While this suggests a seemingly broad connectivity pattern between PPC and FEF, separable pathways may be functionally distinct. Evidence for functional specialization distributed within the frontoparietal network has been found in a study that examined connectivity patterns of different network nodes [42••]. Two pathways between frontal cortex and PPC were identified using diffusion tensor imaging (DTI) and probabilistic tractography, and functional interactions of activity evoked during attention tasks: first, a lateral

pathway connecting FEF and IPS2 and second, a medial pathway connecting the supplementary eye field (SEF) and SPL1 (Figure 3). Intriguingly, Caspase-dependent apoptosis these two pathways appear to mediate different functions. The IPS2-FEF pathway

supports attentional selection in retinotopic, or viewer-centered spatial coordinates, whereas the SEF-SPL1 pathway supports attentional selections based on an object-centered spatial reference frame. Thus, Trametinib supplier the multiple topographic representations in PPC may code for attentional priorities in different spatial reference frames. In sum, a growing body of research demonstrates the broad involvement of frontoparietal cortex in space-based, feature-based, object-based, and category-based selection, Tyrosine-protein kinase BLK consistent with the possible existence of domain-general control centers within the human control network (see Figure 2). An important question that remains unresolved is how a single network can flexibly generate a diverse range of control signals depending on current task demands. Further studies are needed to determine whether separable selection mechanisms are subserved by true domain-general neuronal populations or whether each mechanism recruits distinct subpopulations of neurons within the same regions 23 and 26]. Relatedly,

it remains an open question what individual roles subregions within the network may play in the generation of attentional control signals. The existence of 14 topographic representations in human PPC alone seems, on the face of it, excessive and redundant. As such, an investigation into potential functional dissociations between subunits is warranted. DTI studies lend some support to this line of inquiry, as IPS can be largely subdivided based on structural connectivity patterns alone 37 and 40]. Given that the functional properties of a brain region are necessarily constrained by its anatomical connections, these data imply that subunits of IPS may very well be functionally distinct, but carefully implemented imaging studies are necessary to confirm this hypothesis.

When a peptide sequence containing this amino acid combination is coupled with the energy imparted by the vacuum UV-MALDI ionization and the long trapping times required for FTMS analysis (>10 s), singly protonated orcokinin family peptides undergo so-called “Asp-Xxx cleavages” [47], which result in the production of characteristic C-terminal (y-type)

fragments (see Fig. 2B). Our identification of orcokinin family peptides by MALDI-FTMS relies on the detection of both the [M+H]+ ion and the observation of characteristic y-type ions resulting from Asp-Xxx cleavages. When we analyzed small pieces of eyestalk ganglion tissues directly by MALDI-FTMS, we detected neuropeptide peak profiles 17-AAG cost that reflected differential Gamma-secretase inhibitor distributions of neuropeptides in localized regions of the eyestalk ganglia. For example, the peptides CabTRP I (APSGFLGMRamide at m/z 934.49) and Val1-SIF (VYRKPPFNGSIFamide at m/z 1423.78) were detected in tissues from the LG, XO/MT, MI, and ME but not in the SG. Orcokinin family peptides were detected in many tissues, including the XO/MT, MI, ME, and SG. A representative spectrum from a small piece of XO/MT tissue is shown in Fig. 3A. In contrast with previous studies [10], where we found good agreement between single tissues analyzed directly and by single tissue extraction, our analysis

of 3-mercaptopyruvate sulfurtransferase extracts of tissues from the aforementioned regions of the eyestalk ganglion revealed the presence of a new peptide that had not been detected by direct tissue MALDI-FTMS. For example, Fig. 3C shows the spectrum observed when a small piece of XO/MT tissue was removed by microdissection techniques, placed in extraction solvent, homogenized, sonicated, and centrifuged. While we continue to detect peaks for CabTRP

I and Val1-SIF, an abundant signal at m  /z   1270.57 was observed, which was detected in combination with additional peaks showing the characteristic orcokinin family pattern. The full collection of peaks appeared at m  /z   1270.57, 1253.54, 894.43, 876.42, and 537.28; these peaks were assigned to [M+H]+, [MH−NH3]+, yn+4, yn+4o, and yn+1. Unexpectedly, these masses did not correspond to any orcokinin family members predicted from genomic information for H. americanus [10], nor did they correspond to masses expected from conventional post-translational modifications, including the truncation of full-length orcokinin family peptides. Instead, exact mass measurements (m/z 1270.5692, measured) were consistent with the orcokinin sequence, NFDEIDRSGFA (Orc[Ala11]; m/z 1270.5699, predicted). This peptide has been detected in other studies [4], [16], [19], [30] and [31] with the first characterization, from the crab, C. borealis, reported in a study by Huybrechts et al.

