水稻剑叶性状与产量息息相关,也是水稻遗传学和分子生物学研究的热点之一。剖析水稻剑叶长度的遗传机理,为培育理想株型新品种、提高水稻产量奠定基础。以长剑叶的非洲长雄蕊野生稻(Oryza longistaminata A. Chev.&Roehr.)为父本、...水稻剑叶性状与产量息息相关,也是水稻遗传学和分子生物学研究的热点之一。剖析水稻剑叶长度的遗传机理,为培育理想株型新品种、提高水稻产量奠定基础。以长剑叶的非洲长雄蕊野生稻(Oryza longistaminata A. Chev.&Roehr.)为父本、短剑叶的粳稻Balilla为母本构建F2分离群体。统计2015-2017年近3年的F2群体的表型数据和基因型数据,对剑叶长度性状进行了遗传效应分析和QTL定位。一共检测到10个控制剑叶长度的QTL位点,分布在水稻的第3、4、5、7、8、9、11和12号染色体上。其中位于第9号染色体上的q FLL9-1是群体中控制水稻剑叶长度的主效位点,还鉴定到了两个新位点qFLL4-1和qFLL11-1。针对主效位点q FLL9-1构建了高世代回交群体BC6F2,验证了qFLL9-1在群体中发挥重要的效应。本研究鉴定到qFLL4-1和qFLL11-1两个调控剑叶长度的新QTL位点,验证了1个调控水稻剑叶长度的主效QTL qFLL9-1,研究结果为水稻剑叶长度QTL的精细定位奠定了基础,有利于长雄蕊野生稻种质资源的进一步开发与利用。展开更多
温室高效生产依赖于适宜的温室小气候环境,建立高精度的温室小气候多步预测模型对实现温室环境优化调控具有重要意义。本研究提出一种基于滚动的反向传播神经网络(Rolling Back Propagation Neural Network,R-BP)的温室小气候多步滚动...温室高效生产依赖于适宜的温室小气候环境,建立高精度的温室小气候多步预测模型对实现温室环境优化调控具有重要意义。本研究提出一种基于滚动的反向传播神经网络(Rolling Back Propagation Neural Network,R-BP)的温室小气候多步滚动预测模型。模型主要包括两个阶段:(1)建立初始的BP神经网络。采用自动编码器无监督学习方法获取初始网络参数,并利用改进的粒子群算法优化网络参数。(2)建立滚动的BP神经网络群。将前一个网络输出作为后一个网络的部分输入进行滚动训练和预测,实现温室小气候多步滚动预测。为验证R-BP模型的有效性,在阿拉伯联合酋长国阿布扎比市的自控温室和中国苏州市的非自控温室分别进行试验。验证试验表明,与传统BP神经网络相比,在阿布扎比温室试验中,采用R-BP模型预测未来6h室内温度,其平均误差降低69.9%,预测相对湿度,其平均误差降低47%;在苏州温室试验中,采用R-BP模型预测未来6h室内温度,其平均误差降低43.3%,预测相对湿度,其平均误差降低55.6%。说明R-BP模型能够较准确预测未来6h内温室小气候环境变化,可为制定温室小气候优化调控方案提供依据。展开更多
为了应对高光谱图像同质区域面积分布不均的问题,同时更充分地挖掘空间和光谱信息之间的内在联系,提出了一种基于多尺度空谱鉴别特征的高光谱图像分类方法。该算法首先对图像进行不同尺度的滤波操作,接着分别从得到的多幅图像中提取鉴...为了应对高光谱图像同质区域面积分布不均的问题,同时更充分地挖掘空间和光谱信息之间的内在联系,提出了一种基于多尺度空谱鉴别特征的高光谱图像分类方法。该算法首先对图像进行不同尺度的滤波操作,接着分别从得到的多幅图像中提取鉴别的空谱特征,并使用支持向量机(SVM)进行分类。最后,该算法采取"决策级融合"的策略,来综合不同滤波尺度图像的分类结果。在Indian Pines,Kennedy Space Center和University of Pavia数据集上的实验表明,该算法能够提取较为有效的空间信息,当随机选取10%的像素作为训练样本时,该算法的总体分类准确率均能达到96%以上,其分类精度和Kappa系数均优于其他分类算法。展开更多
The effciencies of free and immobilized bacterial cultures of petroleum hydrocarbon degraders were evaluated and compared in this study. Hydrocarbon-degrading microbial communities with high tolerance to and high degr...The effciencies of free and immobilized bacterial cultures of petroleum hydrocarbon degraders were evaluated and compared in this study. Hydrocarbon-degrading microbial communities with high tolerance to and high degrading ability of crude oil were obtained from the soil contaminated with crude oil in the Yellow River Delta. Then, the microbial cells were immobilized in sodium alginate (SA) beads and sodium alginate-diatomite (SAD) beads. The biodegradation of crude oil in soil by immobilized cells was compared with that by free cells at three inoculation concentrations, 1 × 104 colony forming units (cfu) kg-1 (low concentration, L), 5 × 104 cfu kg-1 (medium concentration, M), and 1 × 105 cfu kg-1 (high concentration, H). At 20 d after inoculation, the maximum degradation rate in the immobilized systems reached 29.8% (SAD-M), significantly higher (P < 0.05) than that of the free cells (21.1%), and the SAD beads showed greater degradation than the SA beads. Moreover, both microbial populations and total microbial activity reached significantly higher level (P < 0.05) in the immobilized systems than free cell systems at a same initial inoculation amount. The scanning electronic microscope (SEM) images also confirmed the advantages of the immobilized microstructure of SAD beads. The enhanced degradation and bacterial growth in the SAD beads indicated the high potential of SAD beads as an effective option for bioremediation of crude oil-contaminated soils in the Yellow River Delta.展开更多
