Matching Items (114)
Description
Caracals (Caracal caracal) are a felid species native to regions of southern Africa and western and central Asia. Despite their relatively high prevalence, the majority of research conducted on caracals has been undertaken on captive individuals, which encounter significantly different environments and exhibit different behaviors in comparison to caracals in the wild. Thereby, they likely have a vastly different virome. The goal of this study was to identify known and unknown DNA viruses associated with free-ranging caracals. Caracal fecal and organ samples were obtained from a caracal surveillance study undertaken in the Western Cape region of South Africa. Parasitic ticks found feeding on caracals were also obtained. Using a viral metagenomic informed approach, a novel circovirus (family Circoviridae) was detected and characterized in caracal fecal, kidney, spleen, and liver samples, as well as in ticks feeding on the caracals. To our knowledge, this is the first circovirus identified in caracals. The novel circovirus was determined to be closely related to a canine circovirus. These findings expand the knowledge of viral diversity and caracals and are greatly important to caracal conservation efforts as well as conservation efforts of other animals within their ecosystem.
ContributorsCollins, Courtney (Author) / Varsani, Arvind (Thesis director) / Dolby, Greer (Committee member) / Kraberger, Simona (Committee member) / Barrett, The Honors College (Contributor) / School of Molecular Sciences (Contributor)
Created2022-05
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
This work presents a thorough analysis of reconstruction of global wave fields (governed by the inhomogeneous wave equation and the Maxwell vector wave equation) from sensor time series data of the wave field. Three major problems are considered. First, an analysis of circumstances under which wave fields can be fully reconstructed from a network of fixed-location sensors is presented. It is proven that, in many cases, wave fields can be fully reconstructed from a single sensor, but that such reconstructions can be sensitive to small perturbations in sensor placement. Generally, multiple sensors are necessary. The next problem considered is how to obtain a global approximation of an electromagnetic wave field in the presence of an amplifying noisy current density from sensor time series data. This type of noise, described in terms of a cylindrical Wiener process, creates a nonequilibrium system, derived from Maxwell’s equations, where variance increases with time. In this noisy system, longer observation times do not generally provide more accurate estimates of the field coefficients. The mean squared error of the estimates can be decomposed into a sum of the squared bias and the variance. As the observation time $\tau$ increases, the bias decreases as $\mathcal{O}(1/\tau)$ but the variance increases as $\mathcal{O}(\tau)$. The contrasting time scales imply the existence of an ``optimal'' observing time (the bias-variance tradeoff). An iterative algorithm is developed to construct global approximations of the electric field using the optimal observing times. Lastly, the effect of sensor acceleration is considered. When the sensor location is fixed, measurements of wave fields composed of plane waves are almost periodic and so can be written in terms of a standard Fourier basis. When the sensor is accelerating, the resulting time series is no longer almost periodic. This phenomenon is related to the Doppler effect, where a time transformation must be performed to obtain the frequency and amplitude information from the time series data. To obtain frequency and amplitude information from accelerating sensor time series data in a general inhomogeneous medium, a randomized algorithm is presented. The algorithm is analyzed and example wave fields are reconstructed.
ContributorsBarclay, Bryce Matthew (Author) / Mahalov, Alex (Thesis advisor) / Kostelich, Eric J (Thesis advisor) / Moustaoui, Mohamed (Committee member) / Motsch, Sebastien (Committee member) / Platte, Rodrigo (Committee member) / Arizona State University (Publisher)
Created2023
Description
Scientists are entrusted with developing novel molecular strategies for effective prophylactic and therapeutic interventions. Antivirals are indispensable tools that can be targeted at viral domains directly or at cellular domains indirectly to obstruct viral infections and reduce pathogenicity. Despite their transformative potential in healthcare, to date, antivirals have been clinically approved to treat only 10 out of the greater than 200 known pathogenic human viruses. Additionally, as obligate intracellular parasites, many virus functions are intimately coupled with host cellular processes. As such, the development of a clinically relevant antiviral is challenged by the limited number of clear targets per virus and necessitates an extensive insight into these molecular processes. Compounding this challenge, many viral pathogens have evolved to evade effective antivirals. Therefore, a means to develop virus- or strain-specific antivirals without detailed insight into each idiosyncratic biochemical mechanism may aid in the development of antivirals against a larger swath of pathogens. Such an approach will tremendously benefit from having the specific molecular recognition of viral species as the lowest barrier. Here, I modify a nanobody (anti-green fluorescent protein) that specifically recognizes non-essential epitopes (glycoprotein M-pHluorin chimera) presented on the extra virion surface of a virus (Pseudorabies virus strain 486). The nanobody switches from having no inhibitory properties (tested up to 50 μM) to ∼3 nM IC50 in in vitro infectivity assays using porcine kidney (PK15) cells. The nanobody modifications use highly reliable bioconjugation to a three-dimensional wireframe deoxyribonucleic acid (DNA) origami scaffold. Mechanistic studies suggest that inhibition is mediated by the DNA origami scaffold bound to the virus particle, which obstructs the internalization of the viruses into cells, and that inhibition is enhanced by avidity resulting from multivalent virus and scaffold interactions. The assembled nanostructures demonstrate negligible cytotoxicity (<10 nM) and sufficient stability, further supporting their therapeutic potential. If translatable to other viral species and epitopes, this approach may open a new strategy that leverages existing infrastructures – monoclonal antibody development, phage display, and in vitro evolution - for rapidly developing novel antivirals in vivo.
