82837 research outputs found
Sort by
Dielectrophoretic Microfluidic Platform Enables Efficient Cell Manipulation for High-throughput Single-cell Analysis
No full textBioanalysis at a single-cell resolution has provided unprecedented insights into the heterogeneity of complex biosamples. A critical aspect of operations of single-cell analysis involves the physical separation and sorting of single cells from suspensions, necessitating the technologies capable of accurate manipulation of individual cells. The development of Lab-on-a-Chip devices has facilitated the integration of large-scale laboratory operations onto chips-scale platforms, leading to reduced labor requirements and enhanced processing speed. This synergy between Lab-on-a-Chip technology and various simultaneous and high-frequency approaches for single-cell sample operations has given rise to microfluidic platforms tailored for high-throughput single-cell analysis.
Among techniques for micro-scale particle manipulation, dielectrophoresis (DEP) stands out as a label-free approach that exploits the dielectric properties of target objects to manipulate individual cells efficiently. On-chip DEP harnesses the induced dipole of dielectric particles, particularly cells, within a non-uniform electric field, offering a range of functionalities, including selective cell capture, release, and more intricate operations such as rotation and movement along predefined routes. In DEP-based microfluidic devices, electrodes and microstructures are intricately designed in various patterns to achieve different geometric boundary conditions for the coupling of the electrical field and the fluid flow, contributing to the versatility and adaptability of this technique in single-cell analysis. In this thesis, we have addressed the limitations of current microfluidic designs in single-cell analysis. Specifically, we proposed a novel DEP-based structure to improve the efficiency of cell-bead pairing, and we developed a microfluidic system design to improve the functionality and engineering reliability for impedance-based on-chip flow cytometry.
Multiple microfluidic platforms utilize microbeads functionalized with different biomolecules to introduce indexing molecules and the necessary biochemical reactants. Such systems tailored for cell-bead pairing have been extensively applied in single-cell analysis. However, existing stochastic-based cell-bead pairing approaches encounter inherent limitations imposed by Poisson statistics, constraining sample utilization and cell pairing rates. To address this challenge, we proposed the 3-D dielectrophoresis-assisted-dual-nanowell-array (ddNA) structure that decouples bead and cell loading processes and achieves high single-cell capture and pairing rates. Experimental validation using human embryonic kidney (HEK) cells demonstrates the suitability and reproducibility of this design, with single-bead capture rates exceeding 97% and cell-bead pairing rates exceeding 75%.
Rapid isolation and precise quantification of target cancer cells are essential for many precision medicine applications. We have also devised a DEP-based particle focusing approach and integrated it into a continuous microfluidic single-cell analysis system to sort and enumerate target cancer cells from the mixed sample. Compared with other purposed designs, our design integrates functions of selective concentration, particle focusing, and single-cell level quantification without introducing complex physical microstructures. In the proposed design, we realized the sheathless focusing with amplified-modulated(AM)-pDEP force and combined it with the side-counter sensing electrodes to overcome the inherent trade-off between sensitivity and throughput of conventional Coulter counter. This platform offers a cost-effective, label-free alternative to conventional optical approaches, presenting a purely electrical strategy for reliable target cell sorting and quantification in biomedical applications.
In this thesis, we envision the engineering advancements of our developed microfluidic platforms contributing to enhancing the practical utility of single-cell analysis in precision medicine and academic research
Insight into government, February 21, 2025
No full textAlberta's independent newsletter on government & politics
From Oral Traditions to Digital Landscapes: Virtual Reality as a Tool for Revitalizing Indigenous Languages
No full textThis thesis investigates the role of virtual reality (VR) as an innovative tool for Indigenous language revitalization, emphasizing its potential to bridge generational and geographical gaps in language transmission. Centered on the Multimodal Indigenous Knowledge Systems (MIKS) framework, the study explores how VR can provide immersive, culturally rich environments that support language learning and cultural connection. Grounded in the principles of embodied cognition, the research highlights how VR can integrate traditional practices, stories, and landscapes to create engaging, contextually relevant learning experiences.
The study examines the perspectives of VR creators, educators, Elders, and youth participants through interviews, focus groups, and workshop data, shedding light on VR's emotional, experiential, and motivational impacts. It also addresses challenges, including technological accessibility, cultural sensitivity, and the need for community-driven development. Findings demonstrate that VR may foster a sense of belonging and cultural pride while supporting language retention and engagement, particularly among Indigenous youth. This research underscores the significance of technology in advancing Indigenous language and cultural revitalization by situating these findings within the broader context of the United Nations Decade of Indigenous Languages and Canada’s Truth and Reconciliation Calls to Action. The results contribute to a growing body of scholarship on digital tools for education, providing actionable recommendations for culturally sensitive VR development and future research in this emerging field
Understanding Youth Experiences of Well-Being Within a School-Based Smoking and Vaping Prevention and Cessation Program
No full textYouth vaping is a fast-growing public health concern in Canada. Many youth are introduced to vaping through friends and have their first experiences with vaping in schools. Therefore, schools are an ideal environment for a smoking and vaping intervention. Interventions should also engage students as peer leaders to influence health behaviour change. The Students Together Moving to Prevent Tobacco Use (STOMP) program is a school-based, peer led smoking and vaping intervention program which applies principles of Comprehensive School Health (CSH) to prevent and reduce commercial tobacco use. The purpose of this research was to explore and understand students’ perceptions of well-being and their experiences with smoking and vaping within the STOMP program. Qualitative methods were used to (1) explore youth perceptions about smoking and vaping as it relates to their overall well-being, and (2) understand the self-perceived impact of STOMP student leaders on the well-being of their school community.
