|
|
|
题名
|
作者
|
年代
|
出处
|
被引量
|
| 1 | Comparative genomic and transcriptomic analyses uncover the molecular basis of high nitrogen-use efficiency in the wheat cultivar Kenong 9204显示文摘Studying the regulatory mechanisms that drive nitrogen-use efficiency(NUE)in crops is important for sustainable agriculture and environmental protection.In this study,we generated a high-quality genome assembly for the high-NUE wheat cultivar Kenong 9204 and systematically analyzed genes related to nitrogen uptake and metabolism.By comparative analyses,we found that the high-affinity nitrate transporter gene family had expanded in Triticeae.Further studies showed that subsequent functional differentiation endowed the expanded family members with saline inducibility,providing a genetic basis for improving the adaptability of wheat to nitrogen deficiency in various habitats.To explore the genetic and molecular mechanisms of high NUE,we compared genomic and transcriptomic data from the high-NUE cultivar Kenong 9204(KN9204)and the low-NUE cultivar Jing 411 and quantified their nitrogen accumulation under high-and low-nitrogen conditions.Compared with Jing 411,KN9204 absorbed significantly more nitrogen at the reproductive stage after shooting and accumulated it in the shoots and seeds.Transcriptome data analysis revealed that nitrogen deficiency clearly suppressed the expression of genes related to cell division in the young spike of Jing 411,whereas this suppression of gene expression was much lower in KN9204.In addition,KN9204 maintained relatively high expression of NPF genes for a longer time than Jing 411 during seed maturity.Physiological and transcriptome data revealed that KN9204 was more tolerant of nitrogen deficiency than Jing 411,especially at the reproductive stage.The high NUE of KN9204 is an integrated effect controlled at different levels.Taken together,our data provide new insights into the molecular mechanisms of NUE and important gene resources for improving wheat cultivars with a higher NUE trait. | Xiaoli Shi Fa Cui Xinyin Han Yilin He Long Zhao Na Zhang Hao Zhang Haidong Zhu Zhexin Liu Bin Ma Shusong Zheng Wei Zhang Jiajia Liu Xiaoli Fan Yaoqi Si Shuiquan Tian Jianqing Niu Huilan Wu Xuemei Liu Zhuo Chen Deyuan Meng Xiaoyan Wang Liqiang Song Lijing Sun Jie Han Hui Zhao Jun Ji Zhiguo Wang Xiaoyu He Ruilin Li Xuebin Chi Chengzhi Liang Beifang Niu Jun Xiao Junming Li Hong-Qing Ling | 2022 | Molecular Plant2022，15，9： | 6 |
| 2 | How Big Data and High-performance Computing Drive Brain Science显示文摘Brain science accelerates the study of intelligence and behavior,contributes fundamental insights into human cognition,and offers prospective treatments for brain disease.Faced with the challenges posed by imaging technologies and deep learning computational models,big data and high-performance computing(HPC)play essential roles in studying brain function,brain diseases,and large-scale brain models or connectomes.We review the driving forces behind big data and HPC methods applied to brain science,including deep learning,powerful data analysis capabilities,and computational performance solutions,each of which can be used to improve diagnostic accuracy and research output.This work reinforces predictions that big data and HPC will continue to improve brain science by making ultrahigh-performance analysis possible,by improving data standardization and sharing,and by providing new neuromorphic insights. | Shanyu Chen Zhipeng He Xinyin Han Xiaoyu He Ruilin Li Haidong Zhu Dan Zhao Chuangchuang Dai Yu Zhang Zhonghua Lu Xuebin Chi Beifang Niu | 2019 | Genomics, Proteomics & Bioinformatics2019，17，4： | 0 |
| 3 | A Study on Investment Casting Directly with Plastic Rapid Prototype Patterns显示文摘 | Songhao Wang Chinwang Shih Xinyin He | 2010 | 材料科学与工程(中英文版)2010，4，11： | 0 |
您所在的机构暂未开通此部分的功能，请联系管理员