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Unveiling Mysteries of the Universe: Axions, Sterile neutrinos, and Curvatons
Get PDFThe Standard Model of particle physics and cosmology is amazingly successful in describing our universe, from at present to even tracing back to just one second after its birth. Yet, it is known to be incomplete. For instance, (i) more than four-fifths of matter in the universe is unknown, (ii) quantum chromodynamics is unnaturally of CP-preserving nature, (iii) the universe produced baryonic particles by nearly equal but slightly larger amount compared to anti-baryonic particles, and (iv) the physics at very early times is not understood yet. This thesis first briefly summarizes the Standard Model of particle physics and cosmology, and then, its limitations and potential solutions: the QCD axion, the sterile neutrino, the Affleck-Dine baryogenesis, and the curvaton inflation. Lastly, the author\u27s previous work is described. The QCD axions and axion-like particles spontaneously and/or are stimulated to decay to two photons. We estimated the detectability of photons from Galactic QCD axion dark matter particles and keV-MeV axion-like particles produced in heavy stars. In addition, we estimated the capture rate of keV sterile neutrino dark matter by a compact astrophysical object and estimated the subsequent cooling/heating effect. The last part of this thesis discusses the viability of the curvaton inflationary scenario where a nonzero baryon number is produced via the Affleck-Dine mechanism
Using “Click” Chemistry and Post-Polymerization Modification to Synthesize Well-Defined Linear Oligocatenanes and Polyacrylonitrile
Get PDFIn the field of supramolecular and polymer chemistry, there is a focus on the structural design of oligomers and polymers at the molecular level. Factors such as intramolecular and intermolecular interactions, stereochemistry, crosslinking, entanglement, chemical bonding, chain length, and side-chain functionality can dictate the properties of resulting macromolecules and polymers. The connection between polymer structures and their properties is often intricate and non-linear, making it difficult to accurately predict. Thus, it becomes necessary to study polymer structures and their resultant properties through traditional models or experiments. In the field of mechanically interlocked molecules, the potential properties of catenane and poly[n]catenane mechanical bonding and topologies have garnered wide interest due to the potential of their toughness and flexibility in materials. These properties make these [n]catenanes and poly[n]catenanes promising candidates for making complex molecular machines, or smart soft materials, respectively. While many variations of [n]catenane complexes have been explored, the straightforward preparation of higher molecular weight linear [n]catenanes has only relatively recently been attempted. In many cases, the synthesis of any catenane is difficult, low-yielding, and usually a slow process. The development of more efficient synthetic protocols for functional catenanes presents a continual, desirable challenge to supramolecular chemists. In the field of traditional polymer chemistry, the synthesis of carbon fiber and the development and enhancement of their structural properties and applications has been widely studied and is of high interest in the community due to the large range of carbon fiber applications in industry and society and its versatile, high-yield macroscopic properties. However, carbon fiber exploration and novel advances have predominantly been concentrated in its post-synthesis, bulk material engineering. To date, development into the synthesis and transformation of polyacrylonitrile (PAN), the polymer precursor to carbon fiber, has been limited to mainly 70,000 – 200,000 g/mol molecular weight polymers due to the limitations of control of the free-radical polymerization of acrylonitrile monomer. While this can still result in intermediate modulus carbon fiber, there is a large potential property enhancement that is left unstudied for PAN, and ultimately carbon fiber. Molecular-level advancement of PAN and carbon fiber synthesis leaves much room for improvement. Overall, the synthetic gap between the synthesis and development of the material properties of catenanes and PAN-based precursor polymers towards carbon fiber need to be bridged
Exploring Social Connectedness Through The Perspectives Of Latina Adolescents At Risk For Suicide, Their Families, And Providers In The Life Is Precious Program
