Role of bacterial pathogens in microbial ecological networks in hydroponic plants.

Plant-associated microbial communities are crucial for plant growth and health. However, assembly mechanisms of microbial communities and microbial interaction patterns remain elusive across vary degrees of pathogen-induced diseases. By using 16S rRNA high-throughput sequencing technology, we investigated the impact of wildfire disease on the microbial composition and interaction network in plant three different compartments. The results showed that pathogen infection significantly affect the phyllosphere and rhizosphere microbial community. We found that the primary sources of microbial communities in healthy and mildly infected plants were from the phyllosphere and hydroponic solution community. Mutual exchanges between phyllosphere and rhizosphere communities were observed, but microbial species migration from the leaf to the root was rarely observed in severely infected plants. Moreover, wildfire disease reduced the diversity and network complexity of plant microbial communities. Interactions among pathogenic bacterial members suggested that Caulobacter and Bosea might be crucial "pathogen antagonists" inhibiting the spread of wildfire disease. Our study provides deep insights into plant pathoecology, which is helpful for the development of novel strategies for phyllosphere disease prediction or prevention.

Feature Extraction and Matching of Humanoid-Eye Binocular Images Based on SUSAN-SIFT Algorithm.

Imitating the visual characteristics of human eyes is one of the important tasks of digital image processing and computer vision. Feature correspondence of humanoid-eye binocular images is a prerequisite for obtaining the fused image. Human eyes are more sensitive to edge, because it contains much information. However, existing matching methods usually fail in producing enough edge corresponding pairs for humanoid-eye images because of viewpoint and view direction differences. To this end, we propose a novel and effective feature matching algorithm based on edge points. The proposed method consists of four steps. First, the SUSAN operator is employed to detect features, for its outstanding edge feature extraction capability. Second, the input image is constructed into a multi-scale structure based on image pyramid theory, which is then used to compute simplified SIFT descriptors for all feature points. Third, a novel multi-scale descriptor is constructed, by stitching the simplified SIFT descriptor of each layer. Finally, the similarity of multi-scale descriptors is measured by bidirectional matching, and the obtained preliminary matches are refined by subsequent procedures, to achieve accurate matching results. We respectively conduct qualitative and quantitative experiments, which demonstrate that our method can robustly match feature points in humanoid-eye binocular image pairs, and achieve favorable performance under illumination changes compared to the state-of-the-art.