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Investigating Hedgehog Signaling and TH17-Mediated Inflammation in Blau Syndrome: Insights into Chronic Inflammation
Get PDFBlau syndrome is a rare, monogenic inflammatory disorder caused by autosomal dominant mutations in NOD2, a cytosolic pattern recognition receptor that regulates immune signaling. Although traditionally considered an autoinflammatory disease driven by innate immune dysfunction, emerging evidence implicates T cells in Blau pathogenesis. Recent studies show that Nod2-deficient mice exhibit elevated IL-17A production despite normal TH17 differentiation, suggesting Nod2 regulates cytokine output independently of canonical lineage commitment. This study investigates how Nod2 mutations influence TH17-mediated inflammation and explores the role of Hedgehog (Hh) signaling, particularly the transcription factor Gli2, in this process.
Using the Nod2R314Q/R314Q mouse model, we evaluated IL-17A production under TH17-differentiation conditions and performed Western blot analysis to assess Gli2 expression in spleen and brain tissue. Additionally, phosphoproteomic profiling of CD3/CD28-stimulated T cells was used to identify signaling pathway alterations. Results revealed no significant increase in IL-17A production in Nod2R314Q/R314Q cells under baseline stimulation, but proteomic data indicated elevated Gli2 expression and other phospho-targets linked to Hh signaling.
These findings suggest that while baseline IL-17A output may not fully capture inflammatory dysregulation, Hedgehog pathway activity is altered in Nod2-mutant T cells. Ongoing studies will explore cytokine regulation under varied stimulation conditions and assess the functional significance of Gli2 isoforms. Together, this work offers new insight into T cell-mediated inflammation in Blau syndrome and identifies signaling axes that may underlie its chronic immune activation
Transportation Policy and Indigenous Ways of Knowing: What Counts as Evidence in Tribal Policy Contexts?
Get PDFEvidence-based policy making is the notion that policy decisions should be based on (1) rigorous data collection and research and (2) focus on ‘what works.’ This approach aligns with the broader theory of New Public Management (NPM), which contends that the public sector should adopt the practices of and operate more like the private sector, including a reliance on measuring performance. That said, the application of evidence-based policy approaches to Tribal contexts is often difficult, if not impossible, due to the reliance on oral traditions rather than data collection. The result is that Tribal contexts are often underrepresented in contemporary policy analyses or overlooked for services that analyses should have otherwise directed policymakers towards.
The questions then are (1) whether evidence-based policy making and Tribal policy contexts are compatible or not and (2) how would either need to adapt accommodate the other? Some social science researchers in Tribal contexts employ Indigenous Research Methods (IRM) in contrast to contemporary positivist approaches, emphasizing storytelling, relationship, and positionality. Unlike evidence-based policy making, which is rooted in positivist views that knowledge is objective and verifiable, IRM suggests a more interpretive worldview and that reality is subjective, socially constructed, and a composite of multiple perspectives.
I argue that the two approaches are not incompatible but instead that evidence-based policy making and IRM can inform one another. The underlying issue separating the two is what constitutes ‘knowledge’ for the purposes of policy decision making. In this paper, I outline the foundations of evidence-based policy making in comparison with Indigenous Research methods, including the assumptions, applications, and gaps implied by both. My intent is to show how the two approaches align and diverge as well as what this means for researchers operating in both context
Short Communication: Multiscale Topographic Complexity Analysis with Pytopocomplexity
Get PDFTopoComplexity is a Python package designed for efficient and customizable quantification of topographic complexity using four advanced methods: two-dimensional continuous wavelet transform analysis, fractal dimension estimation, rugosity index, and terrain position index calculations. This package addresses the lack of open-source software for these advanced terrain analysis techniques essential for modern geomorphology and geohazard research, enhancing data comparison and reproducibility. By assessing topographic complexity across multiple spatial scales, pyTopoComplexity allows users to identify characteristic morphological scales of studied landforms. The software repository also includes a Jupyter Notebook that integrates components from the surface-process modeling platform Landlab (Hobley et al., 2017), facilitating the exploration of how terrestrial processes, such as hillslope diffusion and stream power incision, drive the evolution of topographic complexity over time. When these complexity metrics are calibrated with absolute age dating, they offer a means to estimate in situ hillslope diffusivity and fluvial erodibility, which are critical factors in determining the efficiency of landscape recovery after significant geomorphic disturbances such as landslides. By integrating these features, pyTopoComplexity expands the analytical toolkit for measuring and simulating the time-dependent persistence of geomorphic signatures against environmental and geological forces
