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APPLYING A FLIPPED CLASSROOM MODEL FOR AN INTERNATIONAL SCHOOL IN TAIWAN: OVERCOMING AMOTIVATION AND ANXIETY IN ALGEBRA
No full text“Math anxiety” is an all-too-common feature in many math classrooms; concomitantly, low motivation follows and, hence, math learning can be impeded (Foley et al., 2017). This mixed methods action research study was designed to measure the effectiveness of a flipped classroom (FC) in reducing math anxiety, increasing motivation, and enhancing learning strategies. An action research approach was taken to explore the effectiveness of a FC model, which has been modified by the ARCS model (Attention, Relevance, Confidence, Satisfaction), elements of gamification, and guided by ADDIE (Analysis, Design, Development, Implementation, Evaluation) design principles. This action research study also provides validity to the FC model currently in use by the math department at Jade Mountain International School (JMIS) (pseudonym), an international school located in Taiwan. Quantitative data, extracted from student surveys, was collected and analysed; students were given pre and post intervention surveys using the Math Anxiety Rating Scale - Revised (MARS-R) and Motivated Strategies for Learning Questionnaire (MSLQ) respectively. The FC model was employed for a 10 week period during the 3rd quarter of the academic year. To develop a robust set of best practices, the ADDIE instructional design model was applied to plan, develop, implement, and evaluate the instructional materials. Motivational elements of the instructional materials and methods were guided by the ARCS framework (Keller, 1987, 2000) which is focused on creating attention, relevance, confidence and satisfaction for the students. The rationale for this action research comes from a) the need to support math students at JMIS, who have math anxiety and associated low motivation, b) to provide additional instructional strategies for JMIS math teachers, and c) to add to existing research on FC models, as implemented in international schools located in Taiwan. The data from the FC intervention showed statistically significant results in anxiety reduction and an increase in motivation and learning strategies
Design and Characterization of Antibody-Conjugated T-Cells and Mimetic Nanovesicles for Cancer Therapy
No full textThe development of immune cell therapies (ICTs) such as chimeric antigen receptor T
(CAR-T) cells and bi-specific T cell engagers (BiTEs) are revolutionizing cancer
treatment. However, production of these therapies is challenging, tedious, and costly. To
overcome these obstacles, we are developing a more efficient and cost-effective ICT that
does not require genetic engineering of T cells. This therapy involves metabolically
engineering T cells to incorporate tetraacetylated N-azidoacetyl-D-mannosamine
(Ac4ManNAz) into cell surface proteins, allowing dibenzocyclooctyne (DBCO)-labelled
antibodies to be conjugated on the cell surface using a strain-promoted alkyne-azide
cycloaddition (SPAAC) reaction. The conjugation process does not depend on the identity
of the antibody, enabling antibody-conjugated T cells (ACTs) to be personalized with one
or more antibodies, according to their intended application.
In this study, we engineered T cells by conjugating them to the epidermal growth
factor receptor (EGFR) antibody nimotuzumab. Nimotuzumab-conjugated ACTs
interacted better with EGFR-positive cell lines and enhanced the killing efficacy
compared to unmodified T cells. Current ICTs such as CAR-T cells have a limited
efficacy against solid tumors. To overcome this limitation, we developed a second
strategy to target and kill tumor cells. We produced mimetic nanovesicles (M-NVs) from
activated-T cells. The small size of M-NVs should allow them to better penetrate solid
tumors. M-NVs were labelled with 6-Azidohexanoic acid NHS Ester (NHS-AZ) followed
by DBCO-nimotuzumab conjugation. Nimotuzumab-conjugated M-NVs inhibited
EGFR-positive cancer cell growth better than non-targeted M-NVs.
