Inorganic carbon outwelling from a Mediterranean seagrass meadow using radium isotopes

Seagrass meadows are ‘blue carbon’ ecosystems widely recognised for their potential role in climate change mitigation. Previous studies have focused mainly on carbon storage within meadows and sediments. However, little is known about contribution of outwelling (i.e., lateral transport) to seagrass carbon budgets. Here, radium isotopes (223Ra and 224Ra) were used to assess dissolved inorganic carbon (DIC) and total alkalinity (TA) outwelling from a Mediterranean Posidonia oceanica meadow during early autumn. DIC outwelling was 114 ± 61 mmol m − 2 day − 1 and exceeded above-meadow CO2 outgassing (3 ± 1 mmol m − 2 day − 1). Production of DIC was uncoupled from TA and fuelled by net heterotrophy and aerobic processes within the meadow. The small export of TA (5 ± 6 mmol m−2 day − 1) implied that ∼ 90 % of outwelled DIC may return to the atmosphere as CO2 in offshore waters. Combining these fluxes with above-meadow outgassing suggested a total carbon loss that exceeded long term burial in sediments. Overall, the meadow acted as a carbon source to the atmosphere during the early autumn season. Further studies quantifying outwelling at multiple spatial and temporal scales are required to better resolve seagrass carbon budgets and their contribution to carbon sequestration

Methane emissions in seagrass meadows as a small offset to carbon sequestration

Seagrass meadows are effective carbon sinks due to high primary production and sequestration in sediments. However, methane (CH4) emissions can partially counteract their carbon sink capacity. Here, we measured diffusive sediment-water and sea-air CO2 and CH4 fluxes in a coastal embayment dominated by Posidonia oceanica in the Mediterranean Sea. High-resolution timeseries observations revealed large spatial and temporal variability in CH4 concentrations (2–36 nM). Lower sea-air CH4 emissions were observed in an area with dense seagrass meadows compared to patchy seagrass. A 6%−40% decrease of CH4 concentration in the surface water around noon indicates that photosynthesis likely limits CH4 fluxes. Sediments were the major CH4 source as implied from radon (a natural porewater tracer) observations and evidence for methanogenesis in deeper sediments. CH4 sediment-water fluxes (0.1 ± 0.1–0.4 ± 0.1 mol m−2 d−1) were higher than average sea-air CH4 emissions (0.12 ± 0.10 mol m−2 d−1), suggesting that dilution and CH4 oxidation in the water column could reduce net CH4 fluxes into the atmosphere. Overall, relatively low sea-air CH4 fluxes likely represent the net emissions from subtidal seagrass habitat not influenced by allochthonous CH4 sources. The local CH4 emissions in P. oceanica can offset less than 1% of the carbon burial in sediments (142 ± 69 g CO2eq m−2 yr−1). Combining our results with earlier observations in other seagrass meadows worldwide reveals that global CH4 emissions only offset a small fraction ( \u3c 2%) of carbon sequestration in sediments from seagrass meadows

The removal of multiplicative, systematic bias allows integration of breast cancer gene expression datasets – improving meta-analysis and prediction of prognosis

BACKGROUND: The number of gene expression studies in the public domain is rapidly increasing, representing a highly valuable resource. However, dataset-specific bias precludes meta-analysis at the raw transcript level, even when the RNA is from comparable sources and has been processed on the same microarray platform using similar protocols. Here, we demonstrate, using Affymetrix data, that much of this bias can be removed, allowing multiple datasets to be legitimately combined for meaningful meta-analyses. RESULTS: A series of validation datasets comparing breast cancer and normal breast cell lines (MCF7 and MCF10A) were generated to examine the variability between datasets generated using different amounts of starting RNA, alternative protocols, different generations of Affymetrix GeneChip or scanning hardware. We demonstrate that systematic, multiplicative biases are introduced at the RNA, hybridization and image-capture stages of a microarray experiment. Simple batch mean-centering was found to significantly reduce the level of inter-experimental variation, allowing raw transcript levels to be compared across datasets with confidence. By accounting for dataset-specific bias, we were able to assemble the largest gene expression dataset of primary breast tumours to-date (1107), from six previously published studies. Using this meta-dataset, we demonstrate that combining greater numbers of datasets or tumours leads to a greater overlap in differentially expressed genes and more accurate prognostic predictions. However, this is highly dependent upon the composition of the datasets and patient characteristics. CONCLUSION: Multiplicative, systematic biases are introduced at many stages of microarray experiments. When these are reconciled, raw data can be directly integrated from different gene expression datasets leading to new biological findings with increased statistical power

Search for dark matter produced in association with bottom or top quarks in √s = 13 TeV pp collisions with the ATLAS detector

A search for weakly interacting massive particle dark matter produced in association with bottom or top quarks is presented. Final states containing third-generation quarks and miss- ing transverse momentum are considered. The analysis uses 36.1 fb−1 of proton–proton collision data recorded by the ATLAS experiment at √s = 13 TeV in 2015 and 2016. No significant excess of events above the estimated backgrounds is observed. The results are in- terpreted in the framework of simplified models of spin-0 dark-matter mediators. For colour- neutral spin-0 mediators produced in association with top quarks and decaying into a pair of dark-matter particles, mediator masses below 50 GeV are excluded assuming a dark-matter candidate mass of 1 GeV and unitary couplings. For scalar and pseudoscalar mediators produced in association with bottom quarks, the search sets limits on the production cross- section of 300 times the predicted rate for mediators with masses between 10 and 50 GeV and assuming a dark-matter mass of 1 GeV and unitary coupling. Constraints on colour- charged scalar simplified models are also presented. Assuming a dark-matter particle mass of 35 GeV, mediator particles with mass below 1.1 TeV are excluded for couplings yielding a dark-matter relic density consistent with measurements