Matching Items (113)
Description
Computational models have long been used to describe and predict the outcome of complex immunological processes. The dissertation work described here centers on the construction of multiscale computational immunology models that derives biological insights at the population, systems, and atomistic levels. First, SARS-CoV-2 mortality is investigated through the lens of the predicted robustness of CD8+ T cell responses in 23 different populations. The robustness of CD8+ T cell responses in a given population was modeled by predicting the efficiency of endemic MHC-I protein variants to present peptides derived from SARS-CoV-2 proteins to circulating T cells. To accomplish this task, an algorithm, called EnsembleMHC, was developed to predict viral peptides with a high probability of being recognized by CD T cells. It was discovered that there was significant variation in the efficiency of different MHC-I protein variants to present SARS-CoV-2 derived peptides, and countries enriched with variants with high presentation efficiency had significantly lower mortality rates. Second, a biophysics-based MHC-I peptide prediction algorithm was developed. The MHC-I protein is the most polymorphic protein in the human genome with polymorphisms in the peptide binding causing striking changes in the amino acid compositions, or binding motifs, of peptide species capable of stable binding. A deep learning model, coined HLA-Inception, was trained to predict peptide binding using only biophysical properties, namely electrostatic potential. HLA-Inception was shown to be extremely accurate and efficient at predicting peptide binding motifs and was used to determine the peptide binding motifs of 5,821 MHC-I protein variants. Finally, the impact of stalk glycosylations on NL63 protein dynamics was investigated. Previous data has shown that coronavirus crown glycans play an important role in immune evasion and receptor binding, however, little is known about the role of the stalk glycans. Through the integration of computational biology, experimental data, and physics-based simulations, the stalk glycans were shown to heavily influence the bending angle of spike protein, with a particular emphasis on the glycan at position 1242. Further investigation revealed that removal of the N1242 glycan significantly reduced infectivity, highlighting a new potential therapeutic target. Overall, these investigations and associated innovations in integrative modeling.
ContributorsWilson, Eric Andrew (Author) / Anderson, Karen (Thesis advisor) / Singharoy, Abhishek (Thesis advisor) / Woodbury, Neal (Committee member) / Sulc, Petr (Committee member) / Arizona State University (Publisher)
Created2022
Description
Infrastructure systems are facing non-stationary challenges that stem from climate change and the increasingly complex interactions between the social, ecological, and technological systems (SETSs). It is crucial for transportation infrastructures—which enable residents to access opportunities and foster prosperity, quality of life, and social connections—to be resilient under these non-stationary challenges. Vulnerability assessment (VA) examines the potential consequences a system is likely to experience due to exposure to perturbation or stressors and lack of the capacity to adapt. Post-fire debris flow and heat represent particularly challenging problems for infrastructure and users in the arid U.S. West. Post-fire debris flow, which is manifested with heat and drought, produces powerful runoff threatening physical transportation infrastructures. And heat waves have devastating health effects on transportation infrastructure users, including increased mortality rates. VA anticipates the potential consequences of these perturbations and enables infrastructure stakeholders to improve the system's resilience. The current transportation climate VA—which only considers a single direct climate stressor on the infrastructure—falls short of addressing the wildfire and heat challenges. This work proposes advanced transportation climate VA methods to address the complex and multiple climate stressors and the vulnerability of infrastructure users. Two specific regions were chosen to carry out the progressive transportation climate VA: 1) the California transportation networks’ vulnerability to post-fire debris flows, and 2) the transportation infrastructure user’s vulnerability to heat exposure in Phoenix.
ContributorsLi, Rui (Author) / Chester, Mikhail V. (Thesis advisor) / Middel, Ariane (Committee member) / Hondula, David M. (Committee member) / Pendyala, Ram (Committee member) / Arizona State University (Publisher)
Created2022
Description
Evolution is a key feature of undergraduate biology education: the AmericanAssociation for the Advancement of Science (AAAS) has identified evolution as one of
the five core concepts of biology, and it is relevant to a wide array of biology-related
careers. If biology instructors want students to use evolution to address scientific
challenges post-graduation, students need to be able to apply evolutionary principles to
real-life situations, and accept that the theory of evolution is the best scientific
explanation for the unity and diversity of life on Earth. In order to help students progress
on both fronts, biology education researchers need surveys that measure evolution
acceptance and assessments that measure students’ ability to apply evolutionary concepts.
