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Your search keyword '"Chen, Xiaoying"' showing total 261 results
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261 results on '"Chen, Xiaoying"'

1. Advancements in APOE and dementia research: Highlights from the 2023 AAIC Advancements: APOE conference.

Author

Kloske, Courtney, Belloy, Michael, Blue, Elizabeth, Bowman, Gregory, Carrillo, Maria, Chen, Xiaoying, Chiba-Falek, Ornit, Davis, Albert, Paolo, Gilbert, Garretti, Francesca, Gate, David, Golden, Lesley, Heinecke, Jay, Herz, Joachim, Huang, Yadong, Iadecola, Costantino, Johnson, Lance, Kanekiyo, Takahisa, Karch, Celeste, Khvorova, Anastasia, Koppes-den Hertog, Sascha, Lamb, Bruce, Lawler, Paige, Guen, Yann, Litvinchuk, Alexandra, Liu, Chia-Chen, Mahinrad, Simin, Marcora, Edoardo, Marino, Claudia, Michaelson, Danny, Miller, Justin, Morganti, Josh, Narayan, Priyanka, Naslavsky, Michel, Oosthoek, Marlies, Ramachandran, Kapil, Ramakrishnan, Abhirami, Raulin, Ana-Caroline, Robert, Aiko, Saleh, Rasha, Sexton, Claire, Shah, Nilomi, Shue, Francis, Sible, Isabel, Soranno, Andrea, Strickland, Michael, Tcw, Julia, Thierry, Manon, Tsai, Li-Huei, Tuckey, Ryan, Ulrich, Jason, van der Kant, Rik, Wang, Na, Wellington, Cheryl, Weninger, Stacie, Yassine, Hussein, Zhao, Na, Bu, Guojun, Goate, Alison, and Holtzman, David

Subjects
APOE, Alzheimers disease, apolipoprotein E, conference proceedings, dementia, lipids, microglia, neuroinflammation, risk factor, therapeutics, vasculature, Humans, Apolipoproteins E, Alzheimer Disease, Congresses as Topic, Animals, Amyloid beta-Peptides, Dementia, Biomedical Research
Abstract

INTRODUCTION: The apolipoprotein E gene (APOE) is an established central player in the pathogenesis of Alzheimers disease (AD), with distinct apoE isoforms exerting diverse effects. apoE influences not only amyloid-beta and tau pathologies but also lipid and energy metabolism, neuroinflammation, cerebral vascular health, and sex-dependent disease manifestations. Furthermore, ancestral background may significantly impact the link between APOE and AD, underscoring the need for more inclusive research. METHODS: In 2023, the Alzheimers Association convened multidisciplinary researchers at the AAIC Advancements: APOE conference to discuss various topics, including apoE isoforms and their roles in AD pathogenesis, progress in apoE-targeted therapeutic strategies, updates on disease models and interventions that modulate apoE expression and function. RESULTS: This manuscript presents highlights from the conference and provides an overview of opportunities for further research in the field. DISCUSSION: Understanding apoEs multifaceted roles in AD pathogenesis will help develop targeted interventions for AD and advance the field of AD precision medicine. HIGHLIGHTS: APOE is a central player in the pathogenesis of Alzheimers disease. APOE exerts a numerous effects throughout the brain on amyloid-beta, tau, and other pathways. The AAIC Advancements: APOE conference encouraged discussions and collaborations on understanding the role of APOE.

Published
2024

2. TFCounter:Polishing Gems for Training-Free Object Counting

Author

Ting, Pan, Lin, Jianfeng, Yu, Wenhao, Zhang, Wenlong, Chen, Xiaoying, Zhang, Jinlu, and Huang, Binqiang

Subjects
Computer Science - Computer Vision and Pattern Recognition, 68
Abstract

Object counting is a challenging task with broad application prospects in security surveillance, traffic management, and disease diagnosis. Existing object counting methods face a tri-fold challenge: achieving superior performance, maintaining high generalizability, and minimizing annotation costs. We develop a novel training-free class-agnostic object counter, TFCounter, which is prompt-context-aware via the cascade of the essential elements in large-scale foundation models. This approach employs an iterative counting framework with a dual prompt system to recognize a broader spectrum of objects varying in shape, appearance, and size. Besides, it introduces an innovative context-aware similarity module incorporating background context to enhance accuracy within messy scenes. To demonstrate cross-domain generalizability, we collect a novel counting dataset named BIKE-1000, including exclusive 1000 images of shared bicycles from Meituan. Extensive experiments on FSC-147, CARPK, and BIKE-1000 datasets demonstrate that TFCounter outperforms existing leading training-free methods and exhibits competitive results compared to trained counterparts., Comment: 14pages,11 figuers