The frequency of response concerning cytokine production (IFNγ,

IL2 or TNFα) was evaluated and is shown in Table 2. Regarding the RD1 antigen response within the CD4+ or CD8+ T-cell subsets, no significant difference between the HIV–TB and HIV–LTBI groups was observed (Fig. 1 A-B). To note: the CD4+ T-cell frequency was higher than the CD8+ T-cell frequency in both HIV–TB (in response to RD1 peptides and RD1 proteins p = 0.2 and p = 0.08, respectively) and HIV–LTBI (in response to RD1 peptides and RD1 proteins p = 0.001 and p = 0.08, respectively) ( Fig. 1 A-B). The frequency of response to HIV–GAG peptides (Fig. 1 C-D) and the positive control, staphylococcal enterotoxin B (SEB) (Fig. 1 F), was not dependent on TB status. Differently, a higher frequency of response to Cytomegalovirus (CMV) in CD4+ T-cell and CD8+ T-cell subsets was observed in the HIV–LTBI Selleck Crizotinib group than in the HIV–TB (p = 0.02 and p = 0.03) ( Fig. 1 E), although the proportion GSK-3 cancer of positive serology to CMV was similar in both groups ( Table 1). We further investigated the functional cytokine profile of RD1 antigen-specific CD4+ and CD8+ T-cells in terms of IFNγ, IL2 and TNFα, independent of the simultaneous production of the other cytokines. Fig. 2 A-B shows a flow cytometry panel representing the RD1 response from an HIV–TB subject. Among the CD4+ T-cells, the frequency of IFNγ, IL2 and

TNFα in response to the RD1 antigen was higher in the HIV–TB group than in the HIV–LTBI (Fig. 2 C-D), reaching a statistical significance for IFNγ Nutlin-3 purchase and TNFα response to RD1 peptides (p = 0.007, p = 0.02, respectively) ( Fig. 2 D). Regarding SEB response, there was a significantly

higher frequency of IL2 in the HIV–LTBI group ( Fig. 2 F) compared to the HIV–TB group (p = 0.03). For the CD8+ T-cell-response to RD1, CMV and SEB stimuli, no significant difference was observed (data not shown). Polyfunctional (more than one cytokine) and monofunctional (one cytokine) responses to RD1 antigens were analyzed in CD4+ and CD8+ T-cell subsets (Fig. 3). Considering the CD4+ T-cell response, the HIV–TB group showed a higher frequency of polyfunctional T-cells than the HIV–LTBI, reaching a significant difference in response to RD1 peptides (p = 0.007) ( Fig. 3 B). Considering the HIV–TB group, we observed a higher frequency of polyfunctional CD4+ T-cells than monofunctional; the difference was also significant when evaluating the response to RD1 peptides (p = 0.04) ( Fig. 3 B). Differently, when considering the CD8+ T-cell response to RD1 proteins, we found a significantly higher frequency of monofunctional T-cells than polyfunctional in both the HIV–TB and HIV–LTBI groups (p = 0.03, p = 0.03, respectively) ( Fig. 3 C). The cytokine profiles of CD4+ and CD8+ T-cells were analyzed evaluating the proportion of each cytokine to the total antigen response using the Boolean gate combinations (Fig. 4).

PCP is a phenol derivative that has been extensively used as a wood preservative, insecticide and fungicide [7] and [8]. PCP undergoes oxidative dechlorination to form tetrachlorohydroquinone (TCHQ), a more toxic metabolite of PCP [9] and [10]. PCP toxicity seems to be related mainly to TCHQ-mediated uncoupling of oxidative phosphorylation and the generation of reactive oxygen species (ROS) in mitochondria [11] and [12]. Although it has been shown to promote tumour growth [13], studies suggesting that this compound and its derivative can induce cell death are sparse [14] and [15]. This work was initiated by our preliminary

observations that PCP induces inhibition of CK2 in an ATP-competitive manner. The aim of this study was find more to contribute to the knowledge of the effects of PCP in human pancreatic cancer cells and to shed light on the intracellular signalling pathways involved in PCP-induced cytotoxicity. To our knowledge, this is the first contribution on the characterization of PCP at the molecular level in this type of cells. Protein kinase activity measurements of recombinant CK2α and CK2α2β2 were performed in 40 μl of a reaction mixture containing varying concentrations of C11, PCP or dimethylallylamine (DMA), as indicated in the figure legends, 25 mM Tris/HCl pH 7.5,

