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- Creators: Ira A. Fulton Schools of Engineering
Learning Sparse Representations for Fruit-Fly Gene Expression Pattern Image Annotation and Retrieval
Description
Background
Fruit fly embryogenesis is one of the best understood animal development systems, and the spatiotemporal gene expression dynamics in this process are captured by digital images. Analysis of these high-throughput images will provide novel insights into the functions, interactions, and networks of animal genes governing development. To facilitate comparative analysis, web-based interfaces have been developed to conduct image retrieval based on body part keywords and images. Currently, the keyword annotation of spatiotemporal gene expression patterns is conducted manually. However, this manual practice does not scale with the continuously expanding collection of images. In addition, existing image retrieval systems based on the expression patterns may be made more accurate using keywords.
Results
In this article, we adapt advanced data mining and computer vision techniques to address the key challenges in annotating and retrieving fruit fly gene expression pattern images. To boost the performance of image annotation and retrieval, we propose representations integrating spatial information and sparse features, overcoming the limitations of prior schemes.
Conclusions
We perform systematic experimental studies to evaluate the proposed schemes in comparison with current methods. Experimental results indicate that the integration of spatial information and sparse features lead to consistent performance improvement in image annotation, while for the task of retrieval, sparse features alone yields better results.
Fruit fly embryogenesis is one of the best understood animal development systems, and the spatiotemporal gene expression dynamics in this process are captured by digital images. Analysis of these high-throughput images will provide novel insights into the functions, interactions, and networks of animal genes governing development. To facilitate comparative analysis, web-based interfaces have been developed to conduct image retrieval based on body part keywords and images. Currently, the keyword annotation of spatiotemporal gene expression patterns is conducted manually. However, this manual practice does not scale with the continuously expanding collection of images. In addition, existing image retrieval systems based on the expression patterns may be made more accurate using keywords.
Results
In this article, we adapt advanced data mining and computer vision techniques to address the key challenges in annotating and retrieving fruit fly gene expression pattern images. To boost the performance of image annotation and retrieval, we propose representations integrating spatial information and sparse features, overcoming the limitations of prior schemes.
Conclusions
We perform systematic experimental studies to evaluate the proposed schemes in comparison with current methods. Experimental results indicate that the integration of spatial information and sparse features lead to consistent performance improvement in image annotation, while for the task of retrieval, sparse features alone yields better results.
ContributorsYuan, Lei (Author) / Woodard, Alexander (Author) / Ji, Shuiwang (Author) / Jiang, Yuan (Author) / Zhou, Zhi-Hua (Author) / Kumar, Sudhir (Author) / Ye, Jieping (Author) / Biodesign Institute (Contributor) / Center for Evolution and Medicine (Contributor) / Ira A. Fulton Schools of Engineering (Contributor) / College of Liberal Arts and Sciences (Contributor) / School of Life Sciences (Contributor)
Created2012-05-23
Description
Approximately 248 million people in the world are currently living with chronic Hepatitis B virus (HBV) infection. HBV and HCV infections are the primary cause of liver diseases such as cirrhosis and hepatocellular carcinomas in the world with an estimated 1.4 million deaths annually. HBV in the Republic of Peru was used as a case study of an emerging and rapidly spreading disease in a developing nation. Wherein, clinical diagnosis of HBV infections in at-risk communities such the Amazon Region and the Andes Mountains are challenging due to a myriad of reasons. High prices of clinical diagnosis and limited access to treatment are alone the most significant deterrent for individuals living in at-risk communities to get the much need help. Additionally, limited testing facilities, lack of adequate testing policies or national guidelines, poor laboratory capacity, resource-limited settings, geographical isolation, and public mistrust are among the chief reasons for low HBV testing. Although, preventative vaccination programs deployed by the Peruvian health officials have reduced the number of infected individuals by year and region. To significantly reduce or eradicate HBV in hyperendemic areas and countries such as Peru, preventative clinical diagnosis and vaccination programs are an absolute necessity. Consequently, the need for a portable low-priced diagnostic platform for the detection of HBV and other diseases is substantial and urgent not only in Peru but worldwide. Some of these concerns were addressed by designing a low-cost, rapid detection platform. In that, an immunosignature technology (IMST) slide used to test for reactivity against the presence of antibodies in the serum-sample was used to test for picture resolution and clarity. IMST slides were scanned using a smartphone camera placed on top of the designed device housing a circuit of 32 LED lights at 647 nm, an optical magnifier at 15X, and a linear polarizing film sheet. Tow 9V batteries powered the scanning device LED circuit ensuring enough lighting. The resulting pictures from the first prototype showed that by lighting the device at 647 nm and using a smartphone camera, the camera could capture high-resolution images. These results conclusively indicate that with any modern smartphone camera, a small box lighted to 647 nm, and optical magnifier; a powerful and expensive laboratory scanning machine can be replaced by another that is inexpensive, portable and ready to use anywhere.
