基于电磁感应仪数据的南疆棉田土壤电导率反演模型研究

土壤盐渍化是南疆棉田优质高产的主要障碍因素,定额灌溉可有效改良土壤盐渍化,EM38-MK2可快速、动态获取土壤盐渍化数据,适时监测土壤盐渍化,为棉田定额灌溉提供数字依据。针对电磁感应仪(EM38-MK2)的发射圈和接收圈设计了不同采样方案,在同一条田内采集了6个不同时期的土壤表观电导率数据及相应的剖面土样,分析了不同土壤采集方案及土壤含水量对表观电导率模型精度的影响,对比了以单一时期数据建模的局部模型和6个时期整体数据建模的全局模型的反演精度。结果表明,由单点采集电磁感应仪发射线圈位置土样所构建的模型精度更高,稳定性更好,能有效减少由采样带来的误差。当土壤含水量低于10%时,表观电导率与实测电导率之间的相关性较低,决定系数为0.58,反演模型只具备粗略估计实测电导率的能力;当土壤含水量高于10%时,表观电导率与实测电导率具有很好的相关性,决定系数达到0.80以上,反演模型具有较好的预测能力。EMH+EMV多测定模式下表观电导率与实测电导率之间的模型精度高于EMH或EMV单一测定模式,确立EC_(h0.375)+(EC_(h0.75)+EC_(v0.75))/2+EC_(v1.5)为建模因子,有效提高了反演模型的精度。不同深度土层的局部模型反演精度均高于全局模型精度,局部模型的RPD均大于2.0,具有较好的预测能力。     

Occurrence of prophage and historical perspectives associated with the dissemination of huanglongbing in mainland China

Huanglongbing (HLB), associated with a non-culturable bacterium ‘Candidatus Liberibacter asiaticus’ (CLas), is a highly destructive citrus disease with a long but poorly documented history in China. No effective treatment for HLB is available. The identification of new prophages in abundant CLas genomic sequence data provides new insights into both the diversity of CLas strains and HLB management. In this study, CLas populations from nine provinces were surveyed for the presence of prophage. Two major prophage typing groups (PTGs) were discovered to be associated with two different altitude regions: strains of CLas in PTG1 from high altitude regions (HAR) mainly contained prophage Type 1 only or Types 1 and 3, whereas strains of CLas in PTG2 from low altitude regions (LAR) mainly contained prophage Type 2. The discovery of these CLas population patterns provides evidence for independent origins of HLB in HAR and LAR. Guangdong province is the generally recognized domestic region of origin for HLB and is primarily responsible for the dissemination of HLB in LAR through transport of seedlings. Both Yunnan and Sichuan provinces are the probable regions of origin for HLB in HAR. PTG2 was further divided into two subgroups: PTG2-1, found in Guangdong, Fujian and Guangxi and PTG2-2, found in Jiangxi, Zhejiang and Hunan. These regions and prophage types are correlated with early and late introductions of HLB in LAR. These molecular analyses were supported by studying the history of the dissemination of HLB in historical documents.     

水稻育性调控相关基因RNAi载体的构建及其表型鉴定

利用RNAi干扰技术研究不同基因对花粉发育、卵细胞发育和合子胚发育的影响是一种重要的手段。本研究通过筛选水稻在生殖发育过程中的9个重要调控基因,构建基因的RNAi表达载体,分析转基因植株育性及相关性状表型,以期探明RNAi表达载体对靶标性状的干扰效应。其中,AT61~AT63、AT64~AT66、AT67~AT69表达载体分别靶标花粉育性、卵细胞发育以及合子胚发育的调控。结果表明,除RNAi表达载体AT64没有获得转基因植株外,其余8个RNAi表达载体均获得了转基因植株;对T0代转基因植株的花粉育性、结实率以及潮霉素筛选(40 mg/L)发芽率检测的统计结果显示,RNAi表达载体AT62(花粉发育调控)、AT65(卵细胞发育调控)和AT67(合子胚发育调控)的干扰效应较强。本研究结果将为创制新型水稻基因工程不育系提供策略和选择。     

鸡胚及禽类细胞系在生物医药领域的应用及研究进展

鸡胚因发育过程清楚，长久以来作为基础和应用科学研究领域重要的实验模型，尤其在鸡胚发育早期绒毛尿囊膜阶段，因其血管丰富，是天然的免疫缺陷宿主，是病理学、药理学和肿瘤学等研究领域的理想实验模型。作者简述了发育早期的鸡胚组织结构，并介绍了鸡胚绒毛尿囊膜在肿瘤研究、血管生成、器官移植、烧伤等疾病机理研究中的应用，以及在鸡胚病理模型基础上进行的抗肿瘤药物筛选的应用。重点介绍了鸡胚及禽类细胞系在病毒繁殖和疫苗生产、治疗性蛋白和单抗生产方面的应用研究进展。多种人源病毒、禽源病毒、支原体等可在鸡胚及禽类细胞上增殖，并用于疫苗生产。作者对常用的禽类纤维原细胞和多能干细胞的发展和特点进行了阐述，并总结了商业化的禽类细胞系来源以及部分易感病毒。鸡胚表达系统能够在目的蛋白特定位点产生人源化糖基，减少目的蛋白对人的过敏反应，且禽蛋廉价易得，可作为生产人用单克隆抗体和治疗性蛋白的合适供体。作者介绍了鸡胚及禽类细胞系在生物医药领域应用的最新进展，并对鸡胚作为动物模型在未来的应用进行了展望。     

