Wnt/β-catenin signaling is a conserved pathway crucially governing development, tissue homeostasis and oncogenesis in metazoan. Through screening, we identified a deubiquitinase (DUB) USP10 as a novel modulator of Wnt/β-catenin signaling. Mechanistically, USP10 binds to Axin1 via conserved motifs and stabilizes Axin1 through K48-linked deubiquitination. And in parallel, USP10 tethers Axin1 and β-catenin physically, via stabilizing the phase separation of Axin1 through its intrinsically-disordered regions, which is regardless of its enzymatic activity. Functionally, we show USP10 prominently regulates zebrafish embryonic development and murine intestinal homeostasis by antagonizing Wnt/β-catenin signaling. Additionally in human colorectal cancer, USP10 substantially represses cancer growth and correlates with Wnt/β-catenin magnitude clinically. Collectively, we discovered USP10 functioning in multiple biological processes through repressing Wnt/β-catenin signaling and unearthed a novel DUB-dependent and -independent dual-regulating mechanism by which USP10 utilizes in Wnt regulation context-dependently. Our study also suggested the potential of USP10 inhibitor in treating Wnt-related diseases.
The number of available non-rigid 3D models in various areas increases steadily. The local features are more effective than global features for the search of these non-rigid 3D models. Global descriptors fail to consistently compensate for the intra-class variability of non-rigid 3D models. To solve this problem, we propose a non-rigid 3D model retrieval method based on multi-scale local features. Firstly, we extract keypoints at multiple scales automaticlly. Then, the Heat Kernel Signature (HKS) local descriptors are computed for each keypoint. However, the HKS descriptors are sensitive to scale. In order to solve this problem, the HKS descriptors are put into the Bag-of-Features (BOF) framework. In the BOF framework, we use a kind of histogram equalization technique to make our feature descriptor robust to model scaling. Experimental results on two public benchmarks show that our algorithm can achieve satisfactory retrieval performance for the non-rigid 3D models.
The number of non-rigid 3D models increases steadily in various areas. It is imperative to develop efficient retrieval system for 3D non-rigid models. As we know, global features fail to consistently describe the intra-class variability of non-rigid 3D models, the local features are more effective than global features for the retrieval of non-rigid 3D models. In this paper, we use Heat Kernel Signature (HKS) as the local features to represent non-rigid 3D models and further propose the retrieval method based on scale-invariant local features. Firstly, we extract key-points at multiple scales automatically. Then, the HKS local features are computed for each key-point. However, the HKS features are sensitive to scale. In order to solve this problem, we convert the scale problem into the translation problem using the diffusion Wavelets transform. To solve the translation problem, we use a kind of histogram equalization technique. Finally, we use the bipartite graph matching algorithm to compute similarity between the 3D models. Experimental results on two public benchmarks show that our method outperforms state-of-the-art methods for non-rigid 3D models retrieval.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.