Jasminum sambac is a well-known plant for its attractive and exceptional flower fragrance, and the flowers are used to produce scented tea. Jasmonate (JA), an important plant hormone was first identified in Jasminum species. Jasmine plants contain abundant JA naturally, of which the molecular mechanisms of synthesis and accumulation are not clearly understood. Here, we report a telomere-to-telomere consensus assembly of double-petal J. sambac genome along with two haplotype-resolved genomes. We found that gain-and-loss, positive selection, and allelic specific expression of aromatic votatile related genes contributed to the stronger flower fragrance in double-petal J. sambac compared with single- and multi-petal jasmines. Through comprehensive comparative genomic, transcriptomic, and metabolomic analyses of double-petal J. sambac, we revealed the genetic basis of the production of aromatic volatiles, salicylic acid (SA) and the accumulation of JA under non-stress conditions. We identified several key genes associated with JA biosynthesis, and their non-stress related activities lead to extraordinarily high concentrations of JA in tissues. High JA synthesis coupled with low degradation in J. sambac results in the accumulation of plentiful JA under typical environmental conditions, similar accumulation mechanism of SA. This study offers important insights into the biology of J. sambac, and provides valuable genomic resources for further utilization of natural products.
China’s soybean spot price has historically been highly volatile due to the combined effects of long-term massive import dependence and intricate policies, as well as inherent environmental elements. The accurate prediction of the price is crucial for reducing the amount of soybean-linked risks worldwide and valuable for the long-term sustainability of global agriculture. Therefore, a hybrid prediction model that combines component clustering and a neural network with an attention mechanism has been developed. After fully integrated complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) processing of the price series, the fuzzy entropy of each component is measured as the complexity characteristic. K-means clustering and reconstruction are applied to the components before being input to the CNN-GRU-Attention network for prediction to improve the model ability and adaptability of the sequences. In the empirical analysis, the proposed model outperforms other decomposition techniques and machine learning algorithms regarding prediction accuracy. After applying the decomposition part, the results have RMSE, MAPE, and MAE values of 49.59%, 22.58%, and 21.99% lower than those of the individual prediction part, respectively. This research presents a novel approach for market participants in the soybean industry for risk response. It gives a new perspective on agricultural product prices in sustainable agricultural marketing, while also providing practical tools for developing public policies and decision-making.
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