Matching Items (86)
Description
Microfluidic devices represent a growing technology in the world of analytical chemistry. Serial femtosecond crystallography (SFX) utilizes microfluidic devices to generate droplets of an aqueous buffer containing protein crystals, which are then fired out as a jet in the beam of an X-ray free electron laser (XFEL). A crucial part of the device is its method of droplet detection. This project presents a design for a capacitive sensor that uses a unique electrode configuration to detect the difference in capacitance between the aqueous and oil phases. This design was developed using MATLAB and COMSOL Multiphysics simulations and printed using high-resolution 3D printing. Results show that this design can successfully distinguish between the two immiscible liquids, confirming it as a possible detection method in future SFX experiments.
ContributorsCorder, Cameron Dean (Author) / Ros, Alexandra (Thesis director) / Williams, Peter (Committee member) / Hayes, Mark (Committee member) / School of Molecular Sciences (Contributor) / School of International Letters and Cultures (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
A new challenge on the horizon is to utilize the large amounts of protein found in the atmosphere to identify different organisms from which the protein originated. Included here is work investigating the presence of identifiable patterns of different proteins collected from the air and biological samples for the purposes of remote identification. Protein patterns were generated using high performance liquid chromatography (HPLC). Patterns created could identify high-traffic and low-traffic indoor spaces. Samples were collected from the air using air pumps to draw air through a filter paper trapping particulates, including large amounts of shed protein matter. In complimentary research aerosolized biological samples were collected from various ecosystems throughout Ecuador to explore the relationship between environmental setting and aerosolized protein concentrations. In order to further enhance protein separation and produce more detailed patterns for the identification of individual organisms of interest; a novel separation device was constructed and characterized. The separation device incorporates a longitudinal gradient as well as insulating dielectrophoretic features within a single channel. This design allows for the production of stronger local field gradients along a global gradient allowing particles to enter, initially transported through the channel by electrophoresis and electroosmosis, and to be isolated according to their characteristic physical properties, including charge, polarizability, deformability, surface charge mobility, dielectric features, and local capacitance. Thus, different types of particles are simultaneously separated at different points along the channel distance given small variations of properties. The device has shown the ability to separate analytes over a large dynamic range of size, from 20 nm to 1 μm, roughly the size of proteins to the size of cells. In the study of different sized sulfate capped polystyrene particles were shown to be selectively captured as well as concentrating particles from 103 to 106 times. Qualitative capture and manipulation of β-amyloid fibrils were also shown. The results demonstrate the selective focusing ability of the technique; and it may form the foundation for a versatile tool for separating complex mixtures. Combined this work shows promise for future identification of individual organisms from aerosolized protein as well as for applications in biomedical research.
ContributorsStaton, Sarah J. R (Author) / Hayes, Mark A. (Committee member) / Anbar, Ariel D (Committee member) / Shock, Everett (Committee member) / Williams, Peter (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
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
Mass spectrometric analysis requires that atoms from the sample be ionized in the gas phase. Secondary ion mass spectrometry achieves this by sputtering samples with an energetic primary ion beam. Several investigations of the sputtering and ionization process have been conducted. Oxygen is commonly used in secondary ion mass spectrometry (SIMS) to increase ion yields, but also can complicate the interpretation of SIMS analyses. An 18O implant in silicon has been used to quantify the oxygen concentration at the surface of sputtered silicon in order to study the dependence on oxygen of several sputtering and depth profile phenomena. The ion yield dependence of trace elements in silicon on the surface oxygen concentration is a function of the ionization potential of the element. The ion yield is high and unaffected by oxygen for elements with low ionization potential and ranges over several orders of magnitude for elements with high ionization potential. Depth resolution in sputter profiles has been shown to be degraded by the presence of oxygen, the mechanism of this effect has been investigated using an 18O implant to quantify oxygen levels and it is shown that the process does not appear to be a consequence of surface oxide formation. Molecular ions are a source of mass interference in SIMS analysis, and multiply charged atomic ion signals might be interference-free due to the possible instability of multiply-charged molecular ions. Sputtered SiH2+, AlH2+, BeH2+, Mo22+ and Mg22+ ions have been observed and appear surprisingly stable. The formation mechanism of some of these species has been explored.
