Classification of Camellia (Theaceae) Species Using Leaf Architecture Variations and Pattern Recognition Techniques

Leaf characters have been successfully utilized to classify Camellia (Theaceae) species; however, leaf characters combined with supervised pattern recognition techniques have not been previously explored. We present results of using leaf morphological and venation characters of 93 species from five sections of genus Camellia to assess the effectiveness of several supervised pattern recognition techniques for classifications and compare their accuracy. Clustering approach, Learning Vector Quantization neural network (LVQ-ANN), Dynamic Architecture for Artificial Neural Networks (DAN2), and C-support vector machines (SVM) are used to discriminate 93 species from five sections of genus Camellia (11 in sect. Furfuracea, 16 in sect. Paracamellia, 12 in sect. Tuberculata, 34 in sect. Camellia, and 20 in sect. Theopsis). DAN2 and SVM show excellent classification results for genus Camellia with DAN2's accuracy of 97.92% and 91.11% for training and testing data sets respectively. The RBF-SVM results of 97.92% and 97.78% for training and testing offer the best classification accuracy. A hierarchical dendrogram based on leaf architecture data has confirmed the morphological classification of the five sections as previously proposed. The overall results suggest that leaf architecture-based data analysis using supervised pattern recognition techniques, especially DAN2 and SVM discrimination methods, is excellent for identification of Camellia species

Assessment of the sensitivity of five different cell lines to the triple poliovirus serotypes

Background: The extensive use of poliovaccines has eliminated the wild-type poliovirus in most parts of the world. These conditions were caused due to the utilization of oral polio vaccine (OPV) and inactive polio vaccine (IPV). Since most of the quality control tests for these vaccines are performed on cell beds sensitive to poliovirus, the identification of the most sensitive cell line to poliovirus is a necessity. Materials and Methods: Five monolayer cell lines (Vero, HeLa, Hep-2, MRC-5 and L20-B) were prepared in cell culture flasks (25 cm2). Then serial dilutions of three types of poliovirus with specified titers were added to each cell beds. The inoculated cells were then incubated at 33°C for 14 days and were monitored daily for the presence of cytopathic effects for polioviruses. Results: The results showed that the sensitivity of L20B cell line to polioviruses was more than the other cells. The result also indicated that the sensitivity of cells to poliovirus was declined in Hep-2, HeLa, MRC-5 and Vero cell lines, respectively. Conclusion: It can be concluded that the L20B, Hep-2 and HeLa cell lines, due to their higher sensitivity to triple poliovirus serotypes are considered for vaccine quality control tests

Segmentation of age-related white matter changes in a clinical multi-center study

Age-related white matter changes (WMC) are thought to be a marker of vascular pathology, and have been associated with motor and cognitive deficits. In the present study, an optimized artificial neural network was used as an automatic segmentation method to produce probabilistic maps of WMC in a clinical multi-center study. The neural network uses information from T1- and T2-weighted and fluid attenuation inversion recovery (FLAIR) magnetic resonance (MR) scans, neighboring voxels and spatial location. Generalizability of the neural network was optimized by including the Optimal Brain Damage (OBD) pruning method in the training stage. Six optimized neural networks were produced to investigate the impact of different input information on WMC segmentation. The automatic segmentation method was applied to MR scans of 362 non-demented elderly subjects from 11 centers in the European multi-center study Leukoaraiosis And Disability (LADIS). Semi-manually delineated WMC were used for validating the segmentation produced by the neural networks. The neural network segmentation demonstrated high consistency between subjects and centers, making it a promising technique for large studies. For WMC volumes less than 10 ml, an increasing discrepancy between semi-manual and neural network segmentation was observed using the similarity index (SI) measure. The use of all three image modalities significantly improved cross-center generalizability compared to neural networks using the FLAIR image only. Expert knowledge not available to the neural networks was a minor source of discrepancy, while variation in MR scan quality constituted the largest source of error