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Brown, Richard J C,Brewer, Paul J,Ent, Hugo,Fisicaro, Paola,Horvat, Milena,Kim, Ki-Hyun,Qu?tel, Christophe R BUREAU INTERNATIONAL DES POIDS ET MESURES 2015 METROLOGIA -BERLIN- Vol.52 No.5
<P>This paper considers how decisions on internationally recommended datasets are made and implemented and, further, how the ownership of these decisions comes about. Examples are given of conventionally agreed data and values where the responsibility is clear and comes about through official designation or by common usage and practice over long time periods. The example of the dataset describing the mass concentration of mercury in air at saturation is discussed in detail. This is a case where there are now several competing datasets that are in disagreement with each other, some with historical authority and some more recent but, arguably, with more robust metrological traceability to the SI. Further, it is elaborated that there is no body charged with the responsibility to make a decision on an international recommendation for such a dataset. This has led to the situation where several competing datasets are in use simultaneously. Close parallels are drawn with the current debate over changes to the ozone absorption cross section, which has equal importance to the measurement of ozone amount fraction in air and to subsequent compliance with air quality legislation. It is noted that in the case of the ozone cross section there is already a committee appointed to deliberate over any change. We make the proposal that a similar committee, under the auspices of IUPAC or the CIPM's CCQM (if it adopted a reference data function) could be formed to perform a similar role for the mass concentration of mercury in air at saturation.</P>
Fixed volume sequential standard addition calibration: Value assignment of impurities in zero gas
Brown, Richard J.C.,Brewer, Paul J.,Kim, Ki-Hyun Elsevier 2017 Chemometrics and intelligent laboratory systems Vol.164 No.-
<P><B>Abstract</B></P> <P>A fixed volume case of sequential standard addition calibration (S-SAC) for the value assignment of impurities in zero gas is described. A mathematical description of this technique has been derived and has been shown to exhibit a similar systematic bias during the extrapolation process to that seen for other S-SAC cases. The use of S-SAC in the gas phase has demonstrated that variation in the sample volume for S-SAC in the liquid phase is not the generalised cause of the bias experienced during extrapolation. Instead it is the variation in the volume of the original sample with respect to the overall volume of the mixture following the addition of standard. In addition, the requirement to perform a bias correction on the extrapolated values has been discussed and best practice solutions for correction proposed.</P> <P><B>Highlights</B></P> <P> <UL> <LI> A fixed volume sequential standard addition calibration is described mathematically. </LI> <LI> This has been applied to the determination of impurities in blank gas. </LI> <LI> A systematic bias during the extrapolation process is observed. </LI> <LI> Best practice solutions for correction are proposed. </LI> </UL> </P>
Recon 2.2: from reconstruction to model of human metabolism
Swainston, Neil,Smallbone, Kieran,Hefzi, Hooman,Dobson, Paul D.,Brewer, Judy,Hanscho, Michael,Zielinski, Daniel C.,Ang, Kok Siong,Gardiner, Natalie J.,Gutierrez, Jahir M.,Kyriakopoulos, Sarantos,Laksh Springer US 2016 METABOLOMICS Vol.12 No.7
<P><B>Introduction</B></P><P>The human genome-scale metabolic reconstruction details all known metabolic reactions occurring in humans, and thereby holds substantial promise for studying complex diseases and phenotypes. Capturing the whole human metabolic reconstruction is an on-going task and since the last community effort generated a consensus reconstruction, several updates have been developed.</P><P><B>Objectives</B></P><P>We report a new consensus version, Recon 2.2, which integrates various alternative versions with significant additional updates. In addition to re-establishing a consensus reconstruction, further key objectives included providing more comprehensive annotation of metabolites and genes, ensuring full mass and charge balance in all reactions, and developing a model that correctly predicts ATP production on a range of carbon sources.</P><P><B>Methods</B></P><P>Recon 2.2 has been developed through a combination of manual curation and automated error checking. Specific and significant manual updates include a respecification of fatty acid metabolism, oxidative phosphorylation and a coupling of the electron transport chain to ATP synthase activity. All metabolites have definitive chemical formulae and charges specified, and these are used to ensure full mass and charge reaction balancing through an automated linear programming approach. Additionally, improved integration with transcriptomics and proteomics data has been facilitated with the updated curation of relationships between genes, proteins and reactions.</P><P><B>Results</B></P><P>Recon 2.2 now represents the most predictive model of human metabolism to date as demonstrated here. Extensive manual curation has increased the reconstruction size to 5324 metabolites, 7785 reactions and 1675 associated genes, which now are mapped to a single standard. The focus upon mass and charge balancing of all reactions, along with better representation of energy generation, has produced a flux model that correctly predicts ATP yield on different carbon sources.</P><P><B>Conclusion</B></P><P>Through these updates we have achieved the most complete and best annotated consensus human metabolic reconstruction available, thereby increasing the ability of this resource to provide novel insights into normal and disease states in human. The model is freely available from the Biomodels database (http://identifiers.org/biomodels.db/MODEL1603150001).</P><P><B>Electronic supplementary material</B></P><P>The online version of this article (doi:10.1007/s11306-016-1051-4) contains supplementary material, which is available to authorized users.</P>
International comparison CCQM-K84-carbon monoxide in synthetic air at ambient level
Lee, Jeongsoon,Moon, Dongmin,Lee, Jinbok,Lim, Jeongsik,Hall, Brad,Novelli, Paul,Brewer, Paul J,Miller, Michael,Murugun, Arul,Minarro, Marta Doval,Qiao, Han,Shuguo, Hu,Konopelko, L A,Kustikov, Y A,Kolo BUREAU INTERNATIONAL DES POIDS ET MESURES 2017 METROLOGIA -BERLIN- Vol.54 No.1