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753 results on '"Chen Xiaotian"'

1. Impact of MAFB on polarization and function of tumor associated macrophages

Author

CHEN Xiaotian, CHEN Chong, LUO Yunping

Subjects
mafb, macrophage, m1/m2 polarization, phagocytosis, tumor cytotoxicity, Medicine
Abstract

Objective To investigate the impact of the MAFB transcription factor on macrophage polarization phenotype and function. Methods Analysis of correlation of MAFB expression with survival period of patients of breast cancer and lung adenocarcinoma was performed by RNA-seq data from the Kaplan-Meier database. Flow cytometry was applied to detect MAFB expression in primary human monocytes. Expression of MAFB mRNA or protein of human acute monocytic leukaemia cell line(THP-1) was detected by RT-qPCR or Western blot. Detection of macrophages phenotypes was performed by macrophages polarization in vitro, co-culture, RT-qPCR and flow cytometry. Phagocytosis assay was used to assess changes in phagocytic ability of macrophages. In vitro killing assay to evaluate changes in tumor-killing ability of macrophages. Results Compared to the MAFB low-expression group, patients with high MAFB expression in breast cancer and lung adenocarcinoma exhibited a decreased survival period(P＜0.05). Protein interaction networks showed a correlation between MAFB protein and macrophage polarization proteins. Compared to other cell type, MAFB was specifically highly expressed in human monocyte-macrophage cells. Phorbol myristate ester(PMA) induced an increased MAFB expression in THP-1 cells (P＜0.05). Knockdown of MAFB inhibited the expression of M2-related genes (P＜0.05). Co-culture with tumor cells and knockdown of MAFB suppressed expression of polarization gene (P＜0.05). Knockdown of MAFB did not affect the phagocytic abilityP＞0.05) but enhanced tumor-killing ability of macrophages (P＜0.05). Conclusions This investigation proves potential function of MAFB transcription factor in regulation of macrophage M2 polarization and anti-tumor effect in solid tumors.

Published
2024
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2. Laparoscopic Versus Open Surgery for Early-Stage Intrahepatic Cholangiocarcinoma After Mastering the Learning Curve: A Multicenter Data-Based Matched Study

Author

Yang Jinhuan, Wang Yi, Zheng Yuanwen, Ma Delin, Chen Xiaotian, Wang Yan, Deng Liming, Yu Haitao, Wu Lijun, Deng Tuo, Chen Kaiyu, Hu Jiawei, Zheng Chongming, Wang Daojie, Jin Bin, and Chen Gang

Subjects
intrahepatic cholangiocarcinoma, laparoscopic hepatectomy, liver resection, overall survival, recurrence-free survival, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

BackgroundSurgical resection is the only widely accepted curative method for intrahepatic cholangiocarcinoma (ICC). However, little is known about the efficacy of laparoscopic liver resection for ICC, especially in patients with early-stage disease. The aim of this study was to compare the short-term and long-term effects of laparoscopy and open surgery for the treatment of ICC.MethodsData from 1,084 patients treated at three hospitals from January 2011 to December 2018 were selected and analyzed. Propensity score matching was performed to compare the long-term outcomes (overall survival and recurrence-free survival) and short-term outcomes (perioperative outcomes) of all-stage and early-stage patients.ResultsAfter matching, 244 patients (122 vs. 122) in the all-stage group and 65 patients (27 vs. 38) in the early-stage group were included. The baseline of the two groups was balanced, and no significant differences were found in sex or age. The short-term results of the laparoscopic group were better than those of the open group, including less blood loss [blood loss ≥400 ml 27 (22.1%) vs. 6 (4.92%), p

Published
2022
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11. Degradation of Phenol in Wastewater with Ozone Produced by Self-design Ozone Generator

Author

Wang Shibo, Zhong Dongwei, Qu Guangfei, Ning Ping, Quan Jiankang, and Chen Xiaotian

