Matching Items (77)
Description
Molecular tessellation research aims to elucidate the underlying principles that govern intricate patterns in nature and to leverage these principles to create precise and ordered structures across multiple scales, thereby facilitating the emergence of novel functionalities. DNA origami technology enables the fabrication of nearly arbitrary DNA architectures with nanoscale precision, which can serve as excellent building blocks for the construction of tessellation patterns. However, the size and complexity of DNA origami tessellation systems are currently limited by several unexplored factors relevant to the accuracy of essential design parameters, the applicability of design strategies, and the compatibility between different tiles. Here, a general design and assembly method are described for creating DNA origami tiles that grow into tessellation patterns with micrometer-scale order and nanometer-scale precision. A critical design parameter, interhelical distance (D), was identified, which determined the conformation of monomer tiles and the outcome of tessellation. Finely tuned D facilitated the accurate geometric design of monomer tiles with minimized curvature and improved tessellation capability. To demonstrate the generality of the design method, 9 tile geometries and 15 unique tile designs were generated. The designed tiles were assembled into single-crystalline lattices ranging from tens to hundreds of square micrometers with micrometer-scale, nearly defect-free areas readily visualized by atomic force microscopy. Two strategies were applied to further increase the complexity of DNA origami tessellation, including reducing the symmetry of monomer tiles and co-assembling tiles of various geometries. The designed 6 complex tilings that includes 5 Archimedean tilings and a 12-fold quasicrystal tiling yielded various tiling patterns that great in size and quality, indicating the robustness of the optimized tessellation system. The described design and assembly approach can also be employed to create square DNA origami units for algorithmic self-assembly. As the square units assembled and expanded, they executed the binary function XOR, which generated the Sierpinski triangular pattern according to the predetermined instructions. This study will promote DNA-templated, programmable molecular and material patterning and open up new opportunities for applications in metamaterial engineering, nanoelectronics, and nanolithography.
ContributorsTang, Yue (Author) / Yan, Hao (Thesis advisor) / Guo, Jia (Committee member) / Stephanopoulos, Nicholas (Committee member) / Arizona State University (Publisher)
Created2023
Description
For multiple reasons, the consumption of fresh fruits and vegetables in the United States has progressively increased. This has resulted in increased domestic production and importation of these products. The associated logistics is complex due to the perishability of these products, and most current logistics systems rely on marketing and supply chains practices that result in high levels of food waste and limited offer diversity. For instance, given the lack of critical mass, small growers are conspicuously absent from mainstream distribution channels. One way to obtain these critical masses is using associative schemes such as co-ops. However, the success level of traditional associate schemes has been mixed at best. This dissertation develops decision support tools to facilitate the formation of coalitions of small growers in complementary production regions to act as a single-like supplier. Thus, this dissertation demonstrates the benefits and efficiency that could be achieved by these coalitions, presents a methodology to efficiently distribute the value of a new identified market opportunity among the growers participating in the coalition, and develops a negotiation framework between a buyer(s) and the agent representing the coalition that results in a prototype contract.There are four main areas of research contributions in this dissertation. The first is the development of optimization tools to allocate a market opportunity to potential production regions while considering consumer preferences for special denomination labels such as “local”, “organic”, etc. The second contribution is in the development of a stochastic optimization and revenue-distribution framework for the formation of coalitions of growers to maximize the captured value of a market opportunity. The framework considers the growers’ individual preferences and production characteristics (yields, resources, etc.) to develop supply contracts that entice their participation in the coalition. The third area is the development of a negotiation mechanism to design contracts between buyers and groups of growers considering the profit expectations and the variability of the future demand. The final contribution is the integration of these models and tools into a framework capable of transforming new market opportunities into implementable production plans and contractual agreement between the different supply chain participants.
ContributorsUlloa, Rodrigo (Author) / Villalobos, Jesus (Thesis advisor) / Fowler, John (Committee member) / Mac Cawley, Alejandro (Committee member) / Yan, Hao (Committee member) / Phelan, Patrick (Committee member) / Arizona State University (Publisher)
Created2022
Description
The biological lipid bilayer on cells or the cell membrane is a surface teeming with activity. Several membrane proteins decorate the lipid bilayer to carry out various functionalities that help a cell interact with the environment, gather resources and communicate with other cells. This provides a repertoire of biological structures and processes that can be mimicked and manipulated. Since its inception in the late 20th century deoxyribonucleic acid (DNA) nanotechnology has been used to create nanoscale objects that can be used for such purposes. Using DNA as the building material provides the user with a programmable and functionalizable tool box to design and demonstrate these ideas. In this dissertation, I describe various DNA nanostructures that can insert or interact with lipid bilayers for cargo transport, diagnostics and therapeutics. First, I describe a reversibly gated DNA nanopore of 20.4nm x 20.4nm cross sectional width. Controlled transport of cargoes of various sizes across a lipid bilayer through a channel formed by the DNA nanopore was demonstrated. This demonstration paves the way for a class of nanopores that can be activated by different stimuli. The membrane insertion capability of the DNA nanopore is further utilized to design a nanopore sensor that can detect oligonucleotides of a specific s equence inside a lipid vesicle. The ease with which the sensor can be modified to i dentify different diagnostic markers for disease detection was shown by designing a sensor that can identify the non small cell lung cancer marker micro ribonucleic acid -21 (miRNA21). Finally, I demonstrate the therapeutic capabilities of DNA devices with a DNA tetrabody that can recruit natural killer cells (NK cells) to target cancer cells. The DNA tetrabody functionalized with cholesterol molecules and Her2 affibody inserts into NK cell membrane leading it to Her2 positive cancer cells. This shows that inthe presence of DNA tetrabody, the NK cell activation gets accelerated.
