Directed Correlation Networks, Determined by the Dynamics of COVID-19 Distribution in Various Countries

2020 ◽  
Author(s):  
Dmitry Lande ◽  
Leonard Strashnoy
Keyword(s):  
Correlation Networks

Evolving Cascade-Correlation Networks for Time-Series Forecasting.

1994 ◽  
Author(s):  
J. R. McDonnell ◽  
D. E. Waagen
Keyword(s):  
Time Series ◽  
Time Series Forecasting ◽  
Correlation Networks ◽  
Cascade Correlation

scholarly journals COVID-19 lockdown has altered the dynamics between affective symptoms and social isolation among older adults: results from a longitudinal network analysis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Junhong Yu ◽  
Rathi Mahendran
Keyword(s):  
Older Adults ◽  
Social Isolation ◽  
Anxiety Symptoms ◽  
Community Dwelling ◽  
Depression Symptoms ◽  
Network Analyses ◽  
Correlation Networks ◽  
Affective Symptoms ◽  
Tightly Coupled ◽  
Community Dwelling Older Adults

AbstractThe COVID-19 lockdown has drastically limited social interactions and brought about a climate of fear and uncertainty. These circumstances not only increased affective symptoms and social isolation among community dwelling older adults but also alter the dynamics between them. Using network analyses, we study the changes in these dynamics before and during the lockdown. Community-dwelling older adults (N = 419) completed questionnaires assessing depression, anxiety, and social isolation, before the COVID-19 pandemic, as part of a cohort study, and during the lockdown period. The total scores of these questionnaires were compared across time. For the network analyses, partial correlation networks were constructed using items in the questionnaires as nodes, separately at both timepoints. Changes in edges, as well as nodal and bridge centrality were examined across time. Depression and anxiety symptoms, and social isolation had significantly increased during the lockdown. Significant changes were observed across time on several edges. Greater connectivity between the affective and social isolation nodes at lockdown was observed. Depression symptoms have become more tightly coupled across individuals, and so were the anxiety symptoms. Depression symptoms have also become slightly decoupled from those of anxiety. These changing network dynamics reflect the greater influence of social isolation on affective symptoms across individuals and an increased vulnerability to affective disorders. These findings provide novel perspectives and translational implications on the changing mental health context amidst a COVID-19 pandemic situation.

scholarly journals A Spatio-Temporal Local Association Query Algorithm for Multi-Source Remote Sensing Big Data

2021 ◽  
Vol 13 (12) ◽  
pp. 2333
Author(s):  
Lilu Zhu ◽  
Xiaolu Su ◽  
Yanfeng Hu ◽  
Xianqing Tai ◽  
Kun Fu
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Temporal Correlation ◽  
Local Association ◽  
Sensing Data ◽  
Multi Scale ◽  
Correlation Networks ◽  
Efficient Integration ◽  
Spatio Temporal ◽  
Query Algorithm

It is extremely important to extract valuable information and achieve efficient integration of remote sensing data. The multi-source and heterogeneous nature of remote sensing data leads to the increasing complexity of these relationships, and means that the processing mode based on data ontology cannot meet requirements any more. On the other hand, the multi-dimensional features of remote sensing data bring more difficulties in data query and analysis, especially for datasets with a lot of noise. Therefore, data quality has become the bottleneck of data value discovery, and a single batch query is not enough to support the optimal combination of global data resources. In this paper, we propose a spatio-temporal local association query algorithm for remote sensing data (STLAQ). Firstly, we design a spatio-temporal data model and a bottom-up spatio-temporal correlation network. Then, we use the method of partition-based clustering and the method of spectral clustering to measure the correlation between spatio-temporal correlation networks. Finally, we construct a spatio-temporal index to provide joint query capabilities. We carry out local association query efficiency experiments to verify the feasibility of STLAQ on multi-scale datasets. The results show that the STLAQ weakens the barriers between remote sensing data, and improves their application value effectively.

Reconstruction and analysis of correlation networks based on GC–MS metabolomics data for hypercholesterolemia

2021 ◽  
Vol 553 ◽  
pp. 1-8
Author(s):  
Ya-nan OuYang ◽  
Lu-xin Zhou ◽  
Yue-xin Jin ◽  
Guo-feng Hou ◽  
Peng-fei Yang ◽  
...  
Keyword(s):  
Metabolomics Data ◽  
Correlation Networks

scholarly journals Combining Bayesian Approaches and Evolutionary Techniques for the Inference of Breast Cancer Networks

