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Novel Roles of Cdx Transcription Factors in Intestinal Homeostasis
No full textThe transcription factor Cdx2 is essential for intestinal development and homeostasis. Recent research has demonstrated that Cdx regulates the NLRP3 (NOD-, LRR- and pyrin domain-containing protein 3) inflammasome, a critical driver of inflammation, in agreement with other literature suggesting a role for Cdx2 in inflammatory bowel disease. NLRP3 also induces pyroptosis, an inflammatory form of cell death, via processing of Gasdermin D (GSDMD). However, the effects of Cdx2 on GSDMD, the key executor of pyroptosis, remain undefined. This study investigated the role of Cdx in regulating GSDMD processing necessary to cause pyroptosis in intestinal epithelial cells. Our findings revealed elevated GSDMD cleavage in Cdx mutant cells, as well as increased cell death. Inhibition of the NLRP3 inflammasome significantly reduced GSDMD cleavage, consistent with an impact of Cdx on NLRP3 dependent pyroptosis. Additionally, subcellular fractionation and Western blot analyses demonstrated the translocation and oligomerization of cleaved GSDMD from the cytoplasm to the membrane, in Cdx mutant cells, indicative of formation of GSDMD pores which cause pyroptosis. These findings are consistent with a role for Cdx2 in regulating pyroptosis in the intestinal epithelium, and highlights the potential of Cdx and GSDMD as biomarkers and therapeutic targets for inflammatory conditions of the intestine such as inflammatory bowel disease (IBD) and colorectal cancer (CRC)
Essays in Empirical Asset Pricing and International Finance
No full textChapter 1 introduces the Supervised Dynamic Orthogonal Components (sDOC) method as a novel framework for forecasting the equity risk premium out-of-sample. sDOC advances traditional linear dimension-reduction techniques - most notably Principal Component Analysis (PCA) - by integrating machine learning-based feature selection with the construction of dynamic, volatility-sensitive orthogonal factors. Unlike PCA, which does not capture nonlinear relationships or volatility interactions among predictors, sDOC explicitly models these elements, thereby achieving superior forecasting accuracy. The method is applied to 63 U.S. monthly industry equity portfolios and evaluated against several benchmark models: PCA, univariate GARCH(1,1), random forest (RF), and a three-layer neural network (NN3). Empirical evidence shows that, across most industries, sDOC outperforms PCA, univariate GARCH(1,1), and RF, while delivering predictive performance comparable to NN3. This superiority is evident across multiple evaluation criteria, including out-of-sample R², Mincer-Zarnowitz R², gains in average excess portfolio returns, and a range of risk-adjusted metrics such as the Sharpe ratio, Sortino ratio, Calmar ratio, and gain-to-pain ratio. Moreover, the rolling out-of-sample R² underscores sDOC's adaptability under both calm and turbulent market conditions, positioning it as a robust and interpretable tool for asset return prediction.
Chapter 2 proposes a modeling strategy to investigate the transmission of shocks - whether through interdependence, contagion, or decoupling - between two asset markets during periods of heightened volatility. The regime-switching model captures co-movements in both the mean and volatility processes of asset returns. The mean dynamics incorporate PCA-derived factors that reflect global and market-specific influences, while the variance-covariance structure accounts for common and idiosyncratic shocks, each governed by an independent Markov-switching process. Contagion or decoupling is defined as occurring when a high-volatility idiosyncratic shock originating in one market significantly alters the interdependence of mean returns across the considered assets. To statistically detect these phenomena, we employ a novel bootstrap-based Student's t-test procedure. Applying this methodology to sovereign bond market pairs across three Latin American countries, we find evidence that, on average, decoupling has occurred in the Brazil-Mexico and Argentina-Mexico pairs. For the remaining combinations, our results suggest that shock transmission is characterized primarily by interdependence.
