Zobrazeno 1 - 10
of 111
pro vyhledávání: '"Xinyi Le"'
Publikováno v:
Pharmaceutical Fronts, Vol 06, Iss 03, Pp e294-e304 (2024)
The study aimed to investigate the immunomodulatory effect of Qixian Decoction (QXT) in an asthmatic model. In this study, ovalbumin (OVA)-induced asthma in female SPF BALB/c mice was established. Mice were randomly divided into four groups (n = 8):
Externí odkaz:
https://doaj.org/article/8302f040f76b43d48f628298a1d6769a
Autor:
Kaili Zhong, Xiao Li, Xinyi Le, Xiangyi Kong, Haifeng Zhang, Xiaobo Zheng, Ping Wang, Zhengguang Zhang
Publikováno v:
PLoS Pathogens, Vol 12, Iss 8, p e1005823 (2016)
Dynamins are large superfamily GTPase proteins that are involved in various cellular processes including budding of transport vesicles, division of organelles, cytokinesis, and pathogen resistance. Here, we characterized several dynamin-related prote
Externí odkaz:
https://doaj.org/article/01485cb64ddb45f79cfaf30b87efc9fa
Publikováno v:
IEEE Transactions on Intelligent Vehicles. 8:2123-2134
Publikováno v:
Neurocomputing. 490:380-389
Publikováno v:
Neural Networks. 147:53-62
Anomaly detection is an active research field in industrial defect detection and medical disease detection. However, previous anomaly detection works suffer from unstable training, or non-universal criteria of evaluating feature distribution. In this
Autor:
Xinyi Leng, Bonaventure Y.M. Ip, Sze Ho Ma, Wai Ting Lui, Vincent H.L. Ip, Florence S.Y. Fan, Howan Leung, Vincent C.T. Mok, Simon C.H. Yu, Thomas W. Leung
Publikováno v:
Journal of Stroke, Vol 26, Iss 3, Pp 446-449 (2024)
Externí odkaz:
https://doaj.org/article/459d7d87508642d59ceb542835b34ce0
Publikováno v:
Neural Computing and Applications. 35:9951-9960
Publikováno v:
Complex & Intelligent Systems. 8:1639-1652
Star identification is the foundation of star trackers, which are used to precisely determine the attitude of spacecraft. In this paper, we propose a novel star identification approach based on spectral graph matching. In the proposed approach, we co
Publikováno v:
IEEE Transactions on Instrumentation and Measurement. 71:1-11
Publikováno v:
IEEE Transactions on Neural Networks and Learning Systems. :1-10
Since the last decade, deep neural networks have shown remarkable capability in learning representations. The recently proposed neural ordinary differential equations (NODEs) can be viewed as the continuous-time equivalence of residual neural network