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Fungal nodules and aged fungus gardens are products of termite fungiculture systems, and are the diets of termites. To understand the nutrition flow in fungiculture, we quantified the number and mass of fungal nodules produced along with fungus garden maturation and analysed the α-amino acid and fatty acid compositions of fungal nodules, fungus gardens, and termite tissues of a fungus-growing termite, Odontotermes formosanus. 1 g of fungus garden produced 5,148 fungal nodules (∼68.0 mg). Approximately 7.0% of α-amino acids were allocated to the fungal nodules and the rest (∼93.0%) remained in the fungus gardens. The compositions of α-amino acids or fatty acids in aged fungus gardens and fungal nodules were more similar to that of termite tissues than fresh fungus gardens, which supports the idea that termites nutritionally depend on the fungal products. Among the 18 α-amino acids, tryptophan was an essential amino acid and was the only one missing from fresh and aged fungus gardens, but found in fungal nodules at significantly higher concentrations. Hence, termites must consume fungal nodules to obtain tryptophan for survival. Furthermore, the fungus spores incorporated in nodules, were transferred when nodules were ingested by termites. We propose that allocating tryptophan in fungal nodules is crucial to enhance the mutualism between the fungus and termite. [ABSTRACT FROM AUTHOR]

Rice blast is one of the most devastating diseases that threatens rice production in Taiwan. A rice blast forecasting model is required to guide the precise application of fungicide. Therefore, BlastGRU-TW model based on deep learning algorithms was developed in this study. The input data comprised approximately 1000 rice blast surveys, collected from 50 fields throughout Taiwan between 2014 and 2021, and the corresponding weather data retrieved from weather observation network in Taiwan. Common and easily accessible meteorological factors, i.e. temperature, humidity, precipitation, and wind, were converted into 20 daily meteorological features which were coupled with different time intervals between 1 and 30 days before survey (DBS) to train the model. The results showed that seven meteorological features (daily maximum temperature, daily mean temperature, daily minimum temperature, daily mean humidity, daily mean WV and daily mean Wu) and the interval from −4 to −24 DBS were informative in disease prediction, thereby indicating that the proposed model could predict the risk of rice blast by using meteorological data 4–24 days before new disease symptoms appeared. The proposed BlastGRU-TW model achieved an accuracy of 87.3%. Furthermore, on adding the 3 day forecast weather data from Weather Research and Forecasting (WRF) model in the proposed model, the forecast extended to 7 days ahead of the appearance of new symptoms. Moreover, the BLASTAM model developed in Japan was implemented and validated in Taiwan to evaluate its applicability in different geographical areas. Finally, a rice blast early warning system (https://mycolab.pp.nchu.edu.tw/blast%5fforecast/index%5fen.php) equipped with an interactive web-based map is now available for real-time forecasting of the risk of rice blast in paddy field across Taiwan. • Input data from 1000 rice blast surveys in Taiwan for 2014–2021. • BlastGRU-TW model for deep learning-based rice blast prediction • The model achieves an accuracy of 87.3% • The BLASTAM model developed in Japan was the first to be implemented and validated outside Japan. • Forecast results can be visualized on a user-friendly, interactive web-based map. [ABSTRACT FROM AUTHOR]