VictorJuiz/thesis huggingface dataset:
Integrated Laser-Induced Fluorescence Detection for Parallelizable Microfluidic Single Cell Analysis
Keywords: integrated
laser-induced fluorescence
microball
microfluidics
micro-optics
on-chip

Microfluidic devices employing on-chip capillary electrophoresis (ÂľCE) have the potential to analyze single cells at high throughput while demanding less sample volume. However, to date, detecting the lower limit of the biological dynamic range involves coupling the fluorescence into confocal set-ups using externally cooled photomultiplier tubes (PMTs) which inhibit parallelization of the analysis. The integration of photodetector elements directly on-chip is desirable, but thus far, demonstrated systems are unable to detect targets without increasing target concentration by chemical techniques such as polyermase chain reaction (PCR). To address these demands, this work will investigate a laser-induced fluorescence (LIF) detection scheme using a high-index micro-ball lens embedded in a carbon-blackened polydimethylsiloxane (PDMS) panchromatic filter. The lens-filter layer enables simultaneous excitation rejection and fluorescence enhancement to couple fluorescence from a microfluidic channel to a surface detector. Furthermore, a method is proposed to optimize signal-to-noise ratio (SNR) in these higher dark current environments towards the goal of multiplexable and parallelizable integrated LIF detection.
Epigenetic Vulnerabilities in Triple-Negative Breast Cancer
Keywords: breast cancer
chemical probes
drug screen
epigenetics
SGC
TNBC

Breast cancer is the most commonly diagnosed cancer amongst Canadian women. To date, three main histopathological subtypes based on the expression of estrogen receptor, progesterone receptor and human epidermal growth factor receptor 2 with different clinical outcomes dictate the treatment approach. Among these, the Triple-Negative Breast Cancer (TNBC) subtype does not express any of the hormone receptors and has the least survival rate due to lack of targeted therapy and poor prognosis. Recently, a category of highly selective and potent drug-like molecules ideal to assess therapeutic potential of new targets, known as chemical probes, have been designed to target epigenetic enzymes. Here, we determined the impact of 56 epigenetic chemical probes on the proliferation of TNBC cell lines. We identified distinct response across these cell lines after inhibition of the bromodomains, methyltransferases, methyl lysine transferases and lysine demethylases. Our results revealed epigenetic targets that could lead to potential therapeutic opportunities.
Predicting the Volatility Index Returns Using Machine Learning
Keywords: ensemble method
finance
machine learning
VIX
volatility index
XGBoost

We probe how predictable the short term future behaviour of the Chicago Board Options Exchange (CBOE) Volatility Index (ticker symbol VIX) is given past market price data within the constraints of a simple classic machine learning framework. We use past VIX and SPX price time windows as input to predict the movement direction, i.e. sign of the return, of VIX over the next 1 to 6 weekdays. For successful cases of pre- dicting return direction from one particular weekday to another particular future weekday, we have moderately reliable accuracies of between about 55% and 65% depending on the particular time bridge. We find that 1 day returns are difficult to predict except for a few particular cases, and as the prediction window grows we have models that can predict more and more accurately up to a consistent 62% for both 5 days and 6 days in the future.
Coupling Synthetic Antibodies with Mass Cytometry Allows the Development of a Highly Tailored, Multi-Parametric Single Cell Analysis Strategy
Keywords: BTIC
cyTOF
glioblastoma
mass cytometry
single cell analysis
synthetic antibodies

Increased appreciation for the inherent heterogeneity in cell populations has prompted a demand for more parameters at the single cell level. Synthetic antibody technology allows the rapid development of monoclonal antibodies with exquisite specificity towards any cellular epitope of interest, surpassing traditional hybridoma techniques. A new technology termed mass cytometry (CyTOF) improves on conventional fluorescence detection by labeling antibodies with metal isotope tags, significantly increasing the parameters that can be distinguished simultaneously. In this thesis, the convergence of synthetic antibody technology with mass cytometry is proposed. A comprehensive set of synthetic antibodies towards the Eph Receptor family is amended and tested for use in CyTOF. After validation, this toolset is applied to the study of cancer heterogeneity in Glioblastoma Multiforme. Subsequent analysis by ViSNE and SPADE unearths a putative brain tumour initiating cell population, illustrating an example of the potential afforded through combining these technologies.
The effects of noise-robustness of in-car voice-controlled systems on user perceptions and driving behaviour
Keywords: Cognitive workload
Driver distraction
In-vehicle technologies
User acceptance
Voice recognition accuracy

