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The miR164-TaNAC14 module regulates root development and abiotic-stress tolerance in wheat seedlings 被引量:1
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作者 CHI Qing du lin-ying +6 位作者 MA Wen NIU Ruo-yu WU Bao-wei GUO Li-jian MA Meng LIU Xiang-li ZHAO Hui-xian 《Journal of Integrative Agriculture》 SCIE CAS CSCD 2023年第4期981-998,共18页
Previous studies have revealed the miR164 family and the miR164-targeted NAC transcription factor genes in rice(Oryza sativa)and Arabidopsis that play versatile roles in developmental processes and stress responses.In... Previous studies have revealed the miR164 family and the miR164-targeted NAC transcription factor genes in rice(Oryza sativa)and Arabidopsis that play versatile roles in developmental processes and stress responses.In wheat(Triticum aestivum L.),we found nine genetic loci of tae-miR164(tae-MIR164 a to i)producing two mature sequences that downregulate the expression of three newly identified target genes of TaNACs(TaNAC1,TaNAC11,and TaNAC14)by the cleavage of the respective mRNAs.Overexpression of tae-miR164 or one of its target genes(TaNAC14)demonstrated that the miR164-TaNAC14 module greatly affects root growth and development and stress(drought and salinity)tolerance in wheat seedlings,and TaNAC14 promotes root growth and development in wheat seedlings and enhances drought tolerance,while tae-miR164 inhibits root development and reduces drought and salinity tolerance by downregulating the expression of TaNAC14.These findings identify the miR164-TaNAC14 module as well as other taemiR164-regulated genes which can serve as new genetic resources for stress-resistance wheat breeding. 展开更多
关键词 Triticum aestivum L. tae-miR164 miR164-targeted TaNACs miR164-TaNAC14 module growth and development abiotic-stress tolerance
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改进的BP神经网络对飞机换热器结垢厚度预测 被引量:1
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作者 杜林颖 于鸿彬 +1 位作者 侯立国 汪天京 《计算机仿真》 北大核心 2020年第1期27-30,共4页
为了节约飞机维修成本,准确预测换热器结垢厚度,通过利用改进的BP神经网络预测模型,利用25组数据,建立了换热器结垢厚度与四个因素(环境温度、空调系统进口压力、初级换热器出口温度、次级换热器出口温度)之间的网络预测模型。模型包括... 为了节约飞机维修成本,准确预测换热器结垢厚度,通过利用改进的BP神经网络预测模型,利用25组数据,建立了换热器结垢厚度与四个因素(环境温度、空调系统进口压力、初级换热器出口温度、次级换热器出口温度)之间的网络预测模型。模型包括4个输入神经元,9个隐含层神经元和1个输出层神经元。训练结果表明,改进之后的BP神经网络模型不仅克服了原始BP神经网络收敛速度慢,稳定性差的特点,还可以以较高的精度预测换热器的结垢厚度。 展开更多
关键词 飞机热交换器 结垢厚度 预测
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