26,176 research outputs found

    Anti-Corruption, the Mass Media, and Political Support in China

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    How do anti-corruption campaigns affect public support and elites' behavior in non-democracies, and what roles are played by mass media in this process? This dissertation aims to answer these questions through three empirical chapters on anti-corruption campaigns in China. First, by analyzing nationwide panel surveys, I demonstrate that public political trust is influenced by how the state communicates anti-corruption campaigns to the public via mass media. Information that exposes citizens to instances of local government misconduct diminishes public trust in local governments. Conversely, information highlighting local governments' commitment to rectifying corrupt practices enhances citizens' political trust. Second, I further explore the spatial spillover effects of corruption investigations during anti-corruption campaigns. Empirical findings reveal that corruption investigations in neighboring provinces have a positive spillover impact on public trust in their own local government. In the third study, I shift my focus from the impact of anti-corruption enforcement on the masses to the political elites' behavior during the campaigns. Through an analysis of news reports concerning high-level corruption cases in major official newspapers, I observe that media coverage of (anti)corruption serves as a channel for political elites in China to send political signals to each other. By strategically giving more media attention to high-ranking corrupt officials, who are connected to the top leader's potential rivals, the central top leader demonstrates his/her capability to concentrate power, and local leaders, in turn, signal loyalty to this top leader. Together, these results enhance our understanding of the interactions between anti-corruption campaigns and mass media in China and their impacts on the opinions and behaviors of both the masses and elites

    Pancreatic Islet Microphysiology System for Disease Modeling of Type 2 Diabetes

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    Diabetes has become an increasingly prominent global issue afflicting 10% of world’s population where type 2 diabetes (T2D) comprises the majority of those diagnosed. T2D correlates with a toxic bioenvironment that leads to the body’s inability to properly self-regulate blood glucose due to the dysfunction of insulin producing pancreatic islets. While the outcome of T2D is well recognized, the pathogenesis of the disease still requires greater understanding with most studied mechanisms having a non-human basis. Additionally, current disease models fail to fully replicate disease conditions for drug testing leading to only 10% success in clinical trials. To bridge the gap and more accurately replicate human disease, microphysiological systems (MPS) have risen as a viable alternative since they combine microfluidics and tissue engineering to mimic the in vivo micro-environment. This dissertation focuses on the development of an islet-MPS that utilizes both primary and stem cell-derive tissue to simulate the pathogenesis and drug testing for T2D. In this pursuit, we developed the pancreatic islet (PANIS) system that was able to sustain a healthy islet environment with glucose sensitive insulin secretion from both primary and stem cell-derived islets for more than two weeks. Disease induction was tested by subjecting the primary islet PANIS to toxic conditions correlated with T2D, such as hyperglycemia and/or high free fatty acids (lipotoxicity). The effects of these combinations were studied thoroughly using viability and functionality assays along with RNA sequencing to determine how each toxic factor affected islets and what toxic condition would most closely replicate T2D. This toxic conditioned islet MPS was able to test drug efficacy with dose dependent trials using the anti-oxidant drug, Resveratrol. Additionally, innate immune cell interactions were studied by co-culturing the primary and stem cell-derived islets with neutrophils while simulating disease conditions. The result of this dissertation ultimately provides a robust islet MPS that can be used to model islet-specific disease induction and drug treatment with the capabilities for personalized medicine using human stem cells

    Multi-Scale Spatiotemporal Neural Computation: On the relationship between dynamical attractors, spiking neural networks, and convolutional neural circuits

