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- Member of: ASU Electronic Theses and Dissertations
- Status: Published
Description
Cancer claims hundreds of thousands of lives every year in US alone. Finding ways for early detection of cancer onset is crucial for better management and treatment of cancer. Thus, biomarkers especially protein biomarkers, being the functional units which reflect dynamic physiological changes, need to be discovered. Though important, there are only a few approved protein cancer biomarkers till date. To accelerate this process, fast, comprehensive and affordable assays are required which can be applied to large population studies. For this, these assays should be able to comprehensively characterize and explore the molecular diversity of nominally "single" proteins across populations. This information is usually unavailable with commonly used immunoassays such as ELISA (enzyme linked immunosorbent assay) which either ignore protein microheterogeneity, or are confounded by it. To this end, mass spectrometric immuno assays (MSIA) for three different human plasma proteins have been developed. These proteins viz. IGF-1, hemopexin and tetranectin have been found in reported literature to show correlations with many diseases along with several carcinomas. Developed assays were used to extract entire proteins from plasma samples and subsequently analyzed on mass spectrometric platforms. Matrix assisted laser desorption ionization (MALDI) and electrospray ionization (ESI) mass spectrometric techniques where used due to their availability and suitability for the analysis. This resulted in visibility of different structural forms of these proteins showing their structural micro-heterogeneity which is invisible to commonly used immunoassays. These assays are fast, comprehensive and can be applied in large sample studies to analyze proteins for biomarker discovery.
ContributorsRai, Samita (Author) / Nelson, Randall (Thesis advisor) / Hayes, Mark (Thesis advisor) / Borges, Chad (Committee member) / Ros, Alexandra (Committee member) / Arizona State University (Publisher)
Created2012
Description
Microfluidic platforms have been exploited extensively as a tool for the separation of particles by electric field manipulation. Microfluidic devices can facilitate the manipulation of particles by dielectrophoresis. Separation of particles by size and type has been demonstrated by insulator-based dielectrophoresis in a microfluidic device. Thus, manipulating particles by size has been widely studied throughout the years. It has been shown that size-heterogeneity in organelles has been linked to multiple diseases from abnormal organelle size. Here, a mixture of two sizes of polystyrene beads (0.28 and 0.87 μm) was separated by a ratchet migration mechanism under a continuous flow (20 nL/min). Furthermore, to achieve high-throughput separation, different ratchet devices were designed to achieve high-volume separation. Recently, enormous efforts have been made to manipulate small size DNA and proteins. Here, a microfluidic device comprising of multiple valves acting as insulating constrictions when a potential is applied is presented. The tunability of the electric field gradient is evaluated by a COMSOL model, indicating that high electric field gradients can be reached by deflecting the valve at a certain distance. Experimentally, the tunability of the dynamic constriction was demonstrated by conducting a pressure study to estimate the gap distance between the valve and the substrate at different applied pressures. Finally, as a proof of principle, 0.87 μm polystyrene beads were manipulated by dielectrophoresis. These microfluidic platforms will aid in the understanding of size-heterogeneity of organelles for biomolecular assessment and achieve separation of nanometer-size DNA and proteins by dielectrophoresis.
ContributorsOrtiz, Ricardo (Author) / Ros, Alexandra (Thesis advisor) / Hayes, Mark (Committee member) / Borges, Chad (Committee member) / Arizona State University (Publisher)
Created2021
Description
Traditional Reinforcement Learning (RL) assumes to learn policies with respect to reward available from the environment but sometimes learning in a complex domain requires wisdom which comes from a wide range of experience. In behavior based robotics, it is observed that a complex behavior can be described by a combination of simpler behaviors. It is tempting to apply similar idea such that simpler behaviors can be combined in a meaningful way to tailor the complex combination. Such an approach would enable faster learning and modular design of behaviors. Complex behaviors can be combined with other behaviors to create even more advanced behaviors resulting in a rich set of possibilities. Similar to RL, combined behavior can keep evolving by interacting with the environment. The requirement of this method is to specify a reasonable set of simple behaviors. In this research, I present an algorithm that aims at combining behavior such that the resulting behavior has characteristics of each individual behavior. This approach has been inspired by behavior based robotics, such as the subsumption architecture and motor schema-based design. The combination algorithm outputs n weights to combine behaviors linearly. The weights are state dependent and change dynamically at every step in an episode. This idea is tested on discrete and continuous environments like OpenAI’s “Lunar Lander” and “Biped Walker”. Results are compared with related domains like Multi-objective RL, Hierarchical RL, Transfer learning, and basic RL. It is observed that the combination of behaviors is a novel way of learning which helps the agent achieve required characteristics. A combination is learned for a given state and so the agent is able to learn faster in an efficient manner compared to other similar approaches. Agent beautifully demonstrates characteristics of multiple behaviors which helps the agent to learn and adapt to the environment. Future directions are also suggested as possible extensions to this research.
