Matching Items (289)
Description
This thesis addresses the conception and eventual execution of Walt Disney's model of the city of the future, one in which individuals would work, live and play. EPCOT, representing an Experimental Prototype Community of Tomorrow, was envisioned as a utopian and idealized society in a bubble. Aimed at eliminating the ills that plagued American society of the 1960s by returning individuals to community roots, complete with emerging technologies and innovations to improve lifestyles, EPCOT would take inspiration from unique urban planners and innovators. But EPCOT failed to materialize in its original form once Disney passed away on December 15, 1966. The massive city planning venture eventually evolved into a World's Fair-like theme park called Epcot Center, where the correlations between culture and technology would become blurred in this entertainment venue. The park's success stems from its ability to carry components of its community vision, but to appeal to visitors' interests in experiencing application of new technologies through exposure of other cultures and ideas. Technology and culture, while often interrelated, but sometimes at odds with one another, substantially account for Epcot's development over the past 50 years. This thesis not only reflects on Walt Disney's EPCOT the community, but also details how the Walt Disney World theme park has contended with addressing the dualistic relationship between technology and culture.
ContributorsNachman, Brett Ranon (Author) / Stewart, Pamela (Thesis director) / Dombrowski, Rosemarie (Committee member) / Kurtti, Jeff (Committee member) / Barrett, The Honors College (Contributor) / Department of Psychology (Contributor) / Walter Cronkite School of Journalism and Mass Communication (Contributor)
Created2014-12
Description
Background
Multicellular organisms consist of cells of many different types that are established during development. Each type of cell is characterized by the unique combination of expressed gene products as a result of spatiotemporal gene regulation. Currently, a fundamental challenge in regulatory biology is to elucidate the gene expression controls that generate the complex body plans during development. Recent advances in high-throughput biotechnologies have generated spatiotemporal expression patterns for thousands of genes in the model organism fruit fly Drosophila melanogaster. Existing qualitative methods enhanced by a quantitative analysis based on computational tools we present in this paper would provide promising ways for addressing key scientific questions.
Results
We develop a set of computational methods and open source tools for identifying co-expressed embryonic domains and the associated genes simultaneously. To map the expression patterns of many genes into the same coordinate space and account for the embryonic shape variations, we develop a mesh generation method to deform a meshed generic ellipse to each individual embryo. We then develop a co-clustering formulation to cluster the genes and the mesh elements, thereby identifying co-expressed embryonic domains and the associated genes simultaneously. Experimental results indicate that the gene and mesh co-clusters can be correlated to key developmental events during the stages of embryogenesis we study. The open source software tool has been made available at http://compbio.cs.odu.edu/fly/.
Conclusions
Our mesh generation and machine learning methods and tools improve upon the flexibility, ease-of-use and accuracy of existing methods.
Multicellular organisms consist of cells of many different types that are established during development. Each type of cell is characterized by the unique combination of expressed gene products as a result of spatiotemporal gene regulation. Currently, a fundamental challenge in regulatory biology is to elucidate the gene expression controls that generate the complex body plans during development. Recent advances in high-throughput biotechnologies have generated spatiotemporal expression patterns for thousands of genes in the model organism fruit fly Drosophila melanogaster. Existing qualitative methods enhanced by a quantitative analysis based on computational tools we present in this paper would provide promising ways for addressing key scientific questions.
Results
We develop a set of computational methods and open source tools for identifying co-expressed embryonic domains and the associated genes simultaneously. To map the expression patterns of many genes into the same coordinate space and account for the embryonic shape variations, we develop a mesh generation method to deform a meshed generic ellipse to each individual embryo. We then develop a co-clustering formulation to cluster the genes and the mesh elements, thereby identifying co-expressed embryonic domains and the associated genes simultaneously. Experimental results indicate that the gene and mesh co-clusters can be correlated to key developmental events during the stages of embryogenesis we study. The open source software tool has been made available at http://compbio.cs.odu.edu/fly/.
Conclusions
Our mesh generation and machine learning methods and tools improve upon the flexibility, ease-of-use and accuracy of existing methods.
