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UC San Diego Electronic Theses and Dissertations

Cover page of Towards single-cell chromosome-specific single-base measurement of telomeres with nanopores

Towards single-cell chromosome-specific single-base measurement of telomeres with nanopores

(2022)

Telomeres protect the ends of chromosomes from DNA repair processes. Somatic cells reach senescence as a protective mechanism when telomeres have become critically short. Under certain conditions, a small subset of cells can continue dividing to the point where telomeres are no longer adequately protected, leading to chromosome instability or crisis, at which point the cell is fated to either apoptosis or carcinogenesis. Senescence can be triggered by a single telomere if it is sufficiently short. Chromosomes have been shown to have telomere lengths, and telomere length regulatory factors that can be inherited. Studies on the influence and dynamics of these factors provide insight that is limited by the resolution of tools currently available. Single-cell chromosome-specific techniques are time-consuming, while scalable single-cell methods can only probe the telomere length averaged across all chromosomes. Nanopores have the capability to measure single-molecule telomere lengths with high throughput, and we have developed methods and tools to bring us closer to realizing their potential for this application.

Semiparametric Regression Models for Between- and Within-subject Attributes: Applications to High-Dimensional Data, Asymptotic Efficiency and Beyond

(2022)

Breakthroughs such as high-throughput sequencing are generating flourishing high-dimensional data that provoke challenges in both statistical analyses and interpretations. Since directly modeling such data often suffers from multiple testing and low power, an emerging alternative is to first reduce the dimension at the outset, by comparing two subjects’ genome sequences using dissimilarity metrics, yielding “between-subject attributes.” In the first half of this talk, I will extend the classical generalized linear models (GLM) to establish a new regression paradigm for between-subject attributes, using a class of semiparametric functional response models (FRM). Despite its growing applications, the efficiency of estimators for the FRM has not yet been carefully studied. This is of fundamental importance for semiparametric models due to the efficiency loss at the price of minimum model assumptions. For the next half of the talk, we leverage the Hilbert-Space-based semiparametric efficiency theory to show that estimators from a class of U-statistics-based generalized estimating equation (UGEE) achieve the semiparametric efficiency bound. Thus, like GEE for semiparametric GLM, UGEE estimators also harmonize efficiency and robustness, propelling growing applications in biomedical, psychosocial, and related research.

Cover page of Semantic Optimizations in Modern Hybrid Stores

Semantic Optimizations in Modern Hybrid Stores

(2022)

In recent years, big data applications often involve dealing with diverse datasets in terms of structure: relations flat or nested, complex-structure graphs, documents (JSON or XML), poorly structured logs, or even text data. To handle the heterogeneity of the data, application designers usually rely on several data stores used side-by-side, each supporting a different data model, associated query language (or data access API), and very efficient for some, but not all, kinds of processing on the data. Systems capable of querying disparate data in this fashion are advocated by the database community under terms such as hybrid- or poly-stores.

These systems provide no support for semantic query optimizations, which include (i) exploiting possible data redundancy when the same data may be accessible (with different performance) from distinct data stores; (ii) taking advantage of partial query results (in the style of materialized views), which may be available in the stores; and (iii) reasoning semantically about various data models and query operations’ properties, which can enhance the hybrid workload performance. Motivated by these optimization opportunities, this dissertation makes the following two main contributions:

We design and demonstrate ESTOCADA, an extensible lightweight framework for providing semantic query optimization on top of hybrid stores without modifying their internals. ESTOCADA transparently enables each query to benefit from the best combination of stored data and available processing capabilities. It leverages recent advances in the area of view-based query rewriting under constraints, which we use to describe various data models and stored data. We demonstrate the effectiveness of our approach with an experimental evaluation using the MIMIC real-world dataset and show significant performance gains achieved by ESTOCADA.

Going beyond query workloads covering a variety of data models (relational, JSON, Graph, XML) in hybrid stores, modern applications increasingly need to blend querying and learning on the data, which is primarily expressed using a mix of relational algebra (RA)- and linear algebra (LA)-based languages. Existing specialized solutions for evaluating such hybrid analytical tasks either optimize RA and LA tasks separately, exploiting only RA properties while leaving LA-specific optimizations unexploited, or focus heavily on physical optimizations, leaving semantic query optimization opportunities unexplored. In our second contribution, we take a major step towards filling this gap by proposing HADAD. The novelty of HADAD is to extend the benefits of semantic query rewriting and view-based optimization introduced in ESTOCADA to LA computations, crucial for ML hybrid analytical tasks. Our solution can be naturally and portably applied on top of pure LA and hybrid RA-LA platforms. An extensive empirical evaluation shows that HADAD yields significant performance gains on diverse workloads, ranging from LA-centered to hybrid RA-LA workloads.

