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The advent of CRISPR/Cas9 revolutionized the field of genetic engineering and gave rise to the development of new gene editing tools including prime editing. Prime editing is a versatile gene editing method that mediates precise insertions and deletions and can perform all 12 types of point mutations. In turn, prime editing represents great promise in the design of new gene therapies and disease models where editing was previously not possible using current gene editing techniques. Despite advancements in genome modification technologies, parallel enrichment strategies of edited cells remain lagging behind in development. To this end, this project aimed to enhance prime editing using transient reporter for editing enrichment (TREE) technology to develop a method for the rapid generation of clonal isogenic cell lines for disease modeling. TREE uses an engineered BFP variant that upon a C-to-T conversion will convert to GFP after target modification. Using flow cytometry, this BFP-to-GFP conversion assay enables the isolation of edited cell populations via a fluorescent reporter of editing. Prime induced nucleotide engineering using a transient reporter for editing enrichment (PINE-TREE), pairs prime editing with TREE technology to efficiently enrich for prime edited cells. This investigation revealed PINE-TREE as an efficient editing and enrichment method compared to a conventional reporter of transfection (RoT) enrichment strategy. Here, PINE-TREE exhibited a significant increase in editing efficiencies of single nucleotide conversions, small insertions, and small deletions in multiple human cell types. Additionally, PINE-TREE demonstrated improved clonal cell editing efficiency in human induced pluripotent stem cells (hiPSCs). Most notably, PINE-TREE efficiently generated clonal isogenic hiPSCs harboring a mutation in the APOE gene for in vitro modeling of Alzheimer’s Disease. Collectively, results gathered from this study exhibited PINE-TREE as a valuable new tool in genetic engineering to accelerate the generation of clonal isogenic cell lines for applications in developmental biology, disease modeling, and drug screening.
ContributorsKostes, William Warner (Author) / Brafman, David (Thesis advisor) / Jacobs, Bertram (Committee member) / Lapinaite, Audrone (Committee member) / Tian, Xiaojun (Committee member) / Wang, Xiao (Committee member) / Arizona State University (Publisher)
Created2022
Description
Beta-Amyloid(Aβ) plaques and tau protein tangles in the brain are now widely recognized as the defining hallmarks of Alzheimer’s disease (AD), followed by structural atrophy detectable on brain magnetic resonance imaging (MRI) scans. However, current methods to detect Aβ/tau pathology are either invasive (lumbar puncture) or quite costly and not widely available (positron emission tomography (PET)). And one of the particular neurodegenerative regions is the hippocampus to which the influence of Aβ/tau on has been one of the research projects focuses in the AD pathophysiological progress.
In this dissertation, I proposed three novel machine learning and statistical models to examine subtle aspects of the hippocampal morphometry from MRI that are associated with Aβ /tau burden in the brain, measured using PET images. The first model is a novel unsupervised feature reduction model to generate a low-dimensional representation of hippocampal morphometry for each individual subject, which has superior performance in predicting Aβ/tau burden in the brain. The second one is an efficient federated group lasso model to identify the hippocampal subregions where atrophy is strongly associated with abnormal Aβ/Tau. The last one is a federated model for imaging genetics, which can identify genetic and transcriptomic influences on hippocampal morphometry. Finally, I stated the results of these three models that have been published or submitted to peer-reviewed conferences and journals.
ContributorsWu, Jianfeng (Author) / Wang, Yalin (Thesis advisor) / Li, Baoxin (Committee member) / Liang, Jianming (Committee member) / Wang, Junwen (Committee member) / Wu, Teresa (Committee member) / Arizona State University (Publisher)
Created2022
Description
Structural Magnetic Resonance Imaging analysis is a vital component in the study of Alzheimer’s Disease pathology and several techniques exist as part of the existing research conducted. In particular, volumetric approaches in this field are known to be beneficial due to the increased capability to express morphological characteristics when compared to manifold methods. To aid in the improvement of the field, this paper aims to propose an intrinsic volumetric conic system that can be applied to bounded volumetric meshes to enable a more effective study of subjects. The computation of the metric involves the use of heat kernel theory and conformal parameterization on genus-0 surfaces extended to a volumetric domain. Additionally, this paper also explores the use of the ’TetCNN’ architecture on the classification of hippocampal tetrahedral meshes to detect features that correspond to Alzheimer’s indicators. The model tested was able to achieve remarkable results with a measured classification accuracy of above 90% in the task of differentiating between subjects diagnosed with Alzheimer’s and normal control subjects.
