云南稻种资源表现型分布地和分布民族分析

通过对分布于82个县市的1630份中国云南保存的地方稻种资源多种表现型现象进行分析,结果共有724份稻种资源是具有多种表现型现象的资源,即多型性资源,多型性资源的比例为44．0％,多型性系数X为1．75,多样性指数日为1．34,X与日的相关系数r=0．90。多型性资源的地理分布主要集中在思茅、临沦、版纳和德宏4个地州。以墨江、景洪、龙隆、永德、普洱、孟连和云县等7个县(市)为中心的地带,即云南多型性稻种资源主要集中分布在南部边缘及滇西南地区的野生稻分布区、稻种资源富集区和陆稻的主要种植区。分布地区是傣族、哈尼族、拉祜族和佤族等少数民族的主要聚居地。云南稻种资源的多种表现型现象与当地的地理和民族密切相关。     

葛藤优良单株选择

通过对葛藤表型性状与经济产量的相关关系进行分析,从理论上阐明了优良葛藤单株选择应以分枝数和单根重为主,根粗与茎粗协调搭配为副,进而建立了选优的评分标准,并根据评分标准进行了葛藤优良单株的选择。     

作物根系表型鉴定评价方法的现状与展望

根系是作物固定植株并吸收土壤水分和养分的主要器官,其表型特征直接影响作物的生产力和适应性。优化根系表型被认为是实现第二次“绿色革命”的重要途径之一。然而,根系的隐匿性、复杂性和可塑性极大地制约着根系表型鉴定效率,导致根系优化进程远远滞后于地上部器官。随着光谱成像、机器学习和三维重建等新技术的快速发展,根系表型鉴定方法逐渐由传统取样观测向原位、无损、自动化检测转变,评价依据由二维形态指标向立体构型参数拓展,促进了根系表型鉴定效率大幅提升,根系表型数据快速增长。与此同时,海量数据也带来了信息冗余及利用率低等问题,对根系表型研究提出了规范化和共享化的时代新要求。本文概述了现行主要根系表型鉴定方法的原理和技术要点,从精准度、通量和成本等方面对不同方法进行系统比较,并从使用许可、运行平台和分析方式等方面对常用根系表型量化软件进行归纳总结;进一步提出今后重点研究方向,即开发高效的田间根系表型鉴定方法,建立根系可塑性鉴定评价技术体系,加强根系解剖结构的鉴定和利用,强化分子检测技术在根系表型鉴定中的应用,推进根系表型鉴定技术规范化和数据信息共享化,以期为合理选用和改进作物根系表型鉴定评价方法提供参考,促进作物根系改良。     

A multiple-phenotype imputation procedure as a method for prediction of cheese-making efficiency in Spanish Assaf sheep

Sheep milk is mainly intended to manufacture a wide variety of high-quality cheeses. The ovine cheese industry would benefit from an improvement, through genetic selection, of traits related to the milk coagulation properties (MCPs) and cheese yield-related traits, broadly denoted as “cheese-making traits.” Considering that routine measurements of these traits needed for genetic selection are expensive and time-consuming, this study aimed to evaluate the accuracy of a cheese-making phenotype imputation method based on the information from official milk control records combined with the pH of the milk. For this study, we analyzed records of milk production traits, milk composition traits, and measurements of cheese-making traits available from a total of 1,145 dairy ewes of the Spanish Assaf sheep breed. Cheese-making traits included five related to the MCPs and two cheese yield-related traits. The milk and cheese-making phenotypes were adjusted for significant effects based on a general linear model. The adjusted phenotypes were used to define a multiple-phenotype imputation procedure for the cheese-making traits based on multivariate normality and Markov chain Monte Carlo sampling. Five of the seven cheese-making traits considered in this study achieved a prediction accuracy of 0.60 computed as the correlation between the adjusted phenotypes and the imputed phenotypes. Particularly the logarithm of curd-firming time since rennet addition (logK₂₀) (0.68), which has been previously suggested as a potential candidate trait to improve the cheese ability in this breed, and the logarithm of the ratio between the rennet clotting time and the curd firmness at 60 min (logRCT/A60) (0.65), which has been defined by other studies as an indicator trait of milk coagulation efficiency. This study represents a first step toward the possible use of the phenotype imputation of cheese-making traits to develop a practical methodology for the dairy sheep industry to impute cheese-making traits only based on the analysis of a milk sample without the need of pedigree information. This information could be also used in future planning of specific breeding programs considering the importance of the cheese-making efficiency in dairy sheep and highlights the potential of phenotype imputation to leverage sample size on expensive, hard-to-measure phenotypes.     

