Matching Items (122)
Description
Tempe Town Lake is the site of fifteen years’ worth of chemical data collection by ASU researchers. In 2018 the dataSONDE, an instrument capable of measuring different water quality parameters every thirty minutes for a month at a time was installed in the lake. The SONDE has the potential to completely reduce the need for sampling by hand. Before the SONDE becomes the sole means of gathering data, it is important to verify its accuracy. In this study, the measurements gathered by the SONDE (pH, dissolved oxygen, temperature, conductivity and colored dissolved organic matter) were compared to measurements gathered using the verified methods from the past fifteen years.
ContributorsSauer, Elinor Rayne (Author) / Hartnett, Hilairy (Thesis director) / Glaser, Donald (Committee member) / Shock, Everett (Committee member) / Historical, Philosophical & Religious Studies (Contributor) / School of Molecular Sciences (Contributor) / School of Life Sciences (Contributor) / School of Sustainability (Contributor) / Barrett, The Honors College (Contributor)
Created2020-12
Description
Background
Grading schemes for breast cancer diagnosis are predominantly based on pathologists' qualitative assessment of altered nuclear structure from 2D brightfield microscopy images. However, cells are three-dimensional (3D) objects with features that are inherently 3D and thus poorly characterized in 2D. Our goal is to quantitatively characterize nuclear structure in 3D, assess its variation with malignancy, and investigate whether such variation correlates with standard nuclear grading criteria.
Methodology
We applied micro-optical computed tomographic imaging and automated 3D nuclear morphometry to quantify and compare morphological variations between human cell lines derived from normal, benign fibrocystic or malignant breast epithelium. To reproduce the appearance and contrast in clinical cytopathology images, we stained cells with hematoxylin and eosin and obtained 3D images of 150 individual stained cells of each cell type at sub-micron, isotropic resolution. Applying volumetric image analyses, we computed 42 3D morphological and textural descriptors of cellular and nuclear structure.
Principal Findings
We observed four distinct nuclear shape categories, the predominant being a mushroom cap shape. Cell and nuclear volumes increased from normal to fibrocystic to metastatic type, but there was little difference in the volume ratio of nucleus to cytoplasm (N/C ratio) between the lines. Abnormal cell nuclei had more nucleoli, markedly higher density and clumpier chromatin organization compared to normal. Nuclei of non-tumorigenic, fibrocystic cells exhibited larger textural variations than metastatic cell nuclei. At p<0.0025 by ANOVA and Kruskal-Wallis tests, 90% of our computed descriptors statistically differentiated control from abnormal cell populations, but only 69% of these features statistically differentiated the fibrocystic from the metastatic cell populations.
Conclusions
Our results provide a new perspective on nuclear structure variations associated with malignancy and point to the value of automated quantitative 3D nuclear morphometry as an objective tool to enable development of sensitive and specific nuclear grade classification in breast cancer diagnosis.
Grading schemes for breast cancer diagnosis are predominantly based on pathologists' qualitative assessment of altered nuclear structure from 2D brightfield microscopy images. However, cells are three-dimensional (3D) objects with features that are inherently 3D and thus poorly characterized in 2D. Our goal is to quantitatively characterize nuclear structure in 3D, assess its variation with malignancy, and investigate whether such variation correlates with standard nuclear grading criteria.
Methodology
We applied micro-optical computed tomographic imaging and automated 3D nuclear morphometry to quantify and compare morphological variations between human cell lines derived from normal, benign fibrocystic or malignant breast epithelium. To reproduce the appearance and contrast in clinical cytopathology images, we stained cells with hematoxylin and eosin and obtained 3D images of 150 individual stained cells of each cell type at sub-micron, isotropic resolution. Applying volumetric image analyses, we computed 42 3D morphological and textural descriptors of cellular and nuclear structure.
Principal Findings
We observed four distinct nuclear shape categories, the predominant being a mushroom cap shape. Cell and nuclear volumes increased from normal to fibrocystic to metastatic type, but there was little difference in the volume ratio of nucleus to cytoplasm (N/C ratio) between the lines. Abnormal cell nuclei had more nucleoli, markedly higher density and clumpier chromatin organization compared to normal. Nuclei of non-tumorigenic, fibrocystic cells exhibited larger textural variations than metastatic cell nuclei. At p<0.0025 by ANOVA and Kruskal-Wallis tests, 90% of our computed descriptors statistically differentiated control from abnormal cell populations, but only 69% of these features statistically differentiated the fibrocystic from the metastatic cell populations.