No full textLatina adolescents in the United States experience disproportionately high rates of suicidal thoughts and behaviors, yet culturally responsive interventions remain limited. Social connectedness—a protective factor against suicide—plays a critical role in adolescent well-being but is shaped by complex sociocultural, familial, and systemic influences. This study explores how Latina adolescents with lived experiences of suicidal thoughts and behaviors (N=12), their families (N=5), and providers (N=12) perceive and experience social connectedness within the context of Life is Precious (LIP), a treatment-adjacent community-based intervention designed to support Latina youth at risk for suicide. Guided by Zayas’ Eco-Developmental Model and the Interpersonal Theory of Suicide (IPTS) this qualitative study employs semi-structured interviews and photo-elicitation interviews to examine connectedness across family, peer, school, and community domains. Data from Latina adolescents, their family members, and LIP providers reveal both facilitators and barriers to connectedness, highlighting the dynamic and evolving nature of social relationships. Findings illustrate how cultural values such as familism, gender roles, and acculturation stress shape Latina adolescents’ experiences of belonging and isolation. While LIP enhances connectedness by fostering relationships and emotional support, structural barriers—including stigma, discrimination, and systemic inequities (e.g., immigration-related stress, economic hardship)—continue to limit access to sustained support. This study contributes to the growing body of research on culturally tailored, community-based suicide prevention strategies. By centering the voices of Latina adolescents, their families, and providers, it underscores the urgent need for interventions that not only enhance social connectedness across multiple domains but also address the structural and cultural factors that influence mental health outcomes. Findings have implications for the refinement of LIP and the development of broader suicide prevention initiatives that integrate cultural responsiveness, social support, and community engagement
High-Quality Factor Phase Gradient Metasurfaces with Dynamic Reconfigurability
Get PDFFree-space optical communication holds immense potential for surpassing the bandwidth limitations of conventional physically defined channels. Achieving flexible and efficient communication in free space requires compact, robust optical components capable of dynamically tuning laser light without relying on bulky lenses or mechanically driven mirrors. Metasurfaces, or flat optics, have garnered significant attention due to their densely arranged subwavelength nano-resonators, which impart phase, amplitude, and polarization changes comparable to those of traditional bulky optics. However, conventional phase gradient metasurfaces are constrained by passive designs based on geometric configurations and the weak light-matter interaction inherent in most dielectric materials, limiting their applicability in spatial light modulation. Enhancing the resonance within nanoantennas to amplify far-field radiation and strengthen light-matter interaction is crucial for achieving efficient dynamic tunability. This thesis presents a highly tunable, energy-efficient, subwavelength-resolved reconfigurable phase gradient metasurface with a high-quality factor (high-Q). First, we demonstrate a prototypical meta-reflect-array platform functioning as a universal wavefront modulator. This platform achieves arbitrary high-Q resonances and amplified near-field distributions, both theoretically and experimentally. Phase modulation is realized by translating wavelength-dependent phase shifts into highly sensitive, geometry-dependent shifts at resonant wavelengths. A full 2π phase gradient is achieved with less than 2.6% volume fraction variation in a single antenna. The unique circular radiation pattern, dictated by high-Q dipolar guided mode resonance (DGMR), offers a new universal design for wavefront engineering. Additionally, we propose a novel strategy to eliminate strong coupling between high-Q DGMR antennas within the meta-reflect-array, addressing the fundamental trade-off between the Q-factor and antenna spacing. By introducing anisotropic fins between neighboring high-Q antennas, we selectively enhance the weaker longitudinal polarization component, leading to complete destructive interference with the transverse component. This results in total decoupling of neighboring structures, regardless of their spacing or Q-factor, enabling higher resolution wavefront shaping for applications such as low-power LiDAR, compact AR/VR systems, and dense-resonator metasurfaces for nonlinear optics, nonreciprocal devices, biosensing, and laser beam transmission. Building on this universal platform, we explore its programmability using external electrical inputs. A high-Q phase gradient metasurface amplitude display, featuring low energy consumption and high tuning efficiency, is demonstrated. By leveraging the high sensitivity of the antennas to subtle refractive index changes, we integrate resistive heating Ni wires with gates atop the antennas, achieving thermo-optically programmable shifts with less than 5 V. This platform supports integration into portable amplitude displays, compact LiDAR modules, and augmented reality (AR) and virtual reality (VR) glasses, highlighting its relevance for next-generation mobile and wearable optical systems. Finally, we integrate other active thin-film optical materials onto our universal platform to harness their novel optoelectronic properties. Within the strongly enhanced optical field of the high-Q nanoantennas, materials such as single-layer graphene are primarily explored to unlock their full potential in dynamic optoelectronic applications
