Bulk pollen pollination in maize for efficient construction of introgression populations with high genome coverage

Introgression populations consist of a set of introgression lines or families, constructed by continuous backcrossing to the recurrent parent, while carrying a limited number of chromosome segments from a donor parent in their genomes. Increasing the genome coverage is an important aim when constructing introgression population. In this study, we proposed bulk pollen pollination (BPP) method and used it to increase the genome coverage of a maize introgression population. The results showed that the genome coverage of the introgression population constructed using BPP method reached 100% at BC₃ generation, which accorded with the simulation result. The BPP‐based BC₃F_1:2 population could identify most quantitative trait loci (QTL) detected using the F_2:3 population, especially major QTL. Simulation analysis showed that the genome coverage of introgression population increased with the increase of population size and the number of bulked plants, and decreased with the increase of backcross generation. Our results proved the reliability of the BPP‐based introgression population in increasing genome coverage and detecting QTL, and provided references for constructing high‐coverage introgression populations.     

Interactive effects of family and stocking density on survival and growth of the sea urchin Strongylocentrotus intermedius

To investigate the effects of family, stocking density, and their interaction on mortality, growth rate, and size dispersal in the sea urchin Strongylocentrotus intermedius, 1,440 juveniles from six full‐sib families were exposed to three stocking densities, that is, low stocking density (LD), middle stocking density (MD), and high stocking density (HD), and reared for 10 months in sea‐based suspended cages. The results demonstrated significant family effects on cumulative mortality rate (CMR); specific growth rate for test diameter (SGR for TD); specific growth rate for body weight (SGR for BW); coefficient of variation for body weight at the 10th month (CV for BW₁₀); and significant stocking density effects on SGR for TD, SGR for BW, and CV for BW₁₀. Statistically significant family by stocking density interaction was only found in SGR for TD. A certain degree of family‐ranking inversions occurred in SGR for TD. The present study provides evidence for the existence of family by stocking density interaction on the growth rate of test diameter in the family selection for S. intermedius. More attention should be paid to this interaction effect to select correct parents in S. intermedius.     

Improving wood volume predictions in dry tropical forest in the semi-arid Brazil

The volumetric variability of dry tropical forests in Brazil and the scarcity of studies on the subject show the need for the development of techniques that make it possible to obtain adequate and accurate wood volume estimates. In this study, we analyzed a database of thinning trees from a forest management plan in the Contendas de Sincorá National Forest, southwestern Bahia State, Brazil. The data set included a total of 300 trees with a trunk diameter ranging from 5 to 52 cm. Adjustments, validation and statistical selection of four volumetric models were performed. Due to the difference in height values for the same diameter and the low correlation between both variables, we do not suggest models which only use the diameter at breast height (DBH) variable as a predictor because they accommodate the largest estimation errors. In comparing the best single entry model (Hohenald-Krenn) with the Spurr model (best fit model), it is noted that the exclusion of height as a predictor causes the values of 136.44 and 0.93 for Akaike information criterion (AIC) and adjusted determination coefficient (R2 adj), which are poorer than the second best model (Schumacher-Hall). Regarding the minimum sample size, errors in estimation (root mean square error (RMSE) and bias) of the best model decrease as the sample size increases, especially when a larger number of trees with DBH≥15.0 cm are randomly sampled. Stratified sampling by diameter class produces smaller volume prediction errors than random sampling, especially when considering all trees. In summary, the Spurr and Schumacher-Hall models perform better. These models suggest that the total variance explained in the estimates is not less than 95%, producing reliable forecasts of the total volume with shell. Our estimates indicate that the bias around the average is not greater than 7%. Our results support the decision to use regression methods to build models and estimate their parameters, seeking stratification strategies in diameter classes for the sample trees. Volume estimates with valid confidence intervals can be obtained using the Spurr model for the studied dry forest. Stratified sampling of the data set for model adjustment and selection is necessary, since we find significant results with mean error square root values and bias of up to 70% of the total database.     

山葡萄VaCIPK18原核表达与多克隆抗体制备

类钙调磷酸酶B亚基蛋白(CBLs)互作蛋白(CIPKs) 作为类丝氨酸/苏氨酸蛋白激酶,在植物响应非生物胁迫信号转导中起着重要作用。基于前期山葡萄响应低温胁迫转录组测序结果,发现低温胁迫引发的早期伤害及感知阶段的激酶基因中涉及CBL-CIPK 信号通路的VaCIPK18表达显著上调。为进一步研究山葡萄(Vitis amurensis)VaCIPK18激酶参与低温胁迫的功能,采用同源克隆获得了VaCIPK18基因,其开放阅读框为1 320 bp,编码439个氨基酸。基于对VaCIPK18蛋白生物信息学分析,获取胞外结构域中抗原表位丰富的肽段,并将其C端调控结构域(230~439 aa)构建到原核表达载体pET28a-SUMO。将重组表达载体转化至大肠杆菌(E. coli Rosetta)中,经0.8 mmol·L^-1 IPTG、37℃诱导4 h表达出大小为42 kDa的包涵体蛋白。将重组蛋白作为抗原免疫日本大耳白兔,获得anti-VaCIPK18多克隆抗体,经检测具有高效价及特异性。Western Blot结果表明,该抗体可以与葡萄内源CIPK18特异性结合,且在50 kDa位置出现与预期一致的条带。同时,CIPK18在低温胁迫后葡萄叶片中蛋白表达水平与室温下相一致,但两种状态下均存在可能的磷酸化与泛素化修饰现象。本研究结果为进一步探究VaCIPK18的蛋白定位、表达及其功能奠定了基础。     