ContributorsZhang, Wenlu (Author) / Feng, Daming (Author) / Li, Rongjian (Author) / Chernikov, Andrey (Author) / Chrisochoides, Nikos (Author) / Osgood, Christopher (Author) / Konikoff, Charlotte (Author) / Newfeld, Stuart (Author) / Kumar, Sudhir (Author) / Ji, Shuiwang (Author) / Biodesign Institute (Contributor) / Center for Evolution and Medicine (Contributor) / College of Liberal Arts and Sciences (Contributor) / School of Life Sciences (Contributor)
Created2013-12-28
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 School of Engineering (Contributor) / College of Liberal Arts and Sciences (Contributor) / School of Life Sciences (Contributor)
Created2012-05-23
Description
Background
Drosophila melanogaster has been established as a model organism for investigating the developmental gene interactions. The spatio-temporal gene expression patterns of Drosophila melanogaster can be visualized by in situ hybridization and documented as digital images. Automated and efficient tools for analyzing these expression images will provide biological insights into the gene functions, interactions, and networks. To facilitate pattern recognition and comparison, many web-based resources have been created to conduct comparative analysis based on the body part keywords and the associated images. With the fast accumulation of images from high-throughput techniques, manual inspection of images will impose a serious impediment on the pace of biological discovery. It is thus imperative to design an automated system for efficient image annotation and comparison.
Results
We present a computational framework to perform anatomical keywords annotation for Drosophila gene expression images. The spatial sparse coding approach is used to represent local patches of images in comparison with the well-known bag-of-words (BoW) method. Three pooling functions including max pooling, average pooling and Sqrt (square root of mean squared statistics) pooling are employed to transform the sparse codes to image features. Based on the constructed features, we develop both an image-level scheme and a group-level scheme to tackle the key challenges in annotating Drosophila gene expression pattern images automatically. To deal with the imbalanced data distribution inherent in image annotation tasks, the undersampling method is applied together with majority vote. Results on Drosophila embryonic expression pattern images verify the efficacy of our approach.
Conclusion
In our experiment, the three pooling functions perform comparably well in feature dimension reduction. The undersampling with majority vote is shown to be effective in tackling the problem of imbalanced data. Moreover, combining sparse coding and image-level scheme leads to consistent performance improvement in keywords annotation.
Drosophila melanogaster has been established as a model organism for investigating the developmental gene interactions. The spatio-temporal gene expression patterns of Drosophila melanogaster can be visualized by in situ hybridization and documented as digital images. Automated and efficient tools for analyzing these expression images will provide biological insights into the gene functions, interactions, and networks. To facilitate pattern recognition and comparison, many web-based resources have been created to conduct comparative analysis based on the body part keywords and the associated images. With the fast accumulation of images from high-throughput techniques, manual inspection of images will impose a serious impediment on the pace of biological discovery. It is thus imperative to design an automated system for efficient image annotation and comparison.
Results
We present a computational framework to perform anatomical keywords annotation for Drosophila gene expression images. The spatial sparse coding approach is used to represent local patches of images in comparison with the well-known bag-of-words (BoW) method. Three pooling functions including max pooling, average pooling and Sqrt (square root of mean squared statistics) pooling are employed to transform the sparse codes to image features. Based on the constructed features, we develop both an image-level scheme and a group-level scheme to tackle the key challenges in annotating Drosophila gene expression pattern images automatically. To deal with the imbalanced data distribution inherent in image annotation tasks, the undersampling method is applied together with majority vote. Results on Drosophila embryonic expression pattern images verify the efficacy of our approach.
Conclusion
In our experiment, the three pooling functions perform comparably well in feature dimension reduction. The undersampling with majority vote is shown to be effective in tackling the problem of imbalanced data. Moreover, combining sparse coding and image-level scheme leads to consistent performance improvement in keywords annotation.
