扁蓿豆叶绿体基因组密码子偏好性分析

为阐明扁蓿豆（Medicago ruthenica）叶绿体基因组密码子使用模式，以其50条蛋白编码序列（CDS）为对象，利用软件CodonW 1.4.2、Excel以及在线程序对其ENC，RSCU，GC含量、中性绘图、PR2-plot、最优密码子等进行分析得到以下结果：基因组平均GC含量为40.58%，其中GC1>GC2>GC3；ENC的取值介于35.77～56.62之间，表明密码子使用偏好性较弱；基因组中共有30个密码子RSCU>1，其中29个以A/U结尾；ENC-plot绘图分析结果中多数基因分布于标准曲线下方，22个基因ENC比值在—0.05～0.05之间，密码子偏好性主要受选择影响；PR2-plot分析表明碱基T的使用频率大于A，G大于C，说明密码子偏好性还受其他因素影响；该基因组中共有UUU，UUA，ACU等11个最优密码子并均以A/U结尾。以上研究结果可为扁蓿豆叶绿体基因组优良基因利用提供理论与基础。     

安徽沿江地区油麦后直播棉的品种选用策略

在目前安徽沿江地区不能实现棉花机采的情况下,提出了“植株高度既适于机采也适于人工采收,含絮力适中(不易落花便于集中采收),生育期适中(播种到采收为180天左右),纤维品质及产量性状较优(纤维长度30.0 mm以上,断裂比强度30.0 cN/tex以上,马克隆值5.0以下,籽棉产量3750 kg/hm 2以上,衣分在40%左右)”的油(或麦)后直播棉模式的人工采收品种选用策略。旨在促进安徽沿江地区棉花生产稳定发展。     

水稻赖氨酸含量测定方法改进及其最优动态分类研究

赖氨酸是水稻营养成分中第一限制性氨基酸。为优化赖氨酸的测定方法,设置了本试验,通过超声振荡器取代普通振荡器,进行300W超声功率下酰化用时及染料结合反应用时的研究。结果表明,最佳酰化用时为15 min,染料结合用时为90 min。与国标法相比,优化后的方法酰化用时变长、染料结合用时变短,测定总用时明显缩短,但测定结果和试验精度更高。可见,采用超声波振荡代替传统振荡器振荡能改进赖氨酸的测定方法。另外,采用最优动态聚类法对赖氨酸含量进行分类,能防止人为分类的不确定性,做到分类方案的最优化。     

玉米全膜双垄沟气动直插式穴播机设计与仿真

玉米全膜双垄沟播农艺技术抗旱、增产,实现其机械化膜上播种将是必然趋势。依据玉米全膜双垄沟农艺技术要求,设计了气动直插式穴播机,对作业机关键作业参数进行了设计计算,并结合STEP控制函数对其播种运动轨迹进行仿真分析。为揭示气动播种单体成穴器与种床土壤互作机理,借助ABAQUS有限元法构建了成穴器与种床土壤互作三维模型,分别获得了对应的Mises应力云图、空间位移云图和塑性应变云图。仿真结果分析表明:在相同的气动直插播种作用力下,锥形成穴器与种床土壤互作应力最大值约为1.541 MPa,是楔形成穴器与种床土壤最大值的1.39倍;在相同的载荷与分析步时间内,锥形成穴器对种床土壤作业下的最大塑性变形量约为19.35mm,楔形成穴器对种床土壤的最大塑性变形量约为12.35 mm。因此,锥形成穴器较楔形成穴器形成播种穴孔的能力及动土量效果更好。研究结果将为西北旱区玉米全膜双垄沟机械化播种装置的研发提供参考。     

Comparison of estimators and feature selection procedures in forest inventory based on airborne laser scanning and digital aerial imagery

Digital maps of forest resources are a crucial factor in successful forestry applications. Since manual measurement of this data on large areas is infeasible, maps must be constructed using a sample field data set and a prediction model constructed from remote sensing materials, of which airborne laser scanning (ALS) data and aerial images are currently widely used in management planning inventories. ALS data is suitable for the prediction of variables related to the size and volume of trees, whereas optical imagery helps in improving distinction between tree species. We studied the prediction of forest attributes using field data from National Forest Inventory complemented with ad hoc field plots in combination with ALS and aerial imagery data in Aland province, Finland. We applied feature selection with genetic algorithm and greedy forward selection and compared multiple linear and nonlinear estimators. Maximally around 40 features from a total of 154 were required to achieve the best prediction performances. Tree height was predicted with normalized root mean squared error value of 0.1 and tree volume with a value around 0.25. Predicting the volumes of spruce and broadleaved trees was the most challenging due to small proportions of these tree species in the study area.     

四倍体马铃薯块茎蛋白含量分子标记的开发与验证

块茎蛋白含量是马铃薯的重要品质性状之一,相关分子标记的开发研究较少。本研究以马铃薯高蛋白品种大西洋、低蛋白品种定薯1号及包含173份子代的蛋白含量分离群体为材料,通过高通量简化基因组测序技术与集群分离分析(BSA)相结合,开发了SCAR8-107标记,并以此标记对分离群体的74份高蛋白、42份低蛋白子代及54个四倍体马铃薯品种验证表明,SCAR8-107标记在后代分离群体和四倍体马铃薯品种中的检测结果与表型蛋白含量的对应度分别达到了90.51%和72.97%,经过Pearson’s双侧相关性分析,相关性均达到了极显著水平。SCAR8-107的开发对于四倍体马铃薯块茎蛋白含量标记辅助选择与加速马铃薯品质育种具有重要意义。     

