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  • 1
    Publication Date: 2019
    Description: Water management and nitrogen (N) fertilizers are the two main driving factors of greenhouse gas emissions. In this paper, two irrigation modes, controlled irrigation (CI) and flood irrigation (FI), and four nitrogen fertilizer levels (N0: 0, N1: 85, N2: 110, and N3: 135 kg·hm−2) were set to study the effect of different irrigation modes and N fertilizer amount on greenhouse-gas emissions of paddy fields in cold region by using the static chamber-gas chromatograph method; yield and water consumption were also analyzed. The results showed that, compared with FI, CI significantly reduced CH4 emissions by 19.42~46.94%, but increased N2O emissions by 5.66~11.85%. Under the two irrigation modes, N fertilizers could significantly increase N2O emissions, but the CH4 emissions of each N treatment showed few differences. Compared with FI, appropriate N application under CI could significantly increase grain number per spike, seed-setting rate, and 1000-grain weight, thus increasing yield. Under the two irrigation modes, water consumption increased with the increase of N application rate, and the total water consumption of CI was significantly lower than that of FI. The global warming potential (GWP) of CI was significantly smaller than that of FI. The trend of GWP in each treatment was similar to that of CH4. Through comprehensive comparison and analysis of water productivity (WP), gas emission intensity (GHGI), and the yield of each treatment, we found that CI+N2 treatment had the highest WP (2.05 kg·m−3) and lowest GHGI (0.37 kg CO2-eq·kg−1), while maintaining high yield (10,224.4 kg·hm−2). The results of this study provide an important basis for guiding high yield, water-savings, and emission reduction of paddy fields in cold regions.
    Print ISSN: 1661-7827
    Electronic ISSN: 1660-4601
    Topics: Energy, Environment Protection, Nuclear Power Engineering , Medicine
    Published by MDPI
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  • 2
    Publication Date: 2021-08-19
    Description: DNA methylation may be regulated by genetic variants within a genomic region, referred to as methylation quantitative trait loci (mQTLs). The changes of methylation levels can further lead to alterations of gene expression, and influence the risk of various complex human diseases. Detecting mQTLs may provide insights into the underlying mechanism of how genotypic variations may influence the disease risk. In this article, we propose a methylation random field (MRF) method to detect mQTLs by testing the association between the methylation level of a CpG site and a set of genetic variants within a genomic region. The proposed MRF has two major advantages over existing approaches. First, it uses a beta distribution to characterize the bimodal and interval properties of the methylation trait at a CpG site. Second, it considers multiple common and rare genetic variants within a genomic region to identify mQTLs. Through simulations, we demonstrated that the MRF had improved power over other existing methods in detecting rare variants of relatively large effect, especially when the sample size is small. We further applied our method to a study of congenital heart defects with 83 cardiac tissue samples and identified two mQTL regions, MRPS10 and PSORS1C1, which were colocalized with expression QTL in cardiac tissue. In conclusion, the proposed MRF is a useful tool to identify novel mQTLs, especially for studies with limited sample sizes.
    Print ISSN: 1467-5463
    Electronic ISSN: 1477-4054
    Topics: Biology , Computer Science
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