dataesr/scientific-paragraphs-categorization huggingface dataset:
Winkler et al. [103]  showed that using higher resolution data (30 m for Landsat-based datasets) and including regionally specific historical information alters depicted LULCC patterns and temporal dynamics. For two of the focus countries in ESA-CCI RECCAP2 (Germany and France), we compare the F LUC estimates from DGVM simulations using LUH2 from GCB2021 and HILDA + as LULCC forcing (Fig. 2 ). We further compare these estimates with (i) BK   [81] : an ensemble of 10 in-situ and 11 satellite-based atmospheric inversions (left column) and two simulations by an ensemble of 13DGVMs: one using prescribed wetland extent from the WAD2M datasets (DGVMs Diag. , all 13 models, centre column) and another with prognostically simulated wetland extent (DGVMs Prog. , only 8 out of 13 models, right column).
The top row shows mean annual fluxes, the second row shows inter-annual variability in annual fluxes and the bottom row shows trends in the mean annual fluxes (red for negative trends, indicating reduced emissions, and blue for positive trends, indicating increased emissions). Inversions and DGVMs agree on fWet sources to be mostly located in Scandinavia, Denmark and northern UK but the magnitude of fWet is consistently lower in DGVMs. DGVM runs using prescribed wetland extent (DGVM Diag ) show consistent spatial distribution with inversions, while simulations using prognostic wetland extent (DGVM Prog ) show strong sources in parts of eastern and central Europe. Interannual variability (second row) in inversion datasets is highest in northern and Eastern Europe, while for DGVMs, IAV patterns are more uniform across the whole region and higher for DGVM Prog.
There is wide disagreement between the four datasets for the trends in 2010-2017 (bottom row). More detailed information about the datasets and model simulations is provided in Additional file 1 models from GCB2021, (ii) the BLUE BK model forced with HILDA + by  [32] , and (iii) AFOLU fluxes from FAO (FAOSTAT  [26] ) and from the respective NGHGIs (Fig. 2 ). Note that in both France and Germany, all land is considered to be managed so that we avoid mis-matches due to different definitions. The agreement of each dataset with reference data (BK models, FAO, NGHGIs) depends both on LULCC forcing and model. The differences in F LUC estimated by each individual DGVM using two different LULCC forcing datasets are generally smaller than the differences between DGVMs with the same LULCC forcing. In Germany, F LUC from DGMVs is on the lower range of the uncertainty envelope of BK models from GCB2021 but generally close to NGHGI estimates.
Simulations of DGVMs and BLUE with HILDA + LULCC lead to a weaker LULCC sink but are largely consistent across models. In France, OCN estimates much strong LULCC To estimate F LUC with DGVMs we followed the commonly used approach in Global Carbon Budgets  [31, 70, 83] : we run two simulations forced with changing CO2 and climate, but one with fixed LULCC distribution (in this case in 1950) and another with changing LULCC fields. The difference between the two allows estimating the effect of LULCC on the simulated carbon fluxes. Information about the respective LULCC datasets can be found in the Section on Land cover EO datasets and more details about the forcing datasets and model simulations is provided in Additional file 1 sink than ORCHIDEE-MICT, with the differences between the models being comparable to differences between LULCC forcing.
The estimates by OCN are close to the mean of BK models from GCB2021, which also shows good agreement with NGHGI estimates. These results highlight the important role of model uncertainty in addition to that uncertainty in LULCC forcing. Interactions between model and LULCC forcings results in non-systematic biases, so that using multiple independent LULCC forcing datasets as well as consistent definitions and land-use classes would help constraining F LUC uncertainties. We propose the following recommendations for the use of EO in F LUC estimates: 1. High-resolution remote-sensing data of LULCC (10-30 m) and biomass allow quantifying F LUC in a spatiotemporally explicit and globally consistent manner and cost-effective way. They can therefore provide a key contribution to estimate F LUC in regions/countries with limited capacity to produce detailed national inventories and statistics.
For forest monitoring, we recommend to use directly quantitative information on canopy height, area, and tree cover to define limits of forests. For LULCC products in general, a harmonized combination of available products, based on their common agreement, may reduce the uncertainty effects due to misclassification by the sensors/products. 3. It is currently not possible to assign more confidence to one LULCC dataset over another, especially at global scale, and existing datasets are to some extent dependent on the same underlying data. For example, both HILDA + , LUH2v2 rely on FAO and CCI LC, although with fundamental differences in their methods. We call for more efforts to evaluate and validate LULCC datasets, e. g. , based on regional high-resolution EO data or high-quality inventories, if available, and on a common global framework for benchmarking.
