Evolutionary, Biological and Physiological Characterization of Arabidopsis CTP:phosphocholine Cytidylyltransferase 1 (CCT1)

Phosphatidylcholine (PC) is the major phospholipid class in the non-plastidial membranes of plant cells. The de novo biosynthesis of PC via CDP-choline pathway involves three sequential steps. Among these reactions, CTP:phosphocholine cytidylyltransferase (CCT1) catalyzes the conversion of phosphocholine and CTP into CDP-choline and pyrophosphate, a reaction considered the key regulatory step in certain plant species. The genome of Arabidopsis thaliana encodes two CCT isoforms, known as AthCCT1 and AthCCT2. The overall objective of this doctoral thesis is to advance our understanding of AthCCT1 through a combination of evolutionary, biochemical, and physiological approaches. In the first study, a phylogenetic analysis of plant CCT genes was conducted to investigate the evolutionary history, genetic relationships, and structural variations among CCTs in the green lineage. To further explore the impact of selection pressure on the functional evolution of CCT genes, we conducted a selection pressure analysis on the representative gene AthCCT1 and investigated its biochemical properties through enzyme assays and protein structural analysis. The results revealed a widespread presence of CCT genes across green algae and land plants, with a notable expansion in eudicots. The phylogenetic division of the CCT gene family into eight primary clades was supported by the observed conservation and divergence in gene structures and motif patterns. The selection pressure analysis of AthCCT1, integrated with biochemical assays and three-dimensional structural investigation, has uncovered two amino acid sites under positive selection, emphasizing their important roles in modulating AthCCT1 enzyme activity and substrate affinity. In the second study, the physiological roles of AthCCT1 in lipid biosynthesis and root development under osmotic stress were examined. Due to the lack of lipid profiling data in the cct1 cct2 knockout mutant, the precise role of AthCCT1 in PC biosynthesis is yet to be fully understood. Moreover, AthCCT1 contains a key phosphorylation site, Serine-187 (S187), which is regulated by Sucrose-nonfermentation1-related protein kinase1 (SnRK1). This SnRK1-mediated phosphorylation leads to approximately a 67% reduction in AthCCT1 enzyme activity. However, the effects of the phosphorylation at the S187 site on the dynamic and in vivo functions of AthCCT1 remain unclear. Accordingly, we generated Arabidopsis cct1 knockdown cct2 knockout lines and revealed their reduced PC intensity under normal conditions and impaired root growth in response to osmotic stress compared to the wild type, which could both be rescued by AthCCT1 overexpression. The S187D phosphomimetic mutant, where S187 is substituted by aspartic acid (D) to mimic the negative charge phosphorylation, displayed reduced enzymatic activity and altered structural properties, including reduced lipid-induced conformational changes and a more compact state compared to the native AthCCT1. Moreover, overexpression of the S187D was unable to restore the root growth phenotype under osmotic stress in the cct1 knockdown cct2 knockout lines, indicating that the mimicked phosphorylation state at S187 may influence AthCCT1’s enzyme function. Taken together, these findings highlight the role of AthCCT1 in PC biosynthesis and suggest that its phosphorylation potentially regulates both enzymatic activity and its physiological functions. The third study aimed to further reveal the molecular mechanisms underlying the activity of AthCCT1, with an emphasis on its protein-protein interactions. The combination of yeast two-hybrid and bimolecular fluorescence complementation assays identified several interacting partners of AthCCT1, including AthCCT1 itself, AthCCT2, potential nuclear importin α and β subunits, and an Arabidopsis Sec14 family protein. These results shed light on the dimerization behavior of AthCCT1 and its role in forming potential protein complexes, which likely contribute to PC homeostasis. To summarize, this thesis investigates the roles of AthCCT1 in plant phospholipid metabolism, including its evolutionary history, structural dynamics, physiological functions, and protein-protein interactions. The research provides novel insights into AthCCT1’s involvement in PC biosynthesis, its important roles in plant development under stress conditions, and its participation in protein complexes, thereby contributing to a deeper understanding of its function in plant cellular processes

Uncovering Roles For Myristoylation in Cancer, and NMTs as a New Therapeutic Target in Acute Myeloid Leukemia

