6 R 2-values (colour scale) for linear least-squares regression of F v/F m(λex,λem)

in simulated communities against F v/F m of their a algal and b cyanobacterial subpopulations. Each R 2 value represents the regression of all 465 communities. The regression of community F v/F m(λex,λem) was carried out against F v/F m(470,683) of algal subpopulations and against F v/F m(590,683) of cyanobacterial subpopulations. Grey markers indicate a poor fit (p > 0.001) of the regression model to the data. Numeric markers refer to excitation/emission pairs for which case plots are given in Fig. 8a–c Region 1 shows R 2 close to 1 between community and algal F v/F m (and consequently a R 2 near 0 with the cyanobacterial fraction), under blue excitation in a wide emission band that includes Chla fluorescence and extends into the range of mixed PSI/PSII fluorescence at near-infrared wavelengths. Region AZD2014 supplier 2 is for excitation near 600 nm and emission in the Chla fluorescence

band near 683 nm and returns R 2 above 0.5 for cyanobacteria but 0.2 for algae. In contrast to the correlation with algae in region 1, the excitation range with a high correlation for cyanobacterial F v/F m does not Foretinib purchase extend into the near-infrared. Region 3, similarly to region 2, is found under orange/red excitation, but in the emission range of phycobilipigments (620–650 nm). In this spectral domain, R 2 is greater than 0.4 for cyanobacteria and near 0 for algae, as no algal pigments fluoresce around 650 nm (Fig. 4). While

the presence of highly fluorescent phycobilipigments in cyanobacteria explains strong fluorescence between 600 and 650 nm, the correlation (R 2 > 0.4) to variable fluorescence from PSII Chla is not straightforward, as it has commonly been assumed that phycobilipigment fluorescence is not variable (but see discussion below, and Küpper et al. 2009; Kana et al. 2009). We note that the presence of algae in the community does not influence click here this result as regression of F v/F m(590,650) against F v/F m(590,683) yields the same correlation when BIBW2992 cost measured from the 31 individual cyanobacterial cultures. To find optimal excitation and emission pairs for the separation of cyanobacterial and algal F v/F m in communities, we inspect the data more closely along the emission and excitation lines linked to the previously identified regions 1–3. The PSII Chla emission line (683 nm, Fig. 7a) reveals that the strongest correlations of F v/F m(λex,683) with algal and cyanobacterial F v/F m occurred upon excitation between 440–500 and 590–630 nm, respectively. The 470-nm excitation line (Fig. 7b) reveals that F v/F m(470,λem), particularly for emission >650 nm, was exclusively and strongly correlated with the algal fraction of the community. The emission spectrum along the 590-nm excitation line (Fig. 7c) confirms that emission around 650 and 683 nm was best correlated with cyanobacterial F v/F m (with R 2 in the range 0.4–0.

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54. Edelman SM, Lehti TA, Kainulainen V, Antikainen J, Kylvaja R, Baumann M, Westerlund-Wikstrom B, Korhonen TK: Identification of a high-molecular-mass Lactobacillus epithelium adhesin (LEA) of Lactobacillus crispatus ST1 that binds to stratified squamous epithelium. Microbiology 2012, 158:1713–1722.PubMedCrossRef 55. Watanabe M, Kinoshita H, Huang IN, Eguchi K, Tsurumi T, Kawai Y, Kitazawa H, Kimura K, Taketomo N, Kikuchi D, et al.: An Adhesin-Like Protein, Lam29, from Lactobacillus mucosae ME-340 Binds to Histone H3 and Blood Group Antigens in Human Colonic Mucus. Biosci Biotechnol Biochem 2012, 76:1655–1660.PubMedCrossRef 56. Van Tassell ML, Miller MJ: Lactobacillus adhesion to mucus. Nutrients 2011, 3:613–636.PubMedCrossRef 57. Kainulainen V, Loimaranta V, Pekkala A, Edelman S, Antikainen J, Kylvaja R, Laaksonen M, Laakkonen L, Finne J, Korhonen TK: Glutamine synthetase and glucose-6-phosphate isomerase are adhesive moonlighting proteins of Lactobacillus crispatus released by

epithelial cathelicidin LL-37. J Bacteriol 2012, 194:2509–2519.PubMedCrossRef 58. Murakami M, Ohtake T, Dorschner RA, Gallo RL: Cathelicidin antimicrobial peptides are expressed in salivary glands and saliva. J Dent Res 2002, 81:845–850.PubMedCrossRef 59. Ruhl S, Rayment

