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There are some problems in network traffic classification (NTC), such as complicated statistical features and insufficient training samples, which may cause poor classification effect. A NTC architecture based on one-dimensional Convolutional Neural Network (CNN) and transfer learning is proposed to tackle these problems and improve the fine-grained classification performance. The key points of the proposed architecture include: (1) Model classification--by extracting normalized rate feature set from original data, plus existing statistical features to optimize the CNN NTC model. (2) To apply transfer learning in the classification to improve NTC performance. We collect two typical network flows data from Youku and YouTube, and verify the proposed method through extensive experiments. The results show that compared with existing methods, our method could improve the classification accuracy by around 3-5%for Youku, and by about 7 to 27% for YouTube. [ABSTRACT FROM AUTHOR]

Polymorphism of p53 codon 72 plays an important role in pathogenesis and development of cancer. Published data on the association between the p53 codon 72 polymorphism and endometrial cancer risk are controversial. A meta-analysis was performed to assess whether the polymorphism of p53 codon 72 is associated with endometrial cancer risk. Medline, Embase, China National Knowledge Infrastructure, and Chinese Biomedicine Databases were searched to identify eligible studies. Pooled odds ratios (ORs) and 95% confidence intervals (CIs) for p53 codon 72 polymorphism and endometrial cancer were appropriately derived from fixed-effects or random effects models. A total of 12 studies were enrolled in this meta-analysis. The pooled analyses revealed that p53 codon 72 polymorphism was not associated with endometrial cancer risk. Stratified analysis by Hardy- Weinberg equilibrium exhibited a significantly increased risk of endometrial cancer among studies deviated from Hardy-Weinberg equilibrium in heterozygote comparison (Pro/Arg vs Arg/Arg; OR, 0.61; 95%CI, 0.42Y0.87) and dominant model (Pro/Pro + Pro/Arg vs Arg/Arg; OR, 0.66; 95%CI, 0.47Y0.92). This study indicated that the p53 codon 72 polymorphismmay not be associated with endometrial cancer risk. [ABSTRACT FROM AUTHOR]