Organic matter chemistry controls greenhouse gas emissions from permafrost peatlands

Large tracts of arctic and subarctic peatlands are underlain by permafrost. These peatlands store large quantities of carbon (C), and are currently under severe threat from climate change. The aim of this study was to determine the size and organic chemistry of the easily degradable C pool in permafrost peatlands and link the functional organic chemistry to temperature and moisture controls of greenhouse gas emissions. First, we used a combination of field measurements and laboratory experiments to assess the influence of increased temperature and flooding on CO₂ and CH₄ emissions from sixteen permafrost peatlands in subarctic Sweden and Canada. Second, we determined the variation in organic matter chemistry and the associated microbial community composition of the peat active layer, with depth using quantitative ¹³C solid-state NMR and molecular biomarkers respectively. We demonstrate that the peat organic chemistry strongly controls CO₂ release from peat and that ca. 35 and 26% of the peat organic matter, at the Swedish and Canadian peatlands sites, respectively, is easily degradable by heterotrophic microorganisms. In contrast to CO₂, CH₄ emissions were decoupled from peat functional organic chemistry. We show a strong relationship between the microbial community structure and the peat organic chemistry suggesting that substrate type and abundance is an important driver of microbial composition in sub-arctic peatlands. Despite considerable variation in peat chemistry and microbial community composition with depth the temperature sensitivity was comparable throughout the active layer. Our study shows that functional organic chemistry controls both soil respiration rates and the composition of the microbial community. Furthermore, if these peatlands collapse and flood on thawing, they are unlikely to become large emitters of CH₄ without additional input of labile substrates

The Galactic Faraday rotation sky 2020

Galaxie

A new polygenic score for refractive error improves detection of children at risk of high myopia but not the prediction of those at risk of myopic macular degeneration

Background High myopia (HM), defined as a spherical equivalent refractive error (SER) ≤ −6.00 diopters (D), is a leading cause of sight impairment, through myopic macular degeneration (MMD). We aimed to derive an improved polygenic score (PGS) for predicting children at risk of HM and to test if a PGS is predictive of MMD after accounting for SER. Methods The PGS was derived from genome-wide association studies in participants of UK Biobank, CREAM Consortium, and Genetic Epidemiology Research on Adult Health and Aging. MMD severity was quantified by a deep learning algorithm. Prediction of HM was quantified as the area under the receiver operating curve (AUROC). Prediction of severe MMD was assessed by logistic regression. Findings In independent samples of European, African, South Asian and East Asian ancestry, the PGS explained 19% (95% confidence interval 17–21%), 2% (1–3%), 8% (7–10%) and 6% (3–9%) of the variation in SER, respectively. The AUROC for HM in these samples was 0.78 (0.75–0.81), 0.58 (0.53–0.64), 0.71 (0.69–0.74) and 0.67 (0.62–0.72), respectively. The PGS was not associated with the risk of MMD after accounting for SER: OR = 1.07 (0.92–1.24). Interpretation Performance of the PGS approached the level required for clinical utility in Europeans but not in other ancestries. A PGS for refractive error was not predictive of MMD risk once SER was accounted fo

Broadband polarisation of radio AGN

Item does not contain fulltextLinear polarisation data as a function of wavelength-squared for 100 extragalactic radio sources, selected to be highly polarised at 1.4GHz. The data presented here were obtained using the Australia Telescope Compact Array (ATCA) over 1.1-3.1GHz (16cm) with 1MHz spectral resolution between 2014 April 19-28. The integrated emission from each source, imaged at 10 MHz intervals, is presented below. See Section 2 for details. (2 data files)