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Nose-to-brain delivery of liposomes for the treatment of Alzheimer’s Disease
No full textThe efficacy of Alzheimer’s disease treatment is limited by the blood-brain barrier and the systemic side effects of the medications. To overcome this barrier, we propose the nose-to-brain (NtB) delivery of anti-Alzheimer’s disease medications in combination with siRNA targeting beta-secretase -1 (BACE-1), a key enzyme that produces beta-amyloid peptide. To deliver these therapeutic agents to the brain, we propose a liposomal formulation that can encapsulate these agents. The cationic lipid is incorporated into the liposome to increase the encapsulation of siRNA. We describe the evaluation of the physicochemical properties of the liposomes and their safety, as well as their efficacy in vitro and in vivo. To investigate the efficacy of the treatment groups, we demonstrate the evaluation of the nasal epithelial cell permeability, beta-amyloid peptide levels, and pro-inflammatory mRNA expression of human neuronal cells and mouse brain tissues and the behavioral study of the transgenic mice.Ph.D.Includes bibliographical reference
Development of machine learning potentials for biochemical systems
No full textMLP models present a compelling alternative to traditional computational methods, offering significantly faster performance than ab initio quantum mechanics (QM) methods and higher accuracy than classical force fields, making them particularly suitable for molecular dynamics (MD) simulations of large biochemical systems. In this dissertation, several developments of machine learning potential (MLP) models tailored for biochemical systems are presented. Chapter 3 introduces the Deep Potential Range Correction (DPRc) model to construct efficient and accurate potential energy models for free energy profiles of biochemical reactions in solution. Chapter 4 and Chapter 5 propose the Quantum Deep Potential (QDπ) model, particularly for drug discovery applications, and show its high accuracy. Chapter 6 details the development of DeePMD-kit (version 2), a software suite designed for constructing and deploying MLP models. Chapter 7 introduces the QDπ dataset, comprising high-precision potential energy data for small organic molecules. Finally, chapter 8 presents DeePMD-GNN, a software package enabling the integration of graph neural network (GNN)-based models within the DeePMD-kit framework. Taken together, this work advances the state-of-the-art in computational chemistry, offering novel methodologies and tools for accurate and efficient modeling of complex biochemical systems.Ph.D.Includes bibliographical reference
Mechanisms of asparaginase-associated hepatic steatosis and dysfunction
No full textAsparaginase is an enzyme that degrades asparagine and has been used as a chemotherapeutic agent to treat acute lymphoblastic leukemia (ALL) since the 1960s. As a chemotherapeutic, asparaginase has an unfavorable toxicity profile. Hepatic steatosis is a common adverse event in patients with overweight and obesity. The mechanistic understanding of how hepatic steatosis and dysfunction develops in response to asparaginase is unclear. Asparaginase activates the highly conserved integrated stress response (ISR). The ISR consists of four kinases which respond to different stress stimuli and phosphorylate the α subunit of the eukaryotic initiation factor 2 (eIF2α). The eIF2α kinases that respond to amino acid insufficiency and endoplasmic reticulum (ER) stress are general control nonderepressible 2 (GCN2) and protein kinase R-like ER-resident kinase (PERK), respectively. Phosphorylation of eIF2α suppresses global translation and concomitantly increases the synthesis of stress response transcription factors. These proteins bind DNA to regulate transcription of genes that are involved in cellular protection and recovery and include amino acid synthesis, lipid metabolism, and the cellular catabolic process, autophagy.
