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Abstract Stripe (yellow) rust, caused by Puccinia striiformis f. sp. tritici (Pst), can significantly affect wheat production. Cloning resistance genes is critical for efficient and effective breeding of stripe rust resistant wheat cultivars. One resistance gene (Yr10 CG ) underlying the Pst resistance locus Yr10 has been cloned. However, following haplotype and linkage analyses indicate the presence of additional Pst resistance gene(s) underlying/near Yr10 locus. Here, we report the cloning of the Pst resistance gene YrNAM in this region using the method of sequencing trait-associated mutations (STAM). YrNAM encodes a non-canonical resistance protein with a NAM domain and a ZnF-BED domain. We show that both domains are required for resistance. Transgenic wheat harboring YrNAM gene driven by its endogenous promoter confers resistance to stripe rust races CYR32 and CYR33. YrNAM is an ancient gene and present in wild wheat species Aegilops longissima and Ae. sharonensis; however, it is absent in most wheat cultivars, which indicates its breeding value.

Blockchain is a decentralized architecture emerging with cryptocurrencies,which is credible and robust.A network covert channel model in blockchain environment was proposed for the first time,which was anti-interference,anti-tamper modification,multi-line communication,receiver anonymity and line independence.The shortcomings of network covert channel in existing network environment could be tackled by the new type of network covert channel,such as characteristic defect.etc.Firstly,A network covert channel model in blockchain environment was presented by formal method,its anti-interference and anti-tamper modification was proved.Then,a blockchain network covert channel scenario using service operation interval time was presented.Finally,the undetectability,robustness and rate of the blockchain network covert channel evaluation vectors was proposed.A theoretical foundation was laid for the practicality of the new type of network covert channel in blockchain.

Deep learning-based code vulnerability detection models have gradually become a crucial method for identifying software vulnerabilities due to their high detection efficiency and accuracy. However, Deep Neural Networks (DNNs) have been proven to be susceptible to adversarial attacks, which poses a risk to the detection accuracy of these models. Constructing adversarial attacks against vulnerability detection models not only helps to uncover security flaws in such models but also aids in assessing their robustness and improving their performance through corresponding methods. Existing adversarial attack methods for vulnerability detection models rely on general code transformation tools and do not propose targeted code perturbation operations and decision algorithms, making it difficult to generate effective adversarial samples, and the validity of these samples often depends on manual verification. To address these issues, we propose a reinforcement learning-based adversarial attack method for vulnerability detection models. Our method first designs a series of semantic-preserving and vulnerability-preserving code perturbation operations as a set of perturbations. Then, using code samples with vulnerabilities as input, a reinforcement learning model selects specific sequences of perturbation operations. Finally, potential locations for perturbation are identified based on the node types of the syntax tree of the code samples, and code transformations are performed to generate adversarial samples. We construct two experimental datasets with a total of 14,278 code samples based on SARD and NVD and train four vulnerability detection models with different characteristics as attack targets. For each target model, a reinforcement learning network is trained to conduct adversarial attacks. The results show that our attack method reduces the recall rate of the models by 74.34% and achieves an attack success rate of 96.71%. Compared to baseline methods, the attack success rate is improved by an average of 68.76%. The experiments demonstrate that current vulnerability detection models are at risk of being attacked and require further research to enhance their robustness. [ABSTRACT FROM AUTHOR]