The common bean (Phaseolus vulgaris) is a major source of protein and essential nutrients for humans. To explore the genetic diversity and phylogenetic relationships of P. vulgaris, its complete mitochondrial genome (mitogenome) was sequenced and assembled. The mitogenome is 395,516 bp in length, including 31 unique protein-coding genes (PCGs), 15 transfer RNA (tRNA) genes, and 3 ribosomal RNA (rRNA) genes. Among the 31 PCGs, four genes (mttB, nad1, nad4L, and rps10) use ACG as initiation codons, which are altered to standard initiation codons by RNA editing. In addition, the termination codon CGA in the ccmF_C gene is converted to UGA. Selective pressure analysis indicates that the ccmB, ccmF_C, rps1, rps10, and rps14 genes were under evolutionary positive selection. The proportions of five amino acids (Phe, Leu, Pro, Arg, and Ser) in the whole amino acid profile of the proteins in each mitogenome can be used to distinguish angiosperms from gymnosperms. Phylogenetic analyses show that P. vulgaris is evolutionarily closer to the Glycininae than other leguminous plants. The results of the present study not only provide an important opportunity to conduct further genomic breeding studies in the common bean, they also provide valuable information for future evolutionary and molecular studies of leguminous plants.

Cotton is one of the most important economic crops and the primary source of natural fiber and is an important protein source for animal feed. The complete nuclear and chloroplast (cp) genome sequences ofG. raimondiiare already available but not mitochondria. Here, we assembled the complete mitochondrial (mt) DNA sequence ofG. raimondiiinto a circular genome of length of 676,078 bp and performed comparative analyses with other higher plants. The genome contains 39 protein-coding genes, 6 rRNA genes, and 25 tRNA genes. We also identified four larger repeats (63.9 kb, 10.6 kb, 9.1 kb, and 2.5 kb) in this mt genome, which may be active in intramolecular recombination in the evolution of cotton. Strikingly, nearly all of theG. raimondiimt genome has been transferred to nucleus on Chr1, and the transfer event must be very recent. Phylogenetic analysis reveals thatG. raimondii, as a member of Malvaceae, is much closer to another cotton (G. barbadense) than other rosids, and the clade formed by twoGossypiumspecies is sister to Brassicales. TheG. raimondiimt genome may provide a crucial foundation for evolutionary analysis, molecular biology, and cytoplasmic male sterility in cotton and other higher plants.

Changes on lakes and rivers are of great significance for the study of global climate change. Accurate segmentation of lakes and rivers is critical to the study of their changes. However, traditional water area segmentation methods almost all share the following deficiencies: high computational requirements, poor generalization performance, and low extraction accuracy. In recent years, semantic segmentation algorithms based on deep learning have been emerging. Addressing problems associated to a very large number of parameters, low accuracy, and network degradation during training process, this paper proposes a separable residual SegNet (SR-SegNet) to perform the water area segmentation using remote sensing images. On the one hand, without compromising the ability of feature extraction, the problem of network degradation is alleviated by adding modified residual blocks into the encoder, the number of parameters is limited by introducing depthwise separable convolutions, and the ability of feature extraction is improved by using dilated convolutions to expand the receptive field. On the other hand, SR-SegNet removes the convolution layers with relatively more convolution kernels in the encoding stage, and uses the cascading method to fuse the low-level and high-level features of the image. As a result, the whole network can obtain more spatial information. Experimental results show that the proposed method exhibits significant improvements over several traditional methods, including FCN, DeconvNet, and SegNet.

Willow is a widely used dioecious woody plant ofSalicaceaefamily in China. Due to their high biomass yields, willows are promising sources for bioenergy crops. In this study, we assembled the complete mitochondrial (mt) genome sequence ofS. suchowensiswith the length of 644,437 bp using Roche-454 GS FLX Titanium sequencing technologies. Base composition of theS. suchowensismt genome is A (27.43%), T (27.59%), C (22.34%), and G (22.64%), which shows a prevalent GC content with that of other angiosperms. This long circular mt genome encodes 58 unique genes (32 protein-coding genes, 23 tRNA genes and 3 rRNA genes), and 9 of the 32 protein-coding genes contain 17 introns. Through the phylogenetic analysis of 35 species based on 23 protein-coding genes, it is supported thatSalixas a sister toPopulus. With the detailed phylogenetic information and the identification of phylogenetic position, some ribosomal protein genes and succinate dehydrogenase genes are found usually lost during evolution. As a native shrub willow species, this worthwhile research ofS. suchowensismt genome will provide more desirable information for better understanding the genomic breeding and missing pieces of sex determination evolution in the future.

Water information extraction is always an important aspect of remote sensing image analysis. However, in the actual water images of remote sensing, the backgrounds of water areas are mostly complex buildings and vegetation, which interferes with water detection. In addition, traditional water detection methods were not able to accurately identify small tributaries and edge information. In order to improve to the accuracy of water segmentation, a dense skip connections network with multi-scale features fusion and attention mechanism is proposed for water areas segmentation. The proposed method obtains water feature information by using U-shaped Network (U-Net) as the backbone network and performs dense skip connections between nested convolution modules to reduce the semantic gap between the feature maps of the codec sub-networks and reduce the gradient vanishing problem of network training. In the proposed model, deep feature information is fused with shallow feature information through multi-scale attention modules. The shallow features and large-scale attention modules are used to locate the main body of the water area, while the deep features and small-scale attention modules are used to fine segment the edge of the water area. Combined with the above features and attention modules, waters can be extracted from the backgrounds. Finally, the segmentation accuracy of edge regions is further improved by Conditional Random Field (CRF). The experimental results from China HJ-1A (HJ-1B) satellite imageries and National Aeronautics and Space Administration (NASA) Land Remote-Sensing Satellite (System, Landsat-8) imageries show that the proposed method outperforms the existing methods.