VictorJuiz/thesis huggingface dataset:
Long Term Mental Health Outcomes in Survivors of Adolescent and Young Adult (AYA) Cancer

Cancer can negatively impact the mental well-being of adolescent and young adult (AYA)-onset cancer survivors; their long-term risk of adverse mental health outcomes is not well-characterized. We used population-based clinical and health administrative databases to determine this risk and associated predictors. We identified 2,208 survivors and 10,457 age, sex, FSA-matched population controls without cancer. Compared to controls, survivors experienced an elevated rate of outpatient psychiatry-related visits (adjusted rate ratio (RR) 1.30; p=0.004) and increased risk of severe psychiatric events (adjusted hazard ratio 1.22; p=0.005). Survivors were also at a higher risk of experiencing schizophrenia-related severe psychiatric events, compared to controls. In models restricted to only survivors, those at highest risk of outpatient visits and severe psychiatric episodes could be identified using demographic and/or treatment characteristics. Survivors initially treated for their cancer in an adult versus a pediatric hospital, had a strikingly higher outpatient visit rate (RR 1.79; p=0.03).

Keywords: cancer survivorship
mental health
psychiatric disorders
Advisor: Gupta, Sumit
Intensive Children and Youth Mental Health Services in Ontario: Applying Gelberg-Andersen's Behavioral Model of Health Service Use for Vulnerable Populations

Ontario’s child and youth mental health (CYMH) system is undergoing a transformation aimed at improving access to effective services. Evidence-informed policy directions are the new norm however; few dynamic models exist to understand the utilization of CYMH services. This study adapted the Gelberg-Andersen Behavioral Model of Health Service Use for Vulnerable Populations for use in CYMH and tested its utility. The model was tested using a data sample of children and youth (n=642) referred to one of three levels of intensive mental health treatment (in-home, day treatment or residential). Multivariate analysis determined whether model variables predicted level of care. Significant group differences were found for some variables and predictive models were significant. The enhanced model contributed to an increased understanding of the relationship between individual predisposing, enabling and need characteristics and health service access. Future research is required to test the application of this model with other samples and services.

Advisor: Durbin, Janet
A GaN-Based High-conversion-ratio DC-DC Converter with Fast-transients and Fault-isolation for Automotive Applications

Electric vehicles with autonomous-driving or driver-assistance features have introduced computationally intensive and safety-critical integrated circuit modules. A complementary 48-V network has been proposed to enable more efficient power distribution. An efficient high-conversion-ratio architecture is necessary to interconnect the 48-V bus to the low core-voltages (0.8 V - 1.2 V). The focus of this thesis is the design of a 48-V-to-1-V direct power conversion system, which enables efficient low-voltage, high-current power delivery, near-optimal transient response, and fault-isolation. The 6-to-1 hybrid Dickson switched-capacitor topology satisfies these requirements, particularly fault-isolation, without additional components. A selection methodology is utilized to obtain an optimized set of switches with the minimum necessary voltage rating to support fault-isolation and maximize efficiency. An output-capacitor ripple-current based control strategy, featuring inherent flying-capacitor voltage balancing, near deviation-optimal dynamic behavior, and reduced flying-capacitor voltage drain during transients is presented. A prototype is built to verify and demonstrate the claimed properties.

Keywords: Automotive
Direct Conversion
Gallium Nitride
High-convertion-ratio
Switched-Capacitor
Advisor: Trescases, Olivier
3D Object Detection and Depth Completion for Scene Perception

3D object detection is a fundamental component in the autonomous driving perception pipeline. While images provide rich semantic information, depth information is lost during perspective projection. LiDAR scans provide accurate depth measurements, but the data is sparse at longer distances. In this thesis, we first propose a method to fuse LiDAR and image features in an end-to-end trainable network to achieve robust, real-time 3D detection performance. Next, to alleviate the sparsity of LiDAR data, we propose a fast, CPU based, depth completion algorithm using only classical image processing techniques, which outperforms several learning based methods. The densified depth map is then used to synthesize virtual views of pedestrians, which are incorporated into a proposed orientation estimation pipeline to improve pedestrian heading estimation. Finally, a monocular 3D detection neural network is proposed, which leverages proposal regression and the task of instance reconstruction using densified object point clouds.

