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Modern agriculture is facing new challenges in which ecological and molecular approaches are being integrated to achieve higher crop yields while minimizing negative impacts on the environment. The application of biofertilizers could meet this requirement. Biofertilizer is a natural organic fertilizer that helps to provide all the nutrients required by the plants and helps to increase the quality of the soil with a natural microorganism environment. This paper reviewed the types of biofertilizers, the biological basic of biofertilizers in plant growth promotion. This paper also assayed the bidirectional information exchange between plant-microbes in rhizoshpere and the signal pathway of plant growthpromoting rhizobacteria (PGPR) and plant growth-promoting fungi (PGPF) in the course of plant infection. At last, the challenges of the application and the promising future of biofertilizers were also discussed.

Azafullerenes derived from nitrogen substitution of carbon cage atoms render direct modifications of the cage skeleton, electronic, and physicochemical properties of fullerene. Gas-phase ionized monometallic endohedral azafullerene (MEAF) [La@C81N]+ formed via fragmentation of a La@C82 monoadduct was detected in 1999, but the pristine MEAF has never been synthesized. Here, we report the synthesis, isolation, and characterization of the first pristine MEAF La@C81N, tackling the two-decade challenge. Single-crystal X-ray diffraction study reveals that La@C81N has an 82-atom cage with a pseudo C 3v (8) symmetry. According to DFT computations, the nitrogen substitution site within the C82 cage is proposed to locate at a hexagon/hexagon/pentagon junction far away from the encapsulated La atom. La@C81N exists in stable monomer form with a closed-shell electronic state, which is drastically different from the open-shell electronic state of the original La@C82. Our breakthrough in synthesizing a new type of azafullerene offers a new insight into the skeletal modification of fullerenes.

Multi-modal brain image fusion targets on integrating the salient and complementary features of different modalities of brain images into a comprehensive image. The well-fused brain image will make it convenient for doctors to precisely examine the brain diseases and can be input to intelligent systems to automatically detect the possible diseases. In order to achieve the above purpose, we have proposed a local extreme map guided multi-modal brain image fusion method. First, each source image is iteratively smoothed by the local extreme map guided image filter. Specifically, in each iteration, the guidance image is alternatively set to the local minimum map of the input image and local maximum map of previously filtered image. With the iteratively smoothed images, multiple scales of bright and dark feature maps of each source image can be gradually extracted from the difference image of every two continuously smoothed images. Then, the multiple scales of bright feature maps and base images (i.e., final-scale smoothed images) of the source images are fused by the elementwise-maximum fusion rule, respectively, and the multiple scales of dark feature maps of the source images are fused by the elementwise-minimum fusion rule. Finally, the fused bright feature map, dark feature map, and base image are integrated together to generate a single informative brain image. Extensive experiments verify that the proposed method outperforms eight state-of-the-art (SOTA) image fusion methods from both qualitative and quantitative aspects and demonstrates great application potential to clinical scenarios.

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