中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
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CAS IR Grid
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地理科学与资源研究... [41]
大连化学物理研究所 [23]
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期刊论文 [234]
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Variations and Interseasonal Changes in the Gut Microbial Communities of Seven Wild Fish Species in a Natural Lake with Limited Water Exchange during the Closed Fishing Season
期刊论文
OAI收割
MICROORGANISMS, 2024, 卷号: 12, 期号: 4, 页码: 21
作者:
Liang, Yangyang
;
Wang, Zijia
;
Gao, Na
;
Qi, Xiaoxue
;
Zeng, Juntao
  |  
收藏
  |  
浏览/下载:0/0
  |  
提交时间:2024/05/27
Chaohu Lake
wild fish
gut
microbial communities
seasonal variation
co-occurrence network
Biodiversity conservation and management of lake wetlands based on the spatiotemporal evolution patterns of crane habitats
期刊论文
OAI收割
JOURNAL OF ENVIRONMENTAL MANAGEMENT, 2024, 卷号: 353, 页码: 13
作者:
Zhang, Zihan
;
Wang, Cheng
  |  
收藏
  |  
浏览/下载:4/0
  |  
提交时间:2024/04/30
Lake wetlands
Crane habitats
MaxEnt model
Spatiotemporal evolution
Polymorphic phase transition in CoCrNi medium-entropy alloy under impact loadings
期刊论文
OAI收割
JOURNAL OF MATERIALS RESEARCH AND TECHNOLOGY-JMR&T, 2024, 卷号: 28, 页码: 3583-3596
作者:
Zhou, Wenbo
;
Cao, Fuhua
;
Yang, Zengyu
;
Li, Tong
;
Niu, Yangyang
  |  
收藏
  |  
浏览/下载:6/0
  |  
提交时间:2024/03/11
Medium-entropy alloy
Phase transition
Shock compression
Ramp-wave
Loading path
Obesity induced disruption on diurnal rhythm of insulin sensitivity via gut microbiome-bile acid metabolism
期刊论文
OAI收割
BIOCHIMICA ET BIOPHYSICA ACTA-MOLECULAR AND CELL BIOLOGY OF LIPIDS, 2024, 卷号: 1869, 期号: 1, 页码: 13
作者:
Guo, Xiaozhen
;
Wang, Jiawen
;
Xu, Hualing
;
Wang, Yangyang
;
Cao, Yutang
  |  
收藏
  |  
浏览/下载:32/0
  |  
提交时间:2024/01/16
Diurnal rhythm
Insulin sensitivity
Obesity
Gut microbiota
Ursodeoxycholic acid
Development and validation of a sensitive liquid chromatography-tandem mass spectrometry method for the assay of 12 substances in rat plasma and its application to rat pharmacokinetics of
Epimedium
and
Psoraleae Fructus
herb pair after oral administration
期刊论文
OAI收割
JOURNAL OF SEPARATION SCIENCE, 2024, 卷号: 47, 期号: 1, 页码: 17
作者:
Wang, Linwei
;
Yin, Xiaoying
;
Liu, Huan
  |  
收藏
  |  
浏览/下载:15/0
  |  
提交时间:2024/02/27
compatibility behaviors
Epimedium
LC-MS/MS
pharmacokinetics
Psoraleae Fructus
Individual Tree-Level Monitoring of Pest Infestation Combining Airborne Thermal Imagery and Light Detection and Ranging
期刊论文
OAI收割
FORESTS, 2024, 卷号: 15, 期号: 1, 页码: 16
作者:
Wang, Jingxu
;
Lin, Qinan
;
Meng, Shengwang
;
Huang, Huaguo
;
Liu, Yangyang
  |  
收藏
  |  
浏览/下载:6/0
  |  
提交时间:2024/03/01
pine shoot beetle
shoot damage ratio
canopy temperature
thermal infrared imagery
LiDAR
Deep learning radiomics of multimodal ultrasound for classifying metastatic cervical lymphadenopathy into primary cancer sites: a feasibility study
期刊论文
OAI收割
ULTRASCHALL IN DER MEDIZIN, 2023, 页码: 11
作者:
Zhu, Yangyang
;
Meng, Zheling
;
Wu, Hao
;
Fan, Xiao
;
Lv, Wenhao
  |  
收藏
  |  
浏览/下载:9/0
  |  
提交时间:2024/02/22
Multimodal Ultrasound
Metastatic Cervical Lymphadenopathy
Deep Learning
Primary Cancer Sites
Label-free and highly-sensitive protamine detection by layer-by-layer assembled chitosan/heparin functionalized optical fiber mode interferometer
期刊论文
OAI收割
Sensors and Actuators B: Chemical, 2023, 卷号: 395
作者:
Yan, Minglu
;
Wang, Ruiduo
;
Wang, Qiang
;
Li, Yansong
;
Liu, Beibei
  |  
收藏
  |  
浏览/下载:12/0
  |  
提交时间:2023/10/10
Protamine
Heparin
Fiber mode interferometer
Biosensor
Layer by layer assembly CS/PAA for high sensitivity detection of heavy metal ions using mode interference sensor
期刊论文
OAI收割
OPTICS COMMUNICATIONS, 2023, 卷号: 545
作者:
Yan, Minglu
;
Wang, Ruiduo
;
Liu, Beibei
;
Li, Yangyang
;
Li, Yarong
  |  
收藏
  |  
浏览/下载:14/0
  |  
提交时间:2023/08/28
Fiber optic sensor
Heavy metal ions
Mode interference sensor
Electrostatic self-assembly
Node classification across networks via category-level domain adaptive network embedding
期刊论文
OAI收割
KNOWLEDGE AND INFORMATION SYSTEMS, 2023, 页码: 24
作者:
Shi, Boshen
;
Wang, Yongqing
;
Shao, Jiangli
;
Shen, Huawei
;
Li, Yangyang
  |  
收藏
  |  
浏览/下载:8/0
  |  
提交时间:2023/12/04
Node classification across networks
Graph neural networks
Domain adaptation
Transfer learning