编码杜果丝氨酸/苏氨酸蛋白激酶类蛋白抗病基因同源序列的克隆与分析

根据丝氨酸／苏氨酸蛋白激酶类催化结构域Ⅰ和Ⅸ的氨基酸保守序列设计简并引物,PCR扩增‘紫花芒’杧果嫩绿色叶片基因组DNA。在NCBI中用Blast X比对发现,有6个阳性克隆是抗病基因同源序列,并命名为PK1～PK6,GenBank注册序列号为AY693369-AY693371和AY776277-AY776279。在PK3、PK4和PK6中发现了丝氨酸／苏氨酸蛋白激酶的9个催化结构域Ⅰ～Ⅸ,而其它3个克隆具有部分结构域。氨基酸序列同源性分析还发现,6个克隆编码的氨基酸序列都与受体样蛋白激酶有较高的氨基酸序列同源性。系统进化树分析还表明它们都是可能的抗病基因同源序列。总之,6个克隆不仅是抗病基因同源序列而且是可能的受体样蛋白激酶基因同源序列。     

利用ETM多光谱数据与SPOT5全色数据相融合进行森林资源调查

以ETM多光谱数据与SPOT5全色数据融合的遥感图像为对象,利用遥感图像和地面调查相结合的方法对吉林省东部山区进行森林资源调查,既可节约成本,又可提高调查精度,为遥感技术应用于森林资源调查设计探索了经验。     

人参皂苷Rg3(R),Rg3(S),Rg5/Rk1对乙醇致小鼠记忆阻碍改善作用的影响

采用被动回避穿越法研究了人参皂苷Rg3(R),Rg3(S)和Rg5/Rk1(1∶1)对乙醇致小鼠记忆阻碍改善作用的影响。结果表明:灌服人参皂苷Rg3(R),Rg3(S)和Rg5/Rk110 mg/kg后,显著延长了小鼠避暗的潜伏期,尤其以人参皂苷Rg5/Rk1作用最为显著,Rg5/Rk1处理组的潜伏期是对照组的2.97倍,Rg3(R),Rg3(S)分别为对照组的2.35和2.53倍,差异极显著(P<0.01)。说明这些化合物具有改善乙醇致小鼠的记忆障碍的能力。     

基于改进YOLOv5算法的农田杂草检测

随着智慧农业技术和大田种植技术的不断发展,自动除草具有广阔的市场前景。关于除草剂自动喷洒的有效性,农田杂草的精准、快速地识别和定位是关键技术之一。基于此提出一种改进的YOLOv5算法实现农田杂草检测,该方法通过改进数据增强方式,提高模型泛化性;通过添加注意力机制,增强主干网络的特征提取能力;通过改进框回归损失函数,提升预测框的准确率。试验表明,在芝麻作物和多种杂草的复杂环境下,本文方法的检测平均精度均值mAP为90.6%,杂草的检测平均精度AP为90.2%,比YOLOv5s模型分别提高4.7%和2%。在本文试验环境下,单张图像检测时间为2.8 ms,可实现实时检测。该研究内容可以为农田智能除草设备提供参考。     

利用广亲和基因S5-n的功能标记鉴定特殊配组类型杂交种纯度研究

“粳不育系/广亲和恢复系”配组方式在浙江省得到了越来越多的应用。广亲和基因S5-n的功能标记可快速检测种子纯度。为验证水稻广亲和基因S5-n功能标记鉴定粳不育系/广亲和恢复系配组方式杂种一代的效果,我们分别将长粳1A和6个恢复系获得的F₁代进行大田统计和分子标记鉴定,结果显示,由于广亲和基因S5-n分子标记不能特异性检测出串粉形成的异形株,导致其鉴定结果较大田统计鉴定结果高1.0~2.0百分点。未来还需要进一步优化广亲和基因分子标记技术的特异性,进而提高分子检测的准确性,这将有助于杂交稻的安全生产。     

