48 research outputs found
Veterinarians in the UK on the use of non-steroidal anti-inflammatory drugs (NSAIDs) for post-disbudding analgesia of calves
<p>Top 20 down-regulated genes after DAPT treatment in P0 Lfng-GFP<sup>+</sup> cells.</p
Comparison of the average significance of disease-related modules obtained by DLPCA with <i>β</i><sub>1</sub> = <i>β</i><sub>2</sub>, DLPCA with <i>β</i><sub>1</sub> > <i>β</i><sub>2</sub>, and MLPCA.
<p>Each box shows the average significance of disease-related modules using an approach with different numbers of seeds.</p
Impacts of Steel-Slag-Based Silicate Fertilizer on Soil Acidity and Silicon Availability and Metals-Immobilization in a Paddy Soil
<div><p>Slag-based silicate fertilizer has been widely used to improve soil silicon- availability and crop productivity. A consecutive early rice-late rice rotation experiment was conducted to test the impacts of steel slag on soil pH, silicon availability, rice growth and metals-immobilization in paddy soil. Our results show that application of slag at a rate above higher or equal to 1 600 mg plant-available SiO<sub>2</sub> per kg soil increased soil pH, dry weight of rice straw and grain, plant-available Si concentration and Si concentration in rice shoots compared with the control treatment. No significant accumulation of total cadmium (Cd) and lead (Pb) was noted in soil; rather, the exchangeable fraction of Cd significantly decreased. The cadmium concentrations in rice grains decreased significantly compared with the control treatment. In conclusion, application of steel slag reduced soil acidity, increased plant–availability of silicon, promoted rice growth and inhibited Cd transport to rice grain in the soil-plant system.</p></div
Effect of steel slag fertilizer application on relative content of Cd and Pb in each fraction of soil.
<p>Effect of steel slag fertilizer application on relative content of Cd and Pb in each fraction of soil.</p
The clustering results of each experiment.
<p>The clustering results of each experiment.</p
Illustration of DLPCA compared with MLPA.
<p>(A) The modules in the gene co-expression network obtained using MLPA. (B) Introduction of the pathogenic information of some genes. Here, red nodes represent disease genes and black nodes represent non-disease genes. (C) The new modular structures in the gene co-expression network obtained using DLPCA.</p
Effect of steel slag fertilizer application on heavy metal uptake by rice grain.
<p>Data are means of three replicates. Mean values followed by different letters (a, b, c) in the same season are significantly different (P< 0.05).</p
Comparison of the significance of scatters obtained by DLPCA with <i>β</i><sub>1</sub> = <i>β</i><sub>2</sub>, DLPCA with <i>β</i><sub>1</sub> > <i>β</i><sub>2</sub>, and MLPCA.
<p>Each grouped bar chart represents the results of different approaches with the same numbers of seeds.</p
Comparison of the average significance of disease-related modules obtained by DLPCA with <i>β</i><sub>1</sub> = <i>β</i><sub>2</sub>, DLPCA with <i>β</i><sub>1</sub> > <i>β</i><sub>2</sub>, and MLPCA.
<p>Each grouped bar chart represents the results of different approaches with the same numbers of seeds.</p
The relationship between degree and weighted connectivity.
<p>The scatterplot shows a near-linear correlation between the degree and the weighted connectivity.</p