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Recent Submissions

Access status: Open Access ,
Lipid Phosphate Phosphatases In Skeletal Muscle: Spanning Myogenic Differentiation To Nutritional Stress
(2025-12-05) Fernando, Jeffy; Not Applicable; Master of Science; Department of Biochemistry & Molecular Biology; Received; N/A; Not Applicable; Dr. James M. Kramer; Dr. Yassine El Hiani; Dr. Neale D. Ridgway; Dr. Petra C. Kienesberger
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.
Access status: Embargo ,
Nickel- and Palladium-Catalyzed Mono-α-Arylation of Carbonyl Compounds and the Synthesis of Heterocycles from Nickel-Enolates
(2025-12-04) MacMillan, Joshua; Not Applicable; Doctor of Philosophy; Department of Chemistry; Not Applicable; Dr. Shawn Collins; Not Applicable; Dr. Norman Schepp; Dr. Alex Speed; Dr. Alexander Baker; Dr. Mark Stradiotto
The transition metal-catalyzed mono-α-arylation of carbonyl compounds is a powerful class of C(sp2)-C(sp3) bond forming reactions that is broadly useful for the synthesis of biologically active compounds. Through the application of various ancillary ligands which can dramatically influence the steric and electronic properties of the metal, this class of reactions has greatly matured in scope since its discovery over 20 years ago, encompassing a large array of both (hetero)aryl (pseudo)halide and carbonyl substrates. Notwithstanding the advances in the field of metal-catalyzed α-arylation chemistry, reactions employing earth-abundant transition metals (e.g., nickel) remain underexplored in comparison to palladium-catalyzed methodologies. Furthermore, chemoselective examples are limited, despite the potential utility in the synthesis of complex molecules; and mono-α-arylations of the most cheap and abundant esters, amides, and ketones remains a challenge, where substrate/base pairings can result in undesired reactivity. With the intent of addressing these limitations, this dissertation documenting my doctoral research describes the development of a bisphosphine/palladium-catalyzed protocol for the chemoselective synthesis of α-(o-chloro)aryl ketones from (2-chloro)aryl (pseudo)halides, as well as the development of competent catalyst systems for the nickel-catalyzed α-arylation of cheap and abundant carbonyl compounds with aryl chlorides, featuring the use of the “DalPhos” (DALhousie PHOSphine) ligand family – previously employed for C(sp2)-N/O bond forming reactions. Moreover, the complementarity of these Pd- and Ni-catalyzed α arylation systems is highlighted in the development of two useful new nickel-catalyzed heterocycle-forming reactions, starting from easily accessed α-(o-chloro)aryl ketones and taking advantage of a single mechanistic landscape involving intermediates more commonly associated with the metal-catalyzed α-arylation of carbonyl compounds.
Access status: Open Access ,
Machine Learning for Investigating Urban Systems: Predicting Human Activities, Business Dynamics, and Electric Vehicle Adoption
(2025-12-04) Bhandari, Sagar; No; Master of Applied Science; Department of Civil and Resource Engineering; Not Applicable; n/a; Yes; Dr. Nouman Ali; Dr. Uday Venkatadri; Dr. Muhammad Ahsanul Habib
Cities 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.
