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The Making of a COVID-19 Laboratory: Exploring SARS-CoV-2 Antibody Testing

Description

This thesis project is part of a larger collaboration documenting the history of the ASU Biodesign Clinical Testing Laboratory (ABCTL). There are many different aspects that need to be considered when transforming to a clinical testing laboratory. This includes the different types of tests performed in the laboratory. In addition…

This thesis project is part of a larger collaboration documenting the history of the ASU Biodesign Clinical Testing Laboratory (ABCTL). There are many different aspects that need to be considered when transforming to a clinical testing laboratory. This includes the different types of tests performed in the laboratory. In addition to the diagnostic polymerase chain reaction (PCR) test that is performed detecting the presence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), antibody testing is also performed in clinical laboratories. Antibody testing is used to detect a previous infection. Antibodies are produced as part of the immune response against SARS-CoV-2. There are many different forms of antibody tests and their sensitives and specificities have been examined and reviewed in the literature. Antibody testing can be used to determine the seroprevalence of the disease which can inform policy decisions regarding public health strategies. The results from antibody testing can also be used for creating new therapeutics like vaccines. The ABCTL recognizes the shifting need of the community to begin testing for previous infections of SARS-CoV-2 and is developing new forms of antibody testing that can meet them.

ContributorsRuan, Ellen (Co-author) / Smetanick, Jennifer (Co-author) / Majhail, Kajol (Co-author) / Anderson, Laura (Co-author) / Breshears, Scott (Co-author) / Compton, Carolyn (Thesis director) / Magee, Mitch (Committee member) / School of Life Sciences (Contributor, Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Barrett, The Honors College (Contributor)

Created2021-05

Using twitter to examine animal sightings in urban areas during COVID-19 shut-downs during 2020

Description

In this project, I examined the relationship between lockdowns implemented by COVID-19 and the activity of animals in urban areas. I hypothesized that animals became more active in urban areas during COVID-19 quarantine than they were before and I wanted to see if my hypothesis could be researched through Twitter…

In this project, I examined the relationship between lockdowns implemented by COVID-19 and the activity of animals in urban areas. I hypothesized that animals became more active in urban areas during COVID-19 quarantine than they were before and I wanted to see if my hypothesis could be researched through Twitter crowdsourcing. I began by collecting tweets using python code, but upon examining all data output from code-based searches, I concluded that it is quicker and more efficient to use the advanced search on Twitter website. Based on my research, I can neither confirm nor deny if the appearance of wild animals is due to the COVID-19 lockdowns. However, I was able to discover a correlational relationship between these two factors in some research cases. Although my findings are mixed with regard to my original hypothesis, the impact that this phenomenon had on society cannot be denied.

ContributorsHeimlich, Kiana Raye (Author) / Dorn, Ronald (Thesis director) / Martin, Roberta (Committee member) / Donovan, Mary (Committee member) / School of Mathematical and Statistical Sciences (Contributor) / Barrett, The Honors College (Contributor)

Created2021-05

A Multivariate Analysis of Life History Traits Across Species and Their Statistical Power to Predict Cancer Prevalence

Description

Cancer rates vary between people, between cultures, and between tissue types, driven by clinically relevant distinctions in the risk factors that lead to different cancer types. Despite the importance of cancer location in human health, little is known about tissue-specific cancers in non-human animals. We can gain significant insight into…

Cancer rates vary between people, between cultures, and between tissue types, driven by clinically relevant distinctions in the risk factors that lead to different cancer types. Despite the importance of cancer location in human health, little is known about tissue-specific cancers in non-human animals. We can gain significant insight into how evolutionary history has shaped mechanisms of cancer suppression by examining how life history traits impact cancer susceptibility across species. Here, we perform multi-level analysis to test how species-level life history strategies are associated with differences in neoplasia prevalence, and apply this to mammary neoplasia within mammals. We propose that the same patterns of cancer prevalence that have been reported across species will be maintained at the tissue-specific level. We used a combination of factor analysis and phylogenetic regression on 13 life history traits across 90 mammalian species to determine the correlation between a life history trait and how it relates to mammary neoplasia prevalence. The factor analysis presented ways to calculate quantifiable underlying factors that contribute to covariance of entangled life history variables. A greater risk of mammary neoplasia was found to be correlated most significantly with shorter gestation length. With this analysis, a framework is provided for how different life history modalities can influence cancer vulnerability. Additionally, statistical methods developed for this project present a framework for future comparative oncology studies and have the potential for many diverse applications.

