Genetic and metabolic engineering approaches are powerful tools for improving the tolerance of maize to abiotic stresses because they are faster and can afford greater control over agronomically useful traits. However...Genetic and metabolic engineering approaches are powerful tools for improving the tolerance of maize to abiotic stresses because they are faster and can afford greater control over agronomically useful traits. However, in-depth understanding of the molecular mechanisms controlling response to abiotic stresses is the prerequisite for successful implementation of these strategies. A great flaw to dissect the biological mechanisms by genome sequencing is that genome sequencing approach could not reflect real-time molecular actions of plants especially under the stresses because the living organisms rarely live in unchanging environments. Post-genomics such as transcriptomics, metabolomics, and proteomics can generate knowledge that is closer to the biological processes. With the development of post-genomics, it can be expected that voluminous data will be generated. This paper proposes that future research on maize stress tolerance in the era of post-genomics should focus on metabolomics and proteomics; stress tolerance of whole plant rather than individual tissues or organs; coordination of expression of genes among tissues; characterization of promoters of stress-responsive genes; interrelation between mechanisms for tolerance to, and growth recovery from the stress; hexose metabolism as well as the glycolysis pathway; and foundation genotypes.展开更多
利用全国40个地面台站的观测资料对ERA5及ERA5-Land两种不同空间分辨率的再分析资料开展了地面风速误差评估研究,结果表明:ERA5和ERA5-Land资料多年平均风速偏差的平均值分别为0.08 m s^(−1)、-0.06 m s^(−1),偏差的最大值分别为0.46 m ...利用全国40个地面台站的观测资料对ERA5及ERA5-Land两种不同空间分辨率的再分析资料开展了地面风速误差评估研究,结果表明:ERA5和ERA5-Land资料多年平均风速偏差的平均值分别为0.08 m s^(−1)、-0.06 m s^(−1),偏差的最大值分别为0.46 m s^(−1)、-0.19 m s^(−1),相对偏差的平均值为4.4%、-2.0%,相对偏差的最大值分别为33.0%、-10.1%;月平均风速线性拟合方程的斜率分别为0.93、0.97,截距分别为0.29 m s^(−1)、0.02 m s^(−1),相关系数分别为0.98、0.99;月平均风速均方根误差的平均值分别为0.17 m s^(−1)、0.14 m s^(−1),均方根误差的最大值分别为0.49 m s^(−1)、0.22 m s^(−1),相对均方根误差的平均值为7.4%、5.7%,相对均方根误差的最大值分别为35.2%、13.3%。ERA5-Land高分辨率资料地面风速误差相对较低,有利于提高风能资源评估的准确性。展开更多
提出一种高精度的ZWD模型(tianjin_zwd,TZ)。TZ基于2016-2018年逐小时气压分层的ERA5,欧洲中尺度气象预报中心第五代再分析产品数据,采用BP神经网络建立。然后,根据2019年的ERA5产品导出的ZWD对TZ模型进行了验证。结果表明:相比GPT3模型...提出一种高精度的ZWD模型(tianjin_zwd,TZ)。TZ基于2016-2018年逐小时气压分层的ERA5,欧洲中尺度气象预报中心第五代再分析产品数据,采用BP神经网络建立。然后,根据2019年的ERA5产品导出的ZWD对TZ模型进行了验证。结果表明:相比GPT3模型,TZ模型可提供更贴近真值的ZWD估值;并且,其RMSE由5.0 cm (GPT3)降至4.5 cm,表明10%的精度提升。上述结果表明TZ模型实现了更优的预测性能,该模型的构建策略可为全国其他地区的ZWD建模提供借鉴。展开更多
基金supported by the National Basic Research Program of China(2011CB100100)the 948 Program from the Ministry of Agriculture of China (2001-205)+2 种基金the Development Programs for Guangxi Science and Technology Research, China(Guikegong 10100005-4 and 0228019-6)the Key Laboratory of Ministry of Education for Microbial and Plant Genetic Engineering, China (Director’s grant-06-11)the Opening Project of Guangxi Key Laboratory of Subtropical Bioresource Conservation and Utilization, China (SB0601)
文摘Genetic and metabolic engineering approaches are powerful tools for improving the tolerance of maize to abiotic stresses because they are faster and can afford greater control over agronomically useful traits. However, in-depth understanding of the molecular mechanisms controlling response to abiotic stresses is the prerequisite for successful implementation of these strategies. A great flaw to dissect the biological mechanisms by genome sequencing is that genome sequencing approach could not reflect real-time molecular actions of plants especially under the stresses because the living organisms rarely live in unchanging environments. Post-genomics such as transcriptomics, metabolomics, and proteomics can generate knowledge that is closer to the biological processes. With the development of post-genomics, it can be expected that voluminous data will be generated. This paper proposes that future research on maize stress tolerance in the era of post-genomics should focus on metabolomics and proteomics; stress tolerance of whole plant rather than individual tissues or organs; coordination of expression of genes among tissues; characterization of promoters of stress-responsive genes; interrelation between mechanisms for tolerance to, and growth recovery from the stress; hexose metabolism as well as the glycolysis pathway; and foundation genotypes.
文摘利用全国40个地面台站的观测资料对ERA5及ERA5-Land两种不同空间分辨率的再分析资料开展了地面风速误差评估研究,结果表明:ERA5和ERA5-Land资料多年平均风速偏差的平均值分别为0.08 m s^(−1)、-0.06 m s^(−1),偏差的最大值分别为0.46 m s^(−1)、-0.19 m s^(−1),相对偏差的平均值为4.4%、-2.0%,相对偏差的最大值分别为33.0%、-10.1%;月平均风速线性拟合方程的斜率分别为0.93、0.97,截距分别为0.29 m s^(−1)、0.02 m s^(−1),相关系数分别为0.98、0.99;月平均风速均方根误差的平均值分别为0.17 m s^(−1)、0.14 m s^(−1),均方根误差的最大值分别为0.49 m s^(−1)、0.22 m s^(−1),相对均方根误差的平均值为7.4%、5.7%,相对均方根误差的最大值分别为35.2%、13.3%。ERA5-Land高分辨率资料地面风速误差相对较低,有利于提高风能资源评估的准确性。
文摘提出一种高精度的ZWD模型(tianjin_zwd,TZ)。TZ基于2016-2018年逐小时气压分层的ERA5,欧洲中尺度气象预报中心第五代再分析产品数据,采用BP神经网络建立。然后,根据2019年的ERA5产品导出的ZWD对TZ模型进行了验证。结果表明:相比GPT3模型,TZ模型可提供更贴近真值的ZWD估值;并且,其RMSE由5.0 cm (GPT3)降至4.5 cm,表明10%的精度提升。上述结果表明TZ模型实现了更优的预测性能,该模型的构建策略可为全国其他地区的ZWD建模提供借鉴。