The first aim of this dissertation sought to clarify the relative contributions of obesity and high-fat feeding to hepatic steatosis and ER stress during asparaginase exposure. To accomplish this, lean mice consumed a high-fat, obesogenic diet (OD) and obese mice consumed a low-fat, maintenance diet (MD) during exposure to asparaginase. Acute consumption of an OD to lean mice was sufficient to induce mild hepatic steatosis, but not ER stress. Consumption of a MD to obese mice did not prevent severe hepatic steatosis or ER stress in the liver. The severity of hepatic steatosis in both lean and obese mice corresponded with the magnitude of asparaginase-induced weight loss. Rates of hepatic triglyceride secretion were also measured and this revealed decreased triglyceride export from the liver in obese mice relative to lean mice and especially in combination with asparaginase. In addition, liver metabolomics showed an accumulation of sphingolipids in asparaginase-exposed obese mice, suggesting lipotoxicity and ER stress. Indeed, only obese mice showed activation of PERK during asparaginase exposure, and this activation correlated with a signature of lipid intermediates that included fatty acid metabolites, diacylglycerols, and sphingolipids. The contribution of lipolysis to steatosis was also tested by comparing mice exposed to asparaginase for 24 h to mice fasted the same amount of time, revealing that asparaginase increases hepatic steatosis similar to fasting. Taken together, the findings of this aim show that the greater magnitude of asparaginase-induced hepatic steatosis in obesity is driven by lipolysis associated with body weight loss in combination with reduced triglyceride secretion. ER stress in the liver likely develops as a consequence of increased adipose-derived fatty acid metabolites.
The second aim of this dissertation was to define the role of autophagy in the hepatic stress response to asparaginase. Elevated sphingolipids are associated with dysfunctional autophagy, and the observed correlation between sphingolipids and PERK phosphorylation in Aim 1 suggested a possible role for autophagy in the hepatic stress response to asparaginase. To explore the role of autophagy, asparaginase was administered to mice with a global deletion of a core autophagy gene, autophagy related 7 (Atg7). Atg7 is necessary for expansion of the autophagosomal membrane and the systemic deletion of Atg7 in mice blocks autophagic flux. The deletion of Atg7 in adult mice induces hepatocellular injury, hepatic steatosis, and eventually leads to death. Surprisingly, Atg7-deficient mice (Atg7Δ/Δ) were protected from asparaginase-induced anorexia and weight loss. Interrogation of the ISR in the liver showed that the loss of Atg7 suppresses global translation and transcriptional execution of the ISR. To test the contribution of Atg7, specifically in the liver, to the modulation of food intake and body weight loss, asparaginase was administered to mice with a liver-specific deletion of Atg7 (ls-Atg7KO). Ls-Atg7KO mice were also protected from asparaginase-induced weight loss, but not anorexia. Transcriptional execution of the ISR in the livers of ls-Atg7KO mice was blunted in a similar manner to that of Atg7Δ/Δ mice.
This dissertation provides evidence that obesity predisposes mice to asparaginase-induced hepatic steatosis by a combination of weight loss-induced lipolysis consequential to anorexia alongside reduced rates of hepatic TG secretion. Additionally, this dissertation shows that Atg7 is necessary for the development of hepatic steatosis during asparaginase exposure but does not play a critical role in the protection against asparaginase-induced liver dysfunction. Taken together, these insights provide a foundation for future research for both dietary and pharmaceutical interventions to mitigate this adverse event in patients with ALL.Ph.D.Includes bibliographical reference
Two dimensional dirac semimetals near the magic-angle: excitations, interactions, impurities, and methods
No full textWe theoretically study the excitation spectrum of a two-dimensional Dirac semi-metal inthe presence of an incommensurate potential. Such models have been shown to possess
magic-angle critical points in the single particle wavefunctions, signaled by a momentum
space delocalization of plane wave eigenstates and flat bands due to a vanishing Dirac cone
velocity. Using the kernel polynomial method, we compute the single particle Green’s
function to extract the nature of the single particle excitation energy, damping rate, and
quasi-particle residue. As a result, we are able to clearly demonstrate the redistribution of
spectral weight due to quasi-periodicity-induced downfolding of the Brillouin zone creating
minibands with effective mini Brillouin zones that correspond to emergent superlattices.
By computing the damping rate we show that the vanishing of the velocity and generation
of finite density of states at the magic-angle transition coincides with the development
of an imaginary part in the self energy and a suppression of the quasiparticle residue that
vanishes in a power law like fashion. Observing these effects with ultracold atoms using
momentum resolved radiofrequency spectroscopy is discussed.
We then extended the discussion of two dimensional Dirac semimetal to the case of
with Hubbard interaction and later with coulomb interaction.
We also explore a new method to accelerate the computation of physical properties
similar to Kernel Polynomial method, which is currently widely used in computational
condensed matter, to compute density of states, Green’s function and conductivity. We
used a new method that can approximate the delta function in term of lorentzian functions
in a higher order fashion in the applied math community to introduce this new method.
