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      Warming and redistribution of nitrogen inputs drive an increase in terrestrial nitrous oxide emission factor

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          Abstract

          Anthropogenic nitrogen inputs cause major negative environmental impacts, including emissions of the important greenhouse gas N 2O. Despite their importance, shifts in terrestrial N loss pathways driven by global change are highly uncertain. Here we present a coupled soil-atmosphere isotope model (IsoTONE) to quantify terrestrial N losses and N 2O emission factors from 1850-2020. We find that N inputs from atmospheric deposition caused 51% of anthropogenic N 2O emissions from soils in 2020. The mean effective global emission factor for N 2O was 4.3 ± 0.3% in 2020 (weighted by N inputs), much higher than the surface area-weighted mean (1.1 ± 0.1%). Climate change and spatial redistribution of fertilisation N inputs have driven an increase in global emission factor over the past century, which accounts for 18% of the anthropogenic soil flux in 2020. Predicted increases in fertilisation in emerging economies will accelerate N 2O-driven climate warming in coming decades, unless targeted mitigation measures are introduced.

          Abstract

          Soil nitrogen isotopic composition is used to drive the IsoTONE model, which is constrained with measurements of tropospheric nitrous oxide isotopic composition. The model results reveal causes of rising mean global nitrous oxide emission factor.

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          Re-epithelialization and immune cell behaviour in an ex vivo human skin model

          A large body of literature is available on wound healing in humans. Nonetheless, a standardized ex vivo wound model without disruption of the dermal compartment has not been put forward with compelling justification. Here, we present a novel wound model based on application of negative pressure and its effects for epidermal regeneration and immune cell behaviour. Importantly, the basement membrane remained intact after blister roof removal and keratinocytes were absent in the wounded area. Upon six days of culture, the wound was covered with one to three-cell thick K14+Ki67+ keratinocyte layers, indicating that proliferation and migration were involved in wound closure. After eight to twelve days, a multi-layered epidermis was formed expressing epidermal differentiation markers (K10, filaggrin, DSG-1, CDSN). Investigations about immune cell-specific manners revealed more T cells in the blister roof epidermis compared to normal epidermis. We identified several cell populations in blister roof epidermis and suction blister fluid that are absent in normal epidermis which correlated with their decrease in the dermis, indicating a dermal efflux upon negative pressure. Together, our model recapitulates the main features of epithelial wound regeneration, and can be applied for testing wound healing therapies and investigating underlying mechanisms.
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            Ultrastructural Characterization of the Lower Motor System in a Mouse Model of Krabbe Disease

            Krabbe disease (KD) is a neurodegenerative disorder caused by the lack of β- galactosylceramidase enzymatic activity and by widespread accumulation of the cytotoxic galactosyl-sphingosine in neuronal, myelinating and endothelial cells. Despite the wide use of Twitcher mice as experimental model for KD, the ultrastructure of this model is partial and mainly addressing peripheral nerves. More details are requested to elucidate the basis of the motor defects, which are the first to appear during KD onset. Here we use transmission electron microscopy (TEM) to focus on the alterations produced by KD in the lower motor system at postnatal day 15 (P15), a nearly asymptomatic stage, and in the juvenile P30 mouse. We find mild effects on motorneuron soma, severe ones on sciatic nerves and very severe effects on nerve terminals and neuromuscular junctions at P30, with peripheral damage being already detectable at P15. Finally, we find that the gastrocnemius muscle undergoes atrophy and structural changes that are independent of denervation at P15. Our data further characterize the ultrastructural analysis of the KD mouse model, and support recent theories of a dying-back mechanism for neuronal degeneration, which is independent of demyelination.
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              Occurrence of the potent mutagens 2- nitrobenzanthrone and 3-nitrobenzanthrone in fine airborne particles

              Polycyclic aromatic compounds (PACs) are known due to their mutagenic activity. Among them, 2-nitrobenzanthrone (2-NBA) and 3-nitrobenzanthrone (3-NBA) are considered as two of the most potent mutagens found in atmospheric particles. In the present study 2-NBA, 3-NBA and selected PAHs and Nitro-PAHs were determined in fine particle samples (PM 2.5) collected in a bus station and an outdoor site. The fuel used by buses was a diesel-biodiesel (96:4) blend and light-duty vehicles run with any ethanol-to-gasoline proportion. The concentrations of 2-NBA and 3-NBA were, on average, under 14.8 µg g−1 and 4.39 µg g−1, respectively. In order to access the main sources and formation routes of these compounds, we performed ternary correlations and multivariate statistical analyses. The main sources for the studied compounds in the bus station were diesel/biodiesel exhaust followed by floor resuspension. In the coastal site, vehicular emission, photochemical formation and wood combustion were the main sources for 2-NBA and 3-NBA as well as the other PACs. Incremental lifetime cancer risk (ILCR) were calculated for both places, which presented low values, showing low cancer risk incidence although the ILCR values for the bus station were around 2.5 times higher than the ILCR from the coastal site.
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                Author and article information

