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136 results on '"Zhen-Hua Feng"'

1. EIF4A3-induced circular RNA PRKAR1B promotes osteosarcoma progression by miR-361-3p-mediated induction of FZD4 expression

Author

Zhen-hua Feng, Lin Zheng, Teng Yao, Si-yue Tao, Xiao-an Wei, Ze-yu Zheng, Bing-jie Zheng, Xu-yang Zhang, Bao Huang, Jun-hui Liu, Yi-lei Chen, Zhi Shan, Pu-tao Yuan, Cheng-gui Wang, Jian Chen, Shu-ying Shen, and Feng-dong Zhao

Subjects
Cytology, QH573-671
Abstract

Abstract Emerging evidence indicates that circRNAs are broadly expressed in osteosarcoma (OS) cells and play a crucial role in OS progression. Recently, cancer-specific circRNA circPRKAR1B has been identified by high-throughput sequencing and is recorded in publicly available databases. Nevertheless, the detailed functions and underlying mechanisms of circPRKAR1B in OS remains poorly understood. By functional experiments, we found that circPRKAR1B enhanced OS cell proliferation, migration, and promotes OS epithelial–mesenchymal transition (EMT). Mechanistic investigations suggested that circPRKAR1B promotes OS progression through sponging miR-361-3p to modulate the expression of FZD4. Subsequently, we identified that EIF4A3 promoted cirPRKAR1B formation through binding to the downstream target of circPRKAR1B on PRKAR1B mRNA. Further rescue study revealed that overexpression of the Wnt signalling could impair the onco-suppressor activities of the silencing of circPRKAR1B. Interestingly, further experiments indicated that circPRKAR1B is involved in the sensitivity of chemoresistance in OS. On the whole, our results demonstrated that circPRKAR1B exerted oncogenic roles in OS and suggested the circPRKAR1B/miR-361-3p/FZD4 axis plays an important role in OS progression and might be a potential therapeutic target.

Published
2021
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2. Urolithin A Promotes Angiogenesis and Tissue Regeneration in a Full-Thickness Cutaneous Wound Model

Author

Zhen-hua Feng, Jia Chen, Pu-tao Yuan, Zhong-yin Ji, Si-yue Tao, Lin Zheng, Xiao-an Wei, Ze-yu Zheng, Bing-jie Zheng, Bin Chen, Jian Chen, and Feng-dong Zhao

Subjects
urolithin A, wound healing, PI3K/AKT pathway, angiogenesis, docking, Therapeutics. Pharmacology, RM1-950
Abstract

The treatment of chronic wound is an important topic of current clinical issue. Neovascularization plays a crucial role in skin wound healing by delivering fresh nutrients and oxygen to the wound area. The aim of this study was to investigate the mechanisms of urolithin A (UA) in angiogenesis during wound healing. The results of in vitro experiments showed that treatment with UA (5–20 μM) promoted the proliferation, migration, and angiogenic capacity of HUVECs. Furthermore, we investigated the effect of UA in vivo using a full-thickness skin wound model. Subsequently, we found that UA promoted the regeneration of new blood vessels, which is consistent with the results of accelerated angiogenesis in vitro experiments. After UA treatment, the blood vessels in the wound are rapidly formed, and the deposition and remodeling process of the collagen matrix is also accelerated, which ultimately promotes the effective wound healing. Mechanistic studies have shown that UA promotes angiogenesis by inhibiting the PI3K/AKT pathway. Our study provides evidence that UA can promote angiogenesis and skin regeneration in chronic wounds, especially ischemic wounds.

Published
2022
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3. Analysis of the refractive status in patients with age-related cataract and shallow anterior chamber after phacoemulsification

Author

Juan Sun, Zhen-Hua Feng, and Hui Xu

Subjects
age-related cataract, shallow anterior chamber, phacoemulsification, refraction, anterior chamber depth, Ophthalmology, RE1-994
Abstract

AIM: To explore the characteristics of refractive changes in patients with age-related cataract and shallow anterior chamber after phacoemulsification.METHODS: A retrospective study was conducted. 96 patients(112 affected eyes)with age-related cataract who were admitted to the hospital between March 2018 and June 2019 were divided into the shallow anterior chamber group(ACD≤2.20mm, 52 patients with 62 affected eyes)and the control group(ACD between 2.21 and 2.90 mm, 44 cases with 50 affected eyes)according to the anterior chamber depth(ACD). All subjects were treated with phacoemulsification. The diopter, ACD and anterior chamber volume(ACV)were measured before surgery, at 1d and 3mo after surgery. Pearson linear correlation analysis was performed to analyze the relationship between the refractive status and anterior chamber parameters in patients with age-related cataract and shallow anterior chamber after phacoemulsification.RESULTS: The diopter error in the shallow anterior chamber group was higher than that in the control group at 3mo after surgery(PPP>0.05). In the shallow anterior chamber group, the diopter error at 3mo after surgery was negatively correlated with preoperative ACD(PPP>0.05).CONCLUSION: There is hyperopic shift in patients with age-related cataract and shallow anterior chamber after phacoemulsification. The degree of hyperopic shift is negatively correlated with preoperative ACD and positively correlated with postoperative ACD, namely, the smaller the preoperative ACD, the larger the postoperative ACD, the severer hyperopic shift.

