Matching Items (273)
Description
Immunosignaturing is a new immunodiagnostic technology that uses random-sequence peptide microarrays to profile the humoral immune response. Though the peptides have little sequence homology to any known protein, binding of serum antibodies may be detected, and the pattern correlated to disease states. The aim of my dissertation is to analyze the factors affecting the binding patterns using monoclonal antibodies and determine how much information may be extracted from the sequences. Specifically, I examined the effects of antibody concentration, competition, peptide density, and antibody valence. Peptide binding could be detected at the low concentrations relevant to immunosignaturing, and a monoclonal's signature could even be detected in the presences of 100 fold excess naive IgG. I also found that peptide density was important, but this effect was not due to bivalent binding. Next, I examined in more detail how a polyreactive antibody binds to the random sequence peptides compared to protein sequence derived peptides, and found that it bound to many peptides from both sets, but with low apparent affinity. An in depth look at how the peptide physicochemical properties and sequence complexity revealed that there were some correlations with properties, but they were generally small and varied greatly between antibodies. However, on a limited diversity but larger peptide library, I found that sequence complexity was important for antibody binding. The redundancy on that library did enable the identification of specific sub-sequences recognized by an antibody. The current immunosignaturing platform has little repetition of sub-sequences, so I evaluated several methods to infer antibody epitopes. I found two methods that had modest prediction accuracy, and I developed a software application called GuiTope to facilitate the epitope prediction analysis. None of the methods had sufficient accuracy to identify an unknown antigen from a database. In conclusion, the characteristics of the immunosignaturing platform observed through monoclonal antibody experiments demonstrate its promise as a new diagnostic technology. However, a major limitation is the difficulty in connecting the signature back to the original antigen, though larger peptide libraries could facilitate these predictions.
ContributorsHalperin, Rebecca (Author) / Johnston, Stephen A. (Thesis advisor) / Bordner, Andrew (Committee member) / Taylor, Thomas (Committee member) / Stafford, Phillip (Committee member) / Arizona State University (Publisher)
Created2011
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
This thesis focuses on the erotic depictions of Lucretia and Susanna in Renaissance art. Both noted for displaying exemplary chastity, Lucretia and Susanna gained popularity as Christian and secular role models for women in the late Middle Ages and Renaissance. My examination of the heroines addresses the seductive portrayal of these women in painting, which seemingly contradicts the essence of their celebrity. The images specifically analyzed in this thesis include: Lucas Cranach the Elder's Lucretia from 1525, Lucretia from 1533, and Venus from 1532 as well as Tintoretto's Susanna and the Elders and Annibale Carracci's Susanna and the Elders. The scope of my thesis includes both textual and visual analyses of the myths/figures and the disparity that arises between them. Employing Lucretia and Susanna as examples, my aim is to demonstrate a subtle subversion occurring within images of powerful women that ultimately strips them of their power.
ContributorsWilliamson, Jennifer Marie (Author) / Schleif, Corine (Thesis director) / Geschwind, Rachel (Committee member) / Pratt, Rebekah (Committee member) / Barrett, The Honors College (Contributor) / School of Social Transformation (Contributor) / School of Human Evolution and Social Change (Contributor) / School of Art (Contributor)
Created2013-05
Description
Science fiction themed video games, specifically Role Playing Games (RPGs) like Deus Ex: Human Revolution (DX:HR), that focus on an emerging technology, contain features that help to better inform anticipatory governance. In a game like DX:HR, players vicariously experience human-enhancement technology and its societal effects through their in-game character. Acting as the character, the player explores the topic of human-enhancement technology in various ways, including dialogue with non-player characters (NPCs) and decisions that directly affect the game's world. Because Deus Ex: Human Revolution and games similar to it, allow players to explore and think about the technology itself, the stances on it, and its potential societal effects, they facilitate the anticipatory governance process. In this paper I postulate a theory of anticipatory gaming, which asserts that video games inform the anticipatory governance process for an emerging technology. To demonstrate this theory I examine the parts of the anticipatory governance process and demonstrate RPG's ability to inform it, through a case study of Deus Ex: Human Revolution.
ContributorsShedd, Jesse Bernard (Author) / Wetmore, Jameson (Thesis director) / Fisher, Erik (Committee member) / McKnight, John Carter (Committee member) / Barrett, The Honors College (Contributor) / School of Human Evolution and Social Change (Contributor)
Created2013-05
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
This thesis examines the application of statistical signal processing approaches to data arising from surveys intended to measure psychological and sociological phenomena underpinning human social dynamics. The use of signal processing methods for analysis of signals arising from measurement of social, biological, and other non-traditional phenomena has been an important and growing area of signal processing research over the past decade. Here, we explore the application of statistical modeling and signal processing concepts to data obtained from the Global Group Relations Project, specifically to understand and quantify the effects and interactions of social psychological factors related to intergroup conflicts. We use Bayesian networks to specify prospective models of conditional dependence. Bayesian networks are determined between social psychological factors and conflict variables, and modeled by directed acyclic graphs, while the significant interactions are modeled as conditional probabilities. Since the data are sparse and multi-dimensional, we regress Gaussian mixture models (GMMs) against the data to estimate the conditional probabilities of interest. The parameters of GMMs are estimated using the expectation-maximization (EM) algorithm. However, the EM algorithm may suffer from over-fitting problem due to the high dimensionality and limited observations entailed in this data set. Therefore, the Akaike information criterion (AIC) and the Bayesian information criterion (BIC) are used for GMM order estimation. To assist intuitive understanding of the interactions of social variables and the intergroup conflicts, we introduce a color-based visualization scheme. In this scheme, the intensities of colors are proportional to the conditional probabilities observed.
