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Characterization of Genetically and Epigenetically Engineered Multicellular Systems and Their Application in Tissue Modeling and Hematopoiesis
No full textPluripotent stem cell (PSC)-derived organoids have emerged as novel multicellular models of human tissue development but display can be difficult to rationally engineer, display immature phenotypes, and have been adopted in limited applications. Here, using our preciously established human induced pluripotent stem cell (hiPSC)- derived multilineage human liver organoids we develop tools for epigenetic engineering liver organoids, demonstrate integrated analysis and engineering of gene regulatory networks (GRNs) in PSC-derived multilineage human liver organoids to direct maturation and vascular morphogenesis in vitro through transcription factor engineering, and establish a model of hematopoietic stem cell (HSC) colonization and expansion in the human fetal liver as a platform for expansion of therapeutic HSCs. Collectively, our approach provides an experimental framework to guide epigenetic and genetic engineering of PSC-derived organoids for tissue modeling and novel therapeutic applications
Toll-Like Receptor 7 in Systemic Lupus Erythematosus: Characterization of Cell-Intrinsic Roles and Regulation by NADPH Oxidase
No full textSystemic lupus erythematosus is an autoimmune disease characterized by loss of tolerance to self-nucleic acids, followed by immune cell activation and infiltration into nearly every organ system throughout the body. As such, it is a disease that can greatly impact quality of life and remains a major cause of morbidity and mortality across the globe despite recent therapeutic advances. Although many biological pathways have been implicated in disease pathogenesis, the cardinal pathway responsible for breakdown of tolerance in SLE is signaling through the nucleic acid sensing, endosomal toll-like receptors. Specifically, TLR7 has repeatedly been implicated as a driver of SLE in both mice and humans. However, questions remain regarding how TLR7 promotes disease in specific immune cell subsets and how it is regulated. In this dissertation, I define the B cell and CD11c+ dendritic cell intrinsic roles of TLR7 in SLE, demonstrating that B cell intrinsic TLR7 drives severe lupus when lacking regulation by TLR9. I also elucidate global and B-cell intrinsic regulatory relationships between TLR7 and NOX2, another protein implicated in SLE. Finally, I explore the B cell intrinsic mechanisms of NOX2-mediated negative regulation of TLR7 signaling and structure. This work supports the concept of targeting B cell intrinsic TLR7 in lupus to improve outcomes and quality of life for patients
Psychoneurological Symptoms and their Association with Markers of Genomic Instability from Circulating Tumor DNA in Metastatic Breast Cancer
No full textAmong the 3.8 million breast cancer survivors in the United States, many will experience the psychoneurological (PN) symptom cluster of fatigue, sleep disturbance, anxiety, pain, depressive symptoms, and changes in cognitive function. A variety of patient social, treatment, and biological factors have been associated with this symptom cluster. Genomic instability in cancer cells may be associated with the variability in psychoneurological symptom development, given that markers of systemic inflammation have been associated with both genomic instability and psychoneurological symptoms. Genomic instability can be measured through an emerging technology that sequences circulating tumor DNA in the blood of patients with breast cancer. Individual factors and social determinants of health (SDoH) have also been linked to inflammatory processes and may impact symptoms as well. However, no studies to date have linked genomic instability in cancer cells to variability in the psychoneurological symptom cluster experience. The overall goal of this study was to develop understanding of how genomic instability in breast cancer is associated with the psychoneurological symptom cluster and enable future development of precision health interventions for symptoms based on cancer characteristics. The specific aims of the proposed research were to (1) phenotype patterns and severity of PN symptoms in individuals with metastatic breast cancer (2) investigate the association of PN symptom phenotypes with markers of cancer genomic instability from circulating tumor DNA analysis. This was accomplished through analysis of data from (1) a parent study focused on the circulating tumor DNA in