SA, Schmalz G, Hiller KA, Troxler RF: Proteins in whole saliva during the first year TPCA-1 concentration of infancy. J Dent Res 2005, 84:29–34.PubMedCrossRef Competing interests OH is member of the scientific advisory board of Semper AB. Authors’ contributions IJ, MD, OH, ACRT planned, designed and financed the study. NT coordinated and organized infant participation and sampling. NRV, and PLH coordinated the oral part of the study. NRV, CÖ, CK (qPCR experiments), RC (microbiological identifications) performed laboratory this website experiments. NRV and IJ performed statistics and drafted the manuscript. All authors contributed to completion of the manuscript and approved it.”
“Background Growing concern over the increase in multidrug resistant bacteria has urged the interest for development of new selleck kinase inhibitor types and classes of antimicrobial compounds. One such class is antimicrobial peptides (AMPs), also known as host defence peptides, that are found in all multicellular organisms and form an important part of the innate immune system [1]. They exhibit antimicrobial activity against a wide range of pathogenic microorganisms, have immune-modulatory effects and enhance the host defence against pathogenic bacteria [2–4]. AMPs are usually small cationic and amphiphatic peptides comprised of less than 40 amino acids with immense diversity in sequence, secondary structure motifs, charge and/or the abundance of certain specific amino acids [5].

However, considering the relative instability of the connection of part of the antenna to the supercomplex (Drop et al. 2011), it is possible that the sample properties were not the same in two studies. In conclusion,

PSI-LHCI is not only present in plants, but the antenna size and organization of the various complexes seem to PF01367338 vary for different organisms. What next? Many issues regarding energy transfer and trapping in PSI still need to be fully elucidated. This is mainly due to the high complexity of the system (the core alone contains around 100 Chls), which still represents a great challenge for modeling. In this respect an additional complication is represented by the red forms, which originate from excitonically coupled pigments but also have a strong charge-transfer character. Up to now the properties of these forms could not be reproduced in silico, thus limiting the possibility to study their properties and their effect on the kinetics via modeling. Practically all studies addressing light-harvesting in PSI-LHCI have focused on the complex of higher plants with a few exceptions dealing with the complex from Chlamydomonas reinhardtii. However, the analysis of new organisms indicates that many different PSI-LHCI complexes exist in

nature, varying in the number of antenna complexes and it their spectroscopic properties. This variability seems to be much more pronounced than in the case of PSII where LHCII trimers with properties similar to those of higher plants have been observed in many organisms, suggesting that the antenna complexes of PSI play a role in adaptation. This variability, on the other hand, provides the possibility to compare the functional

CDK inhibitor behavior of PSI complexes which differ in antenna size and energy, in order to determine the robustness of the complex. The comparison of all these complexes and of the environmental conditions in which these host organisms live would help in answering a long-standing question: what is the role of the red forms? Although we nowadays know a lot about their origin and their effect on the excitation trapping, we cannot PCI-32765 concentration answer this fundamental question yet. The possibility to produce plants or algae lacking red forms and to compare their growing Erlotinib datasheet capacity and their performance with those of the corresponding WT will form another strategy to unravel their physiological function. In principle, this is feasible because in vitro mutagenesis has clearly indicated which residues need to be changed to shift the red absorption of Lhca’s to the blue. Finally, in most organisms, the antenna of PSI is not only composed of Lhca, but also of LHCII. Although the PSI-LHCI-LHCII complex of higher plants has now been studied in some detail, very little information is available regarding this complex in other organisms. The case of Chlamydomonas reinhardtii is particularly interesting as it is generally believed that most of the LHCII moves to PSI in state 2.

