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Pragmatic Clinical Trial Design and Outcomes in Acute Stroke
No full textBackground: Pragmatic trials generate generalizable evidence applicable to routine practice, which informs clinical policy decisions rather than simply confirming a physiological or clinical hypothesis. These trials often employ patient-centered outcomes (such as endpoints reflecting pragmatic health care services utilized) rather than purely clinical or biological markers/symptoms. Research Aims: This study examined the uptake of pragmatic trial designs in Phase III acute stroke trials. The second aim examined the association between the type of thrombolysis and the pragmatic health care services utilized in acute ischemic stroke using data from AcT, a multicenter registry-linked acute stroke trial. Methods: A systematic review of published Phase III acute stroke trials was conducted to examine pragmatic uptake. The PRECIS-2 checklist was used to assess the pragmatic elements of the Phase III acute stroke RCT study designs along the pragmatic-explanatory continuum, with prespecified thresholds for the average PRECIS score (≥3) or the total PRECIS score (≥27). For the second aim, data from the AcT trial were used to examine the relationship between the type of thrombolysis received and pragmatic health care utilization outcomes. Administrative, clinical, and demographic data from the Canadian Institute of Health Information, as well as registry-linked secondary data, were used to address this objective. Outcomes included index length of stay, discharge disposition, 90-day and 1-year readmissions, and mortality. Multivariable regression models (logistic for binary outcomes; count/time-to-event models as appropriate) were adjusted for stroke severity (NIHSS), age, sex, morbidity status (none versus one comorbidity versus two or more comorbidities/multimorbidity), and site type (comprehensive versus primary care centres). All covariates were selected a priori based on clinical relevance and prior literature. Model estimates were used to evaluate the direction and magnitude of associations. Results: For Aim #1, of the 136 Phase III acute stroke trials included in the review, 52% of them were classified as pragmatic. The uptake of pragmatic design in acute stroke trials has increased over time, rising from 38% before 2016 to 62% after 2016. For Aim #2, we found that the thrombolytic agent (tenecteplase vs. alteplase) was not associated with greater pragmatic health services utilization. In multivariable-adjusted analyses, there were no significant differences between tenecteplase and alteplase in index length of stay, readmission status, or all-cause mortality at 90 days and 1 year. Age, stroke severity, and multimorbidity were the strongest predictors of hospital utilization across chosen outcomes. Conclusions: Phase III acute stroke trials are increasingly pragmatic, aligning trial design with real-world practice. In a large, registry-linked pragmatic RCT, tenecteplase and alteplase showed comparable downstream pragmatic health services utilization and mortality, providing robust evidence that tenecteplase is a safe and viable alternative to alteplase without increasing long-term healthcare burden, thereby informing recommendations and regulatory approvals in jurisdictions where alteplase remains the standard of care. Additionally, policy and implementation should consider patient risk profiles (e.g., age, severity, and multimorbidity) as influential in pragmatic health services utilization, and should also account for critical factors such as the logistical feasibility, ease of administration, and cost of the thrombolytic agent of choice
AI-Augmented Interfaces for Educational Storytelling
No full textStorytelling serves as a fundamental pillar of education, acting as the primary vehicle for learning through classic mediums such as text, diagrams, or spatial maps. While modern pedagogy emphasizes the value of dynamic learning through interactive simulations, animated videos, and newer modalities like mixed reality, creating these experiences requires specialized technical expertise that most teachers lack or do not have time for. Consequently, teachers are often restricted to passive, static formats like textbooks, although this primary source of teaching is supplemented with external dynamic content. This thesis investigates how AI-augmented interfaces can democratize this process, empowering educators to transform legacy materials into interactive simulations, author original animated narratives, and enable students to experience content through emerging mixed reality modalities. This thesis follows three distinct paths toward designing and en-gineering AI-driven interfaces for educational storytelling. First, we explore a content scaffolding paradigm by addressing the legacy of static diagrams, and present Augmented Physics, a system that enables teachers to semi-automatically transform textbook diagrams into