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Medical classification is affected by many factors, and the traditional medical classification is usually restricted by factors such as too long text, numerous categories and so on. In order to solve these problems, this paper uses word vector and word vector to mine the text deeply, considering the problem of scattered key features of medical text, introducing long-term and short-term memory network to effectively retain the features of historical information in long text sequence, and using the structure of CNN to extract local features of text, through attention mechanism to obtain key features, considering the problems of many diseases, by using hierarchical classification. To stratify the disease. Combined with the above ideas, a deep DLCF model suitable for long text and multi-classification is designed. This model has obvious advantages in CMDD and other datasets. Compared with the baseline models, this model is superior to the baseline model in accuracy, recall and other indicators.
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Reproduction of the sea cucumber Apostichopus japonicus is critical for aquaculture production. Gonadal development is the basis of reproduction, and lipids, which are among the main nutrients required for gonadal development, directly affect reproduction. We investigated whether gonadal and intestinal lipid metabolism differed between male and female A. japonicus. Transcriptome analysis of the intestines of sexually mature male and female wild-caught individuals revealed differences in gene expression, with 27 and 39 genes being up-regulated in females and males, respectively. In particular, the expression of the fatty acid synthase gene was higher in males than in females. Metabolome analysis of the gonads identified 141 metabolites that were up-regulated and 175 metabolites that were down-regulated in the testes compared with the ovaries in the positive/negative mode of an LC-MS/MS analysis. A variety of polyunsaturated fatty acids were found at higher concentrations in the testes than in the ovaries. 16 s rDNA sequencing analysis showed that the composition and structure of the intestinal microbiota were similar between males and females. These results suggest that sex differences in intestinal metabolism of A. japonicus are not due to differences in the microbiota, and we speculate that gonadal metabolism may be related to intestinal morphology. This information might be useful in improving the reproductive efficiency of sea cucumbers in captivity.
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Traditional text classification models have some drawbacks, such as the inability of the model to focus on important parts of the text contextual information in text processing. To solve this problem, we fuse the long and short-term memory network BiGRU with a convolutional neural network to receive text sequence input to reduce the dimensionality of the input sequence and to reduce the loss of text features based on the length and context dependency of the input text sequence. Considering the extraction of important features of the text, we choose the long and short-term memory network BiLSTM to capture the main features of the text and thus reduce the loss of features. Finally, we propose a BiGRU-CNN-BiLSTM model (DCRC model) based on CNN, GRU and LSTM, which is trained and validated on the THUCNews and Toutiao News datasets. The model outperformed the traditional model in terms of accuracy, recall and F1 score after experimental comparison.
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