ContributorsFox, Morgan Shane (Author) / Maley, Carlo C. (Thesis director) / Boddy, Amy (Committee member) / Compton, Zachary (Committee member) / School of Mathematical and Statistical Sciences (Contributor) / School of Molecular Sciences (Contributor) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)

Created2021-05

Healthcare Cost Trend Impacts by COVID-19 in the State of Arizona

Description

Actuaries can analyze healthcare trends to determine if rates are reasonable and if reserves are adequate. In this talk, we will provide a framework of methods to analyze the healthcare trend during the pandemic. COVID-19 may influence future healthcare cost trends in many ways. First, direct COVID-19 costs may increase…

Actuaries can analyze healthcare trends to determine if rates are reasonable and if reserves are adequate. In this talk, we will provide a framework of methods to analyze the healthcare trend during the pandemic. COVID-19 may influence future healthcare cost trends in many ways. First, direct COVID-19 costs may increase the amount of total experienced healthcare costs. However, with the implementation of social distancing, the amount of regularly scheduled care may be deferred to a future date. There are also many unknown factors regarding the transmission of the virus. Implementing epidemiology models allows us to predict infections by studying the dynamics of the disease. The correlation between infection amounts and hospitalization occupancies provide a methodology to estimate the amount of deferred and recouped amounts of regularly scheduled healthcare costs. Thus, the combination of the models allows to model the healthcare cost trend impact due to COVID-19.

ContributorsGabric, Lydia Joan (Author) / Zhou, Hongjuan (Thesis director) / Zicarelli, John (Committee member) / School of Mathematical and Statistical Sciences (Contributor, Contributor) / Barrett, The Honors College (Contributor)

Created2021-05

The synthesis and characterization of Au-core/LDH-shell nanoparticles

Description

In recent years, the field of nanomedicine has progressed at an astonishing rate, particularly with respect to applications in cancer treatment and molecular imaging. Although organic systems have been the frontrunners, inorganic systems have also begun to show promise, especially those based upon silica and magnetic nanoparticles (NPs). Many of…

In recent years, the field of nanomedicine has progressed at an astonishing rate, particularly with respect to applications in cancer treatment and molecular imaging. Although organic systems have been the frontrunners, inorganic systems have also begun to show promise, especially those based upon silica and magnetic nanoparticles (NPs). Many of these systems are being designed for simultaneous therapeutic and diagnostic capabilities, thus coining the term, theranostics. A unique class of inorganic systems that shows great promise as theranostics is that of layered double hydroxides (LDH). By synthesis of a core/shell structures, e.g. a gold nanoparticle (NP) core and LDH shell, the multifunctional theranostic may be developed without a drastic increase in the structural complexity. To demonstrate initial proof-of-concept of a potential (inorganic) theranostic platform, a Au-core/LDH-shell nanovector has been synthesized and characterized. The LDH shell was heterogeneously nucleated and grown on the surface of silica coated gold NPs via a coprecipitation method. Polyethylene glycol (PEG) was introduced in the initial synthesis steps to improve crystallinity and colloidal stability. Additionally, during synthesis, fluorescein isothiocyanate (FITC) was intercalated into the interlayer spacing of the LDH. In contrast to the PEG stabilization, a post synthesis citric acid treatment was used as a method to control the size and short-term stability. The heterogeneous core-shell system was characterized with scanning electron microscopy (SEM), energy dispersive x-ray spectroscopy (EDX), dynamic light scattering (DLS), and powder x-ray diffraction (PXRD). A preliminary in vitro study carried out with the assistance of Dr. Kaushal Rege's group at Arizona State University was to demonstrate the endocytosis capability of homogeneously-grown LDH NPs. The DLS measurements of the core-shell NPs indicated an average particle size of 212nm. The PXRD analysis showed that PEG greatly improved the crystallinity of the system while simultaneously preventing aggregation of the NPs. The preliminary in vitro fluorescence microscopy revealed a moderate uptake of homogeneous LDH NPs into the cells.

