Mildew resistance locus O (MLO) is a plant-specific gene family that plays an important role in the growth and development of plants and their interactions with the environment.  However, the available information on this gene family in pear is limited.  Here, 24 PbrMLO genes were identified and divided into five subfamilies (I, II, III, IV and V).  Whole-genome duplication (WGD) and dispersed duplication contributed to the expansion of the PbrMLO family.  In addition, gene expression analysis revealed that PbrMLO genes were distributed in various pear tissues, suggesting their diverse functions.  We selected PbrMLO23 for further functional analysis.  Expression profile analysis by qRT-PCR showed that PbrMLO23 was highly expressed in pollen.  Subcellular localization analysis showed that PbrMLO23 was located on the plasma membrane.  When the expression level of PbrMLO23 was knocked down by using antisense oligonucleotides, pollen tube lengths increased, indicating that PbrMLO23 plays a functional role in inhibiting pollen tube growth.  In summary, these results provide evolutionary insight into PbrMLO and its functional characteristics and lay a foundation for further analysis of the functions of PbrMLO members in pear.

 

The fall armyworm (FAW), Spodoptera frugiperda (J. E. Smith), is native to the Americas.  It has rapidly invaded 47 African countries and 18 Asian countries since the first detection of invasion into Nigeria and Ghana in 2016.  It is regarded as a super pest based on its host range (at least 353 host plants), its inherent ability to survive in a wide range of habitats, its strong migration ability, high fecundity, rapid development of resistance to insecticides/viruses and its gluttonous characteristics.  The inherently superior biological characteristics of FAW contribute to its invasiveness.  Integrated pest management (IPM) of FAW has relied on multiple applications of monitoring and scouting, agricultural control, chemical pesticides, viral insecticides, sex attractants, bio-control agents (parasitoids, predators and entomopathogens) and botanicals.  Knowledge gaps remain to be filled to: (1) understand the invasive mechanisms of S. frugiperda; (2) understand how to prevent its further spread and (3) provide better management strategies.  This review summarizes the biological characters of FAW, their association with its invasiveness and IPM strategies, which may provide further insights for future management.

Soil waterlogging is a major environmental stress that suppresses the growth and productivity of rapeseed (Brassica napus L.).  Natural genetic variations in waterlogging tolerance (WT) were observed but no QTL mapping has been done for WT related traits in rapeseed. In this study, QTL associated with three WT related traits including relative root length (RRL), relative hypocotyl length (RHL) and relative fresh weight (RFW) were dissected using a set of reciprocal introgression lines (ILs) derived from the cross GH01×ZS9, which showed significant difference in WT.  Genotyping-by-sequencing (GBS) of the populations were performed, totally 1 468 and 1 450 binned SNPs were identified for GIL (GH01 as the recurrent parent) and ZIL (ZS9 as the recurrent parent) population, respectively.  A total of 66 distinct QTLs for WT at the seedling establishment stage including 31 for RRL, 17 for RHL and 18 for RFW were detected.  Among the 66 QTLs, 20 (29.4%) QTLs were detected in both genetic backgrounds and then they were integrated into six QTL clusters, which can be targeted in rapeseed breeding for improvement of WT through marker-assisted selection (MAS).  Based on the physical positions of SNPs and the functional annotation of the Arabidopsis thaliana genome, 56 genes within the six QTL cluster regions were selected as preliminary candidate genes, then the resequencing and transcriptome information about parents were applied to narrow the extent of candidate genes.  Twelve genes were determined as candidates for the six QTL clusters, some of them involved in RNA/protein degradation, most of them involved in oxidation-reduction process.  These findings provided genetic resources, candidate genes to address the urgent demand of improving WT in rapeseed breeding.

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. 

The use of remote sensing to monitor nitrogen (N) in crops is important for obtaining both economic benefit and ecological value because it helps to improve the efficiency of fertilization and reduces the ecological and environmental burden.  In this study, we model the total leaf N concentration (TLNC) in winter wheat constructed from hyperspectral data by considering the vertical N distribution (VND).  The field hyperspectral data of winter wheat acquired during the 2013–2014 growing season were used to construct and validate the model.  The results show that: (1) the vertical distribution law of LNC was distinct, presenting a quadratic polynomial tendency from the top layer to the bottom layer.  (2) The effective layer for remote sensing detection varied at different growth stages.  The entire canopy, the three upper layers, the three upper layers, and the top layer are the effective layers at the jointing stage, flag leaf stage, flowering stages, and filling stage, respectively.  (3) The TLNC model considering the VND has high predicting accuracy and stability.  For models based on the greenness index (GI), mND705 (modified normalized difference 705), and normalized difference vegetation index (NDVI), the values for the determining coefficient (R²), and normalized root mean square error (nRMSE) are 0.61 and 8.84%, 0.59 and 8.89%, and 0.53 and 9.37%, respectively.  Therefore, the LNC model with VND provides an accurate and non-destructive method to monitor N levels in the field.