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    Understanding spatiotemporal neural dynamics and developing biologically-inspired artificial neural networks remain open challenges in computational neuroscience. Critical gaps persist in elucidating cortical rhythms, memory consolidation, and biological networks' remarkable spatiotemporal processing capabilities. This dissertation hypothesizes that asymmetric connectivity and dedicated fast-slow processing pathways in neural systems enhance depth, robustness, and versatility in handling complex spatiotemporal patterns. Our first contribution is elucidating how neurons communicate and synchronize activity via temporally precise spikes by examining the dynamics of spike-coding networks. Developing models of cortical neural oscillators reveal the origins of spontaneous transitions between active and silent states underlying slow-wave sleep rhythms, demonstrating how the intricate balance of excitation and inhibition orchestrates these oscillations. Our second is to establish a mathematical equivalence between Hopfield networks' associative memory models and spike-coding networks by showing that fast and slow asymmetric connectivity weights induce equivalent cyclic attractor dynamics in both systems. Introducing asymmetric weights in slow connections enables both models to learn and generate complex temporal firing sequences, transitioning between quasi-attractor states representing stored memories. Simulations demonstrate the efficacy of spike-coding networks for encoding and retrieving temporal sequences while performing the n-back working memory task. Our third contribution is to harness the potential of generative adversarial networks for unpaired cross-modality translation from 3 Tesla to 7 Tesla magnetic resonance imaging. We propose a fast-slow convolutional network architecture to enhance translation performance by balancing local and global information processing. This dissertation makes significant contributions by elucidating brain mechanisms underlying rhythms and memory, and unifying foundational computational frameworks while extracting principles to improve artificial neural network design

    Tools for Academic Advisors Based on Existing University Student Data

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    Universities already maintain vast stores of student data, but such data are underutilized and unstructured. Typical student data analytics approaches maintain their focus on unstructured data and create tools that give predictions without the structured context the data came from, requiring users to recontextualize the results to be able to diagnose issues and perform interventions. Academic advisors are no different: despite being stewards of the programs they advise for, it is impossible for them to know every concept from every course, let alone understand the relationships between them. Furthermore, it is just as impossible for advisors to know every student and be able to determine what recommendations may be better for current students that are similar to other historical groups of students. I propose reintroducing structured context back into the data flow, with the goal of providing advisors with easy-to-interpret tools that provide data-driven insights. I first show that structured student schedule and grade data can uncover new observations when using existing techniques for student grade prediction, specifically that instructors have a larger than anticipated effect on student grades. With the knowledge that structured data can lead to useful information, I then developed two tools that utilize structured data at different granularities that cater to advisors: (a) StudentPaths, for unsupervised machine learning insights into student performance and scheduling at the course level, and (b) Concept Progression Maps, for insights into student performance within a course at the concept level. These tools were developed and utilized in a single-blind study with academic advisors and students, where only advisors had access to the tools and information. From the studies, I found that despite the challenges that advisors had with the tools, the information derived from these tools can change the conversational dynamic in academic advising sessions and demonstrate that these changes have the potential to make a positive impact on future student performance in their courses

    Spectrum and Decay of Hybrid Mesons in a Constituent Gluon Model

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    For fifty years, Quantum Chromodynamics (QCD) has been accepted as the fundamental theory of strong interactions. The confirmation of the theory took place with the discovery of asymptotic freedom in 1973 and it now represents one of the crowning jewels of the current standard model of particle physics. However, a thorough understanding of strong interactions in the low-energy regime is still out of reach. We still do not have an analytic description of confinement and many of the properties of hadrons remain enigmatic. The need for phenomenological models is therefore apparent. In this work we develop a constituent gluon model of hybrid mesons based on QCD in Coulomb Gauge. This thesis is organized as follows. In Chapter 1 we give a brief introduction to the Standard Model of particle physics and its symmetries. We also discuss the SU(3) structure of QCD and provide the details of the QCD Hamiltonian in Coulomb Gauge, which forms the basis for our model. In Chapter 2 we extensively discuss the properties of conventional and hybrid mesons, starting with the basic properties of quarks. We then give an overview of constituent quark models, mainly non-relativistic models. Prior hybrid models are also covered in this chapter. An extensive overview of hybrid models deserves much more space that is given to it in this thesis. Here, our main goal is to present the various approaches of describing hybrids (bag models, relativistic string models, flux tube models, and constituent gluon models) and see how they they compare to lattice data and experiment. We close the chapter with a discussion of hybrid decay models. The details of our model, including the construction of the hybrid state and the Hamiltonian employed are presented in Chapter 3. This chapter also contains the results for the spin-averaged spectrum of charmonium hybrids, which is then compared to the large body of lattice data. A short section with heavy quarkonium results is also included. Hybrid decays of charmonium and light hybrids are presented in Chapter 4 and Chapter 5, respectively. Conclusions are contained in Chapter 6