Finally, we explore vacancy-induced impurity states in twisted bilayer graphene (TBG)
near the magic angle, where ab-initio calculations and atomic-scale modeling demonstrate
that these vacancies act as quantum impurities. By constructing an Anderson impurity
model and solving it using the numerical renormalization group and kernel polynomial
method, we uncover a dichotomy between vacancies in AA/BB and AB/BA regions, with
the latter exhibiting multifractal wavefunctions and a broad distribution of Kondo temperatures.
These findings highlight the potential of scanning tunneling microscopy to probe
both critical single-particle states and many-body ground states in magic-angle TBG.Ph.D.Includes bibliographical reference
Robots with dynamics: efficient motion planning and analysis of controllers via machine learning
No full textThis thesis aims to improve the efficiency and robustness of motion planning for robots with significant dynamics, leveraging both advances in machine learning as well as contributions in algorithmic and foundational techniques. The key objectives are to (a) efficiently compute safe open-loop trajectories that obey non-trivial robot dynamics so that they are easy to follow with closed-loop controllers, and (b) efficiently analyze and characterize the capabilities of closed-loop robot controllers to enable safe real-world deployment.This effort starts by exploring alternatives to the standard methodology of generating control sequences in sampling-based planning for systems with dynamics. Typically, these methods rely on random controls, which are useful to argue desirable properties, but which lead to slow convergence and low-quality solutions in practice.To address this, the thesis first proposes using machine learning to train goal-reaching controllers via reinforcement learning. Such learned controllers can be integrated with sampling-based planners and help guide the expansion of the underlying planning structure towards the global goal. This is shown to lead to the faster discovery of high-quality trajectories on mobile robot navigation problems, including for physically-simulated challenges with uneven terrains.In addition, this thesis proposes the offline construction of a “roadmap with gaps” data structure for systems with dynamics, which can express the learned controller's reachability capabilities in a target environment. Online, the sampling-based planner uses the “roadmap with gaps” to promote the fact discovery of high-quality trajectories to the goal. The overall approach enhances the efficiency of motion planning in various benchmarks, including physics-based simulations of vehicular systems and aerial robots.The open-loop solutions generated by sampling-based planners require closed-loop feedback control for reliable real-world execution. To this end, the thesis first integrates techniques for identifying approximate analytical models of the robot's dynamics that allow fast motion planning and reduce the model gap. It then focuses on achieving closed-loop operation at both the planning and control levels by proposing a safe replanning framework for kinodynamic motion planning and integrating feedback controllers that reason about robot dynamics. These contributions allow for safe and efficient tracking of planned trajectories on a physical platform.Concurrently, the thesis also addresses the challenge of understanding the global dynamics of robot controllers, including learned ones, which is crucial for safe deployment of such solutions and the composition of controllers. A topological framework (Morse Graphs) is leveraged, and data-driven modeling approaches are proposed to enable data-efficient characterization of controller attractors and their regions of attraction, even for high-dimensional systems.Finally, the thesis contributes an open-source software library, which provides a flexible and efficient framework for integrating machine learning methods into kinodynamic planning and control.Ph.D.Includes bibliographical reference
Design and development of a high-temperature differential calorimeter
No full textDifferential calorimetry is a method that is used to measure the heat flow rate of a sampleby measuring the heat difference between a sample and a reference material. A high-temperature
(room temperature to 700°C) and variable-pressure (10 μTorr – 1 atm) differential calorimeter with
microwatt-level resolution is designed and developed to study the thermal analysis of materials for
energy storage applications. The results show that the calorimeter achieves a heat flow resolution
of 36 μW at 300°C. The calorimeter design has two sample holders, one for the tested material
and one for the reference, and the results show that the calorimeter has a thermal conductance of
11.7 mW/K and 12.35 mW/K at 300 °C, respectively. Since the main goal of the calorimeter is to
measure the thermal response of different chemical reactions, as a case study, we measured the heat
reaction for hydrogenation of metal particles to form a metal hydride. We used titanium particles
with a total mass of 13 mg and measured the heat of titanium hydride formation at various hydrogen
