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Your search keyword '"Wang, Qingjun"' showing total 21 results
Start Over Author "Wang, Qingjun" Publisher wiley
21 results on '"Wang, Qingjun"'

2. ZmLBD5 , a class‐II LBD gene, negatively regulates drought tolerance by impairing abscisic acid synthesis

Author

Feng, Xuanjun, primary, Xiong, Jing, additional, Zhang, Weixiao, additional, Guan, Huarui, additional, Zheng, Dan, additional, Xiong, Hao, additional, Jia, Li, additional, Hu, Yue, additional, Zhou, Hanmei, additional, Wen, Ying, additional, Zhang, Xuemei, additional, Wu, Fengkai, additional, Wang, Qingjun, additional, Xu, Jie, additional, and Lu, Yanli, additional

Published
2022
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3. ABA ‐inducibleDEEPER ROOTING1 improves adaptation of maize to water deficiency

Author

Feng, Xuanjun, primary, Jia, Li, additional, Cai, Yunting, additional, Guan, Huarui, additional, Zheng, Dan, additional, Zhang, Weixiao, additional, Xiong, Hao, additional, Zhou, Hanmei, additional, Wen, Ying, additional, Hu, Yue, additional, Zhang, Xuemei, additional, Wang, Qingjun, additional, Wu, Fengkai, additional, Xu, Jie, additional, and Lu, Yanli, additional

Published
2022
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9. Rapid differentiating colorectal cancer and colorectal polyp using dried blood spot mass spectrometry metabolomic approach

Author

Wang Qingjun, Jingjing Wu, Zhong-Ze Fang, Xue Wu, Yun-Feng Cao, Peng Gao, Zhitu Zhu, Yu Jing, and Chen Li

Subjects
0301 basic medicine, Oncology, medicine.medical_specialty, Colorectal cancer, Metabolite, Clinical Biochemistry, Mass spectrometry, Biochemistry, Causes of cancer, 03 medical and health sciences, chemistry.chemical_compound, 0302 clinical medicine, Metabolomics, Internal medicine, Genetics, medicine, Molecular Biology, business.industry, Cancer, Cell Biology, medicine.disease, Dried blood spot, 030104 developmental biology, chemistry, 030220 oncology & carcinogenesis, Colorectal Polyp, business
Abstract

Colorectal cancer (CRC) is the third leading causes of cancer mortality, and the early-stage detection could significantly enhance survival rates. Cancer influences the important metabolic pathways and the changes in metabolite levels had been used in many studies as the potential biomarkers. This study is aimed at screening metabolite biomarkers with CRC diagnosis potentials. The direct infusion mass spectrometry (MS) metabolomic analysis based on dried blood spot was used to distinguish CRC from polyp. The target metabolites were composed of 23 amino acids and 26 acylcarnitines. The 21 metabolites in blood were selected via multivariate analysis. A regression model was established based on parameters C16, Arg, C4/C8, C5/C3, Val, Phe/Tyr, Ala, C4/C3. Tenfold cross validation (CV) method was used to test this model and showed sensitivity of 81.18% and specificity of 83.95%. The metabolomic analysis is a practicable method for CRC detection. The use of direct MS analysis in metabolite screening could be finished in several minutes and served as a higher-throughput method to distinguish CRC and polyps. © 2017 IUBMB Life, 69(5):347-354, 2017.

Published
2017
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14. Antimicrobial activity and stability of protonectin withD-amino acid substitutions

Author

Qiu, Shuai, primary, Zhu, Ranran, additional, Zhao, Yanyan, additional, An, Xiaoping, additional, Jia, Fengjing, additional, Peng, Jinxiu, additional, Ma, Zelin, additional, Zhu, Yuanyuan, additional, Wang, Jiayi, additional, Su, Jinhuan, additional, Wang, Qingjun, additional, Wang, Hailin, additional, Li, Yuan, additional, Wang, Kairong, additional, Yan, Wenjin, additional, and Wang, Rui, additional

Published
2017
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20. Automated Segmentation of Coronary Arteries Based on Statistical Region Growing and Heuristic Decision Method.

Author

Tian Y, Pan Y, Duan F, Zhao S, Wang Q, and Wang W

Subjects
Humans, Coronary Angiography methods, Coronary Vessels diagnostic imaging, Image Processing, Computer-Assisted methods
Abstract

The segmentation of coronary arteries is a vital process that helps cardiovascular radiologists detect and quantify stenosis. In this paper, we propose a fully automated coronary artery segmentation from cardiac data volume. The method is built on a statistics region growing together with a heuristic decision. First, the heart region is extracted using a multi-atlas-based approach. Second, the vessel structures are enhanced via a 3D multiscale line filter. Next, seed points are detected automatically through a threshold preprocessing and a subsequent morphological operation. Based on the set of detected seed points, a statistics-based region growing is applied. Finally, results are obtained by setting conservative parameters. A heuristic decision method is then used to obtain the desired result automatically because parameters in region growing vary in different patients, and the segmentation requires full automation. The experiments are carried out on a dataset that includes eight-patient multivendor cardiac computed tomography angiography (CTA) volume data. The DICE similarity index, mean distance, and Hausdorff distance metrics are employed to compare the proposed algorithm with two state-of-the-art methods. Experimental results indicate that the proposed algorithm is capable of performing complete, robust, and accurate extraction of coronary arteries.

Published
2016
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21. A vessel active contour model for vascular segmentation.

Author

Tian Y, Chen Q, Wang W, Peng Y, Wang Q, Duan F, Wu Z, and Zhou M

Subjects
Algorithms, Humans, Image Enhancement, Glycosaminoglycans physiology, Image Processing, Computer-Assisted, Models, Theoretical, Pattern Recognition, Automated
Abstract

This paper proposes a vessel active contour model based on local intensity weighting and a vessel vector field. Firstly, the energy function we define is evaluated along the evolving curve instead of all image points, and the function value at each point on the curve is based on the interior and exterior weighted means in a local neighborhood of the point, which is good for dealing with the intensity inhomogeneity. Secondly, a vascular vector field derived from a vesselness measure is employed to guide the contour to evolve along the vessel central skeleton into thin and weak vessels. Thirdly, an automatic initialization method that makes the model converge rapidly is developed, and it avoids repeated trails in conventional local region active contour models. Finally, a speed-up strategy is implemented by labeling the steadily evolved points, and it avoids the repeated computation of these points in the subsequent iterations. Experiments using synthetic and real vessel images validate the proposed model. Comparisons with the localized active contour model, local binary fitting model, and vascular active contour model show that the proposed model is more accurate, efficient, and suitable for extraction of the vessel tree from different medical images.

Published
2014
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