Time trends in Alzheimer's disease mortality attributable to metabolic risks and smoking in China from 1990 to 2019: an age-period-cohort analysis.

Background: With the increase in the aging population worldwide, Alzheimer's disease has become a rapidly increasing public health concern. In the Global Burden of Disease Study 2019, there are three risk factors judged to have evidence for a causal link to Alzheimer's disease and other dementias: smoking, high body-mass index (HBMI), and high fasting plasma glucose (HFPG).

Objective: This study aimed to analyze trends in AD mortality and the relevant burden across China from 1990 to 2019, as well as their correlation with age, period, and birth cohort.

Enhancement of hepatoprotective activity of limonin from citrus seeds against acetaminophen-induced liver injury by HSCCC purification and liposomal encapsulation.

Limonin is a natural tetracyclic triterpenoid compound in citrus seeds that presents hepatoprotective effects but is often discarded as agricultural waste because of its low content and low solubility. Herein, limonin with high purity (98.11%) from citrus seeds was obtained via purification by high-speed counter-current chromatography (HSCCC) and recrystallization.

Missing Data Imputation Method Combining Random Forest and Generative Adversarial Imputation Network.

(1) Background: In order to solve the problem of missing time-series data due to the influence of the acquisition system or external factors, a missing time-series data interpolation method based on random forest and a generative adversarial interpolation network is proposed. (2) Methods: First, the position of the missing part of the data is calibrated, and the trained random forest algorithm is used for the first data interpolation. The output value of the random forest algorithm is used as the input value of the generative adversarial interpolation network, and the generative adversarial interpolation network is used to calibrate the position.

Multiform Informed Machine Learning Based on Piecewise and Weibull for Engine Remaining Useful Life Prediction.

Informed machine learning (IML), which strengthens machine learning (ML) models by incorporating external knowledge, can get around issues like prediction outputs that do not follow natural laws and models, hitting optimization limits. It is therefore of significant importance to investigate how domain knowledge of equipment degradation or failure can be incorporated into machine learning models to achieve more accurate and more interpretable predictions of the remaining useful life (RUL) of equipment. Based on the informed machine learning process, the model proposed in this paper is divided into the following three steps: (1) determine the sources of the two types of knowledge based on the device domain knowledge, (2) express the two forms of knowledge formally in Piecewise and Weibull, respectively, and (3) select different ways of integrating them into the machine learning pipeline based on the results of the formal expression of the two types of knowledge in the previous step.