The Saccharomyces cerevisiae MATa1 and MAT alpha 2 homeodomain proteins, which play a role in determining yeast cell type, form a heterodimer that binds DNA and represses transcription in a cell type-specific manner. Whereas the alpha 2 and a1 proteins on their own have only modest affinity for DNA, the a1/alpha 2 heterodimer binds DNA with high specificity and affinity. The three-dimensional crystal structure of the a1/alpha 2 homeodomain heterodimer bound to DNA was determined at a resolution of 2.5 A. The a1 and alpha 2 homeodomains bind in a head-to-tail orientation, with heterodimer contacts mediated by a 16-residue tail located carboxyl-terminal to the alpha 2 homeodomain. This tail becomes ordered in the presence of a1, part of it forming a short amphipathic helix that packs against the a1 homeodomain between helices 1 and 2. A pronounced 60 degree bend is induced in the DNA, which makes possible protein-protein and protein-DNA contacts that could not take place in a straight DNA fragment. Complex formation mediated by flexible protein-recognition peptides attached to stably folded DNA binding domains may prove to be a general feature of the architecture of other classes of eukaryotic transcriptional regulators.

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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Micro-light-emitting-diodes (μLEDs) with size-independent peak external quantum efficiency behavior was demonstrated from 10 × 10 μm2 to 100 × 100 μm2 by employing a combination of chemical treatment and atomic-layer deposition (ALD) sidewall passivation. The chemical treatment and sidewall passivation improved the ideality factors of μLEDs from 3.4 to 2.5. The results from the combination of chemical treatment and ALD sidewall passivation suggest the issue of size dependent efficiency can be resolved with proper sidewall treatments after dry etching.

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