Oil painting teaching design based on the mobile platform in higher art education.

To improve the current oil painting teaching mode in Chinese universities, this study combines deep learning technology and artificial intelligence technology to explore oil painting teaching. Firstly, the research status of individualized education and related research on image classification based on brush features are analyzed. Secondly, based on a convolutional neural network, mathematical morphology, and support vector machine, the oil painting classification model is constructed, in which the extracted features include color and brush features. Moreover, based on artificial intelligence technology and individualized education theory, a personalized intelligent oil painting teaching framework is built. Finally, the performance of the intelligent oil painting classification model is evaluated, and the content of the personalized intelligent oil painting teaching framework is explained. The results show that the average classification accuracy of oil painting is 90.25% when only brush features are extracted. When only color features are extracted, the average classification accuracy is over 89%. When the two features are extracted, the average accuracy of the oil painting classification model reaches 94.03%. Iterative Dichotomiser3, decision tree C4.5, and support vector machines have an average classification accuracy of 82.24%, 83.57%, and 94.03%. The training speed of epochs data with size 50 is faster than that of epochs original data with size 100, but the accuracy is slightly decreased. The personalized oil painting teaching system helps students adjust their learning plans according to their conditions, avoid learning repetitive content, and ultimately improve students' learning efficiency. Compared with other studies, this study obtains a good oil painting classification model and a personalized oil painting education system that plays a positive role in oil painting teaching. This study has laid the foundation for the development of higher art education.

Automated and real-time validation of gastroesophageal varices under esophagogastroduodenoscopy using a deep convolutional neural network: a multicenter retrospective study (with video).

BACKGROUND AND AIMS: Rupture of gastroesophageal varices is the most common fatal adverse event of cirrhosis. EGD is considered the criterion standard for diagnosis and risk stratification of gastroesophageal variceal bleeding. The aim of this study was to train and validate a real-time deep convolutional neural network (DCNN) system, named ENDOANGEL, for diagnosing gastroesophageal varices and predicting the risk of rupture. METHODS: After training with 8566 images of endoscopic gastroesophageal varices from 3021 patients and 6152 images of normal esophagus/stomach from 3168 patients, ENDOANGEL was also tested with independent images and videos. It was also compared with endoscopists in several aspects. RESULTS: ENDOANGEL, in contrast with endoscopists, displayed higher accuracy of 97.00% and 92.00% in terms of detecting esophageal varices (EVs) and gastric varices (GVs) in an image contest (97.00% vs 93.94% , P < .01; 92.00% vs 84.43%, P < .05). It also surpassed endoscopists for red color signs of EVs and red spots of GVs (84.21% vs 73.45%, P < .01; 85.26% vs 77.52%, P < .05). Moreover, ENDOANGEL achieved comparable performance in the determination of size, form, color, and bleeding signs. ENDOANGEL also had good performance in making treatment suggestions. With regard to predicting risk factors in multicenter videos, ENDOANGEL showed great stability. CONCLUSIONS: Our data suggest that DCNNs were precise in detecting both EVs and GVs and performed excellently in uncovering the endoscopic risk factors of gastroesophageal variceal bleeding. Thus, the application of DCNNs will assist endoscopists in evaluating gastroesophageal varices more objectively and precisely. (Clinical trial registration number: ChiCTR1900023970.).

Evaluating Economic Growth, Industrial Structure, and Water Quality of the Xiangjiang River Basin in China Based on a Spatial Econometric Approach.

This research utilizes the environmental Kuznets curve to demonstrate the interrelationship between economic growth, industrial structure, and water quality of the Xiangjiang river basin in China by employing spatial panel data models. First, it obtains two variables (namely, CODMn, which represents the chemical oxygen demand of using KMnO4 as chemical oxidant, and NH3-N, which represents the ammonia nitrogen content index of wastewater) by pretreating the data of 42 environmental monitoring stations in the Xiangjiang river basin from 2005 to 2015. Afterward, Moran's I index is adopted to analyze the spatial autocorrelation of CODMn and NH3-N concentration. Then, a comparative analysis of the nonspatial panel model and spatial panel model is conducted. Finally, this research estimates the intermediate effect of the industrial structure of the Xiangjiang river basin in China. The results show that spatial autocorrelation exists in pollutant concentration and the relationship between economic growth and pollutant concentration shapes as an inverted-N trajectory. Moreover, the turn points of the environmental Kuznets curve for CODMn are RMB 83,001 and RMB 108,583 per capita GDP. In contrast, the turn points for NH3-N are RMB 50,980 and RMB 188,931 per capita GDP. Additionally, the environmental Kuznets curve for CODMn can be explained by industrial structure adjustment, while that for NH3-N cannot. As a consequence, the research suggests that the effect of various pollutants should be taken into account while making industrial policies.

Identification of a novel gene pairs signature in the prognosis of gastric cancer.

Current prognostic signatures need to be improved in identifying high-risk patients of gastric cancer (GC). Thus, we aimed to develop a reliable prognostic signature that could assess the prognosis risk in GC patients. Two microarray datasets of GSE662254 (n = 300, training set) and GSE15459 (n = 192, test set) were included into analysis. Prognostic genes were screened to construct prognosis-related gene pairs (PRGPs). Then, a penalized Cox proportional hazards regression model identified seven PRGPs, which constructed a prognostic signature and divided patients into high- and low-risk groups according to the signature score. High-risk patients showed a poorer prognosis than low-risk patients in both the training set (hazard ratios [HR]: 6.086, 95% confidence interval [CI]: 4.341-8.533) and test set (1.773 [1.107-2.840]). The PRGPs signature also achieved a higher predictive accuracy (concordance index [C-index]: 0.872, 95% CI: 0.846-0.897) than two existing molecular signatures (0.706 [0.667-0.744] for a 11-gene signature and 0.684 [0.642-0.726] for a 24-lncRNA signature) and TNM stage (0.764 [0.715-0.814]). In conclusion, our study identified a novel gene pairs signature in the prognosis of GC.