Background Yellowhorn ( Xanthoceras sorbifolium Bunge), a deciduous shrub or small tree native to north China, is of great economic value. Seeds of yellowhorn are rich in oil containing unsaturated long-chain fatty acids that have been used for producing edible oil and nervonic acid capsules. However, the lack of a high-quality genome sequence hampers the understanding of its evolution and gene functions. Findings In this study, a whole genome of yellowhorn was sequenced and assembled by integration of Illumina sequencing, Pacific Biosciences single-molecule real-time sequencing, 10X Genomics linked reads, Bionano optical maps, and Hi-C. The yellowhorn genome assembly was 439.97 Mb, which comprised 15 pseudo-chromosomes covering 95.42% (419.84 Mb) of the assembled genome. The repetitive fractions accounted for 56.39% of the yellowhorn genome. The genome contained 21,059 protein-coding genes. Of them, 18,503 (87.86%) genes were found to be functionally annotated with ≥1 "annotation" term by searching against other databases. Transcriptomic analysis showed that 341, 135, 125, 113, and 100 genes were specifically expressed in hermaphrodite flower, staminate flower, young fruit, leaf, and shoot, respectively. Phylogenetic analysis suggested that yellowhorn and Dimocarpus longan diverged from their most recent common ancestor ∼46 million years ago. Conclusions The availability and subsequent annotation of the yellowhorn genome, as well as the identification of tissue-specific functional genes, provides a valuable reference for plant comparative genomics, evolutionary studies, and molecular design breeding.

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In order to ensure the driving safety of vehicles in windy environments, a wind monitoring and warning system is widely used, in which a wind speed prediction algorithm with better stability and sufficient accuracy is one of the key factors to ensure the smooth operation of the system. In this paper, a novel short-term wind speed forecasting model, combining complementary ensemble empirical mode decomposition (CEEMD), auto-regressive integrated moving average (ARIMA), and support vector machine (SVM) technology, is proposed. Firstly, EMD and CEEMD are used to decompose the measured wind speed sequence into a finite number of intrinsic mode functions (IMFs) and a decomposed residual. Each of the IMF subseries has better linear characteristics. The ARIMA algorithm is adopted to predict each of the subseries. Then, a new subseries is reconstructed using the sum of the predicted errors of all subseries. The high nonlinear features of the reconstructed error subseries are modeled using SVM, which is suitable to process nonlinear data. Finally, the superposition of all prediction results is performed to obtain the final predicted wind speed. To verify the stability and accuracy of the model, two typhoon datasets, measured from the south coast of China, are used to test the proposed methods. The results show that the proposed hybrid model has a better predictive ability than single models and other combined models. The root mean squared errors (RMSEs) of the hybrid model for the three wind speed datasets are 0.839, 0.529, and 0.377, respectively. The combination of CEEMD with ARIMA contributes most of the prediction performance to the hybrid model. It is feasible to apply the hybrid model to wind speed prediction.

Yellowhorn (Xanthoceras sorbifolium) is an oil-bearing tree species growing naturally in poor soil. The kernel of yellowhorn contains valuable fatty acids like nervonic acid. However, the genetic basis underlying the biosynthesis of valued fatty acids and adaptation to harsh environments is mainly unexplored in yellowhorn. Here, we presented a haplotype-resolved chromosome-scale genome assembly of yellowhorn with the size of 490.44 Mb containing scaffold N50 of 34.27 Mb. Comparative genomics, in combination with transcriptome profiling analyses, showed that expansion of gene families like long-chain acyl-CoA synthetase and ankyrins contribute to yellowhorn fatty acid biosynthesis and defense against abiotic stresses, respectively. By integrating genomic and transcriptomic data of yellowhorn, we found that the transcription of 3-ketoacyl-CoA synthase gene XS04G00959 was consistent with the accumulation of nervonic and erucic acid biosynthesis, suggesting its critical regulatory roles in their biosynthesis. Collectively, these results enhance our understanding of the genetic basis underlying the biosynthesis of valuable fatty acids and adaptation to harsh environments in yellowhorn and provide foundations for its genetic improvement.

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Wind tunnel tests are carried out for the Commonwealth Advisory Aeronautical Research Council (CAARC) high-rise building with a scale of 1:400 in exposure categories D. The distribution law of extreme pressure coefficients under different conditions is studied. Probability distribution fitting is performed on the measured area-averaged extreme pressure coefficients. The general extreme value (GEV) distribution is preferred for probability distribution fitting of extreme pressure coefficients. From the comparison between the area-averaged coefficients and the value from GB50009-2012, it is indicated that the wind load coefficients from GB50009-2012 may be non-conservative for the CAARC building. The area reduction effect on the extreme wind pressure is smaller than that on the mean wind pressure from the code. The recommended formula of the area reduction factor for the extreme pressure coefficient is proposed in this study. It is found that the mean and the coefficient of variation (COV) for the directionality factors are 0.85 and 0.04, respectively, when the orientation of the building is given. If the uniform distribution is given for the building’s orientation, the mean value of the directionality factors is 0.88, which is close to the directionality factor of 0.90 given in the Chinese specifications.

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