Subjects
Engineering (General). Civil engineering (General), TA1-2040
Abstract

The optimized tube’s structure of the self-design ozone generator was made with the double dielectric inner electrode and small metal cones were embedded in the outside electrode. This ozone generator was used for the degradation of phenol in wastewater. The research was studied from ozone gas flow rate, reaction time, the initial pH and concentration of the phenol in wastewater. In addition, the article also discusses the reaction mechanism of ozone degraded the phenol in wastewater. The results illustrate that the ozone concentration of self-design ozone generator under the pure oxygen flux (1.0 L/min) were 7.06 mg/L, compared with the unoptimized ozone, the optimized ozone generator’s efficiency was improved 56.89%; the phenol removal rate was increased as the initial pH increased in wastewater, when pH was 12, the phenol removal rate was 80.17%; GC-MS analysis was adopted to analyze and determine the phenol intermediates. The results illustrate that the major oxidation degradation products of phenol may be p-benzoquinone benzenequinone, 4-cyclopentene-1, 3-dione and diacetone alcohol.

Published
2016
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13. S2R-DepthNet: Learning a Generalizable Depth-specific Structural Representation

Author

Chen, Xiaotian, Wang, Yuwang, Chen, Xuejin, and Zeng, Wenjun

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Human can infer the 3D geometry of a scene from a sketch instead of a realistic image, which indicates that the spatial structure plays a fundamental role in understanding the depth of scenes. We are the first to explore the learning of a depth-specific structural representation, which captures the essential feature for depth estimation and ignores irrelevant style information. Our S2R-DepthNet (Synthetic to Real DepthNet) can be well generalized to unseen real-world data directly even though it is only trained on synthetic data. S2R-DepthNet consists of: a) a Structure Extraction (STE) module which extracts a domaininvariant structural representation from an image by disentangling the image into domain-invariant structure and domain-specific style components, b) a Depth-specific Attention (DSA) module, which learns task-specific knowledge to suppress depth-irrelevant structures for better depth estimation and generalization, and c) a depth prediction module (DP) to predict depth from the depth-specific representation. Without access of any real-world images, our method even outperforms the state-of-the-art unsupervised domain adaptation methods which use real-world images of the target domain for training. In addition, when using a small amount of labeled real-world data, we achieve the state-ofthe-art performance under the semi-supervised setting. The code and trained models are available at https://github.com/microsoft/S2R-DepthNet., Comment: Accepted by CVPR2021(oral)

Published
2021

16. Three-dimensional eddy current pulsed thermography and its applications

Author

Chen, Xiaotian

Abstract

The measurement and quantification of defects is a challenge for Non-DestructiveTesting and Evaluation (NDT&E). Such challenges include the precise localisation and detection of surface and sub-surface defects, as well as the quantification of such defects. This work first reports a three-dimensional (3D) Eddy Current Pulsed Thermography (ECPT) system via integration with an RGB-D camera. Then, various quantitative measurements and analyses of defects are carried out based on the 3D ECPT system. The ECPT system at Newcastle University has been prooven to be an effective nondestructive testing (NDT) method in surface and sub-surface detection over the past few years. Based on the different numerical or analytical models, it has achieved precise defect detection on the rail tracks, wind turbines, carbon fibre reinforced plastic (CFRP) and so on. The ECPT system has the advantage of fast inspection and a large lift-off range. However, it involves a trade-off between detectable defect size and inspection area compared with other NDT methods. In addition, there are challenges of defect detection in a complex structure. Thus, the quantification of defects gives a higher requirement of the measurement the object geometry information. Furthermore, the analysis of thermal diffusion requires a precise 3D model. For this reason, a 3D ECPT system is proposed that adds each heat pixel with an exact X-Y-Z coordinate. In this work, first, the 3D ECPT system is built. A feature-based automatic calibration of the infrared camera and the RGB-D camera is proposed. Second, the software platform is built. A fast 3D visualization is completed with multi-threading technology and the Point Cloud Library. Lastly, various studies of defect localization, quantification and thermal tomography reconstruction are carried out.

