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

Accurate Automatic Glioma Segmentation in Brain MRI images Based on CapsNet

AbstractGlioma is a highly invasive type of brain tumor with an irregular morphology and blurred infiltrative borders that may affect different parts of the brain. Therefore, it is a challenging task to identify the exact boundaries of the tumor in an MR image. In recent years, deep learning-based Convolutional Neural Networks (CNNs) have gained popularity in the field of image processing and have been utilized for accurate image segmentation in medical applications. However, due to the inherent constraints of CNNs, tens of thousands of images are required for training, and collecting and annotating such a large number of images poses a serious challenge for their practical implementation. Here, for the first time, we have optimized a network based on the capsule neural network called SegCaps, to achieve accurate glioma segmentation on MR images. We have compared our results with a similar experiment conducted using the commonly utilized U-Net. Both experiments were performed on the BraTS2020 challenging dataset. For U-Net, network training was performed on the entire dataset, whereas a subset containing only 20% of the whole dataset was used for the SegCaps. To evaluate the results of our proposed method, the Dice Similarity Coefficient (DSC) was used. SegCaps and U-Net reached DSC of 87.96% and 85.56% on glioma tumor core segmentation, respectively. The SegCaps uses convolutional layers as the basic components and has the intrinsic capability to generalize novel viewpoints. The network learns the spatial relationship between features using dynamic routing of capsules. These capabilities of the capsule neural network have led to a 3% improvement in results of glioma segmentation with fewer data while it contains 95.4% fewer parameters than U-Net.</jats:p