Published
2020
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4. Rs145204276 and rs4759314 affect the prognosis of prostate cancer by modulating the GAS5/miR-1284/HMGB1 and HOTAIR/miR-22/HMGB1 signalling pathways

Author

Zhi-Hai Deng, Gan-Shen Yu, Bin Pan, Zhen-Hua Feng, Qiang Huang, Jian-Zhong Deng, Bo Chen, and Shi-Kun Yang

Subjects
Prostate cancer, rs145204276, rs4759314, miR-1284, miR-22, GAS5, Biotechnology, TP248.13-248.65, Medical technology, R855-855.5
Abstract

Non-coding RNAs play an important role in the pathogenesis of prostate cancer (PC). This study aims to characterize the role of GAS5 rs145204276 and HOTAIR rs4759314 polymorphisms in the pathogenesis of PC. Both INS allele of GAS5 rs145204276 and A allele of HOTAIR rs4759314 were identified to increase the survival of PC patients. And patients carrying DEL/DEL + AG genotypes tend to present higher levels of HMGB1, GAS5, HOTAIR and lower levels of miR-1284 and miR-22. In addition, the transcription activity of GAS5 promoter was increased by the deletion allele of rs145204276 polymorphism, while the G allele of rs4759314 polymorphism increased the transcription activity of HOTAIR promoter. GAS5 and HOTAIR could bind to miR-1284 and miR-22, respectively, while miR-1284 and miR-22 could bind to the 3′UTR of HMGB1. Compared with the control group, the expressions of miR-1284 or miR-22 were decreased with the presence of GAS5 or HOTAIR, and the expression of HMGB1 was the highest in the GAS5 + HOTAIR group. In summary, the findings of this study demonstrated that both GAS5 rs145204276 and HOTAIR rs4759314 polymorphisms could affect the prognosis of PC by modulating the expression of HMGB1 via modulating the GAS5/miR-1284/HMGB1 and HOTAIR/miR-22/HMGB1 signalling pathways.

Published
2020
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5. An enhanced recovery after surgery program in orthopedic surgery: a systematic review and meta-analysis

Author

Zhi-Chao Hu, Lin-Jie He, Dong Chen, Xiao-Bin Li, Zhen-Hua Feng, Cheng-Wei Fu, Jiang-Wei Xuan, Wen-Fei Ni, and Ai-Min Wu

Subjects
Enhanced recovery after surgery, Orthopedic surgery, Meta-analysis, RD701-811, Diseases of the musculoskeletal system, RC925-935
Abstract

Abstract Objectives There is an increased interest in enhanced recovery after surgery (ERAS) minimizing adverse events after orthopedic surgery. Little consensus supports the effectiveness of these interventions. The purpose of present systematic review and meta-analysis is to comprehensively analyze and evaluate the significance of ERAS interventions for postoperative outcomes after orthopedic surgery. Methods PubMed, EMBASE, and Cochrane databases were totally searched from the inception dates to May 31, 2018. Two reviewers independently extracted the data from the selected articles using a standardized form and assessed the risk of bias. The analysis was performed using STATA 12.0. Results A total of 15 published studies fulfilled the requirements of inclusion criteria. We found that the ERAS group showed a significant association with lower incidence of postoperative complications (OR, 0.70; 95% CI, 0.64 to 0.78). Meanwhile, ERAS was also associated with the decline in 30-day mortality rate and Oswestry Disability Index (ODI). However, no significant differences were identified between the two groups regarding the 30-day readmission rate (P = 0.397). Conclusions Our meta-analysis suggested that the ERAS group had more advantages in reducing incidence of postoperative complications, 30-day mortality rate, and ODI after orthopedic surgery, but not of 30-day readmission rate. However, further research with standardized, unbiased methods and larger sample sizes is required for deeper analysis.

Published
2019
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6. Hsa_circ_0088233 Alleviates Proliferation, Migration, and Invasion of Prostate Cancer by Targeting hsa-miR-185-3p

Author

Zhi-Hai Deng, Gan-Shen Yu, Ke-Lei Deng, Zhen-Hua Feng, Qiang Huang, Bin Pan, and Jian-Zhong Deng

Subjects
circRNA, competitive endogenous RNA, invasion, migration, proliferation, prostate cancer, Biology (General), QH301-705.5
Abstract

Prostate cancer is the most common malignant tumor of the urinary system. The mechanisms of the initiation and progression of prostate cancer have not been fully elucidated. Increasing evidence suggests that circular RNAs (circRNAs) are involved in cancer pathogenesis. In this study, we aimed to identify differentially expressed circRNAs in prostate cancer tissues and explored the role of circRNAs in the pathogenesis of prostate cancer. By screening a circRNA microarray assay, we found that circ_0088233 was upregulated in prostate cancer tissues compared to adjacent normal tissues, and this upregulation can be verified in 46 pairs of prostate cancer and adjacent normal tissues examined using quantitative reverse transcription-PCR. The level of circ_0088233 correlated with the TNM stage. Knockdown of circ_0088233 reduced cell proliferation, migration, invasion, and induced G1 phase arrest and apoptosis. In addition, miR-185-3p was identified as the downstream target of circ_0088233 using luciferase reporter assays and a biotinylated circ_0088233 probe pull-down assay. The miR-185-3p level showed a negative correlation with the circ_0088233 level in prostate cancer tissues. Overexpression of circ_0088233 blocked the effects of miR-185-3p on cell proliferation, migration, invasion, cell cycle, and apoptosis. In conclusion, circ_0088233 may function as an oncogene and play an oncogenic role by sponging hsa-miR-185-3p. This study increases the understanding of circRNAs in the progression of prostate cancer. These results implicate circ_0088233 as a potential therapeutic target for prostate cancer.

