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Rethinking the drivers of feline and canine coronavirus virulence and pathogenesis; toward an understanding of the dynamic world of coronavirus mutations, indels and recombination
Olarte-Castillo, Ximena A.; Whittaker, Gary R. (2024)
The viral species Alphacoronavirus-1, which includes feline and canine coronaviruses 1 and 2 (FCoV-1, FCoV-2, CCoV-1 and CCoV-2) as well as transmissible gastroenteritis virus of swine (TGEV), is the cause of a range of disease outcomes in animals and may have zoonotic potential for humans. In cats, feline coronavirus is infamous as the cause of the feline infectious peritonitis (FIP), a lethal disease that can now be treated with antiviral drugs. FCoV-1 disease outcome is driven by a combination of both within- and between-host evolution, whereas FCoV-2 disease appears to be driven by recombination with co-circulating CCoV. This is exemplified by FCoV-23, a novel canine/feline recombinant virus that caused a widespread outbreak of severe disease in Cyprus during 2023. As such, Alphacoronavirus-1 may exist as a dynamic "metavirome" that is in a constant state of flux, presenting notable challenges for disease surveillance and management, and in risk-assessment.
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LIVING ON A PRAYER: FAITH-BASED HOUSING AS A POLICY TOOL
Matthew Goldenberg (2024)
The housing shortage is a pressing issue in the United States, with the situation showing little signs of improvement. The distribution of missing units as a percentage of available supply trends is not evenly distributed by state. Most heartland states east of the Mississippi River see rates of less than 10%. Conversely, California stands at 31%, with Washington, Oregon, Florida, and New York trailing at 26%, 22%, 20%, and 18% respectively (Corinth & Dante, 2022, p. 11). This paper presents faith-based housing as an underutilized resource that, if tapped, would serve to lessen the negative effects of the housing shortage. Core to this position is a quantitative argument that churches and other houses of worship can operate housing more efficiently due to their general exemptions from property taxes, and their ability to realize below market basis for land acquisition costs. This paper does not posit that faith-based housing could in of itself alleviate the housing shortage. As such, this paper is directed towards readers, notably built environment professionals, who are seeking an additional tool which can complement an existing portfolio of housing gap closure measures.
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TOWARDS EFFICIENT AND SCALABLE MACHINE LEARNING FOR FUTURE NEURAL INTERFACES
Zhu, Bingzhao (2023-08)
Closed-loop approaches in systems neuroscience and therapeutic stimulation have the potential to revolutionize our understanding of the brain and develop novel neuromodulation therapies for restoring lost functions. Neural interfaceswith capabilities such as multi-channel neural recording, on-site signal processing, rapid symptom detection, and closed-loop stimulation are crucial for enabling these innovative treatments. However, current closed-loop neural interfaces are limited by their simplicity and lack of sufficient on-chip processing and intelligence. This dissertation focuses on the development of next-generation neural decoders for closed-loop neural interfaces, utilizing on-chip machine learning to detect and suppress symptoms of neurological disorders. These neural decoders offer high versatility, low power consumption, minimal on-chip area, and robustness against neural signal fluctuations. Chapter 2 explores migraine state classification using somatosensory evoked potentials, an emerging application for neural interfaces. In Chapter 3, we introduce a resource-efficient oblique tree model that enables low-power, memory-efficient classifiers for realtime neurological disease detection and motor decoding. Chapter 4 presents a novel Tree in Tree decision graph model with applicability beyond neural data, demonstrating success in general tabular prediction tasks. In Chapter 5, we propose an adaptive machine learning-based decoder to compensate for fluctuations in neural signals during test time. The dissertation concludes with a discussion of future research directions for on-chip neural decoders.