文摘温室高效生产依赖于适宜的温室小气候环境,建立高精度的温室小气候多步预测模型对实现温室环境优化调控具有重要意义。本研究提出一种基于滚动的反向传播神经网络(Rolling Back Propagation Neural Network,R-BP)的温室小气候多步滚动预测模型。模型主要包括两个阶段:(1)建立初始的BP神经网络。采用自动编码器无监督学习方法获取初始网络参数,并利用改进的粒子群算法优化网络参数。(2)建立滚动的BP神经网络群。将前一个网络输出作为后一个网络的部分输入进行滚动训练和预测,实现温室小气候多步滚动预测。为验证R-BP模型的有效性,在阿拉伯联合酋长国阿布扎比市的自控温室和中国苏州市的非自控温室分别进行试验。验证试验表明,与传统BP神经网络相比,在阿布扎比温室试验中,采用R-BP模型预测未来6h室内温度,其平均误差降低69.9%,预测相对湿度,其平均误差降低47%;在苏州温室试验中,采用R-BP模型预测未来6h室内温度,其平均误差降低43.3%,预测相对湿度,其平均误差降低55.6%。说明R-BP模型能够较准确预测未来6h内温室小气候环境变化,可为制定温室小气候优化调控方案提供依据。
文摘为了应对高光谱图像同质区域面积分布不均的问题,同时更充分地挖掘空间和光谱信息之间的内在联系,提出了一种基于多尺度空谱鉴别特征的高光谱图像分类方法。该算法首先对图像进行不同尺度的滤波操作,接着分别从得到的多幅图像中提取鉴别的空谱特征,并使用支持向量机(SVM)进行分类。最后,该算法采取"决策级融合"的策略,来综合不同滤波尺度图像的分类结果。在Indian Pines,Kennedy Space Center和University of Pavia数据集上的实验表明,该算法能够提取较为有效的空间信息,当随机选取10%的像素作为训练样本时,该算法的总体分类准确率均能达到96%以上,其分类精度和Kappa系数均优于其他分类算法。
基金the National Natural Science Foundation of China (No.41073067)the Key Program of the Ministry of Education,China (No.308016)the National Major Special Technological Programme Concerning Water Pollution Control and Management of China (No.2009ZX07010-008)
文摘The effciencies of free and immobilized bacterial cultures of petroleum hydrocarbon degraders were evaluated and compared in this study. Hydrocarbon-degrading microbial communities with high tolerance to and high degrading ability of crude oil were obtained from the soil contaminated with crude oil in the Yellow River Delta. Then, the microbial cells were immobilized in sodium alginate (SA) beads and sodium alginate-diatomite (SAD) beads. The biodegradation of crude oil in soil by immobilized cells was compared with that by free cells at three inoculation concentrations, 1 × 104 colony forming units (cfu) kg-1 (low concentration, L), 5 × 104 cfu kg-1 (medium concentration, M), and 1 × 105 cfu kg-1 (high concentration, H). At 20 d after inoculation, the maximum degradation rate in the immobilized systems reached 29.8% (SAD-M), significantly higher (P < 0.05) than that of the free cells (21.1%), and the SAD beads showed greater degradation than the SA beads. Moreover, both microbial populations and total microbial activity reached significantly higher level (P < 0.05) in the immobilized systems than free cell systems at a same initial inoculation amount. The scanning electronic microscope (SEM) images also confirmed the advantages of the immobilized microstructure of SAD beads. The enhanced degradation and bacterial growth in the SAD beads indicated the high potential of SAD beads as an effective option for bioremediation of crude oil-contaminated soils in the Yellow River Delta.