Search

With the acceleration of the application of information technology in Colleges and universities in China, the efficiency of higher education is constantly improving. The focus of teaching management in Colleges and universities is to continuously improve the teaching level of colleges and universities, and the key is to strengthen the management of students’ performance. Performance warning is a form of student performance management. In recent years, data mining technology is more and more mature, and its application is also very wide. Many students have applied data mining technology to university management. In this paper, we apply data mining technology to college students’ performance early warning, and use Apriori algorithm in association rules to design and build college students’ performance early warning platform, and select two classes of students as the research object to verify. In this study, we choose the English course scores of two classes as the test data, and define the performance warning, which is based on the score below 60. The results show that six students in class a will be subject to performance warning, while seven students in class B will be subject to performance warning. In addition, the performance early warning platform designed by this method, the early warning accuracy rate is as high as 92.85%, the accuracy rate is high, has certain application advantages.

There are many factors affecting students’ grades, which make the prediction of students’ grades present high dimensional and nonlinear characteristics. Therefore, the traditional method has a large error in the prediction results, which is difficult to meet the practical needs. With the rapid development of artificial neural network (Ann), the deep cycle neural network algorithm based on Ann provides a new approach for student achievement prediction. In order to further improve the accuracy of student achievement prediction, this paper proposes a performance prediction model based on deep cycle neural network algorithm. First, principal component analysis is used for data dimensionality reduction processing of the established student writing evaluation system, and the first five principal components are extracted. Then, these principal components are taken as the input of the neural network to construct a three-layer neural network prediction model. The experimental results show that, compared with the single RBF neural network and BP neural network, the prediction model under deep cycle neural network is simple in structure, fast in convergence and 21.6% higher in prediction accuracy, which verifies the effectiveness of the model proposed in this paper.

JARID1B, a histone demethylase, has been reported to be highly expressed in various human cancers. In the present study, we investigated the association of JARID1B level with epithelial ovarian cancer (EOC) and prognosis of patients with EOC. We analyzed JARID1B expression in 20 normal ovaries, 20 benign ovarian tumor (BOT) samples, and 45 epithelial ovarian carcinoma specimens by quantitative PCR (qRT-PCR) and western blotting analyses. JARID1B was further examined in 120 EOC specimens from patients with different histological stages via immunohistochemistry. Possible correlations between JARID1B levels and prognosis as well as chemotherapy resistance of EOC patients were determined by univariate and multivariate analyses. JARID1B level was significantly increased in EOC, as compared to normal ovaries and BOT. Among 120 EOC cases examined, the 5-year progression-free survival (PFS) rates were 17 and 85 % in patients with high and low JARID1B expression, respectively (hazard ratio = 17.85, 95 % confidence interval (CI) 6.31–50.51, P

The present study assessed the effect of photodynamic therapy (PDT) combined with Celecoxib (Cel) on cervical cancer HeLa cells. An MTT assay was performed to detect the inhibitory effects of Cel with different concentrations (10, 50, 100, 150, 200, 250 and 300 µg/ml) on the proliferation of HeLa cells. Subsequently, HeLa cells were divided into control group (group H), 50 g/ml Celecoxib group (group C), PDT group (group P), 50 g/ml Cel + PDT group (group P + C) and western blotting and immunohistochemistry were performed to detect the expression of cyclooxygenase-2 (COX-2) protein in the different groups. Cel inhibited HeLa cells proliferation 24 h following administration, among which 200 µg/ml induced a 50% inhibition rate; the relative expression level of COX-2 protein in group P + C was significantly decreased compared with that in either group C or group P (P