基于Micro-CT的玉米籽粒显微表型特征研究

植物显微表型主要是指植物组织、细胞和亚细胞水平的表型信息,是植物表型组学研究的重要组成部分。针对传统籽粒显微性状检测方法效率低、误差大且指标单一等问题,本研究利用Micro-CT扫描技术对5种类型11个品种玉米籽粒开展显微表型精准鉴定研究。基于对CT序列图像的处理解析,共获取籽粒、胚、胚乳、空腔、皮下空腔、胚乳空腔和胚空腔的34项显微表型指标。其中,胚乳空腔表面积、籽粒体积、胚乳体积比和胚乳空腔比表面积等4项表型指标在不同类型玉米间差异显著（P-value<0.05）。普通玉米胚乳空腔表面积和籽粒体积显著大于其它类型玉米,高油玉米胚乳空腔比表面积最大,甜玉米胚乳空腔比表面积最小,爆裂玉米胚乳体积比最大。进一步利用34项玉米籽粒表型指标开展差异分析和聚类分析,可将11个不同品种玉米分为四类,其中第一类以普通玉米为主,第二类以爆裂玉米为主,第三类是甜玉米,第四类是高油玉米。结果表明,Micro-CT扫描技术不仅可以实现玉米籽粒显微表型的精准鉴定,还可以为玉米籽粒分类、品种检测等提供技术支撑。     

基于迁移学习和双线性CNN的细粒度菌菇表型识别

为了对细粒度菌菇进行表型识别,在双线性卷积神经网络细粒度图像识别框架基础上,提出了一种基于迁移学习和双线性Inception-ResNet-v2网络的菌菇识别方法。利用Inception-ResNet-v2网络的特征提取能力,结合双线性汇合操作,提取菌菇图像数据的细粒度特征,采用迁移学习将ImageNet数据集上预训练的模型参数迁移到细粒度菌类表型数据集上。试验表明,在开源数据集和个人数据集上,识别精度分别达到87.15%和93.94%。开发了基于Flask框架的在线菌类表型识别系统,实现了细粒度菌菇表型的在线识别与分析。     

转胡杨PeDREB2b 基因白三叶表型变异及抗旱性评价

选取W1-2、W1-3、W4-2、W13-4、W17-1、W28-2、W33-3这7个转胡杨(Populus euphratica)PeDREB2b基因白三叶(Trifolium repens)株系为试材,以非转基因白三叶海发为对照,测定了植株的形态特征和永久萎蔫率,以及在不同时间、不同浓度PEG模拟干旱胁迫下的叶片相对含水量、相对电导率和丙二醛含量的变化,旨在评价转基因白三叶株系的抗旱能力。结果表明:1)部分转基因白三叶株系的叶形、叶色、叶片斑纹及叶片大小发生了较明显的变化;2)转基因株系W1-3、W28-2永久萎蔫率明显低于对照,且能维持较长时间的正常生长,表现出一定的抗旱性;3)随着模拟干旱胁迫程度的增加及时间的延长,参试材料的叶片相对含水量呈不同程度下降的趋势,叶片相对电导率、丙二醛含量均有不同程度升高。但转基因株系W1-3、W28-2的叶片相对含水量始终保持较高水平,未出现较大波动,且株系W1-3的相对电导率、丙二醛含量始终保持较低水平,变化较小。综合分析表明,7个转基因白三叶株系的抗旱性强弱依次为W1-3W33-3W28-2W1-2W13-4W17-1W4-2,均优于对照白三叶海发品种。     

