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Genetic polymorphisms of inflammatory disease: periodontal disease and temporomandibular disorders
No full textIntroduction: The impact of genetic variations in inflammatory disease predisposition is not yet fully understood. Associations of genetic polymorphisms reported to play a role in systemic inflammatory diseases may serve as proxies to assess predisposition for oral inflammatory diseases, as well as identify biomarkers to support preventive measures and targeted therapies. Hence, the goal of this study was to assess if genetic variants previously associated with systemic inflammation are associated with temporomandibular disorders (TMD) and periodontal disease (PD).
Methods: Queried repository records identified dental phenotypes (TMD/PD) and systemic inflammatory disease (asthma, obesity, rheumatoid arthritis/autoimmune disease, and type II diabetes mellitus; singular group based upon their shared inflammatory characteristic). Combinations of with and/or without systemic disease, TMD, and PD formed four groups. Single nucleotide polymorphisms (SNPs) in 15 genes (BRINP3, IL10, IL1B, GSK3B, IL4, IL17A, CA9, WNT11, MYO1H, AQP5, ADAM10, MMP2, AXIN2, TGFB1, and MMP9) were selected for TaqMan chemistry genotyping. Genotypic and allelic association tests were performed to identify associations between each SNP and phenotypes of interest using PLINK. Bonferroni correction was applied (=0.001) to denote statistical significance. Logistic regression analyses were conducted to identify associations between systemic and dental disease phenotypes.
Results: Genotypic and/or allelic associations were observed between SNPs in MMP9 with systemic disease phenotypes (asthma, obesity, rheumatoid arthritis/autoimmune disease, and type II diabetes mellitus) without oral disease phenotypes (TMD-, PD-). For Group 2, the same systemic disease phenotypes with signs and symptoms of TMD (TMD+, PD-) are genotypically and allelically associated with SNPs in AXIN2 and MMP9. In Group 3, MMP9 was associated with the systemic disease phenotypes in the presence of periodontal disease, without TMD (TMD-, PD+). In Group 4, an allelic association was found between the SNP in AXIN2 with the systemic disease phenotypes including both TMD and PD positive phenotypes. Logistic Regression analyses did not find associations between all systemic and oral disease phenotype tests after controlling for age and sex at birth.
Conclusion: This study showed that SNPs associated with systemic inflammation were also associated with oral inflammatory diseases. These SNPs may be considered additional markers of oral inflammatory disease
Synthesis, Structure and Properties of Copper Selenide Based Nanoheterostructures
No full textThis dissertation highlights the design of colloidal metal-semiconductor heterostructures, starting from plasmonic, cost-effective and earth abundant semiconductor copper selenide nanoparticles. We first discuss the impact of reaction-insensitive parameter of lattice distortion of copper selenide, on the synthesis of binary metal-semiconductor NHSs, starting from copper selenide nanoparticles (Chapter 2). These results showed us that a system specific design rule can be established for the post-synthetic modification of copper-selenide nanoparticles, to predict if a metal-semiconductor interface will be formed or if competitive cation exchange reactions and metal diffusion would generate multi-metallic semiconductor nanoparticles. Next, we demonstrate how the design rule based on the reaction-insensitive lattice distortion parameter is preserved for the ternary and quaternary metal-metal chalcogenide syntheses; with some heterogeneities in the morphology of complex heterostructures formed (Chapter 3). Taken together, the work in these two chapters give us a basis for designing complex heterostructures, which are important for the downstream applications and synergistic properties discussed in Chapter 1. We then studied the effect of surface ligand on the morphology of metal deposition of gold and platinum on the surface of copper selenide. We found the deposition morphology is uniquely insensitive to several characteristics of the surface chemistry of copper selenide, except for the cooperative dissociation of quaternary ammonium bromide ligands from the NP surface (Chapter 4). Lastly, we established some fundamental plasmonic parameters of copper selenide nanoparticles with various capping ligands, such as extinction coefficient and refractive index sensitivity (Chapter 5)
Abnormal Tau Interactions in Neurodegenerative Disease
No full textMany neurodegenerative disease-related proteins, including Tau and TDP-43, self-assemble into diverse polymeric structures and liquid-like protein phases. These protein phases can organize functionally related proteins, nucleic acids, and biomolecules. These dynamic biomolecular phase transitions give rise to various multicomponent coexisting phases with varying material states known as biomolecular condensates. The most common form of dementia, Alzheimer's disease (AD), as well as >20 other dementias, termed tauopathies, are pathologically defined by insoluble aggregates of the microtubule-associated protein tau (MAPT). Tauopathies represent a wide range of distinct neurodegenerative diseases that display altered expression, mislocalization, and tau protein aggregation as prominent neuropathological features. Although tau aggregation correlates well with AD symptomology, the specific tau species, i.e., monomers, soluble oligomers, and insoluble aggregates that induce neurotoxicity, are incompletely understood. Chapter 2 of this dissertation aims to address some of these questions and overcome the traditional challenges of modeling cellular Tau phase transitions. We develop an optogenetic system (optoTau) that allows light-selective induction of wild-type tau aggregation in cells. Specifically, we created a light-responsive tau protein (optoTAU) and used viscosity-sensitive AggFluor probes to investigate tau aggregation's consequence(s) in human neurons. Furthermore, we also provide proof-of-concept for optoTAU as a pharmacological platform to identify modifiers of tau aggregation.