4%) and all generated negative results for 101 of 107 samples found to be negative by one or more method (94.4%), giving an overall agreement of 82%. Our findings concerning the ability of these methods to GF120918 molecular weight detect mutations in KRAS are similar to those of Whitehall et al. (2009), who compared Dideoxy sequencing, HRM,

the TIB Molbiol kit (Berlin, Germany), and the TheraScreen DxS (Manchester, UK) kit using DNA isolated from frozen colorectal cancer tissues. In their study, all five methods were found to be in concordance with regard to the KRAS mutation status of 66 of the 80 samples tested (83% agreement) [20]. Both our results selleck chemicals and those obtained by Whitehall GSK2245840 nmr [20] show that a significant number of samples from colorectal tumor and NSCLC contain mixtures of KRAS wild-type and KRAS mutant cells, and that in many cases the percentage of mutant cells is below the threshold

that can be detected by direct sequencing. This inherent heterogeneity of bioptic tumor tissues is an universal problem, albeit one that can be partially addressed by concentrating the tumor cells (e.g. by laser capture microdissection) before extracting their DNA. However, the fact that even a pure sample of tumor cells may contain large quantities of wild-type KRAS further complicates the selective identification of mutations in this gene. Consequently, it is desirable that methods for detecting KRAS mutations should be highly sensitive, and this point should be borne in mind when selecting a proper diagnostic method. Our study identified the TheraScreen DxS kit as having the best ability to detect KRAS mutations in clinical samples,

followed by the K-ras StripAssay (Table 4). Table 4 Pairwise concordance between methods for KRAS mutation detection     Direct sequencing TheraScreen DxS K-ras StripAssay Pyrosequencing HRM   + – + – + – + – + – Direct sequencing +   0.338   0.257   0.735 (-)-p-Bromotetramisole Oxalate   0.537   –                 TheraScreen DxS + 5 15   0.790   0.555   0.739   – 1 110             K-ras StripAssay + 5 21 19 7   0.438   0.500   – 1 104 1 104         Pyrosequencing + 6 4 9 1 9 1   0.687   – 0 121 11 110 17 104     HRM + 6 9 12 3 11 4 9 6     – 0 99 4 95 12 87 1 98     Every intersection of method row and method column corresponds to a 2×2 contingency table for two methods. The upper right part of the table is filled with κ concordance metrics. Our results also indicate that direct sequencing is only of limited utility when trying to detect mutations in the KRAS gene in cancer tissues, since this method only detected KRAS mutations in 6 of the 131 DNA samples tested, even though 21 were found to contain mutations by other methods. Though direct sequencing is still being advocated as KRAS genotyping method of choice [21], it missed 72% of all mutations in our cohort.

Apart from contributing to protecting the parasite against the defense mechanisms

of the host, many of them also appear to have the capacity to induce perturbations in the host physiology. HDAC inhibitors cancer Given their abundance, one may speculate that they play a genuine role in the pathology. Some of these proteins may be promising candidates for diagnosis or therapy. As well as degrading proteins, proteases perform highly HSP990 mw specific processing tasks that can affect protein structure, function, life span, and localization. By limited and specific cleavage, proteases can act as switches, turning protein activity on or off, or can modulate protein function in more complex ways, regulating vital processes. Indeed, more than 53 specific hereditary diseases of proteolysis are recognized and it is therefore not surprising that proteases are implicated in many pathologies. Hence, proteases account for 5-10% of drug targets, with protease inhibitor drugs already in use to treat AIDS (acquired immunodeficiency syndrome) by blocking HIV (human immunodeficiency virus) protease-1, cardiovascular disease by targeting angiotensin convertase enzyme and rennin, and multiple myeloma by the reversible covalent proteasome