Keywords: 3D Object Detection
Autonomous Driving
Depth Completion
Scene Understanding
Advisor: Waslander, Steven L
Examining the Molecular Signature of Cardiac Subtypes in Early Embryonic Development

Cardiac development is a dynamic process that requires a high degree of temporal and spatial regulation for proper heart formation. The molecular identities of distinct cardiac subtypes at early embryonic stages are not well understood. Here, we dissect the early heterogeneity of cardiac progenitors by performing single-cell RNA sequencing on gata5:EGFP+ cells at 13 hours post fertilization (hpf) in the zebrafish embryo. Hierarchical clustering analysis reveals distinct cardiac subtypes including ventricular, atrial/fin bud and second heart field (SHF) progenitors. RNA in situ hybridization analyses reveal that SHF progenitors are likely positioned medially in the anterior lateral plate mesoderm (ALPM) at 13 hpf. In addition, ventricular progenitors are positioned laterally to SHF progenitors, and atrial/fin bud progenitors occupy a dorsal posterior region in the lateral plate mesoderm. Examining the molecular signature of early cardiac populations will provide insight in understanding the basis of congenital heart defects at a transcriptional level.

Keywords: anterior lateral plate mesoderm
gata5
heart development
scRNA-seq
second heart field
zebrafish
Advisor: Scott, Ian C
Innovative Pedagogies? Enactment of Active-learning Pedagogies and Policies in Northern, Rural, Tanzania

This ethnographic case study examines how various stakeholders in a northern, rural Tanzanian community responded to one American Non-Governmental Organization’s (ANGO) intervention to implement active-learning (participatory, student-centred) teaching methods in the primary school it co-managed with the Tanzanian government, and at its children’s residential facility. Interviews (with parents, teachers, government official, and children), classroom observations, and document analysis revealed that, despite continuation of some teacher-centred methods in the primary school classrooms, the students’ 2015 national examination passing rate was 98%, topping the regional charts. No visible gender disparity or cultural conflicts were detected. Informed by Ginsburg’s (2010) and Vavrus et al’s (2011) theory and research on active-learning pedagogies in the developing world, and Steiner-Khamsi’s (2013) research on transnational policy-borrowing in education, these analyses suggest that ANGO’s injection of infrastructural and teaching resources – textbooks, in-service training, meals for students – and other material aid had contributed towards the school’s academic excellence.

Keywords: Active-Learning Pedagogies
Education Policy
Learner-Centred Pedagogies
Policy Borrowing
Student-Centred Pedagogies
Teacher-Centred Pedagogies
Advisor: Bickmore, Kathy
An Investigation of Carbon Dioxide Response Function Modelling in Functional Magnetic Resonance Imaging

The main challenge in the measurement of cerebrovascular reactivity using resting-state fMRI is the need to accurately estimate the CO2 response function, HRFPETCO2, which connects spontaneous end-tidal CO2 fluctuations to the fMRI signal. In this work, we compare three compelling methods, namely (1) parametric deconvolution by modeling the HRF as an inverse logit function (IL); (2) parametric deconvolution by estimating the HRF as a linear combination of gamma basis functions using canonical correlation analysis (CCA); (3) non-parametric Bayesian deconvolution using maximum a-posteriori likelihood estimation of the HRF (MAPL). We use simulations based on different ground truths to test the robustness of these methods to the underlying assumptions. We also simulate the effect of Gaussian noise and realistic colored noise at different levels of signal-to-noise ratios (SNRs). We found that the CCA method provides the highest accuracy and precision amongst all tested methods.

