Blast disease caused by the fungus Magnaporthe grisea results in significant yield losses of cereal crops across the world.  To date, very few regulatory genes contributing to blast resistance in grass species have been identified and the genetic basis of blast resistance in cereals remains elusive.  Here, a core collection of foxtail millet (Setaria italica) containing 888 accessions was evaluated through inoculation with the blast strain HN-1 and a genome-wide association study (GWAS) was performed to detect regulators responsible for blast disease resistance in foxtail millet.  The phenotypic variation of foxtail millet accessions inoculated with the blast strain HN-1 indicated that less than 1.60% of the samples were highly resistant, 35.25% were moderately resistant, 57.09% were moderately susceptible, and 6.08% were highly susceptible.  The geographical pattern of blast-resistant samples revealed that a high proportion of resistant accessions were located in lower latitude regions where the foxtail millet growing season has higher rain precipitation.  Using 720 000 SNP markers covering the Setaria genome, GWAS showed that two genomic loci from chromosomes 2 and 9 were significantly associated with blast disease resistance in foxtail millet.  Finally, eight putative genes were identified using rice blast-related transcriptomic data.  The results of this work lay a foundation for the foxtail millet blast resistance biology and provide guidance for breeding practices in this promising crop species and other cereals.

Mikania micrantha Kunth is an invasive alien weed and known as a plant killer around the world.  Accurately and rapidly identifying M. micrantha in the wild is important for monitoring its growth status, as this helps management officials to take the necessary steps to devise a comprehensive strategy to control the invasive weed in the identified area.  However, this approach still mainly depends on satellite remote sensing and manual inspection.  The cost is high and the accuracy rate and efficiency are low.  We acquired color images of the monitoring area in the wild environment using an Unmanned Aerial Vehicle (UAV) and proposed a novel network -MmNet- based on a deep Convolutional Neural Network (CNN) to identify M. micrantha in the images.  The network consists of AlexNet Local Response Normalization (LRN), along with the GoogLeNet and continuous convolution of VGG inception models.  After training and testing, the identification of 400 testing samples by MmNet is very good, with accuracy of 94.50% and time cost of 10.369 s.  Moreover, in quantitative comparative analysis, the proposed MmNet not only has high accuracy and efficiency but also simple construction and outstanding repeatability.  Compared with recently popular CNNs, MmNet is more suitable for the identification of M. micrantha in the wild.  However, to meet the challenge of wild environments, more M. micrantha images need to be acquired for MmNet training.  In addition, the classification labels need to be sorted in more detail.  Altogether, this research provides some theoretical and scientific basis for the development of intelligent monitoring and early warning systems for M. micrantha and other invasive species.