In-car voice-controlled infotainment systems are becoming increasingly common in automobiles, but the effects on users when their accuracy degrades in the presence of background noise has not been examined. This thesis compares use of both noise-sensitive and noise-robust simulated voice-controlled infotainment systems under three background noise conditions. It was found that the sensitive system was perceived to be less useful and satisfying even when it performed identically to the robust system. No differences were observed between the systems in several driving performance metrics (with the exception of brake response), but the use of either system impaired driving performance compared to baseline. Glances and subjective workload demonstrated advantages to the robust system. Increased heart rate was observed with the robust system. The results demonstrate that noise robustness is a key factor in user acceptance, and may mitigate visual distraction generated by voice-controlled systems; however, the effects on driving performance are inconclusive.
A study of the statistical properties of scattered radiation under optical coherence tomography conditions: scattering medium characterization via the power spectrum and the probability density function
Keywords: Brownian motion
Dynamic light scattering
Optical coherence tomography
Scattering
Speckle
Viscosity

This thesis explores theory and data processing techniques that have the potential to extract much more information about a sample than can be gathered from standard optical coherence tomography (OCT) structural images. First, work is done towards making non-invasive glucose measurements via OCT and dynamic light scattering (DLS) techniques. A recently developed theory which models the power spectrum of the radiation scattered from flowing Brownian particles under OCT conditions is described, along with the experiments conducted that validate it. Second, it is demonstrated that the scattering of radiation from tissue-simulating phantoms has non-Gaussian features when there are a small number of scatterers in the OCT scattering volume, and that the statistics are well-described by the K distribution, which allows for scatterer concentration measurements. Subsequent preliminary experiments were conducted on human tissue, and the results show promise for differentiating between different tissue types.
The relationship between seizure adequacy and response to magnetic seizure therapy in patients with treatment-resistant depression
Keywords: Biomarkers of Response
Brain Stimulation
Depression
Electroconvulsive Therapy
Magnetic Seizure Therapy
Treatment-Resistant Depression

Magnetic Seizure Therapy (MST), an alternative to electroconvulsive therapy (ECT), is under investigation for the treatment of depression, and the optimal treatment frequency is still unknown. Electrophysiological (EEG) recordings during treatment seizures can predict response to ECT, but the relationship between seizure characteristics and treatment outcome in MST remains unclear. This study aims to investigate the effect of stimulation frequency on seizure characteristics in MST, and to elucidate the relationship between seizure characteristics and MST treatment outcome. The results indicate that 100 Hz seizures had the lowest quality characteristics than the 25 and 50 Hz groups, particularly with measures of seizure duration. Moreover, shorter polyspike durations and slow-wave amplitudes are correlated with better treatment outcome, but these measures do not significantly differ across frequency groups. The results suggest MST may not have the same mechanisms of action compared to ECT, and that frequency is not an important modifier of treatment response.
Engineering Non-Wetting Antimicrobial Fabrics
Keywords: antimicrobial
bacterial adhesion
fabric