Conclusions
Our results provide a new perspective on nuclear structure variations associated with malignancy and point to the value of automated quantitative 3D nuclear morphometry as an objective tool to enable development of sensitive and specific nuclear grade classification in breast cancer diagnosis.
ContributorsNandakumar, Vivek (Author) / Kelbauskas, Laimonas (Author) / Hernandez, Kathryn (Author) / Lintecum, Kelly (Author) / Senechal, Patti (Author) / Bussey, Kimberly (Author) / Davies, Paul (Author) / Johnson, Roger (Author) / Meldrum, Deirdre (Author) / Ira A. Fulton Schools of Engineering (Contributor) / School of Electrical, Computer and Energy Engineering (Contributor) / Biodesign Institute (Contributor) / Center for Biosignatures Discovery Automation (Contributor) / College of Liberal Arts and Sciences (Contributor) / School of Life Sciences (Contributor) / Department of Physics (Contributor)
Created2012-01-05
Description
Dissolved organic matter (DOM) can have numerous effects on the water chemistry and the biological life within an aquatic system with its wide variety of chemical structures and properties. The composition of the dissolved carbon can be estimated by utilizing the fluorescent properties of some DOM such as aromatic amino acids and humic material. This experiment was used to observe how organic matter could influence hydrothermal systems, such as Sylvan Springs in Yellowstone National Park, USA. Using optical density at 600 nm (OD 600), excitation-emission matrix spectra (EEMS), and Illumina sequencing methods (16S rRNA gene sequencing), changes in dissolved organic matter (DOM) were observed based on long term incubation at 84ºC and microbial influence. Four media conditions were tested over a two-month duration to assess these changes: inoculated pine needle media, uninoculated pine needle media, inoculated yeast extract media, and uninoculated yeast extract media. The inoculated samples contained microbes from a fluid and sediment sample of Sylvan Spring collected July 23, 2018. Absorbance indicated that media containing pine needle broth poorly support life, whereas media containing yeast extract revealed a positive increase in growth. Excitation-Emission Matrix Spectra of the all media conditions indicated changes in DOM composition throughout the trial. There were limited differences between the inoculated and uninoculated samples suggesting that the DOM composition change in this study was dominated by the two-month incubation at 84ºC more than biotic processes. Sequencing performed on a sediment sample collected from Sylvan Spring indicated five main order of prokaryotic phyla: Aquificales, Desulfurococcales, Thermoproteales, Thermodesulfobacteriales, and Crenarchaeota. These organisms are not regarded as heterotrophic microbes, so the lack of significant biotic changes in DOM could be a result of these microorganisms not being able to utilize these enrichments as their main metabolic energy supply.
ContributorsKnott, Nicholas Joseph (Author) / Shock, Everett (Thesis director) / Hartnett, Hilairy (Committee member) / Till, Christy (Committee member) / School of Molecular Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
Finding life beyond Earth could change our understanding of life and habitability. The best place to look for life beyond Earth is Jupiter's moon, Europa. It has been estimated Europa may have a liquid, salt-water subsurface with 2 to 3 times the volume of all Earth's oceans. Knowing that all life requires water, it is in our best interest to explore Europa. This thesis explored the plausibility of life on Europa in four of its environments: on the surface, under the ice shell, in the liquid subsurface, and at the bottom of the liquid subsurface. Each of these environments were defined from science literature and compared to known Earth analogs. Europa's surface is not likely to support life, as there is not liquid water present. There is also extremely high radiation bombardment and extremely low surface temperatures that are estimated to be well out of the range for supporting life. It is more plausible that life could be under Europa's ice shell than on the surface. Under the surface, radiation exposure dramatically reduces. Researchers have found organisms on Earth that can live in similar environments as Europa's ice as well. These organisms require some interaction with liquid water though. Uncertainties about Europa's ice shell thickness and radiation load per depth it experiences, as well as there being limited research on organisms in ice environments, hinder us from definitively assessing the plausibility of life under the surface. The best environment on Europa to look for life on Europa is the subsurface. There remain a lot of uncertainties about the subsurface, however, that make it difficult to assess the plausibility of finding life. These uncertainties include its depth, water activity, salinity, temperature, pressure, and structure. This subsurface may be suitable for life, but until we can further understand the environment of the subsurface, we cannot make definite conclusions. As for assessing the plausibility of life at the bottom of Europa's subsurface, there is not much we know about this environment either. It has been suggested there may be hydrothermal vents, but no evidence has either supported or rejected this idea. Without a clear understanding of the environment at the bottom of the subsurface, the plausibility of life here cannot be definitively answered. It is apparent we need to further study Europa. In particular, we need to focus on understanding the subsurface. When the subsurface is better defined, we can better assess the plausibility of life being present. Fortunately, both NASA and the ESA are currently planning missions to Europa that are scheduled to launch in the 2020s.