patients with metastatic breast cancer and (2) measures of patient-reported symptoms concurrent with measures collection of biological samples abstracted from the electronic medical record. Factor analysis identified a single factor underlying all six measures PN symptoms. Hierarchical clustering identified three distinct symptom phenotypes: mild symptoms, moderate symptoms, and severe mood related symptoms. Deletion of TP53 in circulating tumor DNA was predictive of a more severe symptom phenotype. Prediction of symptoms from cancer biological factors is a novel approach that could improve predictive value of cancer genomic profiling, that is increasingly becoming the standard of care in breast cancer treatment. Additionally, uncovering cancer characteristics associated with PN symptoms could guide future research elucidating the underlying pathways of symptom development
Novel KIF1A Mutations Perturb Axonal TDP-43 Localization and Presynaptic Proteostasis
No full textAxonal trafficking is a key feature unique to the morphology of neurons, facilitating the movement of proteins bidirectionally between the soma and the presynaptic terminal, at times over significantly long distances. In neurons, kinesin motor proteins facilitate the anterograde axonal trafficking of key cargos, including mitochondria, synaptic vesicles, dense core vesicles, and endolysosomes. Kinesin dysfunction has been linked to a variety of neurodevelopmental and neurodegenerative disorders. Here, we investigate novel mutations to kinesin family member 1A KIF1A) in patient derived induced pluripotent stem cell neurons, and their collective role in neuropathology and trafficking mechanisms. Our data suggests that KIF1A is a candidate kinesin motor protein mediating the anterograde axonal trafficking of TAR DNA Binding Protein 43KDa (TDP-43) and RNA, wherein mutations to KIF1A impair the association with and axonal localization of TDP-43. While contemporary research has hypothesized the link between TDP-43 and KIF1A to be conferred through annexin-11 and lysosomes, our data suggests an alternative link between annexin-5 and synaptic vesicles. Additionally, this research infers that these novel KIF1A mutations play pathogenic roles in the loss of synaptic vesicle proteins in the axons, likely leading to deprecated synaptic transmission. Furthermore, we demonstrate that the loss of axonal TDP-43 as a result of mutagenic, and other RNA-binding proteins, leads to a loss of presynaptic transcriptomic and proteostasis. The subsequent loss of axonal proteostasis was found to be in part a result of loss of RNA processing and transcriptional machinery. Lastly, we show that the gross synaptic architecture of axonal protein recovery of neurons is corrupted by mutations in KIF1A and the downstream effects on the axonal transcriptome and proteome. While this data does not identify novel therapeutic targets to address KIF1A associated neurological disorders, the data provides a holistic template to measure the success of future therapies
Exploring the Reality Gap: Deep Reinforcement Learning for Training a 6DOF Robotic Arm to Grasp Target Box
No full textThis thesis investigates a significant challenge in the application of reinforcement learning to robotics: the "reality gap", which refers to the differences in behavior and performance exhibited by robots trained in simulated environments when applied to real-world scenarios. The study focuses on a robotic arm trained to grasp a target box through reinforcement learning, thoroughly analyzing the process of modeling the robotic arm, training it using reinforcement learning techniques, and transferring the learned behaviors to real-world applications. The research underscores the importance of accurately capturing physical properties such as mass, friction, and inertia in simulations and discusses the complexities involved in modeling actuators and control systems for tasks requiring precise manipulation.
Through both qualitative and quantitative analyses, this study examines the discrepancies between simulation and reality, identifying key factors contributing to the reality gap. The research provides a detailed comparison using visual imagery and joint angle data to measure the performance differences between simulated training and real-world execution of the robotic arm. Additionally, the study explores the application of the Deep Deterministic Policy Gradient (DDPG) algorithm in training, highlighting its effectiveness and the challenges faced in translating simulation achievements into real-world operations.
