Plant-associated microbes play important roles in plant health and disease. Mortierella is often found in the plant rhizosphere, and its possible functions are not well known, especially in medical plants. Mortierella alpina isolated from ginseng soil was used to investigate its effects on plant disease. The promoting properties and interactions with rhizospheric microorganisms were investigated in a medium. Further, a pot experiment was conducted to explore its effects on ginseng root rot disease. Physicochemical properties, high-throughput sequencing, network co-occurrence, distance-based redundancy analysis (db-RDA), and correlation analysis were used to evaluate their effects on the root rot pathogen. The results showed that Mortierella alpina YW25 had a high indoleacetic acid production capacity, and the maximum yield was 141.37 mg/L at 4 days. The growth of M. alpina YW25 was inhibited by some probiotics (Bacillus, Streptomyces, Brevibacterium, Trichoderma, etc.) and potential pathogens (Cladosporium, Aspergillus, etc.), but it did not show sensitivity to the soil-borne pathogen Fusarium oxysporum. Pot experiments showed that M. alpina could significantly alleviate the diseases caused by F. oxysporum, and increased the available nitrogen and phosphorus content in rhizosphere soil. In addition, it enhanced the activities of soil sucrase and acid phosphatase. High-throughput results showed that the inoculation of M. alpina with F. oxysporum changed the microbial community structure of ginseng, stimulated the plant to recruit more plant growth-promoting bacteria, and constructed a more stable microbial network of ginseng root. In this study, we found and proved the potential of M. alpina as a biocontrol agent against F. oxysporum, providing a new idea for controlling soil-borne diseases of ginseng by regulating rhizosphere microorganisms.

Previous works on font generation mainly focus on the standard print fonts where character's shape is stable and strokes are clearly separated. There is rare research on brush handwriting font generation, which involves holistic structure changes and complex strokes transfer. To address this issue, we propose a novel GAN-based image translation model by integrating the skeleton information. We first extract the skeleton from training images, then design an image encoder and a skeleton encoder to extract corresponding features. A self-attentive refined attention module is devised to guide the model to learn distinctive features between different domains. A skeleton discriminator is involved to first synthesize the skeleton image from the generated image with a pre-trained generator, then to judge its realness to the target one. We also contribute a large-scale brush handwriting font image dataset with six styles and 15,000 high-resolution images. Both quantitative and qualitative experimental results demonstrate the competitiveness of our proposed model.

In this paper, we present a novel system (denoted as Polaca) to generate poetic Chinese landscape painting with calligraphy. Unlike previous single image-to-image painting generation, Polaca takes the classic poetry as input and outputs the artistic landscape painting image with the corresponding calligraphy. It is equipped with three different modules to complete the whole piece of landscape painting artwork: the first one is a text-to-image module to generate landscape painting image, the second one is an image-to-image module to generate stylistic calligraphy image, and the third one is an image fusion module to fuse the two images into a whole piece of aesthetic artwork.

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