Background Gene expression is a key determinant of cellular response. Natural variation in gene expression bridges genetic variation to phenotypic alteration. Identification of the regulatory variants controlling the gene expression in response to drought, a major environmental threat of crop production worldwide, is of great value for drought-tolerant gene identification. Results A total of 627 RNA-seq analyses are performed for 224 maize accessions which represent a wide genetic diversity under three water regimes; 73,573 eQTLs are detected for about 30,000 expressing genes with high-density genome-wide single nucleotide polymorphisms, reflecting a comprehensive and dynamic genetic architecture of gene expression in response to drought. The regulatory variants controlling the gene expression constitutively or drought-dynamically are unraveled. Focusing on dynamic regulatory variants resolved to genes encoding transcription factors, a drought-responsive network reflecting a hierarchy of transcription factors and their target genes is built. Moreover, 97 genes are prioritized to associate with drought tolerance due to their expression variations through the Mendelian randomization analysis. One of the candidate genes, Abscisic acid 8′-hydroxylase, is verified to play a negative role in plant drought tolerance. Conclusions This study unravels the effects of genetic variants on gene expression dynamics in drought response which allows us to better understand the role of distal and proximal genetic effects on gene expression and phenotypic plasticity. The prioritized drought-associated genes may serve as direct targets for functional investigation or allelic mining.

We propose a mixed language query disambiguation approach by using co-occurrence information from monolingual data only.A mixed language query consists of words in a primary language and a secondary language. Our method translates the query into monolingual queries in either language. Two novel features for disambiguation, namely contextual word voting and 1-best contextual word, are introduced and compared to a baseline feature, the nearest neighbor. Average query translation accuracy for the two features are 81.37% and 83.72%, compared to the baseline accuracy of 75.50%.

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Traditional deception-based cyber defenses (DCD) often adopt the static deployment policy that places the deception resources in some fixed positions in the target network. Unfortunately, the effectiveness of these deception resources has been greatly restricted by the static deployment policy, which also causes the deployed deception resources to be easily identified and bypassed by attackers. Moreover, the existing studies on dynamic deployment policy, which make many strict assumptions and constraints, are too idealistic to be practical. To overcome this limitation, an intelligent deployment policy used to dynamically adjust the locations of these deception resources according to the network security state is developed. Starting with formulating the problem of deception resources deployment, we then model the attacker-defender scenario and the attacker's strategy. Next, the preliminary screening method that can derive the effective deployment locations of deception resources based on threat penetration graph (TPG) is proposed. Afterward, we construct the model for finding the optimal policy to deploy the deception resources using reinforcement learning and design the Q-Learning training algorithm with model-free. Finally, we use the real-world network environment for our experiments and conduct in-depth comparisons with state-of-the-art methods. Our evaluations on a large number of attacks show that our method has a high defense success probability of nearly 80%, which is more efficient than existing schemes.