A network analysis of political incivility dimensions

Although online political incivility has increasingly become an object of scholarly inquiry, there exists little agreement on the construct’s precise definition. The goal of this work was therefore to explore the relational dynamics among previously identified dimensions of online political incivility. The results of a regularized partial correlation network indicated that a communicator’s desire to exclude attitude-discrepant others from discussion played an especially influential role in the variable network. The data also suggested that certain facets of incivility may be likely to be deployed together. Specifically, the data suggested the existence of two identifiable groupings of incivility factors: (1) variables pertaining to violation of speech-based norms and (2) variables pertaining to the violation of the inclusion-based norms that underlie democratic communication processes. These results are discussed in the context of political discussion and deliberation.

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The precuneus/posterior cingulate cortex plays a pivotal role in the default mode network: Evidence from a partial correlation network analysis

NeuroImage ◽

10.1016/j.neuroimage.2008.05.059 ◽

2008 ◽

Vol 42 (3) ◽

pp. 1178-1184 ◽

Cited By ~ 734

Author(s):

Peter Fransson ◽

Guillaume Marrelec

Keyword(s):

Network Analysis ◽

Default Mode Network ◽

Partial Correlation ◽

Cingulate Cortex ◽

Posterior Cingulate Cortex ◽

Pivotal Role ◽

Correlation Network ◽

Default Mode ◽

Posterior Cingulate

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Exploring Young Adulthood Psychopathology Networks Related to Depression Using Partial Correlation Network and Bayesian Network

10.21203/rs.3.rs-131241/v1 ◽

2021 ◽

Author(s):

Min Seob Kim ◽

Bumseok Jeong

Keyword(s):

Network Analysis ◽

Bayesian Network ◽

Young Adulthood ◽

Partial Correlation ◽

Health Management ◽

Verbal Abuse ◽

Correlation Network ◽

Traumatic Experiences ◽

Subjective Distress ◽

Disease Networks

Abstract To characterize young adulthood depression is complicated because it is entangled with a broad spectrum of symptoms as well as traumatic experiences during development. However, previous symptom network studies have focused on undirected transdiagnostic association among depression and anxiety symptoms. Our study investigated both undirected and directed connections among variables potentially associated with depression, such as anxiety, addiction, subjective distress caused by traumatic events, perceived emotional adversities, and support systems. Both the regularized partial correlation network analysis and Bayesian network analysis were applied to 579 subjects screened for depression. Anxiety-related symptoms played a role as a hub node in the partial correlation network and Bayesian network. The vulnerability analysis of the partial correlation network showed that verbal abuse, social anxiety, concentration problems, and suicidal ideation had the strongest influence on changes in the network’s topology. In the Bayesian network analysis, loss of interest, depressed mood, and parental verbal abuse were located as parent nodes in the directed acyclic graph. In the aspect of disease networks, more attention should be paid to certain variables encompassing various domains as well as depressive symptoms in young adults’ mental health management.

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Community Detection in Partial Correlation Network Models

SSRN Electronic Journal ◽

10.2139/ssrn.2776505 ◽

2016 ◽

Cited By ~ 1

Author(s):

Christian T. Brownlees ◽

Gabor Lugosi

Keyword(s):

Community Detection ◽

Partial Correlation ◽

Network Models ◽

Correlation Network

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Differential correlation network analysis identified novel metabolomics signatures for non-responders to total joint replacement in primary osteoarthritis patients

Metabolomics ◽

10.1007/s11306-020-01683-1 ◽

2020 ◽

Vol 16 (5) ◽

Cited By ~ 1

Author(s):

Christie A. Costello ◽

Ting Hu ◽

Ming Liu ◽

Weidong Zhang ◽

Andrew Furey ◽

...

Keyword(s):

Network Analysis ◽

Joint Replacement ◽

Total Joint Replacement ◽

Correlation Network ◽

Differential Correlation ◽

Primary Osteoarthritis

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

Journal of Alzheimer s Disease ◽

10.3233/jad-201347 ◽

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.

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Weighted Correlation Network Analysis (WGCNA) of Japanese Flounder (Paralichthys olivaceus) Embryo Transcriptome Provides Crucial Gene Sets for Understanding Haploid Syndrome and Rescue by Diploidization

Journal of Ocean University of China ◽

10.1007/s11802-018-3656-x ◽

2018 ◽

Vol 17 (6) ◽

pp. 1441-1450 ◽

Cited By ~ 2

Author(s):

Haitao Zhao ◽

Xinxin Du ◽

Kai Zhang ◽

Yuezhong Liu ◽

Yujue Wang ◽

...

Keyword(s):

Network Analysis ◽

Paralichthys Olivaceus ◽

Japanese Flounder ◽

Correlation Network ◽

Gene Sets ◽

Weighted Correlation

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

Frontiers in Cardiovascular Medicine ◽

10.3389/fcvm.2020.631775 ◽

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.

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