Estimating Annual CO2 Flux for Lutjewad Station Using Three Different Gap-Filling Techniques

Long-term measurements of CO2 flux can be obtained using the eddy covariance technique, but these datasets are affected by gaps which hinder the estimation of robust long-term means and annual ecosystem exchanges. We compare results obtained using three gap-fill techniques: multiple regression (MR), multiple imputation (MI), and artificial neural networks (ANNs), applied to a one-year dataset of hourly CO2 flux measurements collected in Lutjewad, over a flat agriculture area near the Wadden Sea dike in the north of the Netherlands. The dataset was separated in two subsets: a learning and a validation set. The performances of gap-filling techniques were analysed by calculating statistical criteria: coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), maximum absolute error (MaxAE), and mean square bias (MSB). The gap-fill accuracy is seasonally dependent, with better results in cold seasons. The highest accuracy is obtained using ANN technique which is also less sensitive to environmental/seasonal conditions. We argue that filling gaps directly on measured CO2 fluxes is more advantageous than the common method of filling gaps on calculated net ecosystem change, because ANN is an empirical method and smaller scatter is expected when gap filling is applied directly to measurements

Genetic susceptibility loci for cardiovascular disease and their impact on atherosclerotic plaques

Background: Atherosclerosis is a chronic inflammatory disease in part caused by lipid uptake in the vascular wall, but the exact underlying mechanisms leading to acute myocardial infarction and stroke remain poorly understood. Large consortia identified genetic susceptibility loci that associate with large artery ischemic stroke and coronary artery disease. However, deciphering their underlying mechanisms are challenging. Histological studies identified destabilizing characteristics in human atherosclerotic plaques that associate with clinical outcome. To what extent established susceptibility loci for large artery ischemic stroke and coronary artery disease relate to plaque characteristics is thus far unknown but may point to novel mechanisms. Methods: We studied the associations of 61 established cardiovascular risk loci with 7 histological plaque characteristics assessed in 1443 carotid plaque specimens from the Athero-Express Biobank Study. We also assessed if the genotyped cardiovascular risk loci impact the tissue-specific gene expression in 2 independent biobanks, Biobank of Karolinska Endarterectomy and Stockholm Atherosclerosis Gene Expression. Results: A total of 21 established risk variants (out of 61) nominally associated to a plaque characteristic. One variant (rs12539895, risk allele A) at 7q22 associated to a reduction of intraplaque fat, P=5.09×10−6 after correction for multiple testing. We further characterized this 7q22 Locus and show tissue-specific effects of rs12539895 on HBP1 expression in plaques and COG5 expression in whole blood and provide data from public resources showing an association with decreased LDL (low-density lipoprotein) and increase HDL (high-density lipoprotein) in the blood. Conclusions: Our study supports the view that cardiovascular susceptibility loci may exert their effect by influencing the atherosclerotic plaque characteristics

Druggable proteins influencing cardiac structure and function: Implications for heart failure therapies and cancer cardiotoxicity

Dysfunction of either the right or left ventricle can lead to heart failure (HF) and subsequent morbidity and mortality. We performed a genome-wide association study (GWAS) of 16 cardiac magnetic resonance (CMR) imaging measurements of biventricular function and structure. Cis-Mendelian randomization (MR) was used to identify plasma proteins associating with CMR traits as well as with any of the following cardiac outcomes: HF, non-ischemic cardiomyopathy, dilated cardiomyopathy (DCM), atrial fibrillation, or coronary heart disease. In total, 33 plasma proteins were prioritized, including repurposing candidates for DCM and/or HF: IL18R (providing indirect evidence for IL18), I17RA, GPC5, LAMC2, PA2GA, CD33, and SLAF7. In addition, 13 of the 25 druggable proteins (52%; 95% confidence interval, 0.31 to 0.72) could be mapped to compounds with known oncological indications or side effects. These findings provide leads to facilitate drug development for cardiac disease and suggest that cardiotoxicities of several cancer treatments might represent mechanism-based adverse effects

Development of CarbonTracker Europe-CH4 – part 1 : system set-up and sensitivity analyses

CarbonTracker Europe-CH4 (CTE-CH4) inverse model versions 1.0 and 1.1 are presented. The model optimizes global surface methane emissions from biosphere and anthropogenic sources using an ensemble Kalman filter (EnKF) based optimization method, using the TM5 chemistry transport model as an observation operator, and assimilating global in-situ atmospheric methane mole fraction observations. In this study, we examine sensitivity of our CH4 emission estimates on the ensemble size, covariance matrix, prior estimates, observations to be assimilated, assimilation window length, convection scheme in TM5, and model structure in the emission estimates by performing CTE-CH4 with several set-ups. The analyses show that the model is sensitive to most of the parameters and inputs that were examined. Firstly, using a large enough ensemble size stabilises the results. Secondly, using an informative covariance matrix reduces uncertainty estimates. Thirdly, agreement with discrete observations became better when assimilating continuous observations. Finally, the posterior emissions were found sensitive to the choice of prior estimates, convection scheme and model structure, particularly to their spatial distribution. The distribution of posterior mole fractions derived from posterior emissions is consistent with the observations to the extent prescribed in the various covariance estimates, indicating a satisfactory performance of our system.peer-reviewe

Magnetism in reduced dimensions

We propose a short overview of a few selected issues of magnetism in reduced dimensions, which are the most relevant to set the background for more specialized contributions to the present Special Issue. Magnetic anisotropy in reduced dimensions is discussed, on a theoretical basis, then with experimental reports and views from surface to single-atom anisotropy. Then conventional magnetization states are reviewed, including macrospins, single domains, multidomains, and domain walls in stripes. Dipolar coupling is examined for lateral interactions in arrays, and for interlayer interactions in films and dots. Finally thermally-assisted magnetization reversal and superparamagnetism are presented. For each topic we sought a balance between well established knowledge and recent developments.Comment: 13 pages. Part of a Special Issue of the C. R. Physique devoted to spinelectronics (2005

