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In Arabidopsis, systemic acquired resistance (SAR) is established beyond the initial infection by a pathogen or is directly induced by treatment with salicylic acid (SA) or its functional analogs, 2,6-dichloroisonicotinic acid (INA) and benzothiadiazole (BTH). NPR1 protein is considered the master regulator of SAR in both SA signal sensing and transduction. In wheat (Triticum aestivum) and barley (Hordeum vulgare), both pathogen infection and BTH treatment can induce broad-spectrum resistance to various diseases, including powdery mildew, leaf rust, Fusarium head blight, etc. However, three different types of SAR-like responses including acquired resistance (AR), systemic immunity (SI), and BTH-induced resistance (BIR) seem to be achieved by activating different gene pathways. Recent research on wheat and barley NPR1 homologs in AR and SI has provided the initial clue for understanding the mechanism of SAR in these two plant species. In this review, the specific features of AR, SI, and BIR in wheat and barley were summarized and compared with that of SAR in model plants of Arabidopsis and rice. Research updates on downstream genes of SAR, including pathogenesis-related (PR) and BTH-induced genes, were highlighted.

Abstract Background To develop a machine learning model for predicting acute respiratory distress syndrome (ARDS) events through commonly available parameters, including baseline characteristics and clinical and laboratory parameters. Methods A secondary analysis of a multi-centre prospective observational cohort study from five hospitals in Beijing, China, was conducted from January 1, 2011, to August 31, 2014. A total of 296 patients at risk for developing ARDS admitted to medical intensive care units (ICUs) were included. We applied a random forest approach to identify the best set of predictors out of 42 variables measured on day 1 of admission. Results All patients were randomly divided into training (80%) and testing (20%) sets. Additionally, these patients were followed daily and assessed according to the Berlin definition. The model obtained an average area under the receiver operating characteristic (ROC) curve (AUC) of 0.82 and yielded a predictive accuracy of 83%. For the first time, four new biomarkers were included in the model: decreased minimum haematocrit, glucose, and sodium and increased minimum white blood cell (WBC) count. Conclusions This newly established machine learning-based model shows good predictive ability in Chinese patients with ARDS. External validation studies are necessary to confirm the generalisability of our approach across populations and treatment practices.