ContributorsVora, Kevin Jatin (Author) / Zhang, Yu (Thesis advisor) / Yang, Yezhou (Committee member) / Praharaj, Sarbeswar (Committee member) / Arizona State University (Publisher)
Created2021
Description
This dissertation consists of three chapters that investigate the rapid adoption and complex implementation of city commitments to transition to 100% renewable energy (100RE). The first paper uses a two-stage, mixed methods approach to examine 100RE commitments across the US, combining a multivariate regression of demographic, institutional, and policy factors in adoption and six interview-based state case studies to discuss implementation. Adoption of this non-binding commitment progressed rapidly for city councils around the US. Results show that many cities passed 100RE commitments with no implementation plan and minimal understanding of implementation challenges. This analysis highlights that many cities will need new institutions and administrative capacities for successful implementation of these ambitious new policies. While many cities abandoned the commitment soon after adoption, collaboration allowed cities in a few states to break through and pursue implementation, examined further in the next two studies. The second paper is a qualitative case study examining policymaking for the Utah Community Renewable Energy Act. Process tracing methods are used to identify causal factors in enacting this legislation at the state level and complementary resolutions at the local level. This Act was passed through the leadership and financial backing of major cities and committed the investor-owned utility to fulfill any city 100RE resolutions passed through 2019. Finally, the third paper is a mixed-methods, descriptive case study of the benefits of Community Choice Aggregation (CCA) in California, which many cities are using to fulfill their 100RE commitments. Cities have adopted CCAs to increase their local voice in the energy process, while fulfilling climate and energy goals. Overall, this research shows that change in the investor-owned utility electricity system is in fact possible from the city scale, though many cities will need institutional innovation to implement these policies and achieve the change they desire. While cities with greater resources are better positioned to make an impact, smaller cities can collaborate to similarly influence the energy system. Communities are interested in lowering energy costs for customers where possible, but the central motivations in these cases were the pursuit of sustainability and increasing local voice in energy decision-making.
ContributorsKunkel, Leah Christine (Author) / Breetz, Hanna L (Thesis advisor) / Parker, Nathan (Committee member) / Salon, Deborah (Committee member) / Arizona State University (Publisher)
Created2022
Description
Plasma and serum are the most commonly used liquid biospecimens in biomarker research. These samples may be subjected to several pre-analytical variables (PAVs) during collection, processing and storage. Exposure to thawed conditions (temperatures above -30 °C) is a PAV that is hard to control, and track and could provide misleading information, that fail to accurately reveal the in vivo biological reality, when unaccounted for. Hence, assays that can empirically check the integrity of plasma and serum samples are crucial. As a solution to this issue, an assay titled ΔS-Cys-Albumin was developed and validated. The reference range of ΔS-Cys-Albumin in cardio vascular patients was determined and the change in ΔS-Cys-Albumin values in different samples over time course incubations at room temperature, 4 °C and -20 °C were evaluated. In blind challenges, this assay proved to be successful in identifying improperly stored samples individually and as groups. Then, the correlation between the instability of several clinically important proteins in plasma from healthy and cancer patients at room temperature, 4 °C and -20 °C was assessed. Results showed a linear inverse relationship between the percentage of proteins destabilized and ΔS-Cys-Albumin regardless of the specific time or temperature of exposure, proving ΔS-Cys-Albumin as an effective surrogate marker to track the stability of clinically relevant analytes in plasma. The stability of oxidized LDL in serum at different temperatures was assessed in serum samples and it stayed stable at all temperatures evaluated. The ΔS-Cys-Albumin requires the use of an LC-ESI-MS instrument which limits its availability to most clinical research laboratories. To overcome this hurdle, an absorbance-based assay that can be measured using a plate reader was developed as an alternative to the ΔS-Cys-Albumin assay. Assay development and analytical validation procedures are reported herein. After that, the range of absorbance in plasma and serum from control and cancer patients were determined and the change in absorbance over a time course incubation at room temperature, 4 °C and -20 °C was assessed. The results showed that the absorbance assay would act as a good alternative to the ΔS-Cys-Albumin assay.
