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Abstract Setaria italica(foxtail millet), a founder crop of East Asian agriculture, is a model plant for C4 photosynthesis and developing approaches to adaptive breeding across multiple climates. Here we established theSetariapan-genome by assembling 110 representative genomes from a worldwide collection. The pan-genome is composed of 73,528 gene families, of which 23.8%, 42.9%, 29.4% and 3.9% are core, soft core, dispensable and private genes, respectively; 202,884 nonredundant structural variants were also detected. The characterization of pan-genomic variants suggests their importance during foxtail millet domestication and improvement, as exemplified by the identification of the yield geneSiGW3, where a 366-bp presence/absence promoter variant accompanies gene expression variation. We developed a graph-based genome and performed large-scale genetic studies for 68 traits across 13 environments, identifying potential genes for millet improvement at different geographic sites. These can be used in marker-assisted breeding, genomic selection and genome editing to accelerate crop improvement under different climatic conditions. 

This paper proposes a simple yet effective method for power system probabilistic transient stability assessment considering the wind farm uncertainties and correlations. Specifically, the inverse Nataf-transformation-based three-point estimation method and the Cornish-Fisher expansion have been integrated together to deal with the uncertainties and the correlations among different wind farms. Then, by resorting to the extended dynamic security region approach, the transient stability criterion is derived as a linear combination of nodal injection vector under a given fault condition. New indices for the identification of critical lines have also been developed. Extensive simulation results carried out on four different systems, including the practical GZ power system in China show that the computational efficiency of the proposed method is much higher than the Monte-Carlo-based method and other methods almost without the loss of accuracy. The effectiveness of the proposed method under different penetrations of wind power with different degree of correlations is also validated. It is shown that correlation among wind farms has a larger impact on the transient stability results with a higher penetration level of renewable energy.