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- Creators: Arizona State University
The field of radio broadcast requires the cohesion of several different skill sets in order to be a success. KHEA Radio has used a traditional form of teaching, which means taking a one-on-one approach. Taking this approach has worked for years in the past and has been the only option for teaching. The down side to this method of teaching is that it requires one seasoned employee to stop their work and take the time to train a new employee. Because of the significant void in the area of instructional content for radio sound engineering, my co-worker and I had to troubleshoot this console and basically teach ourselves its functions. I saw the need for better instructional content on the Internet and in print based on my own experiences. The skills used to create the following instructional content were gained from course work at Arizona State University. The graduate department of Technical Communication makes every effort to equip students with varied skills that can be applied to different fields within the overall scheme of technical communication. This guide serves as a tool for radio broadcast novices to learn the basics of sound board operation.
YourBrandPartner.com exists to provide content to those seeking specific advice and information on purchasing custom promotional items. For this investigation, I conducted a usability test with a select user group to identify user experience issues. The primary goal of this research was to conduct general usability testing through large group survey and a small in-person usability testing group. I designed surveys and tests to investigate if users experienced difficulties in finding the information they were looking for on the website. Based on the results of this study, I recommend reviewing the visual design of the website, increasing site speed, creating a better experience between the blog and e- commerce interactions, and creating an environment that is more accommodating of where the user is in the buying process. This full report includes expanded participant feedback, methodology behind the study, and full recommendations for improvement.
The purpose of this applied project was to research and recommend to Phoenix Children’s Hospital (PCH) improvements to their website in order to provide parents whose child has been newly diagnosed with cancer the most clear and appropriate health information. I conducted a study in order to analyze and evaluate the health information content currently provided to parents at PCH. This was done by through qualitative coding methods on both printed documents provided by The Emily Center Library, as well as interviews conducted with three Hematology/Oncology nurses at PCH. Additionally, I researched the current literature surrounding this topic in order to provide a background of information. Based on the results, I recommended that PCH offer parents a comprehensive cancer database in which all provided information would be searchable via their website. This database would also allow them to expand on their two topic focuses: home care and emotional support. Additionally, I recommended that parents are provided information on how to identify credible and non- credible sources on the Internet so that they can find information that is truly medically valuable when searching for information on their own. Lastly, I offered future recommendations that will require continued research so that PCH’s provided health information can continue to grow and improve.
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.
Learning Sparse Representations for Fruit-Fly Gene Expression Pattern Image Annotation and Retrieval
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.
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.
