Genomic Prediction of Arsenic Tolerance and Grain Yield in Rice: Contribution of Trait-Specific Markers and Multi-Environment Models

Many rice-growing areas are affected by high concentrations of arsenic(As). Rice varieties that prevent As uptake and/or accumulation can mitigate As threats to human health. Genomic selection is known to facilitate rapid selection of superior genotypes for complex traits. We explored the predictive ability(PA) of genomic prediction with single-environment models, accounting or not for trait-specific markers, multi-environment models, and multi-trait and multi-environment models, using the genotypic(1600 K SNPs) and phenotypic(grain As content, grain yield and days to flowering) data of the Bengal and Assam Aus Panel. Under the base-line single-environment model, PA of up to 0.707 and 0.654 was obtained for grain yield and grain As content, respectively; the three prediction methods(Bayesian Lasso, genomic best linear unbiased prediction and reproducing kernel Hilbert spaces) were considered to perform similarly, and marker selection based on linkage disequilibrium allowed to reduce the number of SNP to 17 K, without negative effect on PA of genomic predictions. Single-environment models giving distinct weight to trait-specific markers in the genomic relationship matrix outperformed the base-line models up to 32%. Multi-environment models, accounting for genotype × environment interactions, and multi-trait and multi-environment models outperformed the base-line models by up to 47% and 61%, respectively. Among the multi-trait and multi-environment models, the Bayesian multi-output regressor stacking function obtained the highest predictive ability(0.831 for grain As) with much higher efficiency for computing time. These findings pave the way for breeding for As-tolerance in the progenies of biparental crosses involving members of the Bengal and Assam Aus Panel. Genomic prediction can also be applied to breeding for other complex traits under multiple environments.     

基于物联网的远程温室视觉监控系统设计与实现

在当前智慧农业的大环境下,农作物生长过程的识别与监控问题一直是一项具有挑战性的任务,基于此提出一种基于物联网的远程温室视觉监控系统,系统通过LoRa无线通信技术监测温室内的温湿度、光照强度等环境参数,能够及时监测到农作物的生长状况,并实现自动通风、自动补光等功能。在PC端的Qt上位机实时监测温室内的环境信息并控制环境参数,通过OV9726摄像头对农作物进行监测,所获得的生长状态信息传输到S3C6410集中控制模块进行处理,结合克隆选择算法和朴素贝叶斯分类器对叶片进行识别处理。本系统采用LoRa模块进行自组网来实现环境监测,将Linux操作系统移植到集中控制模块,为视觉系统软硬件平台的搭建做准备工作,所使用的组合算法能够使得农作物叶片识别率达到95.3%,识别时间达到8.4 ms,对于叶片识别精度等方面有着明显的提升,经过实验充分验证本系统所使用的设备与算法的有效性。     

Comparison of selection methods among and within full-sibling progenies in Urochloa humidicola

Hybridization in the Urochloa humidicola breeding programme allowed to explore the genetic variability of the apomictic sources and, thus, to obtain new sexual and apomictic parents with compatible ploidy and/or superior progenies. However, given the high variability arising from crosses, there is a need to adopt efficient selection strategies among and within progenies. The aim of this study was to compare the selection methods among U. humidicola progenies with (t-BLUP) and without (BLUP) the weighting based on prediction error and subsequent selection within progenies by individual simulated (BLUP_IS) or optimal individual BLUP. We evaluated seventy-one U. humidicola full-sib progenies from biparental crosses between nine sexual and ten apomictic parents. The experiment was set up in an incomplete block design with 64 blocks of 15 plots each, consisting of individual plants. Seven consecutive cuts were made and the traits total dry-matter, leaf dry-matter and regrowth scores were measured. Genetic variance was expressive for all traits. The selection procedure among progenies of U. humidicula based on t-BLUP proved to be more advantageous regarding the use of BLUP not weighted by the prediction error, resulting in higher selection gains, taking as reference the optimal procedure of the individual BLUP. Furthermore, the use of BLUP_IS allowed an optimized selection of hybrids, allowing the breeder to explore with variable selection intensity the genetic variability within the selected full-sib progenies based on relative genetic merit.     

H9N2禽流感病毒全基因组密码子使用偏好性及影响因素分析

【目的】探究H9N2禽流感病毒(AIV)全基因组的密码子使用偏好性及影响因素。【方法】选取2010—2018年国内H9N2 AIV流行毒株的全基因组为研究对象,分析其碱基组成特性、最优密码子、密码子使用偏好性的影响因素以及病毒对宿主密码子使用模式的适应性。【结果】H9N2 AIV的全基因组中AU含量高于GC。大部分最优密码子以A或U结尾,有效密码子数(ENC)平均值为52.86,提示存在密码子使用偏好性但偏好性较低。密码子使用偏好性主要受到突变压力和自然选择的共同作用,其中自然选择(所占比例为61.79%～76.15%)作用大于突变压力(所占比例为23.85%～38.21%)。H9N2 AIV对人Homo sapiens的密码子适应指数平均值为0.739～0.741,提示H9N2AIV禽流感病毒可能已适应人类的密码子使用模式。【结论】本研究为H9N2 AIV的基因进化分析、已有疫苗的密码子优化和新型疫苗(密码子去优化疫苗)研制提供了理论依据。     

周晓平(宁化县县属国有林场,福建,宁化365400)