5 mM NaCl for CK2α and 150 mM for CK2α2β2, 18.75 mM MgCl2, 0.5 mM DTT, 190 μM synthetic peptide RRRDDDSDDD (KinaseDetect, Odense, Denmark), 125 μM ATP and 10 μCi [γ-32P-ATP] (3000 Ci/mmol, selleck products Hartmann Analytic, Braunschweig, Germany). After incubation at 30 °C for 10 min, the reactions were stopped on ice and samples were spotted onto a grade P81 cellulose paper (WhatmanTM, GE Healthcare, Brøndby, Denmark). Radioactivity incorporated into the substrate target was determined by scintillation counting in a 1450 MicroBeta2 Plate counter (PerkinElmer, Waltham, MA, USA). The pancreatic ductal adenocarcinoma cell lines Panc-1 and MIA PaCa-2 (ATCC,

Rockville, MD, USA) were cultured according to the manufacturer’s guidelines and maintained at 37 °C in G protein-coupled receptor kinase a humidified atmosphere supplemented with 5% CO2. Cells were treated with C11 (NCI, Bethesda, MD, USA), pentachlorophenol (PCP, AccuStandard, New Haven, CT, USA), dimethylallylamine (DMA, Chemical point, Deisenhofen, Germany) and TNFα (R&D Systems, Abingdon, United Kingdom) as indicated in the figure legends. DMSO (Sigma-Aldrich, Schnelldorf, Germany) was used in all control experiments at a final concentration not exceeding 0.2% (v/v). Cell viability was determined by the WST-1 assay (Roche, Mannheim, Germany) in a 96-well plate. 24 h after seeding, cells were treated with various concentrations of C11, PCP and DMA, respectively, for 48 h. WST-1 reagent was added to the cells according to the manufacturer’s instructions and cell viability was determined 2 h later in a VersaMax ELISA microplate reader (Molecular Devices, CA, USA).

In the coming decade we anticipate this will become a major problem for Hong Kong. In recent times, Hong Kong has developed a growing public awareness of environmental issues, not only as a result of concerns regarding contaminants and their effects, but also through continuing and expanding environmental education. The Hong Kong Government has recognised that environmental sustainability is vital to the socioeconomic development

of Hong Kong. To this end, in 2004, a group of marine environmental http://www.selleckchem.com/screening/gpcr-library.html scientists from six Hong Kong universities were jointly awarded one of only eight “Area of Excellence” research centers in Hong Kong. This center, initially funded a total of $US8.7 million by the University Grants Committee, is known as the Centre for Marine Environmental Research and Innovative Technology – “MERIT”, and is the only “Area of Excellence” dealing with environmental matters. The MERIT team, by the very nature of the venture, is multi-disciplinary, comprising 29 biologists, chemists, physicists, engineers and statisticians from six local universities working closely with nine world class international scientists. MERIT has focused its research on the development of novel chemical, biological and engineering technologies for monitoring,

selleck chemical assessing and controlling anthropogenic activities in our marine environment. At this conference, several members of the MERIT team (many of whom are students) took the opportunity to share some of their exciting findings, which we believe will have a global impact on marine pollution research. Our success in marine environmental

research has also resulted in recognition within the East Asian region and China. The United Nations, via the East Asian Seas Partnership Council, has designated MERIT as the “Regional Centre of Excellence in Marine Pollution”, to play a leading and advisory role on marine pollution issues in the region, and this designation was officially endorsed by its 11 member Arachidonate 15-lipoxygenase countries. In 2009, the Ministry of Science and Technology of the Peoples’ Republic of China approved the formation of a “State Key Laboratory in Marine Pollution”, which is based on the strengths of MERIT, consisting of members from the six local universities and located at City University of Hong Kong. Such recognition of our research efforts has not only made us exceptionally proud, but also inspired us to venture further towards the cutting edge of marine pollution and ecotoxicology research in order to make a real and significant contribution to managing our marine environments in both China and the region. This 6th Conference provided a forum for environmental scientists to meet and discuss research findings, as well as the latest scientific advancements and technologies. The Conference series aims to advance our understanding of local, regional and global marine pollution, with the hope that such problems may be more easily solved in the future.

