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The effect of an anaerobic reductive environment produced by the oxidation of zero valent iron (ZVI) on the microbial reductive dechlorination of trichloroethylene and its applicability to in-situ bioremediation processes was investigated using microcosms and soil column studies. I learned that microbial dechlorination requires a highly reductive environment, as represented by negative values for oxidation-reduction potential (ORP), which can be maintained through the addition of reducing agents such as ZVI, or to a lesser extent, the fermentation of added substrates such as lactate. Microcosm conditions represented distance from an in-situ treatment injection well and contained different types of iron species and dechlorinating bioaugmentation cultures. Diminishing efficacy of microbial reductive dechlorination along a gradient away from the injection zone was observed, characterized by increasing ORP and decreasing pH. Results also suggested that the use of particular biostimulation substrates is key to prioritizing the dechlorination reaction against competing microbial and abiotic processes by supplying electrons needed for microbial dechlorination.
ContributorsMouti, Aatikah (Author) / Krajmalnik-Brown, Rosa (Thesis director) / Delgado, Anca (Committee member) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2017-12
Description
Hydrogen is a key indicator of microbial activity in soils/sediments and groundwater because of its role as an electron donor for reducing sulfate and nitrate and carrying out other metabolic processes. The goal of this study was to quantitatively measure the total biological hydrogen demand (TBHD) of soils and sediments in anaerobic environments. We define the total biological hydrogen demand as the sum of all electron acceptors that can be used by hydrogen-oxidizing microorganisms. Three sets of anaerobic microcosms were set up with different soils/sediments, named Carolina, Garden, and ASM. The microcosms included 25g of soil/sediment and 75 mL of anaerobic medium. 10 mL of hydrogen were pulse-fed for 100 days. Hydrogen consumption and methane production were tracked using gas chromatography. Chemical analysis of each soil was performed at the beginning of the experiment to determine the concentration of electron acceptors in the soils/sediments, including nitrate, sulfate, iron and bicarbonate. An analysis of the microbial community was done at t = 0 and at the end of the 100 days to examine changes in the microbial community due to the metabolic processes occurring as hydrogen was consumed. Carolina consumed 9810 43 mol of hydrogen and produced 19,572 2075 mol of methane. Garden consumed 4006 33 mol of hydrogen and produced 7,239 543 mol of methane. Lastly, ASM consumed 1557 84 mol of hydrogen and produced 1,325 715 mol of methane. I conclude that the concentration of bicarbonate initially present in the soil had the most influence over the hydrogen demand and microbial community enrichment. To improve this research, I recommend that future studies include a chemical analysis of final soil geochemistry conditions, as this will provide with a better idea of what pathway the hydrogen is taking in each soil.
ContributorsLuna Aguero, Marisol (Author) / Krajmalnik-Brown, Rosa (Thesis director) / Delgado, Anca (Committee member) / Civil, Environmental and Sustainable Engineering Programs (Contributor) / School of Sustainability (Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
Description
Open source image analytics and data mining software are widely available but can be overly-complicated and non-intuitive for medical physicians and researchers to use. The ASU-Mayo Clinic Imaging Informatics Lab has developed an in-house pipeline to process medical images, extract imaging features, and develop multi-parametric models to assist disease staging and diagnosis. The tools have been extensively used in a number of medical studies including brain tumor, breast cancer, liver cancer, Alzheimer's disease, and migraine. Recognizing the need from users in the medical field for a simplified interface and streamlined functionalities, this project aims to democratize this pipeline so that it is more readily available to health practitioners and third party developers.