embedded, interactive physics simulations in real time. Second, we explore the narrative scaffolding paradigm by empowering the creation of original visual stories on maps, and we introduce MapStory, a generative tool that allows novices to author com-plex, controllable geospatial animations directly from free-form natural language scripts. Finally, we explore the spatial scaffolding paradigm by looking toward the future of text and situated learning, and we explore MR reading assistance by iteratively developing RealitySummary, which leverages mixed reality to de-liver on-demand, context-aware summaries anchored to the learner’s physical environment. Together, these explorations illustrate the potential of AI-augmented interfaces to evolve educational storytelling: moving from the digitization of existing materials to the generative creation of new narratives, and finally to the ubiquitous augmentation of the physical world with rich educational information on-demand
Challenges and Supports for Teaching
No full textThis narrative literature review identifies and synthesizes relevant research on the supports demonstrated to be effective in addressing the challenges faced by teachers in their initial years of practice. In doing so, this review establishes the foundation for the next stages of the broader project, including instrument design and data analysis. Ultimately, it contributes to the development of recommendations aimed at strengthening early career supports across Alberta’s K–12 system.Alberta Education Research Partnership Grant - Strategic Opportunity grant
A qualitative study examining household context and parental perspectives on preschoolers’ digital media use guidelines
No full textAbstract Background Evidence suggests that the uptake of pediatric guidelines for screen use is generally low, which highlights a need to better understand the familial factors shaping children’s digital media use. This study aimed to explore parents’ perspectives regarding digital media use guidelines, including the factors that influence adherence to recommendations. Methods Semi-structured interviews with 28 parents of young children were analyzed using thematic analysis. Purposive and snowball sampling were employed to recruit parents through notices posted at grocery stores, libraries, preschools, and a university database. Results Three overarching themes emerged: (1) influences and variations on digital media use in families; (2) views regarding guidelines; and (3) recommendations for disseminating and improving uptake of guidelines. Sub-themes included parental attitudes, family context, child characteristics, lack of information, parenting stress, and knowledge dissemination strategies. Conclusions Parental views regarding guidelines are shaped by personal attitudes, family context, child characteristics, availability of information, and societal influences. Recommendations include highlighting the developmental impacts on children, the effects of different types of screen activities, and strategies for limiting digital media use. Dissemination of information should use a multi-faceted approach that involves different sectors such as health and education, providing follow-up assessments, and building supportive environments
Spray coated bentonite/MWCNT composite enzymatic biosensor for uric acid detection
No full textBiosensors are widely used in healthcare and diagnostics, offering rapid and reliable detection of biological analytes. This study presents the development of an enzymatic uric acid (UA) biosensor based on a nanocomposite of bentonite (BT) and multi-walled carbon nanotubes (MWCNTs) fabricated via spray coating. The composite utilizes the high conductivity and surface area of MWCNTs along with the ion-exchange capacity and antifouling properties of BT to improve both electron transfer and long-term stability. Uricase (UOX) was immobilized on the BT/MWCNTs modified electrode using glutaraldehyde (GA) crosslinking, forming a stable enzyme matrix with strong adhesion and reduced leaching. The fabricated BT/MWCNT/GA/UOX biosensor was systematically characterized through morphological, chemical, and electrochemical analyses. Optimizing spray parameters enabled uniform film deposition and consistent surface roughness, thereby improving reproducibility across electrodes. The biosensor exhibited a broad linear detection range, high sensitivity, and a low detection limit of 5.31 µM in phosphate buffer, outperforming the control sensors prepared with BT-only and MWCNT-only films. Excellent selectivity was demonstrated against common interferents such as ascorbic acid and dopamine. The proposed sensor also exhibited good antifouling ability and was applicable to hydrodynamic conditions. Stability studies indicated that after 60 days of storage at 4 °C, the biosensor retained 92% of its initial response, showing minimal signal drift and structural degradation. Furthermore, real-sample validation using spiked human serum demonstrated reliable recovery (92.2–110.8%) and strong correlation (R² = 0.997) with the standard calibration, confirming the biosensor’s clinical applicability. These results confirm the mechanical, electrochemical, and operational robustness of the BT/MWCNT-based biosensor platform and suggest its strong potential for future integration into point-of-care (POC) diagnostic systems for UA and related metabolites
