Filtering by
- Language: English
![131741-Thumbnail Image.png](https://d1rbsgppyrdqq4.cloudfront.net/s3fs-public/styles/width_400/public/2021-07/131741-Thumbnail%20Image.png?versionId=JIOblr9sHi.jPuYBpJ54nKioVsQuL8Cn&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIASBVQ3ZQ42ZLA5CUJ/20240618/us-west-2/s3/aws4_request&X-Amz-Date=20240618T101415Z&X-Amz-SignedHeaders=host&X-Amz-Expires=120&X-Amz-Signature=354b4d7ed3d871f83e3eb689fbe739485e57cd49d55ef27107e8f80c86889871&itok=eKaZ4orC)
![130362-Thumbnail Image.png](https://d1rbsgppyrdqq4.cloudfront.net/s3fs-public/styles/width_400/public/2021-04/130362-Thumbnail%20Image.png?versionId=Foz9OY3A9j73dPUuKVvA7cqJPRhRg3vy&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIASBVQ3ZQ42ZLA5CUJ/20240618/us-west-2/s3/aws4_request&X-Amz-Date=20240618T164712Z&X-Amz-SignedHeaders=host&X-Amz-Expires=120&X-Amz-Signature=f785106a66164c3629b598883d225e53224b39902219b8d0f8350831e2c3308b&itok=FDCSQ6Q1)
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.
![130363-Thumbnail Image.png](https://d1rbsgppyrdqq4.cloudfront.net/s3fs-public/styles/width_400/public/2021-04/130363-Thumbnail%20Image.png?versionId=Egzj_OfJGOoVXrn09BxHlMqv20I0QzaH&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIASBVQ3ZQ42ZLA5CUJ/20240618/us-west-2/s3/aws4_request&X-Amz-Date=20240618T164712Z&X-Amz-SignedHeaders=host&X-Amz-Expires=120&X-Amz-Signature=2d025a1e15e2b43c8602c8d07ef0cbb7021b450554bc3f7e6f91ad19d215cfcf&itok=OG0NXp4C)
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.
![133792-Thumbnail Image.png](https://d1rbsgppyrdqq4.cloudfront.net/s3fs-public/styles/width_400/public/2021-05/133792-Thumbnail%20Image.png?versionId=t7WBD1Ev9gYZlSOsq1HNk.t0KPCm0.WI&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIASBVQ3ZQ42ZLA5CUJ/20240618/us-west-2/s3/aws4_request&X-Amz-Date=20240618T172136Z&X-Amz-SignedHeaders=host&X-Amz-Expires=120&X-Amz-Signature=fea2b7b63e7cd498eb7e1063fbd9941a86e956ed51a181d899ca1d962abe5ae2&itok=dYsFG1pG)
![134334-Thumbnail Image.png](https://d1rbsgppyrdqq4.cloudfront.net/s3fs-public/styles/width_400/public/2021-05/134334-Thumbnail%20Image.png?versionId=7WkaIwtWbPSJgUKmlnrZNmM_xumTvU7E&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIASBVQ3ZQ42ZLA5CUJ/20240618/us-west-2/s3/aws4_request&X-Amz-Date=20240618T165721Z&X-Amz-SignedHeaders=host&X-Amz-Expires=120&X-Amz-Signature=9d126f092b37d0fa2d684acdaff07ec906abf1a713247e67006f483809828177&itok=36K1Wvf9)
![135235-Thumbnail Image.png](https://d1rbsgppyrdqq4.cloudfront.net/s3fs-public/styles/width_400/public/2021-05/135235-Thumbnail%20Image.png?versionId=NDIPMf6tM6qPwA2wL_4LeQHunvc_4Odf&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIASBVQ3ZQ42ZLA5CUJ/20240618/us-west-2/s3/aws4_request&X-Amz-Date=20240618T172136Z&X-Amz-SignedHeaders=host&X-Amz-Expires=120&X-Amz-Signature=3cdbb55d3f4af810ae731b520f2d12d403ec85c8be237601b52388a2303e0159&itok=xIMucKxX)
![168416-Thumbnail Image.png](https://d1rbsgppyrdqq4.cloudfront.net/s3fs-public/styles/width_400/public/2022-08/168416-Thumbnail%20Image.png?versionId=17Ix6XO9PqJ4rxHrzs.n7jrj.NXdF34v&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIASBVQ3ZQ42ZLA5CUJ/20240615/us-west-2/s3/aws4_request&X-Amz-Date=20240615T161723Z&X-Amz-SignedHeaders=host&X-Amz-Expires=120&X-Amz-Signature=09a929d75ce958283f99f7e02b21f98a31131e93afc54f1c5d174402553b4fb3&itok=RvETqE_I)
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.
![171464-Thumbnail Image.png](https://d1rbsgppyrdqq4.cloudfront.net/s3fs-public/styles/width_400/public/2022-12/171464-Thumbnail%20Image.png?versionId=WVN9N6UmN53OYbCrGSxWwiEyLgTQwv7j&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIASBVQ3ZQ42ZLA5CUJ/20240618/us-west-2/s3/aws4_request&X-Amz-Date=20240618T031638Z&X-Amz-SignedHeaders=host&X-Amz-Expires=120&X-Amz-Signature=1fee91668c295b1e48d9eb73921028b0f6e67b034e68b707f6c862e1ebb16da7&itok=c2ytKVOR)
![171365-Thumbnail Image.png](https://d1rbsgppyrdqq4.cloudfront.net/s3fs-public/styles/width_400/public/2022-12/171365-Thumbnail%20Image.png?versionId=foCAXDLnpAb9dB4.D7HNY0MRFAsR91dQ&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIASBVQ3ZQ42ZLA5CUJ/20240618/us-west-2/s3/aws4_request&X-Amz-Date=20240618T164735Z&X-Amz-SignedHeaders=host&X-Amz-Expires=120&X-Amz-Signature=95e2debf32a4d9a793a70089917f30672e34931f33b0914aa049de16d7e6af3b&itok=2mN47IGt)
![187656-Thumbnail Image.png](https://d1rbsgppyrdqq4.cloudfront.net/s3fs-public/styles/width_400/public/2023-06/187656-Thumbnail%20Image.png?versionId=nm8wwdCF9t3eOHF_MZg1M4R0HBJqSTG.&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIASBVQ3ZQ42ZLA5CUJ/20240618/us-west-2/s3/aws4_request&X-Amz-Date=20240618T172136Z&X-Amz-SignedHeaders=host&X-Amz-Expires=120&X-Amz-Signature=0bdf05893128ad527fa37c62577694e95db8bc807deded3d60bd8f6e0683d787&itok=jkYvsP8B)