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Lipid Phosphate Phosphatases In Skeletal Muscle: Spanning Myogenic Differentiation To Nutritional Stress
No full textMy research investigates the expression and functional roles of lipid phosphate phosphatase (LPP) paralogs: LPP1, LPP2, and LPP3, in skeletal muscle under physiological and pathophysiological conditions, including obesity, type 2 diabetes, ER stress, and exposure to lysophosphatidic acid (LPA). Using skeletal muscle cell lines and mouse models, we show that LPP paralogs exhibit differential regulation during muscle differentiation, across fiber types, and in response to metabolic challenges. Notably, LPP3 modulates mitochondrial homeostasis and respiration, suggesting a key role in skeletal muscle energy metabolism. These findings provide a foundation for understanding how LPPs influence skeletal muscle function and metabolic disease.Lipid phosphate phosphatase 3 (LPP3) has been identified as a key regulator of bioactive lipid signaling in cardiac muscle; however, the regulation and functional roles of its paralogs, including LPP1, LPP2, and LPP3, in skeletal muscle remain largely unexplored. To address this gap, our study aimed to examine gene and protein expression of the three LPP paralogs (LPP1, LPP2, and LPP3) and the role of LPP3 in mitochondrial homeostasis in skeletal muscle under physiological and pathophysiological conditions, specifically high-fat diet (HFD)-induced obesity, streptozotocin/HFD induced type 2 diabetes, ER stress, and exogenous LPA exposure. We used skeletal muscle cell lines (C2C12 and L6), mouse models, and targeted molecular and pharmacological interventions to characterize how these enzymes respond to developmental signals, metabolic challenges, and cellular stress.
Our findings reveal that during skeletal muscle cell differentiation, LPP3 and LPP1 display reciprocal expression with LPP3 and LPP1 protein levels decreasing and increasing, respectively. These changes in LPP protein levels appear to be controlled at the post-translational level rather than through transcriptional mechanisms since mRNA levels for LPP1, LPP2, and LPP3 were comparable throughout differentiation. In gastrocnemius muscle from female mice with HFD-induced obesity (DIO) and impaired glucose homeostasis, LPP1 mRNA levels were increased when compared to lean low-fat diet (LFD) fed control mice, an effect that was not observed in male mice. Similarly, LPP3 mRNA levels trended to increase with HFD feeding in female but not male mice, while LPP2 mRNA levels remained unchanged across all groups. At the protein level, LPP3 abundance was influenced by fiber type composition in female mice with reduced LPP3 protein levels in oxidative soleus muscle when compared to glycolytic gastrocnemius muscle. In both male and female mice, HFD feeding did not result in altered LPP3 protein levels when compared to LFD fed mice. Interestingly, in male mice with type 2 diabetes LPP3 protein levels were reduced in glycolytic gastrocnemius, but not oxidative soleus muscle fibers.
In differentiated C2C12 cells, induction of endoplasmic reticulum (ER) stress and incubation with exogenous lysophosphatidic acid (LPA), which mimic aspects of metabolic disease following DIO and type 2 diabetes, induced LPP3 protein upregulation. Consistent with prior data from our lab, LPA treatment suppressed mitochondrial respiration in C2C12 cells. Adenoviral LPP3 overexpression increased protein levels of Tfam, a marker of mitochondrial biogenesis, but paradoxically reduced mitochondrial pyruvate-linked respiration in C2C12 cells.
Collectively, these findings show that all three LPP paralogs are expressed in murine skeletal muscle and that protein and/or mRNA levels of distinct LPP paralogs are altered during skeletal muscle cell differentiation and with fiber type composition and metabolic disease. Our data also show that LPP3 overexpression can influence mitochondrial homeostasis and respiration in skeletal muscle cells. These data provide a foundation for future studies investigating the role of LPPs in skeletal muscle function and energy metabolism
Generalizing the Linear Step-up Procedure for False Discovery Rate Control with Applications to Setwise and High Dimensional Variable Selection
No full textThis thesis presents a unified framework for false discovery rate (FDR) controlled variable selection that addresses three major challenges: (1) the inability of traditional FDR procedures to accommodate structured, non-independent hypothesis testing, (2) the failure of standard FDR control procedures for variable selection in the presence of strong dependence among predictor variables, and (3) the lack of methodology for obtaining valid p-values for applying FDR control in the high-dimensional data where the number of variables exceeds the number of samples (m > n).
