Inhibitory effects of tamoxifen and tanshinone, alone or in combination, on the proliferation of breast cancer cells via activation of p38 MAPK signalling pathway

Purpose: To investigate the effects of tamoxifen and tanshinone administered individually or in combination, on the proliferation of breast cancer (BC) cells, and the underlying mechanism(s) of action. Methods: Human breast cancer cell lines (SNU-306, SNU-334 and SNU-1528), and normal primary mammary epithelial cell line (HMEC) were cultured at 37 °C in Dulbecco's modified Eagle's medium (DMEM) supplemented with 5 % fetal bovine serum (FBS), l glutamine (2 mM), penicillin (100 U/ml) and streptomycin (100 μg/ml) in a humidified incubator containing 5 % CO2. Cell proliferation was determined using MTT assay, while real-time quantitative polymerase chain reaction (qRT-PCR) was used to determine the expressions of apoptosis-related genes. The expressions of p38 mitogenactivated protein kinases (p38 MAPK) were determined by Western blotting. Results: There were only few viable cells in tamoxifen- and tanshinone-treated wells, and cell viability was concentration-dependently reduced. Treatment of SNU-306 cells with tamoxifen (30 µM) or tanshinone (20 µM) alone significantly reduced the expression of Wip1 after 72 h of incubation, and the level of expression was significantly reduced in SNU-306 cells treated with combination of tamoxifen and tanshinones, relative to those treated with tamoxifen or tanshinone alone (p < 0.05). The extent of apoptosis was significantly higher in SNU-306 cells treated with tamoxifen or tanshinone alone or in combination than in control cells (p < 0.05). Expressions of Bax, caspase 3 and p53 were significantly higher in SNU-306 cells than in control cells, and were significantly higher in SNU-306 cells treated with combination of tamoxifen and tanshinone than in those treated with tamoxifen or tanshinone alone (p < 0.05). The level of expression of MAPK was significantly higher in SNU-306 cells treated with tamoxifen or tanshinone alone, and in combination treatment, than in control cells (p < 0.05). Conclusion: Tamoxifen and tanshinone administered alone or in combination promote apoptosis in BC cells via mechanisms involving the up-regulation and phosphorylation of MAPK

Social Metaverse: Challenges and Solutions

Social metaverse is a shared digital space combining a series of interconnected virtual worlds for users to play, shop, work, and socialize. In parallel with the advances of artificial intelligence (AI) and growing awareness of data privacy concerns, federated learning (FL) is promoted as a paradigm shift towards privacy-preserving AI-empowered social metaverse. However, challenges including privacy-utility tradeoff, learning reliability, and AI model thefts hinder the deployment of FL in real metaverse applications. In this paper, we exploit the pervasive social ties among users/avatars to advance a social-aware hierarchical FL framework, i.e., SocialFL for a better privacy-utility tradeoff in the social metaverse. Then, an aggregator-free robust FL mechanism based on blockchain is devised with a new block structure and an improved consensus protocol featured with on/off-chain collaboration. Furthermore, based on smart contracts and digital watermarks, an automatic federated AI (FedAI) model ownership provenance mechanism is designed to prevent AI model thefts and collusive avatars in social metaverse. Experimental findings validate the feasibility and effectiveness of proposed framework. Finally, we envision promising future research directions in this emerging area.Comment: Accepted by Internet of Things Magazine in 23-May 202

Semantic Guided Level-Category Hybrid Prediction Network for Hierarchical Image Classification

Hierarchical classification (HC) assigns each object with multiple labels organized into a hierarchical structure. The existing deep learning based HC methods usually predict an instance starting from the root node until a leaf node is reached. However, in the real world, images interfered by noise, occlusion, blur, or low resolution may not provide sufficient information for the classification at subordinate levels. To address this issue, we propose a novel semantic guided level-category hybrid prediction network (SGLCHPN) that can jointly perform the level and category prediction in an end-to-end manner. SGLCHPN comprises two modules: a visual transformer that extracts feature vectors from the input images, and a semantic guided cross-attention module that uses categories word embeddings as queries to guide learning category-specific representations. In order to evaluate the proposed method, we construct two new datasets in which images are at a broad range of quality and thus are labeled to different levels (depths) in the hierarchy according to their individual quality. Experimental results demonstrate the effectiveness of our proposed HC method.Comment: 3 figure

FrustumFormer: Adaptive Instance-aware Resampling for Multi-view 3D Detection

The transformation of features from 2D perspective space to 3D space is essential to multi-view 3D object detection. Recent approaches mainly focus on the design of view transformation, either pixel-wisely lifting perspective view features into 3D space with estimated depth or grid-wisely constructing BEV features via 3D projection, treating all pixels or grids equally. However, choosing what to transform is also important but has rarely been discussed before. The pixels of a moving car are more informative than the pixels of the sky. To fully utilize the information contained in images, the view transformation should be able to adapt to different image regions according to their contents. In this paper, we propose a novel framework named FrustumFormer, which pays more attention to the features in instance regions via adaptive instance-aware resampling. Specifically, the model obtains instance frustums on the bird's eye view by leveraging image view object proposals. An adaptive occupancy mask within the instance frustum is learned to refine the instance location. Moreover, the temporal frustum intersection could further reduce the localization uncertainty of objects. Comprehensive experiments on the nuScenes dataset demonstrate the effectiveness of FrustumFormer, and we achieve a new state-of-the-art performance on the benchmark. Codes and models will be made available at https://github.com/Robertwyq/Frustum.Comment: Accepted to CVPR 202

4D Unsupervised Object Discovery

Object discovery is a core task in computer vision. While fast progresses have been made in supervised object detection, its unsupervised counterpart remains largely unexplored. With the growth of data volume, the expensive cost of annotations is the major limitation hindering further study. Therefore, discovering objects without annotations has great significance. However, this task seems impractical on still-image or point cloud alone due to the lack of discriminative information. Previous studies underlook the crucial temporal information and constraints naturally behind multi-modal inputs. In this paper, we propose 4D unsupervised object discovery, jointly discovering objects from 4D data -- 3D point clouds and 2D RGB images with temporal information. We present the first practical approach for this task by proposing a ClusterNet on 3D point clouds, which is jointly iteratively optimized with a 2D localization network. Extensive experiments on the large-scale Waymo Open Dataset suggest that the localization network and ClusterNet achieve competitive performance on both class-agnostic 2D object detection and 3D instance segmentation, bridging the gap between unsupervised methods and full supervised ones. Codes and models will be made available at https://github.com/Robertwyq/LSMOL.Comment: Accepted by NeurIPS 2022. 17 pages, 6 figure

Online Adaptation for Implicit Object Tracking and Shape Reconstruction in the Wild

Tracking and reconstructing 3D objects from cluttered scenes are the key components for computer vision, robotics and autonomous driving systems. While recent progress in implicit function has shown encouraging results on high-quality 3D shape reconstruction, it is still very challenging to generalize to cluttered and partially observable LiDAR data. In this paper, we propose to leverage the continuity in video data. We introduce a novel and unified framework which utilizes a neural implicit function to simultaneously track and reconstruct 3D objects in the wild. Our approach adapts the DeepSDF model (i.e., an instantiation of the implicit function) in the video online, iteratively improving the shape reconstruction while in return improving the tracking, and vice versa. We experiment with both Waymo and KITTI datasets and show significant improvements over state-of-the-art methods for both tracking and shape reconstruction tasks. Our project page is at https://jianglongye.com/implicit-tracking .Comment: Accepted to RA-L 2022 & IROS 2022. Project page: https://jianglongye.com/implicit-trackin