Published
2020
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7. An Improved AAM Method for Extracting Human Facial Features

Author

Tao Zhou, Xiao-Jun Wu, Tao Wu, and Zhen-Hua Feng

Subjects
Mathematics, QA1-939
Abstract

Active appearance model is a statistically parametrical model, which is widely used to extract human facial features and recognition. However, intensity values used in original AAM cannot provide enough information for image texture, which will lead to a larger error or a failure fitting of AAM. In order to overcome these defects and improve the fitting performance of AAM model, an improved texture representation is proposed in this paper. Firstly, translation invariant wavelet transform is performed on face images and then image structure is represented using the measure which is obtained by fusing the low-frequency coefficients with edge intensity. Experimental results show that the improved algorithm can increase the accuracy of the AAM fitting and express more information for structures of edge and texture.

Published
2012
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9. The Tenth Visual Object Tracking VOT2022 Challenge Results.

Author

Matej Kristan, Ales Leonardis, Jirí Matas, Michael Felsberg, Roman P. Pflugfelder, Joni-Kristian Kämäräinen, Hyung Jin Chang, Martin Danelljan, Luka Cehovin Zajc, Alan Lukezic, Ondrej Drbohlav, Johanna Björklund, Yushan Zhang, Zhongqun Zhang, Song Yan, Wenyan Yang, Dingding Cai, Christoph Mayer 0007, Gustavo Fernández, Kang Ben, Goutam Bhat, Hong Chang 0001, Guangqi Chen, Jiaye Chen, Shengyong Chen, Xilin Chen 0001, Xin Chen 0032, Xiuyi Chen, Yiwei Chen, Yu-Hsi Chen, Zhixing Chen, Yangming Cheng, Angelo Ciaramella, Yutao Cui, Benjamin Dzubur, Mohana Murali Dasari, Qili Deng, Debajyoti Dhar, Shangzhe Di, Emanuel Di Nardo, Daniel K. Du, Matteo Dunnhofer, Heng Fan 0001, Zhen-Hua Feng, Zhihong Fu, Shang Gao, Rama Krishna Gorthi, Eric Granger, Q. H. Gu, Himanshu Gupta, Jianfeng He, Keji He, Yan Huang 0008, Deepak Jangid, Rongrong Ji, Cheng Jiang 0005, Yingjie Jiang, Felix Järemo Lawin, Ze Kang, Madhu Kiran, Josef Kittler, Simiao Lai, Xiangyuan Lan, Dongwook Lee, Hyunjeong Lee, Seohyung Lee, Hui Li 0037, Ming Li 0010, Wangkai Li, Xi Li 0001, Xianxian Li, Xiao Li, Zhe Li 0008, Liting Lin, Haibin Ling, Bo Liu 0005, Chang Liu 0071, Si Liu 0001, Huchuan Lu, Rafael M. O. Cruz, Bingpeng Ma, Chao Ma 0004, Jie Ma, Yinchao Ma, Niki Martinel, Alireza Memarmoghadam, Christian Micheloni, Payman Moallem, Le Thanh Nguyen-Meidine, Siyang Pan, ChangBeom Park, Danda Pani Paudel, Matthieu Paul, Houwen Peng, Andreas Robinson, Litu Rout, Shiguang Shan, Kristian Simonato, Tianhui Song, Xiaoning Song, Chao Sun, Jingna Sun, Zhangyong Tang, Radu Timofte, Chi-Yi Tsai, Luc Van Gool, Om Prakash Verma, Dong Wang 0004, Fei Wang 0032, Liang Wang 0001, Liangliang Wang, Lijun Wang, Limin Wang 0002, Qiang Wang 0023, Gangshan Wu, Jinlin Wu, Xiaojun Wu 0001, Fei Xie, Tianyang Xu, Wei Xu 0017, Yong Xu 0007, Yuanyou Xu, Wanli Xue, Zizheng Xun, Bin Yan 0004, Dawei Yang, Jinyu Yang, Wankou Yang, Xiaoyun Yang, Yi Yang 0001, Yichun Yang, Zongxin Yang, Botao Ye, Fisher Yu 0001, Hongyuan Yu, Jiaqian Yu, Qianjin Yu, Weichen Yu, Kang Ze, Jiang Zhai, Chengwei Zhang 0001, Chunhu Zhang, Kaihua Zhang, Tianzhu Zhang, Wenkang Zhang, Zhibin Zhang, Zhipeng Zhang, Jie Zhao 0014, Shao-Chuan Zhao, Feng Zheng, Haixia Zheng, Min Zheng, Bineng Zhong, Jiawen Zhu, Xuefeng Zhu, and Yueting Zhuang

Published
2022
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11. The Ninth Visual Object Tracking VOT2021 Challenge Results.

