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- Member of: ASU Electronic Theses and Dissertations
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
There is a considerable need for improved understanding of the outcome and amounts of water used to manage urban landscapes in arid and semiarid cities. Outdoor irrigation in urban parks consists of a large fraction of water demands in Phoenix, Arizona. Hence, ecohydrological processes need to be considered to improve outdoor irrigation management. With the goal of reducing outdoor water use and advancing the general knowledge of water fluxes in urban parks, this study explores water conservation opportunities in an arid city through observations and modeling.Most urban parks in Phoenix consist of a mosaic of turfgrass and trees which receive scheduled maintenance, fertilization and watering through sprinkler or flood irrigation. In this study, the effects that different watering practices, turfgrass management and soil conditions have on soil moisture observations in urban parks are evaluated. Soil moisture stations were deployed at three parks with stations at control plots with no compost application and compost treated sites with either a once or twice per year application instead of traditional fertilizer. An eddy covariance system was installed at a park to help quantify water losses and water, energy and carbon fluxes between the turfgrass and atmosphere. Additional meteorological observations are provided through a network of weather stations. The assessment covers over one year of observations, including the period of turfgrass growth in the warm season, and a period of dormancy during the cool season. The observations were used to setup and test a plot-scale soil water balance model to simulate changes in daily soil moisture in response to irrigation, precipitation and evapotranspiration demand for each park.
Combining modeling and observations of climate-soil-vegetation processes, I provide guidance on irrigation schedules and management that could help minimize water losses while supporting turfgrass health in desert urban parks. The irrigation scenarios suggest that water savings of at least 18% can be achieved at the three sites. While the application of compost treatment to study plots did not show clear improvements in soil water retention when compared to the control plots, this study shows that water conservation can be promoted while maintaining low plant water stress.
ContributorsKindler, Mercedes (Author) / Vivoni, Enrique R (Thesis advisor) / Mascaro, Giuseppe (Committee member) / Garcia, Margaret (Committee member) / Arizona State University (Publisher)
Created2021
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
Observational evidence is mounting on the reduction of winter precipitation and an earlier snowmelt in the southwestern United States. It is unclear, however, how these changes, along with forest thinning, will impact water supplies due to complexities in the precipitation-streamflow transformation. In this study, I use the Triangulated Irregular Network-based Real-time Integrated Basin Simulator (tRIBS) to provide insight into the independent and combined effects of climate change and forest cover reduction on the hydrologic response in the Beaver Creek (~1100 km2) of central Arizona. Prior to these experiments, confidence in the hydrologic model is established using snow observations at two stations, two nested streamflow gauges, and estimates of spatially-distributed snow water equivalent over a long-term period (water years 2003-2018). Model forcings were prepared using station observations and radar rainfall estimates in combination with downscaling and bias correction techniques that account for the orographic controls on air temperature and precipitation. Model confidence building showed that tRIBS is able to capture well the variation in snow cover and streamflow during wet and dry years in the 16 year simulation period. The results from this study show that the climate change experiments increased average annual streamflow by 1.5% at +1°C of warming. However, a 28% decrease in streamflow occurs by +6°C of warming as evapotranspiration (ET) increases by 10%. Forest thinning shifted the warming threshold where ET increases reduce streamflow yield until +4°C of warming as compared to no forest thinning when this threshold occurs at +2°C. An average increase in streamflow of 12% occurs after forest thinning across all climate scenarios. While the snow covered area is unaffected by thinning, the volume of snowmelt increases and is linked to the higher water yield. These findings indicate that water managers can expect decreases in streamflow due to climate change but may be able to offset these impacts up to a warming threshold by thinning forested areas within the Beaver Creek.
