Rice blast, caused by the fungus Magnaporthe oryzae, is the most damaging disease for rice worldwide. However, the reactions of rice to M. oryzae at different growth stages are largely unknown. In the present study, two temperate japonica rice cultivars, M-202 and Nipponbare, were inoculated synchronously at different vegetative growth stages, V1 to V10. Plants of M-202 at each stage from V1 to reproductive stage R8 were inoculated with M. oryzae race (isolate) IB-49 (ZN61) under controlled conditions. Disease reactions were recorded 7 days postinoculation by measuring the percentage of diseased area of all leaves, excluding the youngest leaf. The results showed that the plants were significantly susceptible at the V1 to V4 stages with a disease severity of 26.7 to 46.8% and disease index of 18.62 to 37.76 for M-202. At the V1 to V2 stages, the plants were significantly susceptible with a disease a severity of 28.6 to 39.3% and disease index of 23.65 to 29.82 for Nipponbare. Similar results were observed when plants of M-202 were inoculated at each growth stage with a disease severity of 29.7 to 60.6% and disease index of 21.93 to 59.25 from V1 to V4. Susceptibility decreased after the V5 stage (severity 4.6% and index 2.17) and became completely resistant at the V9 to V10 stages and after the reproductive stages, suggesting that plants have enhanced disease resistance at later growth stages. These findings are useful for managing rice blast disease in commercial rice production worldwide.

In this study, spatial analyses were conducted at three different scales to better understand the epidemiology of rice blast, a major rice disease caused by Magnaporthe oryzae. At the regional scale, across the major rice production regions in China, rice blast incidence was monitored on 101 dates at 193 stations from 10 June to 10 September during 2009 to 2014, and surveyed in 143 fields in September 2016; at the county scale, three surveys were done covering one to five counties in 2015 to 2016; and, at the field scale, blast was evaluated in six fields in 2015 to 2016. Spatial cluster and hot spot analyses were conducted in the geographic information system on the geographical pattern of the disease at regional scale, and geostatistical analysis was performed at all three scales. Cluster and hot spot analyses revealed that high-disease areas were clustered in mountainous areas in China. Geostatistical analyses detected spatial dependence of blast incidence with influence ranges of 399 to 1,080 km at regional scale and 5 to 10 m at field scale but not at county scale. The spatial patterns at different scales might be determined by inherent properties of rice blast and environmental driving forces, and findings from this study provide helpful information to sampling and management of rice blast.

Endogenous microRNAs (miRNAs) play pivotal roles in plant development and responses to various biotic or abiotic stresses. Up to now, more than 500 maize miRNAs have been identified. However, functions of these identified miRNAs remained largely unknown due mainly to the lack of rapid and reliable tools. We previously reported a cucumber mosaic virus strain ZMBJ (ZMBJ-CMV)-based gene silencing vector for rapid and efficient gene function studies in maize lines with agronomical importance. Because ZMBJ-CMV induces very mild disease symptoms but strong gene silencing in maize, we decided to further modify this vector to suppress miRNA expressions in maize. The newly developed ZMBJ-CMV-2b_N81-STTM vector expresses a short tandem target mimic (STTM) containing two target-mimic sequences separated by a short spacer sequence. Our results showed that ZMBJ-CMV-2b_N81-STTM can be used to investigate miRNA function in Nicotiana benthamiana and maize seedlings. The ZMBJ-CMV-2b_N81-STTM-based downregulation of Nbe-miR165/166 or Nbe-miR159 induced specific and strong miRNA-sequestering phenotypes, and increased the expressions of their predicted target genes. For maize, the ZMBJ-CMV-2b_N81-STTM based downregulation of zma-miR167 or zma-miR482 caused a decrease of lateral roots growth and a plant stunting phenotypes, respectively. In both cases, the target genes of zma-miR167- or zma-miR482 were increased significantly. Thus, we consider ZMBJ-CMV based VbMS system as a useful tool for high-throughput investigations of miRNA functions in maize.

Major blast resistance (R) genes confer resistance in a gene-for-gene manner. However, little information is available on interactions between R genes. In this study, interactions between two rice blast R genes, Pi-ta and Pi-b, and other minor blast resistance quantitative trait loci (QTLs) were investigated in a recombinant inbred line (RIL) population comprising 243 RILs from a Cybonnet (CYBT) × Saber (SB) cross. CYBT has the R gene Pi-ta and SB has Pi-b. Ten differential isolates of four Magnaporthe oryzae races (IB-1, IB-17, IB-49, and IE-1K) were used to evaluate disease reactions of the 243 RILs under greenhouse conditions. Five resistance QTLs were mapped on chromosomes 2, 3, 8, 9, and 12 with a linkage map of 179 single nucleotide polymorphism markers. Among them, qBR12 (Q1), was mapped at the Pi-ta locus and accounted for 45.41% of phenotypic variation while qBR2 (Q2) was located at the Pi-b locus and accounted for 24.81% of disease reactions. The additive-by-additive epistatic interaction between Q1 (Pi-ta) and Q2 (Pi-b) was detected; they can enhance the disease resistance by an additive 0.93 using the 0 to 9 standard phenotyping method. These results suggest that Pi-ta interacts synergistically with Pi-b.

The severity of rice blast and its impacts on rice yield are closely related to the inoculum quantity of Magnaporthe oryzae, and automatic detection of the pathogen spores in microscopic images can provide a rapid and effective way to quantify pathogen inoculum. Traditional spore detection methods mostly rely on manual feature extraction and shallow machine learning models, and are mostly designed for the indoor counting of a single spore class, which cannot handle the interference of impurity particles in the field. This study achieved automatic detection of rice blast fungus spores in the mixture with other fungal spores and rice pollens commonly encountered under field conditions by using deep learning based object detection techniques. First, 8959 microscopic images of a single spore class and 1450 microscopic images of mixed spore classes, including the rice blast fungus spores and four common impurity particles, were collected and labelled to form the benchmark dataset. Then, Faster R-CNN, Cascade R-CNN and YOLOv3 were used as the main detection frameworks, and multiple convolutional neural networks were used as the backbone networks in training of nine object detection algorithms. The results showed that the detection performance of YOLOv3_DarkNet53 is superior to the other eight algorithms, and achieved 98.0% mean average precision (intersection over union > 0.5) and an average speed of 36.4 frames per second. This study demonstrated the enormous application potential of deep object detection algorithms in automatic detection and quantification of rice blast fungus spores.

In March 2016, root rot was observed on common bean (Phaseolus vulgaris L.) samples (cultivar Shuangqing 38) collected from a commercial farm in Sanya City, Hainan Province, China. The incidence of this disease in the field was about 30%. The symptoms of infected plants included dwarf and yellowing aboveground, and irregular, scaled, black-brown lesions on roots and lack of lateral roots. Symptomatic root tissues were excised from four plants, cut into 1-cm pieces, surface sterilized with 1% NaClO for 2 min, rinsed in sterilized distilled water three times, then placed on potato dextrose agar (PDA) plates. After incubation at 25°C for 7 days, hyphal tips from colony edge were transferred to a fresh PDA plate and a total of 13 isolates were obtained. Morphological and molecular identification indicated that 11 of these isolates from four plants were fungal species previously reported on common bean, and the other two from 2 individual plants were Plectosphaerella cumuerina. The colonies of these two isolat...