Author

Matej Kristan, Jirí Matas, Ales Leonardis, Michael Felsberg, Roman P. Pflugfelder, Joni-Kristian Kämäräinen, Hyung Jin Chang, Martin Danelljan, Luka Cehovin Zajc, Alan Lukezic, Ondrej Drbohlav, Jani Käpylä, Gustav Häger, Song Yan, Jinyu Yang, Zhongqun Zhang, Gustavo Fernández, Mohamed H. Abdelpakey, Goutam Bhat, Llukman Cerkezi, Hakan Cevikalp, Shengyong Chen, Xin Chen 0032, Miao Cheng, Ziyi Cheng, Yu-Chen Chiu, Ozgun Cirakman, Yutao Cui, Kenan Dai, Mohana Murali Dasari, Qili Deng, Xingping Dong, Daniel K. Du, Matteo Dunnhofer, Zhen-Hua Feng, Zhiyong Feng 0001, Zhihong Fu, Shiming Ge, Rama Krishna Gorthi, Yuzhang Gu, Bilge Günsel, Qing Guo 0005, Filiz Gurkan, Wencheng Han, Yanyan Huang, Felix Järemo Lawin, Shang-Jhih Jhang, Rongrong Ji, Cheng Jiang 0005, Yingjie Jiang, Felix Juefei-Xu, J. Yin, Xiao Ke, Fahad Shahbaz Khan, Byeong Hak Kim, Josef Kittler, Xiangyuan Lan, Jun Ha Lee, Bastian Leibe, Hui Li 0037, Jianhua Li, Xianxian Li, Yuezhou Li, Bo Liu 0005, Chang Liu 0071, Jingen Liu, Li Liu 0036, Qingjie Liu, Huchuan Lu, Wei Lu, Jonathon Luiten, Jie Ma, Ziang Ma, Niki Martinel, Christoph Mayer 0007, Alireza Memarmoghadam, Christian Micheloni, Yuzhen Niu, Danda Pani Paudel, Houwen Peng, Shoumeng Qiu, Aravindh Rajiv, Muhammad Rana, Andreas Robinson, Hasan Saribas, Ling Shao 0001, Mohamed S. Shehata, Furao Shen, Jianbing Shen, Kristian Simonato, Xiaoning Song, Zhangyong Tang, Radu Timofte, Philip H. S. Torr, Chi-Yi Tsai, Bedirhan Uzun, Luc Van Gool, Paul Voigtlaender, Dong Wang 0004, Guangting Wang, Liangliang Wang, Lijun Wang, Limin Wang 0002, Linyuan Wang, Yong Wang 0032, Yunhong Wang, Chenyan Wu, Gangshan Wu, Xiaojun Wu 0001, Fei Xie, Tianyang Xu, Xiang Xu, Wanli Xue, Bin Yan 0004, Wankou Yang, Xiaoyun Yang, Yu Ye 0004, Jun Yin 0003, Chengwei Zhang 0001, Chunhui Zhang, Haitao Zhang, Kaihua Zhang, Kangkai Zhang, Xiaohan Zhang, Xiaolin Zhang, Xinyu Zhang 0017, Zhibin Zhang, Shao-Chuan Zhao, Ming Zhen, Bineng Zhong, Jiawen Zhu, and Xuefeng Zhu

Published
2021
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18. The Eighth Visual Object Tracking VOT2020 Challenge Results.

Author

Matej Kristan, Ales Leonardis, Jiri Matas, Michael Felsberg, Roman P. Pflugfelder, Joni-Kristian Kämäräinen, Martin Danelljan, Luka Cehovin Zajc, Alan Lukezic, Ondrej Drbohlav, Linbo He, Yushan Zhang, Song Yan, Jinyu Yang, Gustavo Fernández, Alexander G. Hauptmann, Alireza Memarmoghadam, álvaro García-Martín, Andreas Robinson, Anton Varfolomieiev, Awet Haileslassie Gebrehiwot, Bedirhan Uzun, Bin Yan 0004, Bing Li 0001, Chen Qian 0006, Chi-Yi Tsai, Christian Micheloni, Dong Wang 0004, Fei Wang 0032, Fei Xie, Felix Järemo Lawin, Fredrik Gustafsson, Gian Luca Foresti, Goutam Bhat, Guangqi Chen, Haibin Ling, Haitao Zhang, Hakan Cevikalp, Haojie Zhao, Haoran Bai, Hari Chandana Kuchibhotla, Hasan Saribas, Heng Fan 0001, Hossein Ghanei-Yakhdan, Houqiang Li, Houwen Peng, Huchuan Lu, Hui Li 0037, Javad Khaghani, Jesús Bescós, Jianhua Li, Jianlong Fu, Jiaqian Yu, Jingtao Xu, Josef Kittler, Jun Yin 0003, Junhyun Lee, Kaicheng Yu, Kaiwen Liu, Kang Yang, Kenan Dai, Li Cheng 0001, Li Zhang 0040, Lijun Wang, Linyuan Wang, Luc Van Gool, Luca Bertinetto, Matteo Dunnhofer, Miao Cheng, Mohana Murali Dasari, Ning Wang 0020, Pengyu Zhang, Philip H. S. Torr, Qiang Wang 0051, Radu Timofte, Rama Krishna Sai Subrahmanyam Gorthi, Seokeon Choi, Seyed Mojtaba Marvasti-Zadeh, Shao-Chuan Zhao, Shohreh Kasaei, Shoumeng Qiu, Shuhao Chen, Thomas B. Schön, Tianyang Xu, Wei Lu, Weiming Hu, Wengang Zhou, Xi Qiu, Xiao Ke, Xiao-Jun Wu 0001, Xiaolin Zhang, Xiaoyun Yang, Xuefeng Zhu, Yingjie Jiang, Yingming Wang, Yiwei Chen, Yu Ye 0004, Yuezhou Li, Yuncon Yao, Yunsung Lee, Yuzhang Gu, Zezhou Wang, Zhangyong Tang, Zhen-Hua Feng, Zhijun Mai, Zhipeng Zhang, Zhirong Wu, and Ziang Ma