Published
2021

22. Structure-Aware Residual Pyramid Network for Monocular Depth Estimation

Author

Chen, Xiaotian, Chen, Xuejin, and Zha, Zheng-Jun

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Monocular depth estimation is an essential task for scene understanding. The underlying structure of objects and stuff in a complex scene is critical to recovering accurate and visually-pleasing depth maps. Global structure conveys scene layouts, while local structure reflects shape details. Recently developed approaches based on convolutional neural networks (CNNs) significantly improve the performance of depth estimation. However, few of them take into account multi-scale structures in complex scenes. In this paper, we propose a Structure-Aware Residual Pyramid Network (SARPN) to exploit multi-scale structures for accurate depth prediction. We propose a Residual Pyramid Decoder (RPD) which expresses global scene structure in upper levels to represent layouts, and local structure in lower levels to present shape details. At each level, we propose Residual Refinement Modules (RRM) that predict residual maps to progressively add finer structures on the coarser structure predicted at the upper level. In order to fully exploit multi-scale image features, an Adaptive Dense Feature Fusion (ADFF) module, which adaptively fuses effective features from all scales for inferring structures of each scale, is introduced. Experiment results on the challenging NYU-Depth v2 dataset demonstrate that our proposed approach achieves state-of-the-art performance in both qualitative and quantitative evaluation. The code is available at https://github.com/Xt-Chen/SARPN., Comment: 7pages, 7figures, Accepted by the 28th International Joint Conference on Artificial Intelligence (IJCAI-2019)

Published
2019

27. Pilot Fatigue Evaluation Based on Eye-Movement Index and Performance Index

Author

Xue, Hongjun, Chen, Xiaotian, Zhang, Xiaoyan, Ma, Ziqi, Qiu, Kai, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Harris, Don, editor, and Li, Wen-Chin, editor

Published
2022
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28. Study on the Mechanism of Pilot Fatigue on Performance

Author

Xue, Hongjun, Ma, Ziqi, Zhang, Xiaoyan, Chen, Xiaotian, Qiu, Kai, Li, Jie, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Harris, Don, editor, and Li, Wen-Chin, editor

Published
2022
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35. Channel state information based physical layer authentication for Wi‐Fi sensing systems using deep learning in Internet of things networks.

Author

Roopak, Monika, Ran, Yachao, Chen, Xiaotian, Tian, Gui Yun, and Parkinson, Simon

Abstract

Security problems loom big in the fast‐growing world of Internet of Things (IoT) networks, which is characterised by unprecedented interconnectedness and data‐driven innovation, due to the inherent susceptibility of wireless infrastructure. One of the most pressing concerns is user authentication, which was originally intended to prevent unwanted access to critical information but has since expanded to provide tailored service customisation. We suggest a Wi‐Fi sensing‐based physical layer authentication method for IoT networks to solve this problem. Our proposed method makes use of raw channel state information (CSI) data from Wi‐Fi signals to create a hybrid deep‐learning model that combines convolutional neural networks and long short‐term memory networks. Rigorous testing yields an astonishing 99.97% accuracy rate, demonstrating the effectiveness of our CSI‐based verification. This technology not only strengthens wireless network security but also prioritises efficiency and portability. The findings highlight the practicality of our proposed CSI‐based physical layer authentication, which provides lightweight and precise protection for wireless networks in the IoT. [ABSTRACT FROM AUTHOR]

Published
2024
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36. Automated Analysis of Doppler Echocardiographic Videos as a Screening Tool for Valvular Heart Diseases

Author

Yang, Feifei, Chen, Xiaotian, Lin, Xixiang, Chen, Xu, Wang, Wenjun, Liu, Bohan, Li, Yao, Pu, Haitao, Zhang, Liwei, Huang, Dangsheng, Zhang, Meiqing, Li, Xin, Wang, Hui, Wang, Yueheng, Guo, Huayuan, Deng, Yujiao, Zhang, Lu, Zhong, Qin, Li, Zongren, Yu, Liheng, Duan, Yongjie, Zhang, Peifang, Wu, Zhenzhou, Burkhoff, Daniel, Wang, Qiushuang, and He, Kunlun

Published
2022
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