ContributorsBaer, Lisa Zhou (Author) / Wu, Teresa (Thesis director) / Wang, Yalin (Committee member) / Computer Science and Engineering Program (Contributor) / W. P. Carey School of Business (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
Description
In the United States, the prevalence of pediatric obesity has increased to 17% in the general population and even more so in the Hispanic pediatric population to 22.4%. These children are at a higher risk for associated comorbidities, including cardiovascular disease and insulin resistance. The purpose of the following study is to determine the effectiveness of the Nutrition and Health Awareness curriculum at reducing childhood obesity by evaluating alterations in the gut microbial composition, diet, and overall health of the students throughout the five-week program. Nutrition and Health Awareness (NHA) is a student organization that strives to reduce the prevalence of obesity, diabetes, and cardiovascular diseases, specifically in children, by providing active nutrition education services through peer mentoring in elementary schools and community programs. This study went through ASU's Institutional Review Board process and all forms were translated into Spanish. The control group maintained their normal routines and the experimental group received the 5 week NHA program and then continued with their normal routines. Anthropometric measures (Body Mass Index, waist-to-hip ratio, and blood pressure), diet measures (Hispanic food frequency questionnaire), fecal swabs, and content surveys were collected on weeks 0, 5, and 8. Contrary to expected, alpha diversity, kilocalorie intake, and macronutrient intake decreased as the study progressed for both the control and experimental groups. Anthropometric measurements were relatively stable. Though not statistically significant, the greatest difference in time points is between weeks 1 and 8. This decrease in alpha diversity and kilocalorie intake could be due to a change in environment since the children started school on week 8. Future implications of this study are that parental involvement is necessary for an effective, sustainable change in these children. More research in different settings is necessary to determine NHA's effectiveness
ContributorsPatel, Kapila Cristina (Author) / Krajmalnik-Brown, Rosa (Thesis director) / Whisner, Corrie (Committee member) / School of Nutrition and Health Promotion (Contributor) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Microorganisms can produce metabolites in the gut including short chain fatty acids, vitamins, and amino acids. Certain metabolites produced in the gut can affect the brain through changes in neurotransmitter concentrations. Serotonin, a neurotransmitter, is associated with mood, appetite, and sleep. Up to 90% of serotonin synthesis is located in the gut, by human enterochromaffin cells. Bacteria known to biosynthesize tryptophan, precursor to serotonin, include Escherichia coli, Enterococcus and Streptococcus. Tryptophan is synthesized by bacteria with the enzyme tryptophan synthase and requires Vitamin B6 (Pyridoxal). We hypothesize that gut isolates from surgical weight loss patients can enhance tryptophan production, which relies on vitamin B6 availability. Our goal was to isolate bacteria in order to test for tryptophan production and to determine how Vitamin B6 concentrations could affect tryptophan production. We isolated gut bacteria was from successful surgical weight loss patient with selective pressures for Enterobacter isolates and Enterococcus isolates. We tested the isolates were tested to determine if they could biosynthesize tryptophan in-vitro. Bacterial cultures were enriched with yeast and enriched with serine and indole, substrates necessary for tryptophan biosynthesis. We analyzed the supernatant samples for tryptophan production using GC-FID. Bacterial isolates most closely related to E. coli and Klebsiella based on 16S rRNA gene sequences, produced tryptophan in vitro. While under serine & indole media conditions, R1, the isolate most similar to Klebsiella produced more tryptophan than R14, the isolate most similar to E. coli. We tested the R1 isolate with a gradient of vitamin B6 concentrations from 0.02 µg/mL to 0.2 µg/mL to determine its effect on tryptophan production. When less than 0.05 µg/mL of Vitamin B6 was added, tryptophan production at 6 hours was higher than tryptophan production with Vitamin B6 concentrations at 0.05 µg/mL and above. The production and consumption of tryptophan by Klebsiella under 0 µg/mL and 0.02 µg/mL concentrations of Vitamin B6 occurred at a faster rate when compared to concentrations 0.05 µg/mL or higher of Vitamin B6.
ContributorsYee, Emily L. (Author) / Krajmalnik-Brown, Rosa (Thesis director) / Ilhan, Zehra (Committee member) / W. P. Carey School of Business (Contributor) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Video object segmentation (VOS) is an important task in computer vision with a lot of applications, e.g., video editing, object tracking, and object based encoding. Different from image object segmentation, video object segmentation must consider both spatial and temporal coherence for the object. Despite extensive previous work, the problem is still challenging. Usually, foreground object in the video draws more attention from humans, i.e. it is salient. In this thesis we tackle the problem from the aspect of saliency, where saliency means a certain subset of visual information selected by a visual system (human or machine). We present a novel unsupervised method for video object segmentation that considers both low level vision cues and high level motion cues. In our model, video object segmentation can be formulated as a unified energy minimization problem and solved in polynomial time by employing the min-cut algorithm. Specifically, our energy function comprises the unary term and pair-wise interaction energy term respectively, where unary term measures region saliency and interaction term smooths the mutual effects between object saliency and motion saliency. Object saliency is computed in spatial domain from each discrete frame using multi-scale context features, e.g., color histogram, gradient, and graph based manifold ranking. Meanwhile, motion saliency is calculated in temporal domain by extracting phase information of the video. In the experimental section of this thesis, our proposed method has been evaluated on several benchmark datasets. In MSRA 1000 dataset the result demonstrates that our spatial object saliency detection is superior to the state-of-art methods. Moreover, our temporal motion saliency detector can achieve better performance than existing motion detection approaches in UCF sports action analysis dataset and Weizmann dataset respectively. Finally, we show the attractive empirical result and quantitative evaluation of our approach on two benchmark video object segmentation datasets.
