[Wheat leaf area index inversion using hyperspectral remote sensing technology].

The wheat leaf area index (LAI) was inverted using hyperspectral remote sensing technology in the present paper. Eighteen kinds of hyperspectral indices were comparatively analyzed, and the index OSAVI, which could reflect wheat LAI most sensitively, was screened out. The models for wheat LAI inversion were built using the field spectra as the training samples. The study showed that the calibration R-square and prediction R-square of the inversion model which were built by hyperspectral index OSAVI were 0.823 and 0.818, respectively, higher than that of other indices, indicating that the accuracy was highest. The inversion model was spatially quantitatively expressed in OMIS image, and then the inversion value and measured value was compared by the method of regression fitting. The R-square and RMSE of the fitting model were 0.756 and 0.500, respectively, indicating that the similarity between the inversion value and measured value was high. The result showed that it was feasible to invert the wheat LAI by hyperspectral indices, and OSVAI was an optimal one.

[Hyperspectral remote sensing image classification based on ICA and SVM algorithm].

A novel method was developed to classify hyperspectral remote sensing image based on independent component analysis (ICA) and support vector machine (SVM) algorithms. The characteristic information of the hyperspectral remote sensing image captured by PHI (made in China, with 80 bands) was extracted by ICA algorithm, and SVM classifier was established with the extracted image data (20 spectral dimensions). After kernel function selecting and parameter optimizing, it was found that the SVM algorithm(RBF kernel function; parameter C = 1093), gamma = 0.05) with accuracy 94.5127% and kappa coefficient 0.9351 has the best classification result, better than the results of four kinds of conventional algorithms, including neural net classification (accuracy 39.4758% and kappa coefficient 0.3155), spectral angle mapper classification (accuracy 80.2826% and kappa coefficient 0.7709), minimum distance classification (accuracy 85.4627% and kappa coefficient 0.8277) and maximum likelihood classification (accuracy 86.0156% and Kappa coefficient 0.8351). In order to control the "pepper and salt" phenomenon which appeared in classification map frequently, the classification result of SVM (RBF kernel) was operated by the method of clump classes using the morphological operators, and that the classification map closer to actual situation was acquired, with the accuracy and kappa coefficient increasing to 94.7584% and 0.9380, respectively. The study indicated that the ICA combined with SVM was an preferred method for hyperspectral remote sensing image classification, and clump classes was a effective method to optimized the classification result.

[Diagnosis of chronic enteritis of alpine musk deer (Moschus chrysogaster) based on visible-near infrared reflectance spectra of feces].

A new method was put forward to diagnose chronic enteritis of alpine musk deer (Moschus chrysogaster) by visible-near infrared reflectance spectra of feces. A total of 125 feces samples, including 70 samples from healthy individuals (healthy samples) and 55 samples from chronic enteritis sufferers (diseased samples), were collected in Xinglongshan musk deer farm, Gansu province. The spectral scan was carried out in the darkroom (temperature 18 degrees C-22 degrees C, humidity 22%-25% and halogen lamp as a sole light source) with an ASD FieldSpec 3 spectrometer. All the samples were divided randomly into two groups, one with 95 samples as the calibration set, and another with 30 samples as the validation set. The samples data were pretreated by the methods of S. Golay smoothing and first derivative. The pretreated spectra were analyzed by principal component analysis (PCA), and the top 6 principal components, which were computed by PCA and accounted for 95.16% variation of the original spectral information, were used for modeling as the new variables. The data of the calibration set were used to build models for diagnosing the chronic enteritis of alpine musk deer by means of back-propagation artificial neural network (ANN-BP), fuzzy pattern recognition, Fisher linear discriminant and Bayes stepwise discriminant, respectively. The predicted outcomes of the 30 unknown samples in validation set showed that the accuracy was 86.7% by themethod of Fisher linear discriminant, 90% by fuzzy pattern recognition and ANN-BP model, and 93.3% by stepwise discrimination. Further analysis found that all misdiagnosed samples were derived from the healthy samples, which were treated as disease samples, and the detection rates of diseased samples were 100% by the four different methods. The results indicated that it was feasible to diagnose the chronic enteritis of alpine musk deer by visible-near infrared reflectance spectra of feces as a rapid and non-contact way, and the PCA combined with Bayes stepwise discriminant was a preferred method.