Essays on Monetary Economics
Get PDFChapter I builds a quantitative New-Keynesian model with a central bank balance sheet, fiscal policy, and a representative financial intermediary facing uncertainty to show how interest rate policy has an additional counter-cyclical fiscal impact via central bank income when reserves are abundant as opposed to scarce. A counterfactual analysis between the US economies with small and large central bank balance sheets of 2005 and 2018 shows that in response to demand, supply, and government spending shocks, the real effects of identical interest rate changes are amplified in the abundant reserves economy. In response to a 1% preference shock, cumulative fluctuations are 4.5% lower in the output gap and 3.4% lower in inflation in the abundant reserves economy. Chapter II utilizes a panel dataset of central bank policy rates to provide evidence for asymmetric interest rate smoothing of central banks globally. During monetary easing interest rate cycles, central banks achieve the terminal policy rate at a faster pace than interest rate tightening cycles. This asymmetry is robust to real side factors in a Taylor type monetary rule. Policy rate cycles for central banks in countries that have higher levels of financial development display a stronger asymmetric smoothing than central banks in countries with less financial development. This finding provides evidence that central banks are cautious during interest rate tightening cycles to avoid financial stability risks which can have real economic impacts
The Role of IL-33 in Itch
Get PDFChronic itch is associated with a broad range of medical conditions, from inflammatory skin diseases to cancer and organ dysfunction, and has a lifetime prevalence of up to 20%. Despite being common and debilitating, the cellular and molecular mechanisms that drive chronic itch are just beginning to be elucidated. Thus, therapeutic options are limited. Recent studies have identified that various pro-inflammatory cytokines can directly activate sensory neurons. This has resulted in mounting interest in the therapeutic potential of inhibiting these neuro-immune interactions. IL-33 is constitutively expressed by epithelial cells, which allows for its rapid release upon tissue damage or stress. Recent studies indicate that IL-33 is important for the development of chronic itch, however, the precise mechanisms by which IL-33 mediates itch remain largely unexplored. IL-33 is a key upstream promoter of type 2 inflammation, and a wide variety of immune cells express its receptor ST2 (IL-33R). Strikingly, IL-33R is also expressed by sensory neurons. Thus, we sought to determine if IL-33R signaling specifically in sensory neurons is necessary for the development of chronic itch in preclinical mouse models of chronic itch. We found that IL-33 is elevated in two different chronic itch conditions in humans, atopic dermatitis (AD) and chronic pruritus of unknown origin (CPUO), as well as their respective mouse models. To evaluate the potential role of neuronal IL-33R in itch, we generated novel mice where IL-33R is conditionally deleted in sensory neurons. Interestingly, sensory neuron-restricted IL-33R signaling was dispensable for itch development in the inflammatory AD-like setting in mice. Instead, neuron-expressed IL-33R was critically required for the development of chronic itch in a mouse model that recapitulates key pathologic features of CPUO. Thus, our findings demonstrate that while the IL-33-IL-33R axis is a known pro-inflammatory axis in AD, it is dispensable at the neuro-immune interface for itch. Rather, neuronal IL-33R emerges as a key regulator of pruriceptor function in the less inflammatory condition CPUO. These findings shed light on the context-dependent nature of IL-33 in driving pathologies like itch as well as current and future trials of anti-IL-33 monoclonal antibody therapies
Novel Therapeutics for Myeloid Malignancies
Get PDFMyeloid malignancies are a group of heterogenous and clonal disorders that include myeloproliferative neoplasms (MPNs) and acute myeloid leukemia (AML). These blood cancers are driven by characteristic oncogenes and propagated by pro-inflammatory niches; MPNs demonstrate hyperactive JAK-STAT signaling and upregulate cytokines including TNF, IL-6, and IL-8. Current therapeutics such as ruxolitinib targeting JAK2 providing symptomatic benefit to MPN patients but are not curative and do not prevent disease transformation to acute myeloid leukemia (AML). As such, greater understanding of the underlying disease biology is still required with means of uncover cancer dependencies. Here, we utilize a series of multi-omic approaches to evaluate novel therapeutics strategies for myeloid malignancies. First, we perform comprehensive profiling of current JAK inhibitors (ruxolitinib, fedratinib, momelotinib, and pacritinib) that are FDA-approved or undergoing phase III clinical trials for MPN patients to help guide the use of specific inhibitors in personalized therapy. Next, we assessed targeting NFκB signaling, another pathway elevated in MPN, via inhibitor pevonedistat as a therapeutic modality for myelofibrosis. Lastly, we uncover a novel DUSP6-RSK1-S6 axis important for MPN disease transformation and RSK1 as a core dependency in myeloid malignancies. Using small molecules including RSK inhibitor PMD-026, currently under evaluation in phase I/Ib clinical trials for breast cancer patients, we demonstrate therapeutic efficacy across a plethora of syngeneic and patient-derived xenograft mouse models of MPN and AML. Together, these efforts establish multiple novel and promising therapeutic strategies for the treatment of myeloid malignancies