ContributorsWang, Yilin (Author) / Li, Baoxin (Thesis advisor) / Wang, Yalin (Committee member) / Cleveau, David (Committee member) / Arizona State University (Publisher)
Created2013
Description
Learning from high dimensional biomedical data attracts lots of attention recently. High dimensional biomedical data often suffer from the curse of dimensionality and have imbalanced class distributions. Both of these features of biomedical data, high dimensionality and imbalanced class distributions, are challenging for traditional machine learning methods and may affect the model performance. In this thesis, I focus on developing learning methods for the high-dimensional imbalanced biomedical data. In the first part, a sparse canonical correlation analysis (CCA) method is presented. The penalty terms is used to control the sparsity of the projection matrices of CCA. The sparse CCA method is then applied to find patterns among biomedical data sets and labels, or to find patterns among different data sources. In the second part, I discuss several learning problems for imbalanced biomedical data. Note that traditional learning systems are often biased when the biomedical data are imbalanced. Therefore, traditional evaluations such as accuracy may be inappropriate for such cases. I then discuss several alternative evaluation criteria to evaluate the learning performance. For imbalanced binary classification problems, I use the undersampling based classifiers ensemble (UEM) strategy to obtain accurate models for both classes of samples. A small sphere and large margin (SSLM) approach is also presented to detect rare abnormal samples from a large number of subjects. In addition, I apply multiple feature selection and clustering methods to deal with high-dimensional data and data with highly correlated features. Experiments on high-dimensional imbalanced biomedical data are presented which illustrate the effectiveness and efficiency of my methods.
ContributorsYang, Tao (Author) / Ye, Jieping (Thesis advisor) / Wang, Yalin (Committee member) / Davulcu, Hasan (Committee member) / Arizona State University (Publisher)
Created2013
Description
The oceanic biological carbon pump is a key component of the global carbon cycle in which dissolved carbon dioxide is taken up by phytoplankton during photosynthesis, a fraction of which then sinks to depth and contributes to oceanic carbon storage. The small-celled phytoplankton (<5 µm) that dominate the phytoplankton community in oligotrophic oceans have traditionally been viewed as contributing little to export production due to their small size. However, recent studies have shown that the picocyanobacterium Synechococcus produces transparent exopolymer particles (TEP), the sticky matrix of marine aggregates, and forms abundant microaggregates (5-60 µm), which is enhanced under nutrient limited growth conditions. Whether other small phytoplankton species exude TEP and form microaggregates, and if these are enhanced under growth-limiting conditions remains to be investigated. This study aims to analyze how nutrient limitation affects TEP production and microaggregate formation of species that are found to be associated with sinking particles in the Sargasso Sea. The pico-cyanobacterium Prochlorococcus marinus (0.8 µm), the nano-diatom Minutocellus polymorphus (2 µm), and the pico-prasinophyte Ostreococcus lucimarinus (0.6 µm) were grown in axenic batch culture experiments under nutrient replete and limited conditions. It was hypothesized that phytoplankton subject to nutrient limitation will aggregate more than those under replete conditions due to an increased exudation of TEP and that Minutocellus would produce the most TEP and microaggregates while Prochlorococcus would produce the least TEP and microaggregates of the three phytoplankton groups. As hypothesized, nutrient limitation increased TEP concentration in all three species, however they were only significant in nitrogen-limited treatments of Prochlorococcus as well as nitrogen- and phosphorus-limited treatments of Minutocellus. Formation of microaggregates was significantly enhanced in Minutocellus and Ostreococcus cultures in distinct microaggregate size ranges. Minutocellus produced the most TEP per cell and aggregated at higher volume concentrations compared to Prochlorococcus and Ostreococcus. Surprisingly, Ostreococcus produced more TEP than Prochlorococcus and Minutocellus per unit cell volume. These findings show for the first time how nutrient limited conditions enhance TEP production and microaggregation of Prochlorococcus, Minutocellus, and Ostreococcus, providing a mechanism for their incorporation into larger, sinking particles and contribution to export production in oligotrophic oceans.
