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Terpenes constitute a large class of plant secondary metabolites. Usually, there is only one type of terpene synthase in seed plants, which is called typical plant terpene synthase. Currently, as a new family of plant terpene synthases, microbial terpene synthase-like (MTPSL) is identificated in nonseed plants. However, our knowledge about the biological function of most MTPSLs in nonseed plants is very limited. Here, we investigated the biochemical and functional characterization of the enzymes encoded by two MTPSLs from moss Stereodon subimponens, SsMTPSL1 and SsMTPSL2. A phylogenetic tree analysis showed that SsMTPSL1 and SsMTPSL2 are homologous to AaMTPSL1, AaMTPSL3, ApMTPSL1, and ApMTPSL3 from hornworts. The enzyme activity experiment demonstrated that SsMTPSL1 has monoterpene synthase and sesquiterpene synthase activity, and SsMTPSL2 has monoterpene synthase activity. Next, we selected SsMTPSL1 to study its biochemical functions. Anti-bacterial activity test in vitro showed that the products of SsMTPSL1 have an anti-bacterial effect on Escherichia coli, Pseudomonas syringae pv. Tomato DC3000 (Pst DC3000), and Staphylococcus aureus. To further understand the function of SsMTPSL1, the transgenic Arabidopsis thaliana plant of SsMTPSL1 is inoculated by Pst DC3000, and the result showed that SsMTPSL1 enhances the resistance of A. thaliana to Pst DC3000. All in all, this study provides new information about the functions of moss MTPSLs.

Selaginella moellendorffii is a lycophyte, a member of an ancient vascular plant lineage. Two distinct types of terpene synthase (TPS) genes were identified from this species, including S. moellendorffii TPS genes (SmTPSs) and S. moellendorffii microbial TPS-like genes (SmMTPSLs). The goal of this study was to investigate the biochemical functions of SmMTPSLs. Here, eight full-length SmMTPSL genes (SmMTPSL5, -15, -19, -23, -33, -37, -46, and -47) were functionally characterized from S. moellendorffii. Escherichia coli-expressed recombinant SmMTPSLs were tested for monoterpenes synthase and sesquiterpenes synthase activities. These enzymatic products were typical monoterpenes and sesquiterpenes that have been previous shown to be generated by typical plant TPSs when provided with geranyl diphosphate (GPP) and farnesyl diphosphate (FPP) as the substrates. Meanwhile, SmMTPSL23, -33, and -37 were up-regulated when induced by alamethicin (ALA) and methyl jasmonate (MeJA), suggesting a role for these genes in plants response to abiotic stresses. Furthermore, this study pointed out that the terpenoids products of SmMTPSL23, -33, and -37 have an antibacterial effect on Pseudomonas syringae pv. tomato DC3000 and Staphylococcus aureus. Taken together, these results provide more information about the catalytic and biochemical function of SmMTPSLs in S. moellendorffii plants.

In real-world applications, different problems can adopt different models. Most of the existing denoising methods use the framework of deep learning, and the most commonly used denoised algorithm evaluation indicators, such as PSNR, MSE, etc., all without exception, require pictures’ ground truth which is needed as a reference. However, there are few real and noise-free pictures in the field of image denoising, only the noise reduction map can be compared with the noise map, which seems to be less persuasive. Therefore, this paper proposes a new criterion for judging the denoising model. The most important thing is that this method does not require noiseless images compared to PSNR when testing. Moreover, we improved the denoising model and verified the reliability of the criterion. At the same time, we conduct statistics on the recognition rate of different types of targets, and analyze the trend of misjudgment. In this paper, the synthetic aperture radar (SAR) image dataset is used as an experimental sample, and different noise parameters are used to obtain denoising data sets with different noise levels. Then we use different denoising models such as DN-CNN to process the data set. Finally, the CNN classification model is used for screening comparison. In this paper, the experimental results show that it is feasible to use classification to judge denoising, so based on this feasibility, this paper modified the denoising network and used classification to judge. The results show that the denoising effect is better and the classification accuracy is higher, that is, the denoising and classification are a complementary relationship.

The doctor determines whether there are lesions in the human body through the diagnosis of medical images, and classifies and identifies the lesions. Therefore, the automatic classification and recognition of medical images has received extensive attention. Since the inflammatory phenomenon of vascular endothelial cells is closely related to the varicose veins of the lower extremities, in order to realize the automatic classification and recognition of varicose veins of the lower extremities, this paper proposes a varicose vein recognition algorithm based on vascular endothelial cell inflammation images and multi-scale deep learning, called MSDCNN. First, we obtained images of vascular endothelial cells in patients with varicose veins of the lower extremities and normal subjects. Second, multiple convolutional layers extract multi-scale features of vascular endothelial cell images. Then, the MFM activation function is used instead of the ReLU activation function to introduce a competitive mechanism that extracts more features that are compact and reduces network layer parameters. Finally, the network uses a 3 × 3 convolution kernel to improve the network feature extraction capability and use the 1 × 1 convolution kernel for dimensionality reduction to further streamline network parameters. The experimental results tell us that the network has the advantages of high recognition accuracy, fast running speed, few network parameters, and is suitable for small-embedded devices.