ContributorsAbraham, Leeza (Author) / Yan, Hao (Thesis advisor) / Liu, Uan (Committee member) / Stephanopoulos, Nicholas (Committee member) / Arizona State University (Publisher)
Created2023
Description
Nonalcoholic Steatohepatitis (NASH) is a severe form of Nonalcoholic fatty liverdisease, that is caused due to excessive calorie intake, sedentary lifestyle and in the
absence of severe alcohol consumption. It is widely prevalent in the United States
and in many other developed countries, affecting up to 25 percent of the population.
Due to being asymptotic, it usually goes unnoticed and may lead to liver failure if
not treated at the right time.
Currently, liver biopsy is the gold standard to diagnose NASH, but being an
invasive procedure, it comes with it's own complications along with the inconvenience
of sampling repeated measurements over a period of time. Hence, noninvasive
procedures to assess NASH are urgently required. Magnetic Resonance Elastography
(MRE) based Shear Stiffness and Loss Modulus along with Magnetic Resonance
Imaging based proton density fat fraction have been successfully combined to predict
NASH stages However, their role in the prediction of disease progression still remains
to be investigated.
This thesis thus looks into combining features from serial MRE observations to
develop statistical models to predict NASH progression. It utilizes data from an experiment
conducted on male mice to develop progressive and regressive NASH and
trains ordinal models, ordered probit regression and ordinal forest on labels generated
from a logistic regression model. The models are assessed on histological data collected
at the end point of the experiment. The models developed provide a framework
to utilize a non-invasive tool to predict NASH disease progression.
ContributorsDeshpande, Eeshan (Author) / Ju, Feng (Thesis advisor) / Wu, Teresa (Committee member) / Yan, Hao (Committee member) / Arizona State University (Publisher)
Created2021
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
As a rapidly evolving field, nucleic acid nanotechnology focuses on creating functional nanostructures or dynamic devices through harnessing the programmbility of nucleic acids including deoxyribonucleic acid (DNA) and ribonucleic acid (RNA), enabled by the predictable Watson-Crick base pairing. The precise control over the sequence and structure, along with the development of simulation softwares for the prediction of the experimental implementation provides the base of designing structures or devices with arbitrary topology and operational logic at nanoscale. Over the past 40 years, the thriving field has pushed the boundaries of nucleic acids, from originally biological macromolecules to functional building blocks with applications in biomedicine, molecular diagnostics and imaging, material science, electronics, crystallography, and more have emerged through programming the sequences and generating the various structures or devices. The underlying logic of nucleic acid programming is the base pairing rule, straightforward and robust. While for the complicated design of sequences and quantitative understanding of the programmed results, computational tools will markedly reduced the level of difficulty and even meet the challenge not available with manual effort. With this thesis three individual projects are presented, with all of them interweaving theory/computation and experiments. In a higher level abstraction, this dissertation covers the topic of biophysical understanding of the dynamic reactions, designing and realizing complex self-assembly systems and finally super-resolutional imaging. More specifically, Chapter 2 describes the study of RNA strand displacement kinetics with dedicated model extracting the reaction rates, providing guidelines for the rational design and regulation of the strand displacement reactions and eventually biochemical processes. In chapter 3 the platform for the design of complex symmetry of the self-assembly target and first experimental implementation of the assembly of pyrochlore lattices with DNA origamis are presented, which potentially can be applied to manipulate lights as optical materials. Chapter 4 focuses on the in solution characterization of the periodicity of DNA origami lattices with super-resolutional microscopy, with algorithms in development for three dimensional structural reconstruction.