ContributorsMakimaa, Heyde (Author) / Holechek, Susan (Thesis director) / Stafford, Phillip (Committee member) / Jayasuriya, Suren (Committee member) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Monoclonal antibody therapy focuses on engineering immune cells to target specific peptide sequences indicative of disease. An impediment in the continued advancement of this market is the lack of an efficient, inexpensive means of characterization that can be broadly applied to any antibody while still providing high-density data. Many characterization methods address an antibody's affinity for its cognate sequence but overlook other important aspects of binding behavior such as off-target binding interactions. The purpose of this study is to demonstrate how the binding intensity between an antibody and a library of random-sequence peptides, otherwise known as an immunosignature, can be evaluated to determine antibody specificity and polyreactivity. A total of 24 commercially available monoclonal antibodies were assayed on 125K and 330K peptide microarrays and analyzed using a motif clustering program to predict candidate epitopes within each antigen sequence. The results support the further development of immunosignaturing as an antibody characterization tool that is relevant to both therapeutic and non-therapeutic antibodies.
ContributorsDai, Jennifer T. (Author) / Stafford, Phillip (Thesis director) / Diehnelt, Chris (Committee member) / School of Life Sciences (Contributor) / W.P. Carey School of Business (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
In this project, biochemical characteristics of peptide binding agents, synthetic antibodies or synbodies, were examined with respect to the capture efficiency and specific binding ability to norovirus. Norovirus, although generally not a deadly pathogen, is the most common cause of acute gastroenteritis and outbreaks present a large social and financial burden to the healthcare and food service industries. With Dr. Diehnelt's laboratory group, a platform has been developed that enables us to rapidly construct peptide-based affinity ligands that can be characterized for binding to norovirus. The design needed to display clear results, be simple to operate, and be inexpensive to produce and use. Four synbodies, originally engineered with a specificity to the GII.4 Minerva genotype were tested with different virus strains varying in similarity to the GII.4 Minerva between 43% and 95.4%. Initial assays utilized norovirus-like particles to qualitatively compare the capture efficiency of the different synbodies without utilizing limited resources. To quantify the amount of actual virus captured by the synbodies, western blots with RT-PCR and RT-qPCR were utilized. The results indicated the synbodies were able to enrich the dilute solutions of the different noroviruses utilizing a magnetic bead pull-down assay. The capture efficiencies of the synbodies were comparable to currently utilized binding agents such as aptamers and porcine gastric mucine magnetic beads. This thesis presents data collected over nearly two years of research at the Center for Innovations in Medicine at the Biodesign Institute located at Arizona State University.
ContributorsSlosky, Rachael Marie (Author) / Diehnelt, Chris (Thesis director) / Stafford, Phillip (Committee member) / Chemical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Yersinia enterocolitica is a major foodborne pathogen found worldwide that causes approximately 87,000 human cases and approximately 1,100 hospitalizations per year in the United States. Y. enterocolitica is a very unique pathogen with the domesticated pig acting as the main animal reservoir for pathogenic bio/serotypes, and as the primary source of human infection. Similar to other gastrointestinal infections, Yersinia enterocolitica is known to trigger autoimmune responses in humans. The most frequent complication associated with Y. enterocolitica is reactive arthritis - an aseptic, asymmetrical inflammation in the peripheral and axial joints, most frequently occurring as an autoimmune response in patients with the HLA-B27 histocompatability antigen. As a foodborne illness it may prove to be a reasonable explanation for some of the cases of arthritis observed in past populations that are considered to be of unknown etiology. The goal of this dissertation project was to study the relationship between the foodborne illness -Y. enterocolitica, and the incidence of arthritis in individuals with and without contact with the domesticated pig.
