Variation in two SNPs and one microsatellite on the Y chromosome was analyzed in a total of 663 rams representing 59 breeds from a large geographic range in northern Eurasia. SNPA-oY1 showed the highest allele frequency (91.55%) across the breeds, whereas SNPG-oY1 was present in only 56 samples. Combined genotypes established seven haplotypes (H4, H5, H6, H7, H8, H12 and H19). H6 dominated in northern Eurasia, and H8 showed the second-highest frequency. H4, which had been earlier reported to be absent in European breeds, was detected in one European breed (Swiniarka), whereas H7, which had been previously identified to be unique to European breeds, was present in two Chinese breeds (Ninglang Black and Large-tailed Han), one Buryatian (Transbaikal Finewool) and two Russian breeds (North Caucasus Mutton-Wool and Kuibyshev). H12, which had been detected only in Turkish breeds, was also found in Chinese breeds in this work. An overall low level of haplotype diversity (median h = 0.1288) was observed across the breeds with relatively higher median values in breeds from the regions neighboring the Near Eastern domestication center of sheep. H6 is the dominant haplotype in northwestern and eastern China, in which the haplotype distribution could be explained by the historical translocations of the H4 and H8 Y chromosomes to China via the Mongol invasions followed by expansions to northwestern and eastern China. Our findings extend previous results of sheep Y chromosomal genetic variability and indicate probably recent paternal gene flows between sheep breeds from distinct major geographic regions.
Just as the heterojunctions in physics, donor–acceptor (D‐A) heterostructures are an emerging class of photoactive materials fabricated from two semiconductive components at the molecular level. Among them, D‐A hybrid heterostructures from organic and inorganic semiconductive components have attracted extensive attention in the past decades due to their combined advantages of high stability for the inorganic semiconductors and modifiability for the organic semiconductors, which are particularly beneficial to efficiently achieve photoinduced charge separation and transfer upon irradiations. In this review, by analogy with the heterojunctions in physics, a definition of the D‐A heterostructures and their general design and synthetic strategies are given. Meanwhile, the D‐A hybrid heterostructures are focused on and their recent advances in potential applications of photochromism, photomodulated luminescence, and photocatalysis summarized.
Water consumption prediction is an integral part of water resource planning and management. Constructing a highly precise water consumption prediction model is of great significance for promoting regional water resource planning and high-quality development of the socio-economy. This paper focuses on the case of the typical karst region in Guizhou Province in China. Based on data on water consumption and its influencing factors spanning 2000–2020, the principal component analysis method was applied to reduce the dimensionality of 16 influencing factors of water consumption in Guizhou; the principal components extracted were used as input samples of the BP neural network and a PCA-BP neural network water consumption prediction model was conducted to predict water consumption of Guizhou Province in the next 10 years. The results show that the mean absolute error and mean relative error of prediction based on the constructed PCA-BP neural network were 2.8% and 2.9%, respectively, with superior performance in terms of prediction error and trends compared with other models. This paper discusses the main influencing factors of water consumption and analyzes their influence on the water consumption forecasting model so that the parameters of the water consumption forecasting model can be selected more efficiently and provide a reference for regional water consumption analysis and water resource planning and management.
Supplementary Information
The online version contains supplementary material available at 10.1007/s11356-022-24604-2.
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