The Growth of the Organic Market: Producers' Perspectives

Demand and Price Analysis,

Demonstration of Mer-Cure Technology for Enhanced Mercury Control: Final Report

Alstom Power Inc. has completed a DOE/NETL-sponsored program (under DOE Cooperative Agreement No. De-FC26-07NT42776) to demonstrate Mer-Cure{trademark}, one of Alstom's mercury control technologies for coal-fired boilers. The Mer-Cure{trademark}system utilizes a small amount of Mer-Clean{trademark} sorbent that is injected into the flue gas stream for oxidation and adsorption of gaseous mercury. Mer-Clean{trademark} sorbents are carbon-based and prepared with chemical additives that promote oxidation and capture of mercury. The Mer-Cure{trademark} system is unique in that the sorbent is injected into an environment where the mercury capture kinetics is accelerated. The full-scale demonstration program originally included test campaigns at two host sites: LCRA's 480-MW{sub e} Fayette Unit No.3 and Reliant Energy's 190-MW{sub e} Shawville Unit No.3. The only demonstration tests actually done were the short-term tests at LCRA due to budget constraints. This report gives a summary of the demonstration testing at Fayette Unit No.3. The goals for this Mercury Round 3 program, established by DOE/NETL under the original solicitation, were to reduce the uncontrolled mercury emissions by 90% at a cost significantly less than 50% of the previous target of $60,000/lb mercury removed. The results indicated that Mer-Cure{trademark} technology could achieve mercury removal of 90$0.75 to $2.00/lb respectively, were$13,400 to $18,700/lb Hg removed. In summary, the results from demonstration testing show that the goals established by DOE/NETL were met during this test program. The goal of 90$60,000/lb mercury removed, significantly less than 50% of the baseline removal cost

Rural Cooperatives Magazine, November/December 2011

Features - Why do members participate in ag co-ops?; Upward spiral; Shine a light; Crash course in cooperation; How co-ops do it; Co-ops saluted as ‘vital economic force’ at USDA event; Mission-driven marketing; Is ‘local’ enough?; Making the connectio

Genome-Wide DNA Methylation Analysis of Systemic Lupus Erythematosus Reveals Persistent Hypomethylation of Interferon Genes and Compositional Changes to CD4+ T-cell Populations

<div><p>Systemic lupus erythematosus (SLE) is an autoimmune disease with known genetic, epigenetic, and environmental risk factors. To assess the role of DNA methylation in SLE, we collected CD4+ T-cells, CD19+ B-cells, and CD14+ monocytes from 49 SLE patients and 58 controls, and performed genome-wide DNA methylation analysis with Illumina Methylation450 microarrays. We identified 166 CpGs in B-cells, 97 CpGs in monocytes, and 1,033 CpGs in T-cells with highly significant changes in DNA methylation levels (p<1×10⁻⁸) among SLE patients. Common to all three cell-types were widespread and severe hypomethylation events near genes involved in interferon signaling (type I). These interferon-related changes were apparent in patients collected during active and quiescent stages of the disease, suggesting that epigenetically-mediated hypersensitivity to interferon persists beyond acute stages of the disease and is independent of circulating interferon levels. This interferon hypersensitivity was apparent in memory, naïve and regulatory T-cells, suggesting that this epigenetic state in lupus patients is established in progenitor cell populations. We also identified a widespread, but lower amplitude shift in methylation in CD4+ T-cells (>16,000 CpGs at FDR<1%) near genes involved in cell division and MAPK signaling. These cell type-specific effects are consistent with disease-specific changes in the composition of the CD4+ population and suggest that shifts in the proportion of CD4+ subtypes can be monitored at CpGs with subtype-specific DNA methylation patterns.</p></div

Estimation of cell-type composition including T and B cell subtypes for whole blood methylation microarray data

DNA methylation levels vary markedly by cell-type makeup of a sample. Understanding these differences and estimating the cell-type makeup of a sample is an important aspect of studying DNA methylation. DNA from leukocytes in whole blood is simple to obtain and pervasive in research. However, leukocytes contain many distinct cell types and subtypes. We propose a two-stage model that estimates the proportions of 6 main cell types in whole blood (CD4+ T cells, CD8+ T cells, monocytes, B cells, granulocytes, and natural killer cells) as well as subtypes of T and B cells. Unlike previous methods that only estimate overall proportions of CD4+ T cell, CD8+ T cells, and B cells, our model is able to estimate proportions of naïve, memory, and regulatory CD4+ T cells as well as naïve and memory CD8+ T cells and naïve and memory B cells. Using real and simulated data, we are able to demonstrate that our model is able to reliably estimate proportions of these cell types and subtypes. In studies with DNA methylation data from Illumina’s HumanMethylation450k arrays, our estimates will be useful both for testing for associations of cell type and subtype composition with phenotypes of interest as well as for adjustment purposes to prevent confounding in epigenetic association studies. Additionally, our method can be easily adapted for use with whole genome bisulfite sequencing data or any other genome-wide methylation data platform

Common and cell type-specific DNA methylation changes in SLE.

<p>Differences in mean methylation between SLE and controls are plotted for each cell type at each probe near two genes. <b>A.</b> The IRF7 gene shows hypomethylation across all three cell-types at a CpG island, plus monocyte-specific hypomethylation further into the gene body. <b>B.</b> The IKZF4 gene shows T-cell-specific hypomethylation at the 5′ end of the gene. Red dots indicate p<1×10⁻⁸. Yellow dots indicate FDR<1%.</p

Disease activity QQ-Plots and the persistence of hypomethylation in quiescent patients.

<p><b>A.</b> QQ-Plots of the p-values from the flare versus quiescent association analysis for each cell type, illustrating the lack of activity-dependent DNA methylation. <b>B.</b> Boxplots of the methylation difference between each individual and the mean of all controls at CpGs in IFN-regulated genes among those that were highly significant in the SLE-control tests. The groups are labeled C, Control, F, SLE collected during a flare, and Q, SLE collected during quiescence.</p

Comparison of the SLE-control methylation differences in sorted T-cell populations.

<p>Each scatter plot represents 1,031 CpGs that had p<1×10⁻⁸ in CD4+ T-cells in our SLE-control association tests. The Y-axis for all plots is the mean SLE-control methylation delta at these CpGs in the initial cohort. The X-axis for each plot is the mean SLE-control methylation delta at the same CpGs in our validation cohort, using (<b>A</b>) total CD4+, (<b>B</b>) CD4+Memory, (<b>C</b>) CD4+Naïve, or (<b>D</b>) CD4+Regulatory cells. The red dots represent CpGs near IFN-regulated genes and the squared correlation coefficients (R²) represent the values for all plotted CpGs (upper left) or IFN CpGs only (lower right).</p

Functional analysis of significant CpGs in three cell types.

<p><a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003678#s2" target="_blank">Results</a> from DAVID/Panther GO term analysis for the highly significant CpGs in each cell type and the mildly significant CpGs in T-cells.</p