Evaluating the ecological vulnerability of Chongqing using deep learning.

This study used deep learning to evaluate the ecological vulnerability of Chongqing, China, discuss the deep learning evaluations of ecological vulnerability, and generate vulnerability maps that support local ecological environment protection and governance decisions and provide reference for future studies. The information gain ratio was used to screen the influencing factors, selecting 16 factors that influence ecological vulnerability. Deep neural network (DNN) and convolutional neural network (CNN) methods were used for modeling, and two ecological vulnerability maps of the study area were generated.

Diagnostic Potential of the Serum lncRNAs HOTAIR, BRM and ICR for Hepatocellular Carcinoma.

Background: Long noncoding RNAs (lncRNAs) are closely associated with the initiation, progression, metastasis, and recurrence of hepatocellular carcinoma (HCC). They could therefore serve as markers for the early diagnosis and for the prognosis of HCC patients.

Methods: This was an observational prospective cohort study.