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Microbiologically Influenced Corrosion and Souring Control under Low and/or High Temperature Conditions.
No full textOil reservoir souring is a process through which sulfate-reducing microorganisms (SRM) produce sulfide, a toxic chemical responsible for endangering human and animal health, and causing environmental harm, corrosion of oilfield infrastructure, and devaluation of petroleum quality. Many souring control strategies have been studied, but bioaugmentation of a reservoir via nitrate reducing microorganism (NRM) culture injection along with nitrate is an understudied option that could provide souring control with fewer disadvantages compared to alternative treatments. Here, NRM injection was tested on simulated reservoir sand-packed sour columns at mesophilic and higher temperatures, with effective souring control after administration of a 30 mM (1920 ppm) nitrate NRM-containing treatment. 16S rRNA gene sequencing showed a shift to columns being dominated by NRM, while qPCR analysis showed sulfate-reduction-associated gene (aprA) copies decreasing and nitrate-reduction-associated gene (narG) copies increasing with successful NRM treatment of souring. While microbial sulfide production is linked to microbiologically influenced corrosion (MIC), some microbes are capable of the more corrosive process of electrical MIC (EMIC), where electrons are sourced from metal surfaces by direct contact or soluble redox mediators, causing severe corrosion. Here, oilfield enrichments under thermophilic conditions were evaluated for their EMIC capabilities by determining corrosion rates via weight loss and electrochemical techniques. Two thermophilic SRM cultures were found to be highly corrosive via electrochemical measurements in the absence of an electron other that carbon steel, suggesting their potential to carry out EMIC. These results contribute to knowledge of souring control and MIC under higher temperatures that characterize many oilfield operations
COVID-19 and School Closure: Breaking with the Past and Imagining the World Anew
No full textPandemic-related disruptions to public education—school closures and subsequent health measures designed to mitigate risks related to COVID-19—had significant impacts on those working and learning in K–12 education systems. These disruptions, while not inconsequential to those involved, do not appear to have occasioned a meaningful shift within broader discourses about the aims and means of public education. This study, which takes a hermeneutic approach influenced by complexity studies and Indigenous research methodologies, explored the notion of educational change through the lens of curriculum reconceptualization, inquiring into the relationships between how we, as educators, attend to disruption, how we respond, and an enlarging of the space of the possible around what it means to educate and be educated. Exploring the properties of pedagogical relationships and the scale at which these properties cease to hold, this study suggests that broad-scale educational change is by nature bureaucratic or mechanistic, rather than adaptive, responsive, or relational. An interesting by-product of this inquiry was how the method itself became an object of study. Among the research participants were two retired curriculum scholars and two retired public education system leaders. My research interactions with these four individuals took on the properties of a pedagogical relationship, leading to a methodologically distinct experience of having been taught. Using this experience as a stepping off point, I discuss a pedagogical approach to conducting educational research as a potentially viable space for further study
The Influence of the Microbiota on T Cell Development
No full textT cell development occurs in the thymus and is a complex and well-regulated process. Errors during this process can lead to immunodeficiencies and autoimmunity. This project will also look at recent thymic emigrants (RTEs). RTEs are the most immature subset and have been shown to have distinct phenotypes compared to mature naïve T cells (MNTs), including being more prone to differentiate into peripherally induced regulatory T cells, which makes the cells interesting in the context of things such as autoimmunity. Previous research has suggested that the microbiota, particularly the gut microbiota, is able to influence T cell development in the thymus, including shaping the T cell receptor (TCR) repertoire in thymocytes. The impact of the microbiota on T cell development, however, still remains an understudied area of research. There has also been no published data exploring the effects of the microbiota on RTE phenotypes and functions. In this project, I aimed to explore the influence of the microbiota on T cell development in the thymus and the influence of the microbiota on RTE phenotypes