Bromus inermis L. (commonly known as smooth bromegrass) is a grass species with high nutritional value, great palatability, cold tolerance, and grazing resistance, which has been widely cultivated for pasture and sand fixation in northern and northwestern China. Salt stress is a main environmental factor limiting growth and production of smooth bromegrass. In this study, we performed PacBio Iso-Seq to construct the first full-length transcriptome database for smooth bromegrass under 300 mM NaCl treatment at different time points. Third-generation full-length transcriptome sequencing yielded 19.67 G polymerase read bases, which were assembled into 355,836 full-length transcripts with an average length of 2,542 bp. A total of 116,578 differentially expressed genes were obtained by comparing the results of third-generation sequencing and second-generation sequencing. GO and KEGG enrichment analyses revealed that multiple pathways were differently activated in leaves and roots. In particular, a number of genes participating in the molecular network of plant signal perception, signal transduction, transcription regulation, antioxidant defense, and ion regulation were affected by NaCl treatment. In particular, the CBL-CIPK, MAPK, ABA signaling network, and SOS core regulatory pathways of Ca²⁺ signal transduction were activated to regulate salt stress response. In addition, the expression patterns of 10 salt-responsive genes were validated by quantitative real-time PCR, which were consistent with those detected by RNA-Seq. Our results reveal the molecular regulation of smooth bromegrass in response to salt stress, which are important for further investigation of critical salt responsive genes and molecular breeding of salt-tolerant smooth bromegrass.

In many remote sensing applications, users usually prefer a multispectral image with both high spectral and high spatial information. This high quality image could be obtained by pan‐sharpening techniques which fuse a high resolution panchromatic (PAN) image and a low resolution multispectral (MS) image. In this paper, we propose a new technique to do so based on the adaptive intensity‐hue‐saturation (IHS) transformation model and evolutionary optimization. The basic idea is to reconstruct the target image through a parameterized adaptive IHS transformation. An optimization objective is thus introduced by considering the relations between the fused image and the original PAN and MS images. The control parameters are optimized by an evolutionary algorithm. Experimental results show that our new approach is practical and performs much better than some state‐of‐the‐art techniques according to the performance metrics.

The objective of the pansharpening task is to integrate multispectral (MS) images with low spatial resolution (LR) and to integrate panchromatic (PAN) images with high spatial resolution (HR) to generate HRMS images. Recently, deep learning-based pansharpening methods have been widely studied. However, traditional deep learning methods lack transparency while deep unrolling methods have limited performance when using one implicit prior for HRMS images. To address this issue, we incorporate one implicit prior with a semi-implicit prior and propose a double prior deep unrolling network (DPDU-Net) for pansharpening. Specifically, we first formulate the objective function based on observation models of PAN and LRMS images and two priors of an HRMS image. In addition to the implicit prior in the image domain, we enforce the sparsity of the HRMS image in a certain multi-scale implicit space; thereby, the feature map can obtain better sparse representation ability. We optimize the proposed objective function via alternating iteration. Then, the iterative process is unrolled into an elaborate network, with each iteration corresponding to a stage of the network. We conduct both reduced-resolution and full-resolution experiments on two satellite datasets. Both visual comparisons and metric-based evaluations consistently demonstrate the superiority of the proposed DPDU-Net.