CRISPR-Cas type I-A Cascade complex couples viral infection surveillance to host transcriptional regulation in the dependence of Csa3b

CRISPR-Cas (clustered regularly interspaced short palindromic repeats and the associated genes) constitute adaptive immune systems in bacteria and archaea and they provide sequence specific immunity against foreign nucleic acids. CRISPR-Cas systems are activated by viral infection. However, little is known about how CRISPR-Cas systems are activated in response to viral infection or how their expression is controlled in the absence of viral infection. Here, we demonstrate that both the transcriptional regulator Csa3b, and the type I-A interference complex Cascade, are required to transcriptionally repress the interference gene cassette in the archaeon Sulfolobus. Csa3b binds to two palindromic repeat sites in the promoter region of the cassette and facilitates binding of the Cascade to the promoter region. Upon viral infection, loading of Cascade complexes onto crRNA-matching protospacers leads to relief of the transcriptional repression. Our data demonstrate a mechanism coupling CRISPR-Cas surveillance of protospacers to transcriptional regulation of the interference gene cassette thereby allowing a fast response to viral infection

Effective and Robust Adversarial Training against Data and Label Corruptions

Corruptions due to data perturbations and label noise are prevalent in the datasets from unreliable sources, which poses significant threats to model training. Despite existing efforts in developing robust models, current learning methods commonly overlook the possible co-existence of both corruptions, limiting the effectiveness and practicability of the model. In this paper, we develop an Effective and Robust Adversarial Training (ERAT) framework to simultaneously handle two types of corruption (i.e., data and label) without prior knowledge of their specifics. We propose a hybrid adversarial training surrounding multiple potential adversarial perturbations, alongside a semi-supervised learning based on class-rebalancing sample selection to enhance the resilience of the model for dual corruption. On the one hand, in the proposed adversarial training, the perturbation generation module learns multiple surrogate malicious data perturbations by taking a DNN model as the victim, while the model is trained to maintain semantic consistency between the original data and the hybrid perturbed data. It is expected to enable the model to cope with unpredictable perturbations in real-world data corruption. On the other hand, a class-rebalancing data selection strategy is designed to fairly differentiate clean labels from noisy labels. Semi-supervised learning is performed accordingly by discarding noisy labels. Extensive experiments demonstrate the superiority of the proposed ERAT framework.Comment: 12 pages, 8 figure