Chapter 3 investigates the impact of country-specific major export commodity shocks on domestic equity returns. Employing a Markov-switching framework, we jointly model and estimate commodity-equity return pairs, capturing co-movements in both the mean and the variance-covariance structure of these assets. Co-movement in the mean reflects the influence of global risk, while the variance-covariance structure accounts for common and idiosyncratic shocks, each governed by an independent Markov-switching process. We then examine the transmission of shocks during crises, focusing on cases where shocks originate in a country's commodity market (idiosyncratic shocks) and spill over into its equity market. Contagion is defined as the amplification of idiosyncratic commodity shocks within equity market returns during a crisis. Using data from six major commodity-exporting countries between 2006 and 2024, and applying likelihood-based tests, we find evidence of contagion during the Global Financial Crisis - most notably in the oil-equity linkages of Norway and the United States. Out-of-sample forecasts further demonstrate that our proposed model consistently outperforms several widely used benchmarks, particularly in predicting equity returns
What are the ethical, legal, and social debates surrounding artificial womb technology? A scoping review protocol
No full textAbstract Background Ectogenesis—the development of the fetus outside the human uterus—is generally attributed to British scientist J.B.S. Haldane as early as 1924. Although efforts to develop artificial womb technology have seen limited success, a number of recent advances suggest that human clinical trials may become possible. The objective of this scoping review is to identify the ethical, legal, and social debates that have emerged regarding the future prospects of artificial womb technology. Methods We will use a pre-defined five-step framework to guide this scoping review. Our primary research question is: “What are the ethical, legal, and social debates surrounding AWT?” We will identify relevant peer-reviewed studies in which the full text is in English from electronic databases including Scopus, PubMed, JSTOR, Proquest, Medline, LexisNexis, Westlaw, HeinOnline, and DOAJ. We will employ a two-stage process to identify relevant articles by (1) searching for articles in databases using keywords and 2) conducting a hand search of the reference lists of all retrieved articles to find any relevant sources not indexed by these databases or keywords. Two independent reviewers will select articles by screening titles/abstracts followed by a full-text appraisal using standardized inclusion criteria. We will extract and synthesize the data and develop a narrative summary with accompanying tables and figures. The final output will adhere to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Extension for Scoping Reviews checklist. Discussion The scoping review will document evidence and gaps in the evidence of key areas of focus for academics, clinicians, scientists, legislators, and public policy decision-makers as they consider how to move forward with artificial womb technology. Given the novelty of this technology, we anticipate that we will identify significant gaps that may inform future research and promote a proactive approach to the modernization of legislation, regulatory frameworks, and existing policies and guidelines that may govern this technology. Systematic review registration We have registered this scoping review protocol with OSF Registries: https://doi.org/10.17605/OSF.IO/D8Q96 .
Locked down, locked out: a cross-sectional study on experiences of intimate partner violence (IPV) and barriers to formal and informal support during COVID-19 lockdowns in Ontario
No full textAbstract Background The COVID-19 pandemic intensified pre-existing social and health inequities, with individuals actively experiencing intimate partner violence (IPV) facing heightened risks and barriers to support. While lockdowns were necessary for public health, they also increased isolation and limited access to essential formal and informal supports. This study explores the association between IPV experienced during Ontario’s COVID-19 lockdowns (March 2020–June 2021) and barriers to both formal (e.g., health, legal, housing) and informal (e.g., friends, family) support systems. Methods A cross-sectional online survey was conducted with 1,344 participants, 18 years or older, who were in an adult relationship and residing in Ontario, Canada during the lockdowns. Participants were recruited through non-probability convenience, quota-based sampling from a pre-existing online panel (Leger Opinion, LEO). Data were analyzed using descriptive statistics, chi-square tests, and multivariable logistic regression to examine associations between IPV and support barriers, controlling for demographic, relational, and health-related factors. Results Nearly one in four participants (23.4%) self-identified as experiencing IPV (i.e., emotional, sexual, physical, mental, financial, coercive, spiritual and/or technology-based abuse) during the lockdowns. IPV survivors had over three times greater odds of facing multiple barriers to formal supports (aOR = 3.4; 95% CI: 2.16–5.38) and 1.6 times higher odds of decreased communication with friends or family (aOR = 1.6; 95% CI: 1.06–2.31). Risk factors for reduced access included low household income, informal caregiving responsibilities, and perceived community violence. Poor physical and mental health were also significant predictors of reduced access to formal and informal supports. Conclusions This study highlights how COVID-19 lockdowns compounded access barriers for participants who self-identified as IPV survivors, limiting both formal services and informal networks. Emergency preparedness plans should maintain IPV service capacity during lockdowns through essential service designations, implement technology-based interventions such as discreet online platforms and text-based safety planning, and create targeted outreach for high-risk groups including low-income households and caregivers. More inclusive frameworks are also needed to ensure that supports are responsive to all survivors—particularly as men and women in this study reported similar IPV experiences and outcomes during lockdowns. Future pandemic responses must proactively fund IPV services rather than rely on reactive, underfunded crisis interventions
