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Your search keyword '"Huang, Chan"' showing total 400 results
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400 results on '"Huang, Chan"'

1. Superfast and sub-wavelength orbital rotation of plasmonic particles in focused Gaussian beams

Author

Zhou, Lei-Ming, Zhu, Xiaoyu, Zheng, Yu, Wang, Long, Huang, Chan, Jiang, Xiaoyun, Shi, Yuzhi, Sun, Fang-Wen, and Hu, Jigang

Subjects
Physics - Optics
Abstract

The use of nanophotonics for optical manipulation has continuously attracted interest in both fundamental research and practical applications, due to its significantly enhanced capabilities at the nanoscale. In this work, we showed that plasmonic particles can be trapped at off-axis location in Gaussian beams assisted by surface plasmon resonance. The off-axis displacement can be tuned at the sub-wavelength scale by the incident light beams. Based on these, we propose that a superfast orbital rotation of particles in continuous-wave laser beam can be realized in tightly focused circularly polarized Gaussian beams. The rotation has a tunable orbital radius at the sub-wavelength scale and a superfast rotation speed (more than 10^4 r/s in water under common laboratory conditions). Our work will aid in the development of optically driven nanomachines, and find applications in micro/nano-rheology, micro-fluid mechanics, and biological research at the nanoscale., Comment: 5 pages, 5 figures

Published
2023
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20. Air Combat Target Threat Assessment Method on Belief Function Theory

Author

Xu, Guojing, Wang, Shiyu, Jiang, Wen, Deng, Xinyang, Huang, Chan, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martin, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, and Zhang, Xinguo, editor

Published
2019
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25. Impacts of illness perception and coping style on social participation in patients receiving surgery for Stanford type A aortic dissection.

Author

LIU Bing, CHEN Hanxi, LIU Wenji, HUANG Yingwen, WANG Yan, HUANG Chan, and YOU Tianhui

Subjects
SOCIAL participation, AORTIC dissection, CONVENIENCE sampling (Statistics), DISSECTION, MEDICAL personnel, SOCIAL perception
Abstract

Objective To explore the impacts of illness perception and coping style on social participation in patients receiving surgery for Stanford type A aortic dissection, so as to provide reference for making intervention measures. Methods A total of 214 patients with Stanford type A aortic dissection who had received surgical treatment were selected by convenience sampling. Brief Illness Perception Questionnaire (BIPQ), Medical Coping Modes Questionnaire (MCMQ), and Impact on Participation and Autonomy questionnaire (IPA) were used for survey. Bootstrapping was used to analyze the association of social participation with illness perception and coping style. Results For the patients, IPA score was (51.74 ± 16.50) points, BIPQ score was (49.74 ± 12.61), and the subscales scores of correspondence to confrontation, avoidance, and acceptance-resignation were (22.87 ± 5.18), (14.96 ± 3.05) and (10.80 ± 3.93), respectively. Illness perception was positively correlated with social participation (P < 0.01), confrontation was negatively correlated with illness perception and social participation (P < 0.01), surrender was positively correlated with illness perception and social participation (P < 0.01). confrontation and acceptance-resignation play a parallel mediating role between illness perception and social participation (P < 0.01), accounting for 29.65% and 16.05% of the total effect, respectively. Conclusions Social participation is at a medium and below level in the patients receiving surgery for Stanford type A aortic dissection, coping style is intermediary variable for illness perception and social participation. By increasing the positive illness perception in the patients, medical staff can instruct them to cope actively with their disease and to enhance their social participation. [ABSTRACT FROM AUTHOR]

Published
2024
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27. Targeted demethylation of the CDO1 promoter based on CRISPR system inhibits the malignant potential of breast cancer cells

Author

Yang, Jiaojiao, primary, Sun, Liyue, additional, Liu, Xiao‐Yun, additional, Huang, Chan, additional, Peng, Junling, additional, Zeng, Xinxin, additional, Zheng, Hailin, additional, Cen, Wenjian, additional, Xu, Yu‐Xia, additional, Zhu, Weijie, additional, Wu, Xiao‐Yan, additional, Ling, Dongyi, additional, Zhang, Lu‐Lu, additional, Wei, Mingbiao, additional, Liu, Ye, additional, Wang, Deshen, additional, Wang, Feng‐Hua, additional, Li, Yu‐Hong, additional, Li, Qin, additional, and Du, Ziming, additional

Published
2023
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30. NEK2 Promotes Hepatoma Metastasis and Serves as Biomarker for High Recurrence Risk after Hepatic Resection

Author

Yu-Ying Chang, Chia-Jui Yen, Shih-Huang Chan, Yi-Wen Chou, Yun-Ping Lee, Ching-Yu Bao, Chien-Jung Huang, and Wenya Huang

