Key molecular lesions in colorectal and other cancers cause beta-catenin-dependent transactivation of T cell factor (Tcf)-dependent genes. Disruption of this signal represents an opportunity for rational cancer therapy. To identify compounds that inhibit association between Tcf4 and beta-catenin, we screened libraries of natural compounds in a high-throughput assay for immunoenzymatic detection of the protein-protein interaction. Selected compounds disrupt Tcf/beta-catenin complexes in several independent in vitro assays and potently antagonize cellular effects of beta-catenin-dependent activities, including reporter gene activation, c-myc or cyclin D1 expression, cell proliferation, and duplication of the Xenopus embryonic dorsal axis. These compounds thus meet predicted criteria for disrupting Tcf/beta-catenin complexes and define a general standard to establish mechanism-based activity of small molecule inhibitors of this pathogenic protein-protein interaction.

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We present a self-supervised Contrastive Video Representation Learning (CVRL) method to learn spatiotemporal visual representations from unlabeled videos. Inspired by the recently proposed self-supervised contrastive learning framework, our representations are learned using a contrastive loss, where two clips from the same short video are pulled together in the embedding space, while clips from different videos are pushed away. We study what makes for good data augmentation for video self-supervised learning and find both spatial and temporal information are crucial.In particular, we propose a simple yet effective temporally consistent spatial augmentation method to impose strong spatial augmentations on each frame of a video clip while maintaining the temporal consistency across frames. For Kinetics-600 action recognition, a linear classifier trained on representations learned by CVRL achieves 64.1% top-1 accuracy with a 3D-ResNet50 backbone, outperforming ImageNet supervised pre-training by 9.4% and SimCLR unsupervised pre-training by 16.1% using the same inflated 3D-ResNet50. The performance of CVRL can be further improved to 68.2% with a larger 3D-ResNet50 (4×) backbone, significantly closing the gap between unsupervised and supervised video representation learning.