Probiotic Strain \u3cem\u3eStenotrophomonas acidaminiphila\u3c/em\u3e BJ1 Degrades and Reduces Chlorothalonil Toxicity to Soil Enzymes, Microbial Communities and Plant Roots

Chlorothalonil, a non-systemic and broad-spectrum fungicide, is widely used to control the pathogens of agricultural plants. Although microbial degradation of chlorothalonil is known, we know little about the colonization and degradation capacity of these microbes in the natural and semi-natural soil environments. Therefore, we studied the colonization and detoxification potential of a chlorothalonil degrading Stenotrophomonas acidaminiphila probiotic strain BJ1 in the soil under green conditions. The results from polymerase chain reaction-denaturing gradient gel electrophoresis demonstrated that probiotic strain BJ1 successfully colonized the soil by competing with the native biota. Moreover, the bacterial inoculation stimulated some members of indigenous soil microbial communities. Meantime, the degradation half-life of chlorothalonil decreased from 9.0 to 4.9 days in the soil environment. Moreover, the results from enzymatic activities and micronucleus test of Vicia faba root tips showed that the probiotic strain BJ1 reduced the ecotoxicity and genotoxicity of chlorothalonil in the soil. We suggest that probiotic strains like BJ1 could potentially alleviate the toxic effects of pesticides on soil microbes and plant roots under greenhouse conditions

When Measures are Unreliable: Imperceptible Adversarial Perturbations toward Top-kk Multi-Label Learning

With the great success of deep neural networks, adversarial learning has received widespread attention in various studies, ranging from multi-class learning to multi-label learning. However, existing adversarial attacks toward multi-label learning only pursue the traditional visual imperceptibility but ignore the new perceptible problem coming from measures such as Precision@$k$ and mAP@$k$. Specifically, when a well-trained multi-label classifier performs far below the expectation on some samples, the victim can easily realize that this performance degeneration stems from attack, rather than the model itself. Therefore, an ideal multi-labeling adversarial attack should manage to not only deceive visual perception but also evade monitoring of measures. To this end, this paper first proposes the concept of measure imperceptibility. Then, a novel loss function is devised to generate such adversarial perturbations that could achieve both visual and measure imperceptibility. Furthermore, an efficient algorithm, which enjoys a convex objective, is established to optimize this objective. Finally, extensive experiments on large-scale benchmark datasets, such as PASCAL VOC 2012, MS COCO, and NUS WIDE, demonstrate the superiority of our proposed method in attacking the top-$k$ multi-label systems.Comment: 22 pages, 7 figures, accepted by ACM MM 202