Non-alcoholic fatty liver disease (NAFLD) is the most common chronic liver disease, and its pathogenesis is complicated and triggered by unbalanced diet, sedentary lifestyle, and genetic background. The aim of this study was to construct and validate a nomogram incorporated lifestyle habits for predicting NAFLD incidence. The overall cohort was divided into training set and test set as using computer-generated random numbers. We constructed the nomogram by multivariate logistic regression analysis in the training set. Thereafter, we validated this model by concordance index, the area under the receiver operating characteristic curve (ROC), net reclassification index, and a calibration curve in the test set. Additionally, we also evaluated the clinical usefulness of the nomogram by decision curve analysis. There were no statistically significant differences about characteristics between training cohort (n = 748) and test cohort (n = 320). Eleven features (age, sex, body mass index, drinking tea, physical exercise, energy, monounsaturated fatty acids, polyunsaturated fatty acids, hypertension, hyperlipidemia, diabetes) were incorporated to construct the nomogram, concordance index, the area under the ROC curve, net reclassification index were 0.801, 0.801, and 0.084, respectively, indicating the nomogram have good discrimination of predicting NAFLD incidence. Also, the calibration curve showed good consistency between nomogram prediction and actual probability. Moreover, the decision curve showed that when the threshold probability of an individual is within a range from approximately 0.5 to 0.8, this model provided more net benefit to predict NAFLD incidence risk than the current strategies. This nomogram can be regarded as a user-friendly tool for assessing the risk of NAFLD incidence, and thus help to facilitate management of NAFLD including lifestyle and medical interventions.

Aftershock-monitoring networks deployed in the epicentral area of a damaging earthquake play important roles in earthquake early warning and ShakeMap estimation, which contribute to hazard mitigation. Using distributed acoustic sensing (DAS) technology with dark fiber can significantly reduce deployment time and cost, and improve spatial sampling, both of which help capture more aftershocks. In this study, we used a 7.6 km dark fiber in Tangshan, China, to monitor seismicity after the 12 July 2020 Ms 5.1 earthquake. The DAS array detected dozens of earthquakes missed by the local permanent network that doubled the number of aftershocks. The relocated aftershocks are distributed mainly north of the DAS array, and the ground-motion pattern changes also hint small-scale features. Our successful results demonstrate the feasibility of using DAS and dark fiber for rapid postearthquake response.

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