In summary, our strategy to construct nimotuzumab-conjugated ACTs and M-NVs
in a simple, robust, and cost-effective manner allows for an adaptive platform that is
translatable to other research labs, being a promising strategy to enhance cancer immune
therapies
Obesity Risk Estimation Accounting Spatial Dependency, Error in Covariate Measurement, and Factors Operating at Multiple Levels
No full textDisease mapping has long been a part of public health, epidemiology, and the study of disease in human populations. Hierarchical spatial models for areal data address the competing goals of accurate small area estimation and fine-scale geographic resolution in disease mapping simultaneously, and it has become a fertile area of research over the last two decades. More recently, there has been increased uptake of the methods in applied research. Nonetheless, there is still scope for the methodological developments. This thesis contributes to the uptake of disease mapping in applied health research through key areas: methodological development, implementation, and application. Chapters 1 and 2 of this thesis provide a brief review of literatures on spatial model, measurement error model, and obesity research. These chapters also summarize methods and data to be utilized in this thesis. Chapter 3 presents an applied research work that demonstrates the importance of incorporating spatial autocorrelation from the observed data into a statistical model, via real and simulated data. The analysis of real data across 117 health regions of Canada is of practical interest, as it identified several obesity clusters with discernible spatial patterns throughout Canada. Chapters 4 and 5 of the thesis present two research works on methodological development. First, covariate measurement error provides biased estimates in standard regression model, violating underlying assumption. In Chapter 4, the classical and Berkson measurement error models were integrated with the well-known Besag-York-Mollie (BYM2) model to incorporate covariate measured with error. The simulation results revealed that the use of a measurement error model for an error-prone covariate in BYM2 model has the advantage of producing a superior fit. The results also demonstrate that a BYM2 model without taking into account covariate measurement error may lead to highly biased estimates for certain parameters. The proposed method was applied for estimating socio-economic and environmental factor’s effect on the obesity counts using aggregated data for 117 health regions of Canada. Second, optimal prediction of risk for an adverse health condition risk at population level requires integrating covariates from multiple levels into a single modeling framework. However, it is a common practice to estimate effects of individual and group-level covariates using multiple models independently. To overcome this methodological gap, this thesis formulated the joint BYM2 model in Chapter 5, that integrates individual- and group-level models through association parameter. The simulation results revealed that the joint BYM2 model performed the same or better than the independent estimation for recovering parameter values. The capability of the proposed model was demonstrated through estimating the risk of developing unhealthy health condition among Canadian secondary school students, integrating individual-, school-, and neighbourhood-level covariates. The neighbourhood-level model incorporated spatially correlated count data and covariates measured with error. Finally, in Chapter 6, the overall findings from this thesis and potential directions for future work are discussed
BIOCONTROL OF ROOT ROT COMPLEX IN FIELD PEA AND LENTIL AND COMPLETE GENOME ANALYSIS OF BIOCONTROL BACTERIA
Get PDFAphanomyces root rot (ARR), caused by the soil-borne oomycete pathogen, Aphanomyces euteiches, is a destructive disease of legumes, most notably to field pea (Pisum sativum L.) and lentil (Lens culinaris L.). It commonly occurs as root rot complex (RRC) along with other soil-borne pathogens, including Fusarium avenaceum and F. oxysporum, which collectively result in significant crop damage leading to complete loss of productivity. Currently, in Canada, the available management strategies against RRC are inadequate. However, a recent study at the University of Saskatchewan identified soil bacteria, Lysobacter capsici K-Hf-H2, Pseudomonas simiae K-Hf-L9 and Pantoea agglomerans PSV1-7, as potential biocontrol agents against ARR in field pea under controlled growth chamber condition. Therefore, the purpose of this study was to i) investigate the potential for biological control of RRC caused by A. euteiches, F. avenaceum and F. oxysporum and ii) unravel the mechanisms by which biocontrol was achieved. To achieve these objectives, L. capsici K-Hf-H2, P. simiae K-Hf-L9 and P. agglomerans PSV1-7 were evaluated against RRC in field pea and lentil under controlled growth chamber conditions, and the strains’ whole genomes were sequenced, annotated, and comparatively analyzed using bioinformatics tools. Also, laboratory-based general functional experiments, siderophores production, proteolytic and cellulolytic capacities, and desiccation tolerance were conducted. Additionally, the current state of the science "biological control of ARR" was determined via a quantitative meta-analysis review using data extracted from published articles investigating the biocontrol of ARR in pea. My meta-analysis findings suggest potential for biological control of ARR and the need for more field trials to demonstrate the higher efficacy level observed under growth chamber conditions. Compared to P. simiae K-Hf-L9 and P. agglomerans PSV1-7, L. capsici K-Hf-H2 demonstrated the highest significant biocontrol efficacy against RRC in field pea and lentil, with higher efficacy in field pea. Moreover, my genome analyses identified several genes and gene clusters encoding various traits potentially involved in the suppression of RRC. Such genetic determinants detected in L. capsici K-Hf-H2 genome include genes encoding for Heat Stable Antifungal Factor (HSAF), endoglucanase (cellulase), chitinase, extracellular zinc proteases (metalloendopeptidase), aminopeptidases and siderophores. In P. simiae K-Hf-L9 and P. agglomerans PSV1-7 genomes, gene and gene clusters encoding iron acquisition, chitin metabolism and protein degradation were detected. I also found evidence that L. capsici K-Hf-H2, P. simiae K-Hf-L9 and P. agglomerans PSV1-7 chelate iron through siderophore production and hydrolyze protein via proteolytic activity. Furthermore, L. capsici K-Hf-H2 and P. simiae K-Hf-L9 were positive for cellulolytic activity. Therefore, my findings indicate the great potential of biological control of RRC in field pea and lentil. Also, the findings in this study represent a significant contribution to the effort of biological control of RRC in field pea and lentil in Canada
Characterizing Lung Inflammation in Agricultural Respiratory Exposures Between the Sexes
Get PDFAgriculture workers, in Saskatchewan and worldwide, are exposed to numerous potential pollutants, including grain dust and pesticides. Although female workers make up approximately 25% of the agricultural working population, the majority of current research on the respiratory and inflammatory effects in agriculture has been conducted on male animal models and workers; very little has been studied of the female response. Females are able to mount a more robust and efficient early immune response leading to improved prognosis in surviving acute infections arising from a variety of pathogens (bacteria, viruses, trauma) compared to males. However, increased efficiency mean females are more predisposed to developing autoimmune diseases. Agriculture workers are commonly exposed to more than one pollutant at a time; how the interaction between glyphosate and lipopolysaccharide (a component of grain dust) will differentially affect the sexes is not known. Currently, there has been minimal work done to evaluate how a respiratory glyphosate exposure may differentially impact the sexes.