The current paper has expanded on this approach. The current findings support Priskin’s (2003b) original conclusion that the public do distinguish between different activities; however Priskin also found that they generally underestimate the negative impacts on the environment compared to that of the marine expert. However, within the current

samples, there were only few differences, with the coastal user sample generally in agreement with the coastal www.selleckchem.com/screening/fda-approved-drug-library.html experts. This may be due to methodological differences such as country, type of shoreline (sandy versus rocky) and the time of data collection (data collected in 1999 for Priskin, and 11 years later for this current work). It could also be because of the reliability of the expert ratings. For this current study, we used 25 coastal experts from around the UK and a further Apitolisib supplier 44 international academics, whilst Priskin relied purely on her own expertise. Overall, the views between experts and coastal users were remarkably similar which can increase our confidence in these perception-based findings. We developed the questionnaire further in Study 2 and can therefore not make direct statistical comparisons between the two data sets. However, the pattern of findings was very similar between the two studies and no differences were

found between coastal experts from the UK as opposed to elsewhere. This seems to indicate that the findings can be seen as more global issues than only relevant to the United Kingdom. However, the exact level of detrimental impact on the environment may be different in other countries and would be interesting to explore further with a more cross-cultural study. In addition to the perceived impacts different activities

have on rocky shores, the open-ended questions offered in-depth insights. As mentioned above, participants used this opportunity to explain the depreciative behaviours linked with foraging activities, including turning rocks over and lack of knowledge 17-DMAG (Alvespimycin) HCl or awareness. Another frequently mentioned theme, especially for the coastal user sample, was littering. Crucially, littering was mentioned spontaneously without a researcher prompt (as this study focussed on purposive recreational activities) yet it turned out to be a consistent key theme. Littering is known to be an important environmental issue, with roughly 2 000 litter items found per kilometre on the UK coastline alone (MCS, 2012). Litter can have numerous effects, including entanglement, ingestion and damage to the environment and its residents (Hall, 2000 and Laist, 1997). Interestingly, however, many of the responses did not only emphasise those detrimental effects of litter on the environment and organisms, but also highlighted the effect it has on visitors’ experiences. This is in line with the finding that marine litter can be a key deterrent for visiting specific beaches (Tudor and Williams, 2006).

In addition, the effects of CdTe-QDs on key HepG2 response biomarkers were compared to those obtained in similar exposures using equivalent amounts of cadmium in form of CdCl2. Overall, the study reveals that CdTe-QDs cause oxidative stress, interfere with antioxidant defenses, and activate protein kinases, leading to Enzalutamide solubility dmso apoptosis via both extrinsic and intrinsic pathways. The results suggest that the toxicity of these NPs might be induced from both cadmium effects and ROS generation. HepG2 cells were obtained from American Type Culture Collection (ATCC)

(Manassas, VA). CdTe-QDs were purchased from Nano Impex Canada (Mississauga, ON). CdTe-QDs were described by the manufacturer as CdTe/CdS core/shell QDs, encapsulated by polyacrylate polymer layers, with a size of 5 nm, a spectral emission of 540 nm, and a concentration of 10 mg/ml in water containing 10% of cadmium. MTT [3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide], dimethyl sulfoxide (DMSO), cadmium chloride (CdCl2), dihydroethidium (DHE), menadione and staurosporine (STS) were obtained from Sigma–Aldrich (St. Louis, MO). Eagle’s minimum essential medium (EMEM), fetal bovine serum

and gentamicin were obtained from Invitrogen (Carlsbad, CA). Spectral and size characterization of test CdTe-QDs were carried out as described in our previous work (Nguyen et al., 2013). HepG2 cells were cultured in EMEM supplemented with 10% fetal bovine serum (FBS) and 100 μg/ml gentamicin Thiazovivin research buy at 37 °C in a humidified atmosphere with 5% CO2. For the MTT assay, cells were seeded into 96-well plates at density of 5 × 104 cells/100 μl well. For confocal microscopy, cells were seeded on cover-slips placed in a 12-well plate, at a concentration of 1 × 105 cells/well in 1 ml of medium. For enzyme-linked immunosorbent assay (ELISA), bead array and enzymatic assays, cells were seeded into 6-well or 96-well plates at density of 5 × 105 cells/2 ml well or 5 × 104 cells/100 μl well, respectively. Cells were