不同种源蒙古栎种子表型性状与淀粉含量的变异分析

The study aims at compare and analyze the variation pattern of the phenotype characters and starch content of Quercus mongolica seed of different provenances. The length, width, length/width ratio, single kernel weight of Q. mongolica seeds from 16 natural distribution regions in China were measured and analyzed. The results showed that there were extremely significant differences among seeds from different region in seed characters. The length and width of seeds ranged from 14.76 mm to 21.08 mm and from 11.87 mm to 15.31 mm, respectively. The length/width ratio of seeds ranged from 1.21 to 1.49. The single kernel weight ranged from 0.60 g to 1.78 g and the contents of starch ranged from 4.42 g·kg^-1 to 5.48 g·kg^-1. The length of seeds has a extremely significant positive correlation with the width and weight of seed, and the width of seed had a significant positive correlation with the weight of seed. The seed width has a significant positive correlation with contents of starch. The width of seeds had a significant negative correlation with altitude, and had a positive correlation with July’s mean temperature. The weight of seed had a significant positive correlation with July's mean temperature too. The correlation of the other phenotypic character and starch content of Q. mongolica seed to geographical climate factors was not significant. According to the results of cluster analysis, sixteen Q. mongolica provenances could be divided into three groups with big fruit and high starch content, middle fruit and starch content, small fruit and low starch content, in which the provenances from Dailing of Heilongjiang Province, Benxi of Liaoning Province and Dayangshu of Inner Mongolia were the superior provenances.     

Mining Elite Sea-Island Cotton Germplasm Based on Phenotyping and SSR Markers

[Objective] An elite germplasm resource of sea-island cotton with outstanding traits was mined in order to accelerate the breeding process of new varieties. [Method] The core collections of sea-island cotton germplasm consisted of 178 accessions were used as experimental materials in this study. Analyses of variability and diversity were performed through detecting phenotypic data of six main breeding-targeted traits, including boll weight, boll number per plant, lint percentage, fiber length, fiber strength, and micronaire. The elite germplasm of sea-island cotton was selected according to 10% optimal sampling strategy based on the phenotypic value of each trait. The 120 pairs of polymorphic simple sequence repeat (SSR) primers were used to analyze the polymorphism of 178 accessions of the core collections. Then, we conducted the population structure and clustering analysis based on the genotyping results. According to the results of cluster analysis, the primary elite germplasm was further selected, and the final elite germplasm of sea-island cotton was identified. [Result] The results showed that there was a high variability and abundant genetic diversity in the 6 studied traits. In 178 accessions of sea-island cotton, 262 alleles were detected by 120 pairs of SSR primers, with an average of 2.18 loci. The average polymorphism information content (PIC) was 0.067 8-0.630 0, with an average of 0.296 0, showing moderate polymorphism. The cluster analysis showed that the core collection of sea-island cotton was divided into six groups. twenty-three elite germplasm resources of sea-island cotton were identified based on phenotypic value and cluster analysis of SSR markers. [Conclusion] The germplasm of sea-island cotton can be analyzed and evaluated based on the phenotyping and SSR markers, and then the elite germplasm of sea-island cotton can be identified. These results provided the material basis for the genetic breeding of sea-island cotton, as well as the important reference and basis for the mining and identification of crop elite germplasm.     

国家果树种质南京桃资源圃初级核心种质构建

 利用国家果树种质南京桃资源圃中保存的630份资源的60个描述项信息,通过比较分组、逐步聚类和随机3类取样方法,以及取样比例的筛选,进行了初级核心种质构建研究。结果显示：逐步聚类取样方法优于分组取样和完全随机取样方法;逐步聚类取样方法的最适取样比例为15%;不同聚类模型间存在差异,聚类后的取样方法间也存在差异;欧式距离和内平方距离法逐步聚类后优先选取高均偏种质的方法更适宜桃初级核心种质的构建。通过缺失表型添加,构建的初级核心种质包括99份资源,占原始库的15.7%,具100%表型保留比例。经多样性指数、表型方差、变异系数检验,以及数量性状的均值和极差符合率检验,初级核心种质具有良好的代表性。