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Description
In an effort to begin validating the large number of discovered candidate biomarkers, proteomics is beginning to shift from shotgun proteomic experiments towards targeted proteomic approaches that provide solutions to automation and economic concerns. Such approaches to validate biomarkers necessitate the mass spectrometric analysis of hundreds to thousands of human samples. As this takes place, a serendipitous opportunity has become evident. By the virtue that as one narrows the focus towards "single" protein targets (instead of entire proteomes) using pan-antibody-based enrichment techniques, a discovery science has emerged, so to speak. This is due to the largely unknown context in which "single" proteins exist in blood (i.e. polymorphisms, transcript variants, and posttranslational modifications) and hence, targeted proteomics has applications for established biomarkers. Furthermore, besides protein heterogeneity accounting for interferences with conventional immunometric platforms, it is becoming evident that this formerly hidden dimension of structural information also contains rich-pathobiological information. Consequently, targeted proteomics studies that aim to ascertain a protein's genuine presentation within disease- stratified populations and serve as a stepping-stone within a biomarker translational pipeline are of clinical interest. Roughly 128 million Americans are pre-diabetic, diabetic, and/or have kidney disease and public and private spending for treating these diseases is in the hundreds of billions of dollars. In an effort to create new solutions for the early detection and management of these conditions, described herein is the design, development, and translation of mass spectrometric immunoassays targeted towards diabetes and kidney disease. Population proteomics experiments were performed for the following clinically relevant proteins: insulin, C-peptide, RANTES, and parathyroid hormone. At least thirty-eight protein isoforms were detected. Besides the numerous disease correlations confronted within the disease-stratified cohorts, certain isoforms also appeared to be causally related to the underlying pathophysiology and/or have therapeutic implications. Technical advancements include multiplexed isoform quantification as well a "dual- extraction" methodology for eliminating non-specific proteins while simultaneously validating isoforms. Industrial efforts towards widespread clinical adoption are also described. Consequently, this work lays a foundation for the translation of mass spectrometric immunoassays into the clinical arena and simultaneously presents the most recent advancements concerning the mass spectrometric immunoassay approach.
ContributorsOran, Paul (Author) / Nelson, Randall (Thesis advisor) / Hayes, Mark (Thesis advisor) / Ros, Alexandra (Committee member) / Williams, Peter (Committee member) / Arizona State University (Publisher)
Created2011
Description
Signal processing techniques have been used extensively in many engineering problems and in recent years its application has extended to non-traditional research fields such as biological systems. Many of these applications require extraction of a signal or parameter of interest from degraded measurements. One such application is mass spectrometry immunoassay (MSIA) which has been one of the primary methods of biomarker discovery techniques. MSIA analyzes protein molecules as potential biomarkers using time of flight mass spectrometry (TOF-MS). Peak detection in TOF-MS is important for biomarker analysis and many other MS related application. Though many peak detection algorithms exist, most of them are based on heuristics models. One of the ways of detecting signal peaks is by deploying stochastic models of the signal and noise observations. Likelihood ratio test (LRT) detector, based on the Neyman-Pearson (NP) lemma, is an uniformly most powerful test to decision making in the form of a hypothesis test. The primary goal of this dissertation is to develop signal and noise models for the electrospray ionization (ESI) TOF-MS data. A new method is proposed for developing the signal model by employing first principles calculations based on device physics and molecular properties. The noise model is developed by analyzing MS data from careful experiments in the ESI mass spectrometer. A non-flat baseline in MS data is common. The reasons behind the formation of this baseline has not been fully comprehended. A new signal model explaining the presence of baseline is proposed, though detailed experiments are needed to further substantiate the model assumptions. Signal detection schemes based on these signal and noise models are proposed. A maximum likelihood (ML) method is introduced for estimating the signal peak amplitudes. The performance of the detection methods and ML estimation are evaluated with Monte Carlo simulation which shows promising results. An application of these methods is proposed for fractional abundance calculation for biomarker analysis, which is mathematically robust and fundamentally different than the current algorithms. Biomarker panels for type 2 diabetes and cardiovascular disease are analyzed using existing MS analysis algorithms. Finally, a support vector machine based multi-classification algorithm is developed for evaluating the biomarkers' effectiveness in discriminating type 2 diabetes and cardiovascular diseases and is shown to perform better than a linear discriminant analysis based classifier.
ContributorsBuddi, Sai (Author) / Taylor, Thomas (Thesis advisor) / Cochran, Douglas (Thesis advisor) / Nelson, Randall (Committee member) / Duman, Tolga (Committee member) / Arizona State University (Publisher)
Created2012
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
TAM19B-7 is the largest, unmelted fine-grained micrometeorite found to date. It has carbonaceous chondritic origins, but the oxygen isotopic composition does not match any known parent bodies. Additionally, carbon-bearing matter and isotopic composition has been extensively characterized in meteorites, but this work has not been done yet for micrometeorites. Using the NanoSIMS 50 L instrument, the bulk δ13C for TAM19B-7 was found to be 3 + 8‰, and four anomalous spots were identified with δ13C values of 12.9‰, 16.8‰, 32.7‰, and -27.1‰.
ContributorsFroh, Victoria (Author) / Bose, Maitrayee (Thesis director) / Williams, Lynda (Committee member) / School of Earth and Space Exploration (Contributor) / School of Molecular Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
We present the isotope yields of two post-explosion, three-dimensional 15 M_sol core-collapse supernova models, 15S and 15A, and compare them to the carbon, nitrogen, silicon, aluminum, sulfur, calcium, titanium, iron, and nickel isotopic compositions of presolar SiC stardust. We find that material from the interior of a core-collapse supernova can form a rare subset of SiC stardust, called SiC D grains, characterized by enrichments of the isotopes 13C and 15N. The innermost material of these core-collapse supernovae is operating in the neutrino-driven regime and undergoes rapid proton capture early in the explosion, providing these isotopes which are not present in such large abundances in other stardust grains of supernova origin.