Protein myristoylation is a form of modification by the attachment of the fatty acid myristate to the N-terminus, helping to regulate protein localization, stability, and function. Myristoylation has been proposed as a therapeutic target in cancer for decades, but only recently have high-quality compounds targeting N-myristoyltransferase 1 and 2 (NMTs, the enzymes catalyzing protein myristoylation) been developed to enable the targeting of this process. Hematopoietic cancers such as Acute Myeloid Leukemia (AML) have been proposed as vulnerable to NMT inhibitors (NMTi) such as zelenirstat. AML is an aggressive cancer with poor clinical outcomes tied to disease relapse and resistance to therapy. Relapses are believed to be driven by leukemic stem cells (LSCs) which are highly resistant to conventional therapies and expand to re-establish the disease. Patient survival rates upon relapse are extremely low, highlighting a need for new therapies capable of both targeting LSCs, and overcoming common mechanisms of resistance. The primary goal of this study was to conduct a pre-clinical evaluation of zelenirstat as a novel therapeutic for AML, providing proof of concept and the necessary investigation to initiate clinical trials. Zelenirstat inhibits signaling through Src-family kinases necessary for oncogenic signaling through clinically relevant FLT3 and KIT receptors, in addition to inhibiting oxidative phosphorylation and AMPK activity necessary for LSC function. AML cell stress and apoptosis was induced by zelenirstat, and cell killing observed in vitro and in vivo, with an apparently selectivity for LSC-enriched populations of the OCI-AML-22 cell model. Zelenirstat also inhibited glycolysis in AML cells, presenting a potent multi-pathway inhibitor capable of targeting LSCs with high potential for synergy with venetoclax. Analysis of AML patient data revealed NMT2 expression functions as a prognostic marker in AML patients experiencing poor outcomes under current standards of care were associated with both low NMT2 expression and high MISS-54 score, predictive of positive response to zelenirstat. Following the broad metabolic impacts of zelenirstat in AML, we further explored the impacts upon mitochondria in HeLa cells. We confirmed inhibition of oxidative phosphorylation and glycolysis in this model, and demonstrated induction of cellular calcium leakage and suppression of glutathione metabolism as a consequence of zelenirstat treatment. Additionally, we provide the first evidence of NMTi disrupting mitochondrial structure, with loss of mitochondrial cristae organization and density upon zelenirstat treatment. Additional validation of the impacts of zelenirstat on AMPK show near-complete loss of activation, compromising the ability of the cell to respond to energetic stress. Next we attempted to contextualize the sensitivity of cancer cells to zelenirstat through pan-cancer analysis of NMT levels, response to zelenirstat, and differential analysis of multi-omics in the context of zelenirstat treatment or genetic NMT ablation. We find that genes related to oxidative phosphorylation are among the most responsive to disruption of protein myristoylation, and present myristoylation inhibition sensitivity signature MISS-54. Developed from differential genomic analysis of more than 1200 cell lines screened for sensitivity against NMTis, MISS-54 allows for prediction of tumor sensitivity to NMTi. MISS-54 is also demonstrated to be lower in cognate healthy tissues than in cancer, supporting the notion of increased sensitivity of cancer to NMTi. Collectively, this work identifies a critical role for myristoylation in oxidative phosphorylation and mitochondria as a significant, pan-cancer target of NMTi. This represents a key mechanism by which NMTi could target both LSCs and cancer stem cells at large, helping to improve and lengthen patient remissions. The large number of pathways in which myristoylated proteins participate give zelenirstat a pleiotropic function to tackle genetically and functionally diverse cancers, smothering resistance. In AML, zelenirstat is an exciting new therapeutic option starting clinical trials with predicted benefit to patients in desperate need of improved outcomes. This study identifies key mechanisms and provides rationale necessary for clinical trials and beyond

Conversion of barley straw and shrimp shell to value-added hydrogels and aerogels using environmentally friendly technologies: Pressurized fluid, high intensity ultrasound, and supercritical CO2 drying