ContributorsCederstrom, Charles Joshua (Author) / Vivoni, Enrique R (Thesis advisor) / Mascaro, Giuseppe (Committee member) / Svoma, Bohumil (Committee member) / Arizona State University (Publisher)
Created2021
Description
Accelerated climate and land use land cover (LULC) changes are anticipated to significantly impact water resources in the Colorado River Basin (CRB), a major freshwater source in the southwestern U.S. The need for actionable information from hydrologic research is growing rapidly, given considerable uncertainties. For instance, it is unclear if the predicted high degree of interannual precipitation variability across the basin could overwhelm the impacts of future warming and how this might vary in space. Climate change will also intensify forest disturbances (wildfire, mortality, thinning), which can significantly impact water resources. These impacts are not constrained, given findings of mixed post-disturbance hydrologic responses. Process-based models like the Variable Infiltration Capacity (VIC) platform can quantitatively predict hydrologic behaviors of these complex systems. However, barriers limit their effectiveness to inform decision making: (1) simulations generate enormous data volumes, (2) outputs are inaccessible to managers, and (3) modeling is not transparent. I designed a stakeholder engagement and VIC modeling process to overcome these challenges, and developed a web-based tool, VIC-Explorer, to “open the black box” of my efforts. Meteorological data was from downscaled historical (1950-2005) and future projections (2006-2099) of eight climate models that best represent climatology under low- and high- emissions. I used two modeling methods: (1) a “top-down” approach to assess an “envelope of hydrologic possibility” under the 16 climate futures; and (2) a “bottom-up” evaluation of hydrology in two climates from the ensemble representing “Hot/Dry” and “Warm/Wet” futures. For the latter assessment, I modified land cover using projections of a LULC model and applied more drastic forest disturbances. I consulted water managers to expand the legitimacy of the research. Results showed Far-Future (2066-2095) basin-wide mean annual streamflow decline (relative to 1976-2005; ensemble median trends of -5% to -25%), attributed to warming that diminished spring snowfall and melt and year-round increased soil evaporation from the Upper Basin, and overall precipitation declines in the Lower Basin. Forest disturbances partially offset warming effects (basin-wide mean annual streamflow up to 12% larger than without disturbance). Results are available via VIC-Explorer, which includes documentation and guided analyses to ensure findings are interpreted appropriately for decision-making.
ContributorsWhitney, Kristen Marie (Author) / Vivoni, Enrique R (Thesis advisor) / Mascaro, Giuseppe (Committee member) / Whipple, Kelin X (Committee member) / White, Dave D (Committee member) / Xu, Tianfang (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
Weather radars provide quantitative precipitation estimates (QPEs) with seamless spatial coverage that can complement limitations of sparse rain gage measurements, including those affecting intensity-duration-frequency (IDF) relations used for infrastructure design. The goal of this M.S. thesis is to assess the ability of 4-km, 1-h QPEs from the Stage IV analysis of the Next-Generation Radar (NEXRAD) network to reproduce the statistics of extreme precipitation (P) in central Arizona, USA, using a dense network of 257 rain gages as reference. The generalized extreme value (GEV) distribution is used to model the frequency of annual P maximum series observed at gages and radar pixels for durations, d, from 1 to 24 h. Estimates of P quantiles from radar QPEs are negatively biased (-20% – -30%) for d = 1 h. The bias tends to 0 and errors are small for d ≥ 6 h, independently of the return period. The presence of scaling for the GEV location and scale parameters, needed to apply IDF scaling models, was found for both radar and gage products. Regional frequency analysis methods combined with bias correction of the GEV shape parameter allow reducing the statistical uncertainty and providing seamless spatial distribution of P quantiles at daily and subdaily durations that address limitations of current IDF relations in southwestern U.S. based on NOAA Atlas 14.