    Synthesis of Oxidatively Cleavable Cyclic Nucleotide Monophosphate Prodrugs

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    Cyclic dinucleotides are potential cancer therapeutics due to their function as agonists of stimulator of interferon genes (STING), which plays an important role in innate immune signaling processes. However, cyclic dinucleotides therapeutic uses are limited due to a few factors, including poor bioavailability and undesired off-target effects, such as non-selective STING activation leading to autoimmune diseases. To mitigate these issues, we envisioned synthesizing vinyl boronate-masked prodrugs of cyclic dinucleotides that would selectively release in conditions that mimic oxidative stress. Unfortunately, efforts towards these prodrugs were unsuccessful due to issues with purification. Cyclic nucleotide monophosphates, such as 3’,5’-cyclic adenosine monophosphate (cAMP) and 3’,5’-cyclic guanosine monophosphate (cGMP) are known as secondary messengers that regulate various cellular functions, such as memory, metabolism, gene regulation, and immunity. Additionally, many prodrugs have been developed using cyclic nucleotide monophosphates that release biologically active molecules, such as anti-hepatitis C and anti-cancer properties. We envisioned developing prodrugs of cyclic nucleotide monophosphates tagged with a vinyl-boronate handle that could selectively release the active cyclic nucleotide monophosphate when exposed to increased levels of hydrogen peroxide. Cells exhibit higher levels of hydrogen peroxide when under oxidative stress. Synthetic efforts successfully led to the formation of a boronate-containing 3’,5’-cyclic thymidine nucleotide monophosphate that selectively releases when exposed to conditions that mimic oxidative stress. The development of a boronate-containing cyclic nucleotide monophosphate allows for the opportunity to further develop prodrugs that can selectively target diseased cells

    Unraveling Ocular Tissue Biomechanics: Characterizing Collagen Microstructural Features and Introducing Direct Fiber Modeling

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    Glaucoma, a progressive optic neuropathy, is primarily associated with elevated intraocular pressure (IOP) and mechanical insult on ocular tissues. The optic nerve head (ONH) and lamina cribrosa (LC) are particularly vulnerable to early nerve damage, while the sclera surrounding the ONH, provides essential mechanical support and stability. Understanding the biomechanics of these ocular structures is crucial for unraveling the mechanisms of glaucoma. The biomechanics of ONH, LC and sclera are intricately connected to their collagen microstructure, necessitating a comprehensive understanding of tissue microstructures and their associated biomechanical properties. However, despite extensive work, several microstructural characteristics have not been considered carefully, largely due to limitations in visualization tools. Furthermore, current modeling approaches in ocular biomechanics often neglect potentially critical fiber characteristics, limiting their ability to describe tissue structure and mechanics, particularly at fiber-level scales. Using advanced imaging techniques like polarized light microscopy, this dissertation characterizes two critical microstructural features that have been ignored: LC insertions and the in-depth collagen fiber organization of corneoscleral shell. We characterized the spatial variations of LC insertions and variations of insertions among species. The discrete insertions of the LC beam indicate that the interaction between LC and the surrounding load-bearing tissue is discontinuous, leading to localized force concentrations. Diverse insertion shapes suggest varying robustness in the LC periphery, potentially influencing susceptibility to glaucomatous damage. We also present a detailed analysis of the in-depth organization of collagen fibers within the corneoscleral shell for a better characterization of the complex three-dimensional collagen architecture. This, in turn, will enhance the understanding of the out-of-plane tissue mechanical properties. Leveraging detailed information available from the imaging, this project proposes and validates a novel direct fiber modeling approach that represents fibrous microstructure, accounting for fiber interweaving, interactions, and specimen-specific collagen architecture. The proposed model replicates anisotropic mechanical behavior observed in different loading protocols and across different samples, providing unprecedented details on fiber-level tissue behavior. In conclusion, this dissertation uncovers crucial microstructural features of ocular tissue and introduces an innovative direct fiber modeling technique. The findings contribute to a deeper understanding of ocular tissue microstructure and biomechanics, advancing our knowledge of glaucoma pathogenesis