Upcycling refers to the process of transforming low value biomass to new materials of higher quality, price, or functionality. Barley straw, an agricultural biomass mainly composed of cellulose (30-40%), hemicellulose (20-25%) and lignin (15-17%), and shrimp shell, an animal biomass composed of chitin (15-22%) and protein (41-49%), are underutilized by-products with low market value. The main objective of this thesis was to use pressurized fluids (PF) to fractionate barley straw and shrimp shell, and then bleach and nanofibrillate the obtained cellulose fiber via high-intensity ultrasound (HIUS) to produce self-assembled scaffolds. Specifically, barley straw and shrimp shell were first upcycled to obtain cellulose nanofiber (CNF) and chitosan (CS), respectively. In addition, CS, a natural cross-linker, was used to produce composite scaffolds with CNF, which were loaded with collagen peptide (COLP) that can be potentially used as scaffolds for tissue engineering. The first two studies investigated the catalytic effect of aqueous ethanol and carboxylic acid at subcritical water (sCW) conditions on the hydrolysis of barley straw (180-220°C, 50-200 bar, flow rate of 5 mL/min for 40 min, 0-100% v/v ethanol) and shrimp shell (140-260oC, 50 bar, 5 mL/min for 10-60 min, 0-10 wt.% citric and malic acid). The maximum amount of hemicellulose sugars was removed from barley straw after sCW treatment at 200°C/40 min, resulting in a cellulose-rich residue (purity 68.43%). Pressurized aqueous ethanol (PAE 60%) at 220oC removed more phenolic compounds (75.38 mg GAE/g straw) and lignin (63.77%). On the other hand, sCW treatment of shrimp shell at 260oC for 60 min resulted in the highest chitin yield of 26.39%, with deproteinization degree of 58.05%, and deacetylation degree of 66.29%. The sCW + malic acid (10 wt.%) treatment at 260oC/60 min improved amino acid removal to 140.11 mg/g shrimp shell. These results indicated that PAE and sCW + carboxylic acid can catalyze the delignification and deproteinization of barley straw and shrimp shell, respectively. The third and fourth studies investigated the effect of HIUS treatment on delignification and nanofibrillation of the obtained cellulose-rich residue. Three different bleaching processes (acidic sodium chlorite bleaching, alkaline hydrogen peroxide bleaching, and HIUS-assisted bleaching) at 75-80°C for 2-6 h were investigated, resulting in cellulose fiber with purity of 91%, and diameter of 3-5 μm. Then, cellulose fiber was nanofibrillated using the HIUS treatment (24-72 kJ/g) to obtain CNF hydrogels with a maximum fibrillation yield of 62 wt.% at 72 kJ/g. After, hydrogels were supercritical CO2 (SC-CO2) dried to form aerogels. The addition of shrimp shell CS improved aerogel stiffness to 3.2 bar, which is good for scaffolds. The last study loaded COLP (1-10 wt.%) in CNF + CS composite hydrogels using a HIUS treatment and freeze-dried to form aerogels. The cumulative release of COLP from CNF + CS aerogels followed a biphasic pattern, where 15.90 and 35.49% of COLP were released within 1 and 48 h, respectively. In summary, the results suggested that PF treatment followed by HIUS and SC-CO2 or freeze drying is a promising strategy for biorefining barley straw and shrimp shell towards nanofiber and potential tissue engineering scaffold production

High-resolution Numerical Modelling to Investigate the Atmospheric, Glacial, and Hydrological Dynamics in the Himalaya

Investigating the atmospheric, glacial, and hydrologic dynamics over the Himalaya is particularly challenging due to its complex topography and limited availability of station data. The uncertainties are further amplified by the profound impacts of global climate change, which significantly influence the region’s atmospheric, glacial, and hydrological responses. While global and regional climate models perform well over relatively flat terrain, they often fail to capture the spatial variability of the atmospheric field in the Himalaya. In this context, well-validated, process-based numerical models offer a promising alternative for studying these dynamics. The Weather Research and Forecasting (WRF) model used in this study is a dynamic atmospheric model used in both operational weather forecasting and atmospheric research. This study uses the WRF model to (a) improve extreme precipitation forecasting skills, (b) explore the intricate relationship between the topography and atmospheric dynamics, and (c) apply the well validated WRF output to drive a new glacial-hydrological model in the Himalaya. This study has demonstrated that the well validated WRF model can accurately reproduce the surface meteorological variables and provide insightful information to investigate the influence of topography in convection, cloud formation, and precipitation generation. To study the glacial-hydrologic dynamics over the Himalaya, a process-based glacial model (Crocus) is coupled with an advanced, fully distributed hydrologic model, WRF-Hydro. The coupled model, thus called the WRF-Hydro/Glacier, when combined with the WRF atmospheric model, offers the potential to accurately simulate the glacial and hydrologic processes in the world’s most complex topography

Predicting Axial Force and Bending Moment in Pipelines Affected by Geohazard Using Machine Learning Techniques

