3D Instance segmentation is a fundamental task in computer vision. Effective segmentation plays an important role in robotic tasks, augmented reality, autonomous driving, etc. With the ascendancy of convolutional neural networks in 2D image processing, the use of deep learning methods to segment 3D point clouds receives much attention. A great convergence of training loss often requires a large amount of human-annotated data, while making such a 3D dataset is time-consuming. This paper proposes a method for training convolutional neural networks to predict instance segmentation results using synthetic data. The proposed method is based on the SGPN framework. We replaced the original feature extractor with ''dynamic graph convolutional neural networks'' that learned how to extract local geometric features and proposed a simple and effective loss function, making the network more focused on hard examples. We experimentally proved that the proposed method significantly outperforms the state-of-the-art method in both Stanford 3D Indoor Semantics Dataset and our datasets.
Gut
bacteria and their metabolites influence the immune microenvironment
of liver through the gut-liver axis, thus representing emerging therapeutic
targets for liver cancer therapy. However, directly manipulating gut
microbiota or their metabolites is not practical in clinic since the
safety concerns and the complicated mechanism of action. Considering
the dysregulated bile acid profiles associated with liver cancer,
here we propose a strategy that directly manipulates the primary and
secondary bile acid receptors through nanoapproach as an alternative
and more precise way for liver cancer therapy. We show that nanodelivery
of bile acid receptor modulators elicited robust antitumor immune
responses and significantly changed the immune microenvironment in
the murine hepatic tumor. In addition, ex vivo stimulation
on both murine and patient hepatic tumor tissues suggests the observation
here may be meaningful for clinical practice. This study elucidates
a novel and precise strategy for liver cancer immunotherapy.
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