RNA dysregulation has emerged as a potential mechanism mediating tau-driven neurodegeneration. In Chapter 3, we aim to expand on current knowledge regarding abnormal Tau and RNA-binding protein (RBP) interactions, specifically Tau and TDP-43. Dysfunctional RNA metabolism associated with a loss of TDP-43 function is considered central to amyotrophic lateral sclerosis (ALS) pathogenesis. Interestingly, hallmarks of TDP-43 pathology are observed to be associated with Tau pathology in diseases including Alzheimer's disease (AD), frontotemporal dementia (FTD), and limbic-predominant age-related TDP-43 encephalopathy (LATE). However, a potential pathologic interplay between Tau and TDP-43 and how Tau may mediate RNA dysregulation across these diseases are poorly understood. Alterations in the abundance, localization, and condensation of protein/RNA complexes, or biomolecular condensates, have been shown to modulate the activity of RBPs, including TDP-43. Therefore, we hypothesized that Tau may act as a modifier of TDP-43 localization, protein interaction networks, and function and thus explored potential cellular mechanisms linking Tau and TDP-43
Combining Quantum Mechanical Calculations with Machine Learning and Genetic Algorithms for the Design of Better Materials
No full textIn the past, the discovery process of new materials was done mainly through trial and error, which was time-consuming and expensive. However, computational simulations and models can quickly filter through a large number of potential candidates and narrow down the search space efficiently and cost-effectively. For example, this process can help identify new electronic materials that use less energy or have novel properties that open the door for new applications and find life-saving drugs for hard-to-cure or rare diseases.
In this work, we present how the combination of several of these computational techniques, namely quantum mechanical (QM) calculations with machine learning (ML) and Genetic Algorithms (GA), can help accelerate the discovery of new materials. We have used GFN2-xTB throughout this work because it has a good balance of accuracy and speed and shows how it can be used as a surrogate for the more costly density functional theory (DFT) and how it can be used to generate molecular features for ML applications.
Three different molecular properties were selected to show how the combination of QM with ML and GAs is greater than the sum of its parts. First, we used GFN2-xTB to calculate geometrical features for a random forest ML algorithm to identify new thiophene-based pi-conjugated polymers with low reorganization energies, achieving a RMSE of 0.036 eV and a speed-up of ~13x over DFT. Second, we used GFN2-xTB calculations in a GA to help identify novel pi-conjugated polymers with stable triplet ground states, finding more than 1,400 potential candidates. Finally, we present QupKake, a graph-neural-networks based ML model that used GFN2-xTB calculated features to predict the micro-pka of drug-like molecules, achieving a improvement over existing models
Synthetic Routes Approaching Functional Micro-Block Copolymers of γ-Substituted ε-Caprolactones
No full textDescribed herein is a generalizable process towards the synthesis micro-block copolymers of γ-substituted ε-caprolactone moieties with reliable block alternation based on thermodynamic preferences in olefin metathesis. These micro-block copolymeric materials, containing oligomeric segments of multiple repeat unit types, inhabit a unique property space and can be used to probe the interface between alternating-like and block-like copolymer bulk behavior when synthesized at scale. The limitations of functional pendant groups are explored, demonstrating that the strategy is limited by both the solubility of the pendant group and its tolerance towards the anionic ring opening strategy employed for chain growth. Based on findings that pendant ester groups are most reactive towards anionic ring opening methods, the major product— chains terminated by an annulation event that occurs via intramolecular transesterification— is thus characterized and the mechanism used to inform alternate potential catalytic approaches. The product distribution of an aluminum salen catalyst with more sterically precluded coordination sites is found to slightly favor the linear product as opposed to its annulated counterpart, more so than is evidenced in the traditional aluminum alkoxide catalyzed oligomerization of the same γ-ester substituted ε-caprolactone monomer. While this change in preference is modest, such an improvement is indicative of potential options for attainable further improvements in the selectivity and tolerance in anionic ring-opening polymerizations of lactides, potentially providing a plethora of options for bespoke functionalized biocompatible polymer materials
Supporting Students Emotionally and Promoting Activism During Climate Change Lessons
No full textClimate change poses an immediate threat to global society. Science educators highlight how human activity, primarily fossil fuel consumption and certain types of agriculture, lead to the rapid warming trends observed over the past one-hundred years. Despite the immediacy of this environmental crisis, the beliefs and emotions of students can make effective teaching of climate science a contentious endeavor. Climate denialism, political influences, impending existential threats, and frustration with the inaction of older generations make navigating the topic of climate change a challenge for teachers.