ContributorsSun, Qian (Author) / Muckatira, Sherin (Author) / Yuan, Lei (Author) / Ji, Shuiwang (Author) / Newfeld, Stuart (Author) / Kumar, Sudhir (Author) / Ye, Jieping (Author) / Biodesign Institute (Contributor) / Center for Evolution and Medicine (Contributor) / College of Liberal Arts and Sciences (Contributor) / School of Life Sciences (Contributor) / Ira A. Fulton School of Engineering (Contributor)
Created2013-12-03
Description
Peoria, a city of about 171,000 residents in the northwest Valley, is recognized as one of Arizona's fastest growing cities. Peoria does not have a news source that engages millennials, despite the fact that adults ages 18 to 34 make up about 20% of Peoria's population. Thus, I created a digital news outlet with a complementary social media presence to target a millennial audience. Peoria Next covers news about Peoria that is either not currently covered by other news outlets or is covered in a different way. The goal of the website is to inform millennials of news and events in Peoria with a focus on topics millennials are interested in. The website receives 40 to 50 unique visitors and around 90 views every month, and Facebook insights show that 32% of the people reached by Peoria Next are in the target age range of 18 to 34. This paper discusses the process involved in creating a news outlet for millennials in Peoria with social media platforms as the main avenue for audience development. The first section discusses the role of local news in a community, using social media to engage the audience and how millennials receive and engage with news. The second section discusses how I developed the website and the overall results.
ContributorsRogers, Anya Bryn (Author) / Leonard, Christina (Thesis director) / Pucci, Jessica (Committee member) / WPC Graduate Programs (Contributor) / Economics Program in CLAS (Contributor) / Walter Cronkite School of Journalism and Mass Communication (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
Description
Examining whether or not a bias exists on individual shows on CNN, FOX News, and MSNBC by using content analysis. Each individual show following the third presidential debate was coded using content analysis, then that information was used to determine whether a bias existed on any of the shows and then whether or not a bias existed across the network as a whole.
ContributorsNelson, Roy Emil (Author) / Kenney, Patrick (Thesis director) / Woodall, Gina (Committee member) / Wells, David (Committee member) / Barrett, The Honors College (Contributor) / Walter Cronkite School of Journalism and Mass Communication (Contributor) / School of Politics and Global Studies (Contributor)
Created2013-05
Description
This project aims to aggregate recent information on broadcast journalism curriculum and propose a website design to help educate broadcast students about the most-needed web skills in newsrooms. Television reporters must go beyond re-hashing their broadcast package and script on the station's website, and this includes knowledge of the best site design practices to house more in-depth content. The Grady Undergraduate Survey, as well as the opinions and experience of professionals today, show that web design and web writing skills are two of the most important skills a broadcast student can possess as they prepare to graduate and seek jobs.
ContributorsPorter, Caroline (Author) / Lodato, Mark (Thesis director) / Carpenter, Serena (Committee member) / Barrett, The Honors College (Contributor) / Walter Cronkite School of Journalism and Mass Communication (Contributor)
Created2012-12
ContributorsHeath, Chelsey (Author) / Goodman, Rebekka (Thesis director) / Gillmor, Dan (Committee member) / McGuire, Tim (Committee member) / Barrett, The Honors College (Contributor) / Walter Cronkite School of Journalism and Mass Communication (Contributor)
Created2012-12
Description
Empathy is a characteristic fully developed and manifested in one creature: the human being. In February 2011, we saw the supercomputer, Watson, challenge highly intelligent human beings on Jeopardy. The human beings put up a brutal battle of wits but ultimately, the computer was declared victor. Scientists have made remarkable leaps when it comes to creating artificial intelligence. We have "smart" phones that sit in the palm of our hand and can do far more than what we expected of bulky desktops in the 90s.
ContributorsFidura, E. L. Monica (Author) / Knopf, Richard (Thesis director) / Sylvester, Edward (Committee member) / Rodriguez, Ariel (Committee member) / Barrett, The Honors College (Contributor) / Walter Cronkite School of Journalism and Mass Communication (Contributor)
Created2012-12
Description
Organizations use news releases to promote coverage of its operations and enhance the image and issues pertinent to the organization. In most cases, the primary focus of press releases and news media coverage differs. This thesis analyzes the resonance between coverage by news organizations and the materials released by the organization. Analysis of coverage by the news media and the NBA illustrates the resonance and connections in coverage by all three organizations. It also shows how information regarding the NBA lockout released by the NBA and news outlets can be differentiated into unique issue arenas. These issue arenas can have influence on each other, while also allowing organizations to provide their own unique perspectives.
ContributorsEckert, Marshall (Author) / Gilpin, Dawn (Thesis director) / Matera, Fran (Committee member) / Barrett, The Honors College (Contributor) / College of Liberal Arts and Sciences (Contributor) / Walter Cronkite School of Journalism and Mass Communication (Contributor)
Created2012-12