inhibitor. In addition, many biomarkers of disease, especially in cancer, are stable fragments generated by proteolysis NU7026 clinical trial and found in biological fluids [52]. Enzymes of nucleotide metabolism are another major class of ESPs represented here by more than 46 protein accessions. This is not unexpected, as T. brucei is incapable of de novo purine nucleotide synthesis and expresses purine salvage enzymes to recover host purines [53]. However, extracellular nucleotides are also signaling molecules that modulate a wide variety of physiological responses in mammalian tissues [54] Tenoxicam and are archetypal activators of the innate immune system [55]. In this context, both hematophagous insects and endoparasites secrete enzymes degrading nucleotides, thus minimizing inflammatory reactions or purinergic signaling provoked by these mediators [56, 57]. As such, the identification of several nucleotide-metabolizing enzymes

in the secretome raises the question of whether T. brucei might exploit such strategies to modulate the concentration of extracellular nucleotides, hence affecting a range of inflammatory responses. If so, Trypanosoma would not only divert the host nucleotides for its own requirements, but also to evade an immune response. Enzymes involved in glycolysis and carbohydrate metabolism are not a major class of the secretome, but this category still numbers more than 36 accessions. Trypanosoma have a simplified energy metabolism entirely dependent on external carbohydrate sources, such as blood glucose. Most glycolysis enzymes are compartmented in glycosomes [58], but three are cytosolic: phosphoglycerate mutase, enolase, and pyruvate kinase [59]. We found all three in the T.

fasciculata. In addition, two kinetoplast-associated proteins of T. cruzi, TcKAP4 and TcKAP6, were cloned, expressed and antisera were generated against recombinant proteins. Imunolabeling

assays revealed a differential distribution of TcKAPs in the kinetoplast of distinct developmental stages of the parasite. Methods Cell culture Epimastigote forms of T. cruzi (Dm28c clone) [22] selleckchem were grown in liver infusion tryptose (LIT) medium supplemented with 10% fetal calf serum at 28°C. Bloodstream trypomastigote forms derived from the blood of Swiss mice were used to infect the LLC-MK2 cells. Trypomastigotes were released seven days after infection in the supernatant and purified by centrifugation. Amastigotes were obtained by disruption of the LLC-MK2 cells after four days of infection with trypomastigotes. It is worth mentioning that the amastigotes released after disruption of the cells

are mixed with intermediate forms, which selleck chemicals llc represent a transitional stage between amastigotes and trypomastigotes [20]. DNA extraction DNA was extracted as described by Medina-Acosta and Cross [23]. Genome search for T. cruzi orthologs of CfKAPs The CfKAPs1–4 protein sequences were retrieved from GenBank® [24] and a BLASTp search [25] was performed against all protein sequences

from trypanosomatids with a complete sequenced genome, available in GenBank® (release 169). All hits having an e-value lower than 1e10-5 were selected for further analyses. Sequences that were redundant or did not contain a discernible nine amino acids presequence, suggestive of kinetoplast import, were selleck chemicals discarded. Evolutionary Ibrutinib cost analysis of trypanosomatids KAPs Multiple sequence alignments (MSAs) were produced with the ClustalW software [26] and a phylogenetic analysis was performed using the MrBayes software [27, 28], running in parallel [29] in a 28 nodes cluster, by 20,000,000 generations, with gamma correction (estimated α = 6.675), allowing for invariant sites. A mixed amino acid model was used and the Wag fixed rate model [30] prevailed with a posterior probability of 1.0. MSAs and trees were visualized with the Jalview [31] and TreeView software [32], respectively Cloning and expression of the TcKAP4 and TcKAP6 genes Primers were designed to amplify the entire coding region of these genes from the T. cruzi Dm28c genome.