We also recommend to compare EO based LULCC datasets with those used by NGHGI. In this case, if NGHGI report spatially explicit datasets instead of nation-wide averages, a detailed comparison can be performed to evaluate the different sources of error. 4. These uncertainties are propagated to estimates of F LUC both in NGHGI and DGVMs or other models  [78]  but also to estimates of the natural sinks (because of the foregone sink capacity) and disturbance fluxes by models (Fig. 3 ). The sensitivity of estimated natural and anthropogenic fluxes to the LULCC forcing is likely to be stronger in regions undergoing intense LUC, i. e. , Brazil compared to Europe. The approach in Rosan et al. [78]  for Brazil provides a prototype of how high-resolution remotesensing data can be combined to target LULCC dataset improvement in other key regions.
Similar efforts should be extended to other countries/regions that have not been comprehensively analysed. 5. Interactive effects between LULCC forcing uncertainty, process representation and model parameterizations make it challenging to track the impacts of differences in LULCC forcing on estimated fluxes  [4, 34, 70]. We propose that that using multiple LULCC forcing datasets in addition to different bookkeeping models or DGVMs may improve the representation of F LUC uncertainties. 6. The impossibility of observational data to separate LULCC and natural processes on a global scale is one of the most important reasons for the application of models, or, on regional scale, the use of ancillary data such as photointerpretation  [73]. EO data needs to be complemented by additional information to separate anthropogenic from natural drivers.
Additional EObased datasets can be used to further constrain these processes/parameters in DGVMs and bookkeeping models and contribute to reduce uncertainties or make them more tractable. Examples of such remotesensing based datasets include: biomass C stocks for bookkeeping model parameterization (recommended in  [4] ) or direct use of remotely-sensed biomass data (next section), satellite-based burned area  [11, 36] , degradation/small scale natural and human disturbances (e. g. land management) or additional vegetation indicators to constrain C uptake  [66].
High-resolution EO biomass changes data to improve national carbon accounting Monitoring biomass change represents a major challenge and yet is key to underpinning NGHGI reporting. Vegetation Optical Depth (VOD) is a vegetation index retrieved from passive or active microwave remote sensing that reflects the attenuation of the microwave signal by the vegetation canopy, however, the signal is also influenced by soil moisture  [28] , leading to uncertainty in VOD retrievals. High frequency products are less sensitive to soil moisture but much more sensitive to foliar dynamics than woody biomass. In a recent intercomparison of nine VOD datasets at different frequencies (X-, C-and L-bands), Li et al. have shown that L-VOD from SMOS-IC V2 and SMAP MT-DCA performs best for predicting biomass  [63].
Consolidating bottom-up approaches with top-down reference estimates of biomass change are important as besides reducing uncertainty there is potential to further identify changes that may be missed in conventional bottom-up reporting such as (1) small scale disturbances or effects of environmental factors on forest growth missing in spatially explicit bottom-up EO based datadriven models, (2) missing or mis-represented processes in DGVM models, and (3) lack of unmanaged lands, under-sampled land use types, limitations of low Tier methodologies in NGHGIs. These independent estimates of biomass changes may also prove useful for better inferring anthropogenic emissions from atmospheric inversions. An emerging application of high-resolution (< 30 m, HR) EO is that through reporting LULCC at fine spatial grain it can be used in combination with region specific auxiliary data, in particular high resolution biomass datasets  [6] , Santoro and Cartus  [80] ) to estimate CO 2 fluxes associated with different natural and anthropogenic processes (Fig.
Acknowledgements We would like to thank  Pierre Brender  for comments on the first version of this manuscript. We thank  Richard Houghton  and  Thomas Gasser  for making the bookkeeping model results of GCB2021 available and  Jean-Pierre Wigneron  for providing the L-VOD dataset. We thank  Christian Mielke ,  Pierre Friedlingstein ,  Benjamin Poulter  and  Josep G. Canadell  for the discussions in the context of RECCAP2 and GCP which helped to shape the work in  ESA-CCI RECCAP2.