Pan-Cancer Universal Targets for Immunotherapy Resulted from Transposable Elements Activation
Get PDFTransposable elements (TEs) contribute to nearly half of the human genome yet have been overlooked in genomic medicines due to the stereotypic impression as “junk DNA” and technical challenges. However, studies in the last two decades demonstrated that dysregulation of the epigenome in cancer cells resurrects the cis-regulatory functions of TEs, including the promoter activities. The tumor-specific TE activation events along with the resulting tumor-specific RNA and protein molecules, provide a potential “gold mine” for identifying tumor-specific therapeutic targets for cancer treatments, including immunotherapy. Here, I present a series of works to investigate the potential of providing actionable, pan-cancer, and inducible immunotherapy targets by leveraging tumor-specific activation of TEs. The first three works validated the universal expression of TE-derived transcripts, and the presentation of the resulting antigens and membrane proteins. More importantly, we demonstrated that these TE-derived proteins are inducible, and the TE-derived antigens can stimulate immune-responses. The fourth work in my thesis focused on investigating the regulatory mechanisms of TP53 on TEs. We demonstrated that despite having an overall repressive effect on TEs, TP53 represses and activates different subsets of TEs in the meantime depending on factors including the presence and the density of TP53 binding motifs in TE sequences. The fifth work in my thesis, as a review, summarized and discussed recent advancements in leveraging TEs for cancer treatments, and technical hurdles for analyzing TEs in genomic medicines. The sixth and the last work in my thesis switched the topic and focused on discussing the role of TEs in maintaining and reshaping the chromatin architecture of the mammalian genomes
Microwave Frequency Probes of Graphene
Get PDFWe investigate high-frequency microwave spectroscopy as a probe of the electronic structure of graphene, focusing on compressibility, magnetic response, and the potential for detection applications. Graphene is a single layer of carbon atoms arranged in a hexagonal lattice, exhibiting unique electronic properties due to its band structure and reduced dimensionality. While traditional compressibility measurements rely on low-frequency capacitance techniques, this thesis explores a high-frequency approach using superconducting resonators to probe the compressibility of graphene in the GHz regime. Originally conceived as a means to examine the diamagnetic response, the method faces fundamental limitations due to resistive losses, revealing key challenges for GHz impedance measurements. Nevertheless, building on this approach, we investigate the potential utility of graphene as a broadband photodetector, leveraging the temperature sensitivity of its impedance. We present numerical calculations, microwave simulations, experimental setups, and improvements in sample fabrication techniques for these measurements. In separate efforts, we summarize contributions made via collaborative work on boron vacancy centers () in isotopically purified hexagonal boron nitride for improved methods of defect quantum sensing with enhanced coherence time and nuclear spin polarization. We also provide an in-depth discussion of relevant fabrication processes and techniques
Advancing Political Science With Machine Learning: A Gaussian Process Approach
Get PDFThe proliferation of data in recent decades including including survey, image and text data, has significantly transformed the landscape of political science research. Machine learning methods have played an instrumental role in analyzing these datasets, yet their application poses challenges in areas where political concepts are not directly measurable or the primary focus of inference is causality. In addition, the essence of machine learning algorithms being trained for prediction performance in a black-boxed manner, makes their outputs hardly interpretable and even unappreciated. This dissertation addresses these issues by proposing a novel methodological framework that employs Gaussian Process (GP) models, a non-parametric Bayesian approach that combines the flexibility to model complex, non-linear relationships and interpretability necessary for causal inference. Through a series of advancement in latent variable measurement, causal inference, prediction and experimental design models, this dissertation demonstrates the effectiveness of the GP framework in addressing core quantitative challenges in political science, thereby advancing the methodological toolkit available to researchers in the field