ContributorsGeorge, John Varghese (Author) / Wang, Yalin (Thesis advisor) / Hansford, Dianne (Committee member) / Gupta, Vikash (Committee member) / Arizona State University (Publisher)
Created2023
Description
Although Saudi Arabia is moving towards a sustainable future, Existing residential buildings in the country are extremely unsustainable. Therefore, there is a necessity for greening the existing residential building. Mostadam green rating systems was developed by the Saudi ministry of housing in 2019 to address the long-term sustainability vision in residential buildings in the country. By setting Mostadam requirements as an objective of the retrofit process, it will ensure that the building achieve sustainability. However, Mostadam is new and there is a lack of knowledge of implementing its requirements on existing buildings. The aim of this research is to develop a framework to green existing residential buildings in Saudi Arabia to achieve Mostadam energy and water minimum requirements. The framework was developed based on an extensive keyword-based search and an analysis of 92 relevant research. The process starts with assessing the building against the minimum requirements of energy and water of Mostadam. After that, optimization phase is conducted. Building information modelling is used in the optimization phase. Energy and water efficiency optimization measures are identified from the analysed literature. Revit is used in the base model authoring and Green building studio cloud is used to simulate the energy and water efficiency measures. Then, payback period is calculated for all the efficiency measured to assess the decision making. A case study of a villa in Riyadh, Saudi Arabia is provided. result shows that the implemented efficiency measures led to an increment of 37.5% in annual energy savings and 26.1% in the annual water savings. Results shows that the application of the proposed framework supports evaluating energy and water efficiency measures to implement it on the buildings to achieve Mostadam minimum energy and water requirements. Recommendations were made for future work to bridge the knowledge gap.
ContributorsMohamed, Sara Murad (Author) / Sullivan, Kenneth (Thesis advisor) / Chong, Oswald (Committee member) / Hurtado, Kristen (Committee member) / Arizona State University (Publisher)
Created2022
Description
Ecology has been an actively studied topic recently, along with the rapid development of human microbiota-based technology. Scientists have made remarkable progress using bioinformatics tools to identify species and analyze composition. However, a thorough understanding of interspecies interactions of microbial ecosystems is still lacking, which has been a significant obstacle in the further development of related technologies. In this work, a genetic circuit design principle with synthetic biology approaches is developed to form two-strain microbial consortia with different inter-strain interactions. The microbial systems are well-defined and inducible. Co-culture experiment results show that our microbial consortia behave consistently with previous ecological knowledge and thus serves as excellent model systems to simulate ecosystems with similar interactions. Colony patterns also emerge when co-culturing multiple species on solid media. With the engineered microbial consortia, image-processing based methods were developed to quantify the shape of co-culture colonies and distinguish microbial consortia with different interactions. Factors that affect the population ratios were identified through induction and variations in the inoculation process. Further time-lapse experiments revealed the basic rules of colony growth, composition variation, patterning, and how spatial factors impact the co-culture colony.
ContributorsChen, Xingwen (Author) / Wang, Xiao (Thesis advisor) / Kuang, Yang (Committee member) / Tian, Xiaojun (Committee member) / Brafman, David (Committee member) / Plaisier, Christopher (Committee member) / Arizona State University (Publisher)
Created2022
Description
Annually, approximately 1.7 million people suffer a traumatic brain injury (TBI) in the United States. After initial insult, a TBI persists as a series of molecular and cellular events that lead to cognitive and motor deficits which have no treatment. In addition, the injured brain activates the regenerative niches of the adult brain presumably to reduce damage. The subventricular zone (SVZ) niche contains neural progenitor cells (NPCs) that generate astrocytes, oligodendrocyte, and neuroblasts. Following TBI, the injury microenvironment secretes signaling molecules like stromal cell derived factor-1a (SDF-1a). SDF-1a gradients from the injury contribute to the redirection of neuroblasts from the SVZ towards the lesion which may differentiate into neurons and integrate into existing circuitry. This repair mechanism is transient and does not lead to complete recovery of damaged tissue. Further, the mechanism by which SDF-1a gradients reach SVZ cells is not fully understood. To prolong NPC recruitment to the injured brain, exogenous SDF-1a delivery strategies have been employed. Increases in cell recruitment following stroke, spinal cord injury, and TBI have been demonstrated following SDF-1a delivery. Exogenous delivery of SDF-1a is limited by its 28-minute half-life and clearance from the injury microenvironment. Biomaterials-based delivery improves stability of molecules like SDF-1a and offer control of its release.