To address the first challenge, we generalize the linear step-up procedure by applying a sizing function to account for dependence structures among hypotheses. This involves a theoretical extension of the linear step-up framework to structured collections of dependent hypotheses, ensuring that FDR control is preserved under dependence when applied in structured hypothesis settings. We provide theoretical guarantees of this generalization for FDR control. These generalized procedures form the foundation for both SHRED and HVS methods we later describe.
To address the second challenge, we propose a family of methods for setwise variable selection that extend the classical FDR framework to non-independent hypothesis spaces. We first introduce the Setwise Hierarchical Rate of Erroneous Discovery (SHRED) methods, which performs FDR-controlled variable selection over hierarchical trees of hypotheses. For these methods, we expand on the notion of variable selection such that we allow for selecting sets of highly correlated surrogate variables, under the assumption that at least one variable in the set is a true variable. We then introduce the Hypergraph Variable Selection (HVS) method, which enables setwise variable selection over a hypergraph of hypotheses representing complex dependencies among variables. In simulation studies we show that both the SHRED and HVS methods have significant advantages over current FDR control methods for variable selection when there is correlation present among predictor variables.
Finally, to overcome the third challenge, the lack of methodology for obtaining valid p-values in high-dimensional settings, we introduce a multi-step p-value estimation procedure that enables the application of linear step-up methods for FDR control when m > n. This procedure begins with a data reduction step to identify a subset of variables believed to contain all true signals, followed by a conservative re-fitting step that yields valid p-values for inference. We provide theoretical guarantees showing that this approach preserves FDR control, as well as simulation studies highlighting the advantages of this method over classic high dimensional variable selection methods for FDR control.
Collectively, these contributions advance the theory and practice of FDR-controlled variable selection, offering a flexible and principled framework that accommodates structure, dependence, and high dimensionality, three fundamental challenges of variable selection with FDR control.This thesis presents a unified framework for false discovery rate (FDR) controlled variable selection that addresses three major challenges: (1) the inability of traditional FDR procedures to accommodate structured, non-independent hypothesis testing, (2) the failure of standard FDR control procedures for variable selection in the presence of strong dependence among predictor variables, and (3) the lack of methodology for obtaining valid p-values for applying FDR control in the high-dimensional data where the number of variables exceeds the number of samples (m > n).
To address the first challenge, we generalize the linear step-up procedure by applying a sizing function to account for dependence structures among hypotheses. This involves a theoretical extension of the linear step-up framework to structured collections of dependent hypotheses, ensuring that FDR control is preserved under dependence when applied in structured hypothesis settings. We provide theoretical guarantees of this generalization for FDR control. These generalized procedures form the foundation for both SHRED and HVS methods we later describe.
To address the second challenge, we propose a family of methods for setwise variable selection that extend the classical FDR framework to non-independent hypothesis spaces. We first introduce the Setwise Hierarchical Rate of Erroneous Discovery (SHRED) methods, which performs FDR-controlled variable selection over hierarchical trees of hypotheses. For these methods, we expand on the notion of variable selection such that we allow for selecting sets of highly correlated surrogate variables, under the assumption that at least one variable in the set is a true variable. We then introduce the Hypergraph Variable Selection (HVS) method, which enables setwise variable selection over a hypergraph of hypotheses representing complex dependencies among variables. In simulation studies we show that both the SHRED and HVS methods have significant advantages over current FDR control methods for variable selection when there is correlation present among predictor variables.
Finally, to overcome the third challenge, the lack of methodology for obtaining valid p-values in high-dimensional settings, we introduce a multi-step p-value estimation procedure that enables the application of linear step-up methods for FDR control when m > n. This procedure begins with a data reduction step to identify a subset of variables believed to contain all true signals, followed by a conservative re-fitting step that yields valid p-values for inference. We provide theoretical guarantees showing that this approach preserves FDR control, as well as simulation studies highlighting the advantages of this method over classic high dimensional variable selection methods for FDR control.