ContributorsLiu, Hui (Author) / Taylor, Thomas (Thesis advisor) / Cochran, Douglas (Thesis advisor) / Zhang, Junshan (Committee member) / Arizona State University (Publisher)
Created2012
Description
This dissertation describes a novel, low cost strategy of using particle streak (track) images for accurate micro-channel velocity field mapping. It is shown that 2-dimensional, 2-component fields can be efficiently obtained using the spatial variation of particle track lengths in micro-channels. The velocity field is a critical performance feature of many microfluidic devices. Since it is often the case that un-modeled micro-scale physics frustrates principled design methodologies, particle based velocity field estimation is an essential design and validation tool. Current technologies that achieve this goal use particle constellation correlation strategies and rely heavily on costly, high-speed imaging hardware. The proposed image/ video processing based method achieves comparable accuracy for fraction of the cost. In the context of micro-channel velocimetry, the usability of particle streaks has been poorly studied so far. Their use has remained restricted mostly to bulk flow measurements and occasional ad-hoc uses in microfluidics. A second look at the usability of particle streak lengths in this work reveals that they can be efficiently used, after approximately 15 years from their first use for micro-channel velocimetry. Particle tracks in steady, smooth microfluidic flows is mathematically modeled and a framework for using experimentally observed particle track lengths for local velocity field estimation is introduced here, followed by algorithm implementation and quantitative verification. Further, experimental considerations and image processing techniques that can facilitate the proposed methods are also discussed in this dissertation. Unavailability of benchmarked particle track image data motivated the implementation of a simulation framework with the capability to generate exposure time controlled particle track image sequence for velocity vector fields. This dissertation also describes this work and shows that arbitrary velocity fields designed in computational fluid dynamics software tools can be used to obtain such images. Apart from aiding gold-standard data generation, such images would find use for quick microfluidic flow field visualization and help improve device designs.
ContributorsMahanti, Prasun (Author) / Cochran, Douglas (Thesis advisor) / Taylor, Thomas (Thesis advisor) / Hayes, Mark (Committee member) / Zhang, Junshan (Committee member) / Arizona State University (Publisher)
Created2011
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
This paper studies the change in social diversity and interaction space from the Classic to Postclassic periods in the Mimbres Valley and East Mimbres Area. Between the Classic and Postclassic periods the Mimbres region of the American Southwest exhibits an increase in diversity of ceramic wares. Previous research suggests that increased diversity of ceramics indicates a more diverse community, which could pose challenges to local social interaction (Nelson et al. 2011). I am interested in whether the architecture of plazas, focal points of communities' social structures, change in response to the growing social diversity. To examine this, I quantify the diversity of painted ceramics at Classic and Postclassic villages as well as the extent of the enclosure of plazas. I find that there is a definite shift towards greater plaza enclosure between the Classic and Postclassic periods. I conclude this paper with a discussion of possible interpretations of this trend regarding the social reactions of Mimbres communities to the changes which reshaped the region between the Classic and Postclassic periods.
ContributorsHarkness, Rebecca Jean (Author) / Nelson, Margaret (Thesis director) / Hegmon, Michelle (Committee member) / Barrett, The Honors College (Contributor) / School of Human Evolution and Social Change (Contributor) / School of Historical, Philosophical and Religious Studies (Contributor)
Created2015-05
Description
Background: Both puberty and diets composed of high levels of saturated fats have been shown to result in central adiposity, fasting hyperinsulinemia, insulin resistance and impaired glucose tolerance. While a significantly insulinogenic phenotypic change occurs in these two incidences, glucose homeostasis does not appear to be affected. Methods: Male, Sprague-dawley rats were fed diets consisting of CHOW or low fat (LF), High Fat Diet and High Fat Diet (HFD) with supplementary Canola Oil (Monounsaturated fat). These rats were given these diets at 4-5 weeks old and given intraperitoneal and oral glucose tolerance tests(IPGTT; OGTT) at 4 and 8 weeks to further understand glucose and insulin behavior under different treatments. (IPGTT: LF-n=14, HFD-n=16, HFD+CAN-n=12; OGTT: LF-n=8, HFD-n=8, HFD+CAN-n=6). Results: When comparing LF fed rats at 8 weeks with 4 week glucose challenge test, area under the curve (AUC) of glucose was 1.2 that of 4 weeks. At 8 weeks, HFD fed rats AUCg was much greater than LF fed rats under both IPGTT and OGTT. When supplemented with Canola oil, HFD fed rats AUC returned to LF data range. Despite the alleviating glucose homeostasis affects of Canola oil the AUC of insulin curve, which was elevated by HFD, remained high. Conclusion: HFD in maturing rats elevates fasting insulin levels, increases insulin resistance and lowers glucose homeostasis. When given a monounsaturated fatty acid (MUFA) supplement fasting hyperinsulinemia, and late hyperinsulinemia still occur though glucose homeostasis is regained. For OGTT HFD also induced late hyper c-peptide levels and compared to LF and HFD+CAN, a higher c-peptide level over time.
ContributorsRay, Tyler John (Author) / Caplan, Michael (Thesis director) / Herman, Richard (Committee member) / Towner, Kali (Committee member) / Barrett, The Honors College (Contributor) / Department of Chemistry and Biochemistry (Contributor) / W. P. Carey School of Business (Contributor) / School of Human Evolution and Social Change (Contributor)
Created2015-05