Keywords: Carbon dioxide response
Cerebrovascular reactivity
Deconvolution
Functional MRI
Hemodynamic response function
Hemodynamics
Advisor: Chen, Jean
Impact of FPGA Architectures on Area and Performance of CGRA Overlays

We investigate Coarse-Grained Reconfgurable Arrays (CGRAs) and synthesize them as overlays on Field-Programmable Gate Arrays (FPGAs), and consider the impact of the underlying FPGA architecture on the performance and area of the produced CGRA overlay. This work extends the open-source CGRA modelling and exploration framework, CGRA-ME, to allow for quick generation of vendor-specific CGRA FPGA-overlays. Performance and area are measured and compared to the naive case CGRA implementation, where the naive case does not attempt to leverage unique FPGA architectural features. Results show a significant improvement over the naive case when CGRA FPGA-overlays are created with the FPGA architecture in mind. By offering quick, architecture-specific CGRA FPGA-overlay generation through CGRA-ME, a designer can physically model these architectures on FPGA platforms, with improved performance and area when compared to a naive implementation.

Keywords: CAD Tools
CGRA
FPGA Architecture
FPGA Overlay
Advisor: Anderson, Jason H
Fear Memory Allocation in the Posterior Basal Amygdala

In the amygdala, neurons compete to be recruited for the encoding of fear and reward memory traces. Previously, our laboratory showed that only ~20 % of lateral amygdala (LA) neurons are allocated to a fear or reward memory trace. Specifically, neurons with increased excitability have a competitive advantage over their less excitable neighbours to be selected to encode a memory. While the basal amygdala (BA) is connected to the LA, its precise role in memory allocation is unknown. Recent reports demonstrated that two different genetic neuronal populations in the BA represent negative and positive valence, localized respectively in the anterior BA and posterior BA (pBA). By increasing pBA neurons’ excitability with CREB, we hoped to artificially allocate a fear memory in this subregion. Instead, we found that CREB in the pBA inhibited fear memory expression. This result supports the hypothesis that the pBA might be responsible for encoding reward, or fear inhibition.

Keywords: allocation
amygdala
CREB
memory
Advisor: Josselyn, Sheena
Searching for Small Molecules in Formalin-fixed Paraffin-embedded Tissue Surrogates

Formalin-fixed paraffin-embedded (FFPE) tissues are housed in the millions within archival banks. They may provide a treasure trove of samples for exploring the small-molecule basis of disease—if they still retain their small-molecule metabolites. Fixation and embedding is chemically harsh, and many metabolites may not survive the process. In this thesis, I use tissue surrogates as a tissue mimic to measure small-molecule recovery from FFPE samples. Using six model small-molecule analytes, I show that much analyte is lost—in whole or in part—during sample processing, and that the extent of loss is compound dependent. Further, I present preliminary data suggesting that tissue-surrogate models produce unbiased testosterone-extraction measurements when used to represent real tissue. These results add to our knowledge about the data quality to be expected from FFPE samples, and thus give future researchers more information when choosing whether to use such specimens in their work.

Keywords: Mass spectrometry
Advisor: Wheeler, Aaron R
Characterizing Sources of Ineffectual Computations in Deep Learning Networks

Hardware accelerators for neural network inference can exploit common data properties for performance gains and reduced memory bandwidth. The properties include using narrower data-types on a coarse or fine granularity, as well as exploiting the ability to skip and compress zero values and bits. This work investigates whether these properties persist in: (1) more recent and accurate image classification networks, (2) models for other applications, such as computational imaging, (3) Long-Short-Term-Memory (LSTM) models for natural language processing, and (4) quantized models. We propose a greedy approach for fixed-point quantization, that achieves between 2 and 13 bits for most networks, with an overall average of 6.5 bits. Sparsity, although not guaranteed to exist, can be induced. Pruning and choice of activation functions can extract 73% and 48% activation sparsity, respectively. Bit-sparsity appears to be an inherent property of all the models studied, with an average 81% over all non-pruned networks.