近些年来宁化县非常注重高质量、高品位绿色发展要求,保护好绿水青山才能成就金山银山的经营理念不断向纵深推进,人们对乡土树种越来越倍加珍惜,因此急需加快乡土树种栽培与保护。本文从乡土树种林业内涵展开分析,阐述了乡土树种在宁化县生态恢复过程中的作用,主要从乡土树种具有生长快_^[1]、适应性强、病虫害少、用途多、改良土壤性强、森林植物群落多样性丰富、林分生产力强、再生繁衍能力强等方面深入探索,从而证明了乡土树种在树种优选方面的重要作用。     

利用MAS技术改良香稻‘R15’的直链淀粉含量

R15是适应性较广,产量较高的中熟晚籼香稻品系,但由于含有Wx^a基因,其直链淀粉含量高达23.5%,严重影响食用口感。因此,我们以含有Wx^b等位基因的不育系Y58S为供体,通过杂交、回交,利用RAD测序技术、分子标记辅助筛选等技术,经过8个世代的筛选,成功用Wx^b基因替换掉R15中的Wx^a基因,培育出1个新的纯合株系——‘深华R16’,其染色体上有10882234 bp长度的碱基,基因型和供体亲本‘Y58S’一致,占2.92%;有362363285 bp长度的DNA片段基因型和受体亲本‘R15’一致,占97.08%;其直链淀粉含量为14%。将‘Y58S’分别与‘深华R16’和‘R15’进行组配,结果表明,‘Y58S’/‘深华R16’组合的直链淀粉含量(13.9%)低于‘Y58S’/‘R15’组合的直链淀粉含量(19.9%),其它米质性状与农艺性状基本一致。本研究为分子标记辅助育种提供了新思路,培育出一新的水稻品系。     

春榆优良家系的苗期选择

本文对大兴安岭地区的春榆在苗期进行了优良家系的初步选择。结果表明:家系平均遗传力为0.61;从50个供试群体中选择出5个优良家系,分别是21号、40号、2号、39号、16号家系,遗传增益为17%。     

影响抗药性杂草发生的因素

文章主要讨论了影响杂草抗药性快速发展的一些主要因素。影响杂草抗药性发展的主要因素是由于一类或一种除草剂以及使用作用靶标相同的不同类型的除草剂的大面积和长期连续使用。另外除草剂和杂草的一些特性也影响杂草的抗药性的快速发展。     

草莓丝核菌根腐病病原菌鉴定及7种杀菌剂的抑菌作用测定

为了解草莓丝核菌根腐病的病原种类及筛选防治丝核菌根腐病的有效杀菌剂,本研究基于形态学特征、细胞核荧光染色、菌丝融合群测定以及rDNA-ITS的序列分析,对北京和河北承德地区的草莓丝核菌根腐病的病原菌进行了鉴定,并利用菌丝生长速率法测定了7种杀菌剂对丝核菌的抑菌作用。结果发现,北京地区的丝核菌为双核丝核菌(binucleate Rhizoctonia,BNR),属于融合群AG-A;河北的丝核菌为立枯丝核菌Rhizoctonia solani,属于融合群AG-4。氟啶胺、吡唑醚菌酯、噻呋酰胺、戊唑醇、咯菌腈、氟硅唑对2种丝核菌均有很强的抑制作用,EC_(50)值为0.063 9～2.485 7μg/mL,抑霉唑的抑制作用较差,EC_(50)值为9.966 8～11.236 8μg/mL。同一种杀菌剂对不同丝核菌的抑制作用存在差异,噻呋酰胺、戊唑醇、氟硅唑、咯菌腈和抑霉唑对立枯丝核菌的抑制作用强于对双核丝核菌。试验结果为生产上合理选用杀菌剂防治草莓丝核菌根腐病提供了科学依据。     

基于浑浊度监测山区河流悬浮泥沙含量的试验研究

针对山区河流悬浮泥沙含量监测缺乏实时性的问题,对浑浊度及悬浮泥沙含量进行监测,分析光照强度、流速、颗粒粒径和水体含沙量对浑浊度的影响,建立浑浊度与悬浮泥沙含量的关系模型式。结果表明： 1）光照强度对浑浊度的影响最大,根据二者之间的关系,将光照强度（E）划分为3个区间：E≤6 800 lx、6 800 lx <E≤22 000 lx、E>22 000 lx,对应于3个区间,确定了浑浊度与悬浮泥沙含量的关系模型; 2）水流流速主要影响上层泥沙颗粒的分布,流速为0.16 m/s时测量区域的浑浊度大于流速为0.018 m/s时的浑浊度; 3）泥沙颗粒的粒径大于扬动流速能带动的临界粒径时,其会沉积在渠底,因此,泥沙级配中大于临界粒径的颗粒占比越大,悬浮泥沙含量越小,浑浊度越小; 4）水体含沙量越大,水沙输移过程中携带悬浮泥沙的量也越多,测量区域上层水样的浑浊度越大; 5）采用本研究确定的浑浊度与悬浮泥沙含量的3个关系模型,预测得到的悬浮泥沙含量与实际悬浮泥沙含量的平均相对误差分别为7.22%、10.00%、8.58%。根据在自然条件下不同光照强度区间测得的浑浊度,可以得出该测量区域的悬浮泥沙含量。由于浑浊度可以现场连续、快速测得,所以将此方法引入山区河流悬浮含沙量实时监测是可行的。