dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2017,  for the years 2005 to 2013) and above 60 • N  (Tan et al. , 2016 (Tan et al. , , for the year 2005)). In this study, we estimate methane emissions during the most recent years (2008 to 2019) through atmospheric inversion based on available in situ measurement data from observation sites located in the Arctic and sub-Arctic. In order to obtain a reliable assessment, we compute a large ensemble of possible posterior emission scenarios using different error estimations that are evaluated concerning their plausibility. The CH 4 emissions are subsequently analysed with particular regards to three different questions: (i) Is the available observation network sufficient to constrain all occurring CH 4 sources and sinks adequately? (ii) Do the different CH 4 sources and sinks show any significant trends between the years 2008 and 2019? (iii) Do the different CH 4 sources and sinks in the posterior state show any shifts in the seasonal cycle in comparison to the prior bottom-up estimates?.
FMI-NOAA The Finnish station Pallas (PAL) is located close to the northern edge of the Scandinavian boreal zone, with a surrounding terrain of wetlands, lakes and patches of forest  (Hatakka et al. , 2003; Aalto et al. , 2007). PAL data are available as FMI Global Atmosphere Watch (GAW) CH 4 data from 2004 onwards at the World Data Centre for Greenhouse Gases (WDCGG). PAL data from 2017 are also available from the ICOS Carbon Portal  (Hatakka and Ri, 2022). Like PAL, the site Tiksi (TIK) is operated by the FMI in cooperation with NOAA GML and is installed on the shore of the Laptev Sea on the Lena River delta  (Uttal et al. , 2013 (Uttal et al. , , 2016)).
Code and data availability. The transport model FLEXPART 10. 4 is open source, and the source code is freely available on the FLEXPART website at https://www. flexpart. eu/downloads/66 (last access: 14 November 2022) (described in detail by  Pisso et al. , 2019). The meteorological forcing fields for FLEXPART are interpolated from the open ERA5 reanalysis, extracted using the open-source flex_extract toolbox  (Tipka et al. , 2020 , https:// www. flexpart. eu/flex_extract; last access: 14 November 2022). Flux data were obtained from the Global Carbon Project -Methane (https://www. icos-cp. eu/GCP-CH4/2019, last access: 14 November 2022; data at https://doi. org/10. 18160/gcp-ch4-2019, Saunois et al. , 2020b). The contribution of the background concentrations was calculated using the Community Inversion Framework available at https://doi.
org/10. 5281/zenodo. 5045730 (Berchet et al. , 2021a). Observations from the NOAA GML network can be downloaded from the dedicated Observation Package (ObsPack) web server at https://www. gml. noaa. gov/ccgg/obspack/ (last access: 14 November 2022; data at https://doi. org/10. 25925/20210518, Masarie et al. , 2014). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/acp-23-6457-2023-supplement. Author contributions. SW designed the analytical inversion system, ran the FLEXPART simulations to compute footprints and performed the scientific analysis presented in the paper. AB and IP provided scientific and technical expertise and contributed to the scientific analysis of this work. MaS and AM provided the CH 4 fluxes and inversion results from the GCP.
JT computed the LMDZ simulations used as background for the study. TM, MoS, MA, DEJW and XL provided measurement data from the NOAA GML and the NIES JR-STATION networks as well as scientific advice. RLT and ES contributed to the code of the FLEXPART interface to the Community Inversion Framework used to compute contributions from the background, as well as scientific advice. Competing interests. The contact author has declared that none of the authors has any competing interests. Disclaimer. Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Special issue statement. This article is part of the special issue "Pan-Eurasian Experiment (PEEX) -Part II". It is not associated with a conference. Acknowledgements.
We thank all principal investigators (PIs) and supporting staff for deploying, maintaining and making available data from numerous (and often remote) observation sites around the Arctic. In particular, we thank our colleagues from the V. E. Zuev Institute of Atmospheric Optics (Tomsk). We acknowledge the data infrastructure of ICOS (Integrated Carbon Observing System; https://www. icos-cp. eu, last access: 14 November 2022), the WDCGG (World Data Centre for Greenhouse Gases; https://gaw. kishou. go. jp/, last access: 14 November 2022) and ObsPack (https://www. gml. noaa. gov/ccgg/obspack/, last access: 14 November 2022) for distributing observations. We thank François Marabelle (LSCE, Laboratoire des Sciences du Climat et de l'Environnement) and the LSCE IT team for the computer resources.
Sophie Wittig was supported by CEA NUMERICS (Commissariat à l'énergie atomique et aux énergies alternatives international PhD programme in numerical simulation), which has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Actions (grant agreement no. 800945). Financial support. This research has been supported by the H2020 Marie Skłodowska-Curie Actions (grant no. 800945). Review statement. This paper was edited by Tim Butler and reviewed by two anonymous referees.
Acknowledgments We are grateful to the  High Performance Computing service of the University of Mu ¨nster  for providing computational resources on the computer cluster PALMA II. We are also thankful to the  INRAE MIGALE Bioinformatics Facility (MIGALE, INRAE , 2018;  Migale Bioinformatics Facility , https://doi. org/10. 15454/1. 5572390655343293E12) for providing computational resources during the early phase of this project.