and functions. In this project, I also aimed to explore the effect of exposure to the maternal microbiota on thymocyte development and RTE phenotypes. This project used the RAG2:GFP model for the identification of RTEs, in which RTEs are identified as GFP positive peripheral T cells. To explore the effects of the microbiota thymocyte development and RTEs, this project also used mice, including the RAG2:GFP model, which had increasingly complex microbiotas; germ free (GF, with no microbiota), Oligo-Mouse-Microbiota (OMM12, with a gut microbiota which consists of 12 well defined bacterial species), and Specific pathogen free (SPF, with complex undefined microbiotas). To study the effects of the maternal microbiota, an in vivo colonisation system with auxotrophic E. coli HA107 was used. In this project, I showed that the microbiota can impact several aspects of thymocyte development, which had not been previously identified, including RAG2 protein expression and thymocyte proliferation, which are associated with TCR diversity. I found that RTE phenotypes, including migration and response to antigen stimulation, were not influenced by the colonisation state of the mouse. Exposure to the maternal microbiota in utero was not sufficient to rescue some aspect of thymocyte development, which was disrupted in GF mice, including RAG2 expression, but appeared to rescue and even promote other aspects, such as thymic FOXP3 expression. Overall, in this project, I identified novel ways in which the microbiota influences T cell development. Identifying mechanisms by which the microbiota is able to impact T cell development may uncover novel therapeutic targets for conditions such as autoimmunity and immunodeficiencies
The Looping Effects of Grief Classifications: Public Discourses Behind the Screen
No full textThe medicalization of grief occurred when Prolonged Grief Disorder (PGD) was incorporated as a mental illness in the two most prominent diagnostic manuals. This inclusion is debated in the field, and some arguments against this diagnostic label emphasize grief is a normal human response to the death of a loved one. Some arguments in support of the diagnosis stress that the disorder will help those struggling in their grief to get proper support. These debates are primarily situated in the academic field of literature and minimally incorporates the voices of those that this diagnosis would impact. In addition, it is valuable to consider how a diagnostic label pertaining to grief impacts those who receive the label. Guided by social constructionism, I addressed these gaps by completing a discourse analysis of public Reddit threads. My research question asks how individuals construct their understanding of the medicalization of grief and its implications within discourses of an online forum. In my analysis, I uncovered nine varied discourses regarding how the public discuss the medicalization of grief. These discourses reflect the current academic debates surrounding PGD, in which many were either opposed to or supported PGD as a diagnostic label. This analysis also highlighted an additional discourse regarding how the public discusses the relationship between insurance providers and their mental health. In my discussion, I frame possible looping effects that may be developing in PGD, and outline implications of this project for the broader discipline, and mental health providers
Coupled Propulsion-Aerodynamics Analysis of a Turbojet-Powered Small-Scale Supersonic Unmanned Aerial Vehicle
No full textSmall-scale supersonic unmanned aerial vehicles (SSUAVs) require powerful and robust propulsion systems that can accelerate the vehicle through the transonic regime. While turbojet, turbofan, ramjet, and combined-cycle engines have been used on larger supersonic vehicles, the feasibility of a miniature turbojet engine to break the sound barrier, particularly in the context of small-scale, supersonic applications, remains underexplored. Therefore, the primary objective of the current work is to quantify the performance of an SSUAV powered by a miniature turbojet. In particular, the key flight conditions (e.g., Mach number, altitude) that correspond to the performance of the turbojet propulsion system will be identified. Due to the large number of possible conditions for evaluation, a combination of one-dimensional (1-D) propulsion modeling and computational fluid dynamics (CFD) simulations is employed instead of full combustion modeling,
which would require prohibitively high computational time and hardware resources. The in-house 1-D propulsion model includes details on the streamtube, Fanno flow, conservation of mass correction, and choked flow condition correction. The CFD solver uses pimpleCentralFoam in OpenFOAM with Reynolds-average Navier-Stokes (RANS) turbulence modeling. An assessment process was conducted to quantify the uncertainty associated with aerodynamics and propulsion modeling based on a systematic comparison of key flow variables predicted by the propulsion model with those obtained from CFD simulations. Simulation results will be analyzed to provide generalized insights and design guidelines for SSUAV propulsion with turbojets