This research presents novel techniques and a review of commercially available fabrics for their antimicrobial potential. Based on previous research into the advantages of superhydrophobic self-cleaning surfaces against bacterial contamination, insights into what can make a superhydrophobic fabric inherently antimicrobial were analyzed. Through comparing the characterization results of scanning electron microscopy (SEM) and optical profilometry to microbiology experiments, hypotheses into the relationship between the contact area of a bacterial solution and the extent of contamination is developed. Contact scenario experiments, involving the use of fluorescence microscopy and calculating colony forming units, proved that the contamination potential of any fabric is due to the wetting state exhibited by the fabric, as well as the extent of surface texturing. Transmission experiments, utilizing a novel technique of stamping a contaminated fabric, outlined the importance of retention of solutions or bacteria during interactions within the hospital environment on the extent of contamination.
Targeted Capture and Sequencing of Immunoglobulin Rearrangements in Multiple Myeloma to Enable Detection of Minimal Residual Disease
Keywords: Genomics
Immunoglobulin
Lymphoproliferative
Multiple Myeloma
Next Generation Sequencing
Targeted Capture Sequencing

Multiple Myeloma (MM) is an incurable plasma cell dyscrasia characterized by recurrent translocations into immunoglobulin loci and a unique V(D)J rearrangement signature that can be used to track disease over time. Modern myeloma therapy results in high response rates, however the majority of patients still relapse, suggesting persistence of minimal residual disease (MRD) beyond current limits of detection. We developed a targeted-capture approach to detect V(D)J rearrangements and recurrent immunoglobulin translocations to enable MRD assessment. We validated this assay in 69 human myeloma cell lines and established a limit of detection of 1/1000 DNA fragments. Our methods agree with MRD measured by multi-parameter flow cytometry in 11/14 patient samples. One sample was MRD positive by MFC and negative by targeted-capture sequencing, two were positive by sequencing and negative by MFC. Targetedcapture sequencing is an alternative strategy to MRD detection and has potential applications for disease monitoring and immune repertoire profiling.
Effect of Fuel Composition on Particulate Matter Emissions from a Gasoline Direct Injection Engine
Keywords: alternative fuels
emissions
engines
particulate emissions
pentane

The effects of fuel composition on reducing PM emissions were investigated using a Ford Focus wall-guided gasoline direct injection engine (GDI). Initial results with a 65% isooctane and 35% toluene blend showed significant reductions in PM emissions. Further experiments determined that this decrease was due to a lack of light-end components in that fuel blend. Tests with pentane content lower than 15% were found to have PN concentrations 96% lower than tests with 20% pentane content. This indicates that there is a shift in mode of soot production. Pentane significantly increases the vapour pressure of the fuel blend, potentially resulting in surface boiling, less homogeneous mixtures, or decreased fuel rebound from the piston. PM mass measurements and PN Index values both showed strong correlations with the PN concentration emissions. In the gaseous exhaust, THC, pentane, and 1,3 butadiene showed strong correlations with the PM emissions.
The Impact of Delay to Anterior Cruciate Ligament Reconstruction on Patient-Reported Outcomes and Concurrent Knee Injuries
Keywords: ACL
cartilage
cost-effectiveness
delay
meniscus
quality of life

The ideal timing of anterior cruciate ligament reconstruction is debatable after ligament tear. Purpose: To investigate the effects of delay from time of injury to reconstruction, on the cost-effectiveness, the incidence of concurrent knee pathology, and the patientsâ quality of life. Methods: We performed 2 systematic reviews and 1 multicenter cohort study, looking at the effects of delay, from injury to surgery, on the cost-effectiveness, concurrent knee injuries, and on quality of life, respectively. Results: Despite substantial heterogeneity, ligament reconstruction was costeffective in each study. A significant increase in medial meniscal and cartilage pathology was found as of 3 monthsâ post-injury, at which point, a decline in quality of life was also detected, 2 years post-operatively. Conclusion: An increase in delay from time of injury to surgery can lead to an increase in cost, medial meniscal and cartilage pathology, and a potential decline of quality of life.
Liquid Water Saturation and Oxygen Transport Resistance in Polymer Electrolyte Membrane Fuel Cell Gas Diffusion Layers
Keywords: Gas diffusion layer
Oxygen transport resistance
Polymer electrolyte membrane fuel cells
Saturation
Synchrotron X-ray radiography
Water management