Pipelines are vital to the safe and efficient transportation of energy resources, playing a critical role in meeting global energy demands and supporting economic stability. However, these critical infrastructures face significant risks from geohazards, particularly landslides, which can lead to sudden ground displacement and severe damage to pipelines. Such events not only compromise the structural integrity of pipelines but also pose environmental, economic, and public safety risks. Understanding the effects of landslides on pipeline design and safety is essential to developing robust strategies for mitigating these risks and ensuring the reliable transport of energy resources under challenging geohazard-induced conditions. To address these challenges, this research focuses on predicting the structural responses of pipelines, including axial force and bending moment, under geohazard-induced conditions, such as landslides. Employing machine learning models, this study aims to provide a robust and efficient alternative to numerical methods. Specifically, Support Vector Regression (SVR), Neural Networks, and Random Forest models are developed and systematically evaluated for their ability to predict these responses, offering insights into the performance and applicability of each technique. The dataset used in this study was generated through Python-based numerical simulations, leveraging theoretical models grounded in the Euler-Bernoulli beam theory. Parameters such as axial displacement (u′), lateral displacement (v′), and curvature (v′′) were sampled over ranges reflective of real-world pipeline deformation scenarios. This comprehensive dataset captures a realistic spectrum of elastic, plastic, and strain-hardening behaviours, ensuring accurate modelling of pipeline responses under diverse loading scenarios. The generated dataset was used to train and evaluate the machine learning models, ensuring a comprehensive representation of diverse pipeline deformation scenarios. Model performance was assessed through key metrics, including Mean Squared Error (MSE), Mean Absolute Error (MAE), and Coefficient of determination (R²), alongside computational efficiency metrics such as training times. These metrics and comparisons were crucial in verifying that the models did not overfit or underfit the data, ensuring their ability to generalize effectively across unseen scenarios and diverse geohazard-induced conditions. Recall performance and trend comparison were conducted to evaluate the models’ consistency and their ability to generalize across diverse scenarios. The recall comparison assessed the efficiency of each model in sequential and batch tasks, providing insights into their suitability for different operational requirements. Trend analysis examined the models' ability to capture theoretical relationships between input parameters and pipeline responses, validating their alignment with established frameworks. The results demonstrated that Neural Networks provided the best balance of accuracy and computational efficiency, achieving high R² values (0.999 for axial force and 0.997 for bending moment) and moderate training times (37 seconds for axial force and 13 seconds for bending moment). SVR exhibited the highest R² values (0.999 for axial force and 0.996 for bending moment), indicating exceptional predictive accuracy; however, this came at the cost of significantly higher training times, particularly for bending moment predictions (3473 seconds). Random Forest, while computationally efficient in sequential recall tasks, lagged in predictive accuracy (R² values of 0.992 for axial force and 0.983 for bending moment) and struggled to capture complex trends, limiting its applicability to the studied scenarios. This study is subject to several limitations. The dataset was generated using numerical simulations based on predefined parameter ranges, which may not fully capture the variability of real-world pipeline deformation scenarios. Additionally, the reliance on synthetic data and the lack of validation against experimental or field data limit the ability to confirm the models’ robustness in practical applications. This research opens several avenues for future studies. Expanding the range of input parameters, such as u′, v′, and v′′, could enhance the generalizability of the predictive models, allowing them to handle a wider variety of deformation scenarios. Customizing material and geometric properties, such as pipe diameter, wall thickness, and soil characteristics, would provide deeper insights into the influence of these factors on axial force and bending moment predictions. Additionally, validating the findings with real-world data, instead of relying solely on synthetic datasets, would test the robustness of the models under practical conditions and increase their applicability to real-world engineering challenges. These efforts could further refine the models and broaden their relevance in pipeline safety and reliability studies

Complexities in K-12 Education and the Barriers Leaders Must Overcome

Being a leader in Alberta schools provides many challenges to fulfill the mandates of the Leadership Quality Standards (Alberta Education, 2020). Issues including meeting the growing demands of classroom complexities created through inclusive education (Alberta Education, 2022), meeting the Calls to Action (Truth and Reconciliation Commission of Canada, 2015), issues with technology and the ethics involved in their use, and parental demands which place ongoing burdens on leaders who are responsible for meeting all the needs and ensuring that all students in Alberta receive a quality education. Through a literature review, issues and policies created to address these demands will be explored. This will be examined through the framework on parental involvement developed by Koutsouveli and Geraki (2022). Their study focused on how school management and climate could enhance parental involvement. They discovered the principal’s role in shaping the climate of the school either opens and welcomes parents in, or causes them to walk away from the building and involvement altogether. This framework analyzed four key elements, starting from the principal’s role at the center, and then flowing outward toward parental involvement, and how it affects school climates. Through a final exploration of leadership styles that best support leaders in implementing change, this paper will recommend actions leaders can take to overcome the barriers to education