CARE-ing for Concussions: Development of the Calgary Adapted aRm Ergometer (CARE) Test: A Novel Upper-Body Post-Concussion Exertion Test
No full textAerobic exercise following concussion is safe, effective, and improves recovery outcomes. Current research examining the relationship between post-concussion exercise and clinical recovery has largely focused on non-disabled athletes who can perform lower body aerobic exercise. For these athletes, post-concussion exercise protocols currently exist and are commonly performed in clinical settings such as the Buffalo Concussion Treadmill Test, Buffalo Concussion Bike Test and the Calgary Concussion Cycle Test (CCCT). However, all of these exertion tests are focused on exercise performed with the lower body and there currently does not exist an upper body specific post-concussion exertion protocol to help inform recovery recommendations. To address the need for more equitable and inclusive concussion research, this research developed a novel arm crank exercise protocol; the Calgary Adapted aRm Ergometer (CARE) post-concussion exertion test and compared its physiological utility as a post- concussion exertion test with the CCCT. Physiological data were collected on 20 non-disabled adult participants and 30 non-disabled adolescent participants who performed both the CARE and CCCT to volitional fatigue. Study findings support the use of the CARE test protocol, demonstrating robust physiological response profiles and moderate comparability with the CCCT. This level of comparability was expected given the smaller active muscle mass recruited during arm crank compared to cycle ergometry and participants limited familiarity with arm crank exercise performed to volitional fatigue. Overall, these findings help inform the safe and effective concussion rehabilitation process for underrepresented populations, promoting equity and inclusivity in sport injury and recovery research. This project fills a critical gap in concussion research, providing an inclusive and accessible alternative to lower-body specific exertion tests which cannot be performed by everyone following concussion
Ability of AI detection tools and humans to accurately identify different forms of AI-generated written content
No full textAbstract Background The increasing use of artificial intelligence (AI) by scholars presents a pressing challenge to healthcare publishing. While legitimate use can potentially accelerate scholarship, unethical approaches also exist, leading to factually inaccurate and biased text that may degrade scholarship. Numerous online AI detection tools exist that provide a percentage score of AI use. These can assist authors and editors in navigating this landscape. In this study, we compared the scores from three AI detection tools (ZeroGPT, PhraslyAI, and Grammarly AI Detector) across five plausible conditions of AI use and evaluated them against human assessments. Methods Thirty open access articles published in the journals Advances in Simulation and Simulation in Healthcare prior to 2022 were selected, and the article introductions were extracted. Five experimental conditions were examined, including: (1) 100% human written; (2) human written, light AI editing; (3) human written, heavy AI editing; (4) AI written text from human content; and (5) 100% AI written from article title. The resulting materials were assessed by three open-access AI detection tools and five blinded human raters. Results were summarized descriptively and compared using repeated measures analysis of variance (ANOVA), intraclass correlation coefficients (ICC), and Bland–Altman plots. Results The three AI detection tools were able to differentiate between the five test conditions (p < 0.001 for all), but varied significantly in absolute score, with ICC ranging from 0.57 to 0.95, raising concerns regarding overall reliability of these tools. Human scoring was far less consistent, with an overall accuracy of 19%, indistinguishable from chance. Conclusion While existing AI detection tools can meaningfully distinguish plausible AI use conditions, reliability across these tools is variable. Human scoring accuracy is uniformly low. Use of AI detection tools by scholars and journal editors may assist in determining potentially unethical use but they should not be relied upon alone at this time
A GAN-Augmented Machine Learning Framework for Predicting Raman Characteristics in Carbon Nanofiber Synthesis
