VictorJuiz/thesis huggingface dataset:
Investigation of Intra-tumour Heterogeneity and Host Immune Response in Triple-negative Breast Cancer
Keywords: breast
cancer
heterogeneity
immune
triple-negative
tumour

Approximately 15% of breast cancer patients have an aggressive subtype termed triple-negative breast cancer (TNBC). Mortality is due to difficulties in treating and controlling metastatic growth. Earlier predictors are urgently needed for accurate classification of patients into specific risk groups. Here, I investigated the relationship between intra-tumour heterogeneity through a gene signature from circulating tumour cells and the degree of lymphocytic infiltration in TNBC tumours to develop a potential biomarker of aggressive tumour progression. I have found that high intra-tumour heterogeneity was correlated with a high infiltration of B and helper T lymphocytes in primary TNBCs. In addition, highly heterogeneous tumours that have high infiltration of B lymphocytes were associated with good prognostic outcomes. These findings may have significant implications in understanding tumour progression in the breast, while potentially improving therapeutic decision-making in the clinic.
Unsupervised Deep Learning for Anomaly Detection and Explanation in Sequential Data
Keywords: Anomaly Detection
Deep Learning
Explanation
Machine Learning
Neural Networks
Recurrent Neural Networks

With recent successes of recurrent neural networks (RNNs) for machine translation, and handwriting recognition tasks, we hypothesize that RNN approaches might be best suited for unsupervised anomaly detection in time series. In this thesis, we first contribute a comprehensive comparative evaluation of RNN-based deep learning methods for anomaly detection across a wide array of popular deep neural network architectures. In our second major contribution we observe that a key gap of deep learning based anomaly detection methods is the inability to identify portions of the data that led to the detected anomaly. To address this, we propose a novel explainability approach that aims to pinpoint regions of an input that lead to the detected anomaly. In sum, this thesis not only advances the state-of-the-art in deep learning based anomaly detection for time series data but it also contributes novel methods for producing explanations and evaluating explanation quality of anomaly detectors.
Seasonal Variations in the Limnology and Ecosystem Metabolism of a Canadian hHgh-Arctic Permafrost Thaw Lake
Keywords: Aquatic ecosystem metabolism
Carbon cycling
High-Arctic
Limnology
Seasonality

Aquatic ecosystem metabolism describes the turnover of carbon biomass and energy available within an ecosystem to support aquatic food webs. This research provides insight on ecosystem responses to seasonality in a high-Arctic thaw lake. Skeleton Lake (Nunavut) was sampled over multiple ice-covered and open water seasons to quantify how the presence and absence of ice effects physical, chemical, and biological factors that drive whole-lake ecosystem metabolism. This provides valuable data on the energetic processes supporting all aquatic life, allowing us to understand how climate change may impact the security of freshwater resources. This research captures a meaningful understanding of year-round carbon metabolism in small Arctic lakes and displays the importance environmental factors have on metabolic processes. Open water provides ample opportunity for primary production in surface waters; while deep below ice, this lake teems with biological activity both in complete darkness and at the first signs of spring light.
The Reuse of a Financial Ontology Driven by Competency Questions
Keywords: Applied Ontology
Knowledge Modelling
Ontology
Ontology Reuse

With the increasing demand for information interoperability, share-ability and reusability in the financial industry, we are looking at ontology applications in this domain. The central goal of this work is to improve the process of ontology reusing. In the first place, existing financial ontologies are studied to see how they are applied in real-world scenarios. Secondly, to support multiple definitions of one term in ontology engineering, existing ontology development methodologies are reviewed and combined with reusing operations proposed by Prof. Grüninger and Dr. Katsumi. We also look at the challenges of ontology partial reusing. To overcome those challenges and enhance the process of partial reusing, existing modularization methods are studied, and new modularization approaches are proposed. Last but not the least, a case study that is the result of collaboration with a Canadian multinational financial services company is provided to demonstrate how proposed methodologies are applied in real-world cases.
Impact of Bariatric Surgery on Circulating Inflammatory and Pro-vascular Progenitor Cell Populations
Keywords: metabolic
obesity
progenitor cell
stem cell
surgery
vascular

