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Item type: Item , Access status: Open Access , The Role of the Mismatch DNA Repair Protein MutL in Antigenic Variation of the Lyme Disease Spirochete(2025-12-10) Shcherbakova, Aleksandra; Chaconas, George; Devinney, Rebekah; Billon, Pierre; Williams, GarethAntigenic variation is one of the immune evasion mechanisms employed by Borrelia burgdorferi to establish a persistent infection in the host through changes of its outer membrane lipoprotein, VlsE, by recombinational events at the vls locus. A recent study identified a requirement for the endonuclease activity of MutL, a canonical DNA mismatch repair protein, for vlsE gene conversion raising the question whether MutL might play a role in recombination or in antigenic variation systems in other bacteria. The aim of this study was to investigate the recombination activity of MutL using B. burgdorferi complementation experiments. I have shown that nanopore sequencing can be used as an expedient method for measuring gene conversion at vlsE that surpasses previously described methods. Current results indicate that MutL from relapsing fever Borrelia species but not from Treponema pallidum or Leptospira interrogans can promote recombination at vlsE in B. burgdorferi suggesting that Borrelia developed a novel sophisticated recombinational switching system to avoid immunosurveillance in a vertebrate host using MutL.Item type: Item , Access status: Open Access , Economic evaluations of cow comfort interventions in dairy production systems: A systematic review protocol(Veterinary Medicine, 2025-12-11) Muunda, Emmanuel; Ceballos, Maria Camila; Ritter, Caroline; VanLeeuwen, John; Rao, Elizaphan James O.; Hall, David C.This protocol outlines a structured approach to evaluating the economic evidence associated with cow comfort interventions across global dairy production systems. Recognizing that inadequate housing, limited resources, and suboptimal management practices can compromise cow welfare and reduce productivity, the review aims to consolidate existing data on the costs, cost-effectiveness, and productivity impacts of interventions designed to improve physical living conditions for dairy cows. By synthesizing studies published between 2005 and 2025, the review seeks to classify intervention types, compare outcomes between high-income and low- and middle-income countries, and identify evidence gaps, particularly in smallholder systems. The anticipated output will inform interventions that enhance both animal welfare and economic performance within diverse dairy farming contexts.Item type: Item , Access status: Open Access , Examining Sex- and Age-Specific Cytokine Profiles in Acute Pediatric Appendicitis(2025-12-10) Khani, Kosar Lotfali; Thompson, Graham; Jenne, Craig; DeBruyn, Jennifer; Gillrie, MarkAcute appendicitis (AA) is a leading cause of emergency abdominal surgery in children, yet timely diagnosis can remain a clinical challenge. Inflammatory cytokine profiling is emerging as a promising method for distinguishing AA from non-appendicitis abdominal pain (NAAP). However, the impact of sex and developmental stage on cytokine profiles is underexplored. Based on sexual dimorphism in immune responses between males and females, this study aimed to evaluate cytokine profiles in pediatric AA and NAAP patients and to determine whether stratification by sex and further by pubertal status enhances diagnostic accuracy. Serum samples from 177 pediatric patients with suspected AA were analyzed using a 48-plex cytokine panel. Multivariate analysis was performed using Principal Component Analysis (PCA) and Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA). The models for the complete patient cohort, in addition to the female and male cohorts, were statistically significant. Interleukin-6 (IL-6) was consistently elevated in all three Profiles. Granulocyte-colony stimulating factor (G-CSF), IL-27, and IL-10 levels were significantly associated with AA, while IL-7 was elevated specifically in AA males. The complete patient model achieved 68% sensitivity, 85% specificity, and an AUROC of 0.88. The model for male patients had 67% sensitivity, 81% specificity, and an AUROC of 0.76, while the model for female patients had 73% sensitivity, 90% specificity, and an AUROC of 0.86. These findings highlight the potential existence of sex-specific cytokine profiles that may be elevated in children with AA and emphasize the need for larger studies to evaluate the role of puberty in clinical diagnostics.Item type: Item , Access status: Embargo , Avoiding Mistakes with Component Selection in Requirements Engineering(2025-12-03) Jaberzadeh Ansari, Mahdi; Barcomb, Ann; Barcomb, Ann; Moussavi, Mahmood; Yanushkevich, Svetlana; Walker, Robert; Wnuk, KrzysztofModern software systems are increasingly being built by combining preexisting building blocks, which are called components, rather than developing everything from scratch [267]. This component-based software development (CBSD) approach can accelerate delivery and reduce cost, but its success is highly dependent on the ability of developers to select appropriate components [133]. Poor component selection can lead to integration failures, reduced main-tainability, security vulnerabilities, and higher costs [157, 355]. Existing research has proposed several methods, tools, and metrics, yet these solutions have struggled to gain traction in industry [157]. Therefore, despite the importance of component selection, current practices remain largely informal and ad hoc, with limited tool support [23, 27]. Developers continue to face uncertainty when evaluating the quality, compatibility, and long-term sustainability of components. At the same time, advances in artificial intelligence (AI) present new opportunities to help developers navigate vast sources of information about components such as GitHub (GH) issues, forums, and