cultured for 24 h to 80% confluency and the medium was replaced just prior to CdTe-QDs exposure. Working solutions of CdTe-QDs and CdCl2 were prepared by diluting the stock solution in phosphate-buffered saline (PBS). For the MTT assay, ADP ribosylation factor cells were treated with different concentrations of CdTe-QD (0.001–10 μg/ml) for different durations. For other assays, cells were treated with 10 μg/ml CdTe-QDs (containing 1 μg/ml of cadmium) for 24 h. PBS alone was used for sham treatments. Except for MTT assay, treatments with 1.63 μg/ml of CdCl2 (containing 1 μg/ml of cadmium) were done in all other assays for comparison purposes. Menadione (25 μM) was used as a positive control in ROS detection assays, and STS (1 μM) was used as the positive control for caspase-3, cleaved PARP, annexin V, Fas, caspase-8, Bax, Bcl2, cytochrome c and phosphoprotein assays.

In step 1, the following 3 FCE tests predicted WC: repetitive reaching, walking speed, and the SED score (data from step 1 are

available on request). The regression coefficients of the 3 FCE tests in the model decreased from step GSK126 2 to step 3 by −.05 for repetitive reaching, −5.45 for walking speed, and −1.76 for SED score. From all 18 predictor variables, 9 (age, sex, body mass index, marital status, duration since injury, attorney involved, work status, education, physical work demands) did not change regression coefficients of the 3 FCE test variables by >10% and were therefore not considered for the next step. In step 4, the remaining 9 predictor variables (WC at baseline, mother language, number of prior injuries, pain level, perceived recovery, perceived functional ability, disability, anxiety, depression), together with the 3 FCE tests and ln (weeks+1), were entered in the model (see table 2, step 3). None of the FCE tests remained significant predictors of future WC. Therefore, FCE tests were excluded from the final model. The final prognostic model included ln (weeks+1) (β=23.74), mother language (β=5.49), WC at baseline (β=1.01), and self-reported disability (β=−.20). All the 2-way interactions between these 4 predictors were explored. Two interactions terms were significant: Time course mediates WC and self-reported disability, as those 2 interaction terms

remained significant. TCL Overall, time course and mother language were the predictors with the highest regression coefficients. Nutlin-3a order To facilitate interpretation of the results of the linear mixed-model analysis, 2 clinical examples were calculated (appendix 2). We conducted a prospective cohort study to determine the prognostic ability of FCE tests to predict WC, and developed a predictive model in a cohort of patients with WADs. Correlation coefficients between FCE tests and WC were <0.4 at baseline

and decreased over the follow-up period. In the multivariate model, outcomes of FCE tests do not predict future WC. Our final model suggested that the strongest predictors were time course, mother language, baseline WC, and self-reported disability. We recommend monitoring variables with the best predictive capacity in those patients who fail to improve in the transition from the acute to the chronic stage of the disorder.31 Values of the prognostic variables identified in this study can easily be recorded. In addition to WC at baseline, NDI scores and mother language were independent predictors. Whereas the NDI was also predictive in other populations and settings, the importance of the mother language may be specific for this rehabilitation setting.29 and 32 Immigrants with different mother languages (ie, cultural backgrounds) form a large part of the workforce in Europe and the United States.

Additionally, false positives (i.e. non-carcinogens detected as mutagens) do occur selleckchem in the Ames test. There are a small number of compounds that are Ames positive mutagens due to their bacterium-specific metabolism e.g. sodium azide and some nitro-group containing compounds (Prival, 1983). The strains of Salmonella typhimurium used in the Ames test contain different mutations in various histidine synthesis genes ( Table 1). The mutations carried by the specific strains prevent the bacteria from growing in media without histidine. However, if the test chemical mutates the defective mutation

back to functional status (revert initial mutation), the bacteria will acquire the ability to grow in histidine-free media and form colonies. These colonies are thus known as revertants ( Ames et al., 1975). All strains except TA102 are missing the uvrB DNA repair gene, thus removing the main error-free DNA excision repair pathway, compared to wild-type cells. This will amplify the mutations as DNA repair, in the absence of excision repair, occurs by error-prone