    Enantio- and regioselective propargylic and allylic functionalization

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    The rapid development of enantioselective allylic or propargylic C − H functionalization methods offers great opportunities for introducing functional groups to readily available starting materials in a stereodefined manner. In this dissertation, we synthesized N-heterocyclic molecules from simple alkynes with good regioselectivity utilizing stoichiometric Fe intermediates. The achievement of highly enantioselective allylic functionalization of non-activated alkenes was realized through the synergistic combination of Fe and Ir catalysts. In addition, we systematically investigated the enantioselective propargylic functionalization with Ir catalysis, thereby expanding the scope pf propargylic functionalization. A wide range of alkynes underwent successful propargylic allylation and silylation, enabling the formation of 1,5-dienes, propargylsilanes and allenylsilanes with good enantioselective and stereoselective control

    Examining the relationships between psychosocial stress exposure, glucocorticoid resistance in immune cells and cardiometabolic risk

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    The present study examined the relationships between chronic stress, glucocorticoid resistance in immune cells and cardiometabolic risk factors. 247 adults completed measures of psychological stress and underwent a blood draw and anthropometric assessment. Glucocorticoid resistance was measured as the concentration of dexamethasone required to reduce IL-6 concentration in half after mitogen exposure, and cardiometabolic risk consisted of a composite score of blood pressure, triglycerides, glucose scores, HDL, and waist circumference. Linear regression analyses revealed largely null results. Stress was not found to be associated with glucocorticoid resistance or cardiometabolic risk across 3 of 4 measures of psychological stress. A small, negative association between a novel measure of chronic stress and glucocorticoid resistance was observed. Results suggest need for further research into the conditions under which stress alters immune and cardiometabolic functioning

    Assessing COVID-19 Transmission Risk: Roommate and Unit Mate Exposures at an Inpatient Behavioral Health Facility

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    Abstract/Objective: The COVID-19 pandemic caused an upsurge in demand for inpatient mental healthcare. Inpatient psychiatric facilities have an inherently increased risk of infection transmission and a heightened possibility of adverse outcomes among patients. This study aimed to evaluate the impact of roommate and unit mate exposures on COVID-19 transmission by index cases to inform on the safest isolation practices at an inpatient behavioral health facility. Methods: This retrospective study evaluated post-exposure COVID-19 test results for roommates and unit mates of patient index cases from July 2020 through August 2023. Electronic health records provided demographics, roommate assignments, unit locations, and testing results. Only inpatient test results recorded during the expected testing timeframe were included in this study. Units and index case exposures where no roommate exposure occurred and exposures with test refusals or discharges before testing during the study period were excluded. Contingency tables were created, stratified by unit location, to display post-exposure test results and relation to the index case. Fisher’s exact or Chi-square tests were conducted for each unit separately. Results: During the 38-month study period, comprising 139,194 patient days, there were 208 COVID-19 positive patient index cases resulting in 2,594 total exposure events. The conversion rate overall was 10.05%, 24.4% for roommates, and 9.3% for unit mates, with an infection odds ratio of 3.14 (1.42, 6.92). For the unit-stratified analysis, 5 of the 6 included units had higher conversion rates among roommates, with a range of differences in conversion rates ranging from -5.6% to 34.2%. The difference in conversion rates was significant in one unit (Unit K) where the conversion rate for roommates was 33.3% and 7.3% for unit mates (Chi-squared p-value <.0001). Conclusion: Roommates were observed to have a higher frequency of conversion that was statistically significant in one hospital unit that primarily treats older adults. Additional exposure event observations are needed to ascertain whether this pattern persists across other units, improving the generalizability of the study. Hospital-wide, those who tested positive post-exposure were likelier to be a roommate. Understanding how to care for the greatest number of patients possible safely is of utmost public health importance
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