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Modeling of Antimicrobial-Resistant Enterococci Exposure Risks in Canadian Beef Cattle Production System Using Existing Data
(2024-04-17) Strong, Kayla; Checkley, Sylvia L.; Checkley, Sylvia L.; Kastelic, John Patrick; Otto, Simon James G.; Reid-Smith, Richard; Waldner, Cheryl Lynne; Lhermie, Guillaume; Noyes, Noelle
Antimicrobial resistance occurs when microbes change, and antimicrobials previously used to treat them are no longer effective. Antimicrobial resistance presents a global risk to health and food safety, with previously treatable infections becoming increasingly costly and challenging. Antimicrobial resistance is a quintessential One Health issue, given its impact and drivers across human, animal, and environmental health, and requires transdisciplinary interpretations and solutions. This thesis considers methods of risk interpretation using a case study of antimicrobial-resistant Enterococcus spp. within Canadian beef production systems. Five objectives were considered: (1) to identify factors associated with antimicrobial-resistant enterococci within Canadian beef production systems; (2) to construct an integrated assessment model for interpretation of factors potentially associated with antimicrobial-resistant enterococci; (3) to construct a risk profile for interpretation of risks associated with antimicrobial-resistant enterococci in Canadian beef; (4) to construct a Bayesian model for interpretation of enterococci resistance within beef production; and (5) to describe integrated strengths and weaknesses of modeling approaches. Factors assessed for association with antimicrobial-resistant Enterococcus spp. within Canadian beef production systems included antimicrobial and nutritional supplement administration to cattle, environmental factors, and type of processing plant. Resistance trends were often nuanced to unique gene and phenotypic resistance. Patterns varied by species of enterococci. When data were available, the integrated assessment model utilized crude odds ratios extracted from identified factors. Limited data for baseline seeding and factor inclusion limited the model's interpretability. Recommendations and best practices are proposed for future model applications. The risk profile was developed to meet the Codex 77 guidelines and demonstrated the scarce evidence of enterococci resistance transference from beef products, and limited human pathogenicity of enterococci from foodborne consumption. The risk profile highlighted the need for Canadian surveillance studies of enterococci in food products for more informed decision-making. The Bayesian model incorporated available evidence with current estimates of enterococci resistance trends, integrating expert opinion within the model. The model suggests that less than 0.3% of beef products carry antimicrobial-resistant E. faecalis. Individual models and risk discussions uniquely fill niches in resistance discussions and interpretations but were insufficient for providing a holistic interpretation required by stakeholders across the production chain. Drawing findings from multiple reports supported a better understanding and enhanced decision-making.
Item
Open Access
Development, Validation, and Implementation of Cytologically Indeterminate Thyroid Nodule Molecular Testing
(2024-04-17) Stewardson, Paul; Eszlinger, Markus; Paschke, Ralf; Demetrick, Douglas J.; Bose, Pinaki; Morris, Donald; Wiseman, Sam M.
Introduction: Genetic testing is increasingly used to diagnose or rule out thyroid cancer in indeterminate fine-needle aspirations. The research in this dissertation aimed to locally develop and validate a novel molecular test (ThyroSPEC), explore ancillary risk stratification, and measure the impacts of introducing molecular testing at the population level including for therapy selection. Methods: Comprehensive clinical data were collected prospectively for the first 615 consecutive patients with indeterminate thyroid nodules (ITNs) in a centralized healthcare system following implementation of ThyroSPEC. Accuracy of molecular testing and the impact of the introduction of molecular testing were calculated. A 16-miRNA panel was developed and validated in 127 air-dried smear indeterminate thyroid FNAC specimens and 157 liquid thyroid FNAC specimens. In a separate study on therapy selection, 86 high risk thyroid cancer patients were retrospectively identified and screened for NTRK fusions and RET fusions/mutations with molecular tests including ThyroSPEC. Results: A locally developed, low-cost molecular test achieved an NPV of 76-91% and a PPV of 46-65% in ITNs using only residual biopsy material. Following implementation of molecular testing, diagnostic yield increased 14% (p=0.2442) and repeat FNAs decreased by 24% (p=0.05). A miRNA expression classifier achieved sensitivity of 100% and specificity of 83% in the air-dried smear sample type as an adjunct to ThyroSPEC. However, miRNA expression was indistinguishable between benign and malignant tumors in local liquid cytology specimens. Molecular testing of advanced thyroid cancer patients resulted in the detection of NTRK or RET fusions in up to 40% of systematically defined patient subsets. Conclusion: Introduction of molecular testing offers clinical benefits, even in a low resection rate setting, and directly influences surgical decision-making. ThyroSPEC improved preoperative risk stratification of indeterminate thyroid nodules but requires further stratification for negative test results and intermediate risk mutations. This research also presented a novel miRNA expression classifier that could be used to incrementally risk stratify indeterminate air-dried smear FNAC and suggests that methanol-based preservation of thyroid liquid FNAC may hinder use of miRNA expression levels for molecular diagnostics. Our findings also indicate that ThyroSPEC may be suitable to detect NTRK and RET fusions in advanced thyroid cancers.