A Changepoint-Based Framework for Climate-Hydrology Teleconnection Analysis
No full textAccurate prediction of hydroclimatic extremes, such as droughts, floods, and water availability, hinges on understanding how remote oceanic forcings modulate continental precipitation. Conventional teleconnection analyses face two critical limitations: (i) their reliance on linear, stationary assumptions, which conceal regime shifts and erode predictive skill, and (ii) their vulnerability to short or fragmented records, which amplifies uncertainty. Although traditional methods can estimate degrees of ocean-land influence, a clear need remains for approaches that link both processes under nonlinear, nonstationary conditions while retaining strong physical interpretability for forecasting and decision-making.
This dissertation advances teleconnection and hydroclimate analysis by: (a) formulating the Point-Slope (PS) similarity metric, which blends Bayesian changepoint detection with segment-specific trend estimation to capture evolving, potentially nonlinear ocean-land connections; (b) generating station-specific Oceanic Regions of Influence (ROIs) that locate where low-frequency climate modes exert maximal control on local rainfall; (c) designing a geographically flexible PS-driven clustering framework that produces hydrologically homogeneous, physically interpretable regions; (d) benchmarking PS against Spearman correlation and Empirical Orthogonal Functions (EOFs) to quantify improvements in signal detection, spatial coherence, and resilience to missing data; and (e) testing PS sensitivity to confirm computational efficiency and methodological stability.
The findings demonstrate that PS (i) uncovers dynamic teleconnections overlooked by conventional metrics; (ii) scales linearly with record length, yielding 0-1 values that directly represent the joint probability of synchronized changepoints and aligned trends; (iii) generates ROI maps that expose fine-scale oceanic zones governing variability at individual rain gauges; (iv) forms clusters with markedly greater monthly-precipitation homogeneity than those produced with Spearman correlation, especially for key modes such as Niño-3.4; and (v) simultaneously delineates oceanic regions of influence across the full predictor field, and their corresponding terrestrial clusters, more accurately than traditional EOF analysis. Together, these advances provide a scalable, reproducible, and transparent framework for incorporating teleconnection insights into regional water-management planning and next-generation predictive systems under a changing climate.
Looking ahead, the PS framework is not intended to replace established methodologies but rather to complement and enhance them. Its probabilistic and segment-based structure provides a foundation that can be readily combined with emerging technologies such as artificial intelligence, machine learning, and high-performance computing. Such integration can improve both the detection and interpretation of teleconnections, enabling hybrid approaches that fuse statistical rigor with computational adaptability. This opens promising avenues for advancing predictive accuracy, refining early-warning systems, and strengthening the scientific basis of climate-informed water-management strategies
The Effects of Chronic Cortisol Elevation on Thermal Tolerance in Zebrafish (Danio rerio)
No full textThe present study tested whether chronic cortisol elevation influences thermal tolerance in zebrafish (Danio rerio), a species living near its upper thermal limit. Four days of cortisol treatment reduced CT_max by ~1°C, an effect that was alleviated once cortisol returned to baseline. Using glucocorticoid receptor (GR) and mineralocorticoid receptor (MR) knockout fish, as well as the GR antagonist RU486, revealed that the GR was the key mediator of cortisol’s action on CT_max. To probe the mechanisms underlying this effect, temperature-sensing proteins (thermoTRPs) were examined. Blockade of TRPV1 using capsazepine, increased CT_max, and cortisol treatment elevated trpv4 splice variant 3 transcripts in gill tissue, suggesting that cortisol's effects on CT_max may be mediated by changes in temperature sensing. Cortisol treatment during early development (0–5 days post-fertilization) produced adults with lower CT_max values than vehicle-treated control fish, suggesting that cortisol in early development has programming effects on thermal tolerance. Overall, our findings reveal that elevated cortisol, and by extension chronic stress, compromises thermal tolerance in zebrafish, with implications for fish health under climate warming
Bond Behaviour of Recycled Coarse Aggregate Concrete Beam-Ends under Monotonic and Cyclic Loading for Seismic Applications
No full textIn pursuit of reducing the carbon footprint and improve the efficiency of the concrete industry, the use of recycled concrete aggregates (RCA) has been proposed and researched as possible solution. However, their reputation has been hampered by the low workability and performance of new RCA concrete mixes. The development of the Equivalent Mortar Volume (EMV) Method in the past decade allowed a better understanding of RCA characteristics and provided way to produce reliable and consistent concrete with properties like those of conventional concrete.
