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• The ranking of environmental factors for soil type mapping was determined. • The optimal environmental covariates for DSM were clarified. • The optimal variable combination was determined. • The best overall accuracy produced by the optimal variable combination was 65.74%. The scientific selection of environmental covariates is an important factor that affects the accuracy of digital soil mapping (DSM).An investigation of the influences of different environmental covariates on soil type identification is vital to understand soil development and predictive soil mapping. In this study, the soil groups in the typical plain and hilly areas of the Jiaodong Peninsula in China were taken as the objects of investigation. Based on five environmental characteristics and remotely sensed indexes (texture, terrain, biology, major spectral components and moisture), 21 covariates sourced from Landsat 8 remote sensing images and a digital elevation map were utilized for the predictive mapping of soils. The identification accuracies of the soil groups were acquired through maximum likelihood classification, and the relevant environmental factors that are important for soil mapping and the influence of different environmental covariates on soil mapping were explored. The results show that texture features had the greatest influence on DSM, followed by terrain, major spectral components, moisture and biological factors. The single environmental covariate with the greatest impact on DSM was the texture entropy parameter. For areas containing plain and hilly landforms, the entropy, elevation, slope, normalized difference vegetation index (NDVI), third principal component (PC3) and normalized difference moisture index (NDMI) indicators compose the optimal covariate combination for soil identification. Clarifying the impacts of different environmental covariates on DSM can improve the soil classification applicability and accuracy. The environmental covariates discussed in this study would be useful in similar environmental areas and would provide a reference for DSM. [ABSTRACT FROM AUTHOR]

Heart fatty acid-binding protein (H-FABP) belongs to a family of intracellular fatty acid-binding proteins that are involved in the transport of long-chain fatty acids. Previous studies have indicated that H-FABP is significantly associated with intramuscular fat (IMF) content in pig. In this study, we compared the mRNA and protein expression of H-FABP between Tibetan pig (with high IMF) and Large White pig (with low IMF). The expression of H-FABP at both mRNA and protein levels in the tissues of backfat, longissimus dorsi muscle and liver was found to be significantly higher in TP than in LW. Single-nucleotide polymorphisms (SNPs) in a 2 kb region upstream of the start codon of the gene were screened using Sanger sequencing. We accordingly identified three SNPs (C-1375G, C-314T and T-158G) between the TP and LW populations and genotyped these based on PCR-restriction fragment length polymorphisms (PCF-RFLPs) analysis. The results showed that the C-1375G site might regulate H-FABP gene expression and thus be associated with fat deposition in pigs. Our study provides important data for further investigating the regulatory mechanism of H-FABP for fat deposition in pigs. • Three novel SNPs of heart fatty acid-binding protein (H-FABP) gene are identified in pigs. • Genotype C-1375G is shown to be associated with fat deposition in pigs. • H-FABP expression positively regulates fat deposition in pigs. [ABSTRACT FROM AUTHOR]

• Pig's 5′ flanking region genes have 9 SNPs, where the linage and mutation occur simultaneously. • 9 linked SNP mutations of porcine MYL4 gene increase the mRNA and protein expression of MYL4 gene in porcine muscle. • Changes in the genotypes of 9 linked SNPs in the MYL4 gene in pigs will affect the promoter activity of MYL4. • The mutation of 9 linked SNPs in pig's MYL4 gene will leading to the increase of the number of muscle fibres in the longissimus dorsi of pigs. Our previous transcriptomic study identified MYL4 (myosin light chain 4) to be associated with muscle development and growth in pigs. In this study, we aimed to investigate the genetic variation of MYL4 and determine the effect of these variations in regulating MYL4 expression and muscle fiber size. After screening the regulatory region of MYL4 in Large White pigs, we identified nine completely linked single nucleotide polymorphisms within the MYL4 regulatory region, which showed two haplotypes (H1 and H2). And in MYL4 may affect the activity of the promoter region and regulate the traits of porcine muscle. The results of Western blotting and qRT-PCR showed that haplotype H2 significantly (p < 0.01) increased the relative mRNA and protein expression of MYL4 gene in pig LD tissues, and tissue sections also showed the number of genotype H2H2 Significantly higher than genotype H1H1, in conclusion, our results suggest that MYL4 may promote muscle growth and development and can affect the transcriptional activity of MYL4 through haplotype H1 and haplotype H2. [ABSTRACT FROM AUTHOR]

The gut microbiota can change to varying degrees because of changes in the environment. In the present study, we performed microbial amplicon sequencing on the feces of people who had long-term exposure to swine farms (F) and that of people living in normal environments (S) to investigate the impact of the environment on the human gut microbiota. A total of 1,283,503 high-quality ordered sequences were obtained, which provided different levels of microbial classification and statistics. We found that different environments did not alter the richness and diversity of the microbial communities in participants, but caused significant changes in the proportion of some bacteria. The main bacterial phyla found in group F participants were Firmicutes (69.44–89.03%), Actinobacteria (1.7–18.95%), and Bacteroidetes (1.17–22.35%); those found in group S participants were Firmicutes (49.93–95.04%), Bacteroidetes (0.62–39.59%), and Proteobacteria (0.98–11.95%). Additionally, because of changes in phylum proportions, the Bugbase phenotypic classification predicted an increase in the proportion of Gram-positive bacteria in group F and an increase in the proportion of Gram-negative bacteria in group S. In conclusion, our findings suggest that human exposure to swine farms can reshape the gut microbiota, resulting in changes in the microbial abundances. This change can potentially reduce the odds of developing bowel disease and contribute to the prevention of intestinal diseases, providing a theoretical basis for improving human health. [Display omitted] • Human gut microbiota is affected by host genetics and the environment. • Environment influences the abundance and diversity of human gut microbes. • Gut microbiota is constantly self-regulating. • There is a high correlation between the microbial community and resistance genes. [ABSTRACT FROM AUTHOR]