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. The results showed that the mean absolute error and root mean square error of the DNN and CNN models were relatively small, and the fitting accuracy was high. The area under the receiver operating characteristic curve of the CNN model was 0.926, which was better than that of the DNN model (0.888). Random forest was applied to calculate the importance of the influencing factors in the two models. Because the main factor was geological features, the relative ecological vulnerability was mainly affected by karst topography. Through the analysis of the ecological vulnerability map, the areas with higher vulnerability are the karst mountains of Dabashan, Wushan, and Qiyaoshan in the northeast and southeast, as well as the valley between mountains and cities in the center and west of the study area. According to the investigation of these areas, the primary ecological problems are low forest quality, structural irregularities caused by self-geological factors, severe desertification, and soil erosion. Human activity is also an important factor that causes ecological vulnerability in the study area. In conclusion, deep learning, particularly CNN models, can be used for ecological vulnerability assessments. The ecological vulnerability maps conformed to the basic cognition of field surveys and can provide references for other deep learning vulnerability studies. While the overall vulnerability of the study area is not high, ecological problems that lead to its vulnerability should be addressed by future ecological protection and management measures.

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. A total of 101 participants were included, comprising patients with HCC (n = 61), liver cirrhosis (LC) (n = 20), or healthy controls (HC) (n = 20). The baseline characteristics of participants in each group were compared. Serum levels of the lncRNAs HOTAIR, BRM and ICR were determined in each group by reverse transcription and quantitative real-time polymerase chain reaction (qRT-PCR). Correlations between the serum levels of the three lncRNAs and multiple clinical parameters were analysed. The receiver operating characteristic (ROC) curve was used to assess the diagnostic potential for HCC of each lncRNA individually, or in combination with AFP. Multivariate Cox regression analysis was used to evaluate the accuracy of these lncRNAs for predicting the outcome and survival of HCC patients. RESULTS: The serum levels of HOTAIR, BRM and ICR were significantly higher in HCC patients compared to LC patients and healthy subjects. The HOTAIR level was positively correlated to tumour-node metastasis (TNM), Barcelona Clinic Liver Cancer (BCLC) stage, extrahepatic metastasis, vascular invasion, portal vein tumour thrombus (PVTT), and tumour size. The BRM level was positively associated with TNM stage, BCLC stage, vascular invasion, PVTT, and tumour size, while the ICR level was positively correlated with PVTT. A combination of the three lncRNAs and AFP showed the highest diagnostic accuracy for HCC, with an AUC of 0.998, sensitivity of 98.4%, and specificity of 100.0%. This combination showed a better diagnostic accuracy than the individual lncRNAs or AFP alone. Serum levels of the HOTAIR and ICR lncRNAs decreased significantly following surgery. CONCLUSIONS: Serum levels of the HOTAIR, BRM and ICR lncRNAs are potential prognostic markers for HCC. Upregulation of HOTAIR, BRM and ICR may facilitate early diagnosis and indicate poor prognosis for HCC. These lncRNAs could potentially serve as therapeutic targets for HCC. Combination of the three lncRNAs with AFP may increase the diagnostic accuracy for HCC. Further studies in larger cohorts of patients are needed to validate these findings.