The Hill Times, Wednesday, January 22, 2025

The newspaper of Parliament

Observer-based Event-Triggered Control of Multi-Agent Systems under Time-Varying Delays

A multi-agent system (MAS) consists of agents that collaborate through local interactions to solve complex tasks, offering advantages such as efficiency, flexibility, and reduced cost. MASs have broad applications in fields such as robotics, smart grids, unmanned aerial vehicle (UAV), etc., effectively addressing problems that are challenging for single agents. However, communication among agents often occurs over networks, introducing challenges such as network congestion, delays, and packet loss, which can adversely affect system performance and stability. In order to reduce the need for constant communication, and mitigate network issues, event-triggering control (ETC) systems are introduced. Nonetheless, communication delays are inevitable and must be considered when designing control algorithms to maintain stability in MASs. This study focuses on developing control strategies for MASs with time-varying communication delays, utilizing ETC to minimize network load. In the first part of the research, we propose a new observer-based asynchronous periodic event-triggered control approach for the consensus of linear MASs to reduce the communication load in both sensor-observer (S-O) and controller-actuator (C-A) channels. In order to deal with the time-varying delays, each agent uses a set of observers to estimate its states as well as its neighbors' states. We demonstrate that all agents' states and observed states converge asymptotically to an agreement point in the presence of time-varying communication delays in both channels. The results show the efficiency of the observer-based ETC in terms of efficient use of communication resources and lower settling time. In the second part, we address the challenge of time-varying formation control in MASs in the presence of time-varying intra- and inter-agent communication delays. We explicitly consider distinct, time-varying delays between each agent and its neighbors, and such delay patterns are more reflective of the intricate delay patterns encountered in real-world MASs. We employ a dynamic event-triggering mechanism (DETM) in S-O and C-A channels, and in the design stage, we guarantee that the closed-loop system of all agents is stable and agents reach the desired formation. Numerical simulations demonstrate that our approach achieves a balance by reducing inter-agent communication frequency while maintaining the desired formation. We address the challenge of asynchronous periodic event-triggered consensus control framework for MASs with general linear dynamics. Achieving consensus in MASs without a shared clock poses a significant challenge due to the asynchronous nature of communication and event-triggering mechanisms. We establish theoretical conditions under which consensus can be reached despite the absence of synchronized communication among agents. Our analysis demonstrates the effectiveness of the proposed approach in achieving consensus while minimizing communication overhead. Finally, we extend the framework of event-triggered control of MASs with time-varying delays to address the scenarios where the communication topology switches based on semi-Markovian rules, which provides a broader design approach for handling more complex MAS environments. We consider the case where transition rates in the switching topologies are partially unknown and implement DETMs on both the S-O and C-A channels to minimize unnecessary data transmissions within the network, which involves utilizing locally triggered sampled data in a distributed manner to optimize resource efficiency. We formulate the event-triggering parameters to ensure the stability of the closed-loop system comprising all agents, thereby achieving consensus. Through numerical simulations and experiments, we demonstrate that our approach effectively balances reducing the frequency of inter-agent communication with ensuring that the agents reach consensus. In addition to the theoretical contributions outlined in this research, we conducted a series of experiments using a multi-agent system consisting of e-puck robots to validate the proposed control strategies. These experiments provided empirical evidence supporting the effectiveness of the event-triggered control frameworks across different scenarios. The results demonstrated the ability of the agents to achieve consensus and maintain the desired formation, aligning with our theoretical findings. The validations highlight the practical applicability of our approaches in achieving efficient and stable coordination among agents in dynamic environments

The Importance of Anti-oppression Education in Chinese Higher Education

This paper explores the existence and roots of oppressive education within the Chinese higher education system, focusing on Confucian traditional influence on educational practices. While Confucianism emphasizes social harmony and moral cultivation, its emphasis on social hierarchy and obedience to authority indirectly contributes to oppressive educational practices. This model prioritizes indoctrination and rote memorization, neglecting critical thinking and the development of student agency, potentially exacerbating educational inequality. This paper analyzes the current system’s shortcomings regarding gender discrimination, regional disparities, and socioeconomic inequality. By comparing Chinese and Western educational models, it highlights the effectiveness of student development. Finally, the article suggests specific reforms, including curriculum reform, teaching training, diversified assessment, and institutional support to achieve equality and inclusion in higher education. The key to building an anti-oppressive system. The article concludes, lie in integrating critical thinking with Confucian principles of reflection, while respecting Chinese cultural traditions