Subjects
Hepatocellular carcinoma, Cancer progression, Cell cycle, Invasion, Hepatitis virus, Specialties of internal medicine, RC581-951
Abstract

Introduction and aim. Developing reliable biomarkers for hepatocellular carcinoma (HCC) patients who are at a high risk of recurrence after curative hepatic resection is very important for determining subsequent therapeutic strategies. We investigated the role of the cell cycle factor NIMA-related kinase 2 (NEK2) in HCC progression in hepatoma cells and post-surgery patients.Material and methods. The effects of NEK2 on proliferation, invasion and migration of hepatoma HuH7 and SK-Hep1 cells were evaluated. In a post-surgery HCC cohort (N = 97), the Nek2 induction levels in the tumors were examined with real-time RT-PCR analysis, and the results were analyzed for their correlations with recurrence.Results. NEK2 promoted G1 to S phase cell cycle progression by causing increases in cyclin D1 and AKT phosphorylation and decreases in the cyclin-dependent kinase inhibitor p27, indicating that NEK2 plays an important role during interphase in addition to its previously identified role in M phase. NEK2 also enhanced the proliferation, migration and invasion of hepatoma cells and regulated the expression of E-cadherin and MMP9. The Nek2 mRNA levels in the tumors were highly correlated with recurrence rates in the post-surgery HCC patients. Combined evaluation of the tumor AJCC stage and the Nek2level can serve as a reliable method for predicting the relative risk of HCC recurrence in these patients. Conclusions. NEK2 plays a significant role in cell cycle progression in the inter- and M-phases. NEK2 enhances HCC metastasis and is correlated with recurrence and thus can potentially serve a promising high-risk biomarker for HCC.

Published
2018
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38. Adaptive Operator-Based Spectral Deconvolution With the Levenberg-Marquardt Algorithm

Author

Huang, Chan, Chen, Feinan, Chang, Yuyang, Han, Lin, Li, Shuang, and Hong, Jin

Abstract

Spectral distortion often occurs in spectral data due to the influence of the bandpass function of the spectrometer. Spectral deconvolution is an effective restoration method to solve this problem. Based on the theory of the maximum posteriori estimation, this paper transforms the spectral deconvolution problem into a multi-parameter optimization problem, and a novel spectral deconvolution method is proposed on the basis of Levenberg-Marquardt algorithm. Furthermore, a spectral adaptive operator is added to the method, which improves the effect of the regularization term. The proposed methods, Richardson-Lucy (R-L) method and Huber-Markov spectroscopic semi-blind deconvolution (HMSBD) method, are employed to deconvolute the white light-emitting diode (LED) spectra with two different color temperatures, respectively. The correction errors, root mean square errors, noise suppression ability, and the computation speed of above methods are compared. The experimental results prove the superiority of the proposed algorithm.

Published
2024
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44. Predicting anaplastic lymphoma kinase rearrangement status in patients with non-small cell lung cancer using a machine learning algorithm that combines clinical features and CT images

Author

Hao, Peng, Deng, Bo-Yu, Huang, Chan-Tao, Xu, Jun, Zhou, Fang, Liu, Zhe-Xing, Zhou, Wu, and Xu, Yi-Kai

Subjects
Cancer Research, Oncology
Abstract

PurposeTo develop an appropriate machine learning model for predicting anaplastic lymphoma kinase (ALK) rearrangement status in non-small cell lung cancer (NSCLC) patients using computed tomography (CT) images and clinical features.Method and materialsThis study included 193 patients with NSCLC (154 in the training cohort, 39 in the validation cohort), 68 of whom tested positive for ALK rearrangements and 125 of whom tested negative. From the nonenhanced CT scans, 157 radiomic characteristics were extracted, and 8 clinical features were collected. Five machine learning (ML) models were assessed to find the best classification model for predicting ALK rearrangement status. A radiomic signature was developed using the least absolute shrinkage and selection operator (LASSO) algorithm. The predictive performance of the models based on radiomic features, clinical features, and their combination was assessed by receiver operating characteristic (ROC) curves.ResultsThe support vector machine (SVM) model had the highest AUC of 0.914 for classification. The clinical features model had an AUC=0.805 (95% CI 0.731–0.877) and an AUC=0.735 (95% CI 0.566–0.863) in the training and validation cohorts, respectively. The CT image-based ML model had an AUC=0.953 (95% CI 0.913–1.0) in the training cohort and an AUC=0.890 (95% CI 0.778–0.971) in the validation cohort. For predicting ALK rearrangement status, the ML model based on CT images and clinical features performed better than the model based on only clinical information or CT images, with an AUC of 0.965 (95% CI 0.826–0.882) in the primary cohort and an AUC of 0.914 (95% CI 0.804–0.893) in the validation cohort.ConclusionOur findings revealed that ALK rearrangement status could be accurately predicted using an ML-based classification model based on CT images and clinical data.

Published
2022

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