This study examined topographic influence on spatial and temporal variability in the normalized difference vegetation index (NDVI) derived from the Satellite Pour l'Observation de la Terre-Vegetation at the regional and landscape scales in the Jiaodong Peninsula. The generalized additive models were used to quantify the spatial variation of NDVI attributable to local terrain and topographically related variables including altitude, exposure to incoming solar radiation, topographic wetness index, distance to the nearest stream and distance from the coast. NDVI distribution shows significant dependence on topography. The variables explained 38.3 % of variance in NDVI at the peninsula, and 30-45.3 % of variance in NDVI at the woodland, cropland, and grassland landscapes. At the Jiaodong Peninsula scale, NDVI is influenced primarily by distance from the coast. However, topographic wetness index has the most explanatory power for NDVI at the woodland, cropland, and grassland landscapes. Through a statistical nonparametric correlation analysis (Spearman's r), the study indicates that spatial distribution of NDVI changes during the period 1998-2009 and future change trend of persistence determined by Hurst exponent is closely associated with topography and topography-based attribution. These results highlight the importance of topographic changes at landscape and regional scales as an important control factor on NDVI patterns.

The rapid development of remote sensing technology provides wealthy data for earth observation. Land-cover mapping indirectly achieves biodiversity estimation at a coarse scale. Therefore, accurate land-cover mapping is the precondition of biodiversity estimation. However, the environment of the wetlands is complex, and the vegetation is mixed and patchy, so the land-cover recognition based on remote sensing is full of challenges. This paper constructs a systematic framework for multisource remote sensing image processing. Firstly, the hyperspectral image (HSI) and multispectral image (MSI) are fused by the CNN-based method to obtain the fused image with high spatial-spectral resolution. Secondly, considering the sequentiality of spatial distribution and spectral response, the spatial-spectral vision transformer (SSViT) is designed to extract sequential relationships from the fused images. After that, an external attention module is utilized for feature integration, and then the pixel-wise prediction is achieved for land-cover mapping. Finally, land-cover mapping and benthos data at the sites are analyzed consistently to reveal the distribution rule of benthos. Experiments on ZiYuan1-02D data of the Yellow River estuary wetland are conducted to demonstrate the effectiveness of the proposed framework compared with several related methods.

Natural product libraries with a remarkable range of biological activities play pivotal roles in drug discoveries due to their extraordinary structural complexity and immense diversity. l-Kynurenine (l-Kyn)-based derivatives are privileged pharmacophores that exhibit diverse therapeutic implications in neurological disorders. However, the difficulty in obtaining l-Kyn analogues with different skeletal structures has recently led to a decline in its medicinal research. Herein, we report a two-step, one-pot protocol for diversity-oriented biosynthesis of a collection of previously intractable l-Kyn-like compounds. The success of these challenging transformations mainly depends on unlocking the new catalytic scope of tryptophan 2,3-dioxygenases, followed by rational site-directed mutagenesis to modify the substrate domains further. As a result, 18 kynurenine analogues with diverse molecular scaffolds can be rapidly assembled in a predictable manner with 20–83% isolated yields, which not only fill the voids of the catalytic profile of tryptophan 2,3-dioxygenases with an array of substituent groups (e.g., F, Cl, Br, I, CH3, OCH3, and NO2) but also update the current understanding of its substrate spectrum. Our work highlights the great potential of existing enzymes in addressing long-standing synthetic challenges for facilitating the development or discovery of new drug candidates. Furthermore, our approach enables translating the reaction parameters from Eppendorf tubes to 1 L scale, affording l-4-Cl-Kyn and l-5-Cl-Kyn both on a gram scale with more than 80% isolated yields, and provides a promising alternative to further industrial applications.

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