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Apple ring rot, which is caused by Botryosphaeria dothidea, severely affects apple production. The mechanisms employed in apple cells against B. dothidea remain unknown. In this research, the pathogen infection mode and the relationship between cell death and disease resistance in ‘Fuji’/B. dothidea interaction pathosystem were investigated. By using transmission electron microscopy (TEM), our research showed that the pathogen infects apple cells both intracellularly and extracellularly. However, compared with that in immature fruit, the incidence of hyphae in the interior of mature apple fruit cells increased dramatically, suggesting that cell wall-mediated penetration resistance could be important in apple resistance against B. dothidea. TEM ultrastructural characterization identified the nuclear morphology of programmed cell death induction in both apple fruit and callus cells under B. dothidea infection. Overexpression of MdVDAC2 (MDP0000271281), which encodes an outer-membrane localized anion channel protein in mitochondria, significantly promoted cell death under B. dothidea infection and simultaneously inhibited pathogen infection, suggesting that cell death represents a disease resistance mechanism in apple against B. dothidea infection. Furthermore, BdCatalase (KAF4307763), a cytochrome P450 family protein BdCYP52A4 (KAF4300696), and subtilisin-domain containing proteins were identified from B. dothidea-secreted proteins, which suggested the potential involvement of active oxygen species and phytoalexins in combating B. dothidea infection and triggering or dampening apple resistance. Collectively, our research suggested that cell wall-mediated penetration resistance, programmed cell death machinery and microbial effector-interrelated signaling were among strategies recruited in apple to combat B. dothidea. The current research laid the foundation for further investigations into resistance mechanisms in apple.

Accurate prediction of taxiing time is important in ensuring efficient and safe operations on the airport surface. It helps improve ground operation efficiency, reduce fuel waste, and improve carbon emissions at the airport. In actual operations, taxiing time is influenced by various factors, including a large number of categorical features. However, few previous studies have focused on selecting such features. Additionally, traditional taxiing time prediction methods are often black-box models that only provide a single prediction result; they fail to provide effective practical references for controllers. Therefore, this paper analyses the features that affect taxiing time from different data types and forms a taxi feature set consisting of nine key features. We also propose a taxiing time prediction method based on adaptive scenario matching rules. This process classifies the scenarios into multiple typical historical scenario sets and adaptively matches the current target scenario to a typical scenario set based on quantified rules. Then, based on the matching results, a pre-trained model obtained from the corresponding scenario set is used to predict the taxiing time of an aircraft in the target scenario, aiming to mitigate the impact of data heterogeneity on prediction results. Experimental results show that compared to baseline methods, the mean absolute error and root mean square error of the proposed method decreased by 4.8% and 12.6%, respectively. This method significantly reduces the fluctuations in results caused by sample heterogeneity and enhances controllers’ acceptance of prediction results from the model. It can be used to further improve auxiliary decision making systems and enhance the precise control capabilities of airport surface operations.