This dissertation improves the measurement of student understanding and acceptance of
evolution by (1) developing a novel Evolutionary Medicine Assessment that measures
students’ ability to apply the core principles of Evolutionary Medicine to a variety of
health-related scenarios, (2) reevaluating existing measures of student evolution
acceptance by using student interviews to assess response process validity, and (3)
correcting the validity issues identified on the most widely-used measure of evolution
acceptance - the Measure of Acceptance of the Theory of Evolution (MATE) - by
developing and validating a revised version of this survey: the MATE 2.0.
ContributorsMisheva, Anastasia Taya (Author) / Brownell, Sara (Thesis advisor) / Barnes, Elizabeth (Committee member) / Collins, James (Committee member) / Cooper, Katelyn (Committee member) / Sterner, Beckett (Committee member) / Arizona State University (Publisher)
Created2023
Description
Machine learning (ML) and deep learning (DL) has become an intrinsic part of multiple fields. The ability to solve complex problems makes machine learning a panacea. In the last few years, there has been an explosion of data generation, which has greatly improvised machine learning models. But this comes with a cost of high computation, which invariably increases power usage and cost of the hardware. In this thesis we explore applications of ML techniques, applied to two completely different fields - arts, media and theater and urban climate research using low-cost and low-powered edge devices. The multi-modal chatbot uses different machine learning techniques: natural language processing (NLP) and computer vision (CV) to understand inputs of the user and accordingly perform in the play and interact with the audience. This system is also equipped with other interactive hardware setups like movable LED systems, together they provide an experiential theatrical play tailored to each user. I will discuss how I used edge devices to achieve this AI system which has created a new genre in theatrical play. I will then discuss MaRTiny, which is an AI-based bio-meteorological system that calculates mean radiant temperature (MRT), which is an important parameter for urban climate research. It is also equipped with a vision system that performs different machine learning tasks like pedestrian and shade detection. The entire system costs around $200 which can potentially replace the existing setup worth $20,000. I will further discuss how I overcame the inaccuracies in MRT value caused by the system, using machine learning methods. These projects although belonging to two very different fields, are implemented using edge devices and use similar ML techniques. In this thesis I will detail out different techniques that are shared between these two projects and how they can be used in several other applications using edge devices.
ContributorsKulkarni, Karthik Kashinath (Author) / Jayasuriya, Suren (Thesis advisor) / Middel, Ariane (Thesis advisor) / Yu, Hongbin (Committee member) / Arizona State University (Publisher)
Created2021
Description
People with disabilities are underrepresented in the Science, Technology, Engineering, and Math (STEM) workforce (NSF, 2016). One way to increase representation of people with disabilities in STEM fields is by supporting students with disabilities (SWDs) at the undergraduate level. In undergraduate education in the United States, SWDs represent approximately 19% of the undergraduate community (U.S. Census Bureau, 2021). However, SWDs have lower graduation and retention rates. This is particularly true for STEM majors, where SWDs make up about 9% of the STEM community in higher education. The AAC&U has defined a list of High-Impact Practices (HIPs), which are active learning practices and experiences that encourage deep learning by promoting student engagement, and could ultimately support student retention (AAC&U). To date, student-centered disability research has not explored the extent to which SWDs participate in HIPs. We hypothesized that SWDs are less likely than students without disabilities to be involved in HIPs and that students who identify as having severe disabilities would participate in HIPs at lower rates. In this study, we conducted a national survey to examine involvement in HIPs for students with disabilities in STEM. We found that disability status significantly affects the probability of participation in undergraduate research, but is not a significant factor for participation in most other HIPs. We also found that self-reported severity of disability did not significantly impact participation in HIPs, though we observed trends that students reporting higher severity generally reported lower participation in HIPs. Our open-ended responses did indicate that SWDs still faced barriers to participation in HIPs.