利用高巧(60%吡虫啉悬浮种衣剂)防治小豆蚜虫效果研究

[目的]探讨小豆蚜虫防治新技术,比较研究种衣剂拌种的防治效果。[方法]选用高巧(60%吡虫啉悬浮种衣剂)进行播前拌种试验,采用50 mL水分别加入高巧2、4、6、8 mL拌种1.0kg的处理方法,播种后比较其出苗率及幼苗的发育情况、蚜虫防治效果、小豆百粒重、产量及增产率。[结果]高巧拌种不会影响小豆出苗,可有效防治小豆蚜虫,但防效随时间延长而降低。防效保持在85%以上,可显著提高小豆百粒重和产量。[结论]该技术可在小豆生产中进行推广应用。     

Deep neural network algorithm for estimating maize biomass based on simulated Sentinel 2A vegetation indices and leaf area index

Accurate estimation of biomass is necessary for evaluating crop growth and predicting crop yield.Biomass is also a key trait in increasing grain yield by crop breeding.The aims of this study were(i)to identify the best vegetation indices for estimating maize biomass,(ii)to investigate the relationship between biomass and leaf area index(LAI)at several growth stages,and(iii)to evaluate a biomass model using measured vegetation indices or simulated vegetation indices of Sentinel 2A and LAI using a deep neural network(DNN)algorithm.The results showed that biomass was associated with all vegetation indices.The three-band water index(TBWI)was the best vegetation index for estimating biomass and the corresponding R2,RMSE,and RRMSE were 0.76,2.84 t ha−1,and 38.22%respectively.LAI was highly correlated with biomass(R2=0.89,RMSE=2.27 t ha−1,and RRMSE=30.55%).Estimated biomass based on 15 hyperspectral vegetation indices was in a high agreement with measured biomass using the DNN algorithm(R2=0.83,RMSE=1.96 t ha−1,and RRMSE=26.43%).Biomass estimation accuracy was further increased when LAI was combined with the 15 vegetation indices(R2=0.91,RMSE=1.49 t ha−1,and RRMSE=20.05%).Relationships between the hyperspectral vegetation indices and biomass differed from relationships between simulated Sentinel 2A vegetation indices and biomass.Biomass estimation from the hyperspectral vegetation indices was more accurate than that from the simulated Sentinel 2A vegetation indices(R2=0.87,RMSE=1.84 t ha−1,and RRMSE=24.76%).The DNN algorithm was effective in improving the estimation accuracy of biomass.It provides a guideline for estimating biomass of maize using remote sensing technology and the DNN algorithm in this region.     

Koi herpesvirus and carp edema virus threaten common carp aquaculture in Croatia

Common carp (Cyprinus carpio) is a very important fish species for warm-water aquaculture in Croatia. All Croatian carp farms are subjected to a surveillance programme for the presence of koi herpesvirus (KHV), causing a deadly disease called koi herpesvirus disease (KHVD). However, there is no surveillance for other viral pathogens of importance like carp edema virus (CEV), a causative agent of koi sleepy disease (KSD). During regular testing within the KHVD surveillance programme, we tested samples for CEV simultaneously. The screening indicated possible outbreaks of KHVD and KSD. During 2016, KHVD broke out in an isolated area and soon thereafter a KHV eradication programme was successfully performed. However, during 2018 and 2019, two additional mortality events occurred in lakes in the southern part of Croatia during the spring. Samples from both events tested positive for CEV. An epidemiological investigation confirmed the introduction of infected carps from an infected farm to one of the lakes. To prevent the spreading of CEV into open waters, it is of utmost importance to introduce CEV testing before fish movement or to perform regular testing of all carp farms in the country to determine CEV prevalence for the purpose of implementation of control measures.     

利用酵母双杂交膜系统筛选与小麦蓝矮植原体免疫膜蛋白互作的异沙叶蝉蛋白

正小麦蓝矮病(Wheat blue dwarf,WBD)是我国西北麦区一种重要植原体病害,在我国西部地区危害严重。该病害由异沙叶蝉(Psammotettix alienus L.)专化性传播,介体传毒成为病害流行的重要中心环节[1]。本实验室前期通过免疫荧光标记研究发现WBD植原体免疫膜蛋白(immunodominant membrane protein, IMP)与介体异沙叶蝉肌动蛋白互作,说明IMP在植原体传播和致病过程中起关键作用。     

The use of infrared spectroscopy and machine learning tools for detection of Meloidogyne infestations

Plant parasitic nematodes are generally soilborne pathogens that attack plants and cause economic losses in many crops. The infested plants show nonspecific symptoms or, often, are symptomless; therefore, diagnosis is performed by taking soil and root tissue samples. Here, we show that a combination of different infrared spectra analysis and machine learning algorithms can be used to detect plant parasitic nematode infestations before symptoms become visible, using leaves instead of roots and soil as samples. We found that tomato and guava plants infested with Meloidogyne enterorlobii produced different spectral patterns compared to uninfested plants. Using partial spectra from 1,450 to 900/cm as the "fingerprint region", principal component analyses indicated that after 5 (tomatoes) or 8 weeks (guava), plants with no visible symptoms of infestations were positively diagnosed. To improve the early detection response, we used machine learning modelling. A support vector machine (SVM) was used to obtain more robust, accurate models. The SVM model contained 34 support vectors, 17 for each level. The overall performance of the model was >97% and the total accuracy was significantly higher, demonstrating the absence of chance prediction. The best prediction of infestation was obtained at the second and fourth weeks for tomatoes and guavas, respectively, reducing the diagnostic time by half. The combined application of these techniques reduces the processing time from field to laboratory and shows enormous advantages by avoiding root and soil sampling.