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1,149 results on '"Gao, Shuo"'

210. Subtype-based analysis of cell-in-cell structures in non-small cell lung cancer

Author

Wei, Yuexian, Niu, Zubiao, Hou, Xinyu, Liu, Mengzhe, Wang, Yuqi, Zhou, Yongan, Wang, Chenxi, Ma, Qunfeng, Zhu, Yichao, Gao, Xinyue, Li, Peiyun, Gao, Shuo, Zhan, Sibo, Yang, Zi, Tai, Yanhong, Shao, Qiuju, Ge, Jianlin, Hua, Jilei, Gao, Lihua, Sun, Qiang, Jiang, Hong, and Huang, Hongyan

Subjects
Original Article
Abstract

Lung cancer is ranked as the leading cause of cancer-related death worldwide, and the development of novel biomarkers is helpful to improve the prognosis of non-small cell lung cancer (NSCLC). Cell-in-cell structures (CICs), a novel functional surrogate of complicated cell behaviors, have shown promise in predicting the prognosis of cancer patients. However, the CIC profiling and its prognostic value remain unclear in NSCLC. In this study, we retrospectively explored the CIC profiling in a cohort of NSCLC tissues by using the “Epithelium-Macrophage-Leukocyte” (EML) method. The distribution of CICs was examined by the Chi-square test, and univariate and multivariate analyses were performed for survival analysis. Four types of CICs were identified in lung cancer tissues, namely, tumor-in-tumor (TiT), tumor-in-macrophage (TiM), lymphocyte-in-tumor (LiT), and macrophage-in-tumor (MiT). Among them, the latter three constituted the heterotypic CICs (heCICs). Overall, CICs were more frequently present in adenocarcinoma than in squamous cell carcinoma (P = 0.009), and LiT was more common in the upper lobe of the lung compared with other lobes (P = 0.020). In univariate analysis, the presence of TiM, heCIC density, TNM stage, T stage, and N stage showed association with the overall survival (OS) of NSCLC patients. Multivariate analysis revealed that heCICs (HR = 2.6, 95% CI 1.25-5.6) and lymph node invasion (HR = 2.6, 95% CI 1.33-5.1) were independent factors associated with the OS of NSCLC. Taken together, we profiled the CIC subtypes in NSCLC for the first time and demonstrated the prognostic value of heCICs, which may serve as a type of novel functional markers along with classical pathological factors in improving prognosis prediction for patients with NSCLC.

Published
2023

211. An Eye Tracking and Brain–Computer Interface-Based Human–Environment Interactive System for Amyotrophic Lateral Sclerosis Patients

Author

Wang, Jiaqi, Xu, Shuoyan, Dai, Yanning, and Gao, Shuo

Abstract

Amyotrophic lateral sclerosis (ALS) patients suffer a great inconvenience in their daily lives due to the gradual loss of their motion abilities. In order to help ALS patients regain their self-care abilities at a certain level, an eye-tracking and brain–computer interface (BCI)-based human–environment interactive (HEI) system is proposed. Through a single-channel EEG recorder and a pair of eye-tracking glasses, the user’s attention levels and gaze points are detected. Based on the two types of intention information, the target objects are recognized by a You Only Look Once v5 (YOLOv5) model. Then, according to voice instructions, users can control the household electric appliances. The system performed a high success rate of 89.3% experimentally, providing a reliable and capable assistive solution for ALS patients.

Published
2023
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212. Piezoelectric and Machine Learning Based Keystroke Dynamics for Highly Secure User Authentication

Author

Tang, Chenyu, Cui, Ziang, Chu, Meng, Lu, Yujiao, Zhou, Fuqiang, and Gao, Shuo

Abstract

Cyber security is of significance in today’s e-commerce applications. In this article, we present a piezoelectric touch sensing supported keystroke dynamics based identity authentication technique, for providing a secure access manner to smartphones. Here, the polyvinylidene fluoride (PVDF) based piezoelectric touch panel can learn detailed force touch habits of users. With a support vector machine (SVM) algorithm, our proposed frequency domain features experimentally demonstrate a better authentication accuracy of 98.3%, compared to the traditional time domain features. The work showcases a feasible method of combining functional materials and artificial intelligence (AI) for satisfying highly secure requirements.

Published
2023
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213. A Piezoresistive Array-Based Force Sensing Technique for Sleeping Posture and Respiratory Rate Detection for SAS Patients

Author

Wang, Zihao, Sui, Zhipeng, Wang, Renrui, Zhang, Aojie, Zhang, Zhao, Lin, Feng, Chen, Junliang, and Gao, Shuo

Abstract

Sleep apnea syndrome (SAS) is a high incidence respiratory disorder disease with potential mortality risk. Thus, researchers continuously present diverse techniques to monitor sleeping posture and respiratory rate (RR) which are closely correlated to the occurrence of SAS. Current techniques can implement high detection accuracy in both sleeping posture and RR, nevertheless, some undesired attributes still remain, such as high device cost, personal privacy leakage, and heavy burden on hardware. To satisfy the expectations, in this paper, we develop an integrated sleep monitoring system, based on flexible piezoresistive architectures and machine learning algorithms, in a low-cost and privacy protection manner. The experimental results successfully demonstrate the feasibility of the proposed technique, by showing a high sleeping posture classification accuracy of 98.1% and RR estimation accuracy of 97.5%, indicating a strong potential in practical utilization for SAS patients.

