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167 results on '"Yuxing Tang"'

1. Influence of Si and SiC Coating on the Microstructures and Mechanical Properties of C/C Bolts

Author

Guodong Sun, Zhiqiang Tan, Qing Zhang, Yi Zhang, Xuqin Li, Qinglai Tian, and Yuxing Tang

Subjects
vapor silicon infiltration, carbon/carbon composite, bolt, mechanical property, Technology, Electrical engineering. Electronics. Nuclear engineering, TK1-9971, Engineering (General). Civil engineering (General), TA1-2040, Microscopy, QH201-278.5, Descriptive and experimental mechanics, QC120-168.85
Abstract

High−performance bolts made of carbon/carbon (C/C) composites are necessary for connecting thermally−insulating structural components of aerospace vehicles. To enhance the mechanical properties of the C/C bolt, a new silicon infiltration−modified C/C (C/C−SiC) bolt was developed via vapor silicon infiltration. The effects of silicon infiltration on microstructure and mechanical properties were systematically studied. Findings reveal that dense and uniform SiC−Si coating has been formed after silicon infiltration of the C/C bolt, strongly bonding with the C matrix. Under tensile stress, the C/C−SiC bolt undergoes a tensile failure of studs, while the C/C bolt is subject to the pull−out failure of threads. The breaking strength of the former (55.16 MPa) is 26.83% higher than the failure strength of the latter (43.49 MPa). Under double−sided shear stress, both the crushing of threads and the shear failure of studs occur within two bolts. As a result, the shear strength of the former (54.73 MPa) exceeds that of the latter (43.88 MPa) by 24.73%. According to CT and SEM analysis, matrix fracture, fiber debonding, and fiber bridging are the main failure modes. Therefore, a mixed coating formed by silicon infiltration can effectively transfer loads from coating to carbon matrix and carbon fiber, thereby enhancing the load−bearing capacity of C/C bolts.

Published
2023
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2. Down-regulation of microRNA-23b aggravates LPS-induced inflammatory injury in chondrogenic ATDC5 cells by targeting PDCD4

Author

Zhongmeng Yang, Yuxing Tang, Qing Zhao, Huading Lu, and Guoyong Xu

Subjects
Inflammatory injury, microRNA-23b, NF-κB/Notch, Osteoarthritis, PDCD4, Medicine
Abstract

Objective(s): Osteoarthritis (OA), characterized by degradation of articular cartilage, is a leading cause of disability. As the only cell type present in cartilage, chondrocytes play curial roles in the progression of OA. In our study, we aimed to explore the roles of miR-23b in the lipopolysaccharide (LPS)-induced inflammatory injury. Materials and Methods: LPS-induced cell injury of ATDC5 cells was evaluated by the loss of cell viability, enhancement of cell apoptosis, alteration of apoptosis-associated proteins, and release of inflammatory cytokines. Then, miR-23b level after LPS treatment was assessed by qRT-PCR. Next, the effects of aberrantly expressed miR-23b on the LPS-induced inflammatory injury were explored. The possible target genes of miR-23b were virtually screened by informatics and verified by luciferase assay. Subsequently, whether miR-23b functioned through regulating the target gene was validated. The involved signaling pathways were investigated finally.Results: Cell viability was decreased but cell apoptosis, as well as release of inflammatory cytokines, was enhanced by LPS treatment. MiR-23b was down-regulated by LPS and its overexpression alleviated LPS-induced inflammatory injury. PDCD4, negatively regulated by miR-23b expression, was verified as a target gene of miR-23b. Following experiments showed miR-23b alleviated LPS-induced cell injury through down-regulating PDCD4 expression. Phosphorylated levels of key kinases in the NF-κB pathway, as well as expressions of key kinases in the Notch pathways, were increased by PDCD4 overexpression.Conclusion: MiR-23b was down-regulated after LPS treatment, and its overexpression ameliorated LPS-induced inflammatory injury in ATDC5 cells by targeting PDCD4, which could activate the NF-κB/Notch pathways.

Published
2018
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5. CancerUniT: Towards a Single Unified Model for Effective Detection, Segmentation, and Diagnosis of Eight Major Cancers Using a Large Collection of CT Scans.

Author

Jieneng Chen, Yingda Xia, Jiawen Yao, Ke Yan 0006, Jianpeng Zhang, Le Lu 0001, Fakai Wang, Bo Zhou 0009, Mingyan Qiu, Qihang Yu, Mingze Yuan, Wei Fang, Yuxing Tang, Minfeng Xu, Jian Zhou, Yuqian Zhao, Qifeng Wang, Xianghua Ye, Xiaoli Yin, Yu Shi, Xin Chen 0058, Jingren Zhou, Alan L. Yuille, Zaiyi Liu, and Ling Zhang 0002

Published
2023
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11. Towards a Single Unified Model for Effective Detection, Segmentation, and Diagnosis of Eight Major Cancers Using a Large Collection of CT Scans.

Author

Jieneng Chen, Yingda Xia, Jiawen Yao, Ke Yan 0006, Jianpeng Zhang, Le Lu 0001, Fakai Wang, Bo Zhou 0009, Mingyan Qiu, Qihang Yu, Mingze Yuan, Wei Fang, Yuxing Tang, Minfeng Xu, Jian Zhou, Yuqian Zhao, Qifeng Wang, Xianghua Ye, Xiaoli Yin, Yu Shi, Xin Chen 0058, Jingren Zhou, Alan L. Yuille, Zaiyi Liu, and Ling Zhang 0002

Published
2023
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37. Automatic mammographic breast density classification in Chinese women: clinical validation of a deep learning model

Author

Xiaohui Lin, Shibin Wu, Lin Li, Rushan Ouyang, Jie Ma, Chunyan Yi, and Yuxing Tang

Subjects
Radiological and Ultrasound Technology, Radiology, Nuclear Medicine and imaging, General Medicine
Abstract

Background High breast density is a strong risk factor for breast cancer. As such, high consistency and accuracy in breast density assessment is necessary. Purpose To validate our proposed deep learning (DL) model and explore its impact on radiologists on density assessments. Material and Methods A total of 3732 mammographic cases were collected as a validated set: 1686 cases before the implementation of the DL model and 2046 cases after the DL model. Five radiologists were divided into two groups (junior and senior groups) to assess all mammograms using either two- or four-category evaluation. Linear-weighted kappa (K) and intraclass correlation coefficient (ICC) statistics were used to analyze the consistency between radiologists before and after implementation of the DL model. Results The accuracy and clinical acceptance of the DL model for the junior group were 96.3% and 96.8% for two-category evaluation, and 85.6% and 89.6% for four-category evaluation, respectively. For the senior group, the accuracy and clinical acceptance were 95.5% and 98.0% for two-category evaluation, and 84.3% and 95.3% for four-category evaluation, respectively. The consistency within the junior group, the senior group, and among all radiologists improved with the help of the DL model. For two-category, their K and ICC values improved to 0.81, 0.81, and 0.80 from 0.73, 0.75, and 0.76. And for four-category, their K and ICC values improved to 0.81, 0.82, and 0.82 from 0.73, 0.79, and 0.78, respectively. Conclusion The DL model showed high accuracy and clinical acceptance in breast density categories. It is helpful to improve radiologists’ consistency.

Published
2023

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