Physics engines face significant challenges when simulating complex contact forces, such as friction and collision forces, leading to substantial discrepancies between computational models and real-world scenarios. The research findings further highlight the limitations of current simulation models, particularly pointing out that limitations in modeling and calculating complex contact forces are among the key factors affecting the transferability of simulated training to practical tasks. The thesis concludes with a discussion on potential strategies to narrow the reality gap, suggesting future research directions aimed at enhancing the accuracy of simulation models and improving the real-world applicability of robotic training. This work aims to bridge the gap between theoretical models and their practical implementation, thereby improving the efficiency and reliability of Reinforcement Learning in real-world applications
Molecular Discovery of Materials for Non-Fullerene Based Organic Solar Cells
No full textOrganic solar cells (OSC) are an emerging photovoltaic technology that uses n-type and p-type organic materials to split excitons into free charges. The n-type material, known as the acceptor, and the p-type material, known as the donor, require interdependent chemical properties such as energy-level alignment of their frontier orbitals and complementary absorption spectra to result in a high power conversion efficiency (PCE). Due to the complexity of finding compatible materials, computational guidance is crucial to quickly discover new OSC materials and uncover their molecular design rules. In the first part of this work, a PCE prediction model is developed that can work on non-fullerene acceptor (NFA)-based OSCs with much higher accuracy than previously published models. This model was then used as the fitness function in a genetic algorithm (GA) to design new unfused NFAs. However, before using the GA, we performed a study to optimize the GA hyperparameters such as population size, elitism percentage, selection method, mutation rate, and convergence criteria for molecular discovery. The results of this study provided a set of best practices for the use of GAs for inverse molecular design that can be generalized over multiple chemical properties. Next, the GA was used to discover more than 1,087 unfused NFAs with a predicted PCE above 18%. In the fourth part of this work, we used a series of genetic algorithms and machine learning to find the best combination of materials for tandem OSCs. The PCE prediction model was improved by training on a larger OSC dataset and used as the fitness function to design better NFAs and compatible donors to those NFAs. The top pairs were used as the active layer in a subcell, while a new GA was used to find compatible NFAs and donors for the other subcell in a tandem device that maximized absorption while minimizing spectral overlap. Lastly, we created the largest OSC dataset available and leveraged its unique size to learn more about the molecular design of NFAs and polymer donors. The results of this meta-analysis can guide chemists on the design of new OSC materials
Compact Real-time Interrogation System for Distributed and Multiplexed Fiber Bragg Grating (FBG) Sensors Demodulation Applied on High Temperature and Vibration Measurements
No full textReal-time measurements of physical parameters like temperature, strain, and vibration are crucial for industrial, aerospace, and infrastructure monitoring applications. Optical fiber sensors, particularly Fiber Bragg Grating (FBG) sensor arrays, excel in high-spatial resolution measurements across a wide range of environments, from cryogenic to extreme high temperatures. Their stability, mechanical robustness, and immunity to electromagnetic interference make them ideal for these applications.
This dissertation explores the development of low-cost, compact, real-time sensor interrogation systems using tunable lasers and embedded systems for high-temperature sensing. The research focuses on real-time temperature measurement with FBG sensor arrays, controlled by a tunable laser and a heterogeneous FPGA/DSP system. The system incorporates signal conditioning circuits, an embedded microcontroller, and a graphical user interface (GUI), achieving accurate temperature measurements up to 910 °C over three weeks. Machine learning algorithms are used to enhance prediction accuracy, resulting in an average Mean Absolute Error (MAE) of 0.98 °C for temperatures around 810 °C. The FBG sensors demonstrate an average temperature sensitivity of 13.74 pm/°C. This embedded interrogator system offers a reliable and precise solution for real-time temperature measurement in harsh environments, suitable for implementation in microcontrollers or low-complexity field devices.
Additionally, the dissertation details the embedding of FBG sensors in aluminum parts using Ultrasonic Additive Manufacturing (UAM) for high-frequency vibration monitoring. Polyimide-coated optical fibers with FBGs are embedded in the parts, enabling strain measurements under vibration frequencies ranging from 1 kHz to 10 kHz. A high-speed interrogation system using a tunable Vertical-Cavity Surface-Emitting Laser (VCSEL) achieves a sampling rate of 120 kHz, detecting strains as low as 2.5 μɛ. Finite Element Analysis (FEA) is used to simulate strain responses under static and high-frequency vibration conditions, validating the system's performance. This integrated approach provides a robust solution for high-frequency vibration monitoring in aerospace, aeronautics, and energy applications. To further enhance system compactness, VCSEL control is integrated onto the embedded board, with temperature and current control managed through onboard analog circuits. This onboard control adjusts the VCSEL scanning wavelength by varying the voltage, combining optical and electronic components to make the system more compact and portable
Ballistic Transport and Persistent Circulation of Exciton-Polariton Condensates
No full textBose-Einstein condensation (BEC) is a quantum macroscopic phenomenon and is closely related to superfluidity and superconductivity. Exciton-polaritons (polaritons) are a kind of quasi-particles, which are superpositions of excitons and photons in semiconductor microcavities. They have low effective mass due to their photon part and interact with each other due to their exciton part. With these properties, polariton BEC can be created at helium temperature (~10K).
This thesis mainly focuses on creating persistent circulation out of polariton condensates in ring structures. In the first experiment, polariton condensate was injected at one point in an etched quasi-1D ring. Rather than observing the circulation, the condensates transported ballistically, and a clear precession of the polarization was seen, which arises from an effective spin-orbit coupling term in the Hamiltonian. In the second experiment, we created a non-circulating condensate by using an optical trap and initiated the circulation in either direction on demand by a short laser pulse. The circulation direction only depends on the short pulse location relative to the intensity distribution of the non-circulating ring and the circulation persisted for at least 13ns, which is much longer than the polariton lifetime (~200ps) or the pulse duration (2ps). This experiment demonstrates the canonical effect of superfluidity and has potential applications for phase memory or optical qubits.
