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  • Author: Wen-qing Huang x
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Guo-Qing Wang, Jin-Long Hou, Huan-Yu Huang and Chao-Wen Yuan

Abstract

Introduction: Bovine postpartum metritis causes great losses. Mast cell (MC)-released mediators participate in uterine inflammation and immune response, but their role in postpartum metritis in cows has not been reported. This study investigated the effect of endometrial MC on the disorder.

Material and Methods: Ten dairy cows, at 6 to 10 days postpartum and with acute purulent metritis made up the experimental group, and 10 comparable healthy cows the control group. Endometrial histamine and IgE levels were determined by ELISA, and the MC particle state and expression of histamine H1 (H1R) and H2 (H2R) mRNA receptors were examined by transmission electron microscope and real-time quantitative PCR, respectively.

Results: Endometrial histamine and IgE levels were significantly higher in the experimental group. In the control group, homogenously distributed size-varied granules were seen in MC cytoplasm of endometrium of lamina propria. In the experimental group however, these showed degranulation with features of reduction. The level of H1R mRNA was lower in the experimental group, but that of H2R mRNA was higher.

Conclusion: The results suggest MC type I hypersensitivity characteristics during metritis, and histamine provocation of local inflammation. High expression of H2R and low expression of H1R inhibited the inflammatory response and prevented excessive uterine tissue damage.

Open access

Wen-Zhi Zeng, Guo-Qing Lei, Hong-Ya Zhang, Ming-Hai Hong, Chi Xu, Jing-Wei Wu and Jie-Sheng Huang

Abstract

For estimation of root-zone moisture content from EO-1/Hyperion imagery, surface soil moisture was first predicted by hyperspectral reflectance data using partial least square regression (PLSR) analysis. The textures of more than 300 soil samples extracted from a 900 m × 900 m field site located within the Hetao Irrigation District in China were used to parameterize the HYDRUS-1D numerical model. The study area was spatially discretized into 18,000 compartments (30 m × 30 m × 0.02 m), and Monte Carlo simulations were applied to generate 2000 different soil-particle size distributions for each compartment. Soil hydraulic properties for each realization were determined by application of artificial neural network analysis and used to parameterize HYDRUS-1D to simulate averaged soil-moisture contents within the root zone (0-40 cm) and surface (approximately 0-4 cm). Then the link between surface moisture and root zone was established by use of linear regression analysis, resulting in R and RMSE of 0.38 and 0.03, respectively. Kriging and co-kriging with observed surface moisture, and co-kriging with surface moisture obtained from Hyperion imagery were also used to estimate root-zone moisture. Results indicated that PLSR is a powerful tool for soil moisture estimation from hyperspectral data. Furthermore, co-kriging with observed surface moisture had the highest R (0.41) and linear regression model, and HYDRUS Monte Carlo simulations had a lowest RMSE (0.03) among the four methods. In regions that have similar climatic and soil conditions to our study area, a linear regression model with HYDRUS Monte Carlo simulations is a practical method for root-zone moisture estimation before sowing and it can be easily coupled with remote sensing technology.