Browsing by Author "Dong Y"

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    Comparative Transcriptomics of Multi-Stress Responses in Pachycladon cheesemanii and Arabidopsis thaliana.
    (MDPI (Basel, Switzerland), 2023-07-11) Dong Y; Gupta S; Wargent JJ; Putterill J; Macknight RC; Gechev TS; Mueller-Roeber B; Dijkwel PP; You FM
    The environment is seldom optimal for plant growth and changes in abiotic and biotic signals, including temperature, water availability, radiation and pests, induce plant responses to optimise survival. The New Zealand native plant species and close relative to Arabidopsis thaliana, Pachycladon cheesemanii, grows under environmental conditions that are unsustainable for many plant species. Here, we compare the responses of both species to different stressors (low temperature, salt and UV-B radiation) to help understand how P. cheesemanii can grow in such harsh environments. The stress transcriptomes were determined and comparative transcriptome and network analyses discovered similar and unique responses within species, and between the two plant species. A number of widely studied plant stress processes were highly conserved in A. thaliana and P. cheesemanii. However, in response to cold stress, Gene Ontology terms related to glycosinolate metabolism were only enriched in P. cheesemanii. Salt stress was associated with alteration of the cuticle and proline biosynthesis in A. thaliana and P. cheesemanii, respectively. Anthocyanin production may be a more important strategy to contribute to the UV-B radiation tolerance in P. cheesemanii. These results allowed us to define broad stress response pathways in A. thaliana and P. cheesemanii and suggested that regulation of glycosinolate, proline and anthocyanin metabolism are strategies that help mitigate environmental stress.
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    Comparison of algorithms for road surface temperature prediction
    (Emerald Publishing Limited, 2018-12-13) Liu B; Shen L; You H; Dong Y; Li J; Li Y
    Purpose: The influence of road surface temperature (RST) on vehicles is becoming more and more obvious. Accurate predication of RST is distinctly meaningful. At present, however, the prediction accuracy of RST is not satisfied with physical methods or statistical learning methods. To find an effective prediction method, this paper selects five representative algorithms to predict the road surface temperature separately. Design/methodology/approach: Multiple linear regressions, least absolute shrinkage and selection operator, random forest and gradient boosting regression tree (GBRT) and neural network are chosen to be representative predictors. Findings: The experimental results show that for temperature data set of this experiment, the prediction effect of GBRT in the ensemble algorithm is the best compared with the other four algorithms. Originality/value: This paper compares different kinds of machine learning algorithms, observes the road surface temperature data from different angles, and finds the most suitable prediction method.