Gaps in climate literacy contribute to skepticism, the propagation of pseudoscience, and societal inertia towards accepting scientific consensus. Addressing these challenges requires integrating robust sustainability and environmental geoscience education into curricula, aiming to cultivate both scientific literacy among the public and leadership capable of informed decision-making for future generations.
This dissertation focuses on the mental health dimensions of climate change education, acknowledging how learning about its causes and solutions can evoke anxiety and negative emotional responses among students. Acknowledging and addressing these psychological impacts is crucial for developing effective educational strategies and garnering broader public support for climate action. This study emphasizes the urgent need for comprehensive approaches that integrate scientific understanding with emotionally responsive teaching and critical pedagogy to foster informed engagement and sustainable solutions in addressing the climate crisis
Engineering endothelialized human kidney organoids for studying kidney injury and HDAC8 targeted therapy
No full textThere is an extreme need to develop representative in vitro models of organs to understand human disease and develop therapeutics. There is no greater need than in the setting of kidney disease, which effects ~850M people annually. Patient derived kidney organoids derived from induced pluripotent stem cells have risen as an attractive model of the kidney for their high throughput nature, patient specificity, complex morphology, and multicellular heterogeneity. Current kidney organoids, however, lack sufficient vascularization, maturity, and certain physiologically relevant cell types. Here, we advanced in vitro kidney organoid models by developing an inducible endothelial stem cell line. This inducible endothelial line generates a heterogenous population of endothelial progenitor cells. When combined to a prior developed kidney organoid protocol, the resulting kidney organoid is highly vascularized with kidney-specific endothelial cells, contains mature epithelial cell populations, and the emergence of renin+ cells. We then utilized this kidney organoid model to understand mechanisms of kidney disease and drug targeting. Based on evidence demonstrating histone deacetylase 8 (HDAC8) as a regulator of kidney injury and cancer mesenchymal transition, we aimed to understand the role HDAC8 plays in kidney disease regulating dedifferentiation. We found that in hemin injured kidney organoids HDAC8 knockout protected against DNA damage and loss of epithelial cell state globally. We then found that in a glomerular toxicity setting, doxorubicin caused dedifferentiation of podocytes and in endothelial cells, subsequent mesenchymal transition. We then found that with HDAC8 inhibitor, PCI-34051, there was preservation of epithelial cell state and prevention of downstream fibrosis and vascular rarefaction. Based on this data, we demonstrated a method for vascularizing human kidney organoids to enhance maturity, as well as a case study in understanding mechanisms of kidney disease and the role HDAC8 plays
Frontiers of Information and Platform Design in Operations Management
No full textThis dissertation revolves around the intricate domains of information and mechanism design in operations management, presented in two essays. The first essay delves into the concerns surrounding customer apprehensions about price discrimination and explores the optimal utilization of information to alleviate such concerns. Employing a correlated Bayesian persuasion framework, we uncover the conditions under which a binary inventory signal complements pricing strategies, concurrently enhancing firm revenue and customer welfare. In the second essay, we study the design of rating platforms in the presence of disconfirmation effects, i.e., when customers incorporate their prior belief of the product into their ratings. The study elucidates the pivotal role of reference effects in shaping rating convergence to true product quality.
In the first essay, we focus on the strategic information transmission between firms and strategic customers. Customers have heterogeneous valuations for the products and services, and firms use various price discrimination tactics where they charge different prices to different customers. This practice, i.e., customizing the price for individual customers, is known as personalized pricing (PP) and is implemented in various industries. We investigate whether pricing can informatively signal PP to customers and how firms should adjust pricing strategies in response to customer reactions. We also investigate whether disclosing inventory information can benefit firms and customers, ultimately advocating for increased transparency in PP practices. By modeling dynamic interactions between firms and a continuum of heterogeneous strategic customers over two periods, we unveil nuanced insights. We find that firms reduce the first-period price to persuade high-valuation customers to purchase in the first period, even when they do not intend to implement PP. This is because the mere presence of PP risk makes customers reluctant to reveal their identity. The firm then must “compensate” customers to persuade them to reveal their valuations. We show that the price alone cannot perfectly signal the firm’s PP intention when the firm takes the strategic customer behavior into account. We next consider whether customers can infer the implementation of PP from the inventory availability information. To study this, we focus on a class of binary signals where the firm marks the inventory low when it is below a threshold. Such inventory signals are commonly adopted by retailers such as IKEA and ZARA. We show that an inventory signal can improve the firm revenue only when customers believe the firm conducts PP with a sufficiently low probability. In this case, an inventory signal alleviates customer PP concerns and allows the firm to set higher prices. Additionally, we demonstrate that disclosing inventory availability information is a strategic complement to the prices when alleviating the customer PP concerns. Furthermore, an inventory signal, in addition to the firm, can benefit all customers. With the growing interest in PP regulation, requiring firms to disclose inventory availability information could be a viable policy to make PP more transparent and credibly reduce customer concerns.