Values are means (n = 3) and the error bars represent ± standard error of the mean. * = Statistically significant difference histone deacetylase activity between MRG and NG (Student’s t-test, P < 0.05). Statistically, pH of the E. coli and S. aureus cultures under HSP990 ic50 MRG and NG conditions were not different in any growth medium with the exception of E. coli at stationary phase in LB (Figure 3). In this case, pH under MRG conditions was significantly higher than the pH in NG controls. Figure 3 pH values of E. coli ( A ) and S. aureus ( B ) culture media under

modeled reduced gravity (MRG) and normal gravity (NG) conditions at different growth phases in different growth media. Values are means (n = 3) and the error bars represent ± standard error of the mean. * = Statistically significant difference between MRG and NG (Student’s t-test, P < 0.05). For E. coli cultures, under MRG compared to NG conditions, dissolved oxygen (DO) concentrations were significantly higher in LB and lower in M9 media at stationary phase, but there were no significant

differences in DO at exponential phase in either medium (Figure NU7026 solubility dmso 4). For S. aureus cultures in dilute LB, under MRG compared to NG conditions, statistically higher and lower DO concentrations were found at exponential and stationary phase, respectively, and in LB DO between MRG and NG treatments were not significantly different. Figure 4 Dissolved oxygen (DO) levels of E. coli ( A ) and S. aureus ( B ) culture media under modeled reduced gravity (MRG) and normal gravity (NG) conditions at different growth phases in different growth media.

Values are means (n = 3) and the error bars represent ± standard error Tenoxicam of the mean. * = Statistically significant difference between MRG and NG (Student’s t-test, P < 0.05). There were no significant differences in E. coli biovolume (based on DAPI staining and subsequent Metamorph image analysis; Figure 5A) and protein amounts per cell (Figure 6A) when cells were grown under MRG compared to NG conditions at either growth phase or in either medium. On the other hand, S. aureus had, on average, a smaller biovolume at exponential phase in dilute LB under MRG compared to NG conditions; there were no other significant differences (Figure 5B). The amount of protein per cell did not differ between MRG and NG conditions for S. aureus (Figure 6) Figure 5 E. coli ( A ) and S. aureus ( B ) biovolume under modeled reduced gravity (MRG) and normal gravity (NG) conditions at different growth phases in different growth media. Values are means (n = 3) and the error bars represent ± standard error of the mean. * = Statistically significant difference between MRG and NG (Student’s t-test, P < 0.05). Figure 6 E. coli ( A ) and S. aureus ( B ) total protein contents under modeled reduced gravity (MRG) and normal gravity (NG) conditions at different growth phases in different growth media. Values are means (n = 3) and the error bars represent ± standard error of the mean.

MH, NR, and GS conceived and designed this study. NR and GS also supervised the project, participated in the discussion on the results, and helped improve the manuscript. All authors read and improved the final manuscript.”
“Background Detection of DNA sequences through hybridization between two complementary single strands is a basic method that is very often exploited at the DNA biosensor development [1]. Now new opportunities have appeared in this route due to synthesis of new nanomaterials which are intensively applied

as the scaffold, transducer, or sensitive detectors. In particular, carbon nanotubes have attracted keen interest of biosensor researchers [2]. STI571 It was found that single-stranded nucleic acid (ssDNA) binds to the single-walled carbon nanotube (SWNT), forming a stable soluble GSI-IX price hybrid in water [3]. In spite of the essential difference in BKM120 ic50 structures of nanotubes and the biopolymer, ssDNA wraps tightly around the nanotube in water when hydrophobic nitrogen bases are adsorbed onto the nanotube surface via π-π stacking, while the hydrophilic sugar-phosphate

backbone is pointed towards water [3, 4]. The hybridization of nucleic acids on SWNT is extensively investigated [5–22], having in sight the development of DNA-hybridized biosensors on the base of nanotubes. Nevertheless, in spite of 10-year investigations in this field, some questions arise upon the study of DNA hybridization on the nanotube especially when the probe polymer was adsorbed to the tube surface directly. One of the keen questions is the effect of DNA interaction with the tube surface on the polymer hybridization. Effective cAMP detection of hybridization of two complementary DNA strands on the nanotube surface was demonstrated in [5–7]; however, in other measurements [12,

