A case report of acute renal failure caused by Amanita neoovoidea poisoning in Anhui Province, eastern China.

Amanita neoovoidea (genus Amanita Pers.) poisoning leads to acute renal failure. Here, we present seven case reports of acute renal failure with acute hepatic failure due to ingestion of A. neoovoidea. Clinical manifestations included gastrointestinal symptoms 1-72 h after ingestion; elevation of renal parameters and blood uric acid, blood urea nitrogen, and creatinine levels; a few abnormal hepatic parameters, primarily albumin decrease and alanine aminotransferase increase; and elevation of zymogram parameters such as cholinesterase and lactate dehydrogenase. To determine whether the hepatic/renal lesions were caused by amanitins, we analyzed the blood and urine samples of patients and specimens of poisonous mushrooms. Morphological and molecular biological analyses indicated that the mushroom was A. neoovoidea. However, no amatoxins and phallotoxins were detected in its basidiomata.

[Hyperspectral remote sensing diagnosis models of rice plant nitrogen nutritional status].

The correlations of rice plant nitrogen content with raw hyperspectral reflectance, first derivative hyperspectral reflectance, and hyperspectral characteristic parameters were analyzed, and the hyperspectral remote sensing diagnosis models of rice plant nitrogen nutritional status with these remote sensing parameters as independent variables were constructed and validated. The results indicated that the nitrogen content in rice plant organs had a variation trend of stem < sheath < spike < leaf. The spectral reflectance at visible light bands was leaf < spike < sheath < stem, but that at near-infrared bands was in adverse. The linear and exponential models with the raw hyperspectral reflectance at 796.7 nm and the first derivative hyperspectral reflectance at 738.4 nm as independent variables could better diagnose rice plant nitrogen nutritional status, with the decisive coefficients (R2) being 0.7996 and 0.8606, respectively; while the model with vegetation index (SDr - SDb) / (SDr + SDb) as independent variable, i. e., y = 365.871 + 639.323 ((SDr - SDb) / (SDr + SDb)), was most fit rice plant nitrogen content, with R2 = 0.8755, RMSE = 0.2372 and relative error = 11.36%, being able to quantitatively diagnose the nitrogen nutritional status of rice.

[Accuracy of winter wheat identification based on multi-temporal CBERS-02 images].

Chinese-Brazil Earth Resources Satellite No. 2 (CBERS-02) has good spatial resolution and abundant spectral information, and a strong ability in detecting vegetation. Based on five CBERS-02 images in winter wheat growth season, the spectral distance between winter wheat and other ground targets was calculated, and then, winter wheat was classified from each individual image or their combinations by using supervised classification. The train and validation samples were derived from high resolution Aerial Images and SPOT5 images. The accuracies and analyses were evaluated for CBERS-02 images at early growth stages, and the results were compared to those of TM images acquired in the same phenological calendars. The results showed that temporal information was the main factor affecting the classification accuracy in winter wheat, but the characteristics of different sensors could affect the classification accuracy. The multi-temporal images could improve the classification accuracy, compared with the results derived from signal stage, with the producer accuracy of optimum periods combination improved 20.0% and user accuracy improved 7.83%. Compared with TM sensor, the classification accuracy from CBERS-02 was a little lower.