Results 221 to 230 of about 4,375,078 (278)

PLXDC2 siRNA‐Mediated Intervention Attenuates Microglial Senescence Through cGAS‐STING Signaling

Advanced Science, EarlyView.
PLXDC2 was identified as a microglial target in Parkinson's disease through human and mouse single‐cell analyses. Ferritin‐armed biomimetic nanovesicles delivered PLXDC2 siRNA, deferoxamine, and CeO2 nanoparticles across the blood–brain barrier, reducing cGAS‐STING activation, iron burden, oxidative stress, and senescence in microglia, thereby ...
Heyue Lu, Zitian Zheng, Feiran Wang, Huanhuan Luo, Fei Ju, Yucheng Zhu, Zheng Zhou, Jie Sun, Zhenghui Qiu, Xiang Luo, Zhongzheng Jia, Jianquan Wang, Hongjie Huang, Bo Zhang   +13 more
wiley   +1 more source

Cognitive risk during underwater exposure: from environmental stressors to assessment and context-aware monitoring. [PDF]

Front Physiol
Zhao H, Wang L, Zhao Y, Zhao N, Zhang S, Wang Y, Yu X, Fang Y.   +7 more
europepmc   +1 more source

3D Human Pose Estimation from Monocular Video Sequences in Underwater Scenarios. [PDF]

Sensors (Basel)
Liang S, Liu H, Liu P, Zhang D, Yu R, Zhou X, Zhou Z.   +6 more
europepmc   +1 more source

Detection of submarine pipeline and cable targets based on depth feature of high resolution sonar image. [PDF]

PLoS One
Liu D, Jin Z, Chen J, Xu Z.
europepmc   +1 more source

Seafood Object Detection Method Based on Improved YOLOv5s. [PDF]

Sensors (Basel)
Zhu N, Liu Z, Wang Z, Xie Z.
europepmc   +1 more source

A Dataset for Fish Segmentation and Tracking in Underwater Videos. [PDF]

Sci Data
Sanchez J, Lisani JL, Catalan IA, Alvarez-Ellacuría A.   +3 more
europepmc   +1 more source

Underwater Target Recognition Based on Improved YOLOv4 Neural Network

Electronics, 2021
The YOLOv4 neural network is employed for underwater target recognition. To improve the accuracy and speed of recognition, the structure of YOLOv4 is modified by replacing the upsampling module with a deconvolution module and by incorporating depthwise ...
Ligang Yao, Weilin Luo, Shunqiang Duan
exaly   +2 more sources

STM: Spectrogram Transformer Model for Underwater Acoustic Target Recognition

Journal of Marine Science and Engineering, 2022
With the evolution of machine learning and deep learning, more and more researchers have utilized these methods in the field of underwater acoustic target recognition.
Qiang Lan, Yongxian Wang, Peng Li
exaly   +2 more sources