ContributorsBrown, Starletta (Author) / Hurtado, Ana M (Thesis advisor) / Chowell-Puente, Gerardo (Committee member) / Hill, Kim (Committee member) / Arizona State University (Publisher)
Created2015
Description
The advent of new high throughput technology allows for increasingly detailed characterization of the immune system in healthy, disease, and age states. The immune system is composed of two main branches: the innate and adaptive immune system, though the border between these two states is appearing less distinct. The adaptive immune system is further split into two main categories: humoral and cellular immunity. The humoral immune response produces antibodies against specific targets, and these antibodies can be used to learn about disease and normal states. In this document, I use antibodies to characterize the immune system in two ways: 1. I determine the Antibody Status (AbStat) from the data collected from applying sera to an array of non-natural sequence peptides, and demonstrate that this AbStat measure can distinguish between disease, normal, and aged samples as well as produce a single AbStat number for each sample; 2. I search for antigens for use in a cancer vaccine, and this search results in several candidates as well as a new hypothesis. Antibodies provide us with a powerful tool for characterizing the immune system, and this natural tool combined with emerging technologies allows us to learn more about healthy and disease states.
ContributorsWhittemore, Kurt (Author) / Sykes, Kathryn (Thesis advisor) / Johnston, Stephen A. (Committee member) / Jacobs, Bertram (Committee member) / Stafford, Phillip (Committee member) / Stout, Valerie (Committee member) / Arizona State University (Publisher)
Created2014
Description
Extraordinary medical advances have led to significant reductions in the burden of infectious diseases in humans. However, infectious diseases still account for more than 13 million annual deaths. This large burden is partly due to some pathogens having found suitable conditions to emerge and spread in denser and more connected host populations, and others having evolved to escape the pressures imposed by the rampant use of antimicrobials. It is then critical to improve our understanding of how diseases spread in these modern landscapes, characterized by new host population structures and socio-economic environments, as well as containment measures such as the deployment of drugs. Thus, the motivation of this dissertation is two-fold. First, we study, using both data-driven and modeling approaches, the the spread of infectious diseases in urban areas. As a case study, we use confirmed-cases data on sexually transmitted diseases (STDs) in the United States to assess the conduciveness of population size of urban areas and their socio-economic characteristics as predictors of STD incidence. We find that the scaling of STD incidence in cities is superlinear, and that the percent of African-Americans residing in cities largely determines these statistical patterns. Since disparities in access to health care are often exacerbated in urban areas, within this project we also develop two modeling frameworks to study the effect of health care disparities on epidemic outcomes. Discrepant results between the two approaches indicate that knowledge of the shape of the recovery period distribution, not just its mean and variance, is key for assessing the epidemiological impact of inequalities. The second project proposes to study, from a modeling perspective, the spread of drug resistance in human populations featuring vital dynamics, stochasticity and contact structure. We derive effective treatment regimes that minimize both the overall disease burden and the spread of resistance. Additionally, targeted treatment in structured host populations may lead to higher levels of drug resistance, and if drug-resistant strains are compensated, they can spread widely even when the wild-type strain is below its epidemic threshold.
ContributorsPatterson-Lomba, Oscar (Author) / Castillo-Chavez, Carlos (Thesis advisor) / Towers, Sherry (Thesis advisor) / Chowell-Puente, Gerardo (Committee member) / Arizona State University (Publisher)
Created2014
Description
Urban scaling analysis has introduced a new scientific paradigm to the study of cities. With it, the notions of size, heterogeneity and structure have taken a leading role. These notions are assumed to be behind the causes for why cities differ from one another, sometimes wildly. However, the mechanisms by which size, heterogeneity and structure shape the general statistical patterns that describe urban economic output are still unclear. Given the rapid rate of urbanization around the globe, we need precise and formal mathematical understandings of these matters. In this context, I perform in this dissertation probabilistic, distributional and computational explorations of (i) how the broadness, or narrowness, of the distribution of individual productivities within cities determines what and how we measure urban systemic output, (ii) how urban scaling may be expressed as a statistical statement when urban metrics display strong stochasticity, (iii) how the processes of aggregation constrain the variability of total urban output, and (iv) how the structure of urban skills diversification within cities induces a multiplicative process in the production of urban output.
