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.     

二氯吡啶酸在油菜及土壤中的残留动态研究

采用高效液相色谱仪测定除草剂二氯吡啶酸在油菜及土壤中的残留消解动态和最终残留量。结果表明:在油菜和土壤上喷施质量分数75%的二氯吡啶酸可溶性粒剂(有效成分338 g/hm2),测出油菜和土壤中的原始沉积量分别为5.63~6.75 mg/kg和0.47~0.48 mg/kg,半衰期为8.49~10.11 d和3.76~4.74 d。对油菜施药1次,施药后45 d测得油菜上残留量为0.15~0.17 mg/kg。     

An investigation into the grazing efficiency of perennial ryegrass varieties

The objective of this study was to compare the grazing efficiency of 30 perennial ryegrass varieties, differing in ploidy and heading date. Plots were grazed by lactating dairy cows and managed under a rotational grazing system with 19 grazing events occurring over two years. Pre-grazing and post-grazing compressed sward heights were measured with a rising plate meter. A mixed model was used to predict the post-grazing sward height of each variety based on year, grazing event, block and pre-grazing sward height. Residual grazed height (RGH) was derived as the difference between the actual and predicted post-grazing sward height and was used as the measure of grazing efficiency. Negative RGH values indicated that the actual herbage removed was greater than that predicted and so indicated a superior grazing efficiency. Varieties differed in their level of grazing efficiency (p < .001), with RGH values ranging from −0.38 to +0.34 cm. Tetraploid varieties exhibited significantly greater grazing efficiency performance than diploids (p < .001), with average RGH values of −0.13 and +0.13 cm respectively. A significant difference in grazing efficiency was found among recommended perennial ryegrass varieties that are not being recorded by mechanically harvested simulated grazing protocols. A variety reappraisal that included grazing efficiency could identify varieties capable of improving on-farm livestock productivity from grass.     

磺酰脲类除草剂的应用及检测方法

磺酰脲类除草剂是上世纪80年代美国杜邦公司研发出来的一种除草剂,其具有高效、广谱、低毒、高选择性等优点,因此,一经问世就得到了广泛应用。但是,由其引起的土壤、地下水污染问题而饱受诟病。有些磺酰脲除草剂难以降解,可对环境造成持续的污染,更有甚者,农作物也会因此而造成农残超标。因此,磺酰脲类除草剂在环境、农产品中的残留问题及和检测技术研究进展值得我们关注。     

基于循环残差注意力的群养生猪实例分割

目的 在群养环境下,实现生猪粘连、杂物遮挡等不同条件下生猪个体的高精度分割。方法 对真实养殖场景下的8栏日龄20~105 d共45头群养生猪进行研究,以移动相机拍摄图像为数据源,并执行改变亮度、加入高斯噪声等数据增强操作获取标注图片3 834张。探究基于2个骨干网络ResNet50、ResNet101与2个任务网络Mask R-CNN、Cascade mask R-CNN交叉结合的多种模型,并将循环残差注意力(RRA)思想引入2个任务网络模型中,在不显著增加计算量的前提下提升模型特征提取能力、提高分割精度。结果 选用Mask R-CNN-ResNet50比Cascade mask R-CNN-ResNet50在AP_0.5、AP_0.75、AP_0.5-0.95和AP_{0.5-0.95-large}指标上分别提升4.3%、3.5%、2.2%和2.2%;加入不同数量的RRA模块以探究其对各个任务模型预测性能影响,试验表明加入2个RRA模块后对各个任务模型的提升效果最为明显。结论 加入2个RRA模块的Mask R-CNN-ResNet50模型可以更精确、有效地对不同场景群养生猪进行分割,为后续生猪身份识别与行为分析提供模型支撑。     