This dissertation investigates SDF-1a delivery strategies for neural regeneration in three ways: 1) elucidating the mechanisms of spatiotemporal SDF-1a signaling across the brain, 2) developing a tunable biomaterials system for SDF-1a delivery to the brain, 3) investigating SDF-1a delivery on SVZ-derived cell migration following TBI. Using in vitro, in vivo, and in silico analyses, autocrine/paracrine signaling was necessary to produce SDF-1a gradients in the brain. Native cell types engaged in autocrine/paracrine signaling. A microfluidics device generated injectable hyaluronic-based microgels that released SDF-1a peptide via enzymatic cleavage. Microgels (±SDF-1a peptide) were injected 7 days post-TBI in a mouse model and evaluated for NPC migration 7 days later using immunohistochemistry. Initial staining suggested complex presence of astrocytes, NPCs, and neuroblasts throughout the frontoparietal cortex.
Advancement of chemokine delivery was demonstrated by uncovering endogenous chemokine propagation in the brain, generating new approaches to maximize chemokine-based neural regeneration.
ContributorsHickey, Kassondra (Author) / Stabenfeldt, Sarah E (Thesis advisor) / Holloway, Julianne (Committee member) / Caplan, Michael (Committee member) / Brafman, David (Committee member) / Newbern, Jason (Committee member) / Arizona State University (Publisher)
Created2021
Description
The RNA editing enzyme adenosine deaminase acting on double stranded RNA 2 (ADAR2) converts adenosine into inosine in regions of double stranded RNA. Here, it was discovered that this critical function of ADAR2 was dysfunctional in amyotrophic lateral sclerosis (ALS) mediated by the C9orf72 hexanucleotide repeat expansion, the most common genetic abnormality associated with ALS. Typically a nuclear protein, ADAR2 was localized in cytoplasmic accumulations in postmortem tissue from C9orf72 ALS patients. The mislocalization of ADAR2 was confirmed using immunostaining in a C9orf72 mouse model and motor neurons differentiated from C9orf72 patient induced pluripotent stem cells. Notably, the cytoplasmic accumulation of ADAR2 coexisted in neurons with cytoplasmic accumulations of TAR DNA binding protein 43 (TDP-43). Interestingly, ADAR2 overexpression in mammalian cell lines induced nuclear depletion and cytoplasmic accumulation of TDP-43, reflective of the pathology observed in ALS patients. The mislocalization of TDP-43 was dependent on the catalytic activity of ADAR2 and the ability of TDP-43 to bind directly to inosine containing RNA. In addition, TDP-43 nuclear export was significantly elevated in cells with increased RNA editing. Together these results describe a novel cellular mechanism by which alterations in RNA editing drive the nuclear export of TDP-43 leading to its cytoplasmic mislocalization. Considering the contribution of cytoplasmic TDP-43 to the pathogenesis of ALS, these findings represent a novel understanding of how the formation of pathogenic cytoplasmic TDP-43 accumulations may be initiated. Further research exploring this mechanism will provide insights into opportunities for novel therapeutic interventions.
ContributorsMoore, Stephen Philip (Author) / Sattler, Rita (Thesis advisor) / Zarnescu, Daniela (Committee member) / Brafman, David (Committee member) / Van Keuren-Jensen, Kendall (Committee member) / Mangone, Marco (Committee member) / Arizona State University (Publisher)
Created2021
Description
As the construction industry in Saudi Arabia was on its way to thriving again. Their growth was due to the unprecedented volume of planned projects such as large-scale and unique projects. Suddenly, the world was faced with one of the most disrupting events in the last century which had a devastating impact on the construction industry specifically. This paper explores mainly the impact of the COVID-19 pandemic on construction projects in Saudi Arabia. Particularly, this paper explores how the pandemic and its related events contributed to the projects' schedule disturbances. This is because most of the projects rely on manpower and supply chains which were heavily disrupted due to the protective measures. For that, a study was conducted to evaluate the impact on the construction projects in Saudi Arabia, to what extent the schedule projects were affected, and what were the main reasons for the schedule delays. The research relied on a field survey and schedule analysis for 12 projects which resulted in identifying several causes of delays and the delayed durations that the projects in Saudi Arabia were facing. This research allows those in construction fields to identify the main causes of delays in order to avoid or minimize the impact of these issues on future projects.
