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Summary: Early steps in the endoplasmic reticulum (ER) lumen and cis-Golgi comprise trimming of N-glycans by class I α-mannosidases (MNSs) play crucial roles in root growth and stress response. Herein, we found that the root growth inhibition in the mns1 mns2 mns3 mutant was partially rescued under alkaline condition, and inhibitor treatment to disrupt auxin transport counteracted this alkaline-maintained root growth. Further study showed that indole-3-acetic acid (IAA) levels were undetectable in mns1 mns2 mns3 at normal condition and recovered at alkaline condition, which corroborate our N-glycopeptide profiling, from which N-glycopeptides related with IAA biosynthesis, amino acid conjugates hydrolysis, and response showed differential abundance between normal and alkaline conditions in mns1 mns2 mns3. Overall, our results linked the need for MNSs-mediated N-glycan processing in the ER and cis-Golgi with maintenance of auxin homeostasis and transport in Arabidopsis roots during the response to alkaline stress.

Summary: We aimed to develop a machine-learning based predictive model to identify 30-day readmission risk in Acute heart failure (AHF) patients. In this study 2232 patients hospitalized with AHF were included. The variance inflation factor value and 5-fold cross-validation were used to select vital clinical variables. Five machine learning algorithms with good performance were applied to develop models, and the discrimination ability was comprehensively evaluated by sensitivity, specificity, and area under the ROC curve (AUC). Prediction results were illustrated by SHapley Additive exPlanations (SHAP) values. Finally, the XGBoost model performs optimally: the greatest AUC of 0.763 (0.703–0.824), highest sensitivity of 0.660, and high accuracy of 0.709. This study developed an optimal XGBoost model to predict the risk of 30-day unplanned readmission for AHF patients, which showed more significant performance compared with traditional logistic regression (LR) model.