1,338 research outputs found

    The consolidated European synthesis of CH₄ and N₂O emissions for the European Union and United Kingdom: 1990–2019

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    Knowledge of the spatial distribution of the fluxes of greenhouse gases (GHGs) and their temporal variability as well as flux attribution to natural and anthropogenic processes is essential to monitoring the progress in mitigating anthropogenic emissions under the Paris Agreement and to inform its global stocktake. This study provides a consolidated synthesis of CH₄ and N₂O emissions using bottom-up (BU) and top-down (TD) approaches for the European Union and UK (EU27 + UK) and updates earlier syntheses (Petrescu et al., 2020, 2021). The work integrates updated emission inventory data, process-based model results, data-driven sector model results and inverse modeling estimates, and it extends the previous period of 1990–2017 to 2019. BU and TD products are compared with European national greenhouse gas inventories (NGHGIs) reported by parties under the United Nations Framework Convention on Climate Change (UNFCCC) in 2021. Uncertainties in NGHGIs, as reported to the UNFCCC by the EU and its member states, are also included in the synthesis. Variations in estimates produced with other methods, such as atmospheric inversion models (TD) or spatially disaggregated inventory datasets (BU), arise from diverse sources including within-model uncertainty related to parameterization as well as structural differences between models. By comparing NGHGIs with other approaches, the activities included are a key source of bias between estimates, e.g., anthropogenic and natural fluxes, which in atmospheric inversions are sensitive to the prior geospatial distribution of emissions. For CH₄ emissions, over the updated 2015–2019 period, which covers a sufficiently robust number of overlapping estimates, and most importantly the NGHGIs, the anthropogenic BU approaches are directly comparable, accounting for mean emissions of 20.5 Tg CH₄ yrc (EDGARv6.0, last year 2018) and 18.4 Tg CH₄ yr⁻¹ (GAINS, last year 2015), close to the NGHGI estimates of 17.5±2.1 Tg CH₄ yr⁻¹. TD inversion estimates give higher emission estimates, as they also detect natural emissions. Over the same period, high-resolution regional TD inversions report a mean emission of 34 Tg CH₄ yr⁻¹. Coarser-resolution global-scale TD inversions result in emission estimates of 23 and 24 Tg CH₄ yr⁻¹ inferred from GOSAT and surface (SURF) network atmospheric measurements, respectively. The magnitude of natural peatland and mineral soil emissions from the JSBACH–HIMMELI model, natural rivers, lake and reservoir emissions, geological sources, and biomass burning together could account for the gap between NGHGI and inversions and account for 8 Tg CH₄ yr⁻¹. For N₂O emissions, over the 2015–2019 period, both BU products (EDGARv6.0 and GAINS) report a mean value of anthropogenic emissions of 0.9 Tg N₂O yr⁻¹, close to the NGHGI data (0.8±55 % Tg N₂O yr⁻¹). Over the same period, the mean of TD global and regional inversions was 1.4 Tg N₂O yr⁻¹ (excluding TOMCAT, which reported no data). The TD and BU comparison method defined in this study can be operationalized for future annual updates for the calculation of CH₄ and N₂O budgets at the national and EU27 + UK scales. Future comparability will be enhanced with further steps involving analysis at finer temporal resolutions and estimation of emissions over intra-annual timescales, which is of great importance for CH₄ and N₂O, and may help identify sector contributions to divergence between prior and posterior estimates at the annual and/or inter-annual scale. Even if currently comparison between CH₄ and N₂O inversion estimates and NGHGIs is highly uncertain because of the large spread in the inversion results, TD inversions inferred from atmospheric observations represent the most independent data against which inventory totals can be compared. With anticipated improvements in atmospheric modeling and observations, as well as modeling of natural fluxes, TD inversions may arguably emerge as the most powerful tool for verifying emission inventories for CH₄, N₂O and other GHGs. The referenced datasets related to figures are visualized at https://doi.org/10.5281/zenodo.7553800 (Petrescu et al., 2023)

    The consolidated European synthesis of CH4 and N2O emissions for the European Union and United Kingdom : 1990-2019