Cover page of Transient Dynamic Analysis of Modified Hopkinson Pressure Bar System for High Strain Rate Tensile Testing

Transient Dynamic Analysis of Modified Hopkinson Pressure Bar System for High Strain Rate Tensile Testing

(2022)

High strain rate testing of specimens within a split Hopkinson pressure bar (SHPB) is a well-established experimental technique used to quantify the compressive properties of materials in high impact events. The aim of this thesis is to investigate high strain rate tensile testing of materials utilizing a modified Hopkinson pressure bar system containing a tension yoke that deforms a test specimen by impacting the yoke strike plate and converting the incident compression wave into tension loading through the specimen. Credence towards the proposed design is built utilizing various finite element benchmark models and verification against the principles of SHPB systems is conducted using computational analysis to quantify the feasibility and performance of the system. Results show that the strain response in the Hopkinson pressure bar shows a strong correlation to the strain response in the test specimen, as assessed via comparison of peak forces and observing relatively low difference between the two quantities. Following this, effects of dispersion are explored further by modifying the impacting pulse shape and comparing results between different pulse shapes. Final stages of analysis reveal that momentum of the tension yoke largely affects the strain response of the tensile specimen relative to the applied pulse. However, the strong correlation of the force pulse developed within the tensile specimen and the force pulse transmitted into the Hopkinson bar remains very consistent, showing the potential of the proposed apparatus design to be used as a functional experimental system for converting impact into high strain rate tensile loading.

I. Transient Induced Molecular Electronic Spectroscopy (TIMES) technique to study the biomolecular interaction on the surface II. Microfluidic droplet-based techniques for single-cell study

(2022)

As the microfluidic technology has been attracted more attentions and has potential for wide range of applications, in our research work we are utilizing this technique to expand its capability in different aspects of study. In this dissertation, I am going to present two topics of the work: Molecular interaction study using microfluidic techniques and Microfluidic droplet-based applications on single-cell analysis.Biosensors are powerful analytical tools for many applications including drug discovery, medical diagnostics, and environment monitoring. Because of the advance of microfluidic technology, biosensors have a significant improvement by merging the biosensor into lab-on-chip (LOC) technology. In most of the microfluidic sensor, the detection mechanism is through the reaction event that occurs when the flowing analyte in the channel physically or chemically react with the immobilized reactant. The immobilization of recognition elements is needed prior to the sample detection, but this can be a disadvantage of using microfluidic device due to one-time use only. To address this issue, we develop a new readout technique, “Transient Induced Molecular Electronic Signal (TIMES)”. The measured signal from TIMES is directly coming from the induced charge generated from the analyte or chemical reaction, and no immobilization of reactant on electrode surface is needed. I am going to present the work on protein-ligand study based on our microfluidic TIMES measurements, which is a highly potential study in drug discovery research area. Besides, TIMES technique has potential to be an alternative readout for lateral flow assay (LFA) study. The recent work we have done with paper-based TIMES study for antibodies binding interaction will be presented. In the second topic, I am going to show our development on microfluidic device that used for single-cell analysis. There are two main workflows to perform single cell analysis: 1.) Plate-based method and 2.) Droplet-based method. The plate-based method is the traditional way, it is costly and time consuming. While in second method, the cells are encapsulated into small droplet, and all the reaction can be done in this tiny droplet, having capability to operate more than 10,000 cells in one reaction, so it saves more use of reagent and time on operation. In our study, we are trying to identify specific cell subtype by using enhancer screening with droplet-based analysis. However, the droplet manipulation is needed in this procedure. To be specific, droplet merging and double emulsion formation are two main steps in droplet-based analysis. Droplet merging is achieved for reagent addition after cells are lysed and RNA is released in the droplet. In our work, we developed a new microfluidic platform to merge droplets by using pillar-induced mechanism. The double emulsion is another main technique for droplet-based analysis. The main purpose for double emulsion formation is to make droplets compatible to water phase so the targeted droplets can be sorted with FACS equipment. In our study, we are going to expand its application on relating cell morphology to gene expression. The double emulsion technique is applied with image-guided sorting system that developed in our lab and combined with gene sequencing will provide value information for cell genotype-phenotype analysis.

Cover page of Four New Species of Osedax Bone-Worms from New Zealand and the Gulf of Mexico and Range Expansions for Pacific Osedax Species

Four New Species of Osedax Bone-Worms from New Zealand and the Gulf of Mexico and Range Expansions for Pacific Osedax Species

(2022)

Osedax is a genus of siboglinid annelids that live on and consume bones in the ocean. Aided by symbiotic bacteria Osedax dissolve the bone matrix for habitat and consume the nutrients inside. Currently there are 27 described and 10 undescribed species of Osedax from 16 localities globally. Using molecular and morphological data we described four new species bringing the total number of Osedax species to 31 and localities to 19. Two species are the first records of Osedax from New Zealand where extensive species diversity is suspected. Two species are from the Gulf of Mexico, one of which is the first species named from a reptile fall. We also expanded the ranges of five described species to Oregon, San Diego, and Costa Rica and conducted population structure analysis on nine species using the COI gene. We found shared haplotypes and evidence of genetic connectivity across broad ranges such as between California and Japan and along the Pacific coast of North and Central America.

