Effect of peak inert-mode temperature on elemental carbon measured using thermal-optical analysis

Thermal-optical analysis is a conventional method for classifying carbonaceous aerosols as organic carbon (OC) and elemental carbon (EC). This article examines the effects of three different temperature protocols on the measured EC. For analyses of parallel punches from the same ambient sample, the protocol with the highest peak helium-mode temperature (870°C) gives the smallest amount of EC, while the protocol with the lowest peak helium-mode temperature (550°C) gives the largest amount of EC. These differences are observed when either sample transmission or reflectance is used to define the OC/EC split. An important issue is the effect of the peak helium-mode temperature on the relative rate at which different types of carbon with different optical properties evolve from the filter. Analyses of solvent-extracted samples are used to demonstrate that high temperatures (870°C) lead to premature EC evolution in the helium-mode. For samples collected in Pittsburgh, this causes the measured EC to be biased low because the attenuation coefficient of pyrolyzed carbon is consistently higher than that of EC. While this problem can be avoided by lowering the peak helium-mode temperature, analyses of wood smoke dominated ambient samples and levoglucosan-spiked filters indicate that too low helium-mode peak temperatures (550°C) allow non-light absorbing carbon to slip into the oxidizing mode of the analysis. If this carbon evolves after the OC/EC split, it biases the EC measurements high. Given the complexity of ambient aerosols, there is unlikely to be a single peak helium-mode temperature at which both of these biases can be avoided. Copyright © American Association for Aerosol Research.

Integrated pathway and epistasis analysis reveals interactive effect of genetic variants at TERF1 and AFAP1L2 loci on melanoma risk.

Genome-wide association studies (GWASs) have characterized 13 loci associated with melanoma, which only account for a small part of melanoma risk. To identify new genes with too small an effect to be detected individually but which collectively influence melanoma risk and/or show interactive effects, we used a two-step analysis strategy including pathway analysis of genome-wide SNP data, in a first step, and epistasis analysis within significant pathways, in a second step. Pathway analysis, using the gene-set enrichment analysis (GSEA) approach and the gene ontology (GO) database, was applied to the outcomes of MELARISK (3,976 subjects) and MDACC (2,827 subjects) GWASs. Cross-gene SNP-SNP interaction analysis within melanoma-associated GOs was performed using the INTERSNP software. Five GO categories were significantly enriched in genes associated with melanoma (false discovery rate ≤ 5% in both studies): response to light stimulus, regulation of mitotic cell cycle, induction of programmed cell death, cytokine activity and oxidative phosphorylation. Epistasis analysis, within each of the five significant GOs, showed significant evidence for interaction for one SNP pair at TERF1 and AFAP1L2 loci (pmeta-int  = 2.0 × 10(-7) , which met both the pathway and overall multiple-testing corrected thresholds that are equal to 9.8 × 10(-7) and 2.0 × 10(-7) , respectively) and suggestive evidence for another pair involving correlated SNPs at the same loci (pmeta-int  = 3.6 × 10(-6) ). This interaction has important biological relevance given the key role of TERF1 in telomere biology and the reported physical interaction between TERF1 and AFAP1L2 proteins. This finding brings a novel piece of evidence for the emerging role of telomere dysfunction into melanoma development.