Objective 1 used focused ethnography as a guiding method and semi-structured focus groups as a data generating strategy. Community-based participatory research was used as a research approach. Youth (n = 59) were recruited from nine school sites across Canada and participated in virtual or in-person focus groups. Focus groups were audio recorded and transcribed verbatim. Four primary themes and twelve subthemes relating to youth experiences of well-being and vaping were identified using inductive thematic analysis. The primary themes included: vaping culture within and outside of school, social aspects of vaping, diverse perspectives and experiences of well-being, and the role of education in supporting well-being. The findings revealed that youth considered vaping to impact various aspects of their well-being and that their interpersonal relationships influenced their vaping behaviours and perceptions of well-being.
Objective 2 used youth participatory action research (YPAR) as a guiding method and draw and tell as the data generating strategy. STOMP student leaders (n=4) from one site in Alberta, Canada participated in the draw and tell activity and subsequent participatory thematic analysis. A semi-structured focus group was used in the draw and tell activity to explore student leaders’ impact on their school community well-being. The focus group was audio recorded and transcribed verbatim. Three themes relating to student leaders’ impact on school community well-being were identified through a participatory thematic analysis process in which students were involved as co-researchers. The three themes were: STOMP leaders can change students' perspectives on vaping, STOMP leaders are advocates in the greater community, and being a student leader inspires personal growth. The findings give insight into student leaders’ perspectives on well-being as it related to their role as leaders in their school community and the interconnectedness of the three areas of impact (peers, greater community, and self).
The findings of this research demonstrated that students’ perceived vaping behaviours within their school community influences their well-being, and that peer-led education can be an appropriate response to address this growing issue. As such, peer leadership in interventions that support youth health should be considered as an essential element in future school-based interventions. Further, interventions should empower student leaders to leverage their role among their peers to promote well-being in a way that builds on social dynamics among peers. Additionally, the findings provide novel insights into youth perspectives on well-being within a school-based peer-led smoking and vaping intervention. Finally, the use of a novel arts-based research method, draw and tell, in a youth population and incorporating youth as co-researchers resulted in rich exploration and understanding of student leaders’ self-perceived impact on various areas of the school community. This method can be used with youth populations to study youth health interventions and meaningfully incorporate youth voice in the research process. This research has the potential to inform future research, practice, and policy related to smoking and vaping prevention and reduction in Canada and strengthen existing school-based smoking and vaping interventions in a way that align with youth perceptions of well-being
Calculation of Mixture Critical Points Based on PC-SAFT EOS
No full textCompositional simulations are important for understanding, analyzing, and optimizing multiphase flows, especially in the petrochemical industry. These simulations are dependent on an accurate Equation of State (EOS) to model the relationships between phases under changing conditions. Although cubic EOS models dominate industrial applications due to their simplicity, the Perturbed Chain Statistical Associating Fluid Theory (PC-SAFT) EOS offers a more profound thermodynamic model. However, PC-SAFT EOS struggles to accurately predict the critical points of pure compounds and mixtures. Two main critical point formulations via EOS exist, namely, the root finding method and the optimization method. These formulations have been modified and improved over the years by many researchers, but mixture critical point calculations using the two distinct methods combined with PC-SAFT EOS, the global optimization method, remains largely unexplored.
In this study, two techniques, namely the Newton-Raphson (NR) method and a global optimization method, are implemented to calculate the critical points of multi component mixtures based on PC-SAFT EOS. The purpose is to assess the effectiveness of the two techniques and draw comparisons between them. Such a study is lacking in the literature. It is found that the NR method demonstrates strong performance, with Absolute Relative Deviation (ARD%) generally below 5% for most mixtures. Similarly, the PVTsim software, which also implements a NR method, also shows reliable results, with ARD% values close to the applied NR method for the majority of mixtures. The global optimization method demonstrates lower accuracy with ARD% values typically ranging between 5% and 10% for some mixtures but higher ARD% values for the others
Developing and Assessing Deeper Learning in Computational Thinking Through an Online Programming Activity
No full textComputational thinking (CT) has been introduced in schools around the world as a way to enable learners to develop problem-solving (PS) skills for the challenges of the 21st century. Although several studies have investigated how to assess and develop CT, research specifically addressing the relationship between CT and PS remains limited. The present research developed and assessed pre-service teachers’ CT (i.e., CT skills and CT dispositions) and problem solving (i.e., actual and perceived PS skills) in an online block-based programming environment. It (1) examined the reliability and validity of the proposed CT assessment; (2) identified the baseline level of pre-service teachers’ CT skills and CT dispositions, and their actual and perceived PS skills; (3) investigated the relationship between CT skills, CT dispositions, actual PS skills, and perceived PS skills; (4) examined the effects of an online CT programming intervention on pre-service teachers’ CT skills, CT dispositions, actual PS skills, and perceived PS skills; (5) determined the role of programming experience and gender on changes in CT and PS; and (6) explored the factors affecting CT skills. Results showed that the proposed assessment had an acceptable level of reliability, CT skills were associated with actual PS skills, and the programming intervention significantly decreased participants’ perceived PS, increased CT dispositions, but it did not change CT skills and actual PS skills; also, CT and PS did not change for those not participating in the intervention. Moreover, there were no interactions between time and gender on CT and PS over time. Of the machine learning algorithms predicting CT skills, Decision Tree outperformed K-Nearest Neighbors, Logistic Regression, and Naive Bayes, with time spent on CT training, prior CT skills, and perceptions of learning content difficulty being the top predictors in this model. Findings advance our understanding of CT, providing educators and researchers with a way of developing and assessing CT that is conducive to problem solving