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    Funding Information: We thank Aurélie Paquirissamy, Géraud Moulas and the ARTTIC team for the great managerial support offered during the project. FAOSTAT statistics are produced and disseminated with the support of its member countries to the FAO regular budget. Annual, gap-filled and harmonized NGHGI uncertainty estimates for the EU and its member states were provided by the EU GHG inventory team (European Environment Agency and its European Topic Centre on Climate change mitigation). Most top-down inverse simulations referred to in this paper rely for the derivation of optimized flux fields on observational data provided by surface stations that are part of networks like ICOS (datasets: 10.18160/P7E9-EKEA , Integrated Non-CO Observing System, 2018a, and 10.18160/B3Q6-JKA0 , Integrated Non-CO Observing System, 2018b), AGAGE, NOAA (Obspack Globalview CH: 10.25925/20221001 , Schuldt et al., 2017), CSIRO and/or WMO GAW. We thank all station PIs and their organizations for providing these valuable datasets. We acknowledge the work of other members of the EDGAR group (Edwin Schaaf, Jos Olivier) and the outstanding scientific contribution to the VERIFY project of Peter Bergamaschi. Timo Vesala thanks ICOS-Finland, University of Helsinki. The TM5-CAMS inversions are available from https://atmosphere.copernicus.eu (last access: June 2022); Arjo Segers acknowledges support from the Copernicus Atmosphere Monitoring Service, implemented by the European Centre for Medium-Range Weather Forecasts on behalf of the European Commission (grant no. CAMS2_55). This research has been supported by the European Commission, Horizon 2020 Framework Programme (VERIFY, grant no. 776810). Ronny Lauerwald received support from the CLand Convergence Institute. Prabir Patra received support from the Environment Research and Technology Development Fund (grant no. JPMEERF20182002) of the Environmental Restoration and Conservation Agency of Japan. Pierre Regnier received financial support from the H2020 project ESM2025 – Earth System Models for the Future (grant no. 101003536). David Basviken received support from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (METLAKE, grant no. 725546). Greet Janssens-Maenhout received support from the European Union's Horizon 2020 research and innovation program (CoCO, grant no. 958927). Tuula Aalto received support from the Finnish Academy (grants nos. 351311 and 345531). Sönke Zhaele received support from the ERC consolidator grant QUINCY (grant no. 647204).Peer reviewedPublisher PD

    Knowledge Distillation and Continual Learning for Optimized Deep Neural Networks

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    Over the past few years, deep learning (DL) has been achieving state-of-theart performance on various human tasks such as speech generation, language translation, image segmentation, and object detection. While traditional machine learning models require hand-crafted features, deep learning algorithms can automatically extract discriminative features and learn complex knowledge from large datasets. This powerful learning ability makes deep learning models attractive to both academia and big corporations. Despite their popularity, deep learning methods still have two main limitations: large memory consumption and catastrophic knowledge forgetting. First, DL algorithms use very deep neural networks (DNNs) with many billion parameters, which have a big model size and a slow inference speed. This restricts the application of DNNs in resource-constraint devices such as mobile phones and autonomous vehicles. Second, DNNs are known to suffer from catastrophic forgetting. When incrementally learning new tasks, the model performance on old tasks significantly drops. The ability to accommodate new knowledge while retaining previously learned knowledge is called continual learning. Since the realworld environments in which the model operates are always evolving, a robust neural network needs to have this continual learning ability for adapting to new changes

    Somatic GNA11/Q mutations- clues to cause and consequence of primary aldosteronism? And Feasibility study of RadioFrequency endoscopic ABlation, with ULtrasound guidance, as a non-surgical, Adrenal Sparing treatment for aldosterone-producing adenomas (FABULAS Study).