C. Propagation model: FLHOWARD3D In the present work, the propagation of sonic booms through homogeneous and isotropic kinematic turbulence is computed with FLHOWARD3D software  (Luquet et al. , 2019). This software is dedicated to the 3D simulation of acoustical weak shock waves propagating through a heterogeneous medium both with scalar (temperature and density) and vectorial (wind) heterogeneities. It is based on a one-way equation  (Coulouvrat, 2012)  including diffraction without any angle restriction, non-linearity, wide-angle propagation through scalar and vectorial heterogeneous media, absorption and dispersion by thermoviscosity and molecular relaxation. It has been already validated using comparisons with analytical solutions  (Luquet et al. , 2019). This method also allows for two-dimensional simulations.
This can be explained by looking at the waveform at 1 km: this rise time includes two shocks with rise times comparable to the one at 196 m. For all metrics, the N-wave turns out more sensitive to turbulence than the rescaled C25D boom. This will be quantified more systematically in the last section. Multiple booms were measured (around 2000 in each site) at different distances from the flight path. When aggregating all data and normalizing the peak amplitude by the median one for a given distance from flight path, cumulative probabilities turn out insensitive to measurement distance from flight track. A dry atmosphere induces a higher variability, but the effect is not dramatic : in both cases, about 99% of booms keep a peak overpressure below twice the median value, and the probability to have a peak overpressure less than half the median value is between 0.
This approach allows access to the level of pressure on the ground (and so all the metrics) and its moments (average, standard deviation and probability density) for a predetermined continuous range of values of σ and L 0 by calculating a meta-model p(x, t, σ, L 0 ) with the help of discrete values for which simulations have been run. In a second stage, different realizations of the state of the frozen turbulence are computed and a gPC study is made for each one of them. This approach made it possible to establish a large database for different sonic boom signatures on the ground with good statistics. In the gPC method, the output random variable p(x, t, σ, L 0 ) is represented with a polynomials series expansion. p(x, t, σ, L 0 ) = ∞ n=0 pn (x, t)Φ n (σ, L 0 ), (12) with (σ, L 0 ) the random variables, pn the coefficients of the expansion and Φ n (σ, L 0 ) polynomials of degree n.
For each study, the signals on the ground have been computed, extracted and collected to make a database with 50 × 3 × 36 = 5400 signals at 256 different distances (ranging from 0 km to 1 km), so the database contains 1382400 signatures. Figure  9  shows the evolution of the mean, the nominal and the probability density function values of different metrics depending on the distance of propagation. This figure is similar to figure 7 but it is plotted for a different random vector (referenced as R 2 , see figure  2 ). As previously, depending on the distance of propagation, the mean propagation and the nominal propagation can be different, showing the importance of the parameters σ and L 0 and the interest to consider them as uncertain. Nevertheless, we can also see that the regions of amplification and the regions where the amplitude drops are different from figure  7.
ACKNOWLEDGMENTS The  RUMBLE (acronym for "RegUlation and norM for low sonic Boom LEvel ") project received funding from the  European Union 's  Horizon 2020 research and innovation programme  under Grant Agreement No. 769896. This document reflects only the authors' view and the Commission is not responsible for any use that may be made of the information it contains. We thank our colleagues in RUMBLE for the exchange during the preparation of this study, and in particular the project coordinator  Jean-François Perelgritz  ( Airbus France ) and  Pierre-Elie Normand  ( Dassault-Aviation ) for sharing the software computing various boom metrics. F18 and C25D input signals were provided to us by  Alexandra Loubeau  ( NASA Langley ). National Aeronautical and Space Administration (NASA)  is thanked for sharing SonicBAT data and permission of reproducing figure 5.
Similar immunofluorescence analyses performed in BCS and Cu conditions on the cell lines sg1-1 (Figure  1E ) and sg1-3 (Figure  1F ) confirmed the absence of ATP7B in the former, while the latter behaves as the parental cell line. For further functional analysis, sg1-1 and sg2-1 cell lines were chosen as models for ATP7B-deficient cell lines, but similar data have been obtained with sg2-1 and sg2-2 cell lines. Expression analysis was further confirmed by immunofluorescence analysis of ATP7B. In the initial HepG2/C3a cell line, ATP7B is localized in the Golgi apparatus upon Cudeprived conditions (Figure  1B , incubation with the Cu chelator BCS) and at bile canaliculi upon exposure to 100 µM Cu (Figure  1C ), while basal conditions led to intermediate location (Figure  1D ). Similar immunofluorescence analyses performed in BCS and Cu conditions on the cell lines sg1-1 (Figure  1E ) and sg1-3 (Figure  1F ) confirmed the absence of ATP7B in the former, while the latter behaves as the parental cell line.
