Three Papers on the Politics of Financial Cooperation and Statecraft

2023Financial globalization has increased interdependence among financial markets in different regions, requiring new frameworks of analysis of the politics of interstate relations to fully understand global financial markets. The three papers in this dissertation manuscript address this demand by formulating multiple hypotheses on what drives financial cooperation between states, how states use asymmetries in interdependence for statecraft, and how monetary policies of one economy can influence the politics of others. The first paper argues that financial cooperation in the international monetary system has a hub-and-spokes structure, with the United States as the hub economy. It demonstrates that this structure affects other economies' motivation to engage in regional financial cooperation. The second paper addresses how volatility in the Fed’s balance sheet affects the level of support for incumbent regimes in other countries. It finds that the effects differ significantly between democracies and autocracies for those with higher reliance on the global financial market. The third paper builds on theories of middle power behavior and emerging economy financial statecraft to develop a theory of middle power financial statecraft and applies it to South Korea

Enterprise Evangelicalism: a new lens for analyzing two centuries of American Christian efforts to proselytize capitalism

2024For the past two centuries, a loose network of American Christians from across the theological spectrum have promoted, defended, and proselytized capitalism as a divinely ordained economic order, claiming the laws of economics mirror moral laws embedded in God’s creation. When properly instituted, these laws shape virtuous citizens who produce prosperous nations, this surprisingly capacious bunch argue. Over the last few decades historians have investigated these Christians’ efforts piecemeal, but no coherent overarching category linking their activities together has yet emerged in the scholarship. Thus, this dissertation crafts a heuristic historiographical lens designed to bring continuities between American Christian evangelists of free enterprise into focus. Enterprise evangelicalism is the name I give to a two-centuries long American Christian tradition which sanctifies capitalism as God’s chosen form of economy. The American Christians who, with great variety and creativity, advance this tradition I call “enterprise evangelicals.” This dissertation thus develops a framework to identify the spectral tradition of enterprise evangelicalism that haunts the annals of American religious history. After doing so, I deploy that framework in proof of concept by analyzing the ways four groups of American Christian elites (groups I call clerical economists, revivalists, crusaders, and edutainers) develop and extend the tradition of enterprise evangelicalism within their spheres of influence. In the conclusion, I invite scholars of religion and economic culture to read their archives through the lens of enterprise evangelicalism and see what surprising insights come into view.2026-09-02T00:00:00

Journal of African Christian Biography: v. 1, no. 5: Booklet, no cover (A4 format), print-ready

The full issue of Journal of African Christian Biography: v. 1, no. 5 is available at: https://hdl.handle.net/2144/3566

Analysis of bone marrow edema: a scoping review

2024BACKGROUND: Axial Spondyloarthritis (axSpA) is characterized by bone marrow edema (BME) on magnetic resonance imaging (MRI) of the sacroiliac joints. The histopathology of BME in axSpA is not fully understood. This study aims to understand the concept of BME in rheumatic diseases, critically examining its histopathological basis and seeking toclarify the true nature of BME as observed on MRI. METHODS: A scoping literature review was conducted focusing on the histopathology of BME in musculoskeletal diseases excluding infection, fracture, and cancer. Vosviewer software was used to visualize interrelationships and frequency of keywords in the literature. RESULTS: The comprehensive literature search yielded 37 studies (28 human and 9 animal studies) meeting the inclusion criteria. Studies of BME in humans included a diverse range of diseases with Osteoarthritis (OA) being the most studied condition, accounting for 8 of the 28 studies, followed by Bone marrow edema syndrome (BMES). A single study analyzed the histopathology of BME in axSpA. Five major histopathological findings were frequently reported across all human and animal studies: edema, necrosis, fibrosis, inflammation, and hypervascularity. Fibrosis was described in most diseases and was present in 82.1% of the studies, often along other pathologies, especially in OA. Edema was reported in only 71.4% of all human studies and was particularly prevalent in cases of BMES and OA. Hypervascularity was a notable feature in BMES and interestingly in Avascular necrosis (AVN). CONCLUSION: The histopathology of MRI BME is not limited to the accumulation of extracellular fluid in the marrow; it also varies depending on the diagnosis, often involving fibrosis, inflammatory infiltrate, and hypervascularity. Only a single study has investigated the association of histopathological characteristics and MRI BME in patients with AS. The limited literature on the histopathology of BME in axSpA can be attributed to the invasive nature of obtaining bone marrow samples, impeding a thorough understanding of axSpA pathogenesis. This gap emphasizes the importance of increased research on the histopathological changes of BME in axSpA

