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The understanding of normal human physiology and disease pathogenesis shows great promise for progress with increasing ability to profile genomic loci and transcripts in single cells in situ. Using biorthogonal cleavable fluorescent oligonucleotides, a highly multiplexed single-cell in situ RNA and DNA analysis is reported. In this report, azide-based cleavable

The understanding of normal human physiology and disease pathogenesis shows great promise for progress with increasing ability to profile genomic loci and transcripts in single cells in situ. Using biorthogonal cleavable fluorescent oligonucleotides, a highly multiplexed single-cell in situ RNA and DNA analysis is reported. In this report, azide-based cleavable linker connects oligonucleotides to fluorophores to show nucleic acids through in situ hybridization. Post-imaging, the fluorophores are effectively cleaved off in half an hour without loss of RNA or DNA integrity. Through multiple cycles of hybridization, imaging, and cleavage this approach proves to quantify thousands of different RNA species or genomic loci because of single-molecule sensitivity in single cells in situ. Different nucleic acids can be imaged by shown by multi-color staining in each hybridization cycle, and that multiple hybridization cycles can be run on the same specimen. It is shown that in situ analysis of DNA, RNA and protein can be accomplished using both cleavable fluorescent antibodies and oligonucleotides. The highly multiplexed imaging platforms will have the potential for wide applications in both systems biology and biomedical research. Thus, proving to be cost effective and time effective.
ContributorsSamuel, Adam David (Author) / Guo, Jia (Thesis director) / Liu, Wei (Committee member) / Wang, Xu (Committee member) / School of Molecular Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
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Description
The ability to profile proteins allows us to gain a deeper understanding of organization, regulation, and function of different biological systems. Many technologies are currently being used in order to accurately perform the protein profiling. Some of these technologies include mass spectrometry, microarray based analysis, and fluorescence microscopy. Deeper analysis

The ability to profile proteins allows us to gain a deeper understanding of organization, regulation, and function of different biological systems. Many technologies are currently being used in order to accurately perform the protein profiling. Some of these technologies include mass spectrometry, microarray based analysis, and fluorescence microscopy. Deeper analysis of these technologies have demonstrated limitations which have taken away from either the efficiency or the accuracy of the results. The objective of this project was to develop a technology in which highly multiplexed single cell in situ protein analysis can be completed in a comprehensive manner without the loss of the protein targets. This was accomplished in the span of 3 steps which is referred to as the immunofluorescence cycle. Antibodies with attached fluorophores with the help of novel azide-based cleavable linker are used to detect protein targets. Fluorescence imaging and data storage procedures are done on the targets and then the fluorophores are cleaved from the antibodies without the loss of the protein targets. Continuous cycles of the immunofluorescence procedure can help create a comprehensive and quantitative profile of the protein. The development of such a technique will not only help us understand biological systems such as solid tumor, brain tissues, and developing embryos. But it will also play a role in real-world applications such as signaling network analysis, molecular diagnosis and cellular targeted therapies.
ContributorsGupta, Aakriti (Author) / Guo, Jia (Thesis director) / Liang, Jianming (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
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Description
Cardiovascular disease (CVD) is the leading cause of mortality yet largely preventable, but the key to prevention is to identify at-risk individuals before adverse events. For predicting individual CVD risk, carotid intima-media thickness (CIMT), a noninvasive ultrasound method, has proven to be valuable, offering several advantages over CT coronary artery

Cardiovascular disease (CVD) is the leading cause of mortality yet largely preventable, but the key to prevention is to identify at-risk individuals before adverse events. For predicting individual CVD risk, carotid intima-media thickness (CIMT), a noninvasive ultrasound method, has proven to be valuable, offering several advantages over CT coronary artery calcium score. However, each CIMT examination includes several ultrasound videos, and interpreting each of these CIMT videos involves three operations: (1) select three enddiastolic ultrasound frames (EUF) in the video, (2) localize a region of interest (ROI) in each selected frame, and (3) trace the lumen-intima interface and the media-adventitia interface in each ROI to measure CIMT. These operations are tedious, laborious, and time consuming, a serious limitation that hinders the widespread utilization of CIMT in clinical practice. To overcome this limitation, this paper presents a new system to automate CIMT video interpretation. Our extensive experiments demonstrate that the suggested system significantly outperforms the state-of-the-art methods. The superior performance is attributable to our unified framework based on convolutional neural networks (CNNs) coupled with our informative image representation and effective post-processing of the CNN outputs, which are uniquely designed for each of the above three operations.
ContributorsShin, Jaeyul (Author) / Liang, Jianming (Thesis advisor) / Maciejewski, Ross (Committee member) / Li, Baoxin (Committee member) / Arizona State University (Publisher)
Created2016
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Description
Computational visual aesthetics has recently become an active research area. Existing state-of-art methods formulate this as a binary classification task where a given image is predicted to be beautiful or not. In many applications such as image retrieval and enhancement, it is more important to rank images based on their

Computational visual aesthetics has recently become an active research area. Existing state-of-art methods formulate this as a binary classification task where a given image is predicted to be beautiful or not. In many applications such as image retrieval and enhancement, it is more important to rank images based on their aesthetic quality instead of binary-categorizing them. Furthermore, in such applications, it may be possible that all images belong to the same category. Hence determining the aesthetic ranking of the images is more appropriate. To this end, a novel problem of ranking images with respect to their aesthetic quality is formulated in this work. A new data-set of image pairs with relative labels is constructed by carefully selecting images from the popular AVA data-set. Unlike in aesthetics classification, there is no single threshold which would determine the ranking order of the images across the entire data-set.

This problem is attempted using a deep neural network based approach that is trained on image pairs by incorporating principles from relative learning. Results show that such relative training procedure allows the network to rank the images with a higher accuracy than a state-of-art network trained on the same set of images using binary labels. Further analyzing the results show that training a model using the image pairs learnt better aesthetic features than training on same number of individual binary labelled images.

Additionally, an attempt is made at enhancing the performance of the system by incorporating saliency related information. Given an image, humans might fixate their vision on particular parts of the image, which they might be subconsciously intrigued to. I therefore tried to utilize the saliency information both stand-alone as well as in combination with the global and local aesthetic features by performing two separate sets of experiments. In both the cases, a standard saliency model is chosen and the generated saliency maps are convoluted with the images prior to passing them to the network, thus giving higher importance to the salient regions as compared to the remaining. Thus generated saliency-images are either used independently or along with the global and the local features to train the network. Empirical results show that the saliency related aesthetic features might already be learnt by the network as a sub-set of the global features from automatic feature extraction, thus proving the redundancy of the additional saliency module.
ContributorsGattupalli, Jaya Vijetha (Author) / Li, Baoxin (Thesis advisor) / Davulcu, Hasan (Committee member) / Liang, Jianming (Committee member) / Arizona State University (Publisher)
Created2016