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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.

Occupancy of vacant taxis brings waste of resources and heavy traffic pressure to urban traffic network. To enable better efficiency of taxis and less traffic congestion, a logit-based model is developed to describe vacant taxi drivers’ route choice behavior and decision-making mechanism when searching for next customer. The proposed model is based on a multinomial logit model (MNL), considering two novel influencing indicators including the path unreliability (PU) and expected rate of return (EROR). In order to simplify the model construction and reduce computational cost, we divide the research area into identical squares with a 0.5-km resolution. Then the customer searching movements are extracted from the high-resolution GPS data of more than 8000 taxis in Shanghai to validate the logit model. The model results show that the customer searching behavior of vacant taxi drivers is significantly influenced by PU and EROR. Moreover, the effect of these two indicators on customer searching behavior varies with the time of day.

In this paper, we envision an emerging Mixed Operation Zone (MOZ) where both autonomous vehicles (AVs) and human-driven vehicles (HVs) are present for on-demand ride services. This paper aims to size and operate a fleet of AVs and HVs in the presence of MOZs and to investigate the impact of MOZs on on-demand ride services. Considering the demand uncertainty, we propose a demand-oriented robust minimum fleet problem (RMFP) and employ a two-stage robust optimization (RO) to model the decision-making. Fluctuant demand is bounded by distribution-free uncertainty sets. For the convenience of solving RO models, we reformulate the second-stage recourse problem with an equivalent mathematical programming formulation. A tailored column-and-constraint generation algorithm is developed to solve the RMFP exactly. The algorithm is proved to converge in a finite number of iterations. Extensive experiments are conducted on the instances based on a real-world on-demand ride service in Chengdu. The developed algorithm performs better than the state-of-the-art Benders decomposition approach. Numerical results imply huge potential benefits from MOZs on improving service performance for ride service platforms.

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.

Previous reports have shown that, when

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.