Collectively, these contributions advance the theory and practice of FDR-controlled variable selection, offering a flexible and principled framework that accommodates structure, dependence, and high dimensionality, three fundamental challenges of variable selection with FDR control
The Experience of Persistent Infertility: Beyond the Medical Model
No full textThis research explores the experience of persistent infertility among 15 childless Canadian women who discontinued fertility treatments when they did not work, who could not access assisted conception, and/or whose infertility is untreatable. By investigating how women understand their experience with persistent infertility and examining the parallels and divergences within and across the participants’ experiences, this analysis indicates that women’s fertility is an intricate lifelong journey, and persistent infertility disrupts this journey in ways that are biological, social, and gendered. Further, persistent infertility fractures the complex relationship between the body and the self. This research theorizes the embodiment of persistent infertility and the meaning placed on reproductive loss events (e.g., failed embryo transfer, miscarriage, and stillbirth), by the women in this study. Lastly, in the medicalized Canadian context, persistent infertility illustrates that hope for maternity is commodified, stratified, and reenforces the biological standard of normative motherhood
Concrete Jungle: Investigating the Bioreceptivity of Biochar-Modified Concrete
No full textThis thesis explores the bioreceptive implications of concrete with added biochar admixture and rough surface texture. An experiment involving four variable concrete panels illustrates the growth and retention of moss on their respective surfaces. Increased bioreceptivity was observed on two biochar-modified concrete test panels. Effective surface geometry observed in the experiment informs a pavilion design that leverages the environmental benefits of the resulting bioreceptive material
INVESTIGATING THE INFLUENCE OF MICROWAVE-ASSISTED PYROLYSIS PARAMETERS ON ADSORPTION CHARACTERISTICS OF BIOCHAR
No full textThis thesis is organized into six chapters. Chapter 1 introduced the background, motivation, and objectives of the study, emphasizing the potential of BSG for biochar production via MAP. Chapter 2 presents a comprehensive literature review on BSG applications in adsorption and the influence of pyrolysis parameters on biochar properties. Chapter 3 outlines the materials and experimental methods used for biochar synthesis, characterization, and adsorption testing. Chapter 4 discusses the results obtained from the optimization studies, material characterization, and adsorption experiments. Chapter 5 summarizes the key findings and implications of the research. The thesis concludes with a list of references that support and contextualize the study.Growing attention to environmental sustainability and circular economy practices has promoted the valorization of agricultural and industrial by-products for resource-efficient waste management. This research converts Brewer’s Spent Grain (BSG), a lignocellulosic biomass waste constituting nearly 85% of brewing industry waste, into functional biochar (BC) as an adsorbent for dye removal from water. Microwave-Assisted Pyrolysis (MAP) was used to prepare BSG-BC, and microwave power, irradiation time, and H3PO4 concentration were optimized using Box-Behnken Design (BBD) and Response Surface Methodology (RSM). Characterization (FTIR, SEM, BET, CHNS, TGA) confirmed improved porosity, surface area, and functional groups; the optimized biochar showed thermal stability and BET surface area ~502.9 m2/g. Adsorption experiments with Crystal Violet (CV) and Orange-II (Or-II) showed PSO kinetics (R2>0.99) and Freundlich/Redlich-Peterson isotherms (R2=0.96–0.99), with capacities of 53.28 mg/g (CV) and 46.97 mg/g (Or-II). Fixed-bed columns agreed with batch results, supporting BSG-BC for batch and continuous wastewater treatment
Quantifying northern bottlenose (Hyperoodon ampullatus) and sperm whale (Physeter macrocephalus) acoustic behavioural responses to anthropogenic noise in Baffin Bay-Davis Strait, Canada
No full textSonar controlled exposure experiments were conducted on northern bottlenose and sperm whales to asses their acoustic behavioural responses to military sonar while foraging and depredating from fishing vessels in Baffin Bay-Davis Strait, Canada.Militaries are expanding operations in the warming Arctic but aim to minimize impacts on marine mammals. To assess the effects of military sonar on northern bottlenose and sperm whales, controlled exposure experiments were conducted in Baffin Bay–Davis Strait, Canada, during fall 2022 and 2023. Acoustic data were collected using DTAGs (n = 2) and drifting hydrophone buoys (n = 8). Sonar (1.86–2.5 kHz, 1 s, max 176.4 dB re 1 µPa at 1 m), vessel (4 h afterwards). These results support the need for proactive military mitigation measures in the Arctic
BRIDGING LEGAL GAPS AND EMPOWERING INDIGENOUS GOVERNANCE: TOWARDS A COMPREHENSIVE IMPACT ASSESSMENT OF SHIPPING FRAMEWORK FOR ARCTIC CANADA
