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- 저자 : ( Jiajun Zhou ) (검색결과 2 건)
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LiYong Chen,Jing Zhou,JiaJun Zhao,FuRong Wang,XiangLan Sun,Ling Gao,YuLian Jiao,XiaoLei Hou,ChengYong Qin 생화학분자생물학회 2010 Experimental and molecular medicine Vol.42 No.3
Chronic and heavy alcohol consumption is one of the causes of heart diseases. However, the effects of ethanol on insulin sensitivity in myocardium has been unclear. To investigate the effects of ethanol on the expression of AMP-activated protein kinase (AMPK), myocyte enhancer factor 2 (MEF2) and glucose transporter 4 (GLUT4), all of which are involved in the regulation of insulin sensitivity, in the myocardium, we performed three parts of experiments in vivo and in vitro. I: Rats were injected with 5-amino-4-imidazolecarboxamide ribonucleotide (AICAR, 0.8 mg.kg-1) for 2 h. II:Rats received different dose (0.5, 2.5 or 5 g.kg-1.d-1) of ethanol for 22-week. III: Primary neonatal rat cardiomyocytes were isolated and treated with or without 100 mM ethanol or 1 mM AICAR for 4 h. The cardiac protein and mRNA expression of AMPKα subunits, MEF2and GLUT4 were observed by western-blotting and RT-PCR, respectively. Serum TNFα levels were assessed by ELISA. The results showed chronic ethanol exposure induced insulin resistance. Ethanol decreased the mRNA levels of AMPKα1 and α2, the protein levels of total- and phospho-AMPKα in cardiomyocytes. Similarly, ethanol showed inhibitory effects on both the mRNA and protein levels of MEF2A and 2D,and GLUT4 in a dose-response-like fashion. Correlation analysis implied an association between phospho-AMPKα and MEF2A or MEF2D, and between the levels of MEF2 protein and GLUT4 transcription. In addition,ethanol elevated serum TNFα level. Taken together,chronic ethanol exposure decreases the expression of AMPKα and MEF2, and is associated with GLUT4 decline in rat myocardium.
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PC-SAN: Pretraining-Based Contextual Self-Attention Model for Topic Essay Generation
( Fuqiang Lin ),( Xingkong Ma ),( Yaofeng Chen ),( Jiajun Zhou ),( Bo Liu ) 한국인터넷정보학회 2020 KSII Transactions on Internet and Information Syst Vol.14 No.8
Automatic topic essay generation (TEG) is a controllable text generation task that aims to generate informative, diverse, and topic-consistent essays based on multiple topics. To make the generated essays of high quality, a reasonable method should consider both diversity and topic-consistency. Another essential issue is the intrinsic link of the topics, which contributes to making the essays closely surround the semantics of provided topics. However, it remains challenging for TEG to fill the semantic gap between source topic words and target output, and a more powerful model is needed to capture the semantics of given topics. To this end, we propose a pretraining-based contextual self-attention (PC-SAN) model that is built upon the seq2seq framework. For the encoder of our model, we employ a dynamic weight sum of layers from BERT to fully utilize the semantics of topics, which is of great help to fill the gap and improve the quality of the generated essays. In the decoding phase, we also transform the target-side contextual history information into the query layers to alleviate the lack of context in typical self-attention networks (SANs). Experimental results on large-scale paragraph-level Chinese corpora verify that our model is capable of generating diverse, topic-consistent text and essentially makes improvements as compare to strong baselines. Furthermore, extensive analysis validates the effectiveness of contextual embeddings from BERT and contextual history information in SANs.
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