This project continues the development path of the EMV Method by studying its influence on the bond behaviour of small-scale structural specimens subjected to cyclic loads to assess the possible use of structural concrete containing RCA in seismic applications. The first stage was dedicated to the development of an EMV mix with a characteristic compressive strength of 35 MPa and a water-to-cement ratio of 0.40. These parameters resulted in a cement mass reduction of 28% and an RCA replacement ratio of 74% when compared to a reference mix without RCA.
The second stage of the project focused on 18 beam-end tests with three bar size - embedment length configurations, namely 15M - 170 mm, 15M - 320 mm, and 25M - 320 mm. Half of the specimens were subjected to monotonic loading and the other half to tension cyclic loading. The chosen combinations allowed the study of different failure mechanisms, such as splitting/pullout failure, bar rupture, and splitting failure, respectively.
The results of the bond tests indicate the EMV mixes were able to follow the performance of the conventional concrete during the cyclic tests across all combinations, closely matching the failure mechanisms, peak loads, and ultimate displacements; thus, following the same trends observed during previous monotonic studies. It is also proved that the current Canadian provisions for conventional concrete could be potentially applied for RCA concrete made with the EMV Method
Analysis of IoT Spatial and Spatiotemporal Data: A Smart Farming Use Case
No full textFarmers will be expected to produce more food due to an increasing population despite challenges like climate change. Precision agriculture (PA) and smart farming can be used to help farmers achieve this goal by reducing input costs and enabling agriculture resource optimizations. Smart farming can enable PA by gathering site-specific agriculture data (agri-data) from a) sensors using in-field gateways and/or b) other data sources. The high spatiotemporal variability of agri-data, privacy concerns, and high system deployment costs act as challenges for PA. PA model performance evaluation risks being over-optimistic if spatial (and/or temporal) structure (such as spatial autocorrelation) is not carefully considered in the model evaluation process. Block cross-validation (CV) can be used to address this to create folds of spatially (or temporally) disjointed blocks of data, although data from new locations (or time periods) may lead to pessimistic model extrapolation performance when using this evaluation technique. Cloud computing-based centralized learning (CL) could be used to train PA models, but CL does not scale well in the Internet of Things (IoT) setting and suffers from poor privacy. In addition to high system deployment costs, ignoring farmers' privacy concerns will lead to poor smart farming system (SFS) adoption rates. Limiting a SFS's farmer user-base would result in less available training data, and this could in turn negatively impact model performance. Fog computing-based local learning (LL) could instead be used, by training many local models at the edge of networks using only local data, but unfortunately, applying LL to agri-data may lead to loss of useful geographical trends. Distributed machine learning (ML) can be applied to address these challenges by only sharing model updates to train models.