Published
2020
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20. The Seventh Visual Object Tracking VOT2019 Challenge Results.

Author

Matej Kristan, Amanda Berg, Linyu Zheng, Litu Rout, Luc Van Gool, Luca Bertinetto, Martin Danelljan, Matteo Dunnhofer, Meng Ni, Min Young Kim, Ming Tang 0001, Ming-Hsuan Yang 0001, Abdelrahman Eldesokey, Naveen Paluru, Niki Martinel, Pengfei Xu 0013, Pengfei Zhang 0005, Pengkun Zheng, Pengyu Zhang, Philip H. S. Torr, Qi Zhang, Qiang Wang 0051, Qing Guo 0005, Radu Timofte, Jani Käpylä, Rama Krishna Sai Subrahmanyam Gorthi, Richard M. Everson, Ruize Han, Ruohan Zhang, Shan You, Shao-Chuan Zhao, Shengwei Zhao, Shihu Li, Shikun Li, Shiming Ge, Gustavo Fernández, Shuai Bai, Shuosen Guan, Tengfei Xing, Tianyang Xu, Tianyu Yang 0003, Ting Zhang 0006, Tomás Vojír, Wei Feng 0005, Weiming Hu, Weizhao Wang, Abel Gonzalez-Garcia, Wenjie Tang, Wenjun Zeng, Wenyu Liu 0001, Xi Chen, Xi Qiu, Xiang Bai, Xiao-Jun Wu 0001, Xiaoyun Yang, Xier Chen, Xin Li 0034, Alireza Memarmoghadam, Xing Sun, Xingyu Chen, Xinmei Tian 0001, Xu Tang, Xuefeng Zhu, Yan Huang 0008, Yanan Chen, Yanchao Lian, Yang Gu, Yang Liu 0003, Andong Lu, Yanjie Chen, Yi Zhang, Yinda Xu, Yingming Wang, Yingping Li, Yu Zhou 0016, Yuan Dong, Yufei Xu, Yunhua Zhang, Yunkun Li, Anfeng He, Zeyu Wang 0008, Zhao Luo, Zhaoliang Zhang, Zhen-Hua Feng, Zhenyu He 0001, Zhichao Song, Zhihao Chen 0004, Zhipeng Zhang, Zhirong Wu, Zhiwei Xiong, Zhongjian Huang, Anton Varfolomieiev, Zhu Teng, Zihan Ni, Antoni B. Chan, Jirí Matas, Ardhendu Shekhar Tripathi, Arnold W. M. Smeulders, Bala Suraj Pedasingu, Bao Xin Chen, Baopeng Zhang, Baoyuan Wu, Bi Li 0005, Bin He, Bin Yan 0004, Bing Bai, Ales Leonardis, Bing Li 0001, Bo Li 0114, Byeong Hak Kim, Chao Ma 0004, Chen Fang, Chen Qian 0001, Cheng Chen, Chenglong Li 0002, Chengquan Zhang, Chi-Yi Tsai, Michael Felsberg, Chong Luo, Christian Micheloni, Chunhui Zhang, Dacheng Tao, Deepak Gupta, Dejia Song, Dong Wang 0004, Efstratios Gavves, Eunu Yi, Fahad Shahbaz Khan, Roman P. Pflugfelder, Fangyi Zhang, Fei Wang 0032, Fei Zhao, George De Ath, Goutam Bhat, Guangqi Chen, Guangting Wang, Guoxuan Li, Hakan Cevikalp, Hao Du 0006, Joni-Kristian Kämäräinen, Haojie Zhao, Hasan Saribas, Ho Min Jung, Hongliang Bai, Hongyuan Yu, Houwen Peng, Huchuan Lu, Hui Li 0037, Jiakun Li, Luka Cehovin Zajc, Jianhua Li, Jianlong Fu, Jie Chen, Jie Gao, Jie Zhao 0014, Jin Tang 0001, Jing Li 0036, Jingjing Wu, Jingtuo Liu, Jinqiao Wang, Ondrej Drbohlav, Jinqing Qi, Jinyue Zhang, John K. Tsotsos, Jong Hyuk Lee, Joost van de Weijer 0001, Josef Kittler, Jun Ha Lee, Junfei Zhuang, Kangkai Zhang, Kangkang Wang, Alan Lukezic, Kenan Dai, Lei Chen, Lei Liu, Leida Guo, Li Zhang 0040, Liang Wang 0001, Liangliang Wang, Lichao Zhang, Lijun Wang, and Lijun Zhou

Published
2019
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21. VisDrone-SOT2019: The Vision Meets Drone Single Object Tracking Challenge Results.