Published
2023
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214. An Inertial Measurement Units and Electromyogram Based Control and Calibration Technique for Reducing Fatigue Issues in Teleoperations

Author

Duan, Xikai, Zhang, Yutong, Zhang, Ailing, and Gao, Shuo

Abstract

Teleoperation has been intensively used in diverse application scenarios in recent years. However, the frequently reported fatigue issue limits its successful use in tasks requiring long term and precise operations. One of key factors giving rise to the fatigue issue is the posture misalignment between the user’s arm and the robotic arm. Although conventional computer vision (CV) based solutions can implement postures alignment, they introduce potential privacy leakage problems at the same time. To address this issue, in this article we present an inertial measurement unit based technique for learning the posture status of the user’s arm and the robotic arm. Through a smart mapping algorithm, the proposed technique is proved to be a feasible manner to synchronize the two postures, potentially reducing the mental fatigue and preventing the privacy leakage possibilities.

Published
2023
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223. Memristor‐Based Intelligent Human‐Like Neural Computing

Author

Wang, Shengbo, primary, Song, Lekai, additional, Chen, Wenbin, additional, Wang, Guanyu, additional, Hao, En, additional, Li, Cong, additional, Hu, Yuhan, additional, Pan, Yu, additional, Nathan, Arokia, additional, Hu, Guohua, additional, and Gao, Shuo, additional

Published
2022
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243. Calycosin prevents bone loss induced by hindlimb unloading

Author

Jin, Xiang, primary, Wang, Hong, additional, Liang, Xuechao, additional, Ru, Kang, additional, Deng, Xiaoni, additional, Gao, Shuo, additional, Qiu, Wuxia, additional, Huai, Ying, additional, Zhang, Jiaqi, additional, Lai, Linbin, additional, Li, Fan, additional, Miao, Zhiping, additional, Zhang, Wenjuan, additional, and Qian, Airong, additional

Published
2022
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246. A New Structure of Cooling Wall Tube for Supercritical CO2 Coal-Fired Power Plants.

Author

Wang, Yanjuan, Gao, Shuo, Jiang, Qiongqiong, Li, Yi, Liu, Qibin, and Xu, Jinliang

Abstract

In terms of developing supercritical CO2 (sCO2) coal-fired power plants, enhancing cooling wall performance is one of significant factors to improve system performance. In this paper, a new cooling wall tube structure is proposed to match the non-uniform heat flux (NUH) with the thermal resistance by changing the cooling wall tube eccentricity. A three-dimensional multi-physical coupling model of cooling wall is constructed to compare the novel structure to the conventional structures. The properties of fluid dynamics, thermal stress, coupled heat transfer and cooling wall deformation are analyzed. In contrast to the traditional structure, the maximum temperature and circumferential temperature difference (CTD) of the proposed structure can be reduced by 2% and 27.4%, respectively. The essential working parameters related to the performances of the cooling wall tube are discussed. The maximum temperature of the new structure is reduced by 8–13 K and the maximum thermal stress is reduced by about 10%–15% under all the simulated working conditions when the eccentricity changes from 0 to 0.2. The proposed structure can effectively reduce the maximum temperature and circumferential temperature gradient under NUH. Consequently, a novel insight is put out for the design and optimization of the cooling wall tube in coal-fired power plants. [ABSTRACT FROM AUTHOR]

Published
2023
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247. News Image-Text Matching With News Knowledge Graph

Author

Liu Limin, Yun Jing, Zhao Yumeng, and Gao Shuo

Subjects
Information retrieval, General Computer Science, Matching (graph theory), Computer science, InformationSystems_INFORMATIONSTORAGEANDRETRIEVAL, Knowledge engineering, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, General Engineering, Construct (python library), Image-text matching, Semantics, TK1-9971, Image (mathematics), Visualization, Consistency (database systems), indirect relations of named entities, General Materials Science, Electrical engineering. Electronics. Nuclear engineering, Electrical and Electronic Engineering, named entity, Semantic gap
Abstract

Image-text matching using the image caption method has made a great progress. However, there are many named entities in news text, and existing approaches are unable to directly generate named entities in the news image caption. It leads to a semantic gap between text and news image caption. Moreover, the existing methods lack the analysis of indirect relations between named entities. Therefore those approaches easily leads to relations error when generating news image caption. To generate the news image caption with named entities by analyzing the indirect relations between named entities. We propose a novel model. In details, we propose the TopNews dataset with related news, which aims to construct the relations between named entities as widely as possible. Then we develop the news knowledge graph by extracting named entities from TopNews dataset. Furthermore, we propose News Knowledge Driven Graph Neural Network (NKD-GNN). We utilize NKD-GNN to analyzing the whole relations of entities in news knowledge graph. In this way, we generate the news image caption with named entities. The results of extensive experiments based on TopNews dataset and common dataset demonstrate that our approach is effective in detecting the consistency of news images and text.

Published
2021

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