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Previous reports have shown that, when Verticillium dahliae localizes at the root surface, many microRNAs (miRNAs) were identified at the early induction stage. Here, we constructed two groups from two timepoints of small RNA (sRNA) in cotton root responses to V. dahliae at the later induction stage, pathogen localizing in the interior of root tissue. We identified 71 known and 378 novel miRNAs from six libraries of the pathogen-induced and the control sRNAs. Combined with degradome and sRNA sequencing, 178 corresponding miRNA target genes were identified, in which 40 target genes from differentially expressed miRNAs were primarily associated with oxidation-reduction and stress responses. More importantly, we characterized the cotton miR477-CBP60A module in the later response of the plant to V. dahliae infection. A β-glucuronidase fusion reporter and cleavage site analysis showed that ghr-miR477 directly cleaved the messenger RNA of GhCBP60A in the posttranscriptional process. The ghr-miR477-silencing decreased plant resistance to this fungus, while the knockdown of GhCBP60A increased plant resistance, which regulated GhICS1 expression to determine salicylic acid level. Our data documented that numerous later-inducible miRNAs in the plant response to V. dahliae, suggesting that these miRNAs play important roles in plant resistance to vascular disease.

Flight departure delay prediction is one of the most critical components of intelligent aviation systems. The accurate prediction of flight departure delays can provide passengers with reliable travel schedules and enhance the service performance of airports and airlines. This article proposes a hybrid method of Random Forest Regression and Maximal Information Coefficient (RFR-MIC) for flight departure delay prediction. Random Forest Regression and Maximal Information Coefficient are inherently fused in terms of Information Consistency. Furthermore, this article focuses on utilizing flight information on multiple air routes for flight departure delay prediction. To validate the proposed flight departure delay prediction model, a numerical study is conducted using flight data collected from Beijing Capital International Airport (PEK). The proposed RFR-MIC model exhibits good performance compared with linear regression (LR), k-nearest neighbors (k-NN), artificial neural network (ANN), and standard Random Forest Regression (RFR). The results also show that flight information on multiple air routes can certainly improve the accuracy of flight departure delay prediction.

Verticillium wilt, caused by Verticillium dahliae, seriously limits cotton production. It is difficult to control this pathogen damage mainly due to the complexity of the molecular mechanism of plant resistance to V. dahliae. Here, we identified three homologous cotton Walls Are Thin (WAT) genes, which were designated as GhWAT1, GhWAT2, and GhWAT3. The GhWATs were predominantly expressed in the roots, internodes, and hypocotyls and induced by infection with V. dahliae and treatment with indole-3-acetic acid (IAA) and salicylic acid (SA). GhWAT1-, GhWAT2-, or GhWAT3-silenced plants showed a comparable phenotype and level of resistance with control plants, but simultaneously silenced three GhWATs (GhWAT123-silenced), inhibited plant growth and increased plant resistance to V. dahliae, indicating that these genes were functionally redundant. In the GhWAT123-silenced plants, the expression of SA related genes was significantly upregulated compared with the control, resulting in an increase of SA level. Moreover, the histochemical analysis showed that xylem development was inhibited in GhWAT123-silenced plants compared with the control. However, lignin deposition increased in the xylem of the GhWAT123-silenced plants compared to the control, and there were higher expression levels of lignin synthesis- and lignifications-related genes in the GhWAT123-silenced plants. Collectively, the results showed that GhWATs in triple-silenced plants acts as negative regulators of plant resistance against V. dahliae. The potential mechanism of the WATs functioning in the plant defence can modulate the SA biosynthesis and lignin deposition in the xylem.

MicroRNAs (miRNAs) participate in plant development and defence through post-transcriptional regulation of the target genes. However, few miRNAs were reported to regulate cotton plant disease resistance. Here, we characterized the cotton miR164-NAC100 module in the later induction stage response of the plant to Verticillium dahliae infection. The results of GUS fusing reporter and transcript identity showed that ghr-miR164 can directly cleave the mRNA of GhNAC100 in the post-transcriptional process. The ghr-miR164 positively regulated the cotton plant resistance to V. dahliae according to analyses of its over-expression and knockdown. In link with results, the knockdown of GhNAC100 increased the plant resistance to V. dahliae. Based on LUC reporter, expression analyses and yeast one-hybrid (Y1H) assays, GhNAC100 bound to the CGTA-box of GhPR3 promoter and repressed its expression, negatively regulating plant disease resistance. These results showed that the ghr-miR164 and GhNAC100 module fine-tunes plant defence through the post-transcriptional regulation, which documented that miRNAs play important roles in plant resistance to vascular disease.