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Data provides a foundation for machine learning, which has accelerated data-driven materials design. The scientific literature contains a large amount of high-quality, reliable data, and automatically extracting data from the literature continues to be a challenge. We propose a natural language processing pipeline to capture both chemical composition and property data that allows analysis and prediction of superalloys. Within 3 h, 2531 records with both composition and property are extracted from 14,425 articles, covering γ′ solvus temperature, density, solidus, and liquidus temperatures. A data-driven model for γ′ solvus temperature is built to predict unexplored Co-based superalloys with high γ′ solvus temperatures within a relative error of 0.81%. We test the predictions via synthesis and characterization of three alloys. A web-based toolkit as an online open-source platform is provided and expected to serve as the basis for a general method to search for targeted materials using data extracted from the literature.

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Background: Anxiety and depression are common mental health problems among patients with cancer. While many psychological variables have been proven to influence anxiety and depressive symptoms, the variables are not mutually exclusive and their integrated effects on patients with oral cancer are yet unknown. The present study aims to explore the prevalence of anxiety and depressive symptoms among patients with oral cancer, to find out key potentially predictive factors associated with anxiety and depressive symptoms. Method: A cross-sectional study was carried out for Chinese patients with oral cancer between May 2016 and October 2017 in two Grade-A Tertiary Hospitals in Shenyang, China. Two hundred thirty patients with oral cancer were interviewed with questionnaires on demographic variables, Zung Self-Rating Anxiety Scale (SAS), Center for Epidemiologic Studies Depression Scale (CES-D), Herth Hope Index (HHI), Social Impact Scale, Multidimensional Scale of Perceived Social Support (MSPSS), Revised Life Orientation Test (LOT-R), Perceived Stress Scale-10 (PSS-10), and General Perceived Self-efficacy Scale(GSE). Chi-square test, nonparametric test, t-test and logistic regression analyses were conducted where appropriate to explore predictive factors of anxiety symptoms and depressive symptoms. Results: The prevalence of anxiety symptoms and depressive symptoms in the sample population was 36.96% (85/230) and 65.21% (150/230), respectively. Social isolation dimension of stigma (β = 0.436, OR = 1.547, CI:1.211~1.975), optimism (β = − 0.276, OR = 0.759, CI:0.624~0.922), and perceived stress (β = 0.217, OR = 1.243, CI:1.092~1.414) were predictors of anxiety symptoms. Marriage (β = 1.648, OR = 5.198, CI:1.427~18.924), positive readiness and expectancy dimension of hope (β = − 0.505, OR = 0.604, CI:0.395~0.923), social isolation dimension of stigma (β = 0.314, OR = 1.368, CI:1.054~1.776) and perceived stress (β = 0.273, OR = 1.314, CI:1.134~1.524) were predictors of depressive symptoms among oral cancer patients. Conclusion: The prevalence of anxiety symptoms and depressive symptoms was high among oral cancer patients in China. The communal predictors of anxiety and depressive symptoms in patients with oral cancer were levels of perceived stress and social isolation of stigma. In addition, optimism was a predictor of anxiety symptoms and hope was a predictor of depressive symptoms.

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