Advisor: Moshovos, Andreas
Explaining Class-of-service Oriented Network Traffic Classification with Superfeatures

Machine learning has gained wide attention in the networking community for application-based traffic classification. However, identifying applications might not be interesting to the service provider being more concerned about class-of-service (CoS) of the incoming flow. In this paper, we consider CoS-oriented classification using delay sensitivity as an example. We show that the direct approach of predicting CoS labels without inferring application types first is significantly more accurate than the two-step approach where the CoS label is given by a predetermined mapping from application labels. To gain insights about the output predictions of the classifier, we propose an explanation framework based on groups of features called superfeatures and the concept of Shapley values from cooperative games. We implement three learning models namely neural network (NN), support vector machine (SVM), and logistic regression (LREG). Our experimental results show that our explanations are quite consistent over different samples and different approaches of classification.

Keywords: Class of Service
Explanation Framework
Machine Learning
Shapley Values
Superfeatures
Traffic Classification
Advisor: Liang, Ben
Endostatin as a Mediator Between Endothelial Function and Cognitive Performance in those at Risk for Vascular Cognitive Impairment

Coronary Artery Disease (CAD), characterized by endothelial dysfunction, increases risk of Vascular Cognitive Impairment (VCI) but the underlying mechanisms are not well understood. This study determined if markers of angiogenesis mediate the relationship between endothelial function and cognition in CAD. Endothelial function, serum vascular endothelial growth factor (VEGF), serum endostatin (anti-angiogenesis), and cognition were assessed cross-sectionally in 50 patients with CAD. A mediation analysis, using a bias corrected inferential bootstrapping method, was used to determine if VEGF or endostatin mediated an association between endothelial function and cognitive performance. Endostatin, but not VEGF, mediated a relationship between endothelial function and cognitive performance. This analysis was also significant when delayed recall was substituted for global cognition scores. Therefore, endostatin may be an important marker of cognition and a potential therapeutic target to prevent cognitive decline in those at risk of VCI.

Keywords: Angiogenesis
Cognition
Endostatin
Endothelial Function
Vascular Cognitive Impairment
Vascular Endothelial Growth Factor
Advisor: Lanctot, Krista L
A Latent Profile Analysis of Elementary School Students’ Story Re-telling Ability

Story re-telling is a language and cognition task familiar to children from diverse cultural backgrounds. The current study investigated the extent to which young students’ story re-tell ability profiles differ by stimulus format (audio vs. text) and further differ by short-term memory ability, grade, and ESL status. One hundred and eighty-five elementary school students completed story re-tell tasks elicited by two different stimuli (aural and written) and a short-term memory task as part of a digital literacy assessment. Stories were scored manually utilizing a story scoring rubric, and supporting details were extracted using Nvivo. Using latent profile analysis with covariates, 4 and 5 latent classes were identified for the audio and text story re-tell tasks, respectively. Furthermore, the results showed that short-term memory, grade, and ESL status influenced profile membership, which highlights the complexity of children’s story retell ability and its linguistic and cognitive sensitivity in classroom-based assessment.

Keywords: Applied Psychology
Educational Assessment
Elementary Education
School Psychology
Advisor: Jang, Eunhee E
Kinetics and Mechanisms of the Heterogeneous Ozonolysis of Tetrahydrocannabinol and Nicotine

Thirdhand smoke (THS) deposits to surfaces following smoking events and is a source of chemical exposure to humans in its vicinity. However, evolution of THS in indoor environments is not well understood. The heterogeneous reaction of Δ9-tetrahydrocannabinol (THC) and nicotine, major components of cannabis and tobacco smoke respectively, with ozone was examined within smoke and as pure compounds. Oxidative decay and product formation were monitored by liquid chromatography-mass spectrometry. Numerous ozonolysis products of THC and nicotine were detected – these products were also found as oxidation products within THS. Heterogeneous reaction rates were determined to be similar whether each compound was pure or within THS. THC was non-volatile, and surface loss was solely due to O3 reaction – in contrast, nicotine was semi-volatile and surface loss was due to volatilization and O3 reaction. These data improve our knowledge on THS evolution and better inform indoor chemistry models.

