As novel mediators of cell‐to‐cell signalling, small extracellular vesicles (sEVs) play a critical role in physiological and pathophysiological processes. To date, the molecular mechanisms that support sEV generation are incompletely understood. Many kinases are reported for their roles in sEV generation or composition, whereas the involvement of phosphatases remains largely unexplored. Here we reveal that pharmacological inhibition and shRNA‐mediated down‐regulation of tyrosine phosphatase Shp2 significantly increases the formation of sEVs. By Co‐immunoprecipitation (Co‐IP) and in vitro dephosphorylation assays, we identified that Shp2 negatively controlled sEV biogenesis by directly dephosphorylating tyrosine 46 of Syntenin, which has been reported as a molecular switch in sEV biogenesis. More importantly, Shp2 dysfunction led to enhanced epithelial sEV generation in vitro and in vivo. The increase of epithelial sEVs caused by shRNA‐mediated down‐regulation of Shp2 promoted macrophage activation, resulting in strengthened inflammation. Our findings highlight the role of Shp2 in regulating sEV‐mediated epithelial‐macrophage crosstalk by controlling sEV biogenesis through dephosphorylation of Syntenin Y46. The present study determines the strengthened inflammatory characteristics of alveolar macrophages elicited by epithelial sEVs transferred intercellularly. These findings provide a basis for understanding the mechanism of sEV formation and relevant function in epithelial‐macrophage crosstalk.

Abstract. Data from the optical satellite imaging sensors running 24/7, is collecting in embarrassing abundance nowadays. Besides more suitable for large-scale mapping, multi-view high-resolution satellite images (HRSI) are cheaper when comparing to Light Detection And Ranging (LiDAR) data and aerial remotely sensed images, which are more accessible sources for digital surface modelling and updating. Digital Surface Model (DSM) generation is one of the most critical steps for mapping, 3D modelling, and semantic interpretation. Computing DSM from this dataset is relatively new, and several solutions exist in the market, both commercial and open-source solutions, the performances of these solutions have not yet been comprehensively analyzed. Although some works and challenges have focused on the DSM generation pipeline and the geometric accuracy of the generated DSM, the evaluations, however, do not consider the latest solutions as the fast development in this domain. In this work, we discussed the pipeline of the considered both commercial and opensource solutions, assessed the accuracy of the multi-view satellite image-based DSMs generation methods with LiDAR-derived DSM as the ground truth. Three solutions, including Satellite Stereo Pipeline (S2P), PCI Geomatica, and Agisoft Metashape, are evaluated on a WorldView-3 multi-view satellite dataset both quantitatively and qualitatively with the LiDAR ground truth. Our comparison and findings are presented in the experimental section.

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DeepLab v3+ neural network shows excellent performance in semantic segmentation. In this paper, we proposed a segmentation framework based on DeepLab v3+ neural network and applied it to the problem of hyperspectral imagery classification (HSIC). The dimensionality reduction of the hyperspectral image is performed using principal component analysis (PCA). DeepLab v3+ is used to extract spatial features, and those are fused with spectral features. A support vector machine (SVM) classifier is used for fitting and classification. Experimental results show that the framework proposed in this paper outperforms most traditional machine learning algorithms and deep-learning algorithms in hyperspectral imagery classification tasks.