BrrTCP4b interacts with BrrTTG1 to suppress the development of trichomes in Brassica rapa var. rapa

The number of trichomes significantly increased in CRISPR/Cas9-edited BrrTCP4b turnip(Brassica rapa var.rapa)plants.However,the underlying molecular mechanism remains to be uncovered.In this study,we performed the Y2H screen using BrrTCP4b as the bait,which unveiled an interaction between BrrTCP4b and BrrTTG1,a pivotal WD40-repeat protein transcription factor in the MYB-bHLH-WD40(MBW)complex.This physical interaction was further validated through bimolecular luciferase complementation and co-immunoprecipitation.Furthermore,it was found that the interaction between BrrTCP4b and BrrTTG1 could inhibit the activity of MBW complex,resulting in decreased expression of BrrGL2,a positive regulator of trichomes development.In contrast,AtTCP4 is known to regulate trichomes development by interacting with AtGL3 in Arabidopsis thaliana.Overall,this study revealed that BrrTCP4b is involved in trichome development by interacting with BrrTTG1 in turnip,indicating a divergence from the mechanisms observed in model plant A.thaliana.The findings contribute to our understanding of the regulatory mechanisms governing trichome development in the non-model plants turnip.

Lightweight and Efficient Attention-Based Superresolution Generative Adversarial Networks

To address the problems of lack of high-frequency information and texture details and unstable training in superresolution generative adversarial net-works,this paper optimizes the generator and discriminator based on the SRGAN model.First,the residual dense block is used as the basic structural unit of the gen-erator to improve the network’s feature extraction capability.Second,enhanced lightweight coordinate attention is incorporated to help the network more precisely concentrate on high-frequency location information,thereby allowing the gener-ator to produce more realistic image reconstruction results.Then,we propose a symmetric and efficient pyramidal segmentation attention discriminator network in which the attention mechanism is capable of derivingfiner-grained multiscale spatial information and creating long-term dependencies between multiscale chan-nel attentions,thus enhancing the discriminative ability of the network.Finally,a Charbonnier loss function and a gradient variance loss function with improved robustness are used to better realize the image’s texture structure and enhance the model’s stability.Thefindings from the experiments reveal that the reconstructed image quality enhances the average peak signal-to-noise ratio(PSNR)by 1.59 dB and the structural similarity index(SSIM)by 0.045 when compared to SRGAN on the three test sets.Compared with the state-of-the-art methods,the reconstructed images have a clearer texture structure,richer high-frequency details,and better visual effects.