ContributorsSchulte, Jack (Author) / Bose, Maitrayee (Thesis director) / Foy, Joseph (Committee member) / School of Earth and Space Exploration (Contributor) / Department of Physics (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
This thesis examines Care Not Cash, a welfare reform measure that replaced traditional cash General Assistance program payments for homeless persons in San Francisco with in-kind social services. Unlike most welfare reform measures, proponents framed Care Not Cash as a progressive policy, aimed at expanding social services and government care for this vulnerable population. Drawing on primary and secondary documents, as well as interviews with homelessness policy experts, this thesis examines the historical and political success of Care Not Cash, and explores the potential need for implementation of a similar program in Phoenix, Arizona.
ContributorsMcCutcheon, Zachary Ryan (Author) / Lucio, Joanna (Thesis director) / Williams, David (Committee member) / Bretts-Jamison, Jake (Committee member) / School of Public Affairs (Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
Description
Subsolidus convection in the mantle of Earth is the driving mechanism behind plate tectonics and provides a central framework linking geophysical, geochemical, petrological, hydrological, and biological processes within the system. Seismic observations have revealed mantle heterogeneities in wide-ranging scales from less than tens of to thousands of kilometers. Understanding the origins and dynamics of these anomalies is critical to advance our knowledge on how mantle convection operates and coevolves with the surface system. This dissertation attempts to constrain the past, present and future of mantle dynamics with lines of evidence from seismology, geodynamics, petrology, geochemistry, and astrophysics. Above Earth’s core, two continent-sized large low shear velocity provinces (LLSVPs) beneath Africa and the Pacific Ocean were seismically detected decades ago. Yet their origin, composition, detailed morphology and influence over mantle convection remain elusive. First, I propose the two LLSVPs may represent the mantle remnants of the Moon-forming impactor Theia. I show that the mantle of Theia is intrinsically denser than Earth’s mantle and would have sunk and accumulated into LLSVP-like structures in the deepest mantle after 4.5 billion years. Second, I examined the maximum height of the two LLSVPs and determined that the African LLSVP is ~1,000 km higher than the Pacific counterpart. Using geodynamic simulations, I find the height of a stable LLSVP is mainly controlled by its density and the ambient mantle viscosity. With ~1,000 numerical experiments, I conclude that the origin of the great height difference between the LLSVPs is that the African LLSVP is less dense, and thus less stable than the Pacific LLSVP. Next, I numerically identified another dynamic scenario accounting for the vastly different height of the two LLSVPs, which is caused by catastrophic sinking of accumulated subducted slabs at the 660-km boundary. Last, targeting one ancient carbonatite above the African LLSVP, I show that lithium isotopes in humite measured by nanoscale secondary ion mass spectrometry was able to uncover the signature of a subducted oceanic crust in its magma source, which may return from the interior to the surface by mantle plumes.
ContributorsYuan, Qian (Author) / Li, Mingming (Thesis advisor) / Garnero, Edward (Committee member) / Shim, Sang-Heon (Committee member) / Hervig, Richard (Committee member) / Bose, Maitrayee (Committee member) / Arizona State University (Publisher)
Created2022
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
Space weathering of planetary surfaces is a complex process involving many mechanisms that work independently over different timescales. This research aims to address outstanding questions related to solar wind rim formation on space weathered regolith and tests a new hypothesis that dielectric breakdown plays an important role in the optical maturation of lunar regolith. The purpose of this work is to highlight the limitations imposed by laboratory equipment to accurately simulate the solar wind’s effects on regolith and to provide physical context for the possible contributions of dielectric breakdown to space weathering. Terrestrial and lunar samples were experimentally irradiated and damage was characterized using electron microscopy techniques. Low-fluence proton irradiation produced differential weathering in a lunar mare basalt, with radiation damage on some phases being inconsistent with that found in the natural lunar environment. Dielectric breakdown of silicates revealed two electrical processes that produce characteristic surface and subsurface damage, in addition to amorphous rims. The results of this research highlight experimental parameters that if ignored, can significantly affect the results and interpretations of simulated solar wind weathering, and provides a framework for advancing space weathering research through experimental studies.
ContributorsShusterman, Morgan (Author) / Robinson, Mark S (Thesis advisor) / Sharp, Thomas G (Thesis advisor) / Hibbits, Charles (Committee member) / Bose, Maitrayee (Committee member) / Semken, Steven (Committee member) / Arizona State University (Publisher)
Created2023
Description
Brown dwarfs are a unique class of object which span the range between the lowest mass stars, and highest mass planets. New insights into the physics and chemistry of brown dwarfs comes from the comparison between spectroscopic observations, and theoretical atmospheric models. In this thesis, I present a uniform atmospheric retrieval analysis of the coolest Y, and late-T spectral type brown dwarfs using the CaltecH Inverse ModEling and Retrieval Algorithms (CHIMERA). In doing so, I develop a foundational dataset of retrieved atmospheric parameters including: molecular abundances, thermal structures, evolutionary parameters, and cloud properties for 61 different brown dwarfs. Comparisons to other modeling techniques and theoretical expectations from the James Webb Space Telescope (JWST) are made. Finally, I describe the techniques used to improve CHIMERA to run on Graphical Processing Units (GPUs), which directly enabled the creation of this large dataset.
ContributorsZalesky, Joseph (Author) / Line, Michael R (Thesis advisor) / Patience, Jennifer (Committee member) / Groppi, Christopher (Committee member) / Young, Patrick (Committee member) / Bose, Maitrayee (Committee member) / Arizona State University (Publisher)
Created2022