Background Sucrose phosphate synthase (SPS) genes play vital roles in sucrose production across various plant species. Modern sugarcane cultivar is derived from the hybridization between the high sugar content species Saccharum officinarum and the high stress tolerance species Saccharum spontaneum, generating one of the most complex genomes among all crops. The genomics of sugarcane SPS remains under-studied despite its profound impact on sugar yield. Results In the present study, 8 and 6 gene sequences for SPS were identified from the BAC libraries of S. officinarum and S. spontaneum, respectively. Phylogenetic analysis showed that SPSD was newly evolved in the lineage of Poaceae species with recently duplicated genes emerging from the SPSA clade. Molecular evolution analysis based on Ka/Ks ratios suggested that polyploidy reduced the selection pressure of SPS genes in Saccharum species. To explore the potential gene functions, the SPS expression patterns were analyzed based on RNA-seq and proteome dataset, and the sugar content was detected using metabolomics analysis. All the SPS members presented the trend of increasing expression in the sink-source transition along the developmental gradient of leaves, suggesting that the SPSs are involved in the photosynthesis in both Saccharum species as their function in dicots. Moreover, SPSs showed the higher expression in S. spontaneum and presented expressional preference between stem (SPSA) and leaf (SPSB) tissue, speculating they might be involved in the differentia of carbohydrate metabolism in these two Saccharum species, which required further verification from experiments. Conclusions SPSA and SPSB genes presented relatively high expression and differential expression patterns between the two Saccharum species, indicating these two SPSs are important in the formation of regulatory networks and sucrose traits in the two Saccharum species. SPSB was suggested to be a major contributor to the sugar accumulation because it presented the highest expressional level and its expression positively correlated with sugar content. The recently duplicated SPSD2 presented divergent expression levels between the two Saccharum species and the relative protein content levels were highest in stem, supporting the neofunctionalization of the SPSD subfamily in Saccharum.

Forecasting pedestrians' future trajectory in unknown complex environments is essential to autonomous navigation in real‐world applications, for example, for self‐driving cars and collision warnings. However, modern observed trajectory‐based prediction methods may easily over‐fit to complex or rare scenes because they do not entirely understand the correlations between scenes and trajectories. To address the over‐fitting problem, an Inverse Reinforcement Learning for Scene‐oriented Trajectory Prediction (IRLSOT) is proposed in this work. The authors' method can be divided into three modules. First, the inverse reinforcement learning module generates the optimal policy by extracting features from scenes and pedestrians' observed trajectories. A lightweight ENet is used to extract features from scenes. Afterwards, the path sampling module introduces a Gumbel Softmax Trick (GST) to improve the accuracy of optimal policy sampling. Different paths are generated on the basis of the optimal policies. Finally, the information fusion module uses the proposed Scene Based Attention (SBA) to fuse the path and trajectory information, then outputs the predicted trajectories. Comparison results show that IRLSOT improves performance on Stanford Drone Database(SDD) by 5.9%$\%$. Furthermore, the authors' test IRLSOT on multi‐agent scenarios and the authors' own data sets, and results demonstrate that IRLSOT can enhance the generalization of trajectory prediction to rare or new scenes.

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