In addition, I did research on polariton condensates in other geometries. One is experimental research, where I observed polariton drag effect in wire structures. In this experiment, the condensate momentum was modified by a direct electrical current. This is equivalent to electrically steering light momentum. The other is numerical simulation of the etching-induced strain. Our goal was to optimize the Y-shape devices because the condensate from the lower arm wouldn't travel to the upper arms. The simulation was first applied to a square-shape pillar, which matched the experiments pretty well. The simulation indicates that the potential at the junction of the Y-shape devices is flat so strain is not the reason that prevented the condensate flow. More research needs to be done to have the best design
Exploring and Advancing Inclusivity in Engineering Education Across Academic Communities
No full textThe overarching theme of this research was to explore different aspects of the science, technology, engineering, and math (STEM) education experience across cultures and contexts in two engineering education communities, higher education and K-12. This dissertation offers new perspectives and resources to the engineering education community and provides a point of view to improve inclusivity and accessibility in engineering for all students.
The research performed in the higher education community focused on the development and pilot of an inclusive classroom practices menu for engineering faculty across three contextually different institutions including predominantly white, Hispanic-serving, and STEM-focused. Inclusive learning communities (ILCs) were also convened to support participating faculty. To assess the impact of the menu and ILCs, faculty and student assessment plans included end of semester surveys and semi-structured interviews. Though literature has highlighted the positive impact of improving inclusivity in STEM classrooms, this research shares both the development of the inclusive classroom tools and the impact of these tools from student, faculty, facilitator, and researcher perspectives. This research also shares a novel conceptual framework of the STEM education environment which centers students’ identities in relation to the people who impact their development as STEM students.
The second part of this research, performed in the K-12 education community, focused on the development and pilot of an international, virtual learning experience for middle and high school students (7th-12th grade) in rural Kenya in response to a local air pollution problem. This research offers a citizen science, problem-based curriculum that utilizes air quality monitoring to guide the learning. This curriculum employed a combination of educational and teaching frameworks which emphasized student-led and hands-on learning. In addition to the curriculum, this part of the research shares the air quality data and analyses, the student learning assessment and results, and recommendations. Compared with previous environmental-focused learning experiences, this research offers a unique perspective as it was conducted virtually and internationally amidst the COVID-19 pandemic.
Results of this dissertation present a larger case for improving inclusivity and accessibility to engineering throughout the education pipeline, particularly in the face of new social, political, and environmental challenges
Childhood antecedents of systemic inflammation in adolescence and adulthood: Contributions of childhood family environment and socioeconomic context
No full textThe childhood family environment has been associated with lifelong health trajectories, including cardiometabolic disease morbidity and mortality. Systemic inflammation is one important preclinical marker of cardiometabolic risk, and prior studies have shown that early-life experiences, including family relationship quality and socioeconomic status, are related to circulating levels of inflammatory markers as early as adolescence. The current study utilized data from the Pittsburgh Girls Study to longitudinally investigate links between a childhood family environment latent variable—indicated by harsh punishment, caregiver-partner conflict, and caregiver depression—and inflammation in adolescence (n=429; M age=10.52) and early adulthood (n=1,362; M age=23.57), while considering additional risk (financial strain) and protective (supportive parenting) factors. Structural equation models found that greater family adversity across ages 5-9 was prospectively associated with higher levels of interleukin (IL)-6 in both adolescence and adulthood, even when adjusting for covariates (e.g., child self-control, pubertal timing). Further adjustment for adolescent waist circumference attenuated the link between the childhood family environment and adult IL-6 to non-significance, and sensitivity analyses revealed that it was a significant mediator of this relationship. When adding financial strain into the models, the childhood family environment was no longer predictive of later inflammation, but there was evidence for associations between childhood financial strain and adult IL-6. Contrary to hypotheses, supportive parenting did not moderate associations between childhood family environment or financial strain and later inflammation. Together, these results suggest that the childhood family environment contributes to long-term systemic inflammation, although not independently of childhood socioeconomic context. The current study offers novel insights into links between childhood stressors and inflammatory profiles across developmental stages and highlights opportunities to further probe biopsychosocial mechanisms underlying these relationships