Matching Items (3)
Description
Deoxyribonucleic acid (DNA) has emerged as an excellent molecular building block for nanoconstruction in addition to its biological role of preserving genetic information. Its unique features such as predictable conformation and programmable intra- and inter-molecular Watson-Crick base pairing interactions make it a remarkable engineering material. A variety of convenient design rules and reliable assembly methods have been developed to engineer DNA nanostructures. The ability to create designer DNA architectures with accurate spatial control has allowed researchers to explore novel applications in directed material assembly, structural biology, biocatalysis, DNA
computing, nano-robotics, disease diagnosis, and drug delivery.
This dissertation focuses on developing the structural design rules for "static" DNA nano-architectures with increasing complexity. By using a modular self-assembly method, Archimedean tilings were achieved by association of different DNA motifs with designed arm lengths and inter-tile sticky end interactions. By employing DNA origami method, a new set of design rules was created to allow the scaffolds to travel in arbitrary directions in a designed geometry without local symmetry restrictions. Sophisticated wireframe structures of higher-order complexity were designed and constructed successfully. This dissertation also presents the use of "dynamic" DNA nanotechnology to construct DNA origami nanostructures with programmed reconfigurations.
computing, nano-robotics, disease diagnosis, and drug delivery.
This dissertation focuses on developing the structural design rules for "static" DNA nano-architectures with increasing complexity. By using a modular self-assembly method, Archimedean tilings were achieved by association of different DNA motifs with designed arm lengths and inter-tile sticky end interactions. By employing DNA origami method, a new set of design rules was created to allow the scaffolds to travel in arbitrary directions in a designed geometry without local symmetry restrictions. Sophisticated wireframe structures of higher-order complexity were designed and constructed successfully. This dissertation also presents the use of "dynamic" DNA nanotechnology to construct DNA origami nanostructures with programmed reconfigurations.
ContributorsZhang, Fei (Author) / Yan, Hao (Thesis advisor) / Liu, Yan (Thesis advisor) / Gould, Ian (Committee member) / Zhang, Peiming (Committee member) / Arizona State University (Publisher)
Created2015
Description
National Airspace Systems (NAS) are complex cyber-physical systems that require swift air traffic management (ATM) to ensure flight safety and efficiency. With the surging demand for air travel and the increasing intricacy of aviation systems, the need for advanced technologies to support air traffic management and air traffic control (ATC) service has become more crucial than ever. Data-driven models or artificial intelligence (AI) have been conceptually investigated by various parties and shown immense potential, especially when provided with a vast volume of real-world data. These data include traffic information, weather contours, operational reports, terrain information, flight procedures, and aviation regulations. Data-driven models learn from historical experiences and observations and provide expeditious recommendations and decision support for various operation tasks, directly contributing to the digital transformation in aviation. This dissertation reports several research studies covering different aspects of air traffic management and ATC service utilizing data-driven modeling, which are validated using real-world big data (flight tracks, flight events, convective weather, workload probes). These studies encompass a range of topics, including trajectory recommendations, weather studies, landing operations, and aviation human factors. Specifically, the topics explored are (i) trajectory recommendations under weather conditions, which examine the impact of convective weather on last on-file flight plans and provide calibrated trajectories based on convective weather; (ii) multi-aircraft trajectory predictions, which study the intention of multiple mid-air aircraft in the near-terminal airspace and provide trajectory predictions; (iii) flight scheduling operations, which involve probabilistic machine learning-enhanced optimization algorithms for robust and efficient aircraft landing sequencing; (iv) aviation human factors, which predict air traffic controller workload level from flight traffic data with conformalized graph neural network. The uncertainties associated with these studies are given special attention and addressed through Bayesian/probabilistic machine learning. Finally, discussions on high-level AI-enabled ATM research directions are provided, hoping to extend the proposed studies in the future. This dissertation demonstrates that data-driven modeling has great potential for aviation digital twins, revolutionizing the aviation decision-making process and enhancing the safety and efficiency of ATM. Moreover, these research directions are not merely add-ons to existing aviation practices but also contribute to the future of transportation, particularly in the development of autonomous systems.
ContributorsPang, Yutian (Author) / Liu, Yongming (Thesis advisor) / Yan, Hao (Committee member) / Zhuang, Houlong (Committee member) / Marvi, Hamid (Committee member) / Ren, Yi (Committee member) / Arizona State University (Publisher)
Created2023
Description
Over the past few decades, medical imaging is becoming important in medicine for disease diagnosis, prognosis, treatment assessment and health monitoring. As medical imaging has progressed, imaging biomarkers are being rapidly developed for early diagnosis and staging of disease. Detecting and segmenting objects from images are often the first steps in quantitative measurement of these biomarkers. While large objects can often be automatically or semi-automatically delineated, segmenting small objects (blobs) is challenging. The small object of particular interest in this dissertation are glomeruli from kidney magnetic resonance (MR) images. This problem has its unique challenges. First of all, the size of glomeruli is extremely small and very similar with noises from images. Second, there are massive of glomeruli in kidney, e.g. over 1 million glomeruli in human kidney, and the intensity distribution is heterogenous. A third recognized issue is that a large portion of glomeruli are overlapping and touched in images. The goal of this dissertation is to develop computational algorithms to identify and discover glomeruli related imaging biomarkers. The first phase is to develop a U-net joint with Hessian based Difference of Gaussians (UH-DoG) blob detector. Joining effort from deep learning alleviates the over-detection issue from Hessian analysis. Next, as extension of UH-DoG, a small blob detector using Bi-Threshold Constrained Adaptive Scales (BTCAS) is proposed. Deep learning is treated as prior of Difference of Gaussian (DoG) to improve its efficiency. By adopting BTCAS, under-segmentation issue of deep learning is addressed. The second phase is to develop a denoising convexity-consistent Blob Generative Adversarial Network (BlobGAN). BlobGAN could achieve high denoising performance and selectively denoise the image without affecting the blobs. These detectors are validated on datasets of 2D fluorescent images, 3D synthetic images, 3D MR (18 mice, 3 humans) images and proved to be outperforming the competing detectors. In the last phase, a Fréchet Descriptors Distance based Coreset approach (FDD-Coreset) is proposed for accelerating BlobGAN’s training. Experiments have shown that BlobGAN trained on FDD-Coreset not only significantly reduces the training time, but also achieves higher denoising performance and maintains approximate performance of blob identification compared with training on entire dataset.
ContributorsXu, Yanzhe (Author) / Wu, Teresa (Thesis advisor) / Iquebal, Ashif (Committee member) / Yan, Hao (Committee member) / Beeman, Scott (Committee member) / Arizona State University (Publisher)
Created2022