129 research outputs found
Comparison of Family 9 Cellulases from Mesophilic and Thermophilic Bacteria
Cellulases containing a family 9 catalytic domain and a family 3c cellulose binding module (CBM3c) are important components of bacterial cellulolytic systems. We measured the temperature dependence of the activities of three homologs: Clostridium cellulolyticum Cel9G, Thermobifida fusca Cel9A, and C. thermocellum Cel9I. To directly compare their catalytic activities, we constructed six new versions of the enzymes in which the three GH9-CBM3c domains were fused to a dockerin both with and without a T. fusca fibronectin type 3 homology module (Fn3). We studied the activities of these enzymes on crystalline cellulose alone and in complex with a miniscaffoldin containing a cohesin and a CBM3a. The presence of Fn3 had no measurable effect on thermostability or cellulase activity. The GH9-CBM3c domains of Cel9A and Cel9I, however, were more active than the wild type when fused to a dockerin complexed to scaffoldin. The three cellulases in complex have similar activities on crystalline cellulose up to 60°C, but C. thermocellum Cel9I, the most thermostable of the three, remains highly active up to 80°C, where its activity is 1.9 times higher than at 60°C. We also compared the temperature-dependent activities of different versions of Cel9I (wild type or in complex with a miniscaffoldin) and found that the thermostable CBM is necessary for activity on crystalline cellulose at high temperatures. These results illustrate the significant benefits of working with thermostable enzymes at high temperatures, as well as the importance of retaining the stability of all modules involved in cellulose degradation
The consolidated European synthesis of CH4 and N2O emissions for the European Union and United Kingdom : 1990-2019
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
The consolidated European synthesis of CH₄ and N₂O emissions for the European Union and United Kingdom: 1990–2019
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)
Interações químico-fisiolóficas entre acidificantes, probióticos, enzimas e lisofosfolipídios na digestão de leitões
Blanket handling concepts for future fusion power plants
In the frame of the power plant conceptual studies (PPCS) launched by the European Commission, two main blanket handling concepts have been investigated with respect to engineering feasibility and the impact on the plant availability and on cost
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