No full textThis thesis explores a hybrid data-driven framework for predicting the structural quality of Carbon Nanofibers (CNFs) synthesized via Chemical Vapor Deposition (CVD). Building upon prior work employing Conditional Tabular GAN (CTGAN) for data augmentation and XGBoost for quality prediction, this thesis expands the methodology by incorporating: (i) an end-to-end reactor data acquisition system (LabVIEW to cloud-based pipeline), and (ii) a new chemical process modeling approach covering hydrocarbon decomposition, catalyst activation, and CNF growth mechanisms. Contributions include physics-informed feature extraction in syn-thesis phases, synthetic data generation to address small data sets, and the integration of machine learning models to predict the intensity ratio of the D and G peaks in a Raman spectrum ( / ), a key indicator of CNF quality. The augmented model achieved 2 ≃ 0.91, and root mean squared error of 0.065 for / , outperforming unaugmented baselines (XGBoost 2 ≃ 0.76; support vector regression 2 ≃ 0.71). CTGAN preserved data characteristics (≃ 96–97 % marginal fidelity / relationship), allowing robust generalization despite the small-n regime
High-Throughput and Dynamic Kidney DECM-based Miniaturized 3D Tissue Culture Platform
No full textThe kidney’s limited regenerative capacity poses a major challenge for modeling renal diseases and developing effective therapeutics. To address this limitation, a kidney-derived decellularized extracellular matrix methacrylate (KdECMMA) bioink and a high-throughput droplet-based bioprinting platform were developed to support kidney organoid culture. The KdECMMA bioink was synthesized to recapitulate the biochemical complexity of native kidney tissue, providing a physiologically relevant microenvironment. Using the developed printing system, uniform and reproducible hydrogel droplets were fabricated for organoid culture under both static and dynamic conditions incorporating air–liquid interface (ALI) with gentle rocking-based convection. Cell viability exceeded 90% under all conditions, confirming cytocompatibility of the printing and culture environments. Dynamic culture significantly enhanced cell morphology and cluster formation compared to static culture, suggesting enhanced organoid-like cluster formation. This integrated platform establishes a foundation for reproducible and scalable kidney organoid culture
Enhancing infant pain assessment and treatment: investigating barriers, facilitators, and implementation outcomes with the ImPaC Resource
No full textAbstract Introduction The Implementation of Infant Pain Practice Change (ImPaC) Resource is a 7-step, multifaceted, web-based implementation strategy to improve pain assessment and treatment in Neonatal Intensive Care Units (NICUs). We explored facilitators and barriers to implementing ImPaC and their relationship to implementation outcomes. Method A hybrid type 1 effectiveness-implementation study was conducted using a cluster randomized controlled trial (reported elsewhere) and a mixed-method exploratory study design. Level 2 and 3 Canadian NICUs with >15 beds were invited to participate and were randomized to intervention (INT, n=12) or usual care (UC, n=11) groups. INT NICUs recruited a change team who accessed ImPaC for 6 months; UC NICUs were waitlisted for 6 months and then offered ImPaC. Focus groups were conducted with all change teams following ImPaC completion. The Consolidated Framework for Implementation Research (CFIR) guided interview questions and analyses. Professionally transcribed interview data were coded and analysed using directed content analysis. Valence (+/-) and strength (–2, –1, 0, +1, +2) were assigned for each CFIR construct/subconstruct. Inductive codes were identified. Relationships between CFIR constructs/subconstructs and ImPaC implementation outcomes (feasibility and fidelity) were determined. Results 83 NICU change team members (median 4/site) participated in focus groups; 1,105 discrete codes relating to 31 CFIR constructs/subconstructs were identified. The most frequent facilitator constructs were Design Quality and Packaging, Compatibility, Available Resources, Champions, Implementation Climate, and Engaging Key Stakeholders. Complexity and Reflecting and Evaluating were salient in 21 transcripts, and Patient Needs and Resources was identified in 20 NICUs. Available Resources and Relative Priority were barriers. A positive association existed between the feasibility of implementing ImPaC and Engaging Key Stakeholders (0.46, p=0.041), Champions (0.82, p=0.001), Relative Priority (0.75, p=0.001) and Networks and Communication (0.60, p=0.023). There was a positive relationship between Engaging Key Stakeholders (0.42, p=0.048), Relative Priority (0.85, p=0.002), Patient Needs and Resources (0.46, p=0.049) and Fidelity. Conclusion Site-specific tailoring to enhance facilitators (e.g., champions, implementation climate) and mitigate local barriers (e.g., resources, relative priority) will provide a viable influence on optimizing implementation outcomes