Background: Dengue Fever/Dengue Hemorrhagic Fever (DF/DHF) has emerged as one of the most important public health concerns in Viet Nam in recent years. Key breeding containers plays an important role in driving dengue vector control in the public. Objectives: The research was conducted to discover the dengue situation, its vectors and key containers to make relevant recommendations in reducing effectively the vector population. Subjects and methods: Two hundred households in two communes of Nam Dinh province (100 in each studied commune) were selected randomly. Vectors collected from the field were identified using mosquito key. The number of dengue larvae and mosquitoes were directly counted or adjustment methods were used to estimate the true number in each type of water storage facility. Results: In Minh Thuan commune, Ae. albopictus larvae were mainly concentrated in jars (49%) and discards (59%), most of Ae. aegypti was found in cement tanks with a volume more than 500L (72%). In Trung Dong commune, larvae of Ae. albopictus concentrated in jars (40%) and discards (25%) and aquariums (15%), while most of the Ae. aegypti larvae was discovered in cement tanks with volumes more than 500L (86%). Mosquito density index of Ae. albopictus in Trung Dong and Minh Thuan communes was 0.56 and 0.38, respectively. The Aedes larvae, houses for larvae and Breteau index were nearly at the threshold of dengue epidemic occurrence for Ae. aegypti and over a certain threshold for Ae. albopictus. Some recommendations were provided to help reduce the dengue vectors. Conclusion: Investigation of key mosquito larvae in water containers was useful in driving the effective dengue vector control. Further studies are required to evaluate the impact and methods to manage water containers in the local area.
Dengue fever ; mosquito ; vector control

An investigation of knowledge, attitude and practice of community and vector colony in 200 households in Huong tra District, Hue City and monitoring of survival ability of mesocyclops after releasing them in to water tanks. Results showed that the participation of community and use of mesocyclops in the field training reduced significantly the mosquito colony and number of patients with haemorrhagic dengue fever. The participation also increased the concepts and knowledge of people in the haemorrhagic dengue fever prevention and control the collaborators and pupils played an important role in the interim of mobilizing the contribution of community.
Community based prevention and control of haemorrhagic dengue fever vector and use of biological agent-mesocyclops

Rapid geographical classification of Bupleuri Radix is important in quality control. In this study, near infrared spectroscopy (NIRS) combined with supervised pattern recognition was attempted to classify Bupleuri Radix according to geographical origins. Three supervised pattern recognitions methods, partial least square discriminant analysis (PLS-DA), quadratic discriminant analysis (QDA) and radial basis function support vector machine (RBF-SVM), were performed to establish the classification models. The QDA and RBF-SVM models were performed based on principal component analysis (PCA). The number of principal components (PCs) was optimized by cross-validation in the model. The results showed that the performance of the QDA model is the optimum among the three models. The optimized QDA model was obtained when 7 PCs were used; the classification rates of the QDA model in the training and test sets are 97.8% and 95.2% respectively. The overall results showed that NIRS combined with supervised pattern recognition could be applied to classify Bupleuri Radix according to geographical origin.
Classification ; Principal Component Analysis ; Quality Control ; Spectrum Analysis ; Support Vector Machine

A novel method was developed for the rapid determination of multi-indicators in corni fructus by means of near infrared (NIR) spectroscopy. Particle swarm optimization (PSO) based least squares support vector machine was investigated to increase the levels of quality control. The calibration models of moisture, extractum, morroniside and loganin were established using the PSO-LS-SVM algorithm. The performance of PSO-LS-SVM models was compared with partial least squares regression (PLSR) and back propagation artificial neural network (BP-ANN). The calibration and validation results of PSO-LS-SVM were superior to both PLS and BP-ANN. For PSO-LS-SVM models, the correlation coefficients (r) of calibrations were all above 0.942. The optimal prediction results were also achieved by PSO-LS-SVM models with the RMSEP (root mean square error of prediction) and RSEP (relative standard errors of prediction) less than 1.176 and 15.5% respectively. The results suggest that PSO-LS-SVM algorithm has a good model performance and high prediction accuracy. NIR has a potential value for rapid determination of multi-indicators in Corni Fructus.
Algorithms ; Calibration ; Cornus ; chemistry ; Drugs, Chinese Herbal ; chemistry ; Fruit ; chemistry ; Least-Squares Analysis ; Models, Theoretical ; Neural Networks (Computer) ; Quality Control ; Spectroscopy, Near-Infrared ; Support Vector Machine