pressures and at a reaction temperature of 270 °C. The results show that at low pressure of hydrogen
(0.02 bar H2), the heat generated from hydrogen absorption is 19.579 J and enthalpy (-72.097
kJ/Moles). For high pressure (0.7 bar of H2), the heat from the reaction is increased to 28.901
J and enthalpy of (-106.422 kJ/Moles). These results are in agreement with past reported values
for heat of titanium hydride formation and demonstrate the instrument’s capability to accurately
measure the heat flow of high-temperature reactions. The capabilities developed in this project
will enable development of high temperature materials and processes for applications including thermal energy storage.M.S.Includes bibliographical reference
Clinical and cognitive implications of mrna and protein-based buccal cell biomarkers for schizophrenia
No full textSchizophrenia (SCZ) is a debilitating and chronic neuropsychiatric disorder characterized by the presence of positive, negative, and cognitive symptoms. SCZ pathogenesis is heterogenous and polygenic in nature, making it difficult to diagnose and treat. This is unsurprising as the diagnostic criteria are solely based on behavioral markers. Thus, there is a critical need for easy-to-collect biomarkers that aid in the treatment of patients with SCZ. To identify novel biomarkers, we recruited a cohort of patients with SCZ (n=27) and age-, race-, and gender-matched control subjects (n=27). Using lysates from buccal cells, we performed real-time quantitative PCR (RT-qPCR) and identified significant differences in SP4 mRNA expression between patients and control subjects as well as significant differences in NOS1AP mRNA expression between Asian patients and control subjects. We then performed label-free quantitative proteomic analysis on extracts from a subset of patients and found decreased abundance of oxidative phosphorylation (OXPHOS) proteins, mitochondrial dysfunction, and immune activation in patient samples. Using targeted mass spectrometry, we identified increased protein abundance of heat shock protein 60 (HSP60) in samples from patients with SCZ. To determine the utility of NOS1AP mRNA, SP4 mRNA, and HSP60 protein as biomarkers, we evaluated their relationship with symptom severity and aberrant cognitive processes. Correlational analyses revealed that both SP4 and HSP60 are significantly associated with symptomology, impairments in response speed on a working memory task, and poorer short-term verbal memory. However, NOS1AP was only significantly associated with symptomology amongst Asian individuals. Cumulatively, these data support the use of buccal cell SP4 mRNA and HSP60 protein as easy-to-collect, novel SCZ biomarkers. These biomarkers have the potential to facilitate the use of biologically relevant criteria in the diagnosis and treatment of SCZ.Ph.D.Includes bibliographical reference
Design, characterization, and optimization of Al0.8Sc0.2N PMUTs for versatile sensing applications
No full textUltrasonic sensing technologies have seen rapid development across industries such as automotive, healthcare, and industrial automation, providing cost-effective and reliable solutions for distance measurement.Traditional ultrasonic sensors are primarily lead-based bulk piezoelectric ceramics, which typically have dimensions in the centimeter range. These sensors face several challenges, including complex fabrication processes, high energy consumption, large size, poor consistency between sensors, and acoustic impedance mismatch with the medium. Such limitations render them inadequate for meeting the current demands of intelligent, miniaturized, and integrated systems. In the past decade, the rapid development of MEMS technology has driven significant advancements in ultrasonic sensing technology. MEMS enables wafer-level manufacturing of ultrasonic sensors through semiconductor processes, significantly reducing manufacturing costs and energy consumption while miniaturizing the sensors to micro- and nanoscale dimensions. Currently, MEMS-based ultrasonic sensors are mainly categorized into capacitive (CMUT) and piezoelectric (PMUT) types. Among these, PMUTs have attracted considerable attention due to their more controllable fabrication process and their ability to operate without requiring a large DC bias voltage. This makes them particularly suitable for air-coupled applications such as distance detection, spatial positioning, and flow monitoring. The core thin-film materials used in PMUTs include PZT, zinc oxide, and aluminum nitride (AlN). While PZT offers a high piezoelectric coefficient, it is environmentally unfriendly due to its lead content. Additionally, PZT has a high dielectric constant, significant leakage current at high frequencies, and a high processing temperature, making it incompatible with CMOS technology and significantly increasing manufacturing costs. Zinc oxide, on the other hand, is simple to fabricate but suffers from low stiffness and poor resistance to high temperatures and corrosion, limiting its applicability under extreme conditions. AlN strikes a balance between manufacturing simplicity and material stability, being compatible with standard CMOS processes and offering excellent mechanical