ContributorsObeid, Muhammad Hasan Hani (Author) / Ariaratnam, Samuel (Thesis advisor) / El Asmar, Mounir (Committee member) / Chong, Oswald (Committee member) / Arizona State University (Publisher)
Created2021
Description
Communicating with computers through thought has been a remarkable achievement in recent years. This was made possible by the use of Electroencephalography (EEG). Brain-computer interface (BCI) relies heavily on Electroencephalography (EEG) signals for communication between humans and computers. With the advent ofdeep learning, many studies recently applied these techniques to EEG data to perform various tasks like emotion recognition, motor imagery classification, sleep analysis, and many more.
Despite the rise of interest in EEG signal classification, very few studies have explored the MindBigData dataset, which collects EEG signals recorded at the stimulus of seeing a digit and thinking about it. This dataset takes us closer to realizing the idea of mind-reading or communication via thought. Thus classifying these signals into the respective digit that the user thinks about is a challenging task. This serves as a motivation to study this dataset and apply existing deep learning techniques to study it.
Given the recent success of transformer architecture in different domains like Computer Vision and Natural language processing, this thesis studies transformer architecture for EEG signal classification. Also, it explores other deep learning
techniques for the same. As a result, the proposed classification pipeline achieves comparable performance with the existing methods.
ContributorsMuglikar, Omkar Dushyant (Author) / Wang, Yalin (Thesis advisor) / Liang, Jianming (Committee member) / Venkateswara, Hemanth (Committee member) / Arizona State University (Publisher)
Created2021
Description
Statistical Shape Modeling is widely used to study the morphometrics of deformable objects in computer vision and biomedical studies. There are mainly two viewpoints to understand the shapes. On one hand, the outer surface of the shape can be taken as a two-dimensional embedding in space. On the other hand, the outer surface along with its enclosed internal volume can be taken as a three-dimensional embedding of interests. Most studies focus on the surface-based perspective by leveraging the intrinsic features on the tangent plane. But a two-dimensional model may fail to fully represent the realistic properties of shapes with both intrinsic and extrinsic properties. In this thesis, severalStochastic Partial Differential Equations (SPDEs) are thoroughly investigated and several methods are originated from these SPDEs to try to solve the problem of both two-dimensional and three-dimensional shape analyses. The unique physical meanings of these SPDEs inspired the findings of features, shape descriptors, metrics, and kernels in this series of works. Initially, the data generation of high-dimensional shapes, here, the tetrahedral meshes, is introduced. The cerebral cortex is taken as the study target and an automatic pipeline of generating the gray matter tetrahedral mesh is introduced. Then, a discretized Laplace-Beltrami operator (LBO) and a Hamiltonian operator (HO) in tetrahedral domain with Finite Element Method (FEM) are derived. Two high-dimensional shape descriptors are defined based on the solution of the heat equation and Schrödinger’s equation. Considering the fact that high-dimensional shape models usually contain massive redundancies, and the demands on effective landmarks in many applications, a Gaussian process landmarking on tetrahedral meshes is further studied. A SIWKS-based metric space is used to define a geometry-aware Gaussian process. The study of the periodic potential diffusion process further inspired the idea of a new kernel call the geometry-aware convolutional kernel. A series of Bayesian learning methods are then introduced to tackle the problem of shape retrieval and classification. Experiments of every single item are demonstrated. From the popular SPDE such as the heat equation and Schrödinger’s equation to the general potential diffusion equation and the specific periodic potential diffusion equation, it clearly shows that classical SPDEs play an important role in discovering new features, metrics, shape descriptors and kernels. I hope this thesis could be an example of using interdisciplinary knowledge to solve problems.
ContributorsFan, Yonghui (Author) / Wang, Yalin (Thesis advisor) / Lepore, Natasha (Committee member) / Turaga, Pavan (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2021