Cytoplasmic male sterility is an important way to utilize wheat heterosis. The purpose of thisstudy was to identify cytoplasmic type of three wheat male sterile lines. Amplified fragment length polymorphism (AFLP) marker technique was used to analyze the wheat mitochondrial DNA. We isolated mitochondria by differential centrifugation and density gradient ultracentrifugation. The results show that the extracted mitochondrial DNA was pure. It was suitable for PCR and genetic analysis. We got 4 pairs of specific primers from 64 primers combinations. Primer E1/M7 amplified 3 specific fragments in ms(Kots)-90-110. Primer E4/M2 generated 2 specific fragments in ms(Ven)-90-110. Primer E7/M6 amplified 2 specific fragments in ms(S)-90-110. Primer E6/M4 produced 2 specific fragments in ms(Kots)-90-110. Four specific primers could be used to identify three cytoplasmic types of Aegilops kotschyi, Ae. ventricosa and Triticum spelta. It provided the molecular basis to further study the mechanism of wheat cytoplasmic male sterility.
Amplified Fragment Length Polymorphism Analysis ; methods ; Cytoplasm ; metabolism ; DNA, Mitochondrial ; genetics ; DNA, Plant ; genetics ; Gene Expression Profiling ; Genotype ; Plant Infertility ; genetics ; Triticum ; genetics

Objective To propose a lung artery segmentation method that integrates shape and position prior knowledge, aiming to solve the issues of inaccurate segmentation caused by the high similarity and small size differences between the lung arteries and surrounding tissues in CT images. Methods Based on the three-dimensional U-Net network architecture and relying on the PARSE 2022 database image data, shape and position prior knowledge was introduced to design feature extraction and fusion strategies to enhance the ability of lung artery segmentation. The data of the patients were divided into three groups: a training set, a validation set, and a test set. The performance metrics for evaluating the model included Dice Similarity Coefficient (DSC), sensitivity, accuracy, and Hausdorff distance (HD95). Results The study included lung artery imaging data from 203 patients, including 100 patients in the training set, 30 patients in the validation set, and 73 patients in the test set. Through the backbone network, a rough segmentation of the lung arteries was performed to obtain a complete vascular structure; the branch network integrating shape and position information was used to extract features of small pulmonary arteries, reducing interference from the pulmonary artery trunk and left and right pulmonary arteries. Experimental results showed that the segmentation model based on shape and position prior knowledge had a higher DSC (82.81%±3.20% vs. 80.47%±3.17% vs. 80.36%±3.43%), sensitivity (85.30%±8.04% vs. 80.95%±6.89% vs. 82.82%±7.29%), and accuracy (81.63%±7.53% vs. 81.19%±8.35% vs. 79.36%±8.98%) compared to traditional three-dimensional U-Net and V-Net methods. HD95 could reach (9.52±4.29) mm, which was 6.05 mm shorter than traditional methods, showing excellent performance in segmentation boundaries. Conclusion The lung artery segmentation method based on shape and position prior knowledge can achieve precise segmentation of lung artery vessels and has potential application value in tasks such as bronchoscopy or percutaneous puncture surgery navigation.