ContributorsJehanathan, Nilojan (Author) / Borges, Chad (Thesis advisor) / Guo, Jia (Committee member) / Van Horn, Wade (Committee member) / Arizona State University (Publisher)
Created2022
Description
With rapid advances in technology development and public adoption, it is crucial to understand how these services will shape the future of travel depending on the extent to which people will use these services; impact the transportation and infrastructure systems such as changes in the use of transit and active modes of travel; and influence how technology developers create and update these transportation technologies to better serve people’s mobility needs. This dissertation explores how two major emerging services, namely ridehailing services and autonomous vehicles (AVs), will be used in the future when they are widely available and vastly used, and how they may impact the transportation infrastructure and societal travel patterns. The four proposed chapters use comprehensive quantitative and qualitative methods to explore the status of these technologies from theory, through robust modeling frameworks, to practice, by investigating the recent AV pilot deployments in real-world settings. In the second chapter, it was found that increased frequency of ridehailing use is significantly associated with a decrease in bus usage, suggesting that ridehailing functions more as a substitute for buses than as a complement and implying that transit agencies should explore ways to incorporate ridehailing services in their plans to enhance transit usage. Next, the third chapter showed that interest in using AVs for running errands had a positive and significant effect on AV ownership intent, even after accounting for a host of variables. The fourth chapter depicted how ridehailing experiences have a considerable effect on the willingness to ride AV-based services in both private and shared modes, suggesting that experience is crucial for future adoption of these services. Then, two recent real-world AV experiences are explored in the fifth chapter. Lessons learned from these experiments reinforced the importance of first-hand experiences in promoting AV awareness and trustworthiness, potentially leading to greater degrees of adoption. Finally, the results and discussions presented in this dissertation strengthen the body of literature on key emerging transportation technologies and inform policymakers and stakeholders to properly prepare cities and the public to welcome these technologies into our transportation system in an efficient, equitable, and complementary way.
ContributorsMagassy, Tassio Bezerra (Author) / Pendyala, Ram M (Thesis advisor) / Khoeini, Sara (Committee member) / Polzin, Steven E (Committee member) / Salon, Deborah (Committee member) / Arizona State University (Publisher)
Created2023
Description
Trace evidence is an essential component of forensic investigations. Anthropogenicmaterials such as fibers and glass have been well studied for use in forensic trace evidence, but the potential use of retroreflective beads found in soils for forensic investigations is largely unexplored. Retroreflective glass beads are tiny spheres mixed into pavement markings to create reflective surfaces to reduce lane departure accidents. Retroreflective glass beads are a potentially new source of trace evidence for forensic investigations.
Analysis of the spatial distribution and chemical compositions of retroreflective glass beads recovered from 17 soil samples were analyzed and compared to see if there are striking variations that can distinguish samples by source. Soil samples taken near marked roads showed significantly higher concentrations of glass beads, averaging from 0.18 bead/g of soil sample to 587 beads/g of soil, while soil samples taken near unmarked roads had average range of concentration of 0 bead/g of soil to 0.21 bead/g of soil. Retroreflective glass beads come from pavement markings, thus soil samples near marked roads are expected to have higher concentrations of glass beads. Analysis of spatial distribution of glass beads showed that as sample collection moved further from the road, concentration of glass beads decreased.
ICP-MS results of elemental concentrations for each sample showed discriminative differences between samples, for most of the elements. An analysis of variance for elemental concentrations was conducted, and results showed statistically significant differences, beyond random chance alone for half of the elements analyzed. For forensic comparisons, a significant difference in even just one element is enough to conclude that the samples came from different sources. The elemental concentrations of glass beads collected from the same location, but of varying differences, was also analyzed. ANOVA results show significant differences for only one or two elements. A pair-wise t-test was conducted to determine which elements are most discriminative among all the samples. Rubidium was found to be the most discriminative, showing significant difference for 67% of the pairs. Beryllium, potassium, and manganese were also highly discriminative, showing significant difference for at least 50% of all the pairs.
