Matching Items (146)
Description
A chimeric, humanized monoclonal antibody that recognizes a highly conserved fusion loop found on flaviviruses was constructed with a geminiviral replicon and transiently expressed in Nicotiana benthamiana plants through Agrobacterium tumefaciens infiltration. Characterization and expression studies were then conducted to confirm correct assembly of the antibody. Once the antibody was purified, an ELISA was conducted to validate that the antibody was able to bind to the flavivirus fusion loop.
ContributorsPardhe, Mary (Author) / Mason, Hugh (Thesis director) / Chen, Qiang (Committee member) / Mor, Tsafrir (Committee member) / School of Life Sciences (Contributor) / Department of Information Systems (Contributor) / W.P. Carey School of Business (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
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
Virus-Like Particles (VLPs) are self-assembling structures that lack the viral genetic material. Therefore they are safer and more immunogenic than other forms of vaccines. The Hepatitis B core (HBc) VLPs are a novel mechanism through which delivery of DNA-based human vaccines are plausible. Production of VLPs require recombinant, rapidly replicating, plant-based systems such as the geminiviral replicon system. This project entails the cloning process of HBc-DIII fusion protein, a VLP that should form Domain III of the Envelope protein on West Nile Virus, into deconstructed geminiviral vector. The cloning process includes the HBc-DIII fusion protein DNA isolation, restriction enzyme digestion with NcoI and SacI, PCR changing the NcoI site on the HBc-DIII insert to XbaI, sequencing, ligation into geminiviral vector and transformation into an agrobacterium strain. The major impediment to the cloning process was the presence of multiple bands instead of the expected two bands while doing restriction enzyme digests. The troubleshooting process enabled speculating that due to the excess of restriction enzymes in the digestion volume, some of the DNA was not digested completely. Hence, multiple bands were observed. However, sequencing analysis and further cloning process ensured the presence of HBc-DIII insert band (approximately 800bp) in the Gemini vector. Lastly, the construct HBc-DIII in Gemini vector was ensured to be in agrobacterium for further experiments such as agro-infiltration.
ContributorsSuresh Kumar, Reshma (Author) / Chen, Qiang (Thesis director) / Zhang, Peiming (Committee member) / School of Molecular Sciences (Contributor) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
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
Vaccines are one of the most effective ways of combating infectious diseases and developing vaccine platforms that can be used to produce vaccines can greatly assist in combating global public health threats. This dissertation focuses on the development and pre-clinical testing of vaccine platforms that are highly immunogenic, easily modifiable, economically viable to produce, and stable. These criteria are met by the recombinant immune complex (RIC) universal vaccine platform when produced in plants. The RIC platform is modeled after naturally occurring immune complexes that form when an antibody, a component of the immune system that recognizes protein structures or sequences, binds to its specific antigen, a molecule that causes an immune response. In the RIC platform, a well-characterized antibody is linked via its heavy chain, to an antigen tagged with the antibody-specific epitope. The RIC antibody binds to the epitope tags on other RIC molecules and forms highly immunogenic complexes. My research has primarily focused on the optimization of the RIC platform. First, I altered the RIC platform to enable an N-terminal antigenic fusion instead of the previous C-terminal fusion strategy. This allowed the platform to be used with antigens that require an accessible N-terminus. A mouse immunization study with a model antigen showed that the fusion location, either N-terminal or C-terminal, did not impact the immune response. Next, I studied a synergistic response that was seen upon co-delivery of RIC with virus-like particles (VLP) and showed that the synergistic response could be produced with either N-terminal or C-terminal RIC co-delivered with VLP. Since RICs are inherently insoluble due to their ability to form complexes, I also examined ways to increase RIC solubility by characterizing a panel of modified RICs and antibody-fusions. The outcome was the identification of a modified RIC that had increased solubility while retaining high immunogenicity. Finally, I modified the RIC platform to contain multiple antigenic insertion sites and explored the use of bioinformatic tools to guide the design of a broadly protective vaccine.
ContributorsPardhe, Mary (Author) / Mason, Hugh S (Thesis advisor) / Chen, Qiang (Committee member) / Mor, Tsafrir (Committee member) / Wilson, Melissa (Committee member) / Arizona State University (Publisher)
Created2021
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