Impact of water content and temperature on the degradation of Cry1Ac protein in leaves and buds of Bt cotton in the soil.

Determining the influence of soil environmental factors on degradation of Cry1Ac protein from Bt cotton residues is vital for assessing the ecological risks of this commercialized transgenic crop. In this study, the degradation of Cry1Ac protein in leaves and in buds of Bt cotton in soil was evaluated under different soil water content and temperature settings in the laboratory. An exponential model and a shift-log model were used to fit the degradation dynamics of Cry1Ac protein and estimate the DT50 and DT90 values. The results showed that Cry1Ac protein in the leaves and buds underwent rapid degradation in the early stage (before day 48), followed by a slow decline in the later stage under different soil water content and temperature. Cry1Ac protein degraded the most rapidly in the early stage at 35°C with 70% soil water holding capacity. The DT50 values were 12.29 d and 10.17 d and the DT90 values were 41.06 d and 33.96 d in the leaves and buds, respectively. Our findings indicated that the soil temperature was a major factor influencing the degradation of Cry1Ac protein from Bt cotton residues. Additionally, the relative higher temperature (25°C and 35°C) was found to be more conducive to degradation of Cry1Ac protein in the soil and the greater water content (100%WHC) retarded the process. These findings suggested that under appropriate soil temperature and water content, Cry1Ac protein from Bt cotton residues will not persist and accumulate in soil.

[A New Method to Decline the SWC Effect on the Accuracy for Monitoring SOM with Hyperspectral Technology].

Soil organic matter (SOM) is one of the most important indexes to reflect the soil fertility, and soil moisture is a main factor to limit the application of hyperspectral technology in monitoring soil attributes. To study the effect of soil moisture on the accuracy for monitoring SOM with hyperspectral remote sensing and monitor the SOM quickly and accurately, SOM, soil water content (SWC) and soil spectrum for 151 natural soil samples in winter wheat field were measured and the soil samples were classified with the method of traditional classification of SWC and Normalized Difference Soil Moisture Index (NSMI) based on the hyperspectral technology. Moreover, the relationship among SWC, SOM and NSMI were analyzed. The results showed that the accuracy of spectral monitor for SOM among the classifications were significantly different, its accuracy was higher than the soils (5%-25%) which was not classified. It indicated that the soil moisture affected the accuracy for monitoring the SOM with hyperspectral technology and the study proved that the most beneficent soil water content for monitoring the SOM was less 10% and higher 20%. On the other hand, the four models for monitoring the SOM by the hyperspectral were constructed by the classification of NSMI, and its accuracy was higher than the classification of SWC. The models for monitoring the SOM by the classification of NSMI were calibrated with the validation parameters of R², RMSE and RPD, and it showed that the four models were available and reliable to quickly and conveniently monitor the SOM by heperspectral. However, the different classifiable ways for soil samples mentioned in the study were naturally similar as all soil samples were classified again with another way. Namely, there may be another optimal classifiable way or method to overcome and eliminate the SWC effect on the accuracy for monitoring SOM. The study will provide some theoretical technology to monitor the SWC and SOM by remote sensing.

Advances in studies on drug adminstration and quality control of poisonous Chinese materia medica / 中草药

The poisonous Chinese materia medica (CMM) has been used since the ancient period as an important part of the traditional Chinese medicine (TCM). However, the drug administration and quality control of poisonous CMM are the key issues which could affect drugs safety and play a crucial role with the modernization of TCM. This paper has reviewed the origins of drugs, using, processing methods, and quality control of poisonous CMM. The practical application in researching of poisonous CMM has also been analyzed, as well as its management problems. The restraints on the poisonous CMM development as well as the medicine quality control and management issues have been expounded, in order to provide the improvements of applying the poisonous CMM.

Integrating remote sensing and GIS for prediction of winter wheat (Triticum aestivum) protein contents in Linfen (Shanxi), China.

In this study, relationships between normalized difference vegetation index (NDVI) and plant (winter wheat) nitrogen content (PNC) and between PNC and grain protein content (GPC) were investigated using multi-temporal moderate-resolution imaging spectroradiometer (MODIS) data at the different stages of winter wheat in Linfen (Shanxi, P. R. China). The anticipating model for GPC of winter wheat was also established by the approach of NDVI at the different stages of winter wheat. The results showed that the spectrum models of PNC passed F test. The NDVI4.14 regression effect of PNC model of irrigated winter wheat was the best, and that in dry land was NDVI4.30. The PNC of irrigated and dry land winter wheat were significantly (P<0.01) and positively correlated to GPC. Both of protein spectral anticipating model of irrigated and dry land winter wheat passed a significance test (P<0.01). Multiple anticipating models (MAM) were established by NDVI from two periods of irrigated and dry land winter wheat and PNC to link GPC anticipating model. The coefficient of determination R(2) (R) of MAM was greater than that of the other two single-factor models. The relative root mean square error (RRMSE) and relative error (RE) of MAM were lower than those of the other two single-factor models. Therefore, test effects of multiple proteins anticipating model were better than those of single-factor models. The application of multiple anticipating models for predication of protein content (PC) of irrigated and dry land winter wheat was more accurate and reliable. The regionalization analysis of GPC was performed using inverse distance weighted function of GIS, which is likely to provide the scientific basis for the reasonable winter wheat planting in Linfen city, China.

Application characteristics and situation analysis of volatile oils in database of Chinese patent medicine / 中国中药杂志

Aromatic traditional Chinese medicines have a long history in China, with wide varieties. Volatile oils are active ingredients extracted from aromatic herbal medicines, which usually contain tens or hundreds of ingredients, with many biological activities. Therefore, volatile oils are often used in combined prescriptions and made into various efficient preparations for oral administration or external use. Based on the sources from the database of Newly Edited National Chinese Traditional Patent Medicines (the second edition), the author selected 266 Chinese patent medicines containing volatile oils in this paper, and then established an information sheet covering such items as name, dosage, dosage form, specification and usage, and main functions. Subsequently, on the basis of the multidisciplinary knowledge of pharmaceutics, traditional Chinese pharmacology and basic theory of traditional Chinese medicine, efforts were also made in the statistics of the dosage form and usage, variety of volatile oils and main functions, as well as the status analysis on volatile oils in terms of the dosage form development, prescription development, drug instruction and quality control, in order to lay a foundation for the further exploration of the market development situations of volatile oils and the future development orientation.