Trust in scientists and their role in society across 68 countries.

Science is crucial for evidence-based decision-making. Public trust in scientists can help decision makers act on the basis of the best available evidence, especially during crises. However, in recent years the epistemic authority of science has been challenged, causing concerns about low public trust in scientists.

Perceptions of science, science communication, and climate change attitudes in 68 countries - the TISP dataset.

Science is integral to society because it can inform individual, government, corporate, and civil society decision-making on issues such as public health, new technologies or climate change. Yet, public distrust and populist sentiment challenge the relationship between science and society. To help researchers analyse the science-society nexus across different geographical and cultural contexts, we undertook a cross-sectional population survey resulting in a dataset of 71,922 participants in 68 countries.

Expression Levels of MUC5AC and MUC5B in Airway Goblet Cells Are Associated with Traits of COPD and Progression of Chronic Airflow Limitation.

Mucins 5AC (MUC5AC) and 5B (MUC5B) are the major mucins providing the organizing framework for the airway's mucus gel. We retrieved bronchial mucosal biopsies and bronchial wash (BW) samples through bronchoscopy from patients with chronic obstructive pulmonary disease ( = 38), healthy never-smokers ( = 40), and smokers with normal lung function ( = 40). The expression of MUC5AC and MUC5B was assessed immunohistochemically.

Predicting the replicability of social and behavioural science claims in COVID-19 preprints.

Replications are important for assessing the reliability of published findings. However, they are costly, and it is infeasible to replicate everything. Accurate, fast, lower-cost alternatives such as eliciting predictions could accelerate assessment for rapid policy implementation in a crisis and help guide a more efficient allocation of scarce replication resources.

Generative language models exhibit social identity biases.

Social identity biases, particularly the tendency to favor one's own group (ingroup solidarity) and derogate other groups (outgroup hostility), are deeply rooted in human psychology and social behavior. However, it is unknown if such biases are also present in artificial intelligence systems. Here we show that large language models (LLMs) exhibit patterns of social identity bias, similarly to humans.