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The expansion of bamboo (Phyllostachys edulis) affects the growth status of trees in colonized forests, but there has been insufficient research on changes in tree water physiology. In this study, we used stable δ2H, δ18O, and 13C isotope ratios to analyze the water sources and water use efficiency (WUE) of bamboo, deciduous broadleaf trees (Alniphyllum fortunei), and evergreen broadleaf trees (Machilus pauhoi and Castanopsis eyrei) in a bamboo-expended broadleaf forest (BEBF), a bamboo-absent broadleaf forest (BABF), and a bamboo forest (BF). We found that the expansion of bamboo had no significant effect on the water sources and WUE of deciduous broadleaf trees, but altered the water sources of evergreen broadleaf trees. During the growing season, evergreen broadleaf trees decrease their uptake fractions of surface soil water by 7.1% to 9.6% and increased their uptake fractions of middle soil water by 5.8%~9.4%. Conversely, during the non-growing season, they increased their uptake fractions of surface soil water by 11.9% and decreased their uptake fractions of deeper soil water by 5.6%~12.9%. Additionally, after expanding into broadleaf forests, bamboo increased its uptake proportion of surface and shallow soil water by 20.0% and 9.4% during the growing season. Its WUE also improved, increasing by 20.0 μmol/mol and 13.0 μmol/mol during the growing and non-growing seasons, respectively. These results indicate that as bamboo expands into broadleaf forests, it enhances its competitiveness for water resources by changing its water use strategy. Compared to deciduous broadleaf trees, evergreen broadleaf trees exhibit more flexible water use strategies under the conditions of bamboo expansion. Our research reveals, for the first time, how broadleaf trees adjust their water use strategies in response to bamboo expansion, and uncovers the mechanisms behind bamboo expansion into evergreen broadleaf forests from the perspective of water use strategies. This will aid future forest management under the conditions of bamboo expansion. [ABSTRACT FROM AUTHOR]

Objectives: To develop a user-friendly nomogram-based predictive model for interstitial lung disease (ILD) in patients with idiopathic inflammatory myositis (IIM)., Methods: A retrospective study was conducted at Shantou Central Hospital, encompassing 205 IIM patients diagnosed between January 2013 and December 2022. We used the LASSO regression method in the discovery set to select features for model construction, followed by efficacy verification through AUC of ROC. Afterwards, KL-6 values and LUS B-lines number were added into this model to evaluate whether these 2 factors added to the model efficiency. Finally, a web version was constructed to make it more available., Results: Among the 205 IIM patients, 115 (56.1%) patients were diagnosed with ILD, and 90 (43.9%) did not. The predictive model, derived from the training set, comprised four independent risk factors, including age, presence of respiratory symptoms, anti-melanoma differentiation-associated gene 5 (MDA-5) antibody positivity, and anti-aminoacyl transfer RNA synthetase (anti-ARS) antibodies positivity. Notably, anti-TIF1-γ antibody positivity emerged as a protective factor. The AUC of the ROC based on these 5 factors was 0.876 in the training set and 0.861 in the validation set. The AUC of the ROC based on the 5 factors plus KL-6 was 0.922, 5 factors plus B-line number was 0.949 and 5 factors plus both KL-6 and B-line number was 0.951. Accordingly, a nomogram and a web version were developed., Conclusions: This predictive model demonstrates robust capability to assess ILD risk in IIM patients, particularly when augmented with serum KL-6 level or/and LUS B-line number.