| 1 | Effects of live yeast on differential genetic and functional attributes of rumen microbiota in beef cattle显示文摘Several studies have evaluated the effects of live yeast supplementation on rumen microbial population;however,its effect on differential microbial genes and their functional potential has not been described. Thus, this study applied shotgun metagenomic sequencing to evaluate the effects of live yeast supplementation on genetic and functional potential of the rumen microbiota in beef cattle. Eight rumen-cannulated Holstein steers were randomly assigned to two treatments in a cross-over design with two 25-day experimental periods and a 10-day wash-out between the two periods. The steers were housed in individual pens and fed 50% concentrate-mix and 50% red clover/orchard hay ad libitum. Treatments were(1) control(CON;basal diet without additive) and(2) yeast(YEA;basal diet plus 15 g/d of live yeast product). Rumen fluid samples were collected at 3, 6, and 9 h after feeding on the last d of each period. Sequencing was done on an Illumina Hi Seq 2500 platform. Dietary yeast supplementation increased the relative abundance of carbohydrate-fermenting bacteria(such as Ruminococcus albus, R.champanellensis, R. bromii, and R. obeum) and lactate-utilizing bacteria(such as Megasphaera elsdenii, Desulfovibrio desulfuricans, and D. vulgaris). A total of 154 differentially abundant genes(DEGs) were obtained(false discovery rate < 0.01). Kyoto Encyclopedia of Genes and Genomes(KEGG) annotation analysis of the DEGs revealed that 10 pathways, including amino sugar and nucleotide sugar metabolism, oxidative phosphorylation, lipopolysaccharide biosynthesis, pantothenate and coenzyme A biosynthesis, glutathione metabolism, beta-alanine metabolism,polyketide sugar unit biosynthesis, protein export, ribosome, and bacterial secretory system, were enriched in steers fed YEA. Annotation analysis of the DEGs in the carbohydrate-active enzymes(CAZy) database revealed that the abundance of genes coding for enzymes belonging to glycoside hydrolases, glycosyltransferases, and carbohydrate binding modules were enriched in steers fed YEA. These results confirm the effectiveness of a live S. cerevisiae product for improving rumen function in beef steers by increasing the abundance of cellulolytic bacteria, lactic acid-utilizing bacteria, and carbohydrate-active enzymes in the rumen. | Ibukun M.Ogunade Jerusha Lay Kenneth Andries Christina J.Mc Manus Frederick Bebe | 2019 | Journal of Animal Science and Biotechnology2019，10，4： | 11 |
| 2 | Detecting Sorghum Plant and Head Features from Multispectral UAV Imagery显示文摘In plant breeding,unmanned aerial vehicles(UAVs)carrying multispectral cameras have demonstrated increasing utility for high-throughput phenotyping(HTP)to aid the interpretation of genotype and environment effects on morphological,biochemical,and physiological traits.A key constraint remains the reduced resolution and quality extracted from“stitched”mosaics generated from UAV missions across large areas.This can be addressed by generating high-quality reflectance data from a single nadir image per plot.In this study,a pipeline was developed to derive reflectance data from raw multispectral UAV images that preserve the original high spatial and spectral resolutions and to use these for phenotyping applications.Sequential steps involved(i)imagery calibration,(ii)spectral band alignment,(iii)backward calculation,(iv)plot segmentation,and(v)application.Each step was designed and optimised to estimate the number of plants and count sorghum heads within each breeding plot.Using a derived nadir image of each plot,the coefficients of determination were 0.90 and 0.86 for estimates of the number of sorghum plants and heads,respectively.Furthermore,the reflectance information acquired from the different spectral bands showed appreciably high discriminative ability for sorghum head colours(i.e.,red and white).Deployment of this pipeline allowed accurate segmentation of crop organs at the canopy level across many diverse field plots with minimal training needed from machine learning approaches. | Yan Zhao Bangyou Zheng Scott CChapman Kenneth Laws Barbara George-Jaeggli Graeme LHammer David RJordan Andries B.Potgieter | 2021 | Plant Phenomics2021，3，1： | 0 |
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