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CREATING AN EXPERIENCE SHARING COMMUNITY IN LEARNING: ENVIRONMENTS, BEHAVIOR, AND LEARNING EXPERIENCE
Zhu, Wangda (2023-08)
In the juncture of virtual and physical learning environments, Experience Sharing Community (ESC) supports learners to create course related posts based on their experience in physical environments, share posts in virtual environments, and interact with peers and instructors. The ESC, via technology and instruction designs, provides students with a place to create, interact, and reflect, influencing diverse learning experience. The idea of ESC is not brand new, similar topics of ESC were explored by studies related to social media use in education for two decades. While there is still a lack of a systematic understanding of how technology and instruction together contribute to learner behavior and experience. This dissertation mainly includes two projects. Study#1 investigates creating an ESC via Instagram in an online learning environment during the Covid-19 pandemic. In a large online course, Introduction to Environmental Psychology, 110 voluntary participants posted photos of their surroundings that were related to the course on Instagram every week. Mixed methods including survey experiment, interview and network analysis were applied to understand their behavior and experience in this community, and how key features of Instagram influenced their experience. The main environments’ features to afford interaction, behaviors including interact, post, and reflect, experience including social presence, cognitive presence, sense of place and sense of belonging, and the relationship between environments, behaviors and experience were identified and explored. While social media such as Instagram was an effective tool for building the ESC, they were not designed for educational settings, and many of their features had to be adapted to better support the ESC. After summarizing the lessons learned from Study#1, I designed and developed my own web application to provide a more tailored experience for students. In Study#2, 114 voluntary participants followed the similar instruction as Study#1 and posted photos on the new app in the same course in a traditional learning setting after the pandemic. This project received positive feedback in terms of engagement, sense of place, and knowledge understanding. Mixed methods including design-based research, interview, and log analysis were applied to further understand how both technology and instruction design influenced student behavior and experience in this community. Based on these two projects, I summarized a framework of conducting longitudinal study to understand participant experience using web applications, regarding its research design and technology needs. This summary extends the scope of the dissertation beyond the ESC, to inspire research methodology development such as Ecological Momentary Assessment. Overall, this dissertation is a unique and valuable addition to the educational landscape, on “what is ESC” and “how to create ESC”, providing students with an opportunity to engage with their peers and expand their knowledge in a dynamic and interactive way. The project also highlights the potential of technology to support and enhance traditional classroom settings, demonstrating the power of innovation and creativity in the pursuit of educational excellence. This learning community can be expanded to broader class settings in the future, including across classes in design fields such as Human-Centered Design, Architecture, and Landscape Architecture. Students across different disciplines can benefit from sharing their knowledge and experience in the community. In addition, the open-sourced technology framework of longitudinal study via web applications including study design, data collection, data analysis and presentation for stakeholders of researchers and instructors can facilitate the research innovation beyond educational technology field.
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RADIAL PROJECTIONS, CONVEX FEASIBILITY PROBLEMS AND MARGIN MAXIMIZATION
Zhou, Song (2023-08)
This work comprises two parts. Part I focuses on the convex feasibility problem (finding or approximating a point in the intersection of finitely many closed convex sets). We avoid the need for orthogonal projections by using radial projections, introduced by Renegar. The main requirement is that an interior point is known in each of the sets considered. By developing Renegar’s theory, we obtain a family of radial projection-based algorithms for the convex feasibility problem which recover the linear convergence rates of orthogonal projection-based methods. Through studying different assumptions on the emptiness of the interior of the intersection set in the convex feasibility problem, we also exhibit how radial projections can be applied to solve constrained optimization problems when certain conditions are met.Part II can be seen as an application of the theory of radial projections developed in Part I. Here, we revisit the notion of maximal-margin classifiers, from around 2000, but now from a general perspective – the intersections of generic closed convex cones, not just half-spaces (i.e., the perceptron). This requires extending concepts and establishing more general theory of the margin function, which is achieved by applying and refining the results in Part I in the conic case. Even more interestingly, we are led to the first Õ(1/ε) first-order method for approximating, within relative error ε, the margin-maximizer of the intersection cone. Previous results, only in the case of the perceptron, were O(1/ε²), making our result a notable improvement even in the most basic of cases.