microRNAs (miRNAs)are endogenous small non-coding RNAs that bind to mRNAs and target them for cleavage and/or translational repression,leading to gene silencing.We previously developed short tandem target mimic (STTM)...microRNAs (miRNAs)are endogenous small non-coding RNAs that bind to mRNAs and target them for cleavage and/or translational repression,leading to gene silencing.We previously developed short tandem target mimic (STTM)technology to deactivate endogenous miRNAs in Arabidopsis.Here,we created hundreds of STTMs that target both conserved and species-specific miRNAs in Arabidopsis,tomato,rice,and maize,providing a resource for the functional interrogation of miRNAs.We not only revealed the functions of several miRNAs in plant development,but also demonstrated that tissue-specific inactivation of a few miRNAs in rice leads to an increase in grain size without adversely affecting overall plant growth and development.RNA-seq and small RNAseq analyses of STTM156/157 and STTM165/166 transgenic plants revealed the roles of these miRNAs in plant hormone biosynthesis and activation,secondary metabolism,and ion-channel activity-associated electrophysiology,demonstrating that STTM technology is an effective approach for studying miRNA functions.To facilitate the study and application of STTM transgenic plants and to provide a useful platform for storing and sharing of information about miRNA-regulated gene networks,we have established an online Genome Browser (https://blossom.ffr.mtu.edu/designindex2.php) to display the transcriptomic and miRNAomic changes in STTMinduced miRNA knockdown plants.展开更多
Relation classification is a crucial component in many Natural Language Processing(NLP) systems. In this paper, we propose a novel bidirectional recurrent neural network architecture(using Long Short-Term Memory,LSTM,...Relation classification is a crucial component in many Natural Language Processing(NLP) systems. In this paper, we propose a novel bidirectional recurrent neural network architecture(using Long Short-Term Memory,LSTM, cells) for relation classification, with an attention layer for organizing the context information on the word level and a tensor layer for detecting complex connections between two entities. The above two feature extraction operations are based on the LSTM networks and use their outputs. Our model allows end-to-end learning from the raw sentences in the dataset, without trimming or reconstructing them. Experiments on the SemEval-2010 Task 8dataset show that our model outperforms most state-of-the-art methods.展开更多
基金the National Science Foundation,USA (IOS-1048216 and IOS-1340001)the National Natural Science Foundation of China (31571679,31501292,31871554)+1 种基金the Major Science and Technology Project of Henan Province (141100110600)the Support Plan of Science and Technology Innovation Team in Universities of Henan Province (171RTSTHN015),and the Key Scientific Research Project in Universities of Henan Province (16A210009).G.T.is also supported by the Guangdong Innovation Research Team Fund (2014ZT058078)and the 111 Project (D16014)to Henan University.S.T.was supported by a post-doctoral fellowship from Henan Agricultural University.F.M.was a visiting scholar supported by the China Scholarship Council (CSC).T.P.,Z.Z.,L.S.,and L.T.were visiting PhD students supported by scholarships from Henan Agricultural University.
文摘microRNAs (miRNAs)are endogenous small non-coding RNAs that bind to mRNAs and target them for cleavage and/or translational repression,leading to gene silencing.We previously developed short tandem target mimic (STTM)technology to deactivate endogenous miRNAs in Arabidopsis.Here,we created hundreds of STTMs that target both conserved and species-specific miRNAs in Arabidopsis,tomato,rice,and maize,providing a resource for the functional interrogation of miRNAs.We not only revealed the functions of several miRNAs in plant development,but also demonstrated that tissue-specific inactivation of a few miRNAs in rice leads to an increase in grain size without adversely affecting overall plant growth and development.RNA-seq and small RNAseq analyses of STTM156/157 and STTM165/166 transgenic plants revealed the roles of these miRNAs in plant hormone biosynthesis and activation,secondary metabolism,and ion-channel activity-associated electrophysiology,demonstrating that STTM technology is an effective approach for studying miRNA functions.To facilitate the study and application of STTM transgenic plants and to provide a useful platform for storing and sharing of information about miRNA-regulated gene networks,we have established an online Genome Browser (https://blossom.ffr.mtu.edu/designindex2.php) to display the transcriptomic and miRNAomic changes in STTMinduced miRNA knockdown plants.
基金supported by the National Natural Science Foundation of China (No. 61572505)ChanXueYan Prospective Project of Jiangsu Province (No. BY201502305)
文摘Relation classification is a crucial component in many Natural Language Processing(NLP) systems. In this paper, we propose a novel bidirectional recurrent neural network architecture(using Long Short-Term Memory,LSTM, cells) for relation classification, with an attention layer for organizing the context information on the word level and a tensor layer for detecting complex connections between two entities. The above two feature extraction operations are based on the LSTM networks and use their outputs. Our model allows end-to-end learning from the raw sentences in the dataset, without trimming or reconstructing them. Experiments on the SemEval-2010 Task 8dataset show that our model outperforms most state-of-the-art methods.
