Matching Items (27)
Filtering by
Description
Second-generation biofuel feedstocks are currently grown in land-based systems that use valuable resources like water, electricity and fertilizer. This study investigates the potential of near-shore marine (ocean) seawater filtration as a source of planktonic biomass for biofuel production. Mixed marine organisms in the size range of 20µm to 500µm were isolated from the University of California, Santa Barbara (UCSB) seawater filtration system during weekly backwash events between the months of April and August, 2011. The quantity of organic material produced was determined by sample combustion and calculation of ash-free dry weights. Qualitative investigation required density gradient separation with the heavy liquid sodium metatungstate followed by direct transesterification and gas chromatography with mass spectrometry (GC-MS) of the fatty acid methyl esters (FAME) produced. A maximum of 0.083g/L of dried organic material was produced in a single backwash event and a study average of 0.036g/L was calculated. This equates to an average weekly value of 7,674.75g of dried organic material produced from the filtration of approximately 24,417,792 liters of seawater. Temporal variations were limited. Organic quantities decreased over the course of the study. Bio-fouling effects from mussel overgrowth inexplicably increased production values when compared to un-fouled seawater supply lines. FAMEs (biodiesel) averaged 0.004% of the dried organic material with 0.36ml of biodiesel produced per week, on average. C16:0 and C22:6n3 fatty acids comprised the majority of the fatty acids in the samples. Saturated fatty acids made up 30.71% to 44.09% and unsaturated forms comprised 55.90% to 66.32% of the total chemical composition. Both quantities and qualities of organics and FAMEs were unrealistic for use as biodiesel but sample size limitations, system design, geographic and temporal factors may have impacted study results.
ContributorsPierre, Christophe (Author) / Olson, Larry (Thesis advisor) / Sommerfeld, Milton (Committee member) / Brown, Albert (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
In 2010, a monthly sampling regimen was established to examine ecological differences in Saguaro Lake and Lake Pleasant, two Central Arizona reservoirs. Lake Pleasant is relatively deep and clear, while Saguaro Lake is relatively shallow and turbid. Preliminary results indicated that phytoplankton biomass was greater by an order of magnitude in Saguaro Lake, and that community structure differed. The purpose of this investigation was to determine why the reservoirs are different, and focused on physical characteristics of the water column, nutrient concentration, community structure of phytoplankton and zooplankton, and trophic cascades induced by fish populations. I formulated the following hypotheses: 1) Top-down control varies between the two reservoirs. The presence of piscivore fish in Lake Pleasant results in high grazer and low primary producer biomass through trophic cascades. Conversely, Saguaro Lake is controlled from the bottom-up. This hypothesis was tested through monthly analysis of zooplankton and phytoplankton communities in each reservoir. Analyses of the nutritional value of phytoplankton and DNA based molecular prey preference of zooplankton provided insight on trophic interactions between phytoplankton and zooplankton. Data from the Arizona Game and Fish Department (AZGFD) provided information on the fish communities of the two reservoirs. 2) Nutrient loads differ for each reservoir. Greater nutrient concentrations yield greater primary producer biomass; I hypothesize that Saguaro Lake is more eutrophic, while Lake Pleasant is more oligotrophic. Lake Pleasant had a larger zooplankton abundance and biomass, a larger piscivore fish community, and smaller phytoplankton abundance compared to Saguaro Lake. Thus, I conclude that Lake Pleasant was controlled top-down by the large piscivore fish population and Saguaro Lake was controlled from the bottom-up by the nutrient load in the reservoir. Hypothesis 2 stated that Saguaro Lake contains more nutrients than Lake Pleasant. However, Lake Pleasant had higher concentrations of dissolved nitrogen and phosphorus than Saguaro Lake. Additionally, an extended period of low dissolved N:P ratios in Saguaro Lake indicated N limitation, favoring dominance of N-fixing filamentous cyanobacteria in the phytoplankton community in that reservoir.