ContributorsGómez-Liévano, Andrés (Author) / Lobo, Jose (Thesis advisor) / Muneepeerakul, Rachata (Thesis advisor) / Bettencourt, Luis M. A. (Committee member) / Chowell-Puente, Gerardo (Committee member) / Arizona State University (Publisher)
Created2014
Description
Peptide microarrays are to proteomics as sequencing is to genomics. As microarrays become more content-rich, higher resolution proteomic studies will parallel deep sequencing of nucleic acids. Antigen-antibody interactions can be studied at a much higher resolution using microarrays than was possible only a decade ago. My dissertation focuses on testing the feasibility of using either the Immunosignature platform, based on non-natural peptide sequences, or a pathogen peptide microarray, which uses bioinformatically-selected peptides from pathogens for creating sensitive diagnostics. Both diagnostic applications use relatively little serum from infected individuals, but each approaches diagnosis of disease differently. The first project compares pathogen epitope peptide (life-space) and non-natural (random-space) peptide microarrays while using them for the early detection of Coccidioidomycosis (Valley Fever). The second project uses NIAID category A, B and C priority pathogen epitope peptides in a multiplexed microarray platform to assess the feasibility of using epitope peptides to simultaneously diagnose multiple exposures using a single assay. Cross-reactivity is a consistent feature of several antigen-antibody based immunodiagnostics. This work utilizes microarray optimization and bioinformatic approaches to distill the underlying disease specific antibody signature pattern. Circumventing inherent cross-reactivity observed in antibody binding to peptides was crucial to achieve the goal of this work to accurately distinguishing multiple exposures simultaneously.
ContributorsNavalkar, Krupa Arun (Author) / Johnston, Stephen A. (Thesis advisor) / Stafford, Phillip (Thesis advisor) / Sykes, Kathryn (Committee member) / Jacobs, Bertram (Committee member) / Arizona State University (Publisher)
Created2014
Description
The healthcare system in this country is currently unacceptable. New technologies may contribute to reducing cost and improving outcomes. Early diagnosis and treatment represents the least risky option for addressing this issue. Such a technology needs to be inexpensive, highly sensitive, highly specific, and amenable to adoption in a clinic. This thesis explores an immunodiagnostic technology based on highly scalable, non-natural sequence peptide microarrays designed to profile the humoral immune response and address the healthcare problem. The primary aim of this thesis is to explore the ability of these arrays to map continuous (linear) epitopes. I discovered that using a technique termed subsequence analysis where epitopes could be decisively mapped to an eliciting protein with high success rate. This led to the discovery of novel linear epitopes from Plasmodium falciparum (Malaria) and Treponema palladium (Syphilis), as well as validation of previously discovered epitopes in Dengue and monoclonal antibodies. Next, I developed and tested a classification scheme based on Support Vector Machines for development of a Dengue Fever diagnostic, achieving higher sensitivity and specificity than current FDA approved techniques. The software underlying this method is available for download under the BSD license. Following this, I developed a kinetic model for immunosignatures and tested it against existing data driven by previously unexplained phenomena. This model provides a framework and informs ways to optimize the platform for maximum stability and efficiency. I also explored the role of sequence composition in explaining an immunosignature binding profile, determining a strong role for charged residues that seems to have some predictive ability for disease. Finally, I developed a database, software and indexing strategy based on Apache Lucene for searching motif patterns (regular expressions) in large biological databases. These projects as a whole have advanced knowledge of how to approach high throughput immunodiagnostics and provide an example of how technology can be fused with biology in order to affect scientific and health outcomes.
ContributorsRicher, Joshua Amos (Author) / Johnston, Stephen A. (Thesis advisor) / Woodbury, Neal (Committee member) / Stafford, Phillip (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Arizona State University (Publisher)
Created2014