We proposed an IoT SFS architecture that uses privacy-aware distributed ML to train PA models without having to share farmers' private data. Expensive sensing equipment is first required to train the models using expensive-to-sense ground truth data and affordable sensors' data, but once the training is complete, new farmers can join the system to benefit from the models without needing any expensive equipment. By leveraging data from a Canadian smart farm, we performed yield prediction and nitrous oxide (NO) emission prediction experiments as use cases to showcase the proposed architecture. We used various forms of spatial and temporal block CV for evaluating PA model performance using datasets of varying heterogeneity (independent and identically distributed (IID) and non-IID datasets). We performed experiments using CL, LL, federated learning, and distributed ensemble learning, where clients/nodes were simulated on a single machine. Our results showed that when using IID datasets, distributed ML could do reasonably well and even compete with CL in terms of model performance. However, the IID dataset experiment results may have been over-optimistic due to the stronger presence of spatial/temporal autocorrelation. When using non-IID datasets (which represents the more realistic scenario of having high spatiotemporal variability in agri-data), we found that distributed ML did more poorly and failed on multiple occasions. Despite this, by using distributed ML and non-IID datasets, we were able to generate useful yield precision maps for most clients. The results reported in this thesis demonstrate that the proposed smart farming IoT architecture combined with distributed ML can potentially be used for achieving high spatiotemporal resolution agri-data sensing in a manner that is a) privacy-aware, b) affordable, and c) scalable, at the expense of reduced sensing accuracy
Effect of IGFBP7 on Cardiac/Renal Vascular Remodeling in Diabetes Mellitus
No full textInsulin-like growth factor (IGF) Binding Protein 7 (IGFBP7) is a regulatory protein of IGF-1 signalling, and has also been demonstrated to be excellent cardiac and acute renal injury biomarkers. But the biological effect of IGFBP7 in diabetic kidney disease (DKD) is still elusive. Utilizing a pre-established DKD mouse model, we found that Igfbp7 was significantly elevated in both serum and kidney tissues of affected mice. In kidney, Igfbp7 was enriched in pericytes and podocytes but not endothelial cells in the glomerulus, suggesting the potential involvement of IGFBP7 in maintaining microvascular networks and glomerular filtration barrier. Our study indicates that IGFBP7 can be applied as a DKD biomarker, as well as its important role in the glomerulus, which provides novel insights about how IGFBP7 could participate in the progression of DKD
Advanced Unsupervised Analysis of Spatio-Temporal Pattern Analytics
No full textThis thesis introduces a unified, scalable framework for unsupervised and annotation-efficient analysis of maritime spatio-temporal data using Automatic Identification System trajectories. Addressing challenges of manual feature engineering and label scarcity, the proposed system integrates transformer-based temporal embeddings, constrained clustering, and Active Learning. The time-series transformer is leveraged to encode vessel movements, enabling clustering to reveal latent behavioural patterns. These patterns are aggregated into bag-of-words features for track-level classification. Geospatial validation and classification accuracy confirm the semantic coherence of the learned representations. A hybrid Active Learning strategy, combining uncertainty and diversity sampling, selectively annotates sequences, accelerating model convergence while reducing required annotations by over 30%. The system addresses real-world challenges like data sparsity and class imbalance, demonstrating robustness and offering a generalizable blueprint for spatio-temporal analytics in maritime and other domains, such as aviation, logistics, and environmental monitoring, paving the way for interactive, human-in-the-loop decision-support systems in dynamic operational settings. --
Cette thèse propose un cadre unifié et extensible pour l'analyse non supervisée et efficace des données spatio-temporelles maritimes via les trajectoires du Système d'Identification Automatique. Pour surmonter l'ingénierie manuelle des caractéristiques et la rareté des étiquettes, le système combine des enchâssements temporels par transformateurs, un regroupement contraint et l'apprentissage actif. Le transformateur encode les mouvements des navires, révélant des schémas comportementaux latents. Ces modèles sont ensuite regroupés en caractéristiques de type sac-de-mots pour la classification des voies. La validation géospatiale et la précision de la classification démontrent la cohérence des représentations. Une stratégie hybride d'apprentissage actif, combinant incertitude et diversité, permet une annotation sélective des séquences, accélérant la convergence et réduisant les annotations de plus de 30%. Ce système, robuste face à la rareté des données et le déséquilibre des classes, offre un modèle généralisable pour l'analyse spatio-temporelle dans le maritime, l'aviation, la logistique ou la surveillance environnementale