RESUMEN
In order to evaluate the genome evolution and systematics, karyotype analysis of mitotic metaphase chromosomes in 51 taxa of Epimedium and two species of Vancouveria was conducted. The 53 taxa were clustered, based on their karyotype similarity coefficients. Results showed that the 53 taxa studied were all diploid with 12 chromosomes (2n = 2x = 12). Each taxon had one pair of satellites located on pair I of homologous chromosomes. Moreover, the karyotype types of the 53 taxa studied were all type 1A or 2A of Stebbins. It can be concluded that the karyotypes between species are indeed very similar and the genome of Epimedium was conservative in evolution. The cluster analysis of karyotype similarity coefficients could provide valuable clues for the systematics and taxonomy of Epimedium. Results of the cluster analysis strongly supported the previous taxonomic division of E. subg. Rhizophyllum and E. subg. Epimedium. The results also showed that the interspecific relationship was closely correlated with geographical distribution in E. subg. Epimedium and the taxa native to east Asia had the highest genetic diversity in Epimedium. Finally, the origin of the modern geographical distribution of Epimedium was inferred. Results of the present study have significant scientific values in further studies on resource utilisation, taxonomy and phylogeny in Epimedium.
RESUMEN
Lung cancer is still the most concerned disease around the world. Lung nodule generates in the pulmonary parenchyma which indicates the latent risk of lung cancer. Computer-aided pulmonary nodules detection system is necessary, which can reduce diagnosis time and decrease mortality of patients. In this study, we have proposed a new computer aided diagnosis (CAD) system for detection of early pulmonary nodule, which can help radiologists quickly locate suspected nodules and make judgments. This system consists of four main sections: pulmonary parenchyma segmentation, nodule candidate detection, features extraction (total 22 features) and nodule classification. The publicly available data set created by the Lung Image Database Consortium (LIDC) is used for training and testing. This study selects 6400 slices from 80 CT scans containing totally 978 nodules, which is labeled by four radiologists. Through a fast segmentation method proposed in this paper, pulmonary nodules including 888 true nodules and 11,379 false positive nodules are segmented. By means of an ensemble classifier, Random Forest (RF), this study acquires 93.2, 92.4, 94.8, 97.6% of accuracy, sensitivity, specificity, area under the curve (AUC), respectively. Compared with support vector machine (SVM) classifier, RF can reduce more false positive nodules and acquire larger AUC. With the help of this CAD system, radiologist can be provided with a great reference for pulmonary nodule diagnosis timely.