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Citric acid may be involved in plant response to high temperature. The objective of this study was to investigate whether exogenous citric acid could improve heat tolerance in a cool-season turfgrass species, tall fescue (Lolium arundinaceum), and to determine the physiological mechanisms of citric acid effects on heat stress tolerance. The grasses were subjected to four citric acid levels (0, 0.2, 2, and 20 mM) and two temperature levels (25/20 and 35/30 ± 0.5°C, day/night) treatments in growth chambers. Heat stress increased an electrolyte leakage (EL) and malonaldehyde (MDA) content, while reduced plant growth, chlorophyll (Chl) content, photochemical efficiency (Fv/Fm), root activity and antioxidant enzyme activities (superoxide dismutase, SOD; catalase, CAT; peroxidase, POD). External citric acid alleviated the detrimental effects of heat stress on tall fescue, which was evidenced by decreased EL and MDA content, and improved plant growth under stress conditions. Additionally, the reduction in Chl content, Fv/Fm, SOD, POD, CAT and root activity were ameliorated in citric acid treated plants under heat stressed conditions. High temperature induced the expression of heat shock protein (HSP) genes, which exhibited greater expression levels after citric acid treatment under heat stress. These results suggest that exogenous citric acid application may alleviate growth and physiological damage caused by high temperature. In addition, the exogenously applied citric acid might be responsible for maintaining membrane stability, root activity, and activation of antioxidant response and HSP genes which could contribute to the protective roles of citric acid in tall fescue responses to heat stress.

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Das Sphingolipid Ceramid wird seit einiger Zeit als sekundärer Botenstoff bei einer Reihe physiologischer und pathologischer Vorgänge, z. B. Autoimmunerkrankungen des Zentralnervensystems wie der multiplen Sklerose, diskutiert und kann durch die Aktion einer sauren oder einer neutralen Sphingomyelinase freigesetzt werden. Das Spiroepoxid 1 ist der erste selektive irreversible Inhibitor der neutralen Sphingomyelinase und kann als Hilfsmittel zur Aufklärung der biologischen Funktion von Ceramid herangezogen werden.

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Using machine learning (ML) techniques to develop disruption predictors is an effective way to avoid or mitigate the disruption in a large-scale tokamak. The recent ML-based disruption predictors have made great progress regarding accuracy, but most of them have not achieved acceptable cross-machine performance. Before we develop a cross-machine predictor, it is very important to investigate the method of developing a cross-tokamak ML-based disruption prediction model. To ascertain the elements which impact the model’s performance and achieve a deep understanding of the predictor, multiple models are trained using data from two different tokamaks, J-TEXT and HL-2A, based on an implementation of the gradient-boosted decision trees algorithm called LightGBM, which can provide detailed information about the model and input features. The predictor models are not only built and tested for performance, but also analyzed from a feature importance perspective as well as for model performance variation. The relative feature importance ranking of two tokamaks is caused by differences in disruption types between different tokamaks. The result of two models with seven inputs showed that common diagnostics is very important in building a cross-machine predictor. This provided a strategy for selecting diagnostics and shots data for developing cross-machine predictors.

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