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    Primary aldosteronism (PA) is the potentially curable cause of high-risk hypertension in 5-10% of unselected patients, and in > 20% of those with resistant hypertension. Landmark Whole exome sequencing (WES) have paved the way to better understanding of this condition. The gold standard treatment for unilateral disease is adrenalectomy where the cure rate achieved varies between 30-60%. The first part of this thesis investigates a molecule, TMEM132E, which was the most consistently upregulated gene in the aldosterone producing adenomas (APA) discovered to have the double somatic mutation of CTNNB1, in exon 3, and either GNA11 or GNAQ, at residue p.Gln209. Transfection of the CTNNB1-mutant adrenocortical cell line, H295R, with wild-type or mutant GNA11, reproduced the increase in CYP11B2 (encoding aldosterone synthase) seen in all APAs, and TMEM132E was the only gene to be significantly upregulated, out of the nine which are uniquely upregulated in the double-mutant APAs. Because TMEM132E is a deafness gene, whose vital function may be to traffic cholinergic ionotropic receptors 9/10, to the plasma membrane, I investigated whether the 30-40% homologous adrenocortical receptor, 7 – also upregulated in double-mutant APAs – enables cholinergic stimulation of aldosterone production. Treatment of H295R cells with Carbachol ( an acetylcholine agonist) increased aldosterone secretion. The second part of this thesis presents results from FABULAS: Feasibility study of RadioFrequency endoscopic ABlation, with ULtrasound guidance, as a non-surgical, Adrenal Sparing treatment for aldosterone producing adenomas. This was a phase 1 study to determine the safety and efficacy of endoscopic ultrasound guided radiofrequency ablation (RFA) as an alternative to adrenal surgery for left sided APAs. By design, surgery was contra-indicated in ~half of the 28 patients because of co-morbidities, or because the diagnosis of unilateral PA was ambiguous. The primary outcome was safety. None of the pre-specified serious adverse events occurred, namely perforation, haemorrhage, or infarction of major organs within the first 24-48 hours. 3 patients had severe events related to the procedure, which rapidly resolved. The secondary outcomes of efficacy – anticipated to be lower than in surgical patients because of the eligibility criteria – were the PASO (Primary Aldosterone Surgical Outcome) criteria of biochemical and clinical success 6-months post-procedure, and changes in CYP11B2 signal, measured by 11C-metomidate PET CT. 14/28 and 4/28 patients achieved complete biochemical or complete clinical success, respectively. There was significant reduction in ARR (aldosterone renin ratio), and defined daily dose of antihypertensive medications, but average blood pressure was unchanged. These results show endoscopic RFA to be safe and potentially effective, enabling a head-to-head comparison with surgery that is now underway

    Transbronchial cryobiopsy and Neutrophil Lymphocyte Ratio - new precision medicine tools and markers in Interstitial Lung Disease

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    The interstitial lung diseases (ILDs) are a group of over 200 disease that may lead to progressive fibrosis and respiratory failure. ILDs are heterogenous, with varying amounts of inflammation and fibrosis, and differ in response to therapy and outcome. The most severe fibrotic (f) ILD, idiopathic pulmonary fibrosis (IPF), has a median survival of just three years. Progressive fILD may respond to antifibrotic treatments which slow down, but do not reverse, fibrosis albeit often with significant side effects. Better treatments or delivery of treatments are needed. Diagnosis of ILD relies on clinical history, imaging and, in some cases lung biopsy, with associated risks. Better diagnostic and prognostic biomarkers in ILD are urgently needed. This thesis examines the approach to diagnosis, prognostication, and treatment in fILDs, and, in particular IPF. It begins with the finding that Neutrophil Lymphocyte Ratio (NLR), derived from a simple, widely available blood test, is a prognostic biomarker in IPF. The role of lung biopsy in the diagnostic pathway is considered and the use of a relatively new minimally invasive technique of transbronchial cryo lung biopsy (TBCB) as an alternative to surgical lung biopsy (SLB) is described. The value of TBCB to obtain lung tissue for research is demonstrated with evaluation of the distribution of inhaled ipratropium in fILD. Using matrix-assisted laser desorption/ionization (MALDI) mass spectrometry (MS) on samples of lung taken using TBCB, it was demonstrated that inhaled medication was able to reach the fibrotic lung, presenting a new approach to drug delivery in fILD. Further discussion focusses on the current role of SLB in the diagnostic pathway in ILD, the presentation of a systematic literature review, and a discussion of future trials to assess the potential benefits of a wider use of TBCB

    Ultrasound Guidance in Perioperative Care

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