Effect of soil pore size distribution on plant-available water and least limiting water range as soil physical quality indicators

Soil pore size distribution(SPSD) is one of the most important soil physical properties. This research investigated the relationships of location and shape parameters of the SPSD curves with plant-available water(PAW) and least limiting water range(LLWR) of the light-textured soils at the Torogh Agricultural Research Station in north-eastern Iran. Soil moisture release curve(SMRC), PAW and LLWR in matric heads of 100 and 330 h Pa for the field capacity and location and shape parameters of the SPSD curves of 30 soils with different texture and organic carbon contents were determined, and the variable relationships were statistically analyzed. The results showed that the median equivalent pore diameter(de), mean de, standard deviation(SD*), and skewness of the SPSD curves were significantly correlated with PAW(PAW330) and LLWR(LLWR330) measured in a matric head of 330 h Pa. Decrease in deand increase in the diversity of soil pore size(SD*) increased PAW330 and LLWR330. The SD* values of all the soil samples were lower than the optimal ranges suggested in literature. Neither PAW nor LLWR values were significantly different in the soils with the optimal modal deand those with non-optimal modal de. Optimal values of median and mean equivalent pore diameters and kurtosis of SPSD curves led to a significant improvement of PAW330 and LLWR330 as soil physical quality indicators. It was recommended to revise the optimal ranges for SD* and modal defor future studies.     

无人机机载激光雷达提取果树单木树冠信息

定株管理是未来果园精准生产管理的趋势，果树单木树冠信息的提取是定株管理的关键。该研究利用无人机采集的苹果园激光探测与测量数据（Light Detection and Ranging，LiDAR）检测和测量每棵果树的树冠面积和树冠直径，并评价空间分辨率对于果树单木树冠检测与提取的影响。该方法主要包括使用反距离权重插值法间接生成冠层高度模型（Canopy Height Model，CHM）；使用局部极大值滤波算法和标记控制分水岭分割算法（Marked-Controlled Watered Segmentation，MCWS）对果树进行单木树冠检测与提取，通过与参考数据的比较，评估了该方法的精度，并定量分析了空间分辨率对于单木树冠检测与信息提取结果的敏感性。结果表明，该方法有效地实现果树单木树冠检测与信息提取，代表果树检测精度的F1得分为94.86%，树冠轮廓提取准确率为86.39%，树冠面积的提取数据集和参考数据集的线性拟合结果决定系数和归一化均方根误差分别为0.81和20.56%，树冠直径的提取数据集和参考数据集的线性拟合结果决定系数和归一化均方根误差分别为0.85和14.79%，树冠面积和直径不同程度地被高估。此外，冠层高度模型的空间分辨率接近果树平均树冠直径的1/10时精度最高，可以有效检测果树单木树冠及提取树冠轮廓，从而准确提取果树单木树冠信息。     

基于光声光谱和TCA迁移学习的稻种活力检测

种子活力是决定水稻产量的最重要因素之一，但目前水稻种子活力的近红外和高光谱等无损检测方法易受种子表皮颜色影响，且所建模型难以适应新品种。该研究提出基于光声光谱技术的稻种活力无损检测方法并结合迁移学习进行新品种稻种活力检测。首先，对Y两优、龙粳、南粳、宁粳、武运粳、新两优等具有区域代表性的典型6种水稻品种，进行高温高湿人工老化处理，得到0~7 d老化时间的水稻种子；再通过调制频率获得8种不同深度的光声光谱信息，用主成分分析、竞争性自适应重加权算法对光谱降维得到特征光谱后，对Y两优、龙粳、南粳、宁粳、武运粳分别建立偏最小二乘回归（Partial Least Squares Regression，PLSR）、反向传播神经网络（Back Propagation Neural Network，BP）、广义回归神经网络（Generalized Regression Neural Network，GRNN）、支持向量回归模型（Support Vector Regression，SVR）、深度卷积神经网络（Convolutional Netural Network，CNN）的稻种活力预测模型，并选择最优调制频率；最后，通过迁移学习将建立的模型迁移到新两优稻种进行活力预测。结果表明，光声光谱最佳扫描频率为300 Hz，CNN预测模型精度较高，相关系数和均方根误差分别优于0.990 9、低于0.967 5；且经过迁移学习，仅需通过对源域数据的训练，即可直接对新品种稻种的活力进行精确预测；通过TCA迁移学习后，新两优稻种活力预测的相关系数从0.718 5提高到0.990 3。研究表明，采用光声光谱深度扫描技术对不同种类稻种的活力进行高精度检测是可行的，且经过迁移学习，仅需80粒新品种稻种信息即可实现稻种活力的精确预测。