Neonicotinoids(NEOs),a large class of organic compounds,are a type of commonly used pesticide for crop protection.Their uptake and accumulation in plants are prerequisites for their intra-and intercellular move-ments,...Neonicotinoids(NEOs),a large class of organic compounds,are a type of commonly used pesticide for crop protection.Their uptake and accumulation in plants are prerequisites for their intra-and intercellular move-ments,transformation,and function.Understanding the molecular mechanisms that underpin NEO uptake by plants is crucial for effective application,which remains elusive.Here,we demonstrate that NEOs enter plant cells primarily through the transmembrane symplastic pathway and accumulate mainly in the cytosol.Two plasma membrane intrinsic proteins discovered in Brassica rapa,BraPIP1;1 and BraPIP2;1,were found to encode aquaporins(AQPs)that are highly permeable to NEOs in different plant species and facilitate NEO subcellular diffusion and accumulation.Their conserved transport function was further demonstrated in Xenopus laevis oocyte and yeast assays.BraPIP1;1 and BraPIP2;1 gene knockouts and interaction as-says suggested that their proteins can form functional heterotetramers.Assessment of the potential of mean force indicated a negative correlation between NEO uptake and the energy barrier of BraPIP1;1 chan-nels.This study shows that AQPs transport organic compounds with greater osmolarity than previously thought,providing new insight into the molecular mechanisms of organic compound uptake and facilitating innovations in systemic pesticides.展开更多
microRNAs(miRNAs)are a class of non-coding functional small RNA composed of 21e23 nucleotides,having multiple associations with liver fibrosis.Fibrosis-associated miRNAs are roughly classified into pro-fibrosis or ant...microRNAs(miRNAs)are a class of non-coding functional small RNA composed of 21e23 nucleotides,having multiple associations with liver fibrosis.Fibrosis-associated miRNAs are roughly classified into pro-fibrosis or anti-fibrosis types.The former is capable of activating hepatic stellate cells(HSCs)by modulating pro-fibrotic signaling pathways,mainly including TGF-b/SMAD,WNT/b-catenin,and Hedgehog;the latter is responsible for maintenance of the quiescent phenotype of normal HSCs,phenotypic reversion of activated HSCs(aHSCs),inhibition of HSCs proliferation and suppression of the extracellular matrix-associated gene expression.Moreover,several miRNAs are involved in regulation of liver fibrosis via alternative mechanisms,such as interacting between hepatocytes and other liver cells via exosomes and increasing autophagy of aHSCs.Thus,understanding the role of these miRNAs may provide new avenues for the development of novel interventions against hepatic fibrosis.展开更多
Aiming at the lack of classification and good standard corpus in the task of joint entity and relationship extraction in the current Chinese academic field, this paper builds a dataset in management science that can b...Aiming at the lack of classification and good standard corpus in the task of joint entity and relationship extraction in the current Chinese academic field, this paper builds a dataset in management science that can be used for joint entity and relationship extraction, and establishes a deep learning model to extract entity and relationship information from scientific texts. With the definition of entity and relation classification, we build a Chinese scientific text corpus dataset based on the abstract texts of projects funded by the National Natural Science Foundation of China(NSFC) in 2018–2019. By combining the word2vec features with the clue word feature which is a kind of special style in scientific documents, we establish a joint entity relationship extraction model based on the Bi LSTM-CNN-CRF model for scientific information extraction. The dataset we constructed contains 13060 entities(not duplicated) and 9728 entity relation labels. In terms of entity prediction effect, the accuracy rate of the constructed model reaches 69.15%, the recall rate reaches 61.03%, and the F1 value reaches 64.83%. In terms of relationship prediction effect, the accuracy rate is higher than that of entity prediction, which reflects the effectiveness of the input mixed features and the integration of local features with CNN layer in the model.展开更多
基金supported in part by the National Natural Science Foundation of China (nos.32172448 and 3177219)the Jiangsu Agricultural Science and Technology Innovation Fund (CX (21)2002)the National Key Research and Development Program (2021YFD1700803),and the USDA (HAW05032R).