14, 17], it was indicated that SWNT hampers effective hybridization of two polymers because of the strong interaction with the nanotube surface, which prevents the necessary conformational mobility of the polymer to be hybridized. Some researchers suppose that the double-stranded DNA (dsDNA) is desorbed from the sidewall of SWNT after hybridization [14, 18–22]. Thus, up to now, the full picture of the biopolymer hybridization on SWNT surface is still unclear, and in some cases, the conclusions are controversial. To clarify this ambiguity, an additional study is required. In this work, we focus our research on the hybridization of polyribocytidylic acid (poly(rC)) adsorbed to the carbon nanotube surface with polyriboinosinic acid (poly(rI)) free in solution.

However, when it comes to the separation of in vivo CO2 and O2 fluxes mass spectrometry is the technique of choice because of its ability to monitor CO2 and O2 species with one instrument and to selectively analyze all isotopes of these gases. The unique fact that makes isotopic approaches particularly

useful in photosynthetic organisms is that the O2 Torin 1 purchase evolved from PSII has the isotopic signature of water while the oxygen uptake reactions consumes the gaseous oxygen. Thus, measurement of gross oxygen evolution and gross Selleck LOXO-101 oxygen uptake can be achieved by the use of enriched 18O2 atmospheres and H 2 16 O (Radmer and Kok 1976). Although there are obvious issues with field deployment, mass spectrometry has been crucial in resolving O2 and CO2 fluxes in plants and algae that can be brought into the laboratory. The first experiments with algae (Radmer and Kok 1976; Radmer and Ollinger 1980b) and leaves (Canvin et al. 1980) answered many important questions regarding CO2 and O2 metabolism in plants. In practice, the measurements are performed on-line with MIMS. The sample cuvette is equipped with a low consumption membrane and operates for example with a 1 ml sample volume to accommodate the

leaf disc and gas additions, Selleckchem MLN2238 see Fig. 2. The sample chamber must also have a gas (O2) tight seal to the outside, as gas leakage invalidates the approach. The plant tissue then can be illuminated to determine rates of photosynthesis: O2 evolution (↑O2), rates of O2 uptake (↓O2), and net rates of others CO2 assimilation. In order to facilitate differentiation between competing O2 fluxes isotopic labeling is undertaken by initially flushing the cuvette with N2 before addition of 12CO2 and 18O2 as substrates for Rubisco and terminal oxidase

proteins. Thus, the 18O2 respiration/uptake fluxes are distinguished from 16O2 evolution from Photosystem II (PSII). The corrections for net rate of O2 uptake and net O2 evolution (Radmer et al. 1978; Canvin et al. 1980; Maxwell et al. 1998; Ruuska et al. 2000) are based upon relative oxygen enrichments, i.e., [16O]/[18O] and the rate of change in the m/z = 36 (∆18O2) or m/z = 32 (∆16O2) signals; i.e. $$ \downarrow \textO_ 2 = \Updelta {}^ 1 8\textO_ 2 \times \left( { 1+ {\frac{{\left[ {{}^ 1 6\textO_ 2 } \right]}}{{\left[ {{}^ 1 8\textO_ 2 } \right]}}}} \right) $$ (6) $$ \uparrow \textO_ 2 = \Updelta{}^ 1 6\textO_ 2 – \Updelta {}^ 1 8\textO_ 2 \left( {{\frac{{\left[ {{}^ 1 6\textO_ 2 } \right]}}{{\left[ {{}^ 1 8\textO_ 2 } \right]}}}} \right) $$ (7)The data from a leaf experiment are shown in Fig. 4. The MIMS cuvettes are custom made and injections can be made via small sealable holes in the cap (Fig. 2a).