Author

Dawei Du, Yue Zhang 0011, Liefeng Bo, Hailin Shi, Rui Zhu 0005, Bo Han 0003, Chunhui Zhang, Guizhong Liu, Han Wu, Hao Wen, Haoran Wang, Pengfei Zhu 0001, Jiaqing Fan, Jie Chen, Jie Gao, Jie Zhang 0091, Jinghao Zhou, Jinliu Zhou, Jinwang Wang, Jiuqing Wan, Josef Kittler, Kaihua Zhang, Longyin Wen, Kaiqi Huang, Kang Yang, Kangkai Zhang, Lianghua Huang, Lijun Zhou, Lingling Shi, Lu Ding, Ning Wang 0020, Peng Wang 0015, Qintao Hu, Xiao Bian, Robert Laganière, Ruiyan Ma, Ruohan Zhang, Shanrong Zou, Shengwei Zhao, Shengyang Li, Shengyin Zhu, Shikun Li, Shiming Ge, Shiyu Xuan, Haibin Ling, Tianyang Xu, Ting He, Wei Shi, Wei Song, Weiming Hu, Wenhua Zhang, Wenjun Zhu, Xi Yu, Xianhai Wang, Xiaojun Wu 0001, Qinghua Hu, Xiaotong Li, Xiaoxue Li, Xiaoyue Yin, Xin Zhang 0008, Xin Zhao 0012, Xizhe Xue, Xu Lei, Xueyuan Yang, Yanjie Gao, Yanyun Zhao, Jiayu Zheng, Yinda Xu, Ying Li 0017, Yong Wang 0032, Yong Yang, Yuting Yang 0008, Yuxuan Li, Zeyu Wang 0008, Zhen-Hua Feng, Zhipeng Zhang, Zhiyong Yu, Tao Peng, Zhizhao Duan, Zhuojin Sun, and Xinyao Wang

Published
2019
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24. The Sixth Visual Object Tracking VOT2018 Challenge Results.

Author

Matej Kristan, Ales Leonardis, Jiri Matas, Michael Felsberg, Roman P. Pflugfelder, Luka Cehovin Zajc, Tomás Vojír, Goutam Bhat, Alan Lukezic, Abdelrahman Eldesokey, Gustavo Fernández, álvaro García-Martín, álvaro Iglesias-Arias, A. Aydin Alatan, Abel González-García, Alfredo Petrosino, Alireza Memarmoghadam, Andrea Vedaldi, Andrej Muhic, Anfeng He, Arnold W. M. Smeulders, Asanka G. Perera, Bo Li 0114, Boyu Chen, Changick Kim, Changsheng Xu, Changzhen Xiong, Cheng Tian, Chong Luo, Chong Sun, Cong Hao, Daijin Kim 0001, Deepak Mishra 0002, Deming Chen, Dong Wang 0004, Dongyoon Wee, Efstratios Gavves, Erhan Gundogdu, Erik Velasco-Salido, Fahad Shahbaz Khan, Fan Yang 0035, Fei Zhao, Feng Li 0031, Francesco Battistone, George De Ath, Gorthi R. K. Sai Subrahmanyam, Guilherme Sousa Bastos, Haibin Ling, Hamed Kiani Galoogahi, Hankyeol Lee, Haojie Li, Haojie Zhao, Heng Fan 0001, Honggang Zhang 0002, Horst Possegger, Houqiang Li, Huchuan Lu, Hui Zhi, Huiyun Li, Hyemin Lee, Hyung Jin Chang, Isabela Drummond, Jack Valmadre, Jaime Spencer Martin, Javaan Singh Chahl, Jin Young Choi 0002, Jing Li 0036, Jinqiao Wang, Jinqing Qi, Jinyoung Sung, Joakim Johnander, João F. Henriques, Jongwon Choi, Joost van de Weijer 0001, Jorge Rodríguez Herranz, José M. Martínez 0001, Josef Kittler, Junfei Zhuang, Junyu Gao 0002, Klemen Grm, Lichao Zhang, Lijun Wang, Lingxiao Yang, Litu Rout, Liu Si, Luca Bertinetto, Lutao Chu, Manqiang Che, Mario Edoardo Maresca, Martin Danelljan, Ming-Hsuan Yang 0001, Mohamed H. Abdelpakey, Mohamed S. Shehata, Myunggu Kang, Namhoon Lee, Ning Wang 0020, Ondrej Miksik, Payman Moallem, Pablo Vicente-Moñivar, Pedro Senna, Peixia Li, Philip H. S. Torr, Priya Mariam Raju, Ruihe Qian, Qiang Wang 0051, Qin Zhou, Qing Guo 0005, Rafael Martin Nieto, Rama Krishna Sai Subrahmanyam Gorthi, Ran Tao 0004, Richard Bowden, Richard M. Everson, Runling Wang, Sangdoo Yun, Seokeon Choi, Sergio Vivas, Shuai Bai, Shuangping Huang, Sihang Wu, Simon Hadfield, Siwen Wang, Stuart Golodetz, Ming Tang 0001, Tianyang Xu, Tianzhu Zhang, Tobias Fischer 0001, Vincenzo Santopietro, Vitomir Struc, Wei Wang 0335, Wangmeng Zuo, Wei Feng 0005, Wei Wu 0021, Wei Zou, Weiming Hu, Wengang Zhou, Wenjun Zeng, Xiaofan Zhang 0002, Xiaohe Wu, Xiao-Jun Wu 0001, Xinmei Tian 0001, Yan Li 0014, Yan Lu 0001, Yee Wei Law, Yi Wu 0001, Yiannis Demiris, Yicai Yang, Yifan Jiao, Yuhong Li, Yunhua Zhang, Yuxuan Sun, Zheng Zhang 0022, Zheng Zhu, Zhen-Hua Feng, Zhihui Wang 0001, and Zhiqun He

Published
2018
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35. Report on the FG 2015 Video Person Recognition Evaluation.

