Summary• Rht-D1c (Rht10) carried by Chinese wheat (Triticum aestivum) line Aibian 1 is an allele at the Rht-D1 locus. Among the Rht-1 alleles, little is known about Rht-D1c although it determines an extreme dwarf phenotype in wheat.• Here, we cloned and functionally characterized Rht-D1c using a combination of Southern blotting, target region sequencing, gene expression analysis and transgenic experiments.• We found that the Rht-D1c allele was generated through a tandem segmental duplication (TSD) of a > 1 Mb region, resulting in two copies of the Rht-D1b. Two copies of Rht-D1b in the TSD were three-fold more effective in reducing plant height than a single copy, and transformation with a segment containing the tandemly duplicated copy of Rht-D1b resulted in the same level of reduction of plant height as the original copy in Aibian 1.• Our results suggest that changes in gene copy number are one of the important sources of genetic diversity and some of these changes could be directly associated with important traits in crops.

A series of novel pyrazole oxime derivatives containing a 2-chloro-5-thiazolyl moiety were synthesized. Their structures were confirmed by (1)H NMR, (13)C NMR, and elemental analysis. The preliminary bioassays showed that all of the title compounds had low acaricidal activity against Tetranychus cinnabarinus . However, most of them exhibited excellent insecticidal activity against Aphis medicagini at the dosage of 0.5 mg/mL, and some compounds still showed good insecticidal activity against A. medicagini even at the dosage of 0.2 mg/mL. Meanwhile, some title compounds displayed fungicidal and plant growth regulatory activities.

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Radio propagation modeling and prediction is fundamental for modern cellular network planning and optimization. Conventional radio propagation models fall into two categories. Empirical models, based on coarse statistics, are simple and computationally efficient, but are inaccurate due to oversimplification. Deterministic models, such as ray tracing based on physical laws of wave propagation, are more accurate and site specific. But they have higher computational complexity and are inflexible to utilize site information other than traditional global information system (GIS) maps.In this article we present a novel method to model radio propagation using deep convolutional neural networks and report significantly improved performance compared to conventional models. We also lay down the framework for data-driven modeling of radio propagation and enable future research to utilize rich and unconventional information of the site, e.g. satellite photos, to provide more accurate and flexible models.