Assessment of microplastic contamination in an eastern Pacific tuna (Katsuwonus pelamis) and evaluation of its health risk implication through molecular docking and metabolomics studies.

This work investigated microplastic (MP) pollution in a commercially-important tuna species Katsuwonus pelamis (K. pelamis) from the Eastern Pacific and health implications. 125 MPs were extracted from gills, esophagus, stomachs, intestinal tracts, and muscle of K. pelamis. MPs in the esophagus was the highest, ∼7.6 times higher than that in the gill. Polyester and polyethylene terephthalate (PET) were dominant. Molecular docking implied that PET stabilized the complex via forming 4 new hydrogen bonds that interacted with Arg83, Gln246, Thr267, and Gly268, given that PET can enter glycerol kinase protein active pocket. Metabonomic results suggested that Glycerol 3-phosphate up expressed 1.66 more times that of control groups with no MPs in the muscle. This confirmed that MPs would lie in the glycerol kinase protein active pocket, which triggered menace to K. pelamis. The results provided insights into suggested the potential influence of MPs on the sustainability of fisheries and seafood safety.

Characteristic mango price forecasting using combined deep-learning optimization model.

Accurate product price forecasting is helpful for scientific decision-making and precise industrial planning. As a characteristic fruit that drives regional development, mango price prediction is of great significance to several economies. However, owing to the strong volatility of mango prices, forecasting is vulnerable to uncertainties and is very challenging. In this study, a deep-learning combination forecasting model based on a back-propagation (BP) long short-term memory (LSTM) neural network is proposed. Using daily mango price data from a large fruit wholesale trading center in China from January 2nd, 2014, to April 18th, 2022, mango price changes are learned and predicted to support the fruit industry. The results show that the root mean-square error, mean absolute percentage error, and the R2 determination coefficient of the BP-LSTM combination model are 0.0175, 0.14%, and 0.9998, respectively. The prediction results of the combined model are better than those of the separate BP and LSTM models. Furthermore, it best fits the actual price profile and has better generalizability.