ContributorsPradhan, Swechchha (Author) / Hariadi, Rizal (Thesis advisor) / Hogue, Ian (Committee member) / Varsani, Arvind (Committee member) / Chen, Qiang (Committee member) / Arizona State University (Publisher)
Created2022
Description
Monkeypox virus (MPXV) is an orthopoxvirus that causes smallpox-like disease and has up to a 10% mortality rate, depending on the infectious strain. The global eradication of the smallpox virus has led to the decrease in smallpox vaccinations, which has led to a drastic increase in the number of human MPXV cases. MPXV has been named the most important orthopoxvirus to infect humans since the eradication of smallpox and has been the causative agent of the 2022 world-wide MPXV outbreak. Despite being highly pathogenic, MPXV contains a natural truncation at the N-terminus of its E3 homologue. Vaccinia virus (VACV) E3 protein has two domains: an N- terminus Z-form nucleic acid binding domain (Z-BD) and a C-terminus double stranded RNA binding domain (dsRBD). Both domains are required for pathogenesis, interferon (IFN) resistance, and protein kinase R (PKR) inhibition. The N-terminus is required for evasion of Z-DNA binding protein 1 (ZBP1)-dependent necroptosis. ZBP1 binding to Z- form deoxyribonucleic acid/ribonucleic acid (Z-DNA/RNA) leads to activation of receptor-interacting protein kinase 3 (RIPK3) leading to mixed lineage kinase domain- like (MLKL) phosphorylation, aggregation and cell death. This study investigated how different cell lines combat MPXV infection and how MPXV has evolved ways to circumvent the host response. MPXV is shown to inhibit necroptosis in L929 cells by degrading RIPK3 through the viral inducer of RIPK3 degradation (vIRD) and by inhibiting MLKL aggregation. Additionally, the data shows that IFN treatment efficiently inhibits MPXV replication in a ZBP1-, RIPK3-, and MLKL- dependent manner, but independent of necroptosis. Also, the data suggests that an IFN inducer with a pancaspase or proteasome inhibitor could potentially be a beneficial treatment against MPXV infections. Furthermore, it reveals a link between PKR and pathogen-induced necroptosis that has not been previously described.
ContributorsWilliams, Jacqueline (Author) / Jacobs, Bertram (Thesis advisor) / Langland, Jeffrey (Committee member) / Lake, Douglas (Committee member) / Varsani, Arvind (Committee member) / Arizona State University (Publisher)
Created2022
Description
Climate is a critical determinant of agricultural productivity, and the ability to accurately predict this productivity is necessary to provide guidance regarding food security and agricultural management. Previous predictions vary in approach due to the myriad of factors influencing agricultural productivity but generally suggest long-term declines in productivity and agricultural land suitability under climate change. In this paper, I relate predicted climate changes to yield for three major United States crops, namely corn, soybeans, and wheat, using a moderate emissions scenario. By adopting data-driven machine learning approaches, I used the following machine learning methods: random forest (RF), extreme gradient boosting (XGB), and artificial neural networks (ANN) to perform comparative analysis and ensemble methodology. I omitted the western US due to the region's susceptibility to water stress and the prevalence of artificial irrigation as a means to compensate for dry conditions. By considering only climate, the model's results suggest an ensemble mean decline in crop yield of 23.4\% for corn, 19.1\% for soybeans, and 7.8\% for wheat between the years of 2017 and 2100. These results emphasize potential negative impacts of climate change on the current agricultural industry as a result of shifting bio-climactic conditions.