ContributorsSawyer, Tyler R (Author) / Neuer, Susanne (Thesis advisor) / Childers, Daniel L. (Committee member) / Sommerfeld, Milton (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
Phytoplankton comprise the base of the marine food web, and, along with heterotrophic protists, they are key players in the biological pump that transports carbon from the surface to the deep ocean. In the world's subtropical oligotrophic gyres, plankton communities exhibit strong seasonality. Winter storms vent deep water into the euphotic zone, triggering a surge in primary productivity in the form of a spring phytoplankton bloom. Although the hydrographic trends of this "boom and bust" cycle have been well studied for decades, community composition and its seasonal and annual variability remains an integral subject of research. It is hypothesized here that proportions of different phytoplankton and protistan taxa vary dramatically between seasons and years, and that picoplankton represent an important component of this community and contributor to carbon in the surface ocean. Monthly samples from the Bermuda Atlantic Time-series Study (BATS) site were analyzed by epifluorescence microscopy, which permits classification by morphology, size, and trophic type. Epifluorescence counts were supplemented with flow cytometric quantification of Synechococcus, Prochlorococcus, and autotrophic pico- and nanoeukaryotes. Results from this study indicate Synechococcus and Prochlorococcus, prymnesiophytes, and hetero- and mixotrophic nano- and dinoflagellates were the major players in the BATS region plankton community. Ciliates, cryptophytes, diatoms, unidentified phototrophs, and other taxa represented rarer groups. Both flow cytometry and epifluorescence microscopy revealed Synechococcus to be most prevalent during the spring bloom. Prymnesiophytes likewise displayed distinct seasonality, with the highest concentrations again being noted during the bloom. Heterotrophic nano- and dinoflagellates, however, were most common in fall and winter. Mixotrophic dinoflagellates, while less abundant than their heterotrophic counterparts, displayed similar seasonality. A key finding of this study was the interannual variability revealed between the two years. While most taxa were more abundant in the first year, prymnesiophytes experienced much greater abundance in the second year bloom. Analyses of integrated carbon revealed further stark contrasts between the two years, both in terms of total carbon and the contributions of different groups. Total integrated carbon varied widely in the first study year but displayed less fluctuation after June 2009, and values were noticeably reduced in the second year.
ContributorsHansen, Amy (Author) / Neuer, Susanne (Thesis advisor) / Krajmalnik-Brown, Rosa (Committee member) / Sommerfeld, Milton (Committee member) / Arizona State University (Publisher)
Created2010
Description
Massive glycerol cluster ions with many charges (~ 106 Da, ~ ±100 charges) have been generated by electrospray to bombard biomolecules and biological sample surfaces. The low impact energy per nucleon facilitates intact sputtering and ionization of biomolecules which can be subsequently imaged. Various lipids, peptides and proteins have been studied. The primary cluster ion source has been coupled with an ion-microscope imaging mass spectrometer (TRIFT-1, Physical Electronics). A lateral resolution of ~3µm has been demonstrated, which is acceptable for sub-cellular imaging of animal cells (e.g. single cancer cell imaging in early diagnosis). Since the available amount of target molecules per pixel is limited in biological samples, the measurement of useful ion yields (ratio of detected molecular ion counts to the sample molecules sputtered) is important to determine whether enough ion counts per pixel can be obtained. The useful ion yields of several lipids and peptides are in the 1-3×10-5 range. A 3×3 µm2lipid bilayer can produce ~260 counts/pixel for a meaningful 3×3 µm2 pixel ion image. This method can probably be used in cell imaging in the future, when there is a change in the lipid contents of the cell membrane (e.g. cancer cells vs. normal cells).