奶牛乳脂性状候选基因EEF1D突变位点功能分析

提高乳脂含量、提升奶质已成为当前奶牛业研究的重点方向之一。前期经GWAS方法研究发现,EEF1D基因5'非翻译区(5'-UTR)的G/A突变与乳脂率极显著相关,为了进一步对候选基因EEF1D的突变位点进行功能分析,首先通过人工合成获得了EEF1D基因突变型与野生型片段,并在细胞水平上对其开展了功能验证实验。研究发现,EEF1D基因5'-UTR的G/A突变引起Sp3转录起始位点后移33 bp,并且突变型的活性大约是野生型的3倍。研究结果表明,该突变可改变转录因子的转录起始位点,从而使EEF1D的表达上调。     

Performance and uncertainty analysis of a short-term climate reconstruction based on multi-source data in the Tianshan Mountains region,China

Short-term climate reconstruction, i.e., the reproduction of short-term(several decades) historical climatic time series based on the relationship between observed data and available longer-term reference data in a certain area, can extend the length of climatic time series and offset the shortage of observations. This can be used to assess regional climate change over a much longer time scale. Based on monthly grid climate data from a Coupled Model Inter-comparison Project phase 5(CMIP5) dataset for the period of 1850–2000, the Climatic Research Unit(CRU) dataset for the period of 1901–2000 and the observed data from 53 meteorological stations located in the Tianshan Mountains region(TMR) of China during the period of 1961–2011, we calibrated and validated monthly average temperature(MAT) and monthly accumulated precipitation(MAP) in the TMR using the delta, physical scaling(SP) and artificial neural network(ANN) methods. Performance and uncertainty during the calibration(1971–1999) and verification(1961–1970) periods were assessed and compared using traditional performance indices and a revised set pair analysis(RSPA) method. The calibration and verification processes were subjected to various sources of uncertainty due to the influence of different reconstructed variables, different data sources, and/or different methods used. According to traditional performance indices, both the CRU and CMIP5 datasets resulted in satisfactory calibrated and verified MAT time series at 53 meteorological stations and MAP time series at 20 meteorological stations using the delta and SP methods for the period of 1961–1999. However, the results differed from those obtained by the RSPA method. This showed that the CRU dataset produced a low degree of uncertainty(positive connection degree) during the calibration and verification of MAT using the delta and SP methods compared to the CMIP5 dataset. Overall, the calibrated and verified MAP had a high degree of uncertainty(negative connection degree) regardless of the dataset or reconstruction method used. Therefore, the reconstructed time series of MAT for the period of 1850(or 1901)–1960 based on the CRU and CMIP5 datasets using the delta and SP methods could be used for further study. The results of this study will be useful for short-term(several decades) regional climate reconstruction and longer-term(100 a or more) assessments of regional climate change.     

大豆GmGRF5基因的组织表达模式与生物信息学分析

Growth Regulating Factor (GRF)家族基因是一类植物特有的转录因子,广泛参与植物多个重要的发育过程。为了研究大豆中的GRF转录因子的功能,本研究从大豆‘天隆1号’品种中克隆获得全长为1 038 bp的cDNA序列,由于该序列的编码蛋白与拟南芥GRF5具有高度的相似性,因此将其命名为GmGRF5基因。通过RT-PCR进行组织部位表达分析,发现GmGRF5基因在大豆各个组织部位均有表达,且在花中的表达量最高。通过ExPASy-Protparam分析发现,该蛋白质的分子量为39.463 48 kD,是具有一定亲水性、不稳定的碱性蛋白。该蛋白含有GRF家族蛋白的两个保守功能域(QLQ和WRC)。利用SOPMA对该蛋白的二级结构预测,显示其主要由无规则卷曲(72.05%)、α-螺旋(13.83%)、延伸链区(9.8%)和β-转角(4.32%)组成。此外,还利用不同的生物信息学软件对其三级结构、跨膜域、亚细胞定位、互作蛋白进行了分析,并构建系统进化树。结果表明,Gm GRF5基因在进化过程中具有高度的保守性,在大豆中具有潜在的功能多样性。