The following study evaluates the differences in the inflammatory respiratory response between the sexes following a short-term agriculture respiratory exposure and is the first study to do so. It uses a mouse model. C57BL/6 mice were intranasally treated with glyphosate (1µg), lipopolysaccharide (LPS) (0.5µg), combined LPS + glyphosate (LPS: 0.5 µg + glyphosate: 1µg), or Hank’s Balanced Salt solution (HBSS) for 5 days. These studies were performed to characterize the inflammatory effects in mice following a short-term intranasal exposure to LPS plus glyphosate including 1) evaluating inflammatory effects of the combined exposure to glyphosate and LPS in female mice compared to exposure to each individual agent; 2) comparing the female response to the combined LPS plus glyphosate exposure vs. the male response; and 3) observing the structural lung changes of the combined exposure to glyphosate and LPS in female mice as measured using multiple image radiography.
Female mice, exposed to LPS and glyphosate for 5 days showed higher levels of inflammatory mediators compared to control animals, or those treated with only LPS or glyphosate. Inflammatory mediators, such as proinflammatory cytokines, were elevated in the LPS plus glyphosate treated animals, indicating that after 5 days, the addition of the glyphosate impacts the ability of female mice to ameliorate the effects of LPS, compared to the animals treated only with LPS or glyphosate.
Further, this study revealed that female mice display a different inflammatory respiratory response compared to male mice. Female mice demonstrated: less lung architecture damage across treatment groups; significantly lower levels of inflammatory markers; and lower levels of proinflammatory cytokine expression as compared to male mice. This is the first study to validate that a significant difference exists between the male and female immune response following a short-term agriculture respiratory exposure to LPS and glyphosate.
Finally, comparisons of lung effects using multiple techniques (multiple image radiography, and histology) were utilized to evaluate a short-term common agriculture respiratory exposure in female mice. Histology revealed greater recruitment of cells into alveolar regions in the lungs of the mice and disruption to the bronchial epithelium from the combined LPS and glyphosate treated group as compared to other treatment groups. MIR images revealed mice exposed to LPS and both LPS plus glyphosate showed compromised lung tissue compared to other treatment groups. Taken together, these results reveal that female mice exposed to the combination of LPS and glyphosate displayed physiological and structural effects that were different from mice exposed to LPS or glyphosate alone. However, the inflammatory effects of the combined exposure were not as pronounced in the female mice compared to the male mice, highlighting the importance of using a structural evaluation technique such as multiple image radiography to reveal the impact to the lungs of such exposures.
Overall, we observed that female mice, exposed to an agriculturally relevant concentration combining LPS plus glyphosate for 5 days, exhibited respiratory inflammatory effects significantly different compared to each single exposure. Additionally, we demonstrated that there is a significantly different respiratory inflammatory response between the females and males at 5 days of LPS plus glyphosate exposure. While the precise mechanisms remain to be elucidated, the differences may be due to the protective effects of estrogen.
This study is the first research to characterize a short term respiratory inflammatory exposure to LPS plus glyphosate in female mice, to compare these results to those obtained from male mice, and to utilize multiple image radiography technology to do so. We were able to detect the differences between exposure groups using MIR and refined this technique during our study. The results suggest that MIR may become a paramount tool in future lung imaging experiments
PHYTOCHEMICAL AND SYNTHETIC INVESTIGATIONAL COMPOUNDS AS MODULATORS OF CYTOMETABOLIC AND CYTOSKELETAL DYNAMICS FOR PRE-CLINICAL TARGETING OF PROSTATE CANCER
No full textThe abstract of this item is unavailable due to an embargo
Satisfaction of Individuals Living with Inflammatory Bowel Disease and Gastroenterology Care Providers with Telephone Care
No full textThe abstract of this item is unavailable due to an embargo
A STUDY OF FLUIDIZATION AND GASIFICATION OF BIOMASS PELLETS IN FLUIDIZED BED REACTORS
No full textThe abstract of this item is unavailable due to an embargo
Mapping the Innovation Ecosystems for the Deployment of Small Modular Reactors in Canada and Mexico: An Innovation Policy Approach Through Strategic Niche Management and Social Network Analysis.