ContributorsRearick, Colton (Author) / Dey, Sandwip K (Thesis advisor) / Krause, Stephen (Committee member) / Ramakrishna, B (Committee member) / Arizona State University (Publisher)

Created2011

The shared cultural knowledge and beliefs about cancer in the Yavapai-Apache community

Description

Native American communities face an ongoing challenge of effectively addressing cancer health disparities, as well as environmental racism issues that may compound these inequities. This dissertation identified the shared cultural knowledge and beliefs about cancer in a southwest American Indian community utilizing a cultural consensus method, an approach that combines…

Native American communities face an ongoing challenge of effectively addressing cancer health disparities, as well as environmental racism issues that may compound these inequities. This dissertation identified the shared cultural knowledge and beliefs about cancer in a southwest American Indian community utilizing a cultural consensus method, an approach that combines qualitative and quantitative data. A community-based participatory research (CBPR) approach was applied at all stages of the study. The three phases of research that were undertaken included: 1) ethnographic interviews - to identifying the themes or the content of the participants' cultural model, 2A) ranking of themes - to provide an understanding of the relative importance of the content of the cultural model, 2B) pile sorts - identify the organization of items within specific domains, and 3) a community survey - access whether the model is shared in the greater community. The cultural consensus method has not been utilized to date in identifying the collective cultural beliefs about cancer prevention, treatment or survivorship in a Native American community. Its use represents a methodological step forward in two areas: 1) the traditional ethnographic inferences used in identifying and defining cultural meaning as it relates to health can be tested more rigorously than in the past, and 2) it addresses the challenge of providing reliable results based on a small number of community informants. This is especially significant when working with smaller tribal/cultural groups where the small sample size has led to questions concerning the reliability and validity of health-related research. Results showed that the key consultants shared strong agreement or consensus on a cultural model regarding the importance of environmental and lifestyle causes of cancer. However, there was no consensus found among the key consultants on the prevention and treatment of cancer. The results of the community survey indicated agreement or consensus in the sub-domains of descriptions of cancer, risk/cause, prevention, treatment, remission/cure and living with cancer. Identifying cultural beliefs and models regarding cancer could contribute to the effective development of culturally responsive cancer prevention education and treatment programs.

ContributorsClaus, Cynthia (Author) / Koss, Joan (Thesis advisor) / Brandt, Elizabeth, (Thesis advisor) / Joe, Jennie (Committee member) / Maupin, Jonathan (Committee member) / Arizona State University (Publisher)

Created2011

System complexity reduction via feature selection

Description

This dissertation transforms a set of system complexity reduction problems to feature selection problems. Three systems are considered: classification based on association rules, network structure learning, and time series classification. Furthermore, two variable importance measures are proposed to reduce the feature selection bias in tree models. Associative classifiers can achieve…

This dissertation transforms a set of system complexity reduction problems to feature selection problems. Three systems are considered: classification based on association rules, network structure learning, and time series classification. Furthermore, two variable importance measures are proposed to reduce the feature selection bias in tree models. Associative classifiers can achieve high accuracy, but the combination of many rules is difficult to interpret. Rule condition subset selection (RCSS) methods for associative classification are considered. RCSS aims to prune the rule conditions into a subset via feature selection. The subset then can be summarized into rule-based classifiers. Experiments show that classifiers after RCSS can substantially improve the classification interpretability without loss of accuracy. An ensemble feature selection method is proposed to learn Markov blankets for either discrete or continuous networks (without linear, Gaussian assumptions). The method is compared to a Bayesian local structure learning algorithm and to alternative feature selection methods in the causal structure learning problem. Feature selection is also used to enhance the interpretability of time series classification. Existing time series classification algorithms (such as nearest-neighbor with dynamic time warping measures) are accurate but difficult to interpret. This research leverages the time-ordering of the data to extract features, and generates an effective and efficient classifier referred to as a time series forest (TSF). The computational complexity of TSF is only linear in the length of time series, and interpretable features can be extracted. These features can be further reduced, and summarized for even better interpretability. Lastly, two variable importance measures are proposed to reduce the feature selection bias in tree-based ensemble models. It is well known that bias can occur when predictor attributes have different numbers of values. Two methods are proposed to solve the bias problem. One uses an out-of-bag sampling method called OOBForest, and the other, based on the new concept of a partial permutation test, is called a pForest. Experimental results show the existing methods are not always reliable for multi-valued predictors, while the proposed methods have advantages.

