Transcriptomic and metabolomic analysis provides insights into lignin biosynthesis and accumulation and differences in lodging resistance in hybrid wheat

The use of hybrid wheat is one way to improve the yield in the future.However,greater plant heights increase lodging risk to some extent.In this study,two hybrid combinations with differences in lodging resistance were used to analyze the stem-related traits during the filling stage,and to investigate the mechanism of the difference in lodging resistance by analyzing lignin synthesis of the basal second internode(BSI).The stem-related traits such as the breaking strength,stem pole substantial degree(SPSD),and rind penetration strength(RPS),as well as the lignin content of the lodging-resistant combination(LRC),were significantly higher than those of the lodgingsensitive combination(LSC).The phenylpropanoid biosynthesis pathway was significantly and simultaneously enriched according to the transcriptomics and metabolomics analysis at the later filling stage.A total of 35 critical regulatory genes involved in the phenylpropanoid pathway were identified.Moreover,42%of the identified genes were significantly and differentially expressed at the later grain-filling stage between the two combinations,among which more than 80%were strongly up-regulated at that stage in the LRC compared with LSC.On the contrary,the LRC displayed lower contents of lignin intermediate metabolites than the LSC.These results suggested that the key to the lodging resistance formation of LRC is largely the higher lignin synthesis at the later grain-filling stage.Finally,breeding strategies for synergistically improving plant height and lodging resistance of hybrid wheat were put forward by comparing the LRC with the conventional wheat applied in large areas.

Anomaly Detection Algorithm of Power System Based on Graph Structure and Anomaly Attention

In this paper, we propose a novel anomaly detection method for data centers based on a combination of graphstructure and abnormal attention mechanism. The method leverages the sensor monitoring data from targetpower substations to construct multidimensional time series. These time series are subsequently transformed intograph structures, and corresponding adjacency matrices are obtained. By incorporating the adjacency matricesand additional weights associated with the graph structure, an aggregation matrix is derived. The aggregationmatrix is then fed into a pre-trained graph convolutional neural network (GCN) to extract graph structure features.Moreover, both themultidimensional time series segments and the graph structure features are inputted into a pretrainedanomaly detectionmodel, resulting in corresponding anomaly detection results that help identify abnormaldata. The anomaly detection model consists of a multi-level encoder-decoder module, wherein each level includesa transformer encoder and decoder based on correlation differences. The attention module in the encoding layeradopts an abnormal attention module with a dual-branch structure. Experimental results demonstrate that ourproposed method significantly improves the accuracy and stability of anomaly detection.

二系杂交小麦混播制种技术研究与利用

二系杂交小麦技术体系是我国原创的利用不育系实现杂交小麦产业化应用的重要成果,其高效制种技术是杂交小麦产业化发展的核心关键技术,选育高配合力和异交性状优良的不育系与恢复系能够有效提高制种异交结实率和产量.本研究通过2016~2020年间二系杂交小麦相关制种试验,对制种过程中的影响因素和效果进行研究,发现恢复系花粉传播距离、花药大小、花药外露率、花粉量和不育系的开颖角度、柱头外露率等异交生物学性状与异交结实性能有显著相关性;父本花期比母本晚2~3 d能够实现时空协同和生理协同的有效配制.按照父本比例5%对父母本进行混播制种比行比制种产量显著提高28.2%.2019~2021年度,在南阳地区进行规模化混播制种生产效果突出,BS1453/JS1规模化制种产量达到5273.4 kg/hm^(2),为二系杂交小麦推广应用和产业化提供了理论与技术支撑.