Brown rot of peach caused by Monilinia fructicola can cause considerable preharvest and postharvest losses in China. Fungicides are increasingly utilized to minimize such losses. Eighty isolates of M. fructicola were collected from commercial peach orchards located in five provinces in China and the sensitivity to carbendazim, azoxystrobin, tebuconazole, and boscalid was determined. Resistance to carbendazim was detected only in the Yunnan province in 15 of 16 isolates. Characterization of carbendazim-resistant isolates revealed stable resistance, no fitness penalty, and negative cross resistance to diethofencarb. Resistant isolates produced disease symptoms on detached fruit sprayed with label rates of formulated carbendazim and possessed the amino acid mutation E198A in β-tubulin. Resistance to azoxystrobin was detected in 3 of 10 isolates from Fujian. In contrast to carbendazim resistance, however, azoxystrobin resistance was unstable, associated with a fitness penalty, and not associated with mutations in the target gene cytochrome b. The concentration at which mycelial growth is inhibited 50% (EC50) values of the azoxystrobin-sensitive isolates were 0.02 to 1.94 μg/ml, with a mean value of 0.54 μg/ml. All isolates were sensitive to tebuconazole, with a mean EC50 value of 0.03 μg/ml. The EC50 values for boscalid were 0.01 to 3.85 μg/ml, with a mean value of 1.02 μg/ml. Our results indicate that methyl benzimidazole carbamates (MBCs), quionon outside inhibitors, demethylation inhibitor fungicides, and succinate dehydrogenase inhibitors are likely to be very effective in controlling brown rot in many peach production areas in China, but that resistance to MBCs is emerging.
Botrytis cinerea, the causal agent of gray mold, can result in considerable preharvest and postharvest losses in many economically valuable plant species. Fungicides were widely used to minimize such losses, but fungicide resistances were detected frequently. In the present study, we collected 164 isolates from nectarine and cherry in China and tested the sensitivity to six fungicides. Among the tested isolates, 71 (43.3%) were resistant to azoxystrobin, 14 (8.5%) to cyprodinil, 7 (4.3%) to boscalid, 4 (2.4%) to carbendazim, 1 (0.6%) to iprodione, and no isolates were found to be resistant to fludioxonil. The EC50 value and resistance factor (RF) of resistant isolates were determined. Fitness analysis showed that there were no significant differences between sensitive and resistant isolates for osmotic stress and pathogenicity, while more conidia production was observed for some resistant isolates. Control efficacy of fungicides showed that the resistant isolates could not be controlled efficiently by using corresponding fungicides. The point mutation G143A was detected in the Cyt b gene of the isolates resistant to azoxystrobin, while the point mutation H272R of SdhB gene was confirmed in boscalid-resistant isolates, and mutations E198V/A of TUB2 gene and mutation I365S of BcOs1 occurred in carbendazim-resistant and iprodione-resistant isolates, respectively. These results indicate that the occurrence of fungicide resistance greatly threatens the management of gray mold on stone fruits nectarine and cherry.
Graph neural network (GNN), as a widely used deep learning model in processing graph-structured data, has attracted numerous studies to apply it in the link prediction task. In these studies, observed edges in a network are utilized as positive samples, and unobserved edges are randomly sampled as negative ones. However, there are problems in randomly sampling unobserved edges as negative samples. First, some unobserved edges are missing edges that are existing edges in the network. Second, some unobserved edges can be easily distinguished from the observed edges, which cannot contribute sufficiently to the prediction task. Therefore, using the randomly sampled unobserved edges directly as negative samples is difficult to make GNN models achieve satisfactory prediction performance in the link prediction task. To address this issue, we propose a policy-based training method (PbTRM) to improve the quality of negative samples. In the proposed PbTRM, a negative sample selector generates the state vectors of the randomly sampled unobserved edges and determines whether to select them as negative samples. We perform experiments with three GNN models on two standard datasets. The results show that the proposed PbTRM can enhance the prediction performance of GNN models in the link prediction task.
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