18525 research outputs found
Sort by
Changes in the Assimilation of Asian Americans from 1860–1940
No full textEconomics Department Honors Thesis.Asian immigration to the United States motivated the first instance of federal immigration legislation with the Chinese Exclusion Act of 1882, but little is known about Asian immigration during the period despite a robust literature on their European counterparts. I use linked cohorts drawn from complete-count census data to find that Asian immigrants displayed the “u-shaped” pattern of occupational assimilation characterizing contemporaneous European immigrants. I also find that they displayed a “catch-up” assimilation phenomenon: successive Asian cohorts steadily reduced their outcome gaps with the native population, and despite starting at a lower occupational tier than European immigrants, they assimilated more than European immigrants in all cohorts but the post-Exclusion cohort of 1880–1900. These findings provide insight into the assimilation process of an understudied immigrant community, furthering the understanding of assimilation in the United States.College of Arts and ScienceEconomic
Overcoming Challenges with Real World Data and Clinical Restrictions in Pharmacokinetic Analyses
No full textReal world observational pharmacokinetic (PK) data, e.g., from Electronic Health Records or measurements collected during routine care, require additional analysis considerations relative to traditional PK trials. These data are more likely to be sparse, imbalanced, or error-prone, and the practical limitations must be considered to implement methods in a clinical setting. In this dissertation, we developed and investigated methodologies to improve studies conducted using real world PK data. First, we developed a natural language processing algorithm medExtractR which targets the extraction of dose information from free-text clinical notes for a specific medication of interest. Contrasted with existing general-purpose medication extraction algorithms, medExtractR was able to achieve better entity-level extraction, and maintained high performance on an external validation set with tuning and customization. Next, we examined an approximate Bayesian model for individualized PK estimation via a simulation study with opportunistic concentration measurements. The intended for therapeutic drug monitoring necessitated approximation methods that were both accurate and computationally efficient. We evaluated the accuracy of uncertainty estimates and implemented the methods into easy-to-use web interface. Finally, we addressed the issue of bias in estimation as a result of time recording errors (TREs) in PK data by evaluating various modifications to study design and data collection. We considered pragmatic strategies minimally invasive to a clinical workflow that could be implemented in an opportunistic data setting. Mitigation strategies included delaying timing of blood draws to non-infusion periods, selecting future draw times based on minimizing bias from simulated errors, and identifying patients whose existing measurements were most sensitive to TREs. For an intravenously infused drug, we found that these strategies reduced bias in estimation of a pharmacodynamic endpoint more for dosing schedules with rapid infusions than those with slower infusions
Think Smaller: Understanding Selenium Pathways in Metal Selenide Nanocrystal Synthesis
No full textAs nanochemistry grows as a field and the applications of nanomaterials emerge, the importance of understanding what happens in the reaction flask during synthesis—thinking smaller than the nanoscale level to the molecular level—has never been more important. Being able to track how the precursors are decomposing and affecting the resulting metal chalcogenide phase leads to more predictable and reproducible nanoparticle syntheses. The following dissertation outlines three mechanistic studies tracking selenium in the production of metal selenide nanoparticles. Diselenides were thoroughly investigated as they are important precursors in synthesizing metstable phases in nanochemistry. First, five hypotheses were explored for why diselenides experience much larger thermal movement in their chemical shift compared to monoselenides or selenols, including 1) rapid equilibrium to an isomer, 2) temperature dependent solute-solvent interactions, 3) shielding, 4) solvent effects, and 5) molecular twisting. Second, molecular pathways from diselenide precursors to all eight copper selenide phases were tracked and two mechanisms were proposed—a Chan-Lam like and hydrogen peroxide like mechanism. Finally, tunable selenoureas and a combination of solvents were explored in the synthesis of iron selenides. Mechanistic deconvolution is still in progress. Ultimately, this dissertation shows progress towards a mechanistic toolbox that can be applied when uncovering pathways in nanoparticle syntheses of metal selenides
The Development of Blood-Brain Barrier Cell Culture Models and Relevant Assessment Tools
No full textWhile animal models are frequently used to explore the macroscale effects of various substances and processes within the blood-brain barrier (BBB), they often fail to fully recapitulate human response or prohibit mechanistic examination. Although in vitro BBB models have the potential to address these challenges, these simplified models have yet to achieve all the characteristics of their in vivo human counterparts. This work explores ways to improve the established transwell cell culture platform to make it a more physiologically relevant model. Human primary, rat primary, and human immortalized cells lines were compared in terms of morphology, transendothelial electrical resistance (TEER), and permeability. Often overlooked culturing parameters are also examined to understand their impact on barrier formation. Special attention was paid to how different preparations and loading styles could potentially alter results. Finally, results are evaluated within the context of current literature standards.