In this thesis, the relative humidity (RH) of the cathode reactant gas was investigated as a factor which influences gas diffusion layer (GDL) liquid water accumulation and mass transport-related efficiency losses over a range of operating current densities in a polymer electrolyte membrane (PEM) fuel cell. Limiting current measurements were used to characterize fuel cell oxygen transport resistance while simultaneous measurements of liquid water accumulation were conducted using synchrotron X-ray radiography. GDL porosity distributions were characterized with micro-computed tomography (ÎźCT). The work presented here can be used by researchers to develop improved numerical models to predict GDL liquid water accumulation and to inform the design of next-generation GDL materials to mitigate mass transport-related efficiency losses. This work also contributes an extensive set of concurrent performance and liquid water visualization data to the PEM fuel cell field that can be used for validating multiphase transport models.
Design and Fabrication of Porous Yttria-Stabilized Zirconia Ceramics for Hot Gas Filtration Applications
Keywords: Gas permeability
Hot gas filtration
Poly methyl methacrylate
Porosity
Porous ceramic fabrication
Yttria-stabilized zirconia

Hot gas filtration has received growing attention in a variety of applications over the past few years. Yttria-stabilized zirconia (YSZ) is a promising candidate for such an application. In this study, we fabricated disk-type porous YSZ filters using the pore forming procedure, in which poly methyl methacrylate (PMMA) was used as the pore-forming agent. After fabricating the pellets, we characterized them to determine their potential for application as gas filters. We investigated the effect of sintering temperature, polymer particle size, and polymer-to-ceramic ratio on the porosity, pore size, gas permeability, and Vickers hardness of the sintered pellets. Furthermore, we designed two sets of experiments to investigate the robustness of the fabricated pellets â i.e., cyclic heating/cooling and high temperature exposure. This study ushers in a robust technique to fabricate such porous ceramics, which have the potential to be utilized in hot gas filtration.
Investigation of Dual-phase Nanocrystalline Ni-Cu Electrodeposits
Keywords: Complexing Agent
Diffusion-limited deposition
Electrodeposition
Nanocrystalline
Ni-Cu
Phase separation

Ni-Cu alloys are employed on a large scale in the marine industry due to their excellent corrosion resistance in natural sea water. In an effort to enhance the erosion resistance of these alloys as well, nanocrystalline Ni-Cu specimens were synthesized by electrodeposition. However, surface and bulk characterization revealed an unexpected macrostructure that displayed separate Ni- and Cu-rich phases. Detailed characterization showed significant differences between the two phases in terms of chemical composition, grain size distribution, and localization of bulk defects. Furthermore, inhomogeneity of this electrodeposit in terms of hardness was found as well, hindering the application of such electrodeposits in industry. The formation of this macrostructure was investigated through assessing key characterization results, and a formation mechanism is postulated which is related to the diffusion-controlled deposition of uncomplexed Cu species, due to inadequate stability of complexing agents in the electrolyte. Some steps to eliminate this undesirable macrostructure are proposed.
Modelling, Testing and Optimization of a Passive Solar Updraft Aeration System for Aquaculture in the Developing World
Keywords: Aeration
CFD
Optimization
Pond Aquaculture
Solar Thermal
Surrogate Modelling

Pond aquaculture is major source of food security and income in the Asia-Pacific region. Over 70% of ponds are operated by smallholder farmers who do not have access to aeration technology for proper management of dissolved oxygen levels. The Solar Updraft Aeration (SUpA) system was developed to provide a low-cost, sustainable alternative to conventional aeration in resource-constrained environments. This thesis presents a framework for identifying a suitable design of the SUpA system for large-scale field testing. A comprehensive model was developed using computational fluid dynamics (CFD) and validated in the bench-scale. The model was used to conduct a broad search of the design space of each configuration. The inverted prism configuration was selected for further optimization based on results from CFD and preliminary field tests. The optimized geometry showed an improvement of 81% over a reference geometry based on previous designs and was selected for testing in the future.
Analytics for Decision-Making in Amateur Sport Organizations
Keywords: Amateur Sport
Analytics
Facility Location
Sports Analytics

