苹果茎沟病毒 (apple stem grooving virus, ASGV) 是一种重要的潜隐类果树病毒，对柑橘、梨和苹果等多种果树的生产构成了严重的威胁。2018年，在中国南方广泛种植的黄金蜜柚 (Citrus grandis cv. Huangjinmiyou) 上观察到了严重的黄化、斑驳和花叶症状，推测其可能由病毒引起。取5株表现相关症状果树的叶片样品构建混库并送高通量测序分析，从其中鉴定到了3个ASGV变异体，通过RT-PCR和RACE技术验证了其基因组序列。序列分析显示，这3个变异体的基因组核苷酸序列一致性为81.03%–82.34%，其基因组结构与过往报道的侵染其它果树的变异体类似。基于病毒全基因组核苷酸序列和外壳蛋白氨基酸序列的系统发育分析显示，3个黄金蜜柚ASGV变异体分别与来自不同寄主和地区的ASGV变异体聚在一枝。重组分析显示，3个ASGV变异体可能来自于ASGV不同株系间的重组。在全国11个主要柑橘种植省份采集了507份黄金蜜柚样品进行RT-PCR检测发现，在每个省份表现上述相关症状的样品中，ASGV的检出率均在92.7%以上，而在40份没有症状的样品中，均未检测到ASGV。将其中6个省份的感病样品嫁接到ASGV的指示植物——Rusk枳橙上，新生的系统叶表现出典型的碎叶症状，进一步验证了黄金蜜柚中ASGV的侵染。进一步探究了病毒和症状与温度的关系，发现嫁接的感病黄金蜜柚样品在30°C–35°C条件下症状消失，同时RT-PCR也检测不到ASGV的存在。而后，再将其置于20°C–24°C的条件下一段时间后，黄金蜜柚症状恢复，且ASGV可以由RT-PCR检测到。本文揭示了黄金蜜柚黄化斑驳花叶病与ASGV侵染的相关性，并提示了该病害大面积流行的风险，为进一步的病害防控提供了相应参考。

    The sclerotia of Villosiclava virens were found commonly in high altitude and the temperate regions, where the temperatures are relatively low in rice filling stage. To make sure if low temperature induce the sclerotial formation in V. virens, the inoculated rice panicles in laboratory and the diseased rice panicles cut from paddy fields were treated under different temperatures. The results showed that 3 days of night temperature at 15°C were enough to induce the sclerotial formation. The low temperature was much more effective for young balls with intact membranes. After appearance of chlamydospores on the ball surfaces, the sclerotium could not differentiate anymore. The sclerotia began to differentiate below the chlamydospore layer and gradually grew onto the ball surfaces. This suggests that low temperature in the early development stage of false smut balls is an important factor to induce the sclerotial differentiation, and rice cultivars with long growth periods are able to produce more sclerotium-bearing balls, which will produce mass of spores in paddy field in the coming year.

Rice false smut is caused by ascomycete Villosiclava virens, whose potential alternative hosts have been assumed previously. Here its potential alternative hosts were surveyed and identified from 2008 to 2013 in the main rice-growing regions in China. Two common weeds in paddy fields, Digitaria sanguinalis Scop. and Echinochloa crusgalli (L.) Beauv., were found to present the similar symptoms to smut diseases in a few individuals in 2012 and 2013 in Zhejiang and Sichuan provinces of China, respectively. After the examinations of the spore morphology, their infection and extension mode in hosts, pathogen cell wall components, and molecular identification, the two pathogens were identified to be the Basidiomycetes, Ustilago syntherismae and Ustilago trichophora, respectively. So far there has been no alternative host of V. virens to be identified in China. These suggest that the alternative hosts of V. virens, if they do exist, are not possible to play an important role in the pathogen life cycle and the disease epidemics.

The complete nucleotide sequence of an isolate of Citrus vein enation virus (CVEV-XZG) from China has been determined for the first time. The genome consisted of 5 983 nucleotides, coding for five open reading frames (ORFs), had a similar genomic organization features with Pea enation mosaic virus (PEMV). Nucleotide and deduced amino acid sequence identity of the five ORFs compared to isolate CVEV VE-1 range from 97.1 to 99.0% and 97.4 to 100.0%, these values compared to isolate PEMV-1 range from 45.2 to 51.6% and 31.1 to 45.2%. Phylogenetic analysis based on the complete genome sequence showed that the isolate CVEV-XZG had close relationship with Pea enation mosaic virus. The results supports CVEV may be a new member of genus Enamovirus. The full sequence of CVEV-XZG presented here may serve as a basis for future study of CVEV in China.

Identification and counting of rice light-trap pests are important to monitor rice pest population dynamics and make pest forecast. Identification and counting of rice light-trap pests manually is time-consuming, and leads to fatigue and an increase in the error rate. A rice light-trap insect imaging system is developed to automate rice pest identification. This system can capture the top and bottom images of each insect by two cameras to obtain more image features. A method is proposed for removing the background by color difference of two images with pests and non-pests. 156 features including color, shape and texture features of each pest are extracted into an support vector machine (SVM) classifier with radial basis kernel function. The seven-fold cross-validation is used to improve the accurate rate of pest identification. Four species of Lepidoptera rice pests are tested and achieved 97.5% average accurate rate.