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Heat stress jeopardised animal health by inducing oxidative stress and inflammation. This study was done to investigate the effects of chitosan oligosaccharides (COS) on oxidative stress and inflammation response in liver and spleen of yellow-feather broilers under high ambient temperature. A total of 144 35-day-old female Chinese indigenous yellow-feather broilers (body weight 450.21 ± 10.05 g) were randomly allocated into 3 dietary treatments with 6 replication pens, each pen had 8 broilers. The treatments were: Control, basal diet; COS100, basal diet with 100 mg/kg COS; COS200, basal diet with 200 mg/kg COS. During day 22 to 42 (57 to 77 days of age), broilers in the COS200 group had higher (p <.05) ADFI than broilers in the Control group. Broilers in the COS200 group had lower (p <.05) serum alanine aminotransferase, aspartate aminotransferase, and tumour necrosis factor-α levels, and spleen malondialdehyde (MDA) content, but had higher (p <.05) liver superoxide dismutase activity and serum interleukin-10 level than broilers in the Control group. Broilers in the COS100 and COS200 groups had lower (p <.05) serum and liver MDA content, and serum interleukin-1β level, but had higher (p <.05) serum glutathione peroxidase activity than broilers in the Control group. In conclusion, dietary COS supplementation can alleviate heat stress-induced oxidative stress and inflammation response in liver and spleen by decreasing lipid peroxidation, increasing anti-oxidant enzyme activities and IL-10 level. Dietary COS decreased serum ALT, AST, IL-1β, TNF-α and MDA. Dietary COS increased serum GSH-Px and IL-10, liver SOD, and spleen GSH-Px. [ABSTRACT FROM AUTHOR]

Accurate grassland aboveground biomass (AGB) estimation is crucial for effective grassland utilization. However, most current satellites cannot provide data with high spatial and temporal resolutions simultaneously. Spatiotemporal fusion models can combine the resolution advantages of different remote sensing data and support high-precision vegetation monitoring. In order to obtain accurate grassland AGB maps with high resolution in the Three-River Headwaters Region (TRHR) of China, we developed an estimation method based on the synthetic 30 m growing season averaged normalized difference vegetation index (GS-NDVI), which was fused from 30 m Landsat 8 Operational Land Imager (OLI) and 250 m Moderate-resolution Imaging Spectroradiometer (MODIS) NDVI data. To choose the optimal fusion model, we investigated the performances of three spatiotemporal fusion models for NDVI fusion, the spatial and temporal adaptive reflectance fusion model (STARFM), the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM), and the rule-based piecewise regression tree model (RPRTM). The three models all produced reasonable NDVI predictions, with the coefficient of determination (R2) ranging from 0.58 to 0.86. RPRTM had the highest efficiency and was more suitable for large-scale spatiotemporal data fusion. Compared with the models generated from 250 m MODIS GS-NDVI, the AGB estimation models based on 30 m synthetic GS-NDVI were more accurate, demonstrating the effectiveness of our methods. The resulting AGB map of 30 m resolution provides spatially detailed AGB information that will be useful for regional ecosystem studies and local land management decisions. [ABSTRACT FROM AUTHOR]