ContributorsSwarup, Shray (Author) / Eikenberry, Steffen (Thesis director) / Mahalov, Alex (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2023-05
Description
Traditional public health strategies for assessing human behavior, exposure, and activity are considered resource-exhaustive, time-consuming, and expensive, warranting a need for alternative methods to enhance data acquisition and subsequent interventions. This dissertation critically evaluated the use of wastewater-based epidemiology (WBE) as an inclusive and non-invasive tool for conducting near real-time population health assessments. A rigorous literature review was performed to gauge the current landscape of WBE to monitor for biomarkers indicative of diet, as well as exposure to estrogen-mimicking endocrine disrupting (EED) chemicals via route of ingestion. Wastewater-derived measurements of phytoestrogens from August 2017 through July 2019 (n = 156 samples) in a small sewer catchment revealed seasonal patterns, with highest average per capita consumption rates in January through March of each year (2018: 7.0 ± 2.0 mg d-1; 2019: 8.2 ± 2.3 mg d-1) and statistically significant differences (p = 0.01) between fall and winter (3.4 ± 1.2 vs. 6.1 ± 2.9 mg d-1; p ≤ 0.01) and spring and summer (5.6 ± 2.1 vs. 3.4 ± 1.5 mg d-1; p ≤ 0.01). Additional investigations, including a human gut microbial composition analysis of community wastewater, were performed to support a methodological framework for future implementation of WBE to assess population-level dietary behavior. In response to the COVID-19 global pandemic, a high-frequency, high-resolution sample collection approach with public data sharing was implemented throughout the City of Tempe, Arizona, and analyzed for SARS-CoV-2 (E gene) from April 2020 through March 2021 (n = 1,556 samples). Results indicate early warning capability during the first wave (June 2020) compared to newly reported clinical cases (8.5 ± 2.1 days), later transitioning to a slight lagging indicator in December/January 2020-21 (-2.0 ± 1.4 days). A viral hotspot from within a larger catchment area was detected, prompting targeted interventions to successfully mitigate community spread; reinforcing the importance of sample collection within the sewer infrastructure. I conclude that by working in tandem with traditional approaches, WBE can enlighten a comprehensive understanding of population health, with methods and strategies implemented in this work recommended for future expansion to produce timely, actionable data in support of public health.
ContributorsBowes, Devin Ashley (Author) / Halden, Rolf U (Thesis advisor) / Krajmalnik-Brown, Rosa (Thesis advisor) / Conroy-Ben, Otakuye (Committee member) / Varsani, Arvind (Committee member) / Whisner, Corrie (Committee member) / Arizona State University (Publisher)
Created2022
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
Poxviruses such as monkeypox virus (MPXV) are emerging zoonotic diseases. Compared to MPXV, Vaccinia virus (VACV) has reduced pathogenicity in humans and can be used as a partially protective vaccine against MPXV. While most orthopoxviruses have E3 protein homologues with highly similar N-termini, the MPXV homologue, F3, has a start codon mutation leading to an N-terminal truncation of 37 amino acids. The VACV protein E3 consists of a dsRNA binding domain in its C-terminus which must be intact for pathogenicity in murine models and replication in cultured cells. The N-terminus of E3 contains a Z-form nucleic acid (ZNA) binding domain and is also required for pathogenicity in murine models. Poxviruses produce RNA transcripts that extend beyond the transcribed gene which can form double-stranded RNA (dsRNA). The innate immune system easily recognizes dsRNA through proteins such as protein kinase R (PKR). After comparing a vaccinia virus with a wild-type E3 protein (VACV WT) to one with an E3 N-terminal truncation of 37 amino acids (VACV E3Δ37N), phenotypic differences appeared in several cell lines. In HeLa cells and certain murine embryonic fibroblasts (MEFs), dsRNA recognition pathways such as PKR become activated during VACV E3Δ37N infections, unlike VACV WT. However, MPXV does not activate PKR in HeLa or MEF cells. Additional investigation determined that MPXV produces less dsRNA than VACV. VACV E3Δ37N was made more similar to MPXV by selecting mutants that produce less dsRNA. By producing less dsRNA, VACV E3Δ37N no longer activated PKR in HeLa or MEF cells, thus restoring the wild-type phenotype. Furthermore, in other cell lines such as L929 (also a murine fibroblast) VACV E3Δ37N, but not VACV WT infection leads to activation of DNA-dependent activator of IFN-regulatory factors (DAI) and induction of necroptotic cell death. The same low dsRNA mutants demonstrate that DAI activation and necroptotic induction is independent of classical dsRNA. Finally, investigations of spread in an animal model and replication in cell lines where both the PKR and DAI pathways are intact determined that inhibition of both pathways is required for VACV E3Δ37N to replicate.
ContributorsCotsmire, Samantha (Author) / Jacobs, Bertram L (Thesis advisor) / Varsani, Arvind (Committee member) / Hogue, Brenda (Committee member) / Haydel, Shelley (Committee member) / Arizona State University (Publisher)
Created2021
Description
Bats are a highly diverse mammal species with a dense virome and fascinating immune system. The following project utilizes metagenomics in order to identify DNA viruses present in populations of silver-haired bats and Mexican free-tailed bats from southern Arizona. A significant number of DNA viruses and novel viruses were identified in the Cressdnaviricota phylum and Microvirdae family.
ContributorsHarding, Ciara (Author) / Varsani, Arvind (Thesis director) / Dolby, Greer (Committee member) / Kraberger, Simona (Committee member) / Barrett, The Honors College (Contributor) / School of Life Sciences (Contributor) / Watts College of Public Service & Community Solut (Contributor)
Created2022-05