Appendix J: Heart Sounds Game PowerPoint

Interactive PowerPoint presentation for the "Name that Heart Sound!" teaching game. This game-based learning resource engages students in recognizing cardiac murmurs and gallops through competitive team play. Includes audio files for 6 cardiac sounds (aortic stenosis, mitral regurgitation, aortic regurgitation, mitral stenosis, S3 and S4 gallops) with clinical scenarios and mnemonics.[Educational Level: Medical Student, Resident, PA Student, NP Student

Advancing deep learning in computational mechanics and biomechanics: overcoming challenges and paving a promising future

2025Deep learning has revolutionized numerous scientific fields, yet its integration within computational mechanics and biomechanics remains limited. This is primarily due to unique challenges including but not limited to the scarcity of suitable benchmark datasets, uncertainties regarding model reliability, and complexities inherent to biological systems. This dissertation systematically identifies and investigates these challenges, adopts methodologies from the broader deep learning and computer vision communities, develops robust computational solutions, and openly shares datasets, tools, and analytical results, enabling and inspiring continued advancements within the research community. The first critical challenge addressed is the notable absence of open-source benchmark datasets within mechanics. In computer vision, benchmark datasets have been fundamental to driving deep learning innovation, fueling the development of increasingly effective algorithms and methods. However, such datasets have historically been lacking within mechanics, significantly limiting progress in applying deep learning methods to this field. To bridge this gap, a novel and challenging dataset was developed using finite element-based phase-field fracture modeling to simulate complex crack propagation in heterogeneous materials. This dataset introduces a unique, rigorous challenge for deep learning—distinct from conventional computer vision tasks such as object recognition or semantic segmentation—thus encouraging innovative model development within the mechanics community. Subsequently, this dissertation tackles the prediction of full-field quantities of interest, such as displacement, damage, and strain fields throughout the entire computational domain, an area comparatively underexplored in the mechanics community. Through this effort, robust neural network architectures are benchmarked, and both the dataset and baseline performance scores are openly shared to facilitate further improvements by other researchers. Although deep learning provides powerful tools capable of uncovering complex patterns and achieving impressive predictive accuracy, these models frequently suffer from inadequate calibration, i.e., the alignment between predicted outcome probabilities and observed occurrences. Model calibration is especially crucial in computational mechanics, where substantial attention is traditionally placed on uncertainty quantification and model reliability. To systematically address this critical issue, extensive investigations were conducted into deep learning model calibration. Rigorous comparative analyses were performed on seven distinct mechanics-specific datasets, evaluating both post-training calibration approaches, such as temperature scaling, and training-time calibration techniques, specifically ensemble model training. Empirical findings from these studies clearly demonstrated that ensemble averaging significantly enhances calibration performance and predictive reliability, directly benefiting applications where accuracy and uncertainty quantification are paramount. Transitioning from simulated mechanical systems to living biological tissues, this dissertation then addresses computational challenges associated with analyzing human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs). These cells hold substantial promise for advancing cardiac research, disease modeling, and regenerative medicine, yet analyzing their structural organization poses significant difficulties due to their inherent biological complexity and structural immaturity. To enable accurate, scalable, and automated analysis, the dissertation introduces SarcGraph, an open-source Python toolkit explicitly designed for quantitative structural analysis at the sarcomere scale. Leveraging recent advancements in state-of-the-art self-supervised deep learning methods, SarcGraph was enhanced to robustly handle structural heterogeneity and accurately identify sarcomere structures, even in highly disordered or immature cellular samples. Furthermore, the improved capabilities of SarcGraph were demonstrated by analyzing an openly available hiPSC-CM imaging dataset, showcasing its effectiveness in quantifying cellular structural organization and providing insights essential for further development of reliable analysis methods. Taken together, the dissertation addresses critical gaps and challenges at the intersection of deep learning, computational mechanics, and biomechanics, offering practical solutions supported by open datasets, tools, and analyses. This work aims to serve as a foundation, fostering broader adoption of deep learning methods within these scientific communities and promoting continuous innovation through collaborative and open-source research practices

