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With the large-scale grid connection of wind and photovoltaic power, the contradiction between renewable energy and thermal power is becoming more and more serious. To solve this problem, this paper comprehensively considers the construction and operation costs of power plants, and constructs an optimal generation expansion planning model of a combined thermal–wind–photovoltaic​ power system with the objective of minimizing total cost. The planning is calculated under the boundary conditions of ensuring the safe operation of the power grid and taking into account the share requirement, utilization requirements and construction requirements for renewable energy. The model is applied to the self-use and supporting power plants in Xinjiang. The results show that the proposed generation expansion plan can realize the coordinated development of three types of power sources under multiple boundary conditions, which has good applicability.

Key message The quantitative trait loci (QTLs) for grain morphological traits were identified via nested association mapping and validated in a natural wheat population via haplotype analysis. Abstract Grain weight, one of the three most important components of crop yield, is largely determined by grain morphological traits. Dissecting the genetic bases of grain morphology could facilitate the improvement of grain weight and yield production. In this study, four wheat recombinant inbred line populations constructed by crossing the modern variety Yanzhan 1 with three semi-wild wheat varieties (i.e., Chayazheda, Yutiandaomai, and Yunnanxiaomai from Xinjiang, Tibet, and Yunnan, respectively) and one exotic accession Hussar from Great Britain were investigated for grain weight and eight morphological traits in seven environments. Eighty-eight QTLs for all measured traits were totally identified through nested association mapping utilizing 14,643 high-quality polymorphic single nucleotide polymorphism (SNP) markers generated by 90 K SNP array. Among them, 64 (72.7%) QTLs have the most favorable alleles donated by semi-wild wheat varieties. For 14 QTL clusters affecting at least two grain morphological traits, nine QTL clusters were located in similar position with known genes/QTL, and the other five were novel. Three important novel QTLs (i.e., qTGW-1B.1, qTGW-1B.2, and qTGW-1A.1) were further validated in a natural wheat population via haplotype analysis. The favorable haplotypes for these three QTLs might be used in marker-assisted selection for the improvement of wheat yield by modifying morphological traits. Electronic supplementary material The online version of this article (10.1007/s00122-019-03410-4) contains supplementary material, which is available to authorized users.

Background Wheat processing quality is an important factor in evaluating overall wheat quality, and dough characteristics are important when assessing the processing quality of wheat. As a notable germplasm resource, semi-wild wheat has a key role in the study of wheat processing quality. Results In this study, four dough rheological characteristics were collected in four environments using a nested association mapping (NAM) population consisting of semi-wild and domesticated wheat varieties to identify quantitative trait loci (QTL) for wheat processing quality. A total of 49 QTL for wheat processing quality were detected, explaining 0.36–10.82% of the phenotypic variation. These QTL were located on all wheat chromosomes except for 2D, 3A, 3D, 6B, 6D and 7D. Compared to previous studies, 29 QTL were newly identified. Four novel QTL, QMlPH-1B.4, QMlPH-3B.4, QWdEm-1B.2 and QWdEm-3B.2, were stably identified in three or more environments, among which QMlPH-3B.4 was a major QTL. Moreover, eight important genetic regions for wheat processing quality were identified on chromosomes 1B, 3B and 4D, which showed pleiotropy for dough characteristics. In addition, out of 49 QTL, 15 favorable alleles came from three semi-wild parents, suggesting that the QTL alleles provided by the semi-wild parent were not utilized in domesticated varieties. Conclusions The results show that semi-wild wheat varieties can enrich the existing wheat gene pool and provide broader variation resources for wheat genetic research.

Fungal disease meets its matchFusariumhead blight (FHB), caused by a fungus, reduces wheat crop yield and introduces toxins into the harvest. From the assembly of the genome ofThinopyrum elongatum, a wild relative of wheat used in breeding programs to improve cultivated wheat, Wanget al.cloned a gene that can address both problems (see the Perspective by Wulff and Jones). The encoded glutathioneS-transferase detoxifies the trichothecene toxin and, when expressed in wheat, confers resistance to FHB.Science, this issue p.eaba5435; see also p.822

Background The scarcity of fossil fuels and their high emissions impact on the environment have forced the rapid development of renewable energy. Wind and photovoltaic power play a more and more important role in today's power system because of their clean and renewable characteristics. However, with the large-scale grid connection of wind and photovoltaic power, the contradiction between renewable energy and thermal power is becoming more and more serious. The unreasonable planning of power generation resources has also caused a lot of waste of electric power.Methods To solve this problem, this paper comprehensively considers the construction and operation costs of power plants, and constructs an optimal generation expansion planning model of a combined thermal-wind-PV power system with the objective of minimizing total cost. The planning is calculated under the boundary conditions of ensuring the safe operation of the power grid and taking into account the share requirement, utilization requirements and construction requirements for renewable energy. The model is applied to the self-use and external power plants in Xinjiang.Results The results show that this generation expansion plan can reduce the total cost of the power plants while ensuring the load demand. The renewable energy has also received more consumption share and its abandonment rate has dropped significantly. Numerical examples show that the optimization model has good applicability.Conclusion The proposed optimization model can realize the coordinated development of three types of power sources under multiple boundary conditions, which can not only guarantee the economy of power construction, but also take into account the optimization of environmental benefits. Reasonable generation expansion planning can improve energy efficiency, achieve clean and low carbon in the process of power generation, and promote the sustainable development of society.