Published
2024

14. Non-canonical functions of UHRF1 maintain DNA methylation homeostasis in cancer cells

Author

Yamaguchi, Kosuke, Chen, Xiaoying, Rodgers, Brianna, Miura, Fumihito, Bashtrykov, Pavel, Bonhomme, Frédéric, Salinas-Luypaert, Catalina, Haxholli, Deis, Gutekunst, Nicole, Aygenli, Bihter Özdemir, Ferry, Laure, Kirsh, Olivier, Laisné, Marthe, Scelfo, Andrea, Ugur, Enes, Arimondo, Paola B., Leonhardt, Heinrich, Kanemaki, Masato T., Bartke, Till, Fachinetti, Daniele, Jeltsch, Albert, Ito, Takashi, and Defossez, Pierre-Antoine

Published
2024
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34. Classification of pain expression images in elderly with hip fractures based on improved ResNet50 network

Author

Yang Shuang, Gong Liangbo, Zhao Huiwen, Liu Jing, Chen Xiaoying, Shen Siyi, Zhu Xiaoya, and Luo Wen

Subjects
expression recognition, Resnet50 network, MTCNN face detection, Bayesian optimization, pain assessment, Medicine (General), R5-920
Abstract

The aim of this study is designed an improved ResNet 50 network to achieve automatic classification model for pain expressions by elderly patients with hip fractures. This study built a dataset by combining the advantages of deep learning in image recognition, using a hybrid of the Multi-Task Cascaded Convolutional Neural Networks (MTCNN). Based on ResNet50 network framework utilized transfer learning to implement model function. This study performed the hyperparameters by Bayesian optimization in the learning process. This study calculated intraclass correlation between visual analog scale scores provided by clinicians independently and those provided by pain expression evaluation assistant(PEEA). The automatic pain expression recognition model in elderly patients with hip fractures, which constructed using the algorithm. The accuracy achieved 99.6% on the training set, 98.7% on the validation set, and 98.2% on the test set. The substantial kappa coefficient of 0.683 confirmed the efficacy of PEEA in clinic. This study demonstrates that the improved ResNet50 network can be used to construct an automatic pain expression recognition model for elderly patients with hip fractures, which has higher accuracy.

Published
2024
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35. Alzheimer’s disease-linked risk alleles elevate microglial cGAS-associated senescence and neurodegeneration in a tauopathy model

Author

Carling, Gillian K., Fan, Li, Foxe, Nessa R., Norman, Kendra, Wong, Man Ying, Zhu, Daphne, Corona, Carlo, Razzoli, Agnese, Yu, Fangmin, Yarahmady, Allan, Ye, Pearly, Chen, Hao, Huang, Yige, Amin, Sadaf, Sereda, Rebecca, Lopez-Lee, Chloe, Zacharioudakis, Emmanouil, Chen, Xiaoying, Xu, Jielin, Cheng, Feixiong, Gavathiotis, Evripidis, Cuervo, Ana Maria, Holtzman, David M., Mok, Sue-Ann, Sinha, Subhash C., Sidoli, Simone, Ratan, Rajiv R., Luo, Wenjie, Gong, Shiaoching, and Gan, Li

Published
2024
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37. Traditional Chinese medicine FYTF-919 (Zhongfeng Xingnao oral prescription) for the treatment of acute intracerebral haemorrhage: a multicentre, randomised, placebo-controlled, double-blind, clinical trial

Author

Anderson, Craig, Song, Lili, Guo, Jianwen, Cai, Yefeng, Zhao, Yang, Chen, Xiaoying, You, Chao, Pan, Suyue, Tang, Guanghai, Lu, Yun, Hankey, Graeme J, Al-Shahi Salman, Rustam, Wang, Jiguang, Campbell, Bruce, Chen, Chris, Hu, Rong, Li, Qiang, Li, Xian, Wan, Yingfeng, You, Shoujiang, Zhang, Congcong, Gui, Zhong, Chen, Man, Ouyang, Shuman, Zhao, Xin, Huo, Chengju, Ban, Ru, Wang, Jingru, Chen, Yajie, Zhang, Lilan, Yu, Wenjing, Wang, Borui, Dong, Le, Ning, Yi, Li, Penggang, Wu, Manli, Wang, Dou, Zheng, Yanwen, Huang, Fengyan, Ren, Zhixuan, Zhang, Jingbei, Zheng, Zhihua, Li, Kunhong, Wang, Xinwei, Li, Yongqi, Cao, Jiamin, Zhu, Huaying, Tan, Xinning, Qiu, Xinhong, Yang, Zhongkang, Lou, Zhenzhen, Xie, Tingting, Zhao, Haining, Tang, Yu, Hu, Dafeng, Cui, Wanzhen, Xie, Mingjiang, Wang, Daxiu, Wu, Xiaoshu, Zheng, Yingyi, Mao, Rui, Chen, Hao, Luo, Min, Xue, Suqin, Li, Chunji, Chen, Jiekai, Jiang, Hongtao, Jing, Hongyan, Zhang, Xu, Xu, Yingnan, Yan, Haiguang, Yang, Guang, Wang, Feilong, Lu, Xiaodi, Yan, Li, Che, Fengyuan, Liu, Zhenchuan, Xie, Zhongxiang, He, Feng, Liu, Wanqi, Zhuang, Ruixue, Su, Qi, Zhang, Xuli, Xia, Zhangyong, Xu, Guangjun, Chen, Tuanzhi, Liang, Zai, Jiang, Qingyan, Su, Guanli, Xiao, Liufu, Zhang, Xiaoying, Shi, Liu, Zheng, Wei, Zhang, Yongjian, Chen, Jiao, Yin, Xiang, Jiang, Qiuhua, Zhang, Zhenyu, Huang, Jinqing, Huang, Qianliang, Guo, Honghua, Meng, Wei, Zhang, Bin, Zhang, Yulin, Dong, Chun, Shi, Jie, Tian, Song, Deng, Xiong, Liang, Zhijun, Zhong, Haixiang, Liu, Chubao, Shao, Xiaoguang, Liu, Enzhi, Fu, Weiqi, Sheng, Wei, Chen, Minghui, Xiang, Yong, Chen, Yanping, Long, Teng, Zhang, Jie, Chang, Tinghao, Tang, Xiaoping, Zhao, Long, Wu, Junde, Hu, Yangjingtian, Li, Zheng, Yu, Rutong, Hua, Lei, Wang, Yong, Ren, Yanhong, Zhang, Haibo, Tang, Jun, Shu, Jianzhong, Luo, Chao, Wang, Gaung, Guan, Yueyue, Wan, Yue, Ke, Shangsheng, He, Dandan, Wang, Zhenyi, Lu, Chenghang, Li, Xie, Zhong, Jianbin, Zhong, Simin, Lai, Yanfang, Zhang, Shijun, Wang, Lixin, Li, Guifu, Huang, Lijin, You, Jinsong, Huang, Tao, Zhen, Zheng, Xue, Daojin, Ke, Guo, He, Qiurong, Lei, Li, Guo, Tie, Li, Hui, Pu, Yunxue, Zhu, Hailin, Yang, Gang, Zheng, Maohua, Hou, Wei, Yu, Yue, Yang, Haijun, He, Gang, Ran, Yuan, Fan, Xiaoli, Xu, Lai, Zou, Shihui, Bai, Xiaoxin, Cai, Jun, Chen, Ruicong, Li, Yinbin, Tu, Huai, Ren, Guohua, Zhao, Jiye, Wang, Yunna, Shi, Jianhui, Bai, Yafeng, Lin, Lin, Li, Dazhi, Cai, Ning, Zhang, Yonghui, Yu, Xiaochen, Jiang, Zonghua, Wang, Cong, He, Dehai, Lai, Jiahe, Yuan, Xujuan, Zhang, Zeshun, Zhao, Minqing, Qin, Defang, Huo, Yonglin, Wang, Bangxiang, Chen, Shaohong, Jiao, Xu, Li, Mingfei, Liu, Xiaoqiu, Ren, Xinwen, Ouyang, Menglu, Billot, Laurent, Wang, Xia, Wen, Zehuai, and Anderson, Craig S

Published
2024
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