Quantifying CO2 leak rates in aquatic environments

The Daylesford region of Victoria (Australia), is a region of natural CO2 seepage. Small bubble streams of CO2 are released into ephemeral river beds proximal to mineral springs that contain high dissolved CO2 content. We study four sites of CO2 degassing to (i) establish the characteristics of CO2 seepage caused by transport to surface of CO2-rich water, (ii) provide an estimate of CO2 flux in the region, and (iii) investigate seasonal effects on CO2 seepage. We observe that bubbling behavior varies considerably between sites, including the number and distribution of bubbling points, and bubble stream the continuity. Total CO2 seep rates at each site were low (< 20 kg/d) but varied substantially between different sites. There were no obvious indicators of total emission rate; the bubble density or other characteristics at the highest emission seep were not remarkably different to the smaller seeps. We find that the total CO2 emission varies inconsistently with season, with some seep rates increasing and other decreasing in the dry season when water levels are lower. We find there are challenges in quantifying the total gas leakage at sites of highly localized and intermittent degassing. Our work has implications for detecting and quantifying leaks from engineered CO2 storage sites which emerge in aqueous environments, which could be these are marine or terrestrial (lakes or rivers)

Novel mutations in TLR genes cause hyporesponsiveness to Mycobacterium avium subsp. paratuberculosis infection

<p>Abstract</p> <p>Background</p> <p>Toll like receptors (TLR) play the central role in the recognition of pathogen associated molecular patterns (PAMPs). Mutations in the TLR1, TLR2 and TLR4 genes may change the ability to recognize PAMPs and cause altered responsiveness to the bacterial pathogens.</p> <p>Results</p> <p>The study presents association between TLR gene mutations and increased susceptibility to <it>Mycobacterium avium </it>subsp. <it>paratuberculosis </it>(MAP) infection. Novel mutations in TLR genes (TLR1- Ser150Gly and Val220Met; TLR2 – Phe670Leu) were statistically correlated with the hindrance in recognition of MAP legends. This correlation was confirmed subsequently by measuring the expression levels of cytokines (IL-4, IL-8, IL-10, IL-12 and IFN-γ) in the mutant and wild type moDCs (mocyte derived dendritic cells) after challenge with MAP cell lysate or LPS. Further <it>in silico </it>analysis of the TLR1 and TLR4 ectodomains (ECD) revealed the polymorphic nature of the central ECD and irregularities in the central LRR (leucine rich repeat) motifs.</p> <p>Conclusion</p> <p>The most critical positions that may alter the pathogen recognition ability of TLR were: the 9^thamino acid position in LRR motif (TLR1–LRR10) and 4^thresidue downstream to LRR domain (exta-LRR region of TLR4). The study describes novel mutations in the TLRs and presents their association with the MAP infection.</p

Modelling spatial patterns of correlations between concentrations of heavy metals in mosses and atmospheric deposition in 2010 across Europe

BackgroundThis paper aims to investigate the correlations between the concentrations of nine heavy metals in moss and atmospheric deposition within ecological land classes covering Europe. Additionally, it is examined to what extent the statistical relations are affected by the land use around the moss sampling sites. Based on moss data collected in 2010/2011 throughout Europe and data on total atmospheric deposition modelled by two chemical transport models (EMEP MSC-E, LOTOS-EUROS), correlation coefficients between concentrations of heavy metals in moss and in modelled atmospheric deposition were specified for spatial subsamples defined by ecological land classes of Europe (ELCE) as a spatial reference system. Linear discriminant analysis (LDA) and logistic regression (LR) were then used to separate moss sampling sites regarding their contribution to the strength of correlation considering the areal percentage of urban, agricultural and forestry land use around the sampling location. After verification LDA models by LR, LDA models were used to transform spatial information on the land use to maps of potential correlation levels, applicable for future network planning in the European Moss Survey.ResultsCorrelations between concentrations of heavy metals in moss and in modelled atmospheric deposition were found to be specific for elements and ELCE units. Land use around the sampling sites mainly influences the correlation level. Small radiuses around the sampling sites examined (5km) are more relevant for Cd, Cu, Ni, and Zn, while the areal percentage of urban and agricultural land use within large radiuses (75-100km) is more relevant for As, Cr, Hg, Pb, and V. Most valid LDA models pattern with error rates of <40% were found for As, Cr, Cu, Hg, Pb, and V. Land use-dependent predictions of spatial patterns split up Europe into investigation areas revealing potentially high (=above-average) or low (=below-average) correlation coefficients.ConclusionsLDA is an eligible method identifying and ranking boundary conditions of correlations between atmospheric deposition and respective concentrations of heavy metals in moss and related mapping considering the influence of the land use around moss sampling sites

CCDC 1496264: Experimental Crystal Structure Determination

An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures

CCDC 1496265: Experimental Crystal Structure Determination

An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures

CCDC 1496263: Experimental Crystal Structure Determination

An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures

CCDC 1504308: Experimental Crystal Structure Determination

An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures

CCDC 1440039: Experimental Crystal Structure Determination

An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures