ARFGAP1 promotes the formation of COPI vesicles, suggesting function as a component of the coat

The role of GTPase-activating protein (GAP) that deactivates ADP-ribosylation factor 1 (ARF1) during the formation of coat protein I (COPI) vesicles has been unclear. GAP is originally thought to antagonize vesicle formation by triggering uncoating, but later studies suggest that GAP promotes cargo sorting, a process that occurs during vesicle formation. Recent models have attempted to reconcile these seemingly contradictory roles by suggesting that cargo proteins suppress GAP activity during vesicle formation, but whether GAP truly antagonizes coat recruitment in this process has not been assessed directly. We have reconstituted the formation of COPI vesicles by incubating Golgi membrane with purified soluble components, and find that ARFGAP1 in the presence of GTP promotes vesicle formation and cargo sorting. Moreover, the presence of GTPγS not only blocks vesicle uncoating but also vesicle formation by preventing the proper recruitment of GAP to nascent vesicles. Elucidating how GAP functions in vesicle formation, we find that the level of GAP on the reconstituted vesicles is at least as abundant as COPI and that GAP binds directly to the dilysine motif of cargo proteins. Collectively, these findings suggest that ARFGAP1 promotes vesicle formation by functioning as a component of the COPI coat

Adaptive Square-Root Unscented Kalman Filter Phase Unwrapping with Modified Phase Gradient Estimation

Phase unwrapping (PU) is a key program in data processing in the interferometric synthetic aperture radar (InSAR) technique, and its accuracy directly affects the quality of final SAR data products. However, PU in regions with large gradient changes and high noise has always been a difficult problem. To overcome the limitation, this article proposes an adaptive square-root unscented Kalman filter PU method. Specifically, a modified phase gradient estimation (PGE) algorithm is proposed, in which a Butterworth low-pass filter is embedded, and the PGE window can be adaptively adjusted according to phase root-mean-square errors of pixels. Furthermore, the outliers of the PGE results are detected and revised to obtain high-precision vertical and horizontal phase gradients. Finally, the unwrapped phase is calculated by the adaptive square-root unscented Kalman filter method. To the best of our knowledge, this article is the first to combine the modified PGE with an adaptive square-root unscented Kalman filter for PU. Two sets of simulated data and a set of TerraSAR-X/TanDEM-X real data were used for experimental verification. The experimental results demonstrated that the various improvement measures proposed in this article were effective. Additionally, compared with the minimum-cost flow algorithm (MCF), statistical-cost network-flow algorithm (SNAPHU) and unscented Kalman filter PU (UKFPU), the proposed method had better accuracy and model robustness