ContributorsZhang, Jitao (Author) / Williams, Peter (Thesis advisor) / Hayes, Mark (Committee member) / Nelson, Randall (Committee member) / Arizona State University (Publisher)
Created2015
Description
Recognition of algae as a “Fit for Purpose” biomass and its potential as an energy and bio-product resource remains relatively obscure. This is due to the absence of tailored and unified production information necessary to overcome several barriers for commercial viability and environmental sustainability. The purpose of this research was to provide experimentally verifiable estimates for direct energy and water demand for the algal cultivation stage which yields algal biomass for biofuels and other bio-products. Algal biomass productivity was evaluated using different cultivation methods in conjunction with assessment for potential reduction in energy and water consumption for production of fuel and feed. Direct water and energy demands are the major focal sustainability metrics in hot and arid climates and are influenced by environmental and operational variables connected with selected algal cultivation technologies. Evaporation is a key component of direct water demand for algal cultivation and directly related to variations in temperature and relative humidity. Temperature control strategies relative to design and operational variables were necessary to mitigate overheating of the outdoor algae culture in panel photobioreactors and sub-optimal cultivation temperature in open pond raceways. Mixing in cultivation systems was a major component in direct energy demand that was provided by aeration in panel bioreactors and paddlewheels in open pond raceways. Management of aeration time to meet required biological interactions provides opportunities for reduced direct energy demand in panel photobioreactors. However, the potential for reduction in direct energy demand in raceway ponds is limited to hydraulics and head loss. Algal cultivation systems were reviewed for potential integration into dairy facilities in order to determine direct energy demand and nutrient requirements for algal biomass production for animal feed. The direct energy assessment was also evaluated for key components of related energy and design parameters for conventional raceway ponds and a gravity fed system. The results of this research provide a platform for selecting appropriate production scenarios with respect to resource use and to ensure a cost effective product with the least environmental burden.
ContributorsBadvipour, Shahrzad (Author) / Sommerfeld, Milton (Thesis advisor) / Downes, Meghan (Committee member) / Abbott, Joshua (Committee member) / Chester, Mikhail (Committee member) / Arizona State University (Publisher)
Created2015
Description
Cell heterogeneity is widely present in the biological world and exists even in an isogenic population. Resolving the protein heterogeneity at the single cell level is of enormous biological and clinical relevance. However, single cell protein analysis has proven to be challenging due to extremely low amount of protein in a single cell and the huge complexity of proteome. This requires appropriate sampling and sensitive detection techniques. Here, a new approach, microfluidics combined with MALDI-TOF mass spectrometry was brought forward, for the analysis of proteins in single cells. The detection sensitivity of peptides as low as 300 molecules and of proteins as low as 10^6 molecules has been demonstrated. Furthermore, an immunoassay was successfully integrated in the microfluidic device for capturing the proteins of interest and further identifying them by subsequent enzymatic digestion. Moreover, an improved microfluidic platform was designed with separate chambers and valves, allowing the absolute quantification by employing iTRAQ tags or an isotopically labeled peptide. The study was further extended to analyze a protein in MCF-7 cell lysate. The approach capable of identifying and quantifying protein molecules in MCF-7 cells is promising for future proteomic studies at the single cell level.
ContributorsYang, Mian (Author) / Ros, Alexandra (Thesis advisor) / Hayes, Mark (Committee member) / Nelson, Randall (Committee member) / Arizona State University (Publisher)
Created2016
Description
There has been considerable advancement in the algae research field to move algae production for biofuels and bio-products forward to become commercially viable. However, there is one key element that humans cannot control, the natural externalities that impact production. An algae cultivation system is similar to agricultural crop farming practices. Algae are grown on an area of land for a certain time period with the aim of harvesting the biomass produced. One of the advantages of using algae biomass is that it can be used as a source of energy in the form of biofuels. Major advances in algae research and development practices have led to new knowledge about the remarkable potential of algae to serve as a sustainable source of biofuel. The challenge is to make the price of biofuels from algae cost-competitive with the price of petroleum-based fuels. The scope of this research was to design a concept for an automated system to control specific externalities and determine if integrating the system in an algae cultivation system could improve the algae biomass production process. This research required the installation and evaluation of an algae cultivation process, components selection and computer software programming for an automated system. The results from the automated system based on continuous real time monitored variables validated that the developed system contributes insights otherwise not detected from a manual measurement approach. The implications of this research may lead to technology that can be used as a base model to further improve algae cultivation systems.
ContributorsPuruhito, Emil (Author) / Sommerfeld, Milton (Thesis advisor) / Gintz, Jerry (Thesis advisor) / Alford, Eddie (Committee member) / Arizona State University (Publisher)
Created2014