Access status: Open Access ,
Development of Phenological Models and Management Strategies for Narrowleaf Goldenrod (Euthamia graminifolia L.) in Wild Blueberry (Vaccinium angustifolium Ait.) Fields
(2025-11-27) Hoeg, Lienna; Not Applicable; Master of Science; Department of Plant, Food and Environmental Sciences; Not Applicable; N/A; Not Applicable; Dr. Travis Esau; Dr. Andrew McKenzie-Gopsill; Dr. Scott White
Narrowleaf goldenrod (Euthamia graminifolia) has emerged as a major perennial weed challenge in wild blueberry (Vaccinium angustifolium) production in Nova Scotia, impacting crop management and yield quality. This thesis investigated the phenological development of E. graminifolia and evaluated integrated strategies for its control. Predictive models based on growing degree days were developed to accurately describe shoot emergence and flowering timing, enabling growers to better schedule post-emergence management. Narrowleaf goldenrod ramet emergence began at 25-71 GDD, and continued to 1,047-1,665 GDD across all study sites. Narrowleaf goldenrod ramets were observed at the flowering bud stage between 710 and 871 GDD (June 21 – 28), and approximately 90% of emerged ramets reached the flowering bud stage between 1303 – 1956 GDD (July 17 – August 24). Emerged ramets began flowering between 1418 – 1626 GDD (July 30 – August 7), and approximately 90% of emerged ramets were flowering between 1992 – 2225 GDD (August 27 – September 12). Cumulative E. graminifolia seedling emergence ranged from 2.4 ± 0.8 to 4 ± 1 seedlings m-2, respectively, and seedling density from soil core samples ranged from 0.02 ± 0.01 to 6.92 ± 1.80 seedlings per core. Seedbank and seedling recruitment studies indicated limited establishment of new plants through sexual reproduction in managed fields, highlighting the necessity of focusing on established populations. Herbicide field trials were established across eleven commercial lowbush blueberry fields in Nova Scotia between 2019 and 2021 to evaluate early-POST, late-POST, and post-harvest, pre-pruning herbicide applications. These studies identified mesotrione-based treatments and targeted fall applications as the most effective methods for long-term suppression with minimal crop injury. The research supports renewed registration efforts for key herbicide programs and provides practical recommendations for sustainable weed management, helping Nova Scotia blueberry growers address the threat of an increasingly dominant, competitive weed species.
Access status: Open Access ,
DYNAMIC BALANCE OF THE SUPPORTING LEG DURING UNILATERAL OBSTACLE CROSSING IN YOUNG ADULTS
(2025-11-25) Kebritchi, Afarin; No; Master of Science; School of Health & Human Performance; Received; Ryan Frayne; No; Derek Rutherford; Christopher MacLean; Michel Ladouceur; David McArthur
Walking biomechanics, especially during tasks requiring postural adjustments such as obstacle navigation, present complex neural and mechanical challenges. Successful obstacle negotiation relies on anticipatory locomotor adjustments and precise dynamic balance control. The present study investigates dynamic balance during unilateral obstacle crossing by examining center of mass behavior and its relationship to the center of pressure across obstacle heights from 0 to 60 cm. Specifically, it evaluates the COM–COP inclination angle and the supporting-leg COM velocity to characterize anticipatory locomotor adjustments and determine potential differences between right and left support limbs. We hypothesize that (A) the COM–COP IA will increase in forward–backward and decrease in side-to-side directions as obstacle height increases, and (B) COM velocity components will vary systematically with higher obstacles. We further propose that lateralization effects may emerge, with inter-limb differences becoming more pronounced at greater heights. This research aims to improve the understanding of adaptive gait strategies.
Access status: Open Access ,
UNDERSTANDING TRAVEL ACTIVITY IN THE DIGITAL ERA: MODELING TRAVELLER PROFILES AND INTERACTION BETWEEN ICT ACCESS, AND WORK ARRANGEMENTS
(2025-11-27) Ibnat, Atkia; Not Applicable; Master of Applied Science; Department of Civil and Resource Engineering; Received; N/A; Yes; Dr. Hamid Afshari; Dr. Lei Liu; Dr. Ahsan Habib
Advances in digital technology have changed how people work, socialize, and travel, yet most transportation planning still focuses on physical trips. This thesis explores how digital access and flexible work arrangements shape mobility and how transportation systems can adapt. It uses the 2024 Calgary CanTRAC Survey, which collected travel diaries and information on ICT ownership, online activity, and lifestyle preferences from 1,474 individuals. A two stage calibration corrected selection bias. Using this weighted dataset, k prototypes clustering identified three groups: Traditionalists, Hybrid Workers, and Active Professionals. A latent segmentation mixed logit model showed that technology access supports hybrid work, while job type and flexibility influence technology adoption. It introduces new ways to integrate digital participation into travel analysis and provides policy guidance to support flexible transit, digital inclusion, and more inclusive urban mobility.