Get PDFSmall Modular Reactors (SMRs) have received considerable attention as their specific designs reduce implementation times and costs, allowing modularity to increase the installed capacity for energy generation. Although SMRs represent a reliable, affordable, and sustainable alternative to meet our growing energy demands, this technology faces deployment obstacles that may require outside interventions to speed up their adoption so that people can enjoy their societal, environmental and economic benefits. Just as a country´s best energy mix approach varies by resource availability and institutional capabilities, the actors promoting SMR adoption constitute an innovation ecosystem uniquely responsive to country-specific characteristics. This thesis uses a Strategic Niche Management (SNM) framework that proposes interventions in protected spaces to determine the optimal conditions for successful deployment and appropriate policy while consolidating a community of early adopters.
Through Social Network Analysis (SNA), this thesis compares how these SMR innovation ecosystems are formed in Canada and Mexico, highlighting structural differences between developed and developing countries. This primary framework and research method are then complemented with the Helix Model IV for a comprehensive review of the governance of SMR innovation ecosystems. Policy and network structures are assumed to have a feedback loop effect on each other and SMR deployment potential. Secondary data were collected from publicly available information and processed under the software Gephi 9.5.
Contrary to most research, which focuses solely on centralized actors in a network, this thesis explores the contributions of both centralized and peripheral actors to the network, so policymakers can discern where to efficiently allocate resources depending on their intervention objectives and their main focus. Results indicate that the Mexican SMR ecosystem, with its visually different network structure in all the snapshots, is more vulnerable than the Canadian ecosystem. This difference is especially apparent in the scene where five of the most centralized actors are removed from the two SMR ecosystems
Supporting complex workflows for data-intensive discovery reliably and efficiently
Get PDFScientific workflows have emerged as well-established pillars of large-scale computational science and appeared as torchbearers to formalize and structure a massive amount of complex heterogeneous data and accelerate scientific progress. Scientists of diverse domains can analyze their data by constructing scientific workflows as a useful paradigm to manage complex scientific computations. A workflow can analyze terabyte-scale datasets, contain numerous individual tasks, and coordinate between heterogeneous tasks with the help of scientific workflow management systems (SWfMSs). However, even for expert users, workflow creation is a complex task due to the dramatic growth of tools and data heterogeneity. Scientists are now more willing to publicly share scientific datasets and analysis pipelines in the interest of open science. As sharing of research data and resources increases in scientific communities, scientists can reuse existing workflows shared in several workflow repositories. Unfortunately, several challenges can prevent scientists from reusing those workflows, which hurts the purpose of the community-oriented knowledge base. In this thesis, we first identify the repositories that scientists use to share and reuse scientific workflows. Among several repositories, we find Galaxy repositories have numerous workflows, and Galaxy is the mostly used SWfMS. After selecting the Galaxy repositories, we attempt to explore the workflows and encounter several challenges in reusing them. We classify the reusability status (reusable/nonreusable). Based on the effort level, we further categorize the reusable workflows (reusable without modification, easily reusable, moderately difficult to reuse, and difficult to reuse). Upon failure, we record the associated challenges that prevent reusability. We also list the actions upon success. The challenges preventing reusability include tool upgrading, tool support unavailability, design flaws, incomplete workflows, failure to load a workflow, etc. We need to perform several actions to overcome the challenges. The actions include identifying proper input datasets, updating/upgrading tools, finding alternative tools support for obsolete tools, debugging to find the issue creating tools and connections and solving them, modifying tools connections, etc. Such challenges and our action list offer guidelines to future workflow composers to create better workflows with enhanced reusability. A SWfMS stores provenance data at different phases of a workflow life cycle, which can help workflow construction. This provenance data allows reproducibility and knowledge reuse in the scientific community. But, this provenance information is usually many times larger than the workflow and input data, and managing provenance data is growing in complexity with large-scale applications. In our second study, we document the challenges of provenance management and reuse in e-science, focusing primarily on scientific workflow approaches by exploring different SWfMSs and provenance management systems. We also investigate the ways to overcome the challenges. Creating a workflow is difficult but essential for data-intensive complex analysis, and the existing workflows have several challenges to be reused, so in our third study, we build a recommendation system to recommend tool(s) using machine learning approaches to help scientists create optimal, error-free, and efficient workflows by using existing reusable workflows in Galaxy workflow repositories. The findings from our studies and proposed techniques have the potential to simplify the data-intensive analysis, ensuring reliability and efficiency