Deciphering the immune contexture of premalignancy in a murine model of lung squamous cell carcinoma

2024Lung cancer (LC) remains the leading cause of cancer death worldwide, taking more lives annually than breast, colon, and prostate cancer combined. Despite the strong association of LC with smoking, not all smokers will develop lung cancer. Expansions of large-scale screening programs and improvements in early detection driven by research programs have increased detection of patients with both early-stage cancer and the premalignant lesions (PMLs) that precede it. However, clinical tools to identify which PMLs will progress to invasive carcinoma to stratify at-risk patients for intervention and prevention strategies are still lacking, and implementation of these strategies remains an urgent need to address LC mortality. Therefore, a greater understanding of the factors contributing to lung cancer tumorigenesis is critical. The PMLs that precede lung squamous cell carcinoma (LUSC), the second most common LC subtype accounting for 20-30% of LC cases, arise within the airway field of injury caused by smoking. Our group and others have established focused efforts defining the molecular alterations that drive progression of these precursor lesions and have found that these changes can identify patients at risk for progression. Among these alterations are downregulation of immune pathways such as antigen processing/presentation, suggesting a defect in immunosurveillance of developing PMLs may play a role in preinvasive disease progression. However, a mechanistic understanding of the precise immune contribution to lesion progression is still lacking, and robust model systems are needed to address this gap. To identify the mechanisms underlying progression, we conducted bulk RNA sequencing (RNAseq) transcriptomic profiling of the carcinogen-induced N-Nitrosotris-2(-chloroethyl)urea (NTCU) murine model of LUSC and found several patterns of gene expression in the NTCU mouse model were significantly associated with those found in human PMLs. Using cell type deconvolution and highly multiplexed imaging mass cytometry (IMC), we observed a reduction in several T cell populations as well as an increase in myeloid populations with increasing PML histological severity, aligning with findings from human preinvasive disease. The parallels in transcriptomic and cell-level alterations between NTCU-induced mouse and human premalignancy suggest that changes in immune response to developing PMLs may drive progression in both settings, making the NTCU model an appropriate representation of human preinvasive disease. A challenge in establishing clinical intervention strategies for squamous PMLs is that many of these lesions regress without intervention. NTCU-induced premalignancy in different inbred laboratory mouse strains reflects this phenotype, as there exists a spectrum of differential sensitivity to NTCU. To further establish how strain differences may reflect progression and regression of squamous PMLs, we used the carcinogen-sensitive A/J and carcinogen-resistant C57BL/6J mouse strains to represent progressive and regressive preinvasive disease, respectively, and conducted a large multimodal profiling study. Using multiplexed IMC and single cell RNAseq we identified an expansion of the Keratin 5+ basal cell population, which had significant copy number variations (CNVs) and may recruit inflammatory myeloid populations to the lesion microenvironment. We also found a reduction in cytotoxic T lymphocytes (CTLs) as well as an increase in FoxP3+ regulatory T cells (Treg cells), macrophage, and neutrophil populations, aligning with our previous studies as well as findings in the human context that suggest an immunosuppressive and inflammatory microenvironment is established early during lesion development. Lastly, we examined the impact of immunomodulation on PML progression in the NTCU-sensitive mouse by inhibiting IL-1β following carcinogen exposure. While IL-1β inhibition did somewhat mitigate tumor progression following treatment cessation, it did not prevent invasive LUSC tumor formation, likely due to preexisting defects in immunosurveillance capacity in the A/J mouse impacting early tumor control. Taken together, this work not only validates the preclinical utility of the NTCU mouse model by demonstrating key similarities to transcriptomic alterations in immune function pathways that may underpin PML progression in the human, but also identifies several immune populations that may contribute to a microenvironment permissive of lesion progression. By increasing our mechanistic understanding of the contribution of the immune system to early LUSC tumorigenesis, this work will support the development of immunomodulatory-based cancer interception strategies, and the rationale for deployment of those interventions during lung squamous precancer.2027-09-03T00:00:00