Keywords: indoor
LC-MS
nicotine
ozone
smoke
THC
Advisor: Abbatt, Jonathan P D
Design and Synthesis of Phosphonate-based Multidentate Polymeric Ligands for Improving Optical and Colloidal Stability of Upconverting Nanoparticles

Lanthanide-doped upconverting nanoparticles (UCNPs) have shown great potential in the field of biomedical research, including the areas of drug delivery, bioimaging and theranostics. However, major challenges still remain for the preparation of biologically compatible UCNPs owing to their susceptibility to surface quenching and poor colloidal stability in aqueous media. Herein, we proposed a surface modification strategy that employs phosphonate-based polymeric ligands to provide NaYF4:Yb,Er nanoparticles with the long-term optical and colloidal stability required for biological applications. Specifically, UCNPs were coated with poly(isobutylene-alt-maleic anhydride) (PIMA) copolymer consisting of different phosphonate anchoring groups and polyethylene glycol (PEG) blocks at varying ratios. A series of mono-, di- and tetraphosphonate-PIMA-PEG ligands were designed and synthesized, and the effect of these ligands on the optical properties and colloidal stability of UCNPs were investigated in both water and 1X PBS solution.

Keywords: polymer coatings
surface modification
upconverting nanoparticles
Advisor: Krull, Ulrich J.
The Role of Inflammation in the Development of Behavioral Changes and Seizure Susceptibility After Experimental Traumatic Brain Injury

The robust inflammatory response following traumatic brain injury (TBI) can be both beneficial and detrimental. This study directly compares the effects of increasing or decreasing TBI-related inflammation on behavioral outcome and seizure susceptibility. Fluid percussion injury was induced in four groups of young adult male Sprague- Dawley rats: 1) sham injury; 2) TBI; 3) TBI with minocycline; 4) TBI with lipopolysaccharide (LPS). Rats underwent a series of behavioral tests including composite neuroscores, rotarod, novel object recognition and Barnes maze at various time points within the first month post-injury. Approximately nine months after injury, a subset of rats from each group was subjected to a pentylenetetrazol – induced seizure test. Decreasing inflammation by minocycline ameliorated increased seizure susceptibility in injured rats. Upregulating inflammation with LPS did not modify the seizure susceptibility. Both minocycline and LPS revealed mixed effects on behavioral outcomes. Findings suggest a dual role of inflammation after TBI.

Keywords: Behavioral Changes
Inflammation
Lipopolysaccharides
Minocycline
Seizure Susceptibility
Traumatic Brain Injury
Advisor: Reid, Aylin Y
Resistance Training-induced Changes in Myofibrillar Protein Synthesis and Skeletal Muscle Hypertrophy in Recreationally Active Young Females

The present study examined the myofibrillar protein synthesis (MyoPS) response following an acute bout of resistance exercise (RE) in the untrained (UT) and trained (T) state to determine its association with hypertrophy in a female population. Ten recreationally active young females underwent ~9 wk of whole-body resistance training (RT). Whole-body fat free mass (FFM), vastus lateralis muscle thickness (MT) and 1RM strength were measured in the UT and T state. MyoPS was measured at 0 and 48 h post-RE following the first and final bout of RE using D2O. Following RT, there was a ~3.4% and ~8.8% increase in FFM (P

Advisor: Moore, Daniel
Classification of Respiratory Physiology with Airwave Oscillometry using Multi-layer Perceptron

Airwave Oscillometry(OSc) is a novel pulmonary function test with the advantages of effort independent operation and high sensitivity to heterogeneous lungs over conventional tests. However, OSc results are high dimensional without well-established interpretation guideline, posing challenge for clinician’s understanding and limiting its clinical utility. While recent advances in Multi-layer Perceptron (MLP), a machine learning model that has proven capability of complex pattern recognition and classification, this thesis proposed to automate OSc output classification using MLP. We have compared the performance of MLP classifying OSc patterns with different number of hidden layers, investigated the effects of training sample size and input selection on the performance of the classification. The classification performance varied with training-validation division and output categories. We found that MLP with hidden layers outperformed MLP without hidden layer and the highest validation accuracy was 82% for binary classification of respiratory physiology with OSc data using a 2-hidden-layer MLP.

Keywords: airwave oscillometry
artificial intelligence
clinical application
machine learning
pulmonary function test
Advisor: Chow, Chung-Wai
Valaee, Shahrokh