SAE Baja Drivetrain Capstone 2024-2025
Get PDFThis thesis reviews the 2024-2025 Baja SAE Drivetrain capstone project. The Baja SAE competition mimics real world engineering problems where teams of students design and build off-road vehicles that can handle tough, rough terrain. The objective of this capstone was to design a functional and reliable drivetrain system for the vehicle, within the constraints of a limited budget and the SAE competition guidelines. As part of a 12-member capstone team divided into three subgroups: frame, suspension, and drivetrain. As part of a four person drivetrain team, we picked up where the 2023-2024 team left off by relying on their past documentation to guide us through their thought process and lead us to the new project plan. With this new project plan, we headed towards a chain driven drivetrain design. Throughout the project, our team engaged in ongoing design, analysis, and decision making processes in order to create a sustainable drivetrain system capable of withstanding the terrains of the Baja competition. Design tools and engineering software were also used to model and evaluate the components of the systems to interpret various challenges that were encountered during this project. At the end of the project, we were able to design a complete chain driven, drivetrain system. Although the Baja car will not be used in competition this year, the work completed serves as a foundation for future development as this capstone project will be available for 2025-2026 seniors
Intersectional Realities, Multi-Dimensional Films: Reading Julie Dash\u27s \u3ci\u3eIllusions\u3c/i\u3e as an Activist Text
Get PDFThis paper reads Julie Dash\u27s 1982 film Illusions as an activist text, which challenges dominant cinematic and historical narratives through its Black feminist perspective. It explores the cinematic and narrative devices that Dash uses--including the weaving of fantasy and history, the strategic use of period aesthetics, and masking--to expose and subvert the mechanisms of white supremacist and patriarchal hegemony within Hollywood. Drawing on intersectional feminist theory from scholars like Kimberlé Crenshaw, Patricia Hill Collins, and bell hooks, alongside cinematic theory, this analysis demonstrates how Illusions critiques the historical erasure of people of color from United States cinema and history. By foregrounding the experiences of its white-passing Black protagonist, Mignon Dupree, the film compels viewers to interrogate their own gaze, revealing the labor and systemic marginalization that underpin idealized on-screen representations
Expression of BioID2 in the Hyperthermophilic Archaeon \u3ci\u3ePyrococcus furiosus\u3c/i\u3e
No full textProximity-dependent biotinylation is an approach that involves the fusion of an enzyme capable of catalyzing the activation of biotin to a bait protein, allowing for the biotinylation of nearby proteins in living cells that can later be captured and identified to study in vivo protein-protein interactions. A commonly used enzyme involves a biotin ligase, and an improved biotin ligase with more-selective targeting of fusion proteins has previously been isolated from Aquifex aeolicus for expression in other biological systems, which has been termed BioID2 . This study aimed to express BioID2 in the hyperthermophilic archaeon Pyrococcus furiosus to gain further insight into the archaeal transcription machinery by fusing BioID2 to the RNA polymerase subunit RpoD, the transcription factor TFB1, and the transcription factor TFB2. Experimental DNA constructs expected to produce BioID2 and BioID2-protein fusions were used to transform COM1 Pyrococcus furiosus through PCR and selection via a pyrF gene. Post-transformation PCR suggested successful transformation for the strains with the BioID2 control and BioID2 fusions proteins. Biotinylation of proteins was checked through a procedure involving SDS-PAGE and probing of a membrane with fluorescent streptavidin. The results of these experiments suggested that biotinylation was occurring, but no novel biotinylation targets were identified when compared to the BioID2 control. This study was centered mainly around the feasibility of expressing proximity biotinylation in P. furiosus, and the results obtained suggest that with further development, BioID2 holds promise as a potentially robust and versatile tool for studying protein-protein interactions in Pyrococcus furiosus
Discovering Sri Lanka: Examining the Marketing Strategies Behind Successful Tourist Destinations