Pause before you prompt

AI tools use algorithms to scan for information and generate responses to prompts. Before you engage with a tool, you will want to reflect on intention, usage, and impact. The following resource is a reflection tool for pressing pause and thinking before using the tool of your choosing in academic and personal contexts

Owiti, Silas Javan Aggrey

[Silas hailed from Kano plains of Kochogo in Kano location, in the western part of Kenya. His father was Jowi Oiko and his mother was Mariam Dede Jowi, from Kodumo in Kabondo, near Kadongo Market in southern Nyanza. Silas was Miriam’s third born. His parents lived in a small village called Apondo situated on the southeastern side of Kisumu town and on the eastern side of the Lake Victoria. Jowi was wealthy and honored by ancient standards of living. He was a polygamous elder among his people and was married to five wives.

Toward an early warning system for climate-sensitive vector-borne diseases: insights from visceral leishmaniasis in Brazil

2025Climate change is reshaping the geographic distribution and transmission mechanisms of infectious diseases worldwide. Vector-borne diseases (VBD) are highly sensitive to environmental fluctuations and are especially vulnerable to these changes. Even marginal temperature increases associated with climate change enable disease vectors to survive in previously uninhabitable locations and accelerate disease transmission. Changes in precipitation and humidity can influence vector populations by creating or eliminating breeding sites, altering host-seeking behavior, and affecting pathogen viability within the vector. However, the relationship between weather variability and VBD risk is often complex, nonlinear, and delayed, necessitating sophisticated methodological approaches for accurate modeling and prediction. Early warning systems that integrate data on meteorological hazards, environmental conditions, and sociodemographic factors show promise in improving disease prevention efforts and mitigating public health burdens.This dissertation uses visceral leishmaniasis (VL) in Brazil as a case study to develop a robust framework for modeling climate-sensitive infectious diseases. VL is a life-threatening vector-borne disease of significant public health concern in Brazil, yet our understanding of its association with climate-related changes remains insufficient. VL is caused by a protozoan parasite (predominantly Leishmania infantum in South America) and is transmitted to humans by the bite of infected female Phlebotomine sandflies (Lutzomyia spp.). As the most severe form of leishmaniasis, VL has a high fatality rate, exceeding 95% in untreated cases, and remains the second-deadliest parasitic disease globally. While effective treatments exist, they are often toxic, challenging to administer, and can be difficult to access in highly impacted regions. Prevention remains a key public health priority, particularly as the dynamics of the parasite and its vector are tightly linked to meteorological conditions such as temperature, humidity, and precipitation. Given projected shifts in weather patterns and ongoing changes in VL distribution due to urbanization, deforestation, and population mobility, integrating meteorological and environmental data into surveillance systems is becoming increasingly essential for proactive disease control. With three primary research aims, this dissertation lays the foundation for developing a climate-informed early warning system for VL in Brazil. First, I employ a spatiotemporal modeling framework to examine the association between weather anomalies and VL incidence across Brazil. I assess how these relationships vary by urbanization and land-use changes, particularly deforestation. Second, I develop a machine learning model to predict VL cases several months in advance based on climate and non-climate-related risk factors. By quantifying the relative contribution of each predictor to the overall forecast, this model identifies key environmental and epidemiological drivers of VL transmission across diverse geographic settings. Third, I construct a seasonal and climate-driven mathematical compartmental model leveraging information on the biological mechanisms underlying VL transmission to evaluate counterfactual intervention scenarios, providing insights into potential strategies for disease mitigation. This research addresses emerging public health challenges as climate change continues to alter vector-borne disease transmission and risk worldwide. Brazil is an ideal case study due to its diverse climatic and ecological conditions, allowing for a comprehensive examination of climate-disease associations. By exploring a combination of data-driven and process-based approaches, this dissertation contributes to the development of scalable and transferable frameworks for modeling and forecasting climate-sensitive infectious diseases. The findings from this work inform the types of models that might, in the future, be most useful in a forecasting framework while directly informing early warning system efforts applicable to VL and other climate-sensitive diseases worldwide.2027-06-03T00:00:00