ContributorsShurtleff, Catrina (Author) / Neuer, Susanne (Thesis advisor) / Lomas, Michael W. (Committee member) / Garcia-Pichel, Ferran (Committee member) / Arizona State University (Publisher)
Created2022
Description
This dissertation constructs a new computational processing framework to robustly and precisely quantify retinotopic maps based on their angle distortion properties. More generally, this framework solves the problem of how to robustly and precisely quantify (angle) distortions of noisy or incomplete (boundary enclosed) 2-dimensional surface to surface mappings. This framework builds upon the Beltrami Coefficient (BC) description of quasiconformal mappings that directly quantifies local mapping (circles to ellipses) distortions between diffeomorphisms of boundary enclosed plane domains homeomorphic to the unit disk. A new map called the Beltrami Coefficient Map (BCM) was constructed to describe distortions in retinotopic maps. The BCM can be used to fully reconstruct the original target surface (retinal visual field) of retinotopic maps. This dissertation also compared retinotopic maps in the visual processing cascade, which is a series of connected retinotopic maps responsible for visual data processing of physical images captured by the eyes. By comparing the BCM results from a large Human Connectome project (HCP) retinotopic dataset (N=181), a new computational quasiconformal mapping description of the transformed retinal image as it passes through the cascade is proposed, which is not present in any current literature. The description applied on HCP data provided direct visible and quantifiable geometric properties of the cascade in a way that has not been observed before. Because retinotopic maps are generated from in vivo noisy functional magnetic resonance imaging (fMRI), quantifying them comes with a certain degree of uncertainty. To quantify the uncertainties in the quantification results, it is necessary to generate statistical models of retinotopic maps from their BCMs and raw fMRI signals. Considering that estimating retinotopic maps from real noisy fMRI time series data using the population receptive field (pRF) model is a time consuming process, a convolutional neural network (CNN) was constructed and trained to predict pRF model parameters from real noisy fMRI data
ContributorsTa, Duyan Nguyen (Author) / Wang, Yalin (Thesis advisor) / Lu, Zhong-Lin (Committee member) / Hansford, Dianne (Committee member) / Liu, Huan (Committee member) / Li, Baoxin (Committee member) / Arizona State University (Publisher)
Created2022
Description
Under current climate conditions northern peatlands mostly act as C sinks; however, changes in climate and environmental conditions, can change the soil carbon decomposition cascade, thus altering the sink status. Here I studied one of the most abundant northern peatland types, poor fen, situated along a climate gradient from tundra (Daring Lake, Canada) to boreal forest (Lutose, Canada) to temperate broadleaf and mixed forest (Bog Lake, MN and Chicago Bog, NY) biomes to assess patterns of microbial abundance across the climate gradient. Principal component regression analysis of the microbial community and environmental variables determined that mean annual temperature (MAT) (r2=0.85), mean annual precipitation (MAP) (r2=0.88), and soil temperature (r2=0.77), were the top significant drivers of microbial community composition (p < 0.001). Niche breadth analysis revealed the relative abundance of Intrasporangiaceae, Methanobacteriaceae and Candidatus Methanoflorentaceae fam. nov. to increase when MAT and MAP decrease. The same analysis showed Spirochaetaceae, Methanosaetaceae and Methanoregulaceae to increase in relative abundance when MAP, soil temperature and MAT increased, respectively. These findings indicated that climate variables were the strongest predictors of microbial community composition and that certain taxa, especially methanogenic families demonstrate distinct patterns across the climate gradient. To evaluate microbial production of methanogenic substrates, I carried out High Resolution-DNA-Stable Isotope Probing (HR-DNA-SIP) to evaluate the active portion of the community’s intermediary ecosystem metabolic processes. HR-DNA-SIP revealed several challenges in efficiency of labelling and statistical identification of responders, however families like Veillonellaceae, Magnetospirillaceae, Acidobacteriaceae 1, were found ubiquitously active in glucose amended incubations. Differences in metabolic byproducts from glucose amendments show distinct patterns in acetate and propionate accumulation across sites. Families like Spirochaetaceae and Sphingomonadaceae were only found to be active in select sites of propionate amended incubations. By-product analysis from propionate incubations indicate that the northernmost sites were acetate-accumulating communities.
These results indicate that microbial communities found in poor fen northern peatlands are strongly influenced by climate variables predicted to change under current climate scenarios. I have identified patterns of relative abundance and activity of select microbial taxa, indicating the potential for climate variables to influence the metabolic pathway in which carbon moves through peatland systems.
ContributorsSarno, Analissa Flores (Author) / Cadillo-Quiroz, Hinsby (Thesis advisor) / Garcia-Pichel, Ferran (Committee member) / Krajmalnik-Brown, Rosa (Committee member) / Childers, Daniel (Committee member) / Arizona State University (Publisher)
Created2022