Using SHARE data, this study was based on an earlier analysis that derived social network types among adults aged 65 and over in Europe. The current effort investigated the transitions that occurred across these network types after 4 years ( = 13,767). Four general network transition patterns were identified according to network type (close-family networks and other networks) and whether a network transition occurred. The associations between network type, network transitions and well-being (depression and life satisfaction) were examined. We regressed depressive symptoms and a life satisfaction measure on the network transition patterns, controlling for socio-demographic background, health and country. The results revealed that a majority of older Europeans experienced a range of network transition, while close-family-based networks tended to prevail over time. Moreover, respondents who remained in or transitioned to close-family networks had fewer depressive symptoms and better life satisfaction than those in other network types. The study, thus, underscores the varied effects of network types and network changes on emotional well-being in late life. It also demonstrates that beneficial changes can be made in one's social network in old age, especially with regard to greater family closeness.
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http://dx.doi.org/10.1007/s10433-019-00545-7 | DOI Listing |
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January 2025
College of Resource and Environment, Henan Polytechnic University, Jiaozuo 454003, China.
Identifying and quantifying the dominant factors influencing heavy metal (HM) pollution sources are essential for maintaining soil ecological health and implementing effective pollution control measures. This study analyzed soil HM samples from 53 different land use types in Jiaozuo City, Henan Province, China. Pollution sources were identified using Absolute Principal Component Score (APCS), with 8 anthropogenic factors, 9 natural factors, and 4 soil physicochemical properties mapped using Geographic Information System (GIS) kernel density estimation.
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December 2024
DDS. Titular Professor. Universidad de Antioquia U de A, Medellín, Colombia. Biomedical Stomatology Research Group, Universidad de Antioquia U de A, Medellín, Colombia.
Background: The RTK-VEGF4 receptor family, which includes VEGFR-1, VEGFR-2, and VEGFR-3, plays a crucial role in tissue regeneration by promoting angiogenesis, the formation of new blood vessels, and recruiting stem cells and immune cells. Machine learning, particularly graph neural networks (GNNs), has shown high accuracy in predicting these interactions. This study aims to predict drug-gene interactions of the RTK-VEGF4 receptor family in periodontal regeneration using graph neural networks.
View Article and Find Full Text PDFNonlinear Dyn
September 2024
Department of Mathematics, University College London, London, UK.
Time series is a data structure prevalent in a wide range of fields such as healthcare, finance and meteorology. It goes without saying that analyzing time series data holds the key to gaining insight into our day-to-day observations. Among the vast spectrum of time series analysis, time series classification offers the unique opportunity to classify the sequences into their respective categories for the sake of automated detection.
View Article and Find Full Text PDFNAR Cancer
March 2025
Department of Pathology and Laboratory Medicine, The Warren Alpert Medical School of Brown University, 593 Eddy Street, Providence, RI 02903, USA.
Cancer is a complex disease with heterogeneous mutational and gene expression patterns. Subgroups of patients who share a phenotype might share a specific genetic architecture including protein-protein interactions (PPIs). We developed the Atlas of Protein-Protein Interactions in Cancer (APPIC), an interactive webtool that provides PPI subnetworks of 10 cancer types and their subtypes shared by cohorts of patients.
View Article and Find Full Text PDFPLoS One
January 2025
Automation School Guangdong University of Petrochemical Technology, Maoming, Guangdong, China.
Centrifugal compressors are widely used in the oil and natural gas industry for gas compression, reinjection, and transportation. Fault diagnosis and identification of centrifugal compressors are crucial. To promptly monitor abnormal changes in compressor data and trace the causes leading to these data anomalies, this paper proposes a security monitoring and root cause tracing method for compressor data anomalies.
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