No full textThe Arctic is undergoing rapid change due to climate change, leading to increased shipping for resource extraction. In Canada’s Arctic waters, particularly Nunavut, this rise presents serious environmental, social, and cultural risks for Inuit communities. Current impact assessment legal frameworks do not sufficiently address the unique, cumulative, and transboundary impacts of Arctic shipping, nor do they meaningfully integrate Inuit Traditional Ecological Knowledge and customary law perspectives. This research critiques Canada’s fragmented legal approach and draws lessons from Indigenous-centred, marine-focused frameworks in jurisdictions such as Greenland and other Arctic states. It calls for a unified, Indigenous rights-based impact assessment regime that meaningfully integrates Inuit governance, emphasizes marine protection, and incorporates adaptive, proactive mechanisms. Grounded in legal pluralism, environmental justice, and Indigenous self-determination, this study reimagines Arctic shipping assessments as more inclusive, coherent, and ecologically responsible processes that reflect the Arctic’s interconnected realities
Spatial and Temporal Mechanisms Controlling Convection Over The Great Plains
No full textThe central United States exhibits an anomalous summertime diurnal cycle of precipitation, characterized by an afternoon maximum over the Rocky Mountains that transitions to a nocturnal maximum over the Great Plains. This phenomenon, which remains a challenge for many numerical models, is governed by the interaction of processes spanning multiple scales. This thesis investigates the spatial, temporal, and dynamical mechanisms controlling this diurnal cycle through a comprehensive, climatologically-grounded multi-year analysis of satellite-derived precipitation data Integrated Multi-satellitE Retrievals for GPM (IMERG) and hourly meteorological analyses Rapid Refresh and Rapid Update Cycle (RAP/RUC).
The research is presented in three parts. First, the spatial variation in the synoptic structure of convective systems is examined. The analysis reveals a distinct transition in dominant forcing mechanisms with distance from the mountains: convection in the “Near Plains” (west of 100◦W) is significantly influenced by mountain-initiated solenoidal circulations, while convection in the “Far Plains” is more closely associated with the dynamics of the Great Plains Low-Level Jet (GPLLJ).
Second, the thesis investigates the diurnal evolution of vertical profiles of convection. A systematic diurnal shift from surface-based to elevated convection is identified,
which consistently occurs as the nocturnal boundary layer stabilizes. This shift is linked to a threshold in the low-level lapse rate of approximately -4 to -5 K/km, providing a quantifiable metric for the influence of boundary layer thermodynamics on the convective mode.
Finally, the thesis examines the climatological eastward propagation of rainfall. The analysis demonstrates that the diurnal, clockwise rotation of the GPLLJ’s wind vector drives a propagating pattern of low-level mass convergence across the plains. This mechanism is modulated by topographically-induced suppression of afternoon convection via enhanced convective inhibition (CIN), enabling the nocturnal, dynamicallydriven rainfall maximum to dominate
Machine Learning for Investigating Urban Systems: Predicting Human Activities, Business Dynamics, and Electric Vehicle Adoption
No full textCities face unprecedented challenges in adapting to rapid technological and behavioral change, exposing the limitations of traditional transportation and urban modelling approaches. This thesis introduces a novel, data-driven and modular approach for urban analytics centered on explainable machine learning, specifically predicting a person’s activity schedule, business establishment dynamics, and household electric vehicle adoption. Leveraging large-scale, multi-source data from Halifax Regional Municipality, the research develops specialized modules, each independently validated yet engineered for interoperability. The activity scheduling system combines interpretable boosting (EBM, 73.7% accuracy) and deep learning (clustered bidirectional LSTM, Macro F1-score 59.9), achieving robust, equity-focused predictions across diverse demographic segments and capturing nuanced daily activity chains. For business establishment dynamics, a spatial Graph Neural Network is developed to forecast the number of businesses at dissemination-area resolution, achieving reliable predictions with an overall R² = 0.739. Firm-level models predict business sales and employment, revealing how establishment characteristics, accessibility, and economic output interrelate through Explainable Boosting Machines, while also capturing divergences between sales revenue and workforce growth that inform more nuanced transportation planning, particularly under surge scenarios. For household vehicle adoption, interpretable machine learning approaches identify population density, household income, and charging infrastructure as the dominant influences on electric vehicle uptake, with the leading model achieving a strong ROC AUC score of 0.65. These modular, transferable machine learning frameworks offer an evidence-based toolkit for urban policy and scenario analysis. This thesis demonstrates that explainable ML delivers actionable insights for urban planning, paving the way for adaptive, transparent modelling approaches that can succeed and eventually replace lengthy and resource-intensive traditional models as the availability of transportation data expands