The Research of Cooperative Emergence Mechanism of Donation Behaviors on Correlation Network

2015 ◽  
Author(s):  
Dongwei Guo ◽  
Fangcai Fu ◽  
Miao Liu
Keyword(s):  
Correlation Network

scholarly journals Investigating the Spatial Heterogeneity and Correlation Network of Green Innovation Efficiency in China

2021 ◽  
Vol 13 (3) ◽  
pp. 1104
Author(s):  
Ke-Liang Wang ◽  
Fu-Qin Zhang
Keyword(s):  
Spatial Heterogeneity ◽  
Critical Path ◽  
Economic Value ◽  
Economic Transformation ◽  
Correlation Network ◽  
Green Innovation ◽  
Net Benefit ◽  
Network Characteristics ◽  
Four Blocks ◽  
Temporal And Spatial Characteristics

With environmental problems becoming increasingly serious worldwide, scholars’ research views on innovation have begun to pay more attention to the technological value from an ecological perspective, instead of simply analyzing the importance of technological innovation from the perspective of economic value. Currently, improving green innovation efficiency (GIE) has been considered as a critical path to realizing economic transformation and green development. Based on the global Super-Epsilon-based measure (EBM) model, Moran index, vector autoregression (VAR) model, and block model, this study investigated the temporal and spatial characteristics of GIE in 30 provinces in China from 2009 to 2017, and analyzed the spatial heterogeneity and spatial correlation network characteristics. The results showed that in spatial terms, China’s GIE presented an extremely unbalanced development model. In provinces with a higher GIE, there was an overall improvement of GIE, but there was a lower impact in provinces with a lower GIE. The efficiency of China’s green innovation could be divided into four blocks. The first block was the main overflow, the second block was the broker, the third block was the bilateral spillover, and the fourth block was the net benefit. The four blocks had their own functions, and a very significant correlation was observed among them.

scholarly journals Lipidomic Network of Mild Cognitive Impairment from the Mayo Clinic Study of Aging

2021 ◽  
pp. 1-11
Author(s):  
Xuewei Wang ◽  
Hai Bui ◽  
Prashanthi Vemuri ◽  
Jonathan Graff-Radford ◽  
Clifford R. Jack Jr ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Network Analysis ◽  
Mayo Clinic ◽  
Plasma Lipids ◽  
Amyloid Β ◽  
Cognitive Status ◽  
Correlation Network ◽  
Lipid Species ◽  
Clinic Study

Background: Lipid alterations contribute to Alzheimer’s disease (AD) pathogenesis. Lipidomics studies could help systematically characterize such alterations and identify potential biomarkers. Objective: To identify lipids associated with mild cognitive impairment and amyloid-β deposition, and to examine lipid correlation patterns within phenotype groups Methods: Eighty plasma lipids were measured using mass spectrometry for 1,255 non-demented participants enrolled in the Mayo Clinic Study of Aging. Individual lipids associated with mild cognitive impairment (MCI) were first identified. Correlation network analysis was then performed to identify lipid species with stable correlations across conditions. Finally, differential correlation network analysis was used to determine lipids with altered correlations between phenotype groups, specifically cognitively unimpaired versus MCI, and with elevated brain amyloid versus without. Results: Seven lipids were associated with MCI after adjustment for age, sex, and APOE4. Lipid correlation network analysis revealed that lipids from a few species correlated well with each other, demonstrated by subnetworks of these lipids. 177 lipid pairs differently correlated between cognitively unimpaired and MCI patients, whereas 337 pairs of lipids exhibited altered correlation between patients with and without elevated brain amyloid. In particular, 51 lipid pairs showed correlation alterations by both cognitive status and brain amyloid. Interestingly, the lipids central to the network of these 51 lipid pairs were not significantly associated with either MCI or amyloid, suggesting network-based approaches could provide biological insights complementary to traditional association analyses. Conclusion: Our attempt to characterize the alterations of lipids at network-level provides additional insights beyond individual lipids, as shown by differential correlations in our study.

scholarly journals Integrative Identification of Hub Genes Associated With Immune Cells in Atrial Fibrillation Using Weighted Gene Correlation Network Analysis

2021 ◽  
Vol 7 ◽  
Author(s):  
Tao Yan ◽  
Shijie Zhu ◽  
Miao Zhu ◽  
Chunsheng Wang ◽  
Changfa Guo
Keyword(s):  
Atrial Fibrillation ◽  
Network Analysis ◽  
Molecular Mechanism ◽  
Immune Cells ◽  
Bioinformatics Analysis ◽  
Functional Enrichment ◽  
Correlation Network ◽  
Hub Genes ◽  
Qrt Pcr ◽  
Gene Correlation

