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Abstract Extracellular matrix (ECM) assembly/disassembly is a critical regulator for airway epithelial development and remodeling. Airway organoid is widely used in respiratory research, yet there is limited study to indicate the roles and mechanisms of ECM organization in epithelial growth and differentiation by using in vitro organoid system. Moreover, most of current Matrigel-based airway organoids are in basal-out orientation where accessing the apical surface is challenging. We present a human apical-out airway organoid using a biochemically defined hybrid hydrogel system. During human nasal epithelial progenitor cells (hNEPCs) differentiation, the gel gradually degrade, leading to the organoid apical surfaces facing outward. The expression and activity of ECM-degrading enzymes, matrix metalloproteinases (MMP7, MMP9, MMP10 and MMP13) increases during organoid differentiation, where inhibition of MMPs significantly suppresses the normal ciliation, resulting in increased goblet cell proportion. Moreover, a decrease of MMPs is found in goblet cell hyperplastic epithelium in inflammatory mucosa. This system reveals essential roles of epithelial-derived MMPs on epithelial cell fate determination, and provides an applicable platform enabling further study for ECM in regulating airway development in health and diseases.

Abstract Action recognition has been applied in fields such as smart homes, gaming, traffic management, and security monitoring. Motion recognition is helpful for biomechanical analysis, auxiliary training systems, table tennis robots, motion-sensing games, virtual reality and other fields. In our study, we collected data on table tennis skill motion, created the TTMD6 dataset, and analyzed the characteristics of table tennis paddle trajectories. We propose a motion recognition algorithm to recognize paddle trajectories. Other research has used multijoint data to identify actions, while we use only the paddle trajectory to recognize table tennis skill motions, accelerating the speed of motion recognition. Therefore, it is feasible to use paddle trajectories to recognize table tennis skill motions.