红茶感官品质及成分近红外光谱快速检测模型建立

以在发酵过程中小叶种工夫红茶为研究对象,分别建立了基于近红外光谱检测技术的感官品质评分和理化品质指标(茶黄素、茶红素、茶褐素、儿茶素和酚氨比)的定量分析模型。在模型建立过程中,探讨了特征变量优选方法对预测模型的影响。首先,对获取的近红外光谱数据进行标准正态变量变换法(standard normal Z transformation,SNV)预处理,进而采用联合区间偏最小二乘回归(synergy interval PLS,Si-PLS)、随机蛙跳算法(shuffled frog leaping algorithm,SFLA)、竞争性自适应权重取样法(competitiveadaptivereweightedsampling,CARS)和连续投影(successive projections algorithm,SPA),筛选出各品质指标的最优特征波长变量;最后基于优选波长分别建立各发酵品质指标的偏最小二乘法(partial least squares regression,PLS)线性预测模型和支持向量机(support vector regression,SVR)非线性预测模型。模型结果比较表明,Si、CARS、SFLA和SPA等变量筛选方法可有效压缩变量,以及进一步提高模型精度。非线性模型的预测均方根误差值(root-mean-square error of prediction,RMSEP)均明显小于PLS模型,相关性系数(correlation coefficient,R)和相对分析误差(relative percent deviation,RPD)均高于PLS模型。对于红茶发酵品质的检测上,非线性模型性能优于线性模型。感官品质、茶褐素和儿茶素的最优变量SVR预测模型的RPD值分别为3.923、3.234和5.462,酚氨比和茶红素模型的RPD值分别为2.815和2.223。除茶黄素的评价模型外(RPD为1.77),基于最优特征波长的各品质指标SVR模型的RPD值均大于2,表明模型具有极好的预测性能。研究结果为实现工夫红茶发酵品质的近红外光谱快速检测的实际应用奠定理论基础。     

Breeding for boron tolerance in lentil (Lens culinaris Medik.) using a high‐throughput phenotypic assay and molecular markers

This study describes the identification of a quantitative trait locus (QTL) in the recombinant inbred line population of ILL2024 × ILL6788 and subsequent validation of associated molecular markers. A high‐quality genetic linkage map was constructed with 758 markers that cover 1,057 cM, with an average intermarker distance of 2 cM. QTL analysis revealed a single genomic region on Lc2 to be associated with B tolerance and accounted for up to 76% of phenotypic variation (Vp). The best markers for B tolerance were assessed for their utility in routine breeding applications using validation panels of diverse lentil germplasm and breeding material derived from ILL2024. A marker generated from the dense genetic map of this study was found to be the most accurate of all markers available for B tolerance in lentil, with a success rate of 93% within a large breeding pool derived from ILL2024. However, given the number of the unrelated lines for which the marker–trait association was not conserved, B tolerance screening is still required at later stages to confirm predicted phenotypes.     

基于甘蔗热带种(LA-purple)全基因组序列的SSR开发及特征分析

现代甘蔗栽培品种(2n=100~130)是由甘蔗热带种(2n=80)与割手密(2n=40~128)种间杂交而来,形成异源多倍体、非整倍体作物,使得甘蔗栽培品种中80%~90%的染色体来源于热带种。开发热带种基因组SSR分子标记,有助于甘蔗遗传多样性分析、分子标记辅助选择、遗传图谱的构建等。本研究基于热带种LA-purple的全基因组测序数据的255 398个预测基因序列(累计总长为1 029 222 285 bp),利用Perl程序与生物信息学软件结合,发掘SSR位点,获得了153 150个SSR位点,平均每1.67个基因有1个SSR位点,其中二、三核苷酸重复基序分别为39 556个和50 072个,占总SSR位点数的58.5%。在二核苷酸重复基序中,TA/AT所占比例最高,占41.4%,CG/GC所占比例最低,占4.6%;在三核苷酸碱基重复基序中,TGT/ACA所占比例最高,为15.6%。在TA/AT重复类型中选取100个基序重复次数在60~90之间的SSR位点,进行引物设计与合成,在12个甘蔗属材料中进行PCR扩增分析,从中筛选出52对具有多态性SSR引物,其中有27对引物在研究的2个甘蔗栽培品种间表现为多态。这些基因组SSR标记的开发,不仅可以用于甘蔗栽培品种DNA指纹图谱分析,而且为甘蔗属不同种的遗传图谱构建、遗传多样性分析和重要性状的遗传机制解析奠定基础,为甘蔗分子育种研究提供重要支撑。     

Identifying Soil and Plant Nutrition Factors Affecting Yield in Irrigated Mature Pistachio Orchards

The main objective of this study was to evaluate the potential use of a hybrid Genetic Algorithm-Artificial Neural Network (GA–ANN) method for predicting pistachio yield and for identifying the determinant factors affecting pistachio yield in Rafsanjan region, Iran. A total of 142 pistachio orchards were selected randomly and soil samples were taken at three depths. Besides, water samples and leaves from branches without fruit were taken in each sampling point. Management information and pistachio yields were achieved by completing a questionnaire. Primarily, 58 variables affecting pistachio yield were measured, and then 26 out of them were selected by minimizing mean square error (MSE) using a feature selection (FS) method. The results showed that the accuracy of the method was acceptable. Furthermore, the sensitivity analysis showed that the main determinant features affecting the pistachio yield were the irrigation water amount, leaf phosphorus, soil soluble magnesium, electrical conductivity (EC), and leaf nitrogen.