ContributorsGomez, Janelle Kate Pacifico (Author) / Montero, Shirly (Thesis advisor) / Herckes, Pierre (Thesis advisor) / Borges, Chad (Committee member) / Gordon, Gwyneth (Committee member) / Arizona State University (Publisher)
Created2023
Description
Type 1 diabetes (T1D) is the result of an autoimmune attack against the insulin-producing β-cells of the pancreas causing hyperglycemia and requiring the individual to rely on life-long exogenous insulin. With the age of onset typically occurring in childhood, there is increased physical and emotional stress to the child as well as caregivers to maintain appropriate glucose levels. The majority of T1D patients have antibodies to one or more antigens: insulin, IA-2, GAD65, and ZnT8. Although antibodies are detectable years before symptoms occur, the initiating factors and mechanisms of progression towards β-cell destruction are still not known. The search for new autoantibodies to elucidate the autoimmune process in diabetes has been slow, with proteome level screenings on native proteins only finding a few minor antigens. Post-translational modifications (PTM)—chemical changes that occur to the protein after translation is complete—are an unexplored way a self-protein could become immunogenic. This dissertation presents the first large sale screening of autoantibodies in T1D to nitrated proteins. The Contra Capture Protein Array (CCPA) allowed for fresh expression of hundreds of proteins that were captured on a secondary slide by tag-specific ligand and subsequent modification with peroxynitrite. The IgG and IgM humoral response of 48 newly diagnosed T1D subjects and 48 age-matched controls were screened against 1632 proteins highly or specifically expressed in pancreatic cells. Top targets at 95% specificity were confirmed with the same serum samples using rapid antigenic protein in situ display enzyme-linked immunosorbent assay (RAPID ELISA) a modified sandwich ELISA employing the same cell-free expression as the CCPA. For validation, 8 IgG and 5 IgM targets were evaluated with an independent serum sample set of 94 T1D subjects and 94 controls. The two best candidates at 90% specificity were estrogen receptor 1 (ESR1) and phosphatidylinositol 4-kinase type 2 beta (PI4K2B) which had sensitivities of 22% (p=.014) and 25% (p=.045), respectively. Receiver operating characteristic (ROC) analyses found an area under curve (AUC) of 0.6 for ESR1 and 0.58 for PI4K2B. These studies demonstrate the ability and value for high-throughput autoantibody screening to modified antigens and the frequency of Type 1 diabetes.
ContributorsHesterman, Jennifer (Author) / LaBaer, Joshua (Thesis advisor) / Borges, Chad (Committee member) / Sweazea, Karen (Committee member) / Mangone, Marco (Committee member) / Arizona State University (Publisher)
Created2022
Description
Public transportation is considered a solution to congestion and a tool for reducing greenhouse gas emissions. It is becoming popular even in cities with the harshest climate conditions as these cities grow rapidly and are trying to provide sustainable alternatives for their vehicle-oriented communities. A lot must be taken into consideration whendesigning transit systems to reduce riders' vulnerability to heat in cities with high temperatures averaging 40°C during the summer and humidity levels reaching 90 percent. Using transit systems in Dubai, United Arab Emirates, and Phoenix Metropolitan, United States, as case studies, this paper focuses on both qualitative and quantitative research methods to observe the built environment around public transit stations and measure the temperatures and humidity levels to compare with the experienced temperatures and the built environment observations. The results show that the design of transit stations and the public realm significantly impacts a rider's experience. The findings show that passive cooling, shading, and vegetation as the best practices in the two case studies. Both transit systems have certain elements that work efficiently and other elements that need improvement to provide a better rider experience. Identifying these best practices helps develop recommendations for the future of designing transit systems in desert cities worldwide.
ContributorsAlbastaki, Mohamed (Author) / King, David (Thesis advisor) / Salon, Deborah (Committee member) / Kelley, Jason (Committee member) / Arizona State University (Publisher)
Created2022
Description
The past two decades have been marked by disruptions in the way transportation is provided to society. Examples are carsharing, ridehailing services, and electric scooters. Understanding how sensitive travel behavior is during transportation disruptions is a key part of planning for the future of transportation. While the effects of people's attitudes and perceptions on travel behavior and choices have been studied in the past, their role in response to disruptions remains under explored. This dissertation explores the effect of attitudes on travel behavior and perceptions for two distinct disruptions: the advent of autonomous vehicles (AVs) and the COVID-19 pandemic. Before diving into such elaborate relationships, it is important to understand how attitudinal data is collected and measured. Thus, a study of the effects of different survey methods on the collection of attitudes towards transportation disruptions is performed. This dissertation finds that having a favorable perception of AVs is the most important factor in defining one’s willingness to use them. More importantly, those who only heard about AVs without knowing much about them were actually less likely to have a favorable perception when compared to those who never heard of AVs prior to the survey, reinforcing the need for thoughtful education and awareness initiatives. Additionally, gender also played an important role in expectations about the AV Future: not only are women less interested in using AVs as a pooled ride service, but also that the effect of attitudes on defining that choice was different for men and women. Regarding the COVID-19 pandemic, two different attitudes towards COVID were identified: concern about the effects of the COVID-19 response, and concern about the health effects of the coronavirus. Both shaped the ways people traveled, and how often they did so. These findings reinforce the need for the broad collection of attitudinal data and the incorporation of such parameters on future travel forecasting.
ContributorsCapasso da Silva, Denise (Author) / Pendyala, Ram M (Thesis advisor) / Khoeini, Sara (Thesis advisor) / Salon, Deborah (Committee member) / Thompson, Marilyn (Committee member) / Arizona State University (Publisher)
Created2021