ContributorsHoward, Cheyenne Whiffen (Author) / Farmer, Jack (Thesis director) / Shock, Everett (Committee member) / School of Earth and Space Exploration (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
Yellowstone National Park has a vibrant variety of flora, fauna, and hydrothermal systems all collected together in one large and complex system. Studies have been conducted for at least several decades in order to make sense of this system in ways that may be relevant to other similar geologies around the world. The latest update in this ever-ongoing study involves the collection and analysis of water samples from 2016. These samples have been analyzed for conductivity, pH, temperature, dissolved organic carbon, dissolved inorganic carbon, carbon isotopes, dissolved oxygen, ferrous iron, sulfide, silica, and more. While not many trends were found in this data in regards to dissolved organic carbon values, this is a substantial addition to a growing body of information that could yield more impressive information in times to come. In addition, factors that have yet to analyzed for this 2016 data, such as concentrations of metals and metalloids, may provide some insights when put through a chloride vs sulfate framework to separate out different reaction regions.
ContributorsDoan, Cuong Le (Author) / Shock, Everett (Thesis director) / Gould, Ian (Committee member) / School of Molecular Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
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
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
The ability to find evidence of life on early Earth and other planets is constrained by the current understanding of biosignatures and our ability to differentiate fossils from abiotic mimics. When organisms transition from the living realm to the fossil record, their morphological and chemical characteristics are modified, usually resulting in the loss of information. These modifications can happen during early and late diagenesis and differ depending on local geochemical properties. These post-depositional modifications need to be understood to better interpret the fossil record. Siliceous hot spring deposits (sinters) are of particular interest for biosignature research as they are early Earth analog environments and targets for investigating the presence of fossil life on Mars. As silica-supersaturated fluids flow from the vent to the distal apron, they precipitate non-crystalline opal-A that fossilizes microbial communities at a range in scales (μm-cm). Therefore, many studies have documented the ties between the active microbial communities and the morphological and chemical biosignatures in hot springs. However, far less attention has been placed on understanding preservation in systems with complex mineralogy or how post-depositional alteration affects the retention of biosignatures. Without this context, it can be challenging to recognize biosignatures in ancient rocks. This dissertation research aims to refine our current understanding of biosignature preservation and retention in sinters. Biosignatures of interest include organic matter, microfossils, and biofabrics. The complex nature of hot springs requires a comprehensive understanding of biosignature preservation that is representative of variable chemistries and post-depositional alterations. For this reason, this dissertation research chapters are field site-based. Chapter 2 investigates biosignature preservation in an unusual spring with mixed opal-A-calcite mineralogy at Lýsuhóll, Iceland. Chapter 3 tracks how silica diagenesis modifies microfossil morphology and associated organic matter at Puchuldiza, Chile. Chapter 4 studies the effects of acid fumarolic overprinting on biosignatures in Gunnuhver, Iceland. To accomplish this, traditional geologic methods (mapping, petrography, X-ray diffraction, bulk elemental analyses) were combined with high-spatial-resolution elemental mapping to better understand diagenetic effects in these systems. Preservation models were developed to predict the types and styles of biosignatures that can be present depending on the depositional and geochemical context. Recommendations are also made for the types of deposits that are most likely to preserve biosignatures.
ContributorsJuarez Rivera, Marisol (Author) / Farmer, Jack D (Thesis advisor) / Hartnett, Hilairy E (Committee member) / Shock, Everett (Committee member) / Garcia-Pichel, Ferran (Committee member) / Trembath-Reichert, Elizabeth (Committee member) / Arizona State University (Publisher)
Created2021