A R T I C L E I N F OKeywords: government social media citizen engagement dialogic communication theory media richness theory emotional valence crisis management A B S T R A C TDuring times of public crises, governments must act swiftly to communicate crisis information effectively and efficiently to members of the public; failure to do so will inevitably lead citizens to become fearful, uncertain and anxious in the prevailing conditions. This pioneering study systematically investigates how Chinese central government agencies used social media to promote citizen engagement during the COVID-19 crisis. Using data scraped from 'Healthy China', an official Sina Weibo account of the National Health Commission of China, we examine how citizen engagement relates to a series of theoretically relevant factors, including media richness, dialogic loop, content type and emotional valence. Results show that media richness negatively predicts citizen engagement through government social media, but dialogic loop facilitates engagement. Information relating to the latest news about the crisis and the government's handling of the event positively affects citizen engagement through government social media. Importantly, all relationships were contingent upon the emotional valence of each Weibo post. )is researcher at the School of Journalism and New Media, Xi'a Jiaotong University in China. She has been studying for information behavior and interaction, and information analysis on the new media.Richard Evans (richard.evans@brunel.ac.uk) is a Senior Lecturer at the College of Engineering, Design and Physical Sciences, Brunel University London, United Kingdom. His research interests are social media and organizational behavior, and knowledge management.

Cells can sense, signal, and organize via mechanical forces. The ability of cells to mechanically sense and respond to the presence of other cells over relatively long distances (e.g., ∼100 μm, or ∼10 cell-diameters) across extracellular matrix (ECM) has been attributed to the strain-hardening behavior of the ECM. In this study, we explore an alternative hypothesis: the fibrous nature of the ECM makes long-range stress transmission possible and provides an important mechanism for long-range cell-cell mechanical signaling. To test this hypothesis, confocal reflectance microscopy was used to develop image-based finite-element models of stress transmission within fibroblast-seeded collagen gels. Models that account for the gel's fibrous nature were compared with homogenous linear-elastic and strain-hardening models to investigate the mechanisms of stress propagation. Experimentally, cells were observed to compact the collagen gel and align collagen fibers between neighboring cells within 24 h. Finite-element analysis revealed that stresses generated by a centripetally contracting cell boundary are concentrated in the relatively stiff ECM fibers and are propagated farther in a fibrous matrix as compared to homogeneous linear elastic or strain-hardening materials. These results support the hypothesis that ECM fibers, especially aligned ones, play an important role in long-range stress transmission.

No abstract

Aims Symptom-based pretest probability scores that estimate the likelihood of obstructive coronary artery disease (CAD) in stable chest pain have moderate accuracy. We sought to develop a machine learning (ML) model, utilizing clinical factors and the coronary artery calcium score (CACS), to predict the presence of obstructive CAD on coronary computed tomography angiography (CCTA). Methods and results The study screened 35 281 participants enrolled in the CONFIRM registry, who underwent ≥64 detector row CCTA evaluation because of either suspected or previously established CAD. A boosted ensemble algorithm (XGBoost) was used, with data split into a training set (80%) on which 10-fold cross-validation was done and a test set (20%). Performance was assessed of the (1) ML model (using 25 clinical and demographic features), (2) ML + CACS, (3) CAD consortium clinical score, (4) CAD consortium clinical score + CACS, and (5) updated Diamond-Forrester (UDF) score. The study population comprised of 13 054 patients, of whom 2380 (18.2%) had obstructive CAD (≥50% stenosis). Machine learning with CACS produced the best performance [area under the curve (AUC) of 0.881] compared with ML alone (AUC of 0.773), CAD consortium clinical score (AUC of 0.734), and with CACS (AUC of 0.866) and UDF (AUC of 0.682), P < 0.05 for all comparisons. CACS, age, and gender were the highest ranking features. Conclusion A ML model incorporating clinical features in addition to CACS can accurately estimate the pretest likelihood of obstructive CAD on CCTA. In clinical practice, the utilization of such an approach could improve risk stratification and help guide downstream management.