scispace - formally typeset
Search or ask a question
Institution

Ocean University of China

EducationQingdao, China
About: Ocean University of China is a education organization based out in Qingdao, China. It is known for research contribution in the topics: Population & Sea surface temperature. The organization has 27604 authors who have published 27886 publications receiving 440181 citations. The organization is also known as: Zhōngguó Hǎiyáng Dàxué & OUC.


Papers
More filters
Journal ArticleDOI
Jingyu Lu1, Na Li1, Shaoyong Zhang1, Zhibin Yu1, Haiyong Zheng1, Bing Zheng1 
TL;DR: This paper proposes an underwater image restoration method based on transferring an underwater style image into a recovered style using Multi-Scale Cycle Generative Adversarial Network (MCycle GAN) System and includes a Structural Similarity Index Measure loss (SSIM loss), which can provide more flexibility to model the detail structural to improve the image restoration performance.
Abstract: Underwater image restoration, which is the keystone to the underwater vision research, is still a challenging work. The key point of underwater image restoration work is how to remove the turbidity and the color distortion caused by the underwater environment. In this paper, we propose an underwater image restoration method based on transferring an underwater style image into a recovered style using Multi-Scale Cycle Generative Adversarial Network (MCycle GAN) System. We include a Structural Similarity Index Measure loss (SSIM loss), which can provide more flexibility to model the detail structural to improve the image restoration performance. We use dark channel prior (DCP) algorithm to get the transmission map of an image and design an adaptive SSIM loss to improve underwater image quality. We input this information into the network for multi-scale calculation on the images, which achieves the combination of DCP algorithm and Cycle-Consistent Adversarial Networks (CycleGAN). By compared the quantitative and qualitative with existing state-of-the-art approaches, our method shows a pleasing performance on the underwater image dataset.

99 citations

Journal ArticleDOI
TL;DR: A feasible system pharmacology model based on chemical, pharmacokinetic and pharmacological data was developed via network construction approach and successfully explained the polypharmcological mechanisms underlying the efficiency of Danggui-Honghua for BSS treatment, and also probed into the potential novel therapeutic strategies for B SS in TCM.
Abstract: Herb pair Danggui-Honghua has been frequently used for treatment of blood stasis syndrome (BSS) in China, one of the most common clinical pathological syndromes in traditional Chinese medicine (TCM). However, its therapeutic mechanism has not been clearly elucidated. In the present study, a feasible system pharmacology model based on chemical, pharmacokinetic and pharmacological data was developed via network construction approach to clarify the mechanisms of this herb pair. Thirty-one active ingredients of Danggui-Honghua possessing favorable pharmacokinetic profiles and biological activities were selected, interacting with 42 BSS-related targets to provide potential synergistic therapeutic actions. Systematic analysis of the constructed networks revealed that these targets such as HMOX1, NOS2, NOS3, HIF1A and PTGS2 were mainly involved in TNF signaling pathway, HIF-1 signaling pathway, estrogen signaling pathway and neurotrophin signaling pathway. The contribution index of every active ingredient also indicated six compounds, including hydroxysafflor yellow A, safflor yellow A, safflor yellow B, Z-ligustilide, ferulic acid, and Z-butylidenephthalide, as the principal components of this herb pair. These results successfully explained the polypharmcological mechanisms underlying the efficiency of Danggui-Honghua for BSS treatment, and also probed into the potential novel therapeutic strategies for BSS in TCM.

99 citations

Journal ArticleDOI
Lin Tan1, Jin-Tai Yu2, Jin-Tai Yu1, Lan Tan1, Lan Tan2 
TL;DR: This work focuses in particular on dysregulation of miRNAs which leads to several neurodegenerative diseases from three aspects: miRNA-generating disorders, mi RNA-targeting genes and epigenetic alterations.
Abstract: Neurodegenerative diseases, including Alzheimer’s disease (AD), Parkinson’s disease (PD), Huntington’s disease (HD) and amyotrophic lateral sclerosis (ALS), originate from a loss of neurons in the central nervous system (CNS) and are severely debilitating. The incidence of neurodegenerative diseases increases with age, and they are expected to become more common due to extended life expectancy. Because of no clear mechanisms, these diseases have become a major challenge in neurobiology. It is well recognized that these disorders become the culmination of many different genetic and environmental influences. Prior studies have shown that microRNAs (miRNAs) are pathologically altered during the inexorable course of some neurodegenerative diseases, suggesting that miRNAs may be the contributing factor in neurodegeneration. Here, we review what is known about the involvement of miRNAs in the pathogenesis of neurodegenerative diseases. The biogenesis of miRNAs and various functions of miRNAs that act as the chief regulators will be discussed. We focus in particular on dysregulation of miRNAs which leads to several neurodegenerative diseases from three aspects: miRNA-generating disorders, miRNA-targeting genes and epigenetic alterations. Furthermore, recent evidences have shown that circulating miRNA expression levels are changed in patients with neurodegenerative diseases. Circulating miRNA expression levels are reported in patients in order to evaluate their application as biomarkers of these diseases. A discussion is included with a potential diagnostic biomarker and the possible future direction in exploring the nexus between miRNAs and various neurodegenerative diseases.

99 citations

Journal ArticleDOI
TL;DR: Experimental data indicated that CM-chitosan was safe in vivo and slightly inhibited growth of sarcoma 180 and enhanced body immunity via elevation of serum IL-2 and TNF-α levels in treated mice (P<0.05).

98 citations

Journal ArticleDOI
TL;DR: The aim of this study was to develop an effective method for the identification of Vibrio harveyi based on using the toxR gene as a taxonomic marker.
Abstract: Aims: The aim of this study was to develop an effective method for the identification of Vibrio harveyi based on using the toxR gene as a taxonomic marker. Methods and Results: Primers for the toxR gene were designed for specificity to V. harveyi, and incorporated in a polymerase chain reaction (PCR). The results of the PCR, which took <5 h from DNA extraction to amplification, revealed positive amplification of the toxR gene fragment in 20 V. harveyi isolates including type strains, whereas DNA from 23 other Vibrionaceae type strains and 13 Vibrio parahaemolyticus strains were negative. The detection limit of the PCR was 4.0 × 103 cells ml−1. In addition, the technique enabled the recognition of V. harveyi from diseased fish. Conclusions: The PCR was specific and sensitive, enabling the identification of V. harveyi within 5 h. Significance and Impact of the Study: The PCR allowed the rapid and sensitive detection of V. harveyi.

98 citations


Authors

Showing all 27836 results

NameH-indexPapersCitations
Guangming Zeng1461676100743
Bin Wang126222674364
Simon A. Wilde11839045547
Yusuke Yamauchi117100051685
Xiaoming Li113193272445
Baoshan Xing10982348944
Peng Wang108167254529
Jun Yang107209055257
Shang-Ping Xie10544136437
M. Santosh103134449846
Qi Li102156346762
Wei Liu102292765228
Tao Wang97272055280
Wei Wang95354459660
Peng Li95154845198
Network Information
Related Institutions (5)
Chinese Academy of Sciences
634.8K papers, 14.8M citations

90% related

South China University of Technology
69.4K papers, 1.2M citations

87% related

Dalian University of Technology
71.9K papers, 1.1M citations

87% related

Nanjing University
105.5K papers, 2.2M citations

87% related

Tianjin University
79.9K papers, 1.2M citations

86% related

Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023103
2022515
20213,161
20202,814
20192,480
20182,068