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Simple Summary: RNA pseudouridine modifications are present in various RNAs across different organisms and play crucial roles in regulating gene expression during biological processes. The accurate identification of pseudouridine sites within RNA sequences is essential for understanding their functional mechanisms. This study proposes a novel ensemble learning predictor named PseUpred-ELPSO, which accurately predicts RNA pseudouridine sites. The predictor demonstrates excellent performance in both cross-validation and independent testing. A user-friendly web server has been established, making it a powerful tool for pseudouridine site identification. RNA pseudouridine modification exists in different RNA types of many species, and it has a significant role in regulating the expression of biological processes. To understand the functional mechanisms for RNA pseudouridine sites, the accurate identification of pseudouridine sites in RNA sequences is essential. Although several fast and inexpensive computational methods have been proposed, the challenge of improving recognition accuracy and generalization still exists. This study proposed a novel ensemble predictor called PseUpred-ELPSO for improved RNA pseudouridine site prediction. After analyzing the nucleotide composition preferences between RNA pseudouridine site sequences, two feature representations were determined and fed into the stacking ensemble framework. Then, using five tree-based machine learning classifiers as base classifiers, 30-dimensional RNA profiles are constructed to represent RNA sequences, and using the PSO algorithm, the weights of the RNA profiles were searched to further enhance the representation. A logistic regression classifier was used as a meta-classifier to complete the final predictions. Compared to the most advanced predictors, the performance of PseUpred-ELPSO is superior in both cross-validation and the independent test. Based on the PseUpred-ELPSO predictor, a free and easy-to-operate web server has been established, which will be a powerful tool for pseudouridine site identification. [ABSTRACT FROM AUTHOR]

Melatonin is a bioactive, edible ingredient that promotes human health and exists widely in plants, but little is known about its biosynthetic routes and underlying molecular mechanisms in the herbaceous peony. In this contribution, we found that herbaceous peony flowers are rich in melatonin that is found in the greatest quantities in the white series, followed by the ink series, the red series and then the pink series. On this basis, the melatonin content fluctuates during flower development and peaks during the bloom stage. Moreover, it is apparent that sun exposure and blue light induce melatonin production whereas green light restrains it during a 24-h light/dark cycle of melatonin content, as there were ‘dual peaks’ at 2 p.m. and 2 a.m. Additionally, the corresponding expression pattern of the herbaceous peony tryptophan decarboxylase gene (TDC) was positively related with melatonin production. These results suggest that color series, development stage and light play an important role in melatonin accumulation, and that TDC is a rate-limiting gene in melatonin biosynthesis. [ABSTRACT FROM AUTHOR]