ContributorsDeng, Houtao (Author) / Runger, George C. (Thesis advisor) / Lohr, Sharon L (Committee member) / Pan, Rong (Committee member) / Zhang, Muhong (Committee member) / Arizona State University (Publisher)

Created2011

A study of boosting based transfer learning for activity and gesture recognition

Description

Real-world environments are characterized by non-stationary and continuously evolving data. Learning a classification model on this data would require a framework that is able to adapt itself to newer circumstances. Under such circumstances, transfer learning has come to be a dependable methodology for improving classification performance with reduced training costs…

Real-world environments are characterized by non-stationary and continuously evolving data. Learning a classification model on this data would require a framework that is able to adapt itself to newer circumstances. Under such circumstances, transfer learning has come to be a dependable methodology for improving classification performance with reduced training costs and without the need for explicit relearning from scratch. In this thesis, a novel instance transfer technique that adapts a "Cost-sensitive" variation of AdaBoost is presented. The method capitalizes on the theoretical and functional properties of AdaBoost to selectively reuse outdated training instances obtained from a "source" domain to effectively classify unseen instances occurring in a different, but related "target" domain. The algorithm is evaluated on real-world classification problems namely accelerometer based 3D gesture recognition, smart home activity recognition and text categorization. The performance on these datasets is analyzed and evaluated against popular boosting-based instance transfer techniques. In addition, supporting empirical studies, that investigate some of the less explored bottlenecks of boosting based instance transfer methods, are presented, to understand the suitability and effectiveness of this form of knowledge transfer.

ContributorsVenkatesan, Ashok (Author) / Panchanathan, Sethuraman (Thesis advisor) / Li, Baoxin (Committee member) / Ye, Jieping (Committee member) / Arizona State University (Publisher)

Created2011

TaxiWorld: developing and evaluating solution methods for multi-agent planning domains

Description

TaxiWorld is a Matlab simulation of a city with a fleet of taxis which operate within it, with the goal of transporting passengers to their destinations. The size of the city, as well as the number of available taxis and the frequency and general locations of fare appearances can all…

TaxiWorld is a Matlab simulation of a city with a fleet of taxis which operate within it, with the goal of transporting passengers to their destinations. The size of the city, as well as the number of available taxis and the frequency and general locations of fare appearances can all be set on a scenario-by-scenario basis. The taxis must attempt to service the fares as quickly as possible, by picking each one up and carrying it to its drop-off location. The TaxiWorld scenario is formally modeled using both Decentralized Partially-Observable Markov Decision Processes (Dec-POMDPs) and Multi-agent Markov Decision Processes (MMDPs). The purpose of developing formal models is to learn how to build and use formal Markov models, such as can be given to planners to solve for optimal policies in problem domains. However, finding optimal solutions for Dec-POMDPs is NEXP-Complete, so an empirical algorithm was also developed as an improvement to the method already in use on the simulator, and the methods were compared in identical scenarios to determine which is more effective. The empirical method is of course not optimal - rather, it attempts to simply account for some of the most important factors to achieve an acceptable level of effectiveness while still retaining a reasonable level of computational complexity for online solving.

ContributorsWhite, Christopher (Author) / Kambhampati, Subbarao (Thesis advisor) / Gupta, Sandeep (Committee member) / Varsamopoulos, Georgios (Committee member) / Arizona State University (Publisher)

Created2011

Signaling pathway deregulation: identification through genomic aberrations and verification through genomic activity

Description

Given the process of tumorigenesis, biological signaling pathways have become of interest in the field of oncology. Many of the regulatory mechanisms that are altered in cancer are directly related to signal transduction and cellular communication. Thus, identifying signaling pathways that have become deregulated may provide useful information…

Given the process of tumorigenesis, biological signaling pathways have become of interest in the field of oncology. Many of the regulatory mechanisms that are altered in cancer are directly related to signal transduction and cellular communication. Thus, identifying signaling pathways that have become deregulated may provide useful information to better understanding altered regulatory mechanisms within cancer. Many methods that have been created to measure the distinct activity of signaling pathways have relied strictly upon transcription profiles. With advancements in comparative genomic hybridization techniques, copy number data has become extremely useful in providing valuable information pertaining to the genomic landscape of cancer. The purpose of this thesis is to develop a methodology that incorporates both gene expression and copy number data to identify signaling pathways that have become deregulated in cancer. The central idea is that copy number data may significantly assist in identifying signaling pathway deregulation by justifying the aberrant activity being measured in gene expression profiles. This method was then applied to four different subtypes of breast cancer resulting in the identification of signaling pathways associated with distinct functionalities for each of the breast cancer subtypes.

ContributorsTrevino, Robert (Author) / Kim, Seungchan (Thesis advisor) / Ringner, Markus (Committee member) / Liu, Huan (Committee member) / Arizona State University (Publisher)

Created2011

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