To improve current BBB quantification strategies, an open-source, low-cost, 3D printed platform was also developed to reliably measure TEER levels in cell monolayers and co-cultures. The device was manufactured by embedding silver wires into a 3D printed housing that can easily be connected to commercial voltohmmeters. This prototype was characterized in comparison to two commercial chopstick electrode standards. In addition, this device was also studied for manufacturing variation and measurement stability over time. The final version produced comparable results to commercial standards with lower overall background while providing researchers with a low-cost customizable device that can be optimized to fulfill their own research needs
The Coevolution of Learned Behavior and Genetic Population Structure
No full textIn order to accurately describe the evolution of traits, researchers must study both their biological and cultural transmission. Socially learned cultural traits have been important for both human and songbird evolution. In humans, social learning influences features such as technology, language, and social structures. Birdsongs are socially learned and have an important role in intra-species competition and mate selection, making birdsong a crucial element in songbird evolution. Culture and genetics evolve together: genetic evolution takes place in an environment made up of cultural traits, and culture can only evolve due to a genetic basis for learning. In this dissertation, I investigate this two-way interaction between genetics and culture in humans and songbirds. I combine computational models with empirical data and simulations to describe the evolution of language, human genetics, and birdsong. I describe how culture has influenced gene flow in human populations, both within and between countries. In human populations, patterns of community organization, kinship, and mobility have all had effects on the geographic distribution of genetic variation, but these effects differed between regions of the world. In birds, I find that the evolution of learning heuristics is affected by the distributions of song traits, and that learning biases have shaped the distribution of songs in a focal species. These analyses of genetic, cultural, and spatial data illustrate the ways in which learned behaviors can shape the dynamics within and between populations
Identifying and Addressing Constraints to Fair De-identification and Data Sharing
No full textImproving health outcomes and achieving health equity requires that researchers have access to large and diverse datasets. The Health Insurance Portability and Accountability Act of 1996 and other privacy legislation permit broad dissemination of person-level data that has been de-identified. The process of de-identification involves removing directly identifying information (e.g., names) and transforming the data in a manner that reduces the risk individual patients can be re-identified (e.g., generalizing date of birth to 5-year age ranges). However, for several reasons, traditional de-identification methods cannot support all use cases. First, they were not designed to support public health research amidst an emerging biosurveillance event. They generally rely on retrospective risk assessments, which delay dataset updates. They also fail to flex with changes in infection rates or population demographics over time, which unnecessarily degrades the data’s utility. Second, traditional methods have not prioritized fairness with respect to both privacy protections and group representation. As such, de-identification may disproportionately expose minority groups to re-identification and/or disproportionately degrade their representation in the dataset and subsequently their potential benefit from research. I address both limitations in this dissertation in three parts. First, I develop a framework to dynamically adapt de-identification for near-real time sharing of person-level surveillance data. I show how this framework can support early detection of underlying disparities while reducing patients’ privacy risk. Second, I formalize the tradeoff between equalizing privacy risk and equalizing data utility between records in de- identified data, proving the impossibility to concurrently equalize both in most real-world settings. Finally, I develop a de-identification method that transcends data transformation conventions to enable cooperative privacy protections. In doing so, I show how certain privacy protections can be altruistically donated by majority groups’ records such that minority groups’ records retain greater data utility than that provided by standard de-identification methods. Collectively, this work identifies and addresses several constraints to share data in a way that both protects privacy and preserves the representation of the full population. The constraints motivate the need for, and should guide the development of, innovative data sharing solutions that supports society’s pursuit of health equity
Seeing the Scientist in Me: A Mixed-Methods Investigative Case Study on Fostering Students’ Science Identities at STEM Skool
No full textLeadership and Learning in Organizations capstone projectThis case study examines the role of STEM Skool, an informal science education organization, in fostering science identities among homeschooled students through its diverse programming. The study employs a mixed-methods approach to evaluate the effectiveness of STEM Skool’s mission to cultivate students’ science identities. Statistical and thematic analyses facilitate exploring the psychological processes of performance, recognition, competence, interest, and belonging, which are key to science identity development. The findings offer research-informed insights and actionable recommendations to enhance student learning experiences and guide the organization’s future programming and expansion efforts
