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    Biophysical and Structural Characterization of the Metronidazole Resistance Protein NimB from Clostridioides difficile
    (University of Waterloo, 2026-08-12) Yazdani, Shauhin
    Clostridioides difficile nitroimidazole reductase B (CdNimB) is a proposed heme-dependent flavin enzyme associated with metronidazole resistance. This thesis characterized recombinant CdNimB to examine its folding, cofactor association, solution-state behaviour, and agreement with predicted structural models. CdNimB was expressed in Escherichia coli, purified after His-SUMO tag removal, and verified by intact protein LC-MS as the expected mature protein. Circular dichroism showed that apo and heme-bound CdNimB were folded in solution. Differential scanning fluorimetry revealed strong hemin-dependent stabilization, increasing the apparent melting temperature from 64.7°C to 87.7°C, while UV-visible spectroscopy and cofactor extraction LC-MS supported heme association. In contrast, FAD supplementation produced limited additional evidence of stable association, leaving the flavin component unresolved. SAXS supported an oligomeric, likely dimeric, solution-state architecture. Apo CdNimB was more consistent with AlphaFold-derived and homologue-informed models, whereas heme-bound CdNimB deviated more strongly, suggesting that heme binding may stabilize a distinct solution-state ensemble. Heme-bound CdNimB crystals diffracted to 2.75 Å, although molecular replacement was unsuccessful. Preliminary anaerobic metronidazole survival assays suggested possible CdNimB-associated protection. Overall, this work supports a model in which CdNimB is a folded, heme-associated, likely dimeric protein whose behaviour changes upon heme binding. These findings provide a purified-protein framework for future studies defining the roles of heme, FAD, His55, and metronidazole binding in CdNimB-dependent resistance.
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    Assessing Count Measurement Systems
    (University of Waterloo, 2026-08-12) French, Megan
    Measurement system analysis provides a method to quantify the sources of variation in observed data collected from a measurement system. Extensive literature describes plans and analyses for assessing a continuous measurement system. However, there has been limited research on how to assess a measurement system when the outcome is a count. Processes with a count outcome occur across various industries, such as manufacturing and medicine. In this thesis, we propose a new model and approach for assessing count measurement systems. First, we introduce measurement system analysis and provide a general framework for this thesis. Then, we introduce a flexible count measurement system assessment model and approach when a gold standard or the true count is available. In this model, we assume that the true counts arise from a Poisson distribution and that the observed counts are subject to two types of errors. We may miss a true defect, and we may obtain a false positive by labeling something flawless as a defect. We initially consider a gold standard plan where we have access to all the information. Then, we consider a second plan where we only know the true count. We compare and discuss these plans empirically and theoretically. We then extend this framework to allow for a scenario in which no gold standard is available. To evaluate the count measurement system, we propose a single-phase and two-phase sampling plan. In the single-phase plan, a small sample of parts is selected at random from the manufacturing process and each selected part is measured repeatedly. With the two-phase plan, we begin with an initial measurement of a larger set of parts, from which a subset of these parts is then selected for additional measurements. In the second phase, the selection of parts for remeasurement is not random, but guided by the initial observations, allowing us to focus on parts that are most informative for assessing measurement system performance. Three selection strategies for the two-phase plan are investigated. We compare and discuss these plans empirically and theoretically. Finally, we show how these methods work in practice by applying them to real experimental data. Finally, we relax the assumption that each defect has the same probability of detection. We assume that each defect has a varying probability of detection based on its severity, which is assumed to follow a Beta distribution. We propose five plans, each with varying levels of information, to assess a count measurement system under these conditions. We then compare these plans empirically and theoretically. Additionally, these plans are compared to the analogous case where the probability of detection is fixed. Finally, we analyze a data example and provide practical insights.
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    The Limited Role of Attention in the Production Effect: Converging Evidence from Behavioural and Computational Methods
    (University of Waterloo, 2026-08-12) Tsapoitis, Patrick
    Encoding techniques can enhance memory and may serve as a means of protection from the negative effects of multitasking on memory. The production effect refers to the reliable memory advantage observed when words are spoken aloud rather than silently during the encoding phase for a list of words. This thesis examined the effectiveness of production in boosting memory, even when encoding is performed under divided attention (DA) conditions. In so doing, I expand its potential benefit into a different context, while also specifying whether the availability of attention influences the magnitude of the effect. In Experiment 1, conducted online using the recruitment platform Prolific, participants were visually presented, in counterbalanced order, with two 20-item lists of words: one under full attention (FA) and another under DA with a concurrently performed auditory digit-monitoring task. Within lists, half of the words were prompted to be read aloud, and half were read silently. Memory was subsequently assessed based on the number of correctly recalled words, retrieved via free recall. As expected, memory was lower overall following DA than FA, but importantly, the relative benefit to memory for words read aloud at encoding was maintained. Experiment 2 replicated this pattern using an in-person sample. These results suggest a limited role of attention on the production effect. In Chapter 3, I aimed to develop a computational model that could mimic existing human data of the production effect and findings from Experiment 1. I employed Nengo, a Python package for building, testing, and deploying spiking neural networks. I used this computational tool to simulate human performance. Two sets of words were presented to the model, one under FA and the other under DA: “read aloud” words (half within each list) were reinforced during initial encoding via increasing item strength by 90%, while words “read silently” were not reinforced. We simulated DA during the encoding phase by destabilizing the model’s memory representation of each word through a reduction in the signal-to-noise ratio. Subsequently, recall was simulated using a competitive accumulation process, and output was measured separately by condition. Recall output from 50 unique simulations was compared to that of 50 human participants randomly selected from Experiment 1. In both human and model data, recall was higher for words read aloud than silently, with the general pattern preserved even under DA conditions; however, the magnitude of the production effect in the modelled data was reduced, likely due to floor effects as overall output was quite low following DA. The results of my thesis suggest that attention plays a limited role in accounting for production’s benefit to memory, with converging evidence provided from two methodologies.