Synthetic Strategies Toward Conipyridoins

(2022)

Decalin-containing natural products along with tetramic acid moieties present characteristic functionalities for antibiotic and antifungal activity. Thus, the total synthesis of a natural product in this class was determined to be of importance in a world where antibiotic resistance is an ever-growing issue. This creates the need to consistently develop novel antibiotics that can effectively attack various targets that are essential to the microorganism’s survival. Exploring the total synthesis of Conipyridoin analogs, which are decalin and tetramic acid-containing natural products, was carried out in this paper in 12 efficient steps. Major transformations in this synthesis include the creation of the decalin moiety through a Lewis acid-mediated intramolecular Diels Alder reaction along with a Lacey-Dieckmann condensation with an amino acid to build the tetramic acid core. This biomimetic synthesis allows for similar analogs of Conipyridoin to be synthesized with ease.

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Novel Human Organoid Impact Device for Application in Investigating Traumatic Brain Injury

(2022)

Traumatic brain injury (TBI) is a common clinical condition in which the brain is subject to a mechanical injury. This results in short- and long-term clinical symptoms and also increases the risk for future neurodegeneration. Previous studies trying to link TBI with its long-term outcomes have only been performed on animal or culture cell models, but very few studies have been performed on 3D human cellular models. These limitations are major barriers for current translation of preclinical science to clinical implementation. Recent studies suggest that 3D brain organoids may be a useful model to study links between TBI and downstream adverse effects. Previous experiments suggest that injuring organoids one at a time through the use of a modified mouse CCI impactor is tedious and creates high variability. In this thesis study, a novel brain organoid impact device was designed, manufactured, and tested to effectively model TBI in a human system. Through phantom organoid and human organoid testing, the impact device demonstrated a more simple, accurate, and efficient model to injure brain organoids, compared to its predecessors. The device allows for simultaneous injury of multiple organoids, consistent and controlled deformation of organoids, and the ability to vary impact force and velocity, based on gravity and the defined compression of a spring. This study represents a dramatic improvement over previous TBI organoid models, and will enable us to better understand downstream effects TBI towards improve translation between preclinical and clinical models. It is anticipated that this device will support a variety of experiments to evaluate TBI related diagnostics, therapeutics, biomarkers, and mechanisms of disease progression.

Cover page of Analyses of Viral Genetic Networks in the Presence of Missing Data

Analyses of Viral Genetic Networks in the Presence of Missing Data

(2022)

Molecular epidemiology is increasingly used to investigate patterns of HIV transmission. To do so, many analyses consider investigating properties of a sexual or transmission network. The use of sampled data to estimate such properties is a common practice; however, in the presence of missing data, even missing completely at random, networks based on sampled data do not represent their population counterparts. As a result, inferences on sampled networks become unreliable. To address this challenge, we propose statistical approaches to accommodating missing data in the analysis of sampled networks.

Cover page of Autonomous Maintenance of Hemostasis in Robotic Surgery

Autonomous Maintenance of Hemostasis in Robotic Surgery

(2022)

Surgical robots are becoming increasingly common in operating rooms, which provides the opportunity to deploy automation algorithms for surgery. Surgical task automation aims to improve patient throughput, reduce quality-of-care variance among surgeries, and potentially deliver complete automated surgery in the future. While progress in developing autonomous surgical tasks has leaped forward, reactive maneuvers to traumatic events, such as hemostasis, represent a critical area that has attracted little attention. Hemostasis describes a state of the surgical field that is achieved when there is no site of active bleeding and the tissues are unobstructed by blood. Unlike previously automated tasks that occur in a more predictable cadence within a procedure, bleeding can be unpredictable, which necessitates hemostatic maneuvers at any time during surgery.

In this dissertation, all the necessary perception, motion planning, and control strategies are presented to autonomously control a robotic suction tool to clear the surgical field from blood. First, a surgical tool tracking technique is proposed that localizes the robotic agent, which will clear the surgical field, in the endoscopic camera frame. The surgical tool tracking is combined with a deformable tissue tracker to completely track a surgical scene before a vessel rupture occurs. The combination of the two trackers is coined SuPer, the Surgical Perception framework. Next, the blood from a vessel rupture scenario is perceived by detecting and reconstructing the flowing blood from the endoscopic camera data. Finally, a controller and a motion planner for the robotic suction tool to clear the surgical field of blood are presented.