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ICE1, a regulator of the cold-inducible transcriptome and freezing tolerance, is currently widely believed to be involved in plant resistance to cold stress. In this study, CfICE1 from Cryptomeria fortunei was transformed into poplar. Physiological indicators of transgenic, empty vector and wild-type poplar after abiotic stress (cold, drought and salt) were determined. Transgenic lines had a higher chlorophyll content, antioxidant enzyme activity and soluble protein content, as well as a lower malondialdehyde and hydrogen peroxide content. The ultrastructure of the plant was observed by transmission electron microscopy, and after stress, the cell structure of the transgenic line was more complete than that of the wild type. CfICE1 was upregulated in transgenic poplar trees after abiotic stress (cold, drought and salt). The CfICE1 transgenic plants improved plant resistance by regulating the CBF gene of poplar under cold and salt stress. In terms of plant responses to abiotic stress, this study showed that overexpression of CfICE1 improved the cold, drought and salt tolerance of poplars. [ABSTRACT FROM AUTHOR]

Low temperatures affect plant growth, development, and geographical distribution. Cryptomeriafortunei (Chinese cedar) is a major industrial tree species used for timber manufacturing in southern China. However, its popularization and application in northern China are limited due to its poor low-temperature resistance (approximately −6 °C), and its overwintering mechanism remains unclear. Here, we performed physiological, metabolomic, and transcriptomic analyses of overwintering C.fortunei needles at three stages: before winter, during winter, and in early spring. Physiological analyses showed that electrolyte leakage, H2O2, malondialdehyde (MDA), soluble sugar, and protein contents increased, while the maximum quantum yield of photosystem II (PSII) (Fv/Fm), effective quantum yield of PSII (YII), and chlorophyll content decreased as overwintering progressed. Metabolomic and transcriptomic analyses revealed that downregulated gibberellin (GA), salicylic acid (SA), cytokinin (CTK), and auxin signal transduction and upregulated abscisic acid (ABA), ethylene, brassinosteroids (BR), and jasmonic acid (JA) signal transduction pathways promoted the winter acclimation of C.fortunei, while the opposite expression pattern promoted the transition from dormancy to growth. In addition, upregulated genes/metabolites involved in phenylpropanoid and flavonoid biosynthesis, starch and sucrose metabolism, cold-related protein and fatty acid desaturases, and downregulated photosynthesis-related pathways promoted winter acclimation, while five (WRKY, AP2/ERF, NAC, MYB, and bHLH) and three (AP2/ERF, MYB, and bHLH) transcription factors were associated with winter acclimation and early spring transition from dormancy to growth, respectively. In summary, we report the first transcriptome of overwintering C.fortunei, providing a foundation for the cultivation of and research on overwintering varieties. [ABSTRACT FROM AUTHOR]

Fault diagnosis of wind turbines is of great importance to reduce operating and maintenance costs of wind farms. At present, most wind turbine fault diagnosis methods are focused on single faults, and the methods for combined faults usually depend on inefficient manual analysis. Filling the gap, this paper proposes a low-pass filtering empirical wavelet transform (LPFEWT) machine learning based fault diagnosis method for combined fault of wind turbines, which can identify the fault type of wind turbines simply and efficiently without human experience and with low computation costs. In this method, low-pass filtering empirical wavelet transform is proposed to extract fault features from vibration signals, LPFEWT energies are selected to be the inputs of the fault diagnosis model, a grey wolf optimizer hyperparameter tuned support vector machine (SVM) is employed for fault diagnosis. The method is verified on a wind turbine test rig that can simulate shaft misalignment and broken gear tooth faulty conditions. Compared with other models, the proposed model has superiority for this classification problem. [ABSTRACT FROM AUTHOR]

MicroRNA (miRNA) expression analysis is very important for investigating its functions. To date, no research on reference genes (RGs) for miRNAs in gymnosperms, including Cryptomeria fortunei, has been reported. Here, ten miRNAs (i.e., pab-miR159a, cln-miR162, cas-miR166d, pab-miR395b, ppt-miR894, cln-miR6725, novel1, novel6, novel14 and novel16) and three common RGs (U6, 5S and 18S) were selected as candidate RGs. qRT-PCR was used to analyse their expressions in C. fortunei under various experimental conditions, including multiple stresses (cold, heat, drought, salt, abscisic acid and gibberellin) and in various tissues (roots, stems, tender needles, cones and seeds). Four algorithms (delta Ct, geNorm, NormFinder and BestKeeper) were employed to assess the stability of candidate RG expression; the geometric mean and RefFinder program were used to comprehensively evaluate RG stability. According to the results, novel16, cln-miR6725, novel1 and U6 were the most stable RGs for studying C. fortunei miRNA expression. In addition, the expression of three target miRNAs (aly-miR164c-5p, aly-miR168a-5p and smo-miR396) was examined to verify that the selected RGs are suitable for miRNA expression normalisation. This study may aid further investigations of miRNA expression/function in the response of C. fortunei to abiotic stress and provides an important basis for the standardisation of miRNA expression in other gymnosperm species. [ABSTRACT FROM AUTHOR]

Cryptomeria fortunei has become one of the main timber afforestation species in subtropical high-altitude areas of China due to its fast growth, good material quality, and strong adaptability, showing broad application prospects. Quantitative real-time PCR (qRT-PCR) is the most accurate and widely used gene expression evaluation technique, and selecting appropriate reference genes (RGs) is essential for normalizing qRT-PCR results. However, suitable RGs for gene expression normalization in C. fortunei have not been reported. Here, we tested the expression stability for 12 RGs in C. fortunei under various experimental conditions (simulated abiotic stresses (cold, heat, drought, and salinity) and hormone treatments (methyl jasmonate, abscisic acid, salicylic acid, and gibberellin) and in different tissues (stems, tender needles, needles, cones, and seeds) using four algorithms (delta Ct, geNorm, NormFinder, and BestKeeper). Then, geometric mean rankings from these algorithms and the RefFinder program were used to comprehensively evaluate RG stability. The results indicated CYP, actin, UBC, and 18S as good choices for studying C. fortunei gene expression. qRT-PCR analysis of the expression patterns of three target genes (CAT and MAPK1/6) further verified that the selected RGs were suitable for gene expression normalization. This study provides an important basis for C. fortunei gene expression standardization and quantification. [ABSTRACT FROM AUTHOR]

Most conventional wind turbine fault diagnosis techniques only use a single type of signal as fault feature and their performance could be limited to such signal characteristics. In this paper, multiple types of signals including vibration, temperature, and stator current are used simultaneously for wind turbine misalignment diagnosis. The model is constructed by integrated methods based on Dempster–Shafer (D–S) evidence theory. First, the time domain, frequency domain, and time–frequency domain features of the collected vibration, temperature, and stator current signal are respectively taken as the inputs of the least square support vector machine (LSSVM). Then, the LSSVM outputs the posterior probabilities of the normal, parallel misalignment, angular misalignment, and integrated misalignment of the transmission systems. The posterior probabilities are used as the basic probabilities of the evidence fusion, and the fault diagnosis is completed according to the D–S synthesis and decision rules. Considering the correlation between the inputs, the vibration and current feature vectors' dimensionalities are reduced by t-distributed stochastic neighbor embedding (t-SNE), and the improved artificial bee colony algorithm is used to optimize the parameters of the LSSVM. The results of the simulation and experimental platform demonstrate the accuracy of the proposed model and its superiority compared with other models. [ABSTRACT FROM AUTHOR]