SUMMARY Histone acetylation plays critical roles in chromatin remodeling, DNA repair, and epigenetic regulation of gene expression, but the underlying mechanisms are unclear. Proteasomes usually catalyze ATP- and polyubiquitin-dependent proteolysis. Here we show that the proteasomes containing the activator PA200 catalyze the polyubiquitin-independent degradation of histones. Most proteasomes in mammalian testes (“spermatoproteasomes”) contain a spermatid/sperm-specific α-subunit α4s/PSMA8 and/or the catalytic β-subunits of immunoproteasomes in addition to PA200. Deletion of PA200 in mice abolishes acetylation-dependent degradation of somatic core histones during DNA double-strand breaks, and delays core histone disappearance in elongated spermatids. Purified PA200 greatly promotes ATP-independent proteasomal degradation of the acetylated core histones, but not polyubiquitinated proteins. Furthermore, acetylation on histones is required for their binding to the bromodomain-like regions in PA200 and its yeast ortholog, Blm10. Thus, PA200/Blm10 specifically targets the core histones for acetylation-mediated degradation by proteasomes, providing mechanisms by which acetylation regulates histone degradation, DNA repair, and spermatogenesis.

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Image inpainting techniques have shown significant improvements by using deep neural networks recently. However, most of them may either fail to reconstruct reasonable structures or restore fine-grained textures. In order to solve this problem, in this paper, we propose a two-stage model which splits the inpainting task into two parts: structure reconstruction and texture generation. In the first stage, edgepreserved smooth images are employed to train a structure reconstructor which completes the missing structures of the inputs. In the second stage, based on the reconstructed structures, a texture generator using appearance flow is designed to yield image details. Experiments on multiple publicly available datasets show the superior performance of the proposed network.

Video anomaly detection under weak labels is formulated as a typical multiple-instance learning problem in previous works. In this paper, we provide a new perspective, i.e., a supervised learning task under noisy labels. In such a viewpoint, as long as cleaning away label noise, we can directly apply fully supervised action classifiers to weakly supervised anomaly detection, and take maximum advantage of these well-developed classifiers. For this purpose, we devise a graph convolutional network to correct noisy labels. Based upon feature similarity and temporal consistency, our network propagates supervisory signals from high-confidence snippets to low-confidence ones. In this manner, the network is capable of providing cleaned supervision for action classifiers. During the test phase, we only need to obtain snippet-wise predictions from the action classifier without any extra post-processing. Extensive experiments on 3 datasets at different scales with 2 types of action classifiers demonstrate the efficacy of our method. Remarkably, we obtain the frame-level AUC score of 82.12% on UCF-Crime.

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