Nipponbare rice was selected as the research object, and the whole process of rice seedlings from normal growth to low temperature to recovery was simulated. In combination with the determination of physiological and biochemical indicators of rice seedlings, it was found that the application of alginic oligosaccharides not only improved the ability of rice seedlings to resist low temperature stress, but also improved root vitality and the activities of SOD, POD, CAT. At the same time, when rice seedlings are subjected to low temperature stress, alginate oligosaccharides could promote the production of soluble sugars and effectively inhibit the increase of malon-dialdehyde content in rice seedlings. Label-free quantification was used to identify and analyze differential proteins and metabolites and study their metabolic pathways. After GO annotation of differentially expressed proteins, the experimental group enriched more entries. The first 10 key pathways were obtained by KEGG enrichment. Metabolomic analysis of rice seedlings by UPLC-Q-TOF-MS showed that the main small molecule differential metabolites were amines, followed by carboxylic acids and alkaloids. Through multiomics analysis, the mechanism of alginate oligosaccharides in improving the cold resistance of rice seedlings was elucidated, which provided a scientific basis for understanding the defense mechanism of cold resistance of rice seedlings as well as the development and utilization of alginate oligosaccharides.

Aiming at the two problems of insufficient utilization of role information and lack of interaction between arguments in general domain event argument extraction research, a role information-oriented multi-turn event argument extraction model is proposed to enhance the semantic information of texts and interactions between arguments. The interactive capability can improve the performance of event argument extraction. First, to better utilize role knowledge to guide argument extraction, the model builds role knowledge based on role definitions, independently encodes role information and text, and uses a method based on attention mechanism to obtain label-knowledge-enhanced representations. Then the augmented embeddings are used to predict whether or not each token is a start or end position for some category. At the same time, in order to make full use of the interaction between event arguments in the extraction process, inspired by the multi-turn dialogue model, this paper designs a multi-turn event argument extraction algorithm. The algorithm refers to the natural logic of “easiness to hardness”, and selects the character with the highest prediction probability, that is, the most predictable character, for extraction each time. In the process of argument extraction, in order to model the interaction between arguments, the model introduces historical embedding, and updates the historical embedding after each prediction to help the extraction of the next round of event arguments. The experimental results show that the guidance of role information and multi round extraction algorithm effectively improve the performance of argument extraction, and the method achieves state-of-the-art performance.