Met (Met1X) (A), GCLM (B), SOD1 (D), and catalase (E) mRNA expression variations were determined following 6 h of exposure to Cu. Met1X (C) mRNA expression variation was also determined following 24 h of exposure to Cu. Results are expressed as the relative change in expression compared to the control. (F) ROS production determined by flow cytometry after exposure to Cu for 24 h and using the H2DCFDA probe following manufacturer instructions (ThermoFisher Scientific, Courtaboeuf, France). All the data are expressed as means +/-standard error of the mean of at least three independent experiments. * stands for data statistically different from the control unexposed to Cu with p < 0. 05, and ** stands for data statistically different from the control unexposed to Cu with p < 0. 01.
As expected, HSPA6 is strongly overexpressed in the HepG2/C3a cell line upon Cu exposure, starting from an about 2-fold increase at 2 µM Cu and reaching almost 150-fold at 200 µM Cu. However, in ATP7B-deficient cell lines, the impact is an order of magnitude higher or more depending on the conditions used. The sg1-1 and sg2-1 cell lines started with a ~10-and ~90-fold increase for 2 µM Cu and ended up at almost 1500-and 2500-fold for 200 µM Cu, respectively. These data indicate the dramatic role of Cu overload on protein unfolding, even at low Cu concentration, and clearly highlight the impact of the absence of ATP7B on this process, which is, therefore, the main problem in the context of Wilson disease. To identify a specific pathway that could be involved in sensing Cu-induced protein unfolding, the expression level of chaperones belonging to the HSP40 family, such as DNAJs, in humans was determined.
We thank the microscopy facility MuLife of  IRIG/DBSCI , funded by  CEA Nanobio  and  GRAL LabEX  ( ANR-10-LABX-49-01 ) financed within the  University Grenoble Alpes graduate school CBH-EUR-GS  ( ANR-17-EURE-0003 ). TDN was supported by the  Emergence program  of the  Chemistry Institute of the CNRS (Emergence 2021 ). This project also received funding from  Gral  and  Arcane Labex , programs from the  Chemistry Biology Health (CBH) Graduate School of University Grenoble Alpes  ( ANR-17-EURE-0003 ). Institutional Review Board Statement: Not applicable.
Acknowledgements We wish to thank all the ERN-Coordinators Group members and their teams. ERN  BOND ,  Pr Luca Sangiorgi  ( Istituto Ortopedico Rizzoli, Bologna, Italy );  ERN-CRANIO ,  Pr Irene Mathijssen  ( Erasmus Medical Centre, Rotterdam, The Netherlands ); ENDO-ERN,  Pr Alberto Pereira  ( Amsterdam University Medical Centre, Amsterdam, The Netherlands ); ERN-EPICARE,  Pr Alexis Arzimanoglou  ( Hospices Civils de Lyon, France );  ERN-ERKnet ,  Pr Franz Schaefer  ( Heidelberg University Hospital, Germany );  ERN-RND ,  Pr Holm Graessner  ( University Hospital T€ ubingen, Germany ); ERNICA,  Pr Rene Wijnen  ( Erasmus Medical Centre, Rotterdam, The Netherlands );  ERN-LUNG ,  Pr Thomas Wagner  ( Klinikum Goethe University Frankfurt, Germany ); ERN-SKIN,  Pr Christine Bodemer  ( Hôpital Necker Enfants Malades, AP-HP,Paris, France ); EURACAN,  Pr Jean-Yves Blay  ( General Cancer Centre L eon B erard, Lyon, France );  ERN EuroBloodNet Pr Pierre Fenaux  ( Hôpital Saint-Louis, AP-HP, Paris, France );  EURO-NMD ,  Teresinha Evangelista  ( Hôpital La Piti e-Salpêtri ere, AP-HP, Paris, France );  ERN-EYE, Pr H el ene Dollfus (Hôpitaux Universitaires de Strasbourg, Strasbourg, France) ;  Nicoline Hoogerbrugge  ( Radboud university medical center, Nijmegen, The Netherlands ); ERN GUARD,  Arthur Wilde  ( Academic Medical Centre, Amsterdam, The Netherlands ); ERN-ITHACA,  Alain Verloes  ( Hôpital Robert Debr e, AP-HP Paris, France ); METAB-ERN,  Maurizio Scarpa  ( University Hospital Udine, Udine, Italy ); ERN-PAEDCAN,  Pr Ruth Ladenstein  ( St.