[PPAR gamma agonist rosiglitazone alleviates hypoxia/reoxygenation-induced oxidative stress and apoptosis in rat cardiac myocytes].

OBJECTIVE: To observe the effects of different concentrations of PPAR gamma agonist rosiglitazone on hypoxia/reoxygenation-induced oxidative stress, cell viability and apoptosis in rat cardiac myocytes. METHODS: Cultured rat cardiac myocytes were divided into 5 groups, namely group I (normal group), group II (20 micromo/L ROS group), group III (I/R group), group IV (I/R+20 micromo/L ROS group), and group V (I/R+80 micromo/L ROS group). Group IV and group V were treated with rosiglitazone 12 h before hypoxia/reoxygenation. The changes in cell morphology were observed under optical and transmission electron microscopy, and levels of malondialdehyde (MDA), superoxide dismutase (SOD) activity, and lactate dehydrogenase (LDH) content were determined after the treatment. MTT assay was performed to assess the cell viability and flow cytometry was used to analyze the cell apoptosis. RESULTS: Hypoxia/reoxygenation resulted in significantly increased MDA and LDH contents and apoptosis of the cardiac myocytes (P<0.05), but lowered SOD activity and the cell viability (P<0.05). The MDA and LDH contents and apoptotic rate were significantly lower but SOD content and cell vitality significantly higher in groups IV and V than in group III (P<0.05). Group V showed significantly lower MDA and LDH contents and apoptotic rate but higher but SOD content and cell vitality than group IV (P<0.05). Electron microscopy revealed obvious apoptotic changes in group III, and only mild changes were found in group V. CONCLUSION: Rosiglitazone can significantly reduce hypoxia/reoxygenation-induced oxidative stress in cardiac myocytes, improve the cell viability and dose-dependently reduce the apoptotic rate of the cardiac myocytes.

[Purity measurement of hybrid rice seed Yixiang 725 with visible-near infrared reflectance spectra].

A rapid and non-invasive method was put forward to measure the purity of hybrid rice seed by visible-near infrared reflectance spectra. Ninety hybrid rice seed samples (Yixiang 725) with the purity of 90%-99% were collected using a FieldSpec 3 visible-near infrared spectometer. All samples were divided randomly into two groups, one group with 75 samples used as calibrated set, and the other with 15 samples used as validated set. Based on the spectra in the range of 380-2 400 nm, the regression model was established using the PLS (partial least square), and different spectra pretreatment methods were compared. The study showed that spectra information can be extracted thoroughly by the pretreatment method of first derivative combined with standard normal variate, with the SEC (standard error of calibration) of 0.002 5, SEP (standard error of prediction) of 0.006 6, and determination coefficients of 0.988 4 (calibration set) and 0.922 7 (validation set) respectively. The spectra, which were pretreated with the method of first derivative combined standard normal variate, were analyzed by principal component analysis (PCA). The top 20 principal components, which were computed by PCA and accounted for 86.09% variation of the original spectral information, were used to build BP-ANN model for measuring the purity of hybrid rice seed as the new variables. The study showed that the SEC and SEP of BP-ANN model were 0.001 7 and 0.006 1, and the determination coefficients of that were 0.995 2 (calibration set) and 0.936 9 (validation set) respectively. Therefore, the predictive power of BP-ANN model was better than that of PLS model. Results indicated that it was feasible to measure the purity of the hybrid rice seed by visible-near reflectance spectra as a rapid and non-contact way, and PCA combined with BP-ANN was a preferred method.