ContributorsPais, Danielle (Author) / Brownell, Sara (Thesis director) / Cooper, Katelyn (Committee member) / Barrett, The Honors College (Contributor) / Historical, Philosophical & Religious Studies, Sch (Contributor) / School of Life Sciences (Contributor) / School of International Letters and Cultures (Contributor)
Created2022-05
Description
There is increasing interest in understanding how active learning affects students’ mental health as science courses transition from traditional lecture to active learning. Prior research has found that active learning can both alleviate and exacerbate undergraduate mental health problems. Existing studies have only examined the relationship between active learning and anxiety. No studies have examined the relationship between active learning and undergraduate depression. To address this gap in the literature, we conducted hour-long exploratory interviews with 29 students with depression who had taken active learning science courses across six U.S. institutions. We probed what aspects of active learning practices exacerbate or alleviate depressive symptoms and how students’ depression affects their experiences in active learning. We found that aspects of active learning practices exacerbate and alleviate students’ depressive symptoms, and depression negatively impacts students’ experiences in active learning. The underlying aspects of active learning practices that impact students’ depression fall into four overarching categories: inherently social, inherently engaging, opportunities to compare selves to others, and opportunities to validate or invalidate intelligence. We hope that by better understanding the experiences of undergraduates with depression in active learning courses we can create more inclusive learning environments for these students.
ContributorsAraghi, Tala (Author) / Cooper, Katelyn (Thesis director) / Brownell, Sara (Committee member) / Busch, Carly (Committee member) / Barrett, The Honors College (Contributor) / School of Life Sciences (Contributor)
Created2022-05
Description
Mounting evidence suggests that gender biases favoring men and racial biases favoring whites and Asians contribute to the underrepresentation of women and underrepresented minorities (URM) in science, technology, engineering, and mathematics (STEM). Systemic issues caused by gender and racial biases create barriers that prevent women and URM from entering STEM from the structure of education to admission or promotions to higher-level positions. One of these barriers is unconscious biases that impact the quality of letters of recommendation for women and URM and their success in application processes to higher education. Though letters of recommendation provide a qualitative aspect to an application and can reveal the typical performance of the applicant, research has found that the unstructured nature of the traditional recommendation letter allows for gender and racial bias to impact the quality of letters of recommendation. Standardized letters of recommendation have been implemented in various fields and have been found to reduce the presence of bias in recommendation letters. This paper reviews the trends seen across the literature regarding equity in the use of letters of recommendation for undergraduates.
ContributorsKolath, Nina (Author) / Brownell, Sara (Thesis director) / Goodwin, Emma (Committee member) / Barrett, The Honors College (Contributor) / School of Criminology and Criminal Justice (Contributor) / School of Life Sciences (Contributor)
Created2022-05
Description
This work focuses on a novel approach to combine electrical current with cyanobacterial technology, called microbial electrophotosynthesis (MEPS). It involves using genetically modified PSII-less Synechocystis PCC 6803 cells to avoid photoinhibition, a problem that hinders green energy. In the work, a cathodic electron delivery system is employed for growth and synthesis. Photoinhibition leads to the dissipation energy and lower yield, and is a major obstacle to preventing green energy from competing with fossil fuels. However, the urgent need for alternative energy sources is driven by soaring energy consumption and rising atmospheric carbon dioxide levels. When developed, MEPS can contribute to a carbon capture technology while helping with energy demands. It is thought that if PSII electron flux can be replaced with an alternative source photosynthesis could be enhanced for more effective production. MEPS has the potential to address these challenges by serving as a carbon capture technology while meeting energy demands. The idea is to replace PSII electron flux with an alternative source, which can be enhanced for higher yields in light intensities not tolerated with PSII. This research specifically focuses on creating the initiation of electron flux between the cathode and the MEPS cells while controlling and measuring the system in real time.
The successful proof-of-concept work shows that MEPS can indeed generate high-light-dependent current at intensities up to 2050 µmol photons m^‒2 s^‒1, delivering 113 µmol electrons h^‒1 mg-chl^‒1. The results were further developed to characterize redox tuning for electron delivery of flux to the photosynthetic electron transport chain and redox-based kinetic analysis to model the limitations of the MEPS system.