Localizing longitudinal changes in volumetric bone mineral density after acute knee injury using voxel-based morphometry
No full textOsteoarthritis (OA) is a progressive, degenerative whole joint disease that leads to pain, stiffness, and substantial reductions in physical activity and quality of life. Since the aetiology of OA is poorly understood, studying at-risk individuals, such as patients who have sustained an anterior cruciate ligament (ACL) injury, provides a unique opportunity to investigate early disease mechanisms. Early joint changes after an ACL injury often manifest in the peri-articular bone, which can be accurately captured using X-ray-based imaging modalities (e.g., dual energy quantitative computed tomography; DE-QCT) that capture bone mineral density (BMD). Voxelbased morphometry (VBM) can provide a comprehensive assessment of the entire bone structure through voxel-by-voxel comparisons, revealing both spatial and temporal patterns in BMD changes. The aim of this thesis was to develop and implement a voxel-wise analysis pipeline for peri-articular bone, enabling the detailed assessment of peri-articular BMD changes over the three years following ACL injury. Population-based atlases of the distal femur, proximal tibia, and patella were developed to facilitate voxel-wise comparisons. The precision of BMD measurements was evaluated both globally and locally, demonstrating excellent global reproducibility with localized variability at the voxel level. The established pipeline was applied to study the spatiotemporal BMD changes in the ACL-injured and contralateral knees, revealing region specific and time-dependent adaptations. A key finding was that early BMD losses often overlapped regions of bbone marrow lesions. This work highlights the value of combining DE-QCT and VBM for understanding of bone adaptations following ACL injury and its potential role in post-traumatic OA development
Moment Constraints in Radiation Therapy Planning Optimization Incorporating Patient-Specific Anatomical Geometry
No full textIntensity-modulated proton therapy (IMPT) is an advanced cancer treatment technique, aimed at maximizing tumor control while minimizing collateral damage to surrounding healthy structures. In inverse planning, dose-volume histogram (DVH) is a key concept for measuring and restricting collateral radiation damage to healthy tissues. Interpreting the DVH as a probability distribution, a framework is proposed to assess the deviation from a reference dose-volume histogram when the so-called gEUD (generalized equivalent uniform dose) constraints are imposed to control the radiation dose. The underlying formulation is equivalent to devising sharp probability bounds subject to moment constraints. To evaluate these bounds numerically, a linear programming-based approach is proposed. Never-theless, such analysis may yield overly conservative bounds due to the base model lacking patient-specific anatomical or dosimetric information. To address that, an enhanced model is proposed that incorporates the so-called dose deposition matrix, thereby requiring reformulating the problem as a mixed-integer program (MIP). By embedding geometric considerations into the formulation, the objective is to produce dose–volume histogram approximations with greater clinical relevance. Computational results show that incorporating patient-specific anatomical geometry substantially enhances the accuracy of patient-specific DVH estima-tion, albeit at considerable computational expense. By evaluating the dose–volume histogram using the first dual bound reported by the MIP solver, clinically meaningful upper-bound approximations of the DVH are obtained within computationally feasible time frames, preserving mathematical validity while circumventing the need for full optimality certification
High-Resolution Remote Sensing and Deep Learning for Assessing Tree Regeneration on Boreal Disturbances