Integrative single-cell meta-analysis reveals disease-relevant vascular cell states and markers in human atherosclerosis

Coronary artery disease (CAD) is characterized by atherosclerotic plaque formation in the arterial wall. CAD progression involves complex interactions and phenotypic plasticity among vascular and immune cell lineages. Single-cell RNA-seq (scRNA-seq) studies have highlighted lineage-specific transcriptomic signatures, but human cell phenotypes remain controversial. Here, we perform an integrated meta-analysis of 22 scRNA-seq libraries to generate a comprehensive map of human atherosclerosis with 118,578 cells. Besides characterizing granular cell-type diversity and communication, we leverage this atlas to provide insights into smooth muscle cell (SMC) modulation. We integrate genome-wide association study data and uncover a critical role for modulated SMC phenotypes in CAD, myocardial infarction, and coronary calcification. Finally, we identify fibromyocyte/fibrochondrogenic SMC markers (LTBP1 and CRTAC1) as proxies of atherosclerosis progression and validate these through omics and spatial imaging analyses. Altogether, we create a unified atlas of human atherosclerosis informing cell state-specific mechanistic and translational studies of cardiovascular diseases.</p

Global methane emission estimates for 2000–2012 from CarbonTracker Europe-CH4 v1.0

We present a global distribution of surface methane (CH4) emission estimates for 2000–2012 derived using the CarbonTracker Europe-CH4 (CTE-CH4) data assimilation system. In CTE-CH4, anthropogenic and biospheric CH4 emissions are simultaneously estimated based on constraints of global atmospheric in situ CH4 observations. The system was configured to either estimate only anthropogenic or biospheric sources per region, or to estimate both categories simultaneously. The latter increased the number of optimizable parameters from 62 to 78. In addition, the differences between two numerical schemes available to perform turbulent vertical mixing in the atmospheric transport model TM5 were examined. Together, the system configurations encompass important axes of uncertainty in inversions and allow us to examine the robustness of the flux estimates. The posterior emission estimates are further evaluated by comparing simulated atmospheric CH4 to surface in situ observations, vertical profiles of CH4 made by aircraft, remotely sensed dry-air total column-averaged mole fraction (XCH4) from the Total Carbon Column Observing Network (TCCON), and XCH4 from the Greenhouse gases Observing Satellite (GOSAT). The evaluation with non-assimilated observations shows that posterior XCH4 is better matched with the retrievals when the vertical mixing scheme with faster interhemispheric exchange is used. Estimated posterior mean total global emissions during 2000–2012 are 516 ± 51 Tg CH4 yr−1 , with an increase of 18 Tg CH4 yr−1 from 2000–2006 to 2007–2012. The increase is mainly driven by an increase in emissions from South American temperate, Asian temperate and Asian tropical TransCom regions. In addition, the increase is hardly sensitive to different model configurations (< 2 Tg CH4 yr−1 difference), and much smaller than suggested by EDGAR v4.2 FT2010 inventory (33 Tg CH4 yr−1 ), which was used for prior anthropogenic emission estimates. The result is in good agreement with other published estimates from inverse modelling studies (16–20 Tg CH4 yr−1 ). However, this study could not conclusively separate a small trend in biospheric emissions (−5 to +6.9 Tg CH4 yr−1 ) from the much larger trend in anthropogenic emissions (15–27 Tg CH4 yr−1 ). Finally, we find that the global and North American CH4 balance could be closed over this time period without the previously suggested need to strongly increase anthropogenic CH4 emissions in the United States. With further developments, especially on the treatment of the atmospheric CH4 sink, we expect the data assimilation system presented here will be able to contribute to the ongoing interpretation of changes in this important greenhouse gas budget.peer-reviewe

Gene-based meta-analysis of genome-wide association studies implicates new loci involved in obesity

To date, genome-wide association studies (GWASs) have identified >100 loci with single variants associated with body mass index (BMI). This approach may miss loci with high allelic heterogeneity; therefore, the aim of the present study was to use gene-based meta-analysis to identify regions with high allelic heterogeneity to discover additional obesity susceptibility loci. We included GWAS data from 123 865 individuals of European descent from 46 cohorts in Stage 1 and Metabochip data from additional 103 046 individuals from 43 cohorts in Stage 2, all within the Genetic Investigation of ANthropometric Traits (GIANT) consortium. Each cohort was tested for association between ∼2.4 million (Stage 1) or ∼200 000 (Stage 2) imputed or genotyped single variants and BMI, and summary statistics were subsequently meta-analyzed in 17 941 genes. We used the ‘VErsatile Gene-based Association Study’ (VEGAS) approach to assign variants to genes and to calculate gene-based P-values based on simulations. The VEGAS method was applied to each cohort separately before a gene-based meta-analysis was performed. In Stage 1, two known (FTO and TMEM18) and six novel (PEX2, MTFR2, SSFA2, IARS2, CEP295 and TXNDC12) loci were associated with BMI (P < 2.8 × 10−6 for 17 941 gene tests). We confirmed all loci, and six of them were gene-wide significant in Stage 2 alone. We provide biological support for the loci by pathway, expression and methylation analyses. Our results indicate that gene-based meta-analysis of GWAS provides a useful strategy to find loci of interest that were not identified in standard single-marker analyses due to high allelic heterogeneity