stability and resistance to high-temperature and corrosive environments. Furthermore, Sc doping significantly enhances the piezoelectric properties of AlN, making Sc-doped AlN the most commercially promising core piezoelectric thin-film material for PMUTs. Despite the widespread application of Sc-doped AlN-based PMUTs, several challenges remain, particularly in air-coupled applications: 1. The functional performance of Sc-doped AlN in high-temperature environments has not been extensively studied. While existing research indicates that Sc-doped AlN maintains high piezoelectric performance at elevated temperatures, critical parameters such as frequency drift, electromechanical coupling coefficient, and stress variations under extreme conditions remain unexplored. This lack of understanding hinders its reliable application in high-temperature environments. 2. Current application scenarios impose stringent frequency requirements on PMUTs. Low frequencies are preferred to reduce sound attenuation in the medium and extend the measurement range, while high frequencies are desired to shorten wavelengths and improve measurement accuracy. Existing approaches to achieving multi-frequency PMUTs typically involve controlling the amplitude and timing of array elements or integrating devices with different frequencies within the array. However, these methods compromise the advantages of PMUTs in terms of small size and low power consumption. 3. In near-field applications, PMUTs suffer from blind spots due to lingering vibrations after emitting sound waves. During this ringdown period, any received signals may be aliased. Additionally, the extended duration of the echo signals caused by this trailing vibration results in poor axial resolution. When two objects are close together, their reflected signals may overlap, further degrading resolution.end{enumerate} To address the issues above, this dissertation explores the design, characterization, and optimization of PMUTs for high-performance applications, with a focus on enhancing operational versatility, precision, and scalability. The research content can be divided into three main points: 1. Fabrication and Characterization of Scandium-Doped Aluminum Nitride Thin Films}
This study begins with the deposition of 20% scandium-doped aluminum nitride () films using physical vapor deposition (PVD) technology, followed by thin film etching. Stress and surface roughness uniformity of nine etched thin films were characterized to ensure the reliability of the fabrication parameters. Additionally, advanced material characterization techniques such as XRD, SEM, and EDS were employed to analyze the morphology and composition of the films. Finally, high-performance wafer-scale Al0.8Sc0.2N PMUT devices were successfully fabricated. 2. Performance Evaluation of PMUTs under High-Temperature Conditions The performance of PMUTs under high-temperature conditions was studied, including parameters such as resonant frequency, electromechanical coupling coefficient, and temperature stability. Finite element analysis (FEA) and experimental validations were conducted to investigate the effects of structural parameters, such as piezoelectric layer thickness, structural layer thickness, and back cavity diameter, as well as material variations like scandium doping, on the performance under extreme temperatures. The study also examined the reliability of PMUTs under thermal cycling conditions and characterized the frequency response at low temperatures. Results showed that when the operating temperature increased from room temperature to 200°C, PMUT devices with a back cavity diameter of 1000 µm and a top silicon thickness of 4 µm exhibited a temperature drift rate of 47.3%. The maximum electromechanical coupling coefficient improved by 68.6% at approximately 100°C compared to room temperature. Moreover, thermal cycling experiments revealed that the initial frequency shifted after temperature variations and could not recover over time, indicating the need for frequency calibration to ensure accurate measurements under extreme conditions. Low-temperature experiments further demonstrated that PMUTs maintained stable piezoelectric performance even at -30°C, confirming their reliability in extreme temperature environments. 3. Development and Characterization of a Mode-Switchable Multi-Frequency PMUT A novel mode-switchable multi-frequency PMUT (MS-MF-PMUT) was developed and characterized to achieve high-frequency consistency at the wafer scale, providing a mass-producible design for complex applications such as medical imaging and smart city technologies. This design addresses the challenges of low transmission sensitivity and high production complexity faced by current multi-frequency PMUTs. Simulations were first performed to obtain the frequency response characteristics of the device under different driving modes. Electrical characterizations were then conducted to analyze the frequency response, and dynamic vibration testing was used to study its resonant characteristics under different modes. Additionally, wafer-level frequency uniformity was investigated to assess its feasibility for mass production. Results showed that the device achieved maximum amplitudes of 350.2 nm/V and 218.6 nm/V in the two driving modes, respectively, and wafer-level frequency uniformity was maintained within ±8.14% and ±6.58% of the central frequency. These findings highlight its significant potential for large-scale production. 