Obesity is a growing healthcare concern with more than 2 billion adults worldwide affected by unhealthy weight gain. Since bariatric surgery can induce significant cardiometabolic improvements and reduce adverse cardiovascular events, we hypothesized that these benefits may occur through decreased circulating inflammatory cell burden and increased pro-vascular progenitor cell content. High aldehyde dehydrogenase- (ALDH) activity and cell surface markers were assessed by flow cytometry for circulating progenitor cell content, M1/M2 macrophage balance, and pro-inflammatory cell burden between normal weight controls and individuals before and after bariatric surgery. At 3 months, ALDHhiSSChi granulocytes were reduced 2-fold and ALDHhiSSCmid monocyte/macrophage precursors were increased 2-fold, to frequencies equivalent to normal weight controls. Pro-angiogenic ALDHhiSSClow cells with primitive CD34+/CD133+ co-expression were enriched following surgery. Collectively, these data suggest that bariatric surgery induced beneficial changes in circulating progenitor cell content, consistent with improved vascular regenerative capacity and lower cardiovascular risk.
Neuronal Differentiation of Cartilage-derived Stem Cells In Vitro and their Delivery to the Spinal Cord in a Mouse Model of ALS Using MRI-guided Focused Ultrasound
Keywords: Amyotrophic Lateral Sclerosis
Cartilage-Derived Stem Cells
MRI-guided Focused Ultrasound
Spinal Cord

Amyotrophic Lateral Sclerosis (ALS) is an incurable neurodegenerative disorder for which stem cells are attractive therapeutic interventions. However, the complications of immunosuppression for allografts and invasive surgeries for blood-spinal cord barrier (BSCB) penetration have hampered their success. First, we investigated the in vitro neuronal differentiation potential of immunosuppressive, cartilage-derived stem cells (CDCSs). Next, we examined the safety and survival of intravenously injected, GFP-expressing, neuronally-differentiated CDSCs in an ALS mouse model (SOD-G93A), following MRI-guided focused ultrasound (MRIgFUS)-mediated opening of the BSCB. CDSCs’ neuronal differentiation was successful. GFP+ cells were not detected within the spinal cords of treated mice; no behavioural improvements were noted. Despite lack of efficacy, disease onset and survival in treated SOD1-G93A mice remained similar to the control SOD1-G93A mice, indicating that treatment did not hamper motor performance. This is the first study to demonstrate neuronal differentiation of CDSCs and feasibility of MRIgFUS-mediated BSCB opening in SOD1-G93A mice.
Long Term Mental Health Outcomes in Survivors of Adolescent and Young Adult (AYA) Cancer
Keywords: cancer survivorship
mental health
psychiatric disorders

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).
A GaN-Based High-conversion-ratio DC-DC Converter with Fast-transients and Fault-isolation for Automotive Applications
Keywords: Automotive
Direct Conversion
Gallium Nitride
High-convertion-ratio
Switched-Capacitor

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.
3D Object Detection and Depth Completion for Scene Perception
Keywords: 3D Object Detection
Autonomous Driving
Depth Completion
Scene Understanding

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.
Examining the Molecular Signature of Cardiac Subtypes in Early Embryonic Development
Keywords: anterior lateral plate mesoderm
gata5
heart development
scRNA-seq
second heart field
zebrafish

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.
Innovative Pedagogies? Enactment of Active-learning Pedagogies and Policies in Northern, Rural, Tanzania
Keywords: Active-Learning Pedagogies
Education Policy
Learner-Centred Pedagogies
Policy Borrowing
Student-Centred Pedagogies
Teacher-Centred Pedagogies

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.
An Investigation of Carbon Dioxide Response Function Modelling in Functional Magnetic Resonance Imaging
Keywords: Carbon dioxide response
Cerebrovascular reactivity
Deconvolution
Functional MRI
Hemodynamic response function
Hemodynamics

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.
Impact of FPGA Architectures on Area and Performance of CGRA Overlays
Keywords: CAD Tools
CGRA
FPGA Architecture
FPGA Overlay

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.
Fear Memory Allocation in the Posterior Basal Amygdala
Keywords: allocation
amygdala
CREB
memory

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.
Searching for Small Molecules in Formalin-fixed Paraffin-embedded Tissue Surrogates
Keywords: Mass spectrometry

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.
Explaining Class-of-service Oriented Network Traffic Classification with Superfeatures
Keywords: Class of Service
Explanation Framework
Machine Learning
Shapley Values
Superfeatures
Traffic Classification

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.
Endostatin as a Mediator Between Endothelial Function and Cognitive Performance in those at Risk for Vascular Cognitive Impairment
Keywords: Angiogenesis
Cognition
Endostatin
Endothelial Function
Vascular Cognitive Impairment
Vascular Endothelial Growth Factor

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.
A Latent Profile Analysis of Elementary School Students’ Story Re-telling Ability
Keywords: Applied Psychology
Educational Assessment
Elementary Education
School Psychology

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.
Kinetics and Mechanisms of the Heterogeneous Ozonolysis of Tetrahydrocannabinol and Nicotine
Keywords: indoor
LC-MS
nicotine
ozone
smoke
THC

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.