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Open Access
Advancing Temporal and Quality Adaptation in Video Streaming with AV1
(2024-04-18) Ansari, Mohd Akram; Wang, Mea; Boyd, Jeffrey Edwin; Drew, Steve H.
Traditional Adaptive BitRate (ABR) streaming faces the challenge of providing a smooth experience under highly variable network conditions. Many forms of quality adaptation have been proposed, mainly exploring quality or temporal adaptation. The emerging AV1 codec not only advances video encoding and transcoding but also presents opportunities for more flexible quality adaptation. In this thesis, our objective is to push the limit of quality and temporal adaptation with AV1 frame and segment structure. We propose Temporal Adaptive Streaming over QUIC (TASQ) technique and S-frame Adaptive Bitrate (SABR) streaming for temporal and quality adaptation. Our evaluation results show that both TASQ and SABR can significantly improve the smoothness of the streaming playback while conserving bandwidth and improving the Quality- of-Experience (QoE). The only trade-off is short stutters. To smooth out the stutter, we proposed the IFNet-based Frame Filling (IFF) technique. Collectively, TASQ, SABR, and IFF offer a complete quality and temporal adaptation solution.
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Open Access
Exploration of Techniques for Working with Sparse Data when Applying Natural Language Processing to Assist a Qualitative Data Analysis of a COVID-19 Open Innovation Community
(2024-04-17) Yamani, Shirin; Barcomb, Ann; Far, Behrouz; Abbasi, Zahra
This thesis undertakes a novel integration of Natural Language Processing (NLP) with Qualitative Data Analysis (QDA) to investigate the dynamics of volunteer involvement within the TeamOSV community, a collective formed in response to the COVID-19 pandemic. Central to this study is the exploration of roles and interaction patterns among episodic and habitual volunteers, alongside an analysis of the factors influencing their engagement and disengagement within the community. A significant methodological contribution of this work lies in addressing the sparse data challenge, a common constraint in qualitative research, particularly within multi-class classification contexts. The study employs and critically evaluates a range of NLP techniques, with a focus on data augmentation strategies, to enhance the efficacy of various models, including Logistic Regression, Naive Bayes, Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and particularly the Self-Attention model. The proposed framework, identified for its superior performance, demonstrates a noteworthy ability to process and interpret sparse qualitative data, surpassing both traditional approaches in its effectiveness. Furthermore, the thesis explores an in-depth analysis of model variations, assessing the impact of differing configurations of Self-Attention blocks and layers of feed-forward neural networks. It also explores the implications of pre-training on model performance, offering insights into the architectural complexities and training dynamics of NLP models. A crucial aspect of this exploration is the consideration of the trade-offs between model complexity and computational efficiency, highlighting the practical challenges and considerations in deploying these models in qualitative research contexts. Qualitatively, the study offers a detailed examination of volunteer roles within the TeamOSV community. It identifies the distinct contributions and challenges associated with episodic volunteers, characterized by their sporadic engagement patterns, and habitual volunteers, who provide stability and long-termvision. The research also sheds light on the reasons behind volunteer disengagement, such as lifestyle changes and diminishing interest, providing a holistic understanding of volunteer participation in open-source, community-driven projects. The thesis concludes by emphasizing the collaborative strengths of merging NLP with QDA, a union that significantly augments the depth of qualitative research. It proposes a roadmap for future investigations, concentrating on enhancing insights into volunteer coordination within open innovation settings and broadening the application range of NLP in qualitative data examination.
Item
Open Access
Electrokinetic Transport of Silica Nanoparticles in a Biomimetic Porous Medium
(2024-04-18) Sreeram, Priyanka; Natale, Giovanniantonio; Benneker, Anne; Kim, Keekyoung; Hejazi, Hossein
Colloidal particles can be manipulated under an applied external field, one of which is an electric field, to transport them to their desired location. The movement of charged particles or fluids due to an applied electric field is known as electrokinetics. This has widely been used in drug delivery and electrokinetic soil remediation. This phenomenon allows for an easy way to manipulate charged particles. Previous work investigating the application of drug delivery has focused on porous mediums that is homogeneous and physiochemically different from that found in tissues. The electrokinetic and hydrodynamic transport of nanoparticles in biomimetic porous medium has not been studied extensively. In this work, we have incorporated Gelatin methacrylamide (GelMA) hydrogel in a microfluidic chip to explore the transport of silica nanoparticles through the hydrogel. To accomplish this, we studied the transport of silica nanoparticles at varying crosslinker concentration, pressure driven flow and electric field intensity. Our results indicate that by increasing the crosslinker concentration, the pore size gets smaller and the nanoparticle transport is more constrained. Increasing the flow rate increases the distance that the nanoparticles can travel while also increasing the number of aggregates formed. We also explore the transport of silica nanoparticles at varying electric field strengths. We observed that increasing the electric field strength increases the distance travelled by the nanoparticles through the hydrogel and reduces the number of aggregates formed which benefits the transport of the nanoparticle. The experimental results in this thesis open up routes for electrokinetic drug delivery through heterogeneous porous media, and allow for further investigation of the effect of electric fields and more directed drug delivery.