In the second essay, we study the customers’ social learning problem upon observing the product ratings. Customers and platforms increasingly rely on online ratings to assess the quality of products and services. However, customer ratings are susceptible to various biases. Disconfirmation bias is a specific form where customers incorporate the discrepancy between their prior expectations and post purchase experiences into their ratings. We study the asymptotic behavior of ratings in the presence of disconfirmation bias in three rating systems: (i) complete system, where customers observe the entire rating history; (ii) aggregate system, where only the frequency of each rating option is available; and (iii) average ratings, where customers solely use the average of past ratings. Customers are Bayesian and update their quality beliefs upon observing the ratings. After experiencing the product, they rate it according to their heterogeneous ex-post utility and disconfirmation bias. In complete and aggregate systems, we show that customer beliefs converge to the intrinsic quality when disconfirmation bias is small. When this bias is large, there will be a discrepancy between converged beliefs and the intrinsic quality, although this discrepancy could be arbitrarily small. When the disconfirmation bias is intermediate, beliefs may diverge significantly from the intrinsic quality or not converge. However, we establish that the platform can guarantee correct learning by designing a sufficiently granular rating system, i.e., a system with more rating options. We confirm all these results in the system with average ratings, albeit with a bias-correcting rule. Finally, we characterize the learning speed in the aggregate system.
In summary, this thesis contributes to the literature on the interface of information, platform, and mechanism design in operations management, unraveling the intricacies of pricing strategies, information revelation mechanisms, and rating platforms
Characterization, Modeling, And Design Of Microcavity Exciton-Polariton Samples
No full textExciton-polaritons are particles combining the aspects of solid matter with light. They interact and scatter off one another, while also having a very light effective mass. They thermalize well, but are also bosons, meaning they can condense into Bose-Einstein condensates at much higher temperatures than many other systems. They are typically generated in semiconductor structures grown in extremely precise molecular beam epitaxy machines. Very high quality samples are commonplace, and long polariton lifetimes over 100 ps are observed.
However, when the samples are of such high quality, the polaritons may no longer be easily observed in reflectivity measurements, because they have surpassed the resolution of normal laboratory spectrometers. Also, one of the two polariton states, known as the upper polariton, is no longer visible in standard photoluminescence measurements, because its decay is dominated by down-scattering into lower polariton states. This has been a consistent problem for calculating the exciton fraction, which is an essential parameter in many-body theories that include the polariton-polariton interactions.
In this work, I present my work on accurate calculation of the exciton fraction of polaritons in semiconductor microcavities. This work includes redesign of several experiments, creation of a photoluminescence excitation measurement setup, which allows detection of of the upper polariton, and simulation of the electromagnetic properties of polariton microcavity structures using the transfer-matrix method. These simulations show great success, and may be used for designing future samples. Additionally, I present work on cavity structures with transition-metal dichalcogenide monolayers, as a means towards room temperature polariton physics
The Layered World
No full textWe’re surrounded by medium-sized dry goods: tables, rocks, cats, etc. At higher levels or larger scales, there are planets and galaxies. At lower or more fundamental levels, there are molecules, atoms, and electrons. How are these different things, or different levels of descriptions, related to one another? What can we know about tables from studying the theories of particles? Can the former be reduced to the latter? Questions like these concern inter-level relations (that is, relations between certain aspects of the world at different levels or scales) as well as inter-theoretic relations. This dissertation uses examples to demonstrate how taking inter-level relations into account affects our philosophical understanding of the physical world. It focuses on three particular aspects: (1) ontology and theory, (2) fundamentality, and (3) laws of nature.
Chapter 1 draws on a case study from statistical mechanics to argue for the significance of drawing a distinction between two ways in which ontological and inter-theoretic reduction can be related. Chapter 2 addresses a problem of proposals that aim to draw the fundamental ontology of the world while focusing on non-fundamental theories (such as nonrelativistic quantum particle mechanics). Chapter 3 examines prominent accounts of laws that focus on the fundamental laws (Lewis’ Best System Account and Loewer’s Package Deal Account), and argues that they are at odds with scientific realism