Network-based interventions are gaining prominence in the treatment of chronic illnesses; however, little is known about what aspects of network structure are easily identified by non-experts when shown network visualizations. This study examines which structural features are recognizable by non-experts. Nineteen non-experts were asked to pile-sort 68 network diagrams. Results were analyzed using multidimensional scaling, discriminant analysis, cluster analysis, and PROFIT analysis. Participants tended to sort networks along the dimensions of isolates and size of largest component, suggesting that interventions aimed at helping individuals understand and change their social environments could benefit from incorporating visualizations of social networks.
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http://dx.doi.org/10.1177/1525822X11399702 | DOI Listing |
J Proteomics
January 2025
Institute for Advanced Biosciences, CR Inserm U1209, CNRSUMR 5309, University of Grenoble-Alpes, 38000 Grenoble, France; Platform BioPark Archamps, 74160 Archamps, France. Electronic address:
The venom of scorpions has been the subject of numerous studies. However, their taxonomic identification is not a simple task, leading to misidentifications. This study aims to provide a practical approach for identifying scorpions based on the venom molecular mass fingerprint (MFP).
View Article and Find Full Text PDFEcol Evol
July 2024
Laboratório de Sistemática de Collembola e Conservação, Instituto de Biologia de Solo Universidade Estadual da Paraíba João Pessoa PB Brazil.
Eur J Protistol
August 2024
Research Department for Limnology, Mondsee, University of Innsbruck, Austria. Electronic address:
Despite their high abundance and wide distribution in ecosystems, most protists remain unknown to the public. Although science communication approaches were developed in historical times to raise public awareness of these 'enigmatic' taxa, many aspects have not been considered in the spotlight of modern techniques. We present selected ideas and activities on how to attract the public to unicellular eukaryotes.
View Article and Find Full Text PDFIEEE/ACM Trans Comput Biol Bioinform
May 2024
Predicting the physical interaction of proteins is a cornerstone problem in computational biology. New classes of learning-based algorithms are actively being developed, and are typically trained end-to-end on protein complex structures extracted from the Protein Data Bank. These training datasets tend to be large and difficult to use for prototyping and, unlike image or natural language datasets, they are not easily interpretable by non-experts.
View Article and Find Full Text PDFJ Imaging Inform Med
August 2024
School of Computer Science and Software Engineering, Southwest Petroleum University, Chengdu, Sichuan, China.
Automated recognition of heart shunts using saline contrast transthoracic echocardiography (SC-TTE) has the potential to transform clinical practice, enabling non-experts to assess heart shunt lesions. This study aims to develop a fully automated and scalable analysis pipeline for distinguishing heart shunts, utilizing a deep neural network-based framework. The pipeline consists of three steps: (1) chamber segmentation, (2) ultrasound microbubble localization, and (3) disease classification model establishment.
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