Author

J. Ross Beveridge, Hao Zhang 0013, Bruce A. Draper, Patrick J. Flynn, Zhen-Hua Feng, Patrik Huber 0001, Josef Kittler, Zhiwu Huang, Shaoxin Li 0001, Yan Li 0014, Meina Kan, Ruiping Wang 0001, Shiguang Shan, Xilin Chen 0001, Haoxiang Li, Gang Hua 0001, Vitomir Struc, Janez Krizaj, Changxing Ding, Dacheng Tao, and P. Jonathon Phillips

Published
2015
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46. SP-GAN: Self-Growing and Pruning Generative Adversarial Networks

Author

Xiaojun Wu, Dong-Jun Yu, Zhen-Hua Feng, Yao Chen, Guosheng Hu, and Xiaoning Song

Subjects
Source code, Computer Networks and Communications, Computer science, media_common.quotation_subject, Stability (learning theory), 02 engineering and technology, Function (mathematics), Computer Science Applications, Set (abstract data type), Artificial Intelligence, 0202 electrical engineering, electronic engineering, information engineering, Redundancy (engineering), 020201 artificial intelligence & image processing, Pruning (decision trees), Algorithm, Software, Generator (mathematics), media_common
Abstract

This article presents a new Self-growing and Pruning Generative Adversarial Network (SP-GAN) for realistic image generation. In contrast to traditional GAN models, our SP-GAN is able to dynamically adjust the size and architecture of a network in the training stage by using the proposed self-growing and pruning mechanisms. To be more specific, we first train two seed networks as the generator and discriminator; each contains a small number of convolution kernels. Such small-scale networks are much easier and faster to train than large-capacity networks. Second, in the self-growing step, we replicate the convolution kernels of each seed network to augment the scale of the network, followed by fine-tuning the augmented/expanded network. More importantly, to prevent the excessive growth of each seed network in the self-growing stage, we propose a pruning strategy that reduces the redundancy of an augmented network, yielding the optimal scale of the network. Finally, we design a new adaptive loss function that is treated as a variable loss computational process for the training of the proposed SP-GAN model. By design, the hyperparameters of the loss function can dynamically adapt to different training stages. Experimental results obtained on a set of data sets demonstrate the merits of the proposed method, especially in terms of the stability and efficiency of network training. The source code of the proposed SP-GAN method is publicly available at https://github.com/Lambert-chen/SPGAN.git .

Published
2021

47. Adaptive Channel Selection for Robust Visual Object Tracking with Discriminative Correlation Filters

Author

Josef Kittler, Zhen-Hua Feng, Xiaojun Wu, and Tianyang Xu

Subjects
Elastic net regularization, 0303 health sciences, business.industry, Computer science, Stability (learning theory), Pattern recognition, 02 engineering and technology, Active appearance model, 03 medical and health sciences, Discriminative model, Artificial Intelligence, Robustness (computer science), Feature (computer vision), Video tracking, Pattern recognition (psychology), 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Computer Vision and Pattern Recognition, Artificial intelligence, business, Software, 030304 developmental biology
Abstract

Discriminative Correlation Filters (DCF) have been shown to achieve impressive performance in visual object tracking. However, existing DCF-based trackers rely heavily on learning regularised appearance models from invariant image feature representations. To further improve the performance of DCF in accuracy and provide a parsimonious model from the attribute perspective, we propose to gauge the relevance of multi-channel features for the purpose of channel selection. This is achieved by assessing the information conveyed by the features of each channel as a group, using an adaptive group elastic net inducing independent sparsity and temporal smoothness on the DCF solution. The robustness and stability of the learned appearance model are significantly enhanced by the proposed method as the process of channel selection performs implicit spatial regularisation. We use the augmented Lagrangian method to optimise the discriminative filters efficiently. The experimental results obtained on a number of well-known benchmarking datasets demonstrate the effectiveness and stability of the proposed method. A superior performance over the state-of-the-art trackers is achieved using less than $$10\%$$ 10 % deep feature channels.

