Microwave-assisted digestion and NaOH treatment of waste-activated sludge to recover phosphorus by crystallizing struvite

<p>A number of studies of waste-activated sludge (WAS) pretreatments, aimed at releasing phosphorus (P) from WAS and increasing the amount of P that can be recovered, have been performed. Here, a microwave-assisted digestion and NaOH treatment (MWs & NaOH) coupled crystallizing struvite, to promote the solubilization, transformation, and recovery of P from WAS, is proposed. Microwaves (MWs) can cause cavities to form in WAS, weakening the bonds between extracellular polymeric substances and the solid phase. Irradiating with MWs significantly increased the efficiency at which P was dissolved (i.e. transferred from the solid to the liquid phase) and the efficiency at which organic P was hydrolyzed and transformed into inorganic P when the NaOH treatment was performed. The P solubilization and transformation characteristic achieved in different treatments was examined by scanning electron microscopy and three-dimensional excitation emission matrix analysis. The MWs & NaOH method released 34.20–43.73% of total P from WAS, and 23.48–32.07% of the total P was recovered by crystallizing struvite at pH 9.5 and Mg:P ratio of 1.5:1. It would cost about USD 85–103 per ton of dry WAS to treat WAS using the MWs & NaOH method.</p

Relations between soil organic matter (SOM) concentrations and grain yield without fertilizer addition for on-farm trials (Yield-CK) in 5 major irrigated cereal-based cropping systems in China.

<p>(a) winter wheat in north China (n=354); (b) summer maize in north China (n=425) (c) early rice in south of China (n=697); (d) late rice in south of China (n=688); (e) single rice in Yangtze River Basin (n=2474). Solid and dashed lines in this figure indicate median and mean yield, respectively. The box boundaries indicate upper and lower quartiles, the whisker caps indicate 90th and 10th percentiles, and the circles indicate the 95th and 5th percentiles.</p

Relations between grain yields without fertilizer addition (Yield-CK) and yields under the best management practices (Yield-BMPs) from on-farm trials in major irrigated cereal-based cropping systems in China.

<p>Note: Paired sites involved 621 were for wheat, 457 for maize, 1614 for early rice, 1592 for late rice and 3126 for single rice.</p

Temporal changes in crop yields in plots without fertilizer addition (Yield-CK) in China’s typical long term experiments of major irrigated cereal-based cropping systems.

<p>Note: N, north; S, South of China; and YRB, Yangtze River Basin. See supporting information (Table S1 in File S1) on slopes of each of yield dynamics.</p

Relative contributions of improvements in inherent soil productivity to yield under BMPs in 5 major irrigated cereal-based cropping systems in China.

<p>(a) winter wheat in north China (n=457); (b) summer maize in north China (n=621); (c) early rice in South of China (n=1614); (d) late rice in South of China (n=1592); (e) single rice in Yangtze River Basin (n=3126). Solid and dashed lines in this figure indicate median and mean yield, respectively. The box boundaries indicate upper and lower quartiles, the whisker caps indicate 90th and 10th percentiles, and the circles indicate the 95th and 5th percentiles.</p

Integrated Analysis of DNA Methylation and RNA Transcriptome during <i>In Vitro</i> Differentiation of Human Pluripotent Stem Cells into Retinal Pigment Epithelial Cells

<div><p>Using the paradigm of <i>in vitro</i> differentiation of hESCs/iPSCs into retinal pigment epithelial (RPE) cells, we have recently profiled mRNA and miRNA transcriptomes to define a set of RPE mRNA and miRNA signature genes implicated in directed RPE differentiation. In this study, in order to understand the role of DNA methylation in RPE differentiation, we profiled genome-scale DNA methylation patterns using the method of reduced representation bisulfite sequencing (RRBS). We found dynamic waves of <i>de novo</i> methylation and demethylation in four stages of RPE differentiation. Integrated analysis of DNA methylation and RPE transcriptomes revealed a reverse-correlation between levels of DNA methylation and expression of a subset of miRNA and mRNA genes that are important for RPE differentiation and function. Gene Ontology (GO) analysis suggested that genes undergoing dynamic methylation changes were related to RPE differentiation and maturation. We further compared methylation patterns among human ESC- and iPSC-derived RPE as well as primary fetal RPE (fRPE) cells, and discovered that specific DNA methylation pattern is useful to classify each of the three types of RPE cells. Our results demonstrate that DNA methylation may serve as biomarkers to characterize the cell differentiation process during the conversion of human pluripotent stem cells into functional RPE cells.</p></div

The correlation of gene expression and DNA methylation in hESC-/hiPSC-RPEs and fRPEs.

<p>(A) Scatterplots of gene expression fold change vs DNA methylation change in hESC- and hiPSC- RPEs vs. fRPEs. The correlation coefficients were −0.306 (p-value = 0.051) and −0.288 (p-value = 0.067), respectively. (B) Gene expression heatmap analysis of selected genes that are methylated in H9 ESC-RPE, but not in fetal RPE.</p

Analysis of differentially methylated genes during RPE differentiation from human H9 ESCs.

<p>(A) Heatmap analysis of promoter methylation of differentially methylated genes during RPE differentiation. ESC is undifferentiated H9 hESCs, PD: partial differentiated H9 cells, PC: pigmented cluster. RPE: H9 ESC-RPE. (B, C, D) GO analysis of differentially methylated genes during RPE differentiation. Bar graphs showing significance of enrichment terms for sets of demethylated genes from PD into PC cells (B, as indicated by the two blue boxes in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0091416#pone-0091416-g002" target="_blank">figure 2A</a>) and during the course of RPE maturation from PC to RPE (C, the yellow box in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0091416#pone-0091416-g002" target="_blank">figure 2A</a>), and remethylated genes in mature RPE (D, the two light blue boxes in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0091416#pone-0091416-g002" target="_blank">figure 2A</a>) as listed in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0091416#pone.0091416.s007" target="_blank">Table S2</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0091416#pone.0091416.s008" target="_blank">S3</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0091416#pone.0091416.s009" target="_blank">S4</a>. P-values<0.05.</p

DNA methylation profiles of RPE and ESC signature genes during the course of RPE differentiation.

<p>Heatmap showing the average promoter methylation levels of (A) RPE and (B) ESC mRNA signature genes during the conversion of stem cells into functional RPE.</p

Different cell types exhibit distinctive global DNA methylation patterns.

<p>DNA methylation profiles of different cell types were clustered using either unbiased hierarchical clustering or principal component analysis (PCA) based on the DNA methylation levels of all shared CGs in all cell lines analyzed. A) Hierarchal clustering using Pearson correlation distance between methylation levels of 535,376 shared CGs. Defined cell types show a high overall similarity in the methylation patterns and thus cluster together. B) 2D-biplot of the first two principal components. For this analysis, we report the average principal component scores after randomly sampling of 100,000 shared CGs over 200 iterations.</p