                Contributors
                eliza.harris@sdsc.ethz.ch
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                25 July 2022
                25 July 2022
                2022
                : 13
                : 4310
                Affiliations
                [1 ]GRID grid.5801.c, ISNI 0000 0001 2156 2780, Swiss Data Science Centre, ETH Zurich, ; 8092 Zurich, Switzerland
                [2 ]GRID grid.5771.4, ISNI 0000 0001 2151 8122, Functional Ecology Research Group, Institute of Ecology, , University of Innsbruck, ; 6020 Innsbruck, Austria
                [3 ]GRID grid.12527.33, ISNI 0000 0001 0662 3178, Institute of Environment and Ecology, Tsinghua Shenzhen International Graduate School (SIGS), , Tsinghua University, ; Shenzhen, 518055 China
                [4 ]GRID grid.7354.5, ISNI 0000 0001 2331 3059, Laboratory for Air Pollution & Environmental Technology, Empa, Swiss Federal Laboratories for Materials Science and Technology, ; 8600 Duebendorf, Switzerland
                [5 ]GRID grid.492990.f, ISNI 0000 0004 0402 7163, Climate Science Centre, CSIRO Oceans and Atmosphere, ; Aspendale, VIC 3195 Australia
                [6 ]GRID grid.27446.33, ISNI 0000 0004 1789 9163, Key Laboratory of Geographical Processes and Ecological Security of Changbai Mountains, Ministry of Education, School of Geographical Sciences, , Northeast Normal University, ; Changchun, 130024 China
                [7 ]GRID grid.5801.c, ISNI 0000 0001 2156 2780, Department of Environmental Systems Science, ETH Zurich, ; 8092 Zurich, Switzerland
                [8 ]GRID grid.5342.0, ISNI 0000 0001 2069 7798, Isotope Bioscience Laboratory - ISOFYS, Department of Green Chemistry and Technology, , Ghent University, ; Coupure Links 653, 9000 Ghent, Belgium
                [9 ]GRID grid.47894.36, ISNI 0000 0004 1936 8083, Natural Resource Ecology Laboratory, , Colorado State University, ; Fort Collins, 80523 CO USA
                [10 ]CSIRO Agriculture and Food, Locked bag 2, Glen Osmond, SA 5064 Australia
                [11 ]GRID grid.419500.9, ISNI 0000 0004 0491 7318, Department of Biogeochemical Integration, Max Planck Institute for Biogeochemistry, ; Jena, Germany
                [12 ]GRID grid.1031.3, ISNI 0000000121532610, Centre for Coastal Biogeochemistry, , Southern Cross University, ; Lismore, NSW 2480 Australia
                [13 ]GRID grid.16488.33, ISNI 0000 0004 0385 8571, Department of Soil and Physical Sciences, Agriculture and Life Sciences, , Lincoln University, ; Lincoln, 7647 New Zealand
                [14 ]GRID grid.1008.9, ISNI 0000 0001 2179 088X, School of Geography, Earth and Atmospheric Sciences, , University of Melbourne, ; Parkville, VIC 3052 Australia
                [15 ]GRID grid.1008.9, ISNI 0000 0001 2179 088X, Melbourne Climate Futures Climate and Energy College, , University of Melbourne, ; Parkville, VIC 3052 Australia
                Author information
                http://orcid.org/0000-0002-7102-8305
                http://orcid.org/0000-0002-2127-6343
                http://orcid.org/0000-0002-4614-6203
                http://orcid.org/0000-0002-9799-1001
                http://orcid.org/0000-0002-6637-4887
                http://orcid.org/0000-0003-0495-6504
                http://orcid.org/0000-0003-0978-6639
                http://orcid.org/0000-0003-3998-0010
                http://orcid.org/0000-0003-4562-2738
                http://orcid.org/0000-0002-4884-3678
                http://orcid.org/0000-0001-6424-1098
                http://orcid.org/0000-0001-5736-1112
                http://orcid.org/0000-0001-9336-4185
                http://orcid.org/0000-0002-7195-8115
                http://orcid.org/0000-0001-9914-5418
                http://orcid.org/0000-0001-7482-9776
                http://orcid.org/0000-0001-7707-6298
                Article
                32001
                10.1038/s41467-022-32001-z
                9314393
                35879348
                00736aa2-dc4b-4e22-bc63-978af3c8b193
                © The Author(s) 2022

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 10 January 2022
                : 11 July 2022
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100002428, Austrian Science Fund (Fonds zur Förderung der Wissenschaftlichen Forschung);
                Award ID: P31132
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/501100001711, Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (Swiss National Science Foundation);
                Award ID: 163075
                Award Recipient :
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                © The Author(s) 2022

                Uncategorized
                element cycles,agriculture,ecological modelling,stable isotope analysis
                Uncategorized
                element cycles, agriculture, ecological modelling, stable isotope analysis

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