Single-cell transcriptome-wide Mendelian randomization and colocalization reveals immune-mediated regulatory mechanisms and drug targets for COVID-19Research in context by Hui Ying, Xueyan Wu, Xiaojing Jia, Qianqian Yang, Haoyu Liu, Huiling Zhao, Zhihe Chen, Min Xu, Tiange Wang, Mian Li, Zhiyun Zhao, Ruizhi Zheng, Shuangyuan Wang, Hong Lin, Yu Xu, Jieli Lu, Weiqing Wang, Guang Ning, Jie Zheng, Yufang Bi

“…For pathway analyses, we found the putative causal genes were enriched in natural killer (NK) recruiting cells but de-enriched in NK cells. Using a deep learning model, we found 107 (81%) of the putative causal genes (41 novel genes) were predicted to interact with SARS-COV-2 proteins. …”
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Review on meta-learning by Yingzhao ZHU, Man LI

“…Deep learning and reinforcement learning are limited by small sample data set, which is impossible to realize the strong generalization learning ability.Meta-learning can make up for their shortcomings effectively.The values formed by accumulated experience feedback the corresponding signals to promote the model to adjust itself.It allows the artificial intelligence to learn to complete complex tasks quickly, which implements true artificial intelligence.Firstly, the basic principles of meta-learning were outlined.Secondly, according to the different forms of meta-knowledge, the research status of various methods was analyzed in depth.Finally, the application potential and the future development trends of meta-learning was discussed .…”
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Incremental Instance-Oriented 3D Semantic Mapping via RGB-D Cameras for Unknown Indoor Scene by Wei Li, Junhua Gu, Benwen Chen, Jungong Han

“…To ensure an efficient reconstruction of 3D objects with semantic and instance IDs, the input RGB images are operated by a real-time deep-learned object detector. To obtain accurate point cloud cluster, we adopt the Gaussian mixture model as an optimizer after processing 2D to 3D projection. …”
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