Oxidized Lipid Species Promote Atherogenic Phenotypes in Innate and Adaptive Immune Cells
No full textAtherosclerosis is an immune mediated hyperinflammatory disease driven by the retention and oxidation of low-density lipoprotein (LDL) in the vessel wall. The goal of this work was to determine how oxidized LDL (oxLDL) reshapes the phenotype and function of innate and adaptive cells. Chapter 2 addresses the gap in knowledge regarding what causes regulatory T cell (Treg) dysfunction in atherosclerosis, and shows that oxidized phospholipids within oxLDL, namely oxidized 1-palmitoyl-2-arachidonoyl-sn-glycero-3-phosphocholine (oxPAPC), alters Treg differentiation and inhibits their atheroprotective function. This oxPAPC-driven effect is partially IFN- signaling dependent, prompting an investigation of the role of Treg IFN- receptor (IFNR) signaling in a bone marrow transplant mouse model of atherosclerosis in Chapter 3. Treg IFNR deficiency alters atherosclerosis severity in mice but does so in a sex-dependent manner, with deficiency increasing disease burden in females and reducing it in males. This builds on previous work that has shown sex-dependent outcomes for IFN- inhibition in atherosclerosis mouse models, and adds nuance to the described proinflammatory function of IFN-. Chapter 4 demonstrates oxLDL immune complexes, which are increased in the circulation of atherosclerosis and autoimmune patients, trigger long-term changes in DCs that are distinct from alterations caused by oxLDL alone, better characterizing how this prevalent atherosclerotic antigen might contribute to chronic inflammation. The summary of these chapters and potential future directions of this work is the focus of Chapter 5. Ultimately, these studies add to our understanding of how oxidized lipids in atherosclerosis modulate the immune responses that are critical to driving and resolving disease
Multiscale Investigation of the Compression Failure in Laminated CFRP Composites with and without Z-pin Reinforcements
No full textUnderstanding how advanced composites behave under critical conditions is necessary for effective im- plementation of these materials in aerospace structures. Common design involves advanced laminated carbon- fiber reinforced polymer (CFRP) configurations with fiber orientations tailored for expected load conditions such as flight maneuvers or landing. Among the characteristics of CFRP laminates, compression strength is a critical weakness. The compression behavior is complicated due to the presence of multiple failure mechanisms which may interact during the loading process. Among the failure mechanisms, kink bands are typically driving ultimate failure, while splitting, matrix cracking, and delaminations may additionally be present and affecting the overall behavior. Additional complications such as existing regions of material failure, or damage, due to an impact event weaken the structural response. Under compression-after-impact (CAI) test conditions, interlaminar properties are a crucial weakness and associated with delamination-driven failure. Technologies proposed to arrest delamination growth and increase the CAI strength include through- thickness reinforcements called z-pins. Delamination and other CAI failure mechanisms in the presence of z-pins are not yet fully understood. This dissertation presents multiscale investigations of the compression response of laminated CFRP composites with and without z-pins undertaken using state-of-the-art compu- tational damage analysis models and experimental methods. Physics-based prediction of the composite re- sponse is accomplished by integrating state-of-the-art multiscale and cohesive zone models in finite element analyses. Experimental campaigns are used to support the development, calibration, and validation of the computational models. Damage progression predictions supplement experimental results and demonstrate advancements in modeling capabilities to predict compression failure in laminates with and without z-pins
Impact of Entropy on Black Hole Astrophysics
No full textAs an irreversible natural process, mergers between two black holes will increase the entropy of the universe. By imposing thermodynamical constraints derived from general relativity and quasi-circular hypothesis, we showcase BRAHMA – a novel framework to infer the properties and astrophysical implications of binary black hole mergers in LIGO-Virgo-KAGRA (LVK). We apply the framework as an IMR (inspiral-merger-ringdown) consistency test to 10 heaviest binary black hole merger events reported by LVK Collaboration and perform a systematic investigation on the consistency between phenomenological waveform and ringdown models (analysis data available on Zenodo). In doing so, we obtain astrophysical insights into the origins of black holes for GW190521 and GW191109, two of the heaviest confirmed merger events. We also show the high consistency of the NRSur7dq4 waveform and Kerr221 ringdown model, providing insights into the timing of the ringdown stage. For events without QNM measurements due to low SNR of ringdown, we use the post-merger conditions inferred from phenomenological waveforms to compute the IBBH (Merger Entropy Index: measures the efficiency of entropy transfer during BBH merger) distribution across all GW events. IBBH shows high differentiability in formation channels, which can be used as a tool to classify compact binary populations. Therefore, we employ IBBH to identify compact objects in the lower mass gap (2.5 ∽ 5⊙), including the newest discovery GW230529 in the 4th observing run.College of Arts and ScienceDepartment of Physics and Astronom