Published
2021

48. Complementary Discriminative Correlation Filters Based on Collaborative Representation for Visual Object Tracking

Author

Zhen-Hua Feng, Xue-Feng Zhu, Tianyang Xu, Josef Kittler, and Xiaojun Wu

Subjects
Boosting (machine learning), Artificial neural network, Computer science, business.industry, Feature extraction, Representation (systemics), Pattern recognition, 02 engineering and technology, Discriminative model, Feature (computer vision), Robustness (computer science), Video tracking, 0202 electrical engineering, electronic engineering, information engineering, Media Technology, 020201 artificial intelligence & image processing, Artificial intelligence, Electrical and Electronic Engineering, business
Abstract

In recent years, discriminative correlation filter (DCF) based algorithms have significantly advanced the state of the art in visual object tracking. The key to the success of DCF is an efficient discriminative regression model trained with powerful multi-cue features, including both hand-crafted and deep neural network features. However, the tracking performance is hindered by their inability to respond adequately to abrupt target appearance variations. This issue is posed by the limited representation capability of fixed image features. In this work, we set out to rectify this shortcoming by proposing a complementary representation of a visual content. Specifically, we propose the use of a collaborative representation between successive frames to extract the dynamic appearance information from a target with rapid appearance changes, which results in suppressing the undesirable impact of the background. The resulting collaborative representation coefficients are combined with the original feature maps using a spatially regularised DCF framework for performance boosting. The experimental results on several benchmarking datasets demonstrate the effectiveness and robustness of the proposed method, as compared with a number of state-of-the-art tracking algorithms.

Published
2021

49. Site-1 protease controls osteoclastogenesis by mediating LC3 transcription

Author

Zeyu Zheng, Zhi Shan, Xuyang Zhang, Jian Chen, Fengdong Zhao, Shunwu Fan, Xiaoan Wei, Hao Wu, Bao Huang, Yilei Chen, Junhui Liu, and Zhen-hua Feng

Subjects
0301 basic medicine, Osteoclasts, CHOP, Article, Bone remodeling, Mice, 03 medical and health sciences, 0302 clinical medicine, Downregulation and upregulation, Osteoclast, Lysosome, Autophagy, medicine, Animals, Humans, Molecular Biology, Transcription factor, Chemistry, ATF6, Serine Endopeptidases, Cell Differentiation, Cell Biology, Cell biology, 030104 developmental biology, medicine.anatomical_structure, 030220 oncology & carcinogenesis, lipids (amino acids, peptides, and proteins), Proprotein Convertases, Microtubule-Associated Proteins, Signal Transduction
Abstract

Site-1 protease (S1P) is a Golgi-located protein that activates unique membrane-bound latent transcription factors, and it plays an indispensable role in endoplasmic reticulum stress, lipid metabolism, inflammatory response and lysosome function. A patient with S1P mutation exhibits severe skeletal dysplasia with kyphoscoliosis, dysmorphic facial features and pectus carinatum. However, whether S1P regulates bone remodeling by affecting osteoclastogenesis remains elusive. Here, we show that S1P is indeed a positive regulator of osteoclastogenesis. S1P ablation in mice led to significant osteosclerosis compared with wild-type littermates. Mechanistically, S1P showed upregulated during osteoclastogenesis and was identified as a direct target of miR-9-5p. S1P deletion in bone marrow monocytes (BMMs) inhibited ATF6 and SREBP2 maturation, which subsequently impeded CHOP/SREBP2-complex-induced LC3 expression and autophagy flux. Consistently, transfection of LC3 adenovirus evidently rescued osteoclastogenesis in S1P-deficient BMMs. We then identified the interaction regions between CHOP and SREBP2 by Co-immunoprecipitation (Co-IP) and molecular docking. Furthermore, S1P deletion or inhibitor efficaciously rescued ovariectomized (OVX)- and LPS-induced bone loss in vivo. Collectively, we showed that S1P regulates osteoclast differentiation in a LC3 dependent manner and so is a potential therapy target for osteoporosis.

Published
2021

50. From RGB to Depth: Domain Transfer Network for Face Anti-Spoofing

Author

Zhen-Hua Feng, Xiaoning Song, Xiaojun Wu, Wang Yahang, and Tianyang Xu

Subjects
Source code, Spoofing attack, Computer Networks and Communications, business.industry, Image quality, Computer science, media_common.quotation_subject, Feature extraction, Facial recognition system, Depth map, Face (geometry), RGB color model, Computer vision, Artificial intelligence, Safety, Risk, Reliability and Quality, business, media_common
Abstract

With the rapid development in face recognition, most of the existing systems can perform very well in unconstrained scenarios. However, it is still a very challenging task to detect face spoofing attacks, thus face anti-spoofing has become one of the most important research topics in the community. Though various anti-spoofing models have been proposed, the generalisation capability of these models usually degrades for unseen attacks in the presence of challenging appearance variations, e.g. , background, illumination, diverse spoofing materials and low image quality. To address this issue, we propose to use a Generative Adversarial Network (GAN) that transfers an input face image from the RGB domain to the depth domain. The generated depth clue enables biometric preservation against challenging appearance variations and diverse image qualities. To be more specific, the proposed method has two main stages. The first one is a GAN-based domain transfer module that converts an input image to its corresponding depth map. By design, a live face image should be transferred to a depth map whereas a spoofing face image should be transferred to a plain (black) image. The aim is to improve the discriminative capability of the proposed system. The second stage is a classification model that determines whether an input face image is live or spoofing. Benefit from the use of the GAN-based domain transfer module, the latent variables can effectively represent the depth information, complementarily enhancing the discrimination of the original RGB features. The experimental results obtained on several benchmarking datasets demonstrate the effectiveness of the proposed method, with superior performance over the state-of-the-art methods. The source code of the proposed method is publicly available at https://github.com/coderwangson/DFA .

Published
2021

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