ContributorsSrivastava, Nehal Ansh (Author) / Mascaro, Giuseppe (Thesis advisor) / Chester, Mikhail (Committee member) / Garcia, Margaret (Committee member) / Papalexiou, Simon Michael (Committee member) / Arizona State University (Publisher)
Created2022
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
Cities are facing complex problems in urban water management due to unprecedented changes in climate, natural and built environment. The shift in urban hydrology from pre-development to post-development continues to accelerate the challenges of managing excess stormwater runoff, mitigating urban flood hazards and flood damages. Physically based hydrologic-hydraulic stormwater models are a useful tool for broad subset of urban flood management including risk and hazard assessment, flood forecasting, and infrastructure adaptation decision making and planning. The existing limitations in data availability, gaps in data, and uncertainty in data preclude reliable model construction, testing, deployment, knowledge generation, effective communication of flood risks, and adaptation decision making. These challenges that affect both the science and practice motivate three chapters of this dissertation. The first study conducts diagnostic analysis of the effects of stormwater infrastructure data completeness on model’s ability to simulate flood duration, flooding flow rate; and assesses the combined effects of data gaps and model resolution to simulate flood depth, extent and volume (chapter 2). The analysis showed the significance of complete stormwater infrastructure data and high model resolution to reduce error, bias and uncertainty; this study also presented an approach for filling infrastructure data gaps using available data and design standards. The second study addresses the lack of long-term hydrological observation in urban catchment by investigating the process and benefits of leveraging novel data sources in urban flood model construction and testing (chapter 3). A proof-of-concept demonstrated the application and benefits of leveraging novel data sources for urban flood monitoring and modeling. Furthermore, it highlights the need for developing and streamlining novel data collection infrastructure. The third study applies the hydrologic-hydraulic model as an adaptation planning tool and assess the effects of uncertainty in design precipitation estimates and land use change on the optimal configuration of green infrastructure (chapter 4). Several uncertainties affect infrastructure decision making as showed by variation in optimal green infrastructure configuration under precipitation estimates and land use change. Thus, the study further highlights the need of flexible planning process in infrastructure decision making.
ContributorsShrestha, Ashish (Author) / Garcia, Margaret (Thesis advisor) / Mascaro, Giuseppe (Committee member) / Chester, Mikhail (Committee member) / Fletcher, Sarah (Committee member) / Arizona State University (Publisher)
Created2022
Description
Integrated water resources management for flood control, water distribution, conservation, and food security require understanding hydrological spatial and temporal trends. Proliferation of monitoring and sensor data has boosted data-driven simulation and evaluation. Developing data-driven models for such physical process-related phenomena, and meaningful interpretability therein, necessitates an inventive methodology. In this dissertation, I developed time series and deep learning model that connected rainfall, runoff, and fish species abundances. I also investigated the underlying explainabilty for hydrological processes and impacts on fish species. First, I created a streamflow simulation model using computer vision and natural language processing as an alternative to physical-based routing. I tested it on seven US river network sections and showed it outperformed time series models, deep learning baselines, and novel variants. In addition, my model explained flow routing without physical parameter input or time-consuming calibration. On the basis of this model, I expanded it from accepting dispersed spatial inputs to adopting comprehensive 2D grid data. I constructed a spatial-temporal deep leaning model for rainfall-runoff simulation. I tested it against a semi-distributed hydrological model and found superior results. Furthermore, I investigated the potential interpretability for rainfall-runoff process in both space and time. To understand impacts of flow variation on fish species, I applied a frequency based model framework for long term time series data simulation. First, I discovered that timing of hydrological anomalies was as crucial as their size. Flooding and drought, when properly timed, were both linked with excellent fishing productivity. To identify responses of various fish trait groups, I used this model to assess mitigated hydrological variation by fish attributes. Longitudinal migratory fish species were more impacted by flow variance, whereas migratory strategy species reacted in the same direction but to various degrees. Finally, I investigated future fish population changes under alternative design flow scenarios and showed that a protracted low flow with a powerful, on-time flood pulse would benefit fish. In my dissertation, I constructed three data-driven models that link the hydrological cycle to the stream environment and give insight into the underlying physical process, which is vital for quantitative, efficient, and integrated water resource management.
ContributorsDeng, Qi (Author) / Sabo, John (Thesis advisor) / Grimm, Nancy (Thesis advisor) / Ganguly, Auroop (Committee member) / Li, Wenwen (Committee member) / Mascaro, Giuseppe (Committee member) / Arizona State University (Publisher)
Created2022