g. , for CO 2 fixation, oxidation of acetyl-CoA produced by various catabolic pathways (50)], and several nonmethanogen lineages encode Wood-Ljungdahl pathway genes (3). Thus, presence of both Mcr and Wood-Ljungdahl pathway enzymes in the LACA does not directly imply ability for CO 2 -reducing methanogenesis. The origin of the Mtr complex is therefore of fundamental importance. The most recent phylogeny of Mtr including sequences from TACK methanogens ("Ca. Methanohydrogenales"/"Ca. Nezhaarchaeota" and "Ca. Bathyarchaeota") shows that Mtr was acquired horizontally in separate events from Class I/II methanogens (111) (Figure  4b ). The authors thus proposed that Mtr emerged within Euryarchaeota. Nevertheless, another analysis with similar datasets reached opposite conclusions (present in the LACA) (4).
, , 2011)). For PFAS, the picture was more complex, with a decline for PFOS, but overall increases of PFCAs  (Ahrens et al. , 2011; Bustnes et al. , 2015). The present study examined 10 additional years (2010-2019) for 14 OCs and 12 PFASs, providing a data series spanning over 34 years (breeding seasons). Based on the previous studies and emission statistics, we hypothesized that legacy OCs would be stabilizing over the last decade and that PFOS would continue to decline. For PFCAs, we predicted that previously documented increases would be levelling off. The annual collection of eggs and the large sample size (n = 234 for OCs and n = 172 for PFAS) enabled us to assess temporal trends with relatively high precision.
For each response, we rescaled and ranked each model (i) relative to the model with the lowest second-order Akaike's Information Criterion (AICc) value (Δ i denotes this for model i). From this, we selected the simplest model (as judged by the model's estimated number of parameters) with a Δ i ≤ 1. 5 value (e. g. ,  Anderson, 2008; Burnham & Anderson, 2002)  using the AICcmodavg-package in R  (Mazerolle, 2019). We speak of temporal trends if either, or both, the main effect of Year or Year 2 was statistically significant. We mainly refer to the figures  when we speak of the resulting trends, because models including polynomials may take various forms based on the sign and strengths of the estimates. Finally, the different responses were treated differently depending on the number of samples in each analysis at or below the LOD by creating our own rule of thumb based on defining the LOD for each contaminant as its minimum value.
First, we fitted linear models using the lm-function if >15% of samples were ≤LOD. Second, we used logistic models with a binary response, classifying each observation (X i ) as 0 if X i ≤ LOD and 1 if X i > LOD, using the glm-function with a logisticlink assuming a binomial distribution (both the lm-and glmfunctions are a part of the stats-package: see, e. g. ,  Fox 2002; Zuur et al. 2009  for how these models are fitted to data). Third, we fitted a tobit-regression model using the vglm-function in the vgam-package (using the tobit-family and the contaminantspecific LOD as the lower limit: see Yee & Moler 2020 for how these models were specified) if between 15% and 50% of the samples were ≤LOD. The tobit-regression is a left-censored model meaning that whenever X i = LOD, the observation is, in reality, ≤LOD (even though X i ≥ 0) and were fitted similar as in  Thomas et al.
For PFOS, this analysis shows a decline of approximately 3. 2% per year (Figure  4  and Supporting Information, Table  S4 ), suggesting that the phase-out of PFOS production has had a similar impact on PFOS as the banning had on legacy OCs. However, PFOS is still used in some products, which may account for the relatively slower decline. The reported decline is in line with other studies from Scandinavia investigating both terrestrial and marine species  (Holmström et al. , 2010; Jouanneau et al. , 2020; Sun et al. , 2019). For both PFHxS and PFHpS, there were indications of a decline until 2009  (Ahrens et al. , 2011) , but only for PFHpS did this continue when including data for the last decade (Figure  4  and Supporting Information, Table  S3 ). Moreover, PFOSA was very low in concentration and was less likely to be detected after 2005 (Figure  5  and Supporting Information, Table  S3 ).
Acknowledgments-The project has been funded by the  Norwegian Research Council , Projects:  EnviStress  (No. 268482 ) and by the  FRAM Centre Flagship Program  "Hazardous substances-effects on ecosystems and human health" (project: Impacts of environmental contaminants and natural stressors on northern raptors: RAPTOR). We are very grateful to  L. Ahrens  for conducting some of the chemical analyses and to  M. Gamain  for valuable help with laboratory analyses. Finally, we thank two anonymous reviewers for comments that greatly improved the manuscript.