Background: Atrial fibrillation (AF) is the most common tachyarrhythmia in the clinic, leading to high morbidity and mortality. Although many studies on AF have been conducted, the molecular mechanism of AF has not been fully elucidated. This study was designed to explore the molecular mechanism of AF using integrative bioinformatics analysis and provide new insights into the pathophysiology of AF.Methods: The GSE115574 dataset was downloaded, and Cibersort was applied to estimate the relative expression of 22 kinds of immune cells. Differentially expressed genes (DEGs) were identified through the limma package in R language. Weighted gene correlation network analysis (WGCNA) was performed to cluster DEGs into different modules and explore relationships between modules and immune cell types. Functional enrichment analysis was performed on DEGs in the significant module, and hub genes were identified based on the protein-protein interaction (PPI) network. Hub genes were then verified using quantitative real-time polymerase chain reaction (qRT-PCR).Results: A total of 2,350 DEGs were identified and clustered into eleven modules using WGCNA. The magenta module with 246 genes was identified as the key module associated with M1 macrophages with the highest correlation coefficient. Three hub genes (CTSS, CSF2RB, and NCF2) were identified. The results verified using three other datasets and qRT-PCR demonstrated that the expression levels of these three genes in patients with AF were significantly higher than those in patients with SR, which were consistent with the bioinformatic analysis.Conclusion: Three novel genes identified using comprehensive bioinformatics analysis may play crucial roles in the pathophysiological mechanism in AF, which provide potential therapeutic targets and new insights into the treatment and early detection of AF.

scholarly journals Exploring the Predictability of 30-Day Extreme Precipitation Occurrence Using a Global SST–SLP Correlation Network

2016 ◽  
Vol 29 (3) ◽  
pp. 1013-1029 ◽  
Author(s):  
Mengqian Lu ◽  
Upmanu Lall ◽  
Jaya Kawale ◽  
Stefan Liess ◽  
Vipin Kumar
Keyword(s):  
Extreme Precipitation ◽  
Time Lag ◽  
Principal Component Regression ◽  
Principal Component ◽  
Correlation Network ◽  
Climatic Data ◽  
Wet Season ◽  
Predictive Skill ◽  
Correlation Networks ◽  
Time Lagged

Abstract Correlation networks identified from financial, genomic, ecological, epidemiological, social, and climatic data are being used to provide useful topological insights into the structure of high-dimensional data. Strong convection over the oceans and the atmospheric moisture transport and flow convergence indicated by atmospheric pressure fields may determine where and when extreme precipitation occurs. Here, the spatiotemporal relationship among sea surface temperature (SST), sea level pressure (SLP), and extreme global precipitation is explored using a graph-based approach that uses the concept of reciprocity to generate cluster pairs of locations with similar spatiotemporal patterns at any time lag. A global time-lagged relationship between pentad SST anomalies and pentad SLP anomalies is investigated to understand the linkages and influence of the slowly changing oceanic boundary conditions on the development of the global atmospheric circulation. This study explores the use of this correlation network to predict extreme precipitation globally over the next 30 days, using a logistic principal component regression on the strong global dipoles found between SST and SLP. Predictive skill under cross validation and blind prediction for the occurrence of 30-day precipitation that is higher than the 90th percentile of days in the wet season is indicated for the selected global regions considered.

scholarly journals The Use of RNA Sequencing and Correlation Network Analysis to Study Potential Regulators of Crabapple Leaf Color Transformation

2018 ◽  
Vol 59 (5) ◽  
pp. 1027-1042 ◽  
Author(s):  
Tuo Yang ◽  
Keting Li ◽  
Suxiao Hao ◽  
Jie Zhang ◽  
Tingting Song ◽  
...  
Keyword(s):  
Network Analysis ◽  
Rna Sequencing ◽  
Correlation Network ◽  
Leaf Color ◽  
Color Transformation

Correlation Network Analysis and Knowledge Integration

2011 ◽  
pp. 251-280 ◽  
Author(s):  
Thomas N. Plasterer ◽  
Robert Stanley ◽  
Erich Gombocz
Keyword(s):  
Network Analysis ◽  
Knowledge Integration ◽  
Correlation Network
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