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| 1 | Antigenicity of tissues and organs from GGTA1/CMAH/β4GalNT2 triple gene knockout pigs显示文摘Clinical xenotransplantations have been hampered by human preformed antibody-mediated damage of the xenografts.To overcome biological incompatibility between pigs and humans,one strategy is to remove the major antigens[Gal,Neu5 Gc,and Sd(a)]present on pig cells and tissues.Triple gene(GGTAI,CMAH,and β4 GalNT2)knockout(TKO)pigs were produced in our laboratory by CRISPR-Cas9 targeting.To investigate the antigenicity reduction in the TKO pigs,the expression levels of these three xenoantigens in the cornea,heart,liver,spleen,lung,kidney,and pancreas tissues were examined.The level of human IgG/IgM binding to those tissues was also investigated,with wildtype pig tissues as control.The results showed that aGal,Neu5 Gc,and Sd(a)were markedly positive in all the examined tissues in wildtype pigs but barely detected in TKO pigs.Compared to wildtype pigs,the liver,spleen,and pancreas of TKO pigs showed comparable levels of human IgG and IgM binding,whereas corneas,heart,lung,and kidney of TKO pigs exhibited significantly reduced human IgG and IgM binding.These results indicate that the antigenicity of TKO pig is significantly reduced and the remaining xenoantigens on porcine tissues can be eliminated via a gene targeting approach. | Ronggen Wang Miaomiao Ruan Runjie Zhang Lei Chen Xiaoxue Li Bin Fang Chu Li Xueyang Ren Jiying Liu Qiang Xiong Lining Zhang Yong Jin Lin Li Rongfeng Li Ying Wang Haiyuan Yang Yifan Dai | 2019 | The Journal of Biomedical Research2019，33，4： | 3 |
| 2 | CFD and digital particle tracking to assess flow characteristics in the labyrinth flow path of a drip irrigation emitter显示文摘 | Yunkai Li Peiling Yang Tingwu Xu Shumei Ren Xiongcai Lin Runjie Wei Hongbing Xu | 2008 | Irrigation Science2008，，5： | 2 |
| 3 | CFD and digital particle tracking to assess flow characteristics in the labyrinth flow path of a drip irrigation emitter显示文摘 | Yunkai Li Peiling Yang Tingwu Xu Shumei Ren Xiongcai Lin Runjie Wei Hongbing Xu | 2008 | Irrigation Science2008，，5： | 1 |
| 4 | Large sequence models for sequential decision-making:a survey显示文摘Transformer architectures have facilitated the development of large-scale and general-purpose sequence models for prediction tasks in natural language processing and computer vision,e.g.,GPT-3 and Swin Transformer.Although originally designed for prediction problems,it is natural to inquire about their suitability for sequential decision-making and reinforcement learning problems,which are typically beset by long-standing issues involving sample efficiency,credit assignment,and partial observability.In recent years,sequence models,especially the Transformer,have attracted increasing interest in the RL communities,spawning numerous approaches with notable effectiveness and generalizability.This survey presents a comprehensive overview of recent works aimed at solving sequential decision-making tasks with sequence models such as the Transformer,by discussing the connection between sequential decision-making and sequence modeling,and categorizing them based on the way they utilize the Transformer.Moreover,this paper puts forth various potential avenues for future research intending to improve the effectiveness of large sequence models for sequential decision-making,encompassing theoretical foundations,network architectures,algorithms,and efficient training systems. | Muning WEN Runji LIN Hanjing WANG Yaodong YANG Ying WEN Luo MAI Jun WANG Haifeng ZHANG Weinan ZHANG | 2023 | Frontiers of Computer Science2023，17，6： | 0 |
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