pathways. TA102 bacteria strain maintains the excision repair system to be able to detect DNA cross-linking agents such as mitomycin C. Otherwise compounds with DNA cross-link mechanism of action will not be detected, as unrepaired cross-links are lethal to the cell. In addition, all strains have the mutation known as deep rough or rfa genotype. This is an alteration of the phenotype, where the polysaccharide capsule surrounding the cell is no longer selleck screening library present. Therefore, larger compounds are able to enter through the cell membrane reaching the bacterial DNA. Various strains possess the plasmid pKM101 which contains the operon muc. Enzymes encoded by this operon allow the damaged DNA to continue its synthesis. The effect of ID-8 this operon is to amplify the translation of DNA damage to mutations. The plasmid also contains a gene coding for resistance to the antibiotic ampicillin. This ampicillin-resistant property

permits the selection of mutants containing the plasmid. Alternatively, some Escherichia coli strains can be used to screen for mutagens. These strains have base change mutations in one of the tryptophan synthesis operon genes (trpE) instead of the histidine operon genes. Strains with and without the uvrA mutation are available as are strains with and without the plasmid pKM101. E. coli WP2 strains are equivalent to TA102 in terms of types of mutagen detected (including oxidative mutagens). However, if a cross-linking effect is to be detected, then the E. coli strain must have an intact excision repair system. The rfa mutation is not required as E. coli cells are naturally permeable to larger molecules. Each strain of bacteria used in the Ames test detects a different spectrum of mutagens.

Measured 13ɛ values were used to investigate shifts towards larger fractionations expected for oil uptake and in mass-balance isotope mixing models ( Spies and DesMarais, 1983) to calculate % www.selleckchem.com/products/BIBW2992.html oil use: %oil=100*(13εCONTROL-13εSAMPLE)/(13εCONTROL-13εOIL)A similar mass

balance mixing equation was used to calculate % oil use from Δ14C data: %oil=100*(Δ14CCONTROL-Δ14CSAMPLE)/(Δ14CCONTROL-(-1000)) For Δ14C results for barnacles collected in 2000, a post-analysis decay correction was extrapolated from published data (Druffel et al., 2010) and applied to account for the higher 14C activity of seawater in 2000 than in 2010. This change in seawater 14C activity is due to ongoing loss of bomb radiocarbon that was added to the atmosphere and biosphere during aboveground nuclear bomb tests in the 1950s and 1960s. To account for this change in activity, 29‰ has been subtracted from the measured Δ14C values for year 2000 results, to give a common baseline for comparison with all 2010 results, which are reported as analyzed. The detection limit for the % oil calculations

was about 0.3% oil incorporation, based on the average 95% confidence limits for Δ14C means of triplicate individual samples listed in Table 1. Incubations of estuarine water showed no evidence for enhanced respiration in Barataria Bay due to microbial use of Deepwater Horizon oil. Incubations were performed at multiple stations (Fig. 1) along the Barataria transect hypothesized to be impacted by oil and along the Breton Sound transect that lacked oil inputs.

Respiration rates Epigenetic phosphorylation for the transects, given in average mmol oxygen consumed m−3 d−1 ± standard error of the mean (N), were 28 ± 2 (10) for Barataria in late August, 27 ± 2 (17) for Barataria in early October, and 41 ± 5 (9) for Breton Sound in early October. Barataria respiration rates were significantly lower (P < 0.05, unpaired t test) rather than higher than Breton Sound respiration rates. Barnacles and mussels were analyzed initially for δ13C to test for oil uptake in comparisons of Calpain animals collected from oiled vs. unoiled control sites. Mussel δ13C values were very similar at oiled and unoiled sites and animals from oiled sites did not show shifts towards larger 13ɛ values expected for oil incorporation (Fig. 2). Barnacle δ13C values were more variable across the salinity gradients of the transects, and the 2010 Barataria samples collected after the spill had lower δ13C values that would be consistent with oil uptake (Fig. 3). But this apparent shift towards oil values largely disappeared after baseline inorganic carbon effects were normalized out using shell δ13C values, and 13ɛ values were similar for all collections (Fig. 3). Thus, 13ɛ averages ± SEM (N) for the 2010 post-spill barnacles were 17.2 ± 0.3‰(18) and not significantly different (P > 0.05, t tests) than control values of 17.4 ± 0.