Get PDFBuilding on the observation that Sri Lanka is emerging as a tourist destination, this thesis compares the national destination marketing campaigns of Thailand, Indonesia, and Sri Lanka to identify how their strengths have contributed to their success. Using a comparative analysis, the research examines each destination’s brand positioning, distribution channels, campaign messaging, visual identity, and brand consistency. A deeper analysis of the strengths, weaknesses, opportunities, and threats (SWOT analysis) is conducted on Sri Lanka\u27s destination marketing campaign to present the study\u27s findings and communicate recommendations. Ultimately, the analysis is used to recommend strategies for Sri Lanka\u27s destination marketing efforts to enhance its global perception, increase competitiveness, and attract international tourists. This research contributes to the study of destination marketing by highlighting the importance of cohesive destination branding and offering insights for national tourism marketers aiming to compete in a global market
Optical Coherence Tomography Harmonization with Anatomy-Guided Latent Metric Schrödinger Bridges
No full textMedical image harmonization aims to reduce the differences in appearance caused by scanner hardware variations to allow for consistent and reliable comparisons across devices. Harmonization based on paired images from different devices has limited applicability in real-world clinical settings. On the other hand, unpaired harmonization typically does not guarantee anatomy consistency, which is problematic because anatomical information preservation is paramount. The Schrödinger bridge framework has achieved state-of-the-art style transfer performance with natural images by matching distributions of unpaired images, but this approach can also introduce anatomy changes when applied to medical images. We show that such changes occur because the Schrödinger bridge uses the square of the Euclidean distance between images as the transport cost in an entropy-regularized optimal transport problem. Such a transport cost is not appropriate for measuring anatomical distances, as medical images with the same anatomy need not have a small Euclidean distance between them. In this paper, we propose a latent metric Schrödinger bridge (LMSB) framework to improve the anatomical consistency for the harmonization of medical images. We develop an invertible network that maps medical images into a latent Euclidean metric space where the distances among images with the same anatomy are minimized using the pullback latent metric. Within this latent space, we train a Schrödinger bridge to match distributions. We show that the proposed LMSB is superior to the direct application of a Schrödinger bridge to harmonize optical coherence tomography (OCT) images
Signal Processing of Ecologically Significant Signals for Transient Bedload Characterization
No full textWhen rivers achieve a great enough flow rate, sediment along their riverbed becomes mobilized and transported downstream. This process is known as bedload transport. As bedload transport initiates, micro-collisions of various sediments and gravel generate underwater acoustic signals that can be detected by hydrophones. While there is extensive research on underwater acoustic theory and surrogate methods for monitoring bedload transport, there is a lack of heuristic work that analyzes collected bedload samples in conjunction with underwater acoustic recordings. The aim of this work is to complete an analysis on collected bedload samples and the simultaneous underwater acoustic recordings via signal processing to identify methods by which acoustic recordings can be used to determine quantity and size distribution of bedload in motion. We expect an inverse relationship between sediment size and the maximum impulse frequency, so we hypothesize an algorithm exists that would infer sediment particle size and count based on detected impulses. These results are of particular significance to hydrologists and conservationists, as it provides a safer and more efficient method to estimate the sizes of sediment in transport compared to manual bedload collection and can be used to better assess suitable spawning habitat of, e.g., salmon
JHTDB-Wind: A Web-Accessible Large-Eddy Simulation Database of a Wind Farm with Virtual Sensor Querying
No full textThis paper introduces JHTDB-wind (https://turbulence.idies.jhu.edu/datasets/windfarms), a publicly accessible database containing large-eddy simulation (LES) data from wind farms. Building on the framework of the Johns Hopkins Turbulence Database (JHTDB), which hosts direct numerical and some large-eddy simulation datasets of canonical turbulent flows, JHTDB-wind stores the full space-time (4D) history of the flow and provides users the ability to access and query the data via a web-based virtual sensor interface. The initial dataset comprises LES results from a large wind farm with 6 X 10 turbines, modeled using a filtered actuator line method, under conventionally neutral atmospheric conditions. This data comprises one hour of flow field data (velocity, pressure, potential temperature, and others, approximately 15 TB) and wind turbine data—including both turbine-level operational quantities and blade-level aerodynamic quantities (approximately 1.3 TB)—stored in Zarr and Parquet formats, respectively. Data retrieval is facilitated by the Giverny Python package, allowing remote users to query the database in Python or Matlab (C and Fortran support are available for flow field data). This paper details the simulation setup and demonstrates data access through examples that analyze wind farm flow structures and turbine performance. The framework is extensible to future datasets, including the JHTDB-wind diurnal cycle simulation analyzed in Xiao et. al (2025)