No full textIn western Canada, the leading cause of forest fragmentation is seismic lines—corridors cleared for hydrocarbon exploration. These linear features have been linked to habitat degradation and predation-driven declines of boreal woodland caribou populations, prompting efforts to restore tree cover on seismic lines within caribou habitat. Prior to restoration, existing tree regeneration is assessed using aerial surveys and field visits to triage lines for treatment. However, these approaches are costly and labour-intensive and could be improved through automated methods that integrate high-resolution remote sensing and deep learning for individual tree detection and crown delineation (ITDCD) and species classification. Despite growing interest, the data sources and seedling conditions (i.e., species and size) under which these approaches are effective remain insufficiently explored. This thesis explores the use of a YOLO (You Only Look Once) object detection model and three remote sensing data sources—standard RGB (red–green–blue) imagery, multispectral imagery (RGB–red edge–near infrared), and LiDAR (light detection and ranging)—for ITDCD and species classification of black spruce (Picea mariana) and tamarack (Larix laricina) on regenerating seismic lines in the Canadian boreal forest. I found that standard RGB imagery (1 cm ground sample distance; GSD) consistently outperformed multispectral imagery (5 cm GSD), with F1 score improvements of up to 17.8 percentage points (pp). This was observed for both species and was most pronounced for smaller seedlings. At equivalent GSD, the inclusion of red edge and near-infrared bands yielded modest F1 improvements (up to 4.0 pp), although these effects were species- and size-dependent. Low-level fusion of optical imagery with LiDAR-derived surface models did not consistently improve performance relative to optical-only models (0.0–1.2 pp F1), with variable effects across seedling species and sizes. These findings indicate that sensor selection and data acquisition parameters should prioritize high spatial resolution for assessing tree regeneration on seismic lines, with additional spectral bands as a secondary consideration. Under the strategies tested, LiDAR provided limited additional benefit and may not justify the added operational complexity
Structural Neural Connectivity Correlates of Early Language and Reading Development and Prenatal Alcohol Exposure
No full textReading and language difficulties are common in children and can have long-lasting effects. Studying the brain’s structural connectivity from an early age can provide insight into the roots of reading and language development and may help with early identification and interventions. Prior studies examining the structural neural correlates of language and pre-reading/reading abilities have mainly focused on older, typically developing children. Unfortunately, little research has been done on preschool children, despite this being a critical learning period. Additionally, the effects of adverse early environments such as prenatal alcohol exposure (PAE) on these associations remain unclear. This thesis aimed to gain a better understanding of the associations between brain structural networks and early language and reading abilities in young children, and the impact of PAE on these associations. Brain imaging data were previously collected using diffusion weighted imaging (DWI) in two
cohorts of children from Canada and South Africa. Children completed skills assessments. DWI images were processed, and structural connectivity was assessed using graph theory. The associations between early language and structural connectivity, and the effect of PAE on these associations were analyzed. We found that 1) phonological processing skills were associated with connectivity in a network consisting of reading and language regions in preschool aged children, 2) PAE negatively moderated the relationship between pre-reading skills and network measures in preschool aged children, and 3) PAE-related alterations to language abilities and brain-language associations are present during the toddler years. Our findings expand the current literature, supporting the idea that the structural brain correlates of reading and language are present early, and showing that PAE moderates them. These moderation effects imply that reading and language deficits in children with PAE begin early, suggesting that the roots of reading and language (dis)ability are present well before children go to school. This highlights the need for early diagnosis and interventions to support positive outcomes for children. Our findings provide a better understanding of the neural correlates of pre-reading and language from an early age and can lay the groundwork for earlier interventions for reading and language disabilities
Membrane Enhancement for Electrochemical Carbon Conversion to Valuable Chemicals at High Partial Current Densities