4. A novel driving method was proposed to improve axial resolution for near-range detection by employing forced vibration instead of the traditional resonant driving mode. Theoretical analysis was conducted to establish the relationship between the forced vibration driving frequency and the number of bursts. This relationship was verified through simulations and dynamic vibration testing. Acoustic tests were then performed to evaluate blind spots and axial resolution in near-range applications. Results indicated that, compared to traditional resonant driving, the proposed forced vibration method improved axial resolution by 440% and reduced blind spots by 37.5%. This demonstrates the effectiveness of the method in enhancing the near-range detection capabilities of PMUT sensors. The findings of this research contribute to the advancement of ultrasonic sensing technology, offering new insights into the design of PMUTs that are not only capable of multi-frequency and high-temperature operations but also optimized for both short-range and long-range applications. These innovations enhance the feasibility of PMUTs in diverse industries, including automotive, healthcare, and industrial sensing.Ph.D.Includes bibliographical reference
Representing the economic boom and its anxieties: Italy and Japan
No full textIn my dissertation, I explore the cultural anxieties surrounding the economic miracles of Japan (1950-1970) and Italy (1958-1963) in the post-war period, focusing on the interplay of American influence, tradition, and modernity, as well as the evolving constructions of masculinity, gender roles, and patriarchal structures. This study highlights the shared struggles and distinct responses to rapid modernization and societal transformation by examining key literary and cinematic works from both nations. I argue that the economic miracles in Japan and Italy reveal common struggles with identity, gender, and societal expectations during rapid change. By comparing the two nations, this work demonstrates how similar challenges can manifest differently in unique cultural contexts while highlighting shared anxieties. First, I establish a connection between Italy and Japan by analyzing the translations of tankas by Gherardo Marone and Shimoi Harukichi. Then, I examine texts and cinematic representations of Italy and Japan: Un amore (1963) by Dino Buzzati, Una bambolona (1967) by Alba De Cespedes, American Hijiki (1967) by Nosaka Akiyuki, and the films Un amore (1965) by Gianni Vernuccio, La bambolona (1968) by Franco Giraldi, The Life of Oharu (1952) by Kenji Mizoguchi, and Flowing (1956) by Naruse Mikio. I explore the patriarchal anxiety in a changing world – Eastern and Western – and how the rapid economic growth intensified these anxieties about masculinity and gender roles. I also show how modernization and consumer culture commodified the relationships between gender roles: women were reduced to objects of desire, and they fought against the patriarchal system to revert it. In Chapter One, I establish a historical frame for Italy and Japan, exploring the dichotomy of tradition vs. modernity and the theme of alienation. In Chapter Two, I analyze the tankas translated in La Diana by Gherardo Marone and Harukichi Shimoi. In Chapter Three, I examine the commodification of gender roles, the alienation caused by consumer culture and patriarchal society, and the threat to masculinity in Italian novels and films. Finally, Chapter Four investigates films depicting women’s lives constrained by patriarchal structures amidst Japan’s modernization.Ph.D.Includes bibliographical reference
A disease conceptual model for syngap1-related neurodevelopmental disorders
No full textSYNGAP1-related disorders are rare neurodevelopmental and epileptic encephalopathies that were first identified in 2009. These disorders are known to manifest with a great range of clinical presentations and neurodevelopmental trajectories; however, the impacts of symptoms are not well understood. To understand symptoms and their impacts, we conducted twelve qualitative interviews with caregivers of individuals with SYNGAP1-related disorders to form a preliminary disease concept model. An inductive thematic analysis approach was used to identify concepts described by participants using DeDoose software. Disease concept models (DCMs) are frameworks that examine the relationship between symptoms, symptom impacts on quality of life, and symptom impacts on caregivers to provide a basis for generating outcome measures. We comprehensively mapped symptoms and their impact on caregivers and their families to generate a disease model as a foundation for clinical endpoints in future trials. The findings of this study identified concepts that are beneficial to include in a SYNGAP1-related disorders preliminary disease concept model that would be applicable for treatment.M.S.Includes bibliographical reference