In recent years, RG-I pectin isolated by low-temperature alkaline extraction methods has attracted the attention of a large number of researchers due to its huge health benefits. However, studies on other applications of RG-I pectin are still lacking. In this study, we summarized the sources (e.g. potato pulp, sugar beet pulp, okra, apple pomace, citrus peel, pumpkin, grapefruit, ginseng, etc.), extraction methods, fine structure and applications of RG-I pectin in physiological activities (e.g. anti-cancer, anti-inflammatory, anti-obesity, anti-oxidation, immune regulation, prebiotics, etc.), emulsions, gels, etc. These neutral sugar side chains not only endow RG-I pectin with various physiological activities but the entanglement and cross-linking of these side chains also endow RG-I pectin with excellent emulsifying and gelling properties. We believe that this review can not only provide a comprehensive reading for new workers interested in RG-I pectin, but also provide a valuable reference for future research directions of RG-I pectin.

Objectives: Sepsis is caused by infection and subsequent overreaction of immune system and will severely threaten human life. The early prediction is important for the treatment of sepsis. This report aims to develop an early prediction method for sepsis 6 hours ahead on the basis of clinical electronic health records. Data Sources: Challenge data are released by PhysioNet/Computing in Cardiology Challenge 2019 and obtained from ICU patients in three separate hospital systems. Part of the data from two datasets, including 40,336 subjects, are publicly available, and the remaining are used as hidden test set. A normalized utility score defined by the organizing committee is used for model performance evaluation. Study Selection: The supervised machine learning is applied to tackle this challenge. Specifically, we establish the prediction model under the framework of ensemble learning by integrating the artificial features based on clinical prior knowledge of sepsis with deep features automatically extracted by long short-term memory neural network. Data Extraction: Forty clinical variables, including eight vital signs, 26 laboratory values, and six demographics, were measured and recorded once an hour for each individual, and the binary label (0 or 1) was simultaneously provided for each item. Data Synthesis: The proposed model was evaluated by 30-fold cross-validation. The sensitivity, specificity, and normalized utility score were 0.641 ± 0.022, 0.844 ± 0.007, and 0.401 ± 0.019 on publicly available datasets, respectively. The final normalized utility score our team (UCAS_DataMiner) has obtained was 0.313 on full hidden test set (0.406, 0.373, and –0.215 on test set A, B, and C, respectively). Conclusions: We realized a 6-hour ahead early-onset prediction of sepsis on the basis of clinical electronic health record by ensemble learning. The results indicated the proposed model functioned well in the early prediction of sepsis. In particular, ensemble learning had a significant (p < 0.01) improvement than any single model in performance.

This paper reports a high performance micromechanical resonant electrostatic field sensor (EFS) that is fabricated with a three-layer polysilicon surface micromachining process. The EFS has a high resolution of 100V/m when used in ambient air at room temperature. The driving voltage is 25V DC and 0.3Vp-p AC lower than other reported electrostatic comb driven devices. Experimental results show that the EFS can be succeeded in atmospheric electric field detecting.