2017 ◽  
Author(s):  
Stefano Beretta ◽  
Mauro Castelli ◽  
Ivo Gonçalves ◽  
Ivan Merelli ◽  
Daniele Ramazzotti
Keyword(s):  
Breast Cancer ◽  
Graphical Models ◽  
Protein Interactions ◽  
Large Scale ◽  
Small Sample Size ◽  
Small Sample ◽  
Cancer Data ◽  
Correlation Networks ◽  
Model Complex ◽  
Cancer Networks

AbstractGene and protein networks are very important to model complex large-scale systems in molecular biology. Inferring or reverseengineering such networks can be defined as the process of identifying gene/protein interactions from experimental data through computational analysis. However, this task is typically complicated by the enormously large scale of the unknowns in a rather small sample size. Furthermore, when the goal is to study causal relationships within the network, tools capable of overcoming the limitations of correlation networks are required. In this work, we make use of Bayesian Graphical Models to attach this problem and, specifically, we perform a comparative study of different state-of-the-art heuristics, analyzing their performance in inferring the structure of the Bayesian Network from breast cancer data.

scholarly journals The Replicability and Generalizability of Internalizing Symptom Networks Across Five Samples

2019 ◽  
Author(s):  
Carter J. Funkhouser ◽  
Kelly Correa
Keyword(s):  
Network Structure ◽  
Internalizing Symptoms ◽  
Rank Order ◽  
Network Models ◽  
Clinical Samples ◽  
Cross Sectional ◽  
Total N ◽  
Correlation Networks ◽  
Global Strength ◽  
Central Symptom

The popularity of network analysis in psychopathology research has increased exponentially in recent years. Yet, little research has examined the replicability of cross-sectional psychopathology network models, and those that have used single items for symptoms rather than multi-item scales. The present study therefore examined the replicability and generalizability of regularized partial correlation networks of internalizing symptoms within and across five samples (total N = 2,573) using the Inventory for Depression and Anxiety Symptoms, a factor analytically-derived measure of individual internalizing symptoms. As different metrics may yield different conclusions about the replicability of network parameters, we examined both global and specific metrics of similarity between networks. Correlations within and between nonclinical samples suggested considerable global similarities in network structure (rss = .53-.87) and centrality strength (rss = .37-.86), but weaker similarities in network structure (rss = .36-.66) and centrality (rss = .04-.54) between clinical and nonclinical samples. Global strength (i.e., connectivity) did not significantly differ across all five networks and few edges (0-5.5%) significantly differed between networks. Specific metrics of similarity indicated that, on average, approximately 80% of edges were consistently estimated within and between all five samples. The most central symptom (i.e., dysphoria) was consistent within and across samples, but there were few other matches in centrality rank-order. In sum, there were considerable similarities in network structure, the presence and sign of individual edges, and the most central symptom within and across internalizing symptom networks estimated from nonclinical samples, but global metrics suggested network structure and symptom centrality had weak to moderate generalizability from nonclinical to clinical samples.

scholarly journals Bifidobacterial Succession and Correlation Networks in a Large Unselected Cohort of Mothers and Their Children

2012 ◽  
Vol 79 (2) ◽  
pp. 497-507 ◽  
Author(s):  
E. Avershina ◽  
O. Storrø ◽  
T. Øien ◽  
R. Johnsen ◽  
R. Wilson ◽  
...  
Keyword(s):  
Human Populations ◽  
Bifidobacterium Adolescentis ◽  
Content Type ◽  
Individual Level ◽  
Correlation Networks ◽  
Age Related ◽  
Related Development ◽  
Healthy Cohort ◽  
Stool Samples ◽  
The Individual

ABSTRACTBifidobacteria are a major microbial component of infant gut microbiota, which is believed to promote health benefits for the host and stimulate maturation of the immune system. Despite their perceived importance, very little is known about the natural development of and possible correlations between bifidobacteria in human populations. To address this knowledge gap, we analyzed stool samples from a randomly selected healthy cohort of 87 infants and their mothers with >90% of vaginal delivery and nearly 100% breast-feeding at 4 months. Fecal material was sampled during pregnancy, at 3 and 10 days, at 4 months, and at 1 and 2 years after birth. Stool samples were predicted to be rich in the speciesBifidobacterium adolescentis,B. bifidum,B. dentium,B. breve, andB. longum. Due to high variation, we did not identify a clear age-related structure at the individual level. Within the population as a whole, however, there were clear age-related successions. Negative correlations between theB. longumgroup andB. adolescentiswere detected in adults and in 1- and 2-year-old children, whereas negative correlations betweenB. longumandB. brevewere characteristic for newborns and 4-month-old infants. The highly structured age-related development of and correlation networks between bifidobacterial species during the first 2 years of life mirrors their different or competing nutritional requirements, which in turn may be associated with specific biological functions in the development of healthy gut.

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.

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