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    Advancing Characterization of Municipal Wastewater Treatment Implications to Aquatic Phosphorus and Eutrophication in Receiving Streams
    (University of Waterloo, 2026-08-12) Nunes Vianini, Kelvin
    Municipal wastewater treatment remains a critical intervention for reducing phosphorus inputs to receiving waters and mitigating nutrient loadings to prevent eutrophication. However, treatment performance is still commonly evaluated using total phosphorus, which provides limited insight into the environmental relevance of discharged phosphorus because it does not distinguish between dissolved, particulate, reactive, and potentially bioavailable forms. This limitation is particularly important for advanced treatment systems that reduce total phosphorus but may also alter the balance and relative distribution of specific phosphorus fractions discharged to receiving waters. Under these conditions, concentration-based assessment alone may obscure whether residual phosphorus is likely to remain inert or become available after discharge. The goal of this research was to advance the characterization and interpretation of phosphorus in municipal wastewater effluents by evaluating how treatment affects phosphorus form, release behaviour, and contributions to receiving waters. Full-scale wastewater effluents were characterized across secondary and tertiary treatment configurations using solids collection, phosphorus fractionation, and controlled desorption experiments. These measurements were used to assess the composition and bioavailability of effluent particulate phosphorus, quantify changes associated with tertiary cloth filtration, evaluate phosphorus release behaviour from effluent solids in relation to established sediment-based concepts, and apply a multi-form phosphorus evaluation approach with hydrologic regime analysis to evaluate conditions under which WWTP discharges may materially influence bioavailable phosphorus concentrations post-discharge. Collectively, the findings demonstrated that wastewater phosphorus relevance depends not only on how total phosphorus is removed, but on how remaining phosphorus is partitioned and transformed after discharge. Effluent particulate phosphorus was dominated by reactive inorganic forms, indicating that residual solids can represent a bioavailable phosphorus pool rather than an inert particulate fraction. Tertiary treatment reduced total and particulate phosphorus and lowered the release potential of effluent solids, but did not eliminate dissolved phosphorus inputs or the capacity of remaining solids to act as phosphorus sources. Desorption behaviour was highly influenced by temperature, while pH effects were most evident outside circumneutral conditions. When these experimentally derived behaviours were evaluated under receiving water conditions, the contribution of bioavailable phosphorus was controlled strongly by dilution, background phosphorus concentration, and effluent flow. This thesis demonstrates that total phosphorus removal alone is insufficient to characterize eutrophication-relevant wastewater performance. A more complete assessment requires explicit consideration of phosphorus speciation, particulate phosphorus bioavailability and post-discharge release, and receiving water context. By linking treatment performance to phosphorus form and flow patterns, this work provides a stronger basis for interpreting wastewater phosphorus controls in relation to downstream bioavailable phosphorus exposure, without assuming that concentration changes alone directly predict ecological response.
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    Multilingual Embeddings: Data, Training, and Understanding
    (University of Waterloo, 2026-08-12) Zhang, Xinyu
    Embedding models have been a central component of modern information access systems, including search engines, question answering systems, retrieval-augmented generation, and nowadays agentic search pipelines. By converting text-space search into vector-space search, embedding models provide semantic matching beyond exact lexical overlap. However, their progress has been uneven across languages. While English dense retrieval has benefited from large training collections, mature benchmarks, and well-studied training recipes, many other languages still lack reliable retrieval resources, practical modeling guidance, and a clear understanding of why multilingual transfer works. This thesis studies multilingual embedding models for retrieval from three connected perspectives: data, training, and understanding. First, it introduces two multilingual retrieval resources, Mr. TYDI and MIRACL. Mr. TYDI establishes the first large-scale mono-lingual retrieval benchmark over eleven typologically diverse languages, while MIRACL expands the setting to eighteen languages with ten times richer human relevance annotations. Together, these datasets provide both the supervision needed to train multilingual dense retrievers and the benchmarks needed to evaluate them reliably across diverse languages and scripts. Second, we investigate how to train multilingual dense retrievers under realistic resource conditions. Starting from the observation that plain multilingual DPR can perform only marginally better than BM25 in unsupervised settings, this part studies cases where target-language training data, target-language pretrained models, or both may be unavailable. The analysis compares pretrained backbones, translated and in-language data, multi-stage fine-tuning, cross-lingual transfer, knowledge distillation, and monolingual versus multilingual transformers. These experiments provide practical guidance for building effective multilingual retrieval models when resources differ across languages. Third, this thesis examines how multilingual language models may understand across languages. It analyzes the roles of shared tokens across languages and their impact at the embedding finetuning stage, and then examines how language models may understand token-level semantic concepts, revealing that multilingual understanding and cross-lingual transfer largely depend on token-level semantic structures within multilingual vocabularies and embedding spaces. Overall, this thesis contributes datasets, training strategies, and model analyses that move multilingual retrieval research from infrastructure to practice to interpretation, advancing the development of retrieval systems that can support information access more reliably across languages, scripts, and resource conditions.