ContributorsLiu, Hao (Author) / Yan, Hao (Thesis advisor) / Sulc, Petr (Thesis advisor) / Guo, Jia (Committee member) / Heyden, Matthias (Committee member) / Arizona State University (Publisher)
Created2023
Description
Assembly lines are low-cost production systems that manufacture similar finished units in large quantities. Manufacturers utilize mixed-model assembly lines to produce customized items that are not identical but share some general features in response to consumer needs. To maintain efficiency, the aim is to find the best feasible option to balance the lines efficiently; allocating each task to a workstation to satisfy all restrictions and fulfill all operational requirements in such a way that the line has the highest performance and maximum throughput. The work to be done at each workstation and line depends on the precise product configuration and is not constant across all models. This research seeks to enhance the subject of assembly line balancing by establishing a model for creating the most efficient assembly system. Several realistic characteristics are included into efficient optimization techniques and mathematical models to provide a more comprehensive model for building assembly systems. This involves analyzing the learning growth by task, employing parallel line designs, and configuring mixed models structure under particular constraints and criteria. This dissertation covers a gap in the literature by utilizing some exact and approximation modeling approaches. These methods are based on mathematical programming techniques, including integer and mixed integer models and heuristics. In this dissertation, heuristic approximations are employed to address problem-solving challenges caused by the problem's combinatorial complexity. This study proposes a model that considers learning curve effects and dynamic demand. This is exemplified in instances of a new assembly line, new employees, introducing new products or simply implementing engineering change orders. To achieve a cost-based optimal solution, an integer mathematical formulation is proposed to minimize the production line's total cost under the impact of learning and demand fulfillment. The research further creates approaches to obtain a comprehensive model in the case of single and mixed models for parallel lines systems. Optimization models and heuristics are developed under various aspects, such as cycle times by line and tooling considerations. Numerous extensions are explored effectively to analyze the cost impact under certain constraints and implications. The implementation results demonstrate that the proposed models and heuristics provide valuable insights.
ContributorsAlhomaidi, Esam (Author) / Askin, Ronald G (Thesis advisor) / Yan, Hao (Committee member) / Iquebal, Ashif (Committee member) / Sefair, Jorge (Committee member) / Arizona State University (Publisher)
Created2023
Description
Sequential event prediction or sequential pattern mining is a well-studied topic in the literature. There are a lot of real-world scenarios where the data is released sequentially. People believe that there exist repetitive patterns of event sequences so that the future events can be predicted. For example, many companies build their recommender system to predict the next possible product for the users according to their purchase history. The healthcare system discovers the relationships among patients’ sequential symptoms to mitigate the adverse effect of a treatment (drugs or surgery). Modern engineering systems like aviation/distributed computing/energy systems diagnosed failure event logs and took prompt actions to avoid disaster when a similar failure pattern occurs. In this dissertation, I specifically focus on building a scalable algorithm for event prediction and extraction in the aviation domain. Understanding the accident event is always the major concern of the safety issue in the aviation system. A flight accident is often caused by a sequence of failure events. Accurate modeling of the failure event sequence and how it leads to the final accident is important for aviation safety. This work aims to study the relationship of the failure event sequence and evaluate the risk of the final accident according to these failure events. There are three major challenges I am trying to deal with. (1) Modeling Sequential Events with Hierarchical Structure: I aim to improve the prediction accuracy by taking advantage of the multi-level or hierarchical representation of these rare events. Specifically, I proposed to build a sequential Encoder-Decoder framework with a hierarchical embedding representation of the events. (2) Lack of high-quality and consistent event log data: In order to acquire more accurate event data from aviation accident reports, I convert the problem into a multi-label classification. An attention-based Bidirectional Encoder Representations from Transformers model is developed to achieve good performance and interpretability. (3) Ontology-based event extraction: In order to extract detailed events, I proposed to solve the problem as a hierarchical classification task. I improve the model performance by incorporating event ontology. By solving these three challenges, I provide a framework to extract events from narrative reports and estimate the risk level of aviation accidents through event sequence modeling.
ContributorsZhao, Xinyu (Author) / Yan, Hao (Thesis advisor) / Liu, Yongming (Committee member) / Ju, Feng (Committee member) / Iquebal, Ashif (Committee member) / Arizona State University (Publisher)
Created2022
Description
Nucleic acid nanotechnology is a field of nanoscale engineering where the sequences of deoxyribonucleicacid (DNA) and ribonucleic acid (RNA) molecules are carefully designed to create self–assembled nanostructures with higher spatial resolution than is available to top–down fabrication methods. In the 40 year history
of the field, the structures created have scaled from small tile–like structures constructed from a few hundred
individual nucleotides to micron–scale structures assembled from millions of nucleotides using the technique
of “DNA origami”. One of the key drivers of advancement in any modern engineering field is the parallel
development of software which facilitates the design of components and performs in silico simulation of the
target structure to determine its structural properties, dynamic behavior, and identify defects. For nucleic acid
nanotechnology, the design software CaDNAno and simulation software oxDNA are the most popular choices
for design and simulation, respectively. In this dissertation I will present my work on the oxDNA software
ecosystem, including an analysis toolkit, a web–based graphical interface, and a new molecular visualization
tool which doubles as a free–form design editor that covers some of the weaknesses of CaDNAno’s lattice–based design paradigm. Finally, as a demonstration of the utility of these new tools I show oxDNA simulation
and subsequent analysis of a nanoscale leaf–spring engine capable of converting chemical energy into dynamic motion. OxDNA simulations were used to investigate the effects of design choices on the behavior of
the system and rationalize experimental results.
ContributorsPoppleton, Erik (Author) / Sulc, Petr (Thesis advisor) / Yan, Hao (Committee member) / Forrest, Stephanie (Committee member) / Stephanopoulos, Nicholas (Committee member) / Arizona State University (Publisher)
Created2022
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