Traditional strategies for designing new materials with targeted property including methods such as trial and error, and experiences of domain experts, are time and cost consuming. In the present study, we propose a machine learning design system involving three features of machine learning modeling, compositional design and property prediction, which can accelerate the discovery of new materials. We demonstrate better efficiency of on a rapid compositional design of high-performance copper alloys with a targeted ultimate tensile strength of 600-950 MPa and an electrical conductivity of 50.0% international annealed copper standard. There exists a good consistency between the predicted and measured values for three alloys from literatures and two newly made alloys with designed compositions. Our results provide a new recipe to realize the property-oriented compositional design for highperformance complex alloys via machine learning.

No abstract

Our study observed further hypoadiponectinemia and IR in NAFLD with metabolic syndrome. The T45G and G276T of the adiponectin gene may not be the important determinants of NAFLD in Chinese people, but some of them still influence serum ALT, BMI, IR, lipid, glucose metabolism and plasma adiponectin concentration.

No abstract

Background Heart rate, acidosis, consciousness, oxygenation, and respiratory rate (HACOR) have been used to predict noninvasive ventilation (NIV) failure. However, the HACOR score fails to consider baseline data. Here, we aimed to update the HACOR score to take into account baseline data and test its predictive power for NIV failure primarily after 1–2 h of NIV. Methods A multicenter prospective observational study was performed in 18 hospitals in China and Turkey. Patients who received NIV because of hypoxemic respiratory failure were enrolled. In Chongqing, China, 1451 patients were enrolled in the training cohort. Outside of Chongqing, another 728 patients were enrolled in the external validation cohort. Results Before NIV, the presence of pneumonia, cardiogenic pulmonary edema, pulmonary ARDS, immunosuppression, or septic shock and the SOFA score were strongly associated with NIV failure. These six variables as baseline data were added to the original HACOR score. The AUCs for predicting NIV failure were 0.85 (95% CI 0.84–0.87) and 0.78 (0.75–0.81) tested with the updated HACOR score assessed after 1–2 h of NIV in the training and validation cohorts, respectively. A higher AUC was observed when it was tested with the updated HACOR score compared to the original HACOR score in the training cohort (0.85 vs. 0.80, 0.86 vs. 0.81, and 0.85 vs. 0.82 after 1–2, 12, and 24 h of NIV, respectively; all p values < 0.01). Similar results were found in the validation cohort (0.78 vs. 0.71, 0.79 vs. 0.74, and 0.81 vs. 0.76, respectively; all p values < 0.01). When 7, 10.5, and 14 points of the updated HACOR score were used as cutoff values, the probability of NIV failure was 25%, 50%, and 75%, respectively. Among patients with updated HACOR scores of ≤ 7, 7.5–10.5, 11–14, and > 14 after 1–2 h of NIV, the rate of NIV failure was 12.4%, 38.2%, 67.1%, and 83.7%, respectively. Conclusions The updated HACOR score has high predictive power for NIV failure in patients with hypoxemic respiratory failure. It can be used to help in decision-making when NIV is used.

No abstract

No abstract

No abstract