documentation [23]. This thesis investi-gates how AI, and in particular natural language processing (NLP), can support developers in assessing components to make more informed selection decisions. The research was carried out in three phases. First, the problem was defined through a literature survey that synthesized recent work on component selection methods, tools, and evaluation criteria, highlighting the strengths, weaknesses, and gaps that limit adoption in practice. Second, the problem was further explored through a survey with almost 100 devel-opers and CBSD experts, which revealed expectations and challenges of practitioners, as well as a strong demand for automated support and evidence-based evaluation of components. Third, a potential solution was introduced in the form of a prototype approach, which applied two subsets of NLP, natural language inference (NLI) and large language models (LLMs), to analyze discussions around components and automatically present pros and cons related to a set of selected quality criteria. The effectiveness of this approach was then evaluated through a second survey involving 37 developers, who evaluated its usefulness and limitations. The results showed that while AI techniques have promise in providing developers with richer and more contextual information, their current limitations in accuracy, transparency, and trustworthiness must be carefully managed. The research contributes an updated review of the literature on component selection practices, insight into the needs of the practitioner from a survey study, and an initial validation of AI-driven techniques for component evaluation. The findings highlight the potential for future work to integrate AI-driven selection support into practical developer workflows, with an eye on applicability, explainability, and performance. This work makes three concrete contributions to the field of component-based software engineering (CBSE). First, it provides a comprehensive literature review that organizes exist-ing selection methods and catalogs over 700 quality attributes into a structured taxonomy, identifying gaps such as the lack of automated component selection tools and overlooked factors like licensing and community health. Second, it offers empirical insights from a sur-vey of nearly 100 practitioners, revealing which criteria developers prioritize (e.g., reliability, documentation, security) and highlighting common pitfalls in current practice, as well as a strong demand for better tool support. Third, it introduces and evaluates a novel AI-driven approach to component evaluation. A curated dataset of 271 developer discussions was labeled for key quality attributes, and a prototype tool was built using NLI and LLM techniques to automatically extract and present evidence (pros and cons) from public sources. An initial user study with 37 developers demonstrated the prototype’s potential to reduce evaluation effort, while also underscoring the need for transparency and explainability in AI-assisted tools. Together, these contributions advance the understanding of component selection challenges and lay the groundwork for integrating intelligent support into developer workflows, ultimately aiming to make CBSD more informed, reliable, and efficient.Item type: Item , Access status: Open Access , Street2School in Motion: Lessons Learned from Engaging in Lab2Market Validate(2025-12-01) Amaechi, Emmanuel Chukwunenye; Huba, Mahmood; Dr. Jacobsen, MicheleStreet2School is a youth-led nonprofit that creates mobile, community-based learning spaces for out-of-school children in Nigeria. Lab2Market Validate is a program offered in partnership with academic institutions that engages researchers and graduate students in testing their ideas for the entrepreneurial world. Over 16 weeks, participants dive into a guided experience characterized by funding support, hands-on exercises, practical workshops, and mentorship from industry experts. The Lab2Market Validate process is designed to help researchers explore, question, and understand the potential of their ideas. This report documents the first author’s participation in the Lab2Market Validate program, supported by a Propel Business Project, Haskayne School of Business, Master of Management student, and a faculty supervisor. This report documents how the Lab2Market experience helped the team to strengthen both the Street2School venture and its leadership. Street2School was entered into the Lab2Market Validate process to test critical assumptions about users, partners, delivery models, and economics, and to clarify whether a mobile classroom model could achieve problem-solution fit. In carrying out the validation process, our team drew on three main sources of information. First, structured customer discovery conversations with parents, community leaders, NGOs, and education officials. Second, weekly mentor scrums, sprint logs, and debrief notes. Third, internal retrospectives and simple pre and post-guidance from mentors on key indicators such as clarity of target segments, value proposition, unit economics, and partnership status. These materials were examined using reflective thematic analysis. The validation process led to a sharper definition of priority segments, a refined value proposition, and a clearer business model for a community-owned and potentially licensable Street2School model. It also supported early partnership progress. We conclude that the Lab2Market Validate process, combined with Propel student leadership, is a powerful platform for validating social ventures that sit at the intersection of education and entrepreneurship. The lessons learned from this validation process now inform next steps in enacting Street2School’s goal of reaching at least ten thousand out-of-school children by 2030 and offer practical guidance for other founders who seek to scale mission-driven models without losing their core values.