No full textThe continuous rise in global carbon dioxide (CO2) emissions poses a significant environmental threat, with projections suggesting a potential 4 °C increase in Earth’s average temperature above pre-industrial levels if current trends persist. This has driven industries to pursue technologically advanced and economically feasible strategies to mitigate CO2 emissions. Electrochemical conversion of carbon dioxide and carbon monoxide (CO2/CO) to valuable chemicals represents a promising pathway for carbon utilization; however, its industrial deployment requires simultaneous achievement of high current density, Faradaic efficiency, energy efficiency, favorable cell potential, rapid CO2/CO conversion rates, and long-term operational stability. A critical component influencing product selectivity and durability in CO2/CO electrolyzers is the ion-exchange membrane. Commercially available membranes often suffer from high cell voltages due to excessive thickness and limited interfacial contact with the catalyst layer, which compromises stability at elevated current densities. To address these challenges, we implemented a direct deposition method to fabricate an ultrathin membrane for CO electrolysis, enabling precise tuning of membrane properties and thereby enhancing overall electrochemical performance. In this thesis work, we have developed a direct deposition process which allows to produce an ultrathin anion exchange ionomer (i.e. Sustainion XA-9), which led to demonstrate over two-folds higher partial current density and 8% lower cell potential at high current densities compared to the stand-alone, thick and commercially available membrane counterpart. A loading of 12.5 μl/cm2 of XA-9, forming a ~1.76 μm uniform coating on a copper oxide catalyst supported on a carbon-based gas diffusion layer, exhibited the best electrochemical performance for multi-carbon (C2+) products at high current densities. At this optimized configuration, partial current densities of 307 mA/cm2 for ethylene and 253 mA/cm2 for ethanol were achieved at a total current density of 900 mA/cm2. Product analysis at this current density revealed generation of 34% ethylene, 28% ethanol, 3% propanol, and 17% acetate, corresponding to a total C2+ partial current density of 741 mA/cm2. Electrochemical impedance spectroscopy (EIS) analysis showed that the ultrathin ionomer coating reduced the polarization resistance from approximately 13 Ω·cm2 to 6 Ω·cm2 and the ohmic resistance from about 2.2 Ω·cm2 to 1.6 Ω·cm2. This improvement is attributed to the reduced membrane thickness and enhanced interfacial contact between the catalyst and ionomer layers, which facilitated improved mass transport compared to the stand-alone membrane counterpart. Diffusion and ion transport analyses revealed that the thicker stand-alone membrane facilitated excessive cation transport to the cathode, which, together with limited free water availability in the reaction microenvironment, resulted in decreased selectivity towards C2+ products and a pronounced hydrogen evolution reaction (HER), particularly at high current densities. In contrast, the thinner directly deposited ionomer coating provided a more favorable reaction environment, enabling improved C2+ product selectivity even at high current densities. By fine-tuning the cation concentration, regulating the free water availability and improving the CO accessibility at the
reaction microenvironment, the spray coated ionomer layer enabled superior electrochemical performance for CO reduction reaction (CORR). Another major challenge affecting the economic viability of electrochemical CO2 reduction as a carbon-utilization pathway is the significantly high upstream energy demand for capturing and regeneration of CO2 from point sources. CO2 emissions from industrial processes are typically released as flue gas mixtures containing only 10- 15% CO2. Conventional CO2 electrochemical conversion methods using 100% pure inlet reactant gas stream therefore require an additional upstream CO2 purification step, which is both costly and energy intensive. To overcome this limitation, integrated CO2 capture and electrochemical conversion systems have emerged as a promising alternative, where CO2 separation and conversion occur simultaneously. We integrated a CO2-selective and highly permeable mixed matrix membrane (MMM), composed of the metal–organic framework (MOF) ZIF-8 and the polymer matrix Pebax 1657, into an electrochemical cell for combined CO2 separation and conversion. The optimized MMM, containing 90 wt% ZIF-8 and 10 wt% Pebax 1657 at a loading of 1 mg/cm2, enhanced CO selectivity from 39% to 63% compared to the control system without the MMM at a current density of 20 mA/cm2. Although the incorporation of the MMM improved CO selectivity in a mixed CO2/N2 gas feed, the partial current density for CO still remained significantly lower than that achieved with a pure CO2 stream, limiting operation at high current densities. This drop in CO2 reduction reaction (CO2RR) performance is primarily attributed to the lower CO2 availability and partial pressure in the feed, which hinder mass transport to the catalytic sites required especially at high current densities under ambient conditions. To address this limitation, we propose the development of a high-pressure electrochemical system incorporating the MMM to enhance CO2 availability at the cathodic reaction environment. Permeation studies further revealed that CO2/N2 selectivity of the MMM increased ~7 times compared to the bare substrate, with higher polymer content relative to MOF content, and at higher overall MMM loading, suggesting potential for further optimization under pressurized conditions