ContributorsLewis, Christine Michelle (Author) / Torres, César I (Thesis advisor) / Fromme, Petra (Thesis advisor) / Woodbury, Neal (Committee member) / Hayes, Mark (Committee member) / Arizona State University (Publisher)
Created2023
Description
Cities globally are experiencing substantial warming due to ongoing urbanization and climate change. However, existing efforts to mitigate urban heat focus mainly on new technologies, exacerbate social injustices, and ignore the need for a sustainability lens that considers environmental, social, and economic perspectives. Heat in urban areas is amplified and urgently needs to be considered as a critical sustainability issue that crosses disciplinary and sectoral (traditional) boundaries. The missing urgency is concerning because urban overheating is a multi-faceted threat to the well-being and performance of individuals as well as the energy efficiency and economy of cities. Urban heat consequences require transformation in ways of thinking by involving the best available knowledge engaging scientists, policymakers, and communities. To do so, effective heat mitigation planning requires a considerable amount of diverse knowledge sources, yet urban planners face multiple barriers to effective heat mitigation, including a lack of usable, policy-relevant science and governance structures. To address these issues, transdisciplinary approaches, such as co-production via partnerships and the creation of usable, policy-relevant science, are necessary to allow for sustainable and equitable heat mitigation that allow cities to work toward multiple Sustainable Development Goals (SDGs) using a systems approach. This dissertation presents three studies that contribute to a sustainability lens on urban heat, improve the holistic and multi-perspective understanding of heat mitigation strategies, provide contextual guidance for reflective pavement as a heat mitigation strategy, and evaluate a multilateral, sustainability-oriented, co-production partnership to foster heat resilience equitably in cities. Results show that science and city practice communicate differently about heat mitigation strategies while both avoid to communicate disservices and trade-offs. Additionally, performance evaluation of heat mitigation strategies for decision-making needs to consider multiple heat metrics, people, and background climate. Lastly, the partnership between science, city practice, and community needs to be evaluated to be accountable and provide a pathway of growth for all partners. The outcomes of this dissertation advance research and awareness of urban heat for science, practice, and community, and provide guidance to improve holistic and sustainable decision-making in cities and partnerships to address SDGs around urban heat.
ContributorsSchneider, Florian Arwed (Author) / Middel, Ariane (Thesis advisor) / Vanos, Jennifer K (Committee member) / Withycombe Keeler, Lauren (Committee member) / Arizona State University (Publisher)
Created2023
Description
Extreme heat and its human impacts are significant public health challenges that disproportionately affect certain populations. Often, people with the least resources to cope with the heat also live in the hottest regions of cities. Previous heat vulnerability research has predominantly been conducted at a coarse geographic scale, yet translating relationships measured at aggregated scales to the individual level can result in ecological fallacy. Prior work has also primarily studied the most severe health outcomes: hospitalization/emergency care and mortality. It is likely that magnitudes more people are experiencing negative health impacts from heat that do not necessarily result in medical care. Such less severe impacts are under-researched in the literature.This dissertation addresses these knowledge gaps by identifying how social characteristics and physical measurements of heat at the individual and household level act independently and in concert to influence human heat-related outcomes, especially less severe outcomes. In the first paper, meta-analysis was used to quantify the summary effects of vulnerability indicators on incidence of heat-related illness. More proximal vulnerability indicators (e.g., residential air conditioning use, indoor heat exposure, etc.) tended to have the strongest impact on odds of experiencing heat-related illness than more distal indicators. In the next paper, indoor air temperature observations were related to the social characteristics of the residents. The strongest predictor of indoor air temperature was the residents’ ideal thermally comfortable temperature, despite affordability. In the final paper, fine scale biometeorological observations of the outdoor thermal environment near residents’ homes were linked to their experience with heat-related illness. The outdoor thermal environment appeared to have a stronger, more consistent impact on heat-related illness among households in a lower income neighborhood compared to a higher income one. These findings affirm the value of employing residential heat mitigation solutions at the individual and household scale, indoors and outdoors. Across all chapters, the indoor thermal environment, and the ability to modify it, had a clear impact on residents’ comfort and health. Solutions that target the most proximal causal factors of heat-related illness will likely have the greatest impact on reducing the burden of heat on human health and well-being.
ContributorsWright, Mary K (Author) / Hondula, David M (Thesis advisor) / Larson, Kelli L (Committee member) / Middel, Ariane (Committee member) / Arizona State University (Publisher)
Created2023