Pedestrian-Accessible Infrastructure Inventory: Enabling and Assessing Zero-Shot Segmentation on Multi-Mode Geospatial Data for All Pedestrian Types.

In this paper, a Segment Anything Model (SAM)-based pedestrian infrastructure segmentation workflow is designed and optimized, which is capable of efficiently processing multi-sourced geospatial data, including LiDAR data and satellite imagery data. We used an expanded definition of pedestrian infrastructure inventory, which goes beyond the traditional transportation elements to include street furniture objects that are important for accessibility but are often omitted from the traditional definition. Our contributions lie in producing the necessary knowledge to answer the following three questions. First, how can mobile LiDAR technology be leveraged to produce comprehensive pedestrian-accessible infrastructure inventory? Second, which data representation can facilitate zero-shot segmentation of infrastructure objects with SAM? Third, how well does the SAM-based method perform on segmenting pedestrian infrastructure objects? Our proposed method is designed to efficiently create pedestrian-accessible infrastructure inventory through the zero-shot segmentation of multi-sourced geospatial datasets. Through addressing three research questions, we show how the multi-mode data should be prepared, what data representation works best for what asset features, and how SAM performs on these data presentations. Our findings indicate that street-view images generated from mobile LiDAR point-cloud data, when paired with satellite imagery data, can work efficiently with SAM to create a scalable pedestrian infrastructure inventory approach with immediate benefits to GIS professionals, city managers, transportation owners, and walkers, especially those with travel-limiting disabilities, such as individuals who are blind, have low vision, or experience mobility disabilities.

Endothelial Cell-Derived Interleukin-18 Released During Ischemia Reperfusion Injury Selectively Expands T Peripheral Helper Cells to Promote Alloantibody Production.

BACKGROUND: Ischemia reperfusion injury (IRI) predisposes to the formation of donor-specific antibodies, a factor contributing to chronic rejection and late allograft loss. METHODS: We describe a mechanism underlying the correlative association between IRI and donor-specific antibodies by using humanized models and patient specimens. RESULTS: IRI induces immunoglobulin M-dependent complement activation on endothelial cells that assembles an NLRP3 (NOD-like receptor pyrin domain-containing protein 3) inflammasome via a Rab5-ZFYVE21-NIK axis and upregulates ICOS-L (inducible costimulator ligand) and PD-L2 (programmed death ligand 2). Endothelial cell-derived interleukin-18 (IL-18) selectively expands a T-cell population (CD4+CD45RO+PD-1hiICOS+CCR2+CXCR5-) displaying features of recently described T peripheral helper cells. This population highly expressed IL-18R1 and promoted donor-specific antibodies in response to IL-18 in vivo. In patients with delayed graft function, a clinical manifestation of IRI, these cells were Ki-67+IL-18R1+ and could be expanded ex vivo in response to IL-18. CONCLUSIONS: IRI promotes elaboration of IL-18 from endothelial cells to selectively expand alloreactive IL-18R1+ T peripheral helper cells in allograft tissues to promote donor-specific antibody formation.