高砷煤矿周围旱作土壤重金属污染特征及农作物健康风险评价

为了掌握高砷煤矿周边旱作农田重金属污染状况,运用单因子污染指数法、内梅罗综合污染指数法和地积累指数法对高砷煤矿周边旱作土壤重金属（As、Cd、Hg、Cu、Zn）进行污染风险评价,并采用富集系数法、目标危害商数法和致癌风险指数法对农作物和人体健康风险进行评价。结果表明：调查区域土壤综合污染指数均值为2.12,属中度污染水平;土壤中As、Cd、Hg、Zn、Cu的含量均值分别为全国土壤背景值的7.91、2.57、5.26、1.53、3.44倍;As和Hg的污染等级高且范围广,Cu污染等级低但污染范围广,Cd的污染等级较低且范围较小,Zn基本无污染。富集系数法评价结果显示,作物对5种重金属的吸收能力表现为Cd>Zn>Cu>Hg>As。农作物健康风险评价结果显示,5种农作物的健康风险主要为非致癌风险,As是可食用农作物（除玉米外）的主要致癌风险因子和非致癌风险因子,Cd是烟草的主要致癌风险因子和非致癌风险因子。食用本地农作物对人体造成的健康风险依次为As>Cd>Hg>Cu>Zn。研究表明：研究区域土壤处于中度污染水平,As的风险较高;可食农作物中重...     

吡虫啉在番茄和土壤上的残留动态

采用Spherisoib 5μ-C18色谱柱建立以乙腈：水为流动相的高效液相色谱条件，测定了吡虫啉在番茄及土壤上的残留动态。结果表明：吡虫啉在番茄及土壤上的添加回收率分别为84％～120％，80％～100％；原始沉积量分别为0．12～0．22mg/kg，0．21～0．62mg／kg；半衰期(t1/2)分别为0．7～6．3d，8．2～25．0d。     

残膜回收机带式卷膜装置设计与试验

为解决现有残膜回收机集膜装置在集膜和卸膜过程中残膜质地松散、作业效率低等问题,设计了一种带式卷膜装置。阐述了该带式卷膜装置的基本结构和工作原理,通过理论分析确定了关键部件结构参数,分析了卷膜作业过程,经过计算分析得到可卷收残膜膜卷的最大直径、卷膜速比范围、卷膜倾角范围。采用三因素三水平Box Behnken试验设计方法,建立了各因素与膜卷密度之间的数学模型,确定了较优工作参数组合为:机具前进速度5.38 km/h、卷膜速比为1.19、卷膜倾角为80°,此时平均膜卷密度为122.7 kg/m³。田间试验表明,卷膜装置作业性能稳定,膜卷质量良好,满足设计和实际作业要求。     

基于原型网络的小样本禽蛋图像特征检测方法

机器视觉因具有检测速度快、稳定性高及成本低等优点,已发展成为禽蛋无损检测领域主流检测手段。使用该技术对禽蛋进行无损检测时,需要依赖大量禽蛋图像作为数据支撑才能取得较好的检测效果。由于养殖安全等限制,禽蛋图像数据的采集成本较高,针对该问题,提出了一种适应于小样本禽蛋图像检测的原型网络（Prototypical network）。该网络利用引入注意力机制的逆残差结构搭建的卷积神经网络将不同类别的禽蛋图像映射至嵌入空间,并利用欧氏距离度量测试禽蛋图像在嵌入空间的类别,从而完成禽蛋图像的分类。本文利用该网络分别验证了小样本条件下受精蛋与无精蛋、双黄蛋与单黄蛋及裂纹蛋与正常蛋的分类检测效果,其检测精度分别为95%、98%、88%。试验结果表明本文方法能够有效地解决禽蛋图像检测中样本不足的问题,为禽蛋图像无损检测研究提供了新的思路。     

基于猪粪水热炭化的生物炭性能及残液成分分析

为系统研究不同炭化温度条件下猪粪水热炭化规律,本研究以猪粪和发酵猪粪为供试材料,采用水热炭化工艺在系列温度条件下（180、240℃和300℃）制备生物炭,对其元素含量、热稳定性、孔隙结构、表面官能团等理化性质进行表征,并对水热炭化残液进行成分分析。结果表明,猪粪生物炭和发酵猪粪生物炭均具有发达的孔隙结构、丰富的表面官能团等优良特性,其H/C原子比和热失重率均随炭化温度升高而减小,表明热化学稳定性随炭化温度升高而增强。水热炭化残液的成分主要包括有机酸、醇、酯、醛、吡嗪、苯酚等物质,较高炭化温度条件下残液中化合物种类更丰富。与猪粪相比,发酵猪粪水热炭化残液的成分仍然以酚、烯、酮类物质为主,但呋喃、吡啶、吡嗪类毒性化合物消失。研究表明,发酵猪粪在300℃条件下水热炭化的残液用作液态肥料的安全性更高,在资源化利用方面更具优势。