Background The class III peroxidase (PRX) gene family is a plant-specific member of the PRX superfamily that is closely related to various physiological processes, such as cell wall loosening, lignification, and abiotic and biotic stress responses. However, its classification, evolutionary history and gene expression patterns are unclear in wheat and Aegilops tauschii. Results Here, we identified 374, 159 and 169 PRXs in Triticum aestivum, Triticum urartu and Ae. tauschii, respectively. Together with PRXs detected from eight other plants, they were classified into 18 subfamilies. Among subfamilies V to XVIII, a conserved exon-intron structure within the “001” exon phases was detected in the PRX domain. Based on the analysis, we proposed a phylogenetic model to infer the evolutionary history of the exon-intron structures of PRX subfamilies. A comparative genomics analysis showed that subfamily VII could be the ancient subfamily that originated from green algae (Chlamydomonas reinhardtii). Further integrated analysis of chromosome locations and collinearity events of PRX genes suggested that both whole genome duplication (WGD) and tandem duplication (TD) events contributed to the expansion of T. aestivum PRXs (TaePRXs) during wheat evolution. To validate functions of these genes in the regulation of various physiological processes, the expression patterns of PRXs in different tissues and under various stresses were studied using public microarray datasets. The results suggested that there were distinct expression patterns among different tissues and PRXs could be involved in biotic and abiotic responses in wheat. qRT-PCR was performed on samples exposed to drought, phytohormone treatments and Fusarium graminearum infection to validate the microarray predictions. The predicted subcellular localizations of some TaePRXs were consistent with the confocal microscopy results. We predicted that some TaePRXs had hormone-responsive cis-elements in their promoter regions and validated these predicted cis-acting elements by sequencing promoters. Conclusion In this study, identification, classification, evolution, and expression patterns of PRXs in wheat and relative plants were performed. Our results will provide information for further studies on the evolution and molecular mechanisms of wheat PRXs. Electronic supplementary material The online version of this article (10.1186/s12864-019-6006-5) contains supplementary material, which is available to authorized users.

The basic rules for long and medium-term power market(LAMPM) trading clearly require that power dispatch center is responsible for the security check of trading results and ensure its implementation. However, the technical standard for security check in LAMPM has not formed yet. Besides the research is not sufficient about the influence of intermittent new energy such as wind power and photovoltaic on security check, which results in the lack of transparency and accuracy in the process of security check in LAMPM. To this end, a two-layer framework of LAMPM security check based on multi-scenario prediction of new energy is proposed, which is characterized by "long-term unit commitment + typical day economic dispatch". Then the key points including of long-term unit commitment model, typical day economic dispatch model and check judgement criteria are introduced respectively. Finally, a case study based on IEEE-30 buses system is used to prove the validity of the proposed method.

Figure S9. Upstream sequences and predicted cis-acting elements related to stress and hormone responses in TaePRXs. Various predicted cis-acting elements are circled by different colours. The corresponding information is provided in Additional file 23: Table S11. (PDF 6070 kb)

Figure S10. Sequencing data of the predicted c is-elements in upstream. Pair alignments were performed between upstream sequences and sequencing data. Matched cis-acting elements between them are circled by red boxes. Cis-acting elements with tiny differences are circled by green boxes. (PDF 1069 kb)

Figure S7. Heatmap of the expression patterns of T. aestivum class III peroxidase genes under abiotic stress treatments. The expression patterns of 170 class III peroxidase genes under abiotic stress treatments (cold, heat, drought and nutrient deficiency) are presented. Normalized gene expression values are provided in Additional file 21: Table S9. (PDF 1333 kb)

Figure S5. Quality control of GEO microarray datasets. RLE (Relative log expression) and NUSE (Normalized unscaled standard errors) values of each GEO microarray dataset. (PDF 2638 kb)

Figure S2. Exonâ intron and domain diagrams of class III peroxidases in T. aestivum, T. urartu, Ae. tauschii, B. distachyon, Z. mays, O. sativa, A. thaliana, V. vinifera, S. moellendorffii, P. patens and C. reinhardtii. The descriptions of the domain and exon phases are the same as those in Fig. 2. The lengths of the boxes and lines are scaled based on the lengths of the genes. (PDF 487 kb)

Figure S4. Chromosome locations of class III peroxidases in T. aestivum. (PDF 49 kb)

Figure S8. Heatmap of the expression patterns of T. aestivum class III peroxidase genes under seven phytohormone treatments. The expression patterns of 170 class III peroxidase genes under seven phytohormone treatments, including IAA, GA (GA3), ABA, ET, CK (trans-zeatin), SA and MeJA, are presented. Normalized gene expression values are provided in Additional file 21: Table S9. (PDF 1226 kb)

Figure S6. Heatmap of the expression patterns of T. aestivum class III peroxidase genes in different tissues. The expression patterns of 170 class III peroxidase genes in different tissues: coleoptile, root and embryo of germinating seed; root, crown and leaf of seedling; immature inflorescence; floral bracts, pistil and anthers before anthesis; 3-5 DAP (day after planting) caryopsis; 22 DAP embryos. The heatmap was generated using MeV (Multiple Experiment Viewer) software, version 4.9. Red and green correspond to upregulation and downregulation, respectively. Normalized gene expression values and p values are provided in Additional file 20: Table S8. (PDF 1350 kb)

Figure S3. Conserved exonâ intron and domain diagrams of class III peroxidases in T. aestivum, B. distachyon, S. moellendorffii and P. patens. The descriptions of the domain and exon phases are the same as those in Fig. 2. The lengths of the boxes and lines are scaled based on the lengths of the genes. (PDF 30 kb)