ContributorsSobers, Richard Carlisle, Jr (Author) / Williams, Peter (Thesis advisor) / Hayes, Mark (Committee member) / Petuskey, William (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
Serial femtosecond crystallography (SFX) with an X-ray free-electron laser (XFEL) has enabled the determination of protein structures and protein reaction intermediates in millisecond to microsecond time resolutions. Mix-and-Inject crystallography (MISC) at XFELs enables fast mixing in the magnitude of milliseconds in order to achieve desired reaction time points. For these experiments, numerical simulations of a hydrodynamic flow mixer capable of fast mixing by diffusion has been developed using both COMSOL Multiphysics 5.6 and QuickerSims Computational Fluid Dynamics (CFD) Toolbox for MATLAB. These simulation programs were compared by calculations of mixing times and concentration flow profiles. Mixing times in the range of 1-10 ms were calculated in COMSOL under certain flow rate conditions whereas mixing times in the range of 6-15 ms were calculated with QuickerSims. From these mixing times, reaction intermediates can be varied from sub-millisecond to several hundred millisecond time points for a MISC experiment. Explanations for the discrepancies between the two models were attributed to variations in parameter definitions and meshing. Further analysis on the mixing characteristics were investigated by calculating an analytical solution to the convection-diffusion equation for fluid flow in a two-dimensional rectangular channel. The concentration profile along the width of the channel for the analytical solution was compared with the numerical solution obtained with COMSOL and QuickerSims. Upon comparison, it was determined that the diffusion coefficient may not be a significant factor for the disagreement between the two hydrodynamic flow models.
ContributorsGuzman, Manuel Alexander (Author) / Ros, Alexandra (Thesis director) / Williams, Peter (Committee member) / Hayes, Mark (Committee member) / School of Mathematical and Statistical Sciences (Contributor) / School of Molecular Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
In the digital humanities, there is a constant need to turn images and PDF files into plain text to apply analyses such as topic modelling, named entity recognition, and other techniques. However, although there exist different solutions to extract text embedded in PDF files or run OCR on images, they typically require additional training (for example, scholars have to learn how to use the command line) or are difficult to automate without programming skills. The Giles Ecosystem is a distributed system based on Apache Kafka that allows users to upload documents for text and image extraction. The system components are implemented using Java and the Spring Framework and are available under an Open Source license on GitHub (https://github.com/diging/).
ContributorsLessios-Damerow, Julia (Contributor) / Peirson, Erick (Contributor) / Laubichler, Manfred (Contributor) / ASU-SFI Center for Biosocial Complex Systems (Contributor)
Created2017-09-28
Description
Many natural and synthetic quinones have shown biological and pharmacological activity. Some of them have also shown anticancer activity. Ubiquinone (CoQ10) which is a natural quinone, is a component of the electron transport chain and participates in generation of ATP (adenosine triphosphate). Cellular oxidative stress is key feature of many neurodegenerative diseases such as Friedreich's ataxia, Alzheimer's disease and Parkinson's disease. The increased generation of reactive oxygen species damages cell membranes and leads to cell death. Analogues of ubiquinone in the form of pyrimidinols and pyridinols, were effective in protecting Friedreich's ataxia lymphocytes from oxidative stress- induced cell death. There were some structural features which could be identified that should be useful for the design of the analogues for cellular protection against oxidative stress. There are quinones such as doxorubicin, daunomycin and topopyrones which have anticancer activity. Here I evaluated topopyrone analogues which poison both topoisomerases I and II. The topopyrone analogues were lethal to human breast cancer cells, but these analogues were not as potent as camptothecin.
ContributorsRaghav, Nidhi (Author) / Hecht, Sidney M. (Thesis advisor) / Gould, Ian R (Committee member) / Williams, Peter (Committee member) / Arizona State University (Publisher)
Created2011
Description
Honey bee workers display remarkable flexibility in the aging process. This plasticity is closely tied to behavioral maturation. Workers who initiate foraging behavior at earlier ages have shorter lifespans, and much of the variation in total lifespan can be explained by differences in pre-foraging lifespan. Vitellogenin (Vg), a yolk precursor protein, influences worker lifespan both as a regulator of behavioral maturation and through anti-oxidant and immune functions. Experimental reduction of Vg mRNA, and thus Vg protein levels, in wild-type bees results in precocious foraging behavior, decreased lifespan, and increased susceptibility to oxidative damage. We sought to separate the effects of Vg on lifespan due to behavioral maturation from those due to immune and antioxidant function using two selected strains of honey bees that differ in their phenotypic responsiveness to Vg gene knockdown. Surprisingly, we found that lifespans lengthen in the strain described as behaviorally and hormonally insensitive to Vg reduction. We then performed targeted gene expression analyses on genes hypothesized to mediate aging and lifespan: the insulin-like peptides (Ilp1 and 2) and manganese superoxide dismutase (mnSOD). The two honey bee Ilps are the most upstream components in the insulin-signaling pathway, which influences lifespan in Drosophila melanogaster and other organisms, while manganese superoxide dismutase encodes an enzyme with antioxidant functions in animals. We found expression differences in the llps in fat body related to behavior (llp1 and 2) and genetic background (Ilp2), but did not find strain by treatment effects. Expression of mnSOD was also affected by behavior and genetic background. Additionally, we observed a differential response to Vg knockdown in fat body expression of mnSOD, suggesting that antioxidant pathways may partially explain the strain-specific lifespan responses to Vg knockdown.
ContributorsIhle, Kate (Author) / Fondrk, M. Kim (Author) / Page, Robert (Author) / Amdam, Gro (Author) / College of Liberal Arts and Sciences (Contributor) / School of Life Sciences (Contributor)
Created2015-01-01