We are grateful to Dr. Jiankang Zhu for providing us with the vector 35S-Cas9-SK. No conflict of interest declared.

Summary For glucose-stimulated insulin secretion (GSIS) insulin granules have to be localized close to the plasma membrane. The role of microtubule-dependent transport in granule positioning and GSIS has been debated. Here, we report that microtubules, counterintuitively, restrict granule availability for secretion. In β cells, microtubules originate at the Golgi and form a dense non-radial meshwork. Non-directional transport along these microtubules limits granule dwelling at the cell periphery, restricting granule availability for secretion. High glucose destabilizes microtubules, decreasing their density; such local microtubule depolymerization is necessary for GSIS, likely because granule withdrawal from the cell periphery becomes inefficient. Consistently, microtubule depolymerization by nocodazole blocks granule withdrawal, increases their concentration at exocytic sites, and dramatically enhances GSIS in vitro and in mice. Furthermore, glucose-driven MT destabilization is balanced by new microtubule formation, which likely prevents over-secretion. Importantly, microtubule density is greater in dysfunctional β cells of diabetic mice.

In this paper, we focus on exploring the robustness of the 3D object detection in point clouds, which has been rarely discussed in existing approaches. We observe two crucial phenomena: 1) the detection accuracy of the hard objects, e.g., Pedestrians, is unsatisfactory, 2) when adding additional noise points, the performance of existing approaches decreases rapidly. To alleviate these problems, a novel TANet is introduced in this paper, which mainly contains a Triple Attention (TA) module, and a Coarse-to-Fine Regression (CFR) module. By considering the channel-wise, point-wise and voxel-wise attention jointly, the TA module enhances the crucial information of the target while suppresses the unstable cloud points. Besides, the novel stacked TA further exploits the multi-level feature attention. In addition, the CFR module boosts the accuracy of localization without excessive computation cost. Experimental results on the validation set of KITTI dataset demonstrate that, in the challenging noisy cases, i.e., adding additional random noisy points around each object, the presented approach goes far beyond state-of-the-art approaches. Furthermore, for the 3D object detection task of the KITTI benchmark, our approach ranks the first place on Pedestrian class, by using the point clouds as the only input. The running speed is around 29 frames per second.