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To enhance our understanding of forest carbon sequestration, climate change mitigation and drought impact on forest ecosystems, the availability of high-resolution annual forest growth maps based on tree-ring width (TRW) would provide a significant advancement to the field. Site-specific characteristics, which can be approximated by high-resolution Earth observation by satellites (EOS), emerge as crucial drivers of forest growth, influencing how climate translates into tree growth. EOS provides information on surface reflectance related to forest characteristics and thus can potentially improve the accuracy of forest growth models based on TRW. Through the modelling of TRW using EOS, climate and topography data, we showed that species-specific models can explain up to 52 % of model variance (Quercus petraea), while combining different species results in relatively poor model performance (R 2  = 13 %). The integration of EOS into models based solely on climate and elevation data improved the explained variance by 6 % on average. Leveraging these insights, we successfully generated a map of annual TRW for the year 2021. We employed the area of applicability (AOA) approach to delineate the range in which our models are deemed valid. The calculated AOA for the established forest-type models was 73 % of the study region, indicating robust spatial applicability. Notably, unreliable predictions predominantly occurred in the climate margins of our dataset. In conclusion, our large-scale assessment underscores the efficacy of combining climate, EOS and topographic data to develop robust models for mapping annual TRW. This research not only fills a critical void in the current understanding of forest growth dynamics but also highlights the potential of integrated data sources for comprehensive ecosystem assessments., Competing Interests: Declaration of competing interest The authors declare no competing interests., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Background: The COPD Assessment Test (CAT) has been proposed to help guide therapy in chronic obstructive pulmonary disease (COPD). It is important to understand the distribution of scores in different COPD populations and their determinants., Methods: The POPE study is an international, observational cross-sectional study of COPD subjects in 11 Central and Eastern European countries aimed at characterizing COPD phenotypes. Here we report the analysis of CAT scores with the objective of identifying their determinants, evaluating symptom load and investigating the distribution of scores among the participating countries. Additionally, we investigated the discrepancies between the CAT and modified Medical Research Council (mMRC) scores when used to classify patients according to the GOLD strategy., Results: The study included 3452 patients (69.2% men, mean forced expiratory volume in 1 s (FEV1% predicted) 52.5%). The mean CAT score was 17.5 (SD = 7.8), ranging from 15.1 in Hungary to 21.2 in Bulgaria. Multiple linear regression analysis showed six variables significantly associated with CAT scores: depression, number of previous exacerbations, 6-min walking distance, FEV1(%), mMRC and country and explained 47.2% of the variance of CAT. According to either CAT or mMRC, up to 23.9% patients would be classified in different GOLD groups., Conclusions: The CAT score may be predicted by factors related to COPD severity, depression and exercise capacity, with significant differences in the distribution of CAT scores in different countries. According to our results CAT >10 is not equivalent to mMRC >2 for assessing symptom burden., Trial Registration: ClinicalTrials.gov, identifier NCT02119494., (Copyright © 2019 Elsevier Ltd. All rights reserved.)