In this thesis, we demonstrate the value that analytics can have in guiding decisions at amateur sport organizations. We ďŹ rst use machine learning methods to estimate how the demand for tennis is distributed across Canada on a geographical level; geographical prediction of demand for sport has not been seen in the sport management literature. We develop novel validation techniques to show that our estimates outperform the naive assumption that the same proportion of tennis players lives in each region in Canada. To present the estimated demand to decision-makers, we build interactive maps that display the supply and demand for tennis across Canada. Through a case study in facility location, we show that the predicted demand can be used to determine the optimal locations of new tennis programs. Lastly, we present interactive tools that we built for decision-makers to analyze their membership and make targeted decisions about recruitment and retention strategies.
COMPARATIVE STUDY BETWEEN STATISTICAL FRAUD DETECTION METHODS ON ECOMMERCE NETWORKS
Keywords: ecommerce
fraud
stochastic

In the eCommerce industry, the problem of consumer fraud is becoming an increasingly troublesome problem posing a multitude of challenges. Large eCommerce firms, such as Alibaba, eBay, Amazon (to name a few) serve the needs of hundreds of millions customers. On a daily basis, a large number of transactions are made and recorded, as more consumers are becoming susceptible to fraudulent behaviour provided the scale of operations. This research aims to develop a robust real-time fraud detection algorithm based on the construction of a behavioural reference model. Time series features pertaining to a data set of customers on a large eCommerce service are collected and assigned to two classes (non-fraudulent or fraudulent) and modelled using a general stochastic model. The classification performance of this model is compared with more conventional classification techniques, such as logistic regression, tree-based methods, and Na誰ve Bayesian Classifier.
Characterizing Vascular and Neuronal Defects in Williams-Beuren Syndrome and 7q11.23 Duplication Syndrome Using Induced Pluripotent Stem Cells
Keywords: 7q11.23 duplication syndrome
disease modelling
induced pluripotent stem cells
neuron
smooth muscle
Williams-Beuren syndrome

Williams-Beuren syndrome (WBS) and 7q11.23 duplication syndrome (Dup7) are caused by the deletion and duplication, respectively, of 26-28 genes on chromosome 7 in humans, affecting the cardiovascular and nervous systems. To model WBS and Dup7, we generated induced pluripotent stem cells (iPSCs) from multiple WBS, Dup7, and healthy control individuals. In iPSC-derived smooth muscle cells (SMCs), I found an increase in proliferation and reduction in differentiation among WBS SMCs, while also finding elevated proportions of SMC marker in Dup7 precursors, indicating premature differentiation. Assessing neuronal morphology in iPSC-derived neurons, when co-cultured and compared with similarly derived control neurons, axon and dendrite lengths were increased in Dup7 neurons and soma size was increased in both WBS and Dup7 neurons. Further characterization of such phenotypes will provide insight into the pathology underlying the cardiovascular and neurobehavioural symptoms of WBS and Dup7 and inform how they can be addressed at a clinical level.
Inverse linear optimization for the recovery of constraint parameters in robust and non-robust problems
Keywords: Inverse optimization
Nonlinear programming
Robust optimization

Most inverse optimization models impute unspecified parameters of an objective function to make an observed solution optimal for a given optimization problem. In this thesis, we propose two approaches to impute unspecified left-hand-side constraint coefficients in addition to a cost vector for a given linear optimization problem. The ďŹ rst approach minimally perturbs prior estimates of the unspecified parameters to satisfy strong duality, while the second identifies parameters minimizing the duality gap, if it is not possible to satisfy the optimality conditions exactly. We apply these two approaches to the general linear optimization problem. We also use them to impute unspecified parameters of the uncertainty set for robust linear optimization problems under interval and cardinality constrained uncertainty. Each inverse optimization model we propose is nonconvex, but we show that a globally optimal solution can be obtained by solving a ďŹ nite number of tractable optimization problems.