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16. FTO rs1121980 polymorphism contributes to coronary artery disease susceptibility in a Chinese Han population

Author

Xue Min, Yu-Lan Zhou, Yun-Fei Qu, Zhao-Fu Liao, Heng Li, Jie Cheng, Li-Li Liang, Hai-Liang Mo, Zhu-Guo Wu, and Xing-Dong Xiong

Subjects
FTO, Single nucleotide polymorphism, Lipids, Coronary artery disease, Myocardial infarction, Risk, Nutritional diseases. Deficiency diseases, RC620-627
Abstract

Abstract Background The fat mass and obesity-associated protein (FTO) has been showed to be involved in the pathogenesis and progression of coronary artery disease (CAD). However, the effects of FTO variants on CAD risk remain poorly understood. We herein genotyped three SNPs (rs1121980, rs72803657, and rs4783818) in FTO to investigate the influence of FTO polymorphisms on individual susceptibility to CAD. Methods Genotyping for the three SNPs (rs1121980, rs72803657, and rs4783818) was conducted in a cohort of 712 CAD cases with 349 myocardial infarction (MI) cases and 701 control participants, utilizing the polymerase chain reaction-ligation detection reaction (PCR-LDR) technique. The associations of these SNPs with CAD were analyzed using multivariate logistic regression, and the associations with lipid profiles were assessed by the Kruskal-Wallis or Wilcoxon-Mann-Whitney tests. Results The A allele (OR = 1.26, 95% CI = 1.01–1.57, and P = 0.044) and the AA genotype (OR = 3.13, 95% CI = 1.53–6.38, and P = 0.002) of FTO rs1121980 were significantly associated with an elevated risk of CAD. Similarly, the A allele (OR = 1.54, 95% CI = 1.18–2.02, and P = 0.002) and the AA genotype (OR = 5.61, 95% CI = 2.57–12.27, and P

Published
2025
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17. Visual/Near-Infrared Spectroscopy Combined with Linear Discriminant Analysis and Machine Learning for Classification of Apple Damage

Author

ZHANG Yu, ZHANG Chongyang, DUAN Xinxin, MA Shaoge, ZHAO Fu, WANG Juxia

Subjects
apple, visible/near-infrared spectroscopy, machine learning, linear discriminant analysis, damage classification, Food processing and manufacture, TP368-456
Abstract

This study investigated the combined application of visible/near-infrared (Vis-NIR) spectroscopy with linear discriminant analysis (LDA) and machine learning (ML) for the classification of apples with different degrees of damage. The Vis-NIR spectral data of apples with different degrees of damage were collected, and the effect of different spectral preprocessing methods on the support vector machine (SVM) classification model was analyzed. LDA was used to reduce the dimensionality of the preprocessed spectral data, and five machine learning models including SVM, random forest (RF), K-nearest neighbor (KNN), decision tree (DT) and extreme gradient boosting (XGBoost) were constructed and compared for the classification of apple damage. The results showed that the SVM model based on preprocessed spectra with Savitzky-Golay (SG) smoothing had the best classification performance, with an accuracy of 87.3%. After dimensionality reduction using LDA, the classification accuracy of all the models was significantly improved, with the highest increase of 16% being observed in the DT model. The KNN model showed the best classification performance, with an accuracy of 96.0% and a precision of 96.4%. This study provides a basis for efficient and accurate assessment of the degree of mechanical damage in apples.

Published
2024
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18. Leontopodium nyingchiense (Asteraceae), a new species from Xizang (Tibet), China

Author

Wen-Qi He, Fang-Yu Zhao, Zhao-Fu Chu, Guo-Zhu Chai, Kai-Hui Zhao, Jing-Qin Tian, Bao-Xin Zhang, Fang-Yuan Zhang, Zhi-Hua Liao, Wei-Lie Zheng, and Xiao-Zhong Lan

Subjects
Botany, QK1-989
Abstract

Leontopodium nyingchiense, a new species of Asteraceae from the Xizang (Tibet) Autonomous Region of China, is described and illustrated here. Morphologically, it is most similar to L. lingianum but can be distinguished by the combination of monoecious and dioecious individuals, involucral bracts arranged in 3–5 series (with outer series herbaceous and middle to inner series membranous), fimbriate apices on female florets, rough-edged lobes on male florets, and achenes lacking costae. Phylogenetic analyses further support the separation of this new species from related taxa. Finally, we characterize this new species through both morphological comparisons and molecular analyses.

Published
2024
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46. Single-shot wide-field optical section imaging

Author

Hu, Yuyao, Liang, Dong, Wang, Jing, Xuan, Yaping, Zhao, Fu, Liu, Jun, and Li, Ruxin

Subjects
Physics - Optics, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Optical sectioning technology has been widely used in various fluorescence microscopes owing to its background removing capability. Here, a virtual HiLo based on edge detection (V-HiLo-ED) is proposed to achieve wide-field optical sectioning, which requires only single wide-field image. Compared with conventional optical sectioning technologies, its imaging speed can be increased by at least twice, meanwhile maintaining nice optical sectioning performance, low cost, and excellent artifact suppression capabilities. Furthermore, the new V-HiLo-ED can also be extended to other non-fluorescence imaging fields. This simple, cost-effective and easy-to-extend method will benefit many research and application fields that needs to remove out-of-focus blurred images., Comment: 22 pages 9 figures

Published
2021

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