文摘Neonicotinoids(NEOs),a large class of organic compounds,are a type of commonly used pesticide for crop protection.Their uptake and accumulation in plants are prerequisites for their intra-and intercellular move-ments,transformation,and function.Understanding the molecular mechanisms that underpin NEO uptake by plants is crucial for effective application,which remains elusive.Here,we demonstrate that NEOs enter plant cells primarily through the transmembrane symplastic pathway and accumulate mainly in the cytosol.Two plasma membrane intrinsic proteins discovered in Brassica rapa,BraPIP1;1 and BraPIP2;1,were found to encode aquaporins(AQPs)that are highly permeable to NEOs in different plant species and facilitate NEO subcellular diffusion and accumulation.Their conserved transport function was further demonstrated in Xenopus laevis oocyte and yeast assays.BraPIP1;1 and BraPIP2;1 gene knockouts and interaction as-says suggested that their proteins can form functional heterotetramers.Assessment of the potential of mean force indicated a negative correlation between NEO uptake and the energy barrier of BraPIP1;1 chan-nels.This study shows that AQPs transport organic compounds with greater osmolarity than previously thought,providing new insight into the molecular mechanisms of organic compound uptake and facilitating innovations in systemic pesticides.
基金supported by grants from the National Natural Science Foundation of China(No.32072889,U1703104)Key R&D Program of Zhejiang Province(China)(No.2019C02052)Scientific Research and Development Talent Fund of Zhejiang Agriculture and Forestry University,China(No.2021LFR038).
文摘microRNAs(miRNAs)are a class of non-coding functional small RNA composed of 21e23 nucleotides,having multiple associations with liver fibrosis.Fibrosis-associated miRNAs are roughly classified into pro-fibrosis or anti-fibrosis types.The former is capable of activating hepatic stellate cells(HSCs)by modulating pro-fibrotic signaling pathways,mainly including TGF-b/SMAD,WNT/b-catenin,and Hedgehog;the latter is responsible for maintenance of the quiescent phenotype of normal HSCs,phenotypic reversion of activated HSCs(aHSCs),inhibition of HSCs proliferation and suppression of the extracellular matrix-associated gene expression.Moreover,several miRNAs are involved in regulation of liver fibrosis via alternative mechanisms,such as interacting between hepatocytes and other liver cells via exosomes and increasing autophagy of aHSCs.Thus,understanding the role of these miRNAs may provide new avenues for the development of novel interventions against hepatic fibrosis.
基金Supported by the National Natural Science Foundation of China (71804017)the R&D Program of Beijing Municipal Education Commission (KZ202210005013)the Sichuan Social Science Planning Project (SC22B151)。
文摘Aiming at the lack of classification and good standard corpus in the task of joint entity and relationship extraction in the current Chinese academic field, this paper builds a dataset in management science that can be used for joint entity and relationship extraction, and establishes a deep learning model to extract entity and relationship information from scientific texts. With the definition of entity and relation classification, we build a Chinese scientific text corpus dataset based on the abstract texts of projects funded by the National Natural Science Foundation of China(NSFC) in 2018–2019. By combining the word2vec features with the clue word feature which is a kind of special style in scientific documents, we establish a joint entity relationship extraction model based on the Bi LSTM-CNN-CRF model for scientific information extraction. The dataset we constructed contains 13060 entities(not duplicated) and 9728 entity relation labels. In terms of entity prediction effect, the accuracy rate of the constructed model reaches 69.15%, the recall rate reaches 61.03%, and the F1 value reaches 64.83%. In terms of relationship prediction effect, the accuracy rate is higher than that of entity prediction, which reflects the effectiveness of the input mixed features and the integration of local features with CNN layer in the model.