Background Plant microbiome highlights the importance of endosphere microbiome for growth and health of the host plant. Microbial community analysis represents an elegant way to identify keystone microbial species that have a more central position in the community. The aim of this study was to access the interactions between the keystone bacterial species and plants during banana Fusarium wilt process, by comparing the endophytic bacterial and fungal community in banana roots and shoot tips during growth and wilting processes. The keystone bacterial species were isolated and further engineered to improve banana wilt resistance. Results Banana endosphere microbiome structure varied during plant growth and wilting processes. Bacterial and fungal diversity in the shoot tips and roots increased with the development of the banana plantlets. The bacterial groups belonging to the Enterobacteriaceae family with different relative abundances were detected in all the samples. The Klebsiella spp. might be the keystone bacteria during the growth of banana plantlets. The relative abundance of Fusarium associated with the wilt disease did not increase during the wilting process. The endophytic Enterobacteriaceae strains Enterobacter sp. E5, Kosakonia sp. S1, and Klebsiella sp. Kb were isolated on Enterobacteriaceae selective medium and further engineered by expressing 1-aminocyclopropane-1-carboxylate (ACC) deaminase on the bacterial cell walls (designated as E5P, S1P, and KbP, respectively). Pot experiments suggested that plants inoculated with strains E5, E5P, S1, and S1P increased resistance to the Fusarium wilt disease compared with the controls without inoculation, whereas the Klebsiella inoculation (Kb and KbP) did not increase the wilt resistance. Compared with the inoculation with the wild strains E5 and S1, the inoculation with engineered strains E5P and S1P significantly increased wilt resistance and promoted plant growth, respectively. The results illustrated that the keystone species in the banana microbiome may not be dominant in numbers and the functional role of keystone species should be involved in the wilt resistance. Conclusion The ACC deaminase activity of engineered bacteria was essential to the Fusarium wilt resistance and growth promotion of banana plants. Engineering keystone bacteria in plant microbiome with ACC deaminase on the cell walls should be a promising method to improve plant growth and disease resistance. Electronic supplementary material The online version of this article (10.1186/s40168-019-0690-x) contains supplementary material, which is available to authorized users....

Federated self-supervised learning (FSSL) has recently emerged as a promising paradigm that enables the exploitation of clients' vast amounts of unlabeled data while preserving data privacy. While FSSL offers advantages, its susceptibility to backdoor attacks, a concern identified in traditional federated supervised learning (FSL), has not been investigated.To fill the research gap, we undertake a comprehensive investigation into a backdoor attack paradigm, where unscrupulous clients conspire to manipulate the global model, revealing the vulnerability of FSSL to such attacks. In FSL, backdoor attacks typically build a direct association between the backdoor trigger and the target label. In contrast, in FSSL, backdoor attacks aim to alter the global model's representation for images containing the attacker's specified trigger pattern in favor of the attacker's intended target class, which is less straightforward. In this sense, we demonstrate that existing defenses are insufficient to mitigate the investigated backdoor attacks in FSSL, thus finding an effective defense mechanism is urgent. To tackle this issue, we dive into the fundamental mechanism of backdoor attacks on FSSL, proposing the Embedding Inspector (EmInspector) that detects malicious clients by inspecting the embedding space of local models. In particular, EmInspector assesses the similarity of embeddings from different local models using a small set of inspection images (e.g., ten images of CIFAR100) without specific requirements on sample distribution or labels. We discover that embeddings from backdoored models tend to cluster together in the embedding space for a given inspection image. Evaluation results show that EmInspector can effectively mitigate backdoor attacks on FSSL across various adversary settings. Our code is avaliable at https://github.com/ShuchiWu/EmInspector.

Data poisoning attacks and backdoor attacks aim to corrupt a machine learning classifier via modifying, adding, and/or removing some carefully selected training examples, such that the corrupted classifier makes incorrect predictions as the attacker desires. The key idea of state-of-the-art certified defenses against data poisoning attacks and backdoor attacks is to create a majority vote mechanism to predict the label of a testing example. Moreover, each voter is a base classifier trained on a subset of the training dataset. Classical simple learning algorithms such as k nearest neighbors (kNN) and radius nearest neighbors (rNN) have intrinsic majority vote mechanisms. In this work, we show that the intrinsic majority vote mechanisms in kNN and rNN already provide certified robustness guarantees against data poisoning attacks and backdoor attacks. Moreover, our evaluation results on MNIST and CIFAR10 show that the intrinsic certified robustness guarantees of kNN and rNN outperform those provided by state-of-the-art certified defenses. Our results serve as standard baselines for future certified defenses against data poisoning attacks and backdoor attacks.

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A high STST score is strongly associated with gastric cancer risk. STST can be used to evaluate an inherited characteristic of salt preference, and it is a simple index to verify the salt intake in clinic.