P. C. has received funding from the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Sklodowska-Curie  grant agreement  PolyPath 844369. F. B. is funded by the  ERC   StG 949208  EvoComBac. F. D. is funded by  ANR-19-CE45-0009-01 TheoGeneDrive. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
e. King, 1985; Dyke et al. , 2003; Dalton et al. , 2020) , the chronology of most of the major moraine systems marking stabilizations/readvances of the retreating LIS margin in the region is still poorly constrained. In this paper, we report on the deglaciation history of the eastern sector of the LIS by using terrestrial cosmogenic 10 Be exposure dating of boulders from major moraine systems. This chronological dataset coupled with previously published and new radiocarbon ages allow us to (1) constrain the position of the ice margin during its retreat, and (2) assess retreat rates of the ice margin in order to identify time intervals of important changes. These results provide a revised deglacial chronological framework across five former ice-marginal stillstand/ readvance positions located along a 500-km-long transect from the coast to the Labrador hinterland.
0  (Balco et al. , 2008;  https://hess. ess. washington. edu/). Calibration was done using the northeastern North America (NENA) 10 Be production rate of 4. 04 ± 0. 27 atoms g -1 a -1  (Balco et al. , 2009)  and the nuclide-and time-dependent LSDn scaling scheme  (Lifton et al. , 2014). The NENA production rate is statistically identical to other high-latitude production-rate calibration datasets such as the Baffin Bay  (Young et al. , 2013)  and the Rannoch Moor  (Putnam et al. , 2019)  production rates. For comparison, the global production rate  (Borchers et al. , 2016)  would result in ages that are ~5% younger. By comparison, using the St or Lm scaling schemes  (Lal, 1991; Stone, 2000)  would result in exposure ages that are ~5% older than the LSDn-derived 10 Be ages. The effects of postglacial erosion on 10 Be ages were neglected as sampled bedrock and boulder surfaces commonly displayed glacial striations, suggesting little surface erosion since deglaciation.
65 g cm -3 was applied for all samples. No erosion and no topographic shielding were accounted for in our calculations. Snow cover corrections were not applied (Supporting Information). Asterisks (*) mark the samples that are considered as outliers and were not included in the mean age calculations (in bold). Numbers in parentheses includes the production rate uncertainty. years using the online software Calib 8. 2  (Stuiver & Reimer, 1993 ; http://calib. org/) with the Marine20 radiocarbon age calibration curve  (Heaton et al. , 2020). A local reservoir correction (ΔR) of -2 ± 69 was used to account for the regional offset of the world ocean 14 C age  (McNeely et al. , 2006). These four ages complement a dataset of 41 previously published 14 C ages that have also been recalibrated. Marine samples were calibrated using the aforementioned parameters, whereas terrestrial samples were calibrated using the IntCal20 radiocarbon age calibration curve  (Reimer et al.
These retreat rates represent minimum values as they do not take into account possible readvances of the LIS margin and their respective durations. The AMS 14 C ages were calibrated within the age-depth modelling process, using the online software Calib 8. 2 with the Marine20  (Heaton et al. , 2020)  and IntCal20  (Reimer et al. , 2020)  radiocarbon age calibration curves. A local reservoir correction (ΔR) of -2 ± 69 was used to account for the regional offset of the world ocean 14 C age, as determined by  McNeely et al. (2006).
Based on their stratigraphic position, the Belles Amours Moraine is expected to be older than the Paradise Moraine and, consequently, significantly older than the Little Drunken Moraine. To cope with these discrepancies, published cosmogenic exposure ages  (Ullman et al. , 2016)  were incorporated in our age model. Their recalculations show ages of 9. 4 ± 0. 8 and 8. 5 ± 0. 7 ka for the Paradise Moraine and Québec North Shore-Little Drunken Moraine, respectively (see Supporting Information Table  S3 ). While there are discrepancies between the ages from the original publication and our recalculations, the uncertainties still overlap between datasets. However, two key parameters could partly explain the younger ages of the samples in regard to the actual targeted landforms: (i)  Ullman et al.
(2016)  sampled boulders sometimes located well behind morainic crests (up to 25 km), therefore systematically minimizing the moraine ages; and (ii) some samples were collected below the reported marine limit of 150 m  (Carlson, 2020)  for the site CL1, consequently delaying their exposure ages by a few centuries. These ages, representing minimum values for the abandonment of the moraines, are nonetheless coherent with minimum-limiting radiocarbon ages in the region and, consequently, complete our dataset without any chronological overlap between moraine segments. Combining both datasets could therefore allow the correlation of the Paradise Moraine to the 10. 3-ka event and the Little Drunken Moraine to the 9. 3-ka event, as suggested by  Ullman et al. (2016). While the cause of the 10. 3-ka cooling event remains elusive, its consequences, recorded by (i) the presence of DCP 2 (Fig.
Acknowledgments. This project was funded by the  ArcticNet Network of Centers of Excellence, Natural Sciences and Engineering Council of Canada (NSERC)  and  Sentinelle Nord (Apogée Canada)  grants to P. L. ,  Institut de Physique du Globe de Strasbourg (IPGS)  grants to J. F. G. , as well as  Université de Strasbourg  and  Fondation Famille Choquette  grants to P. O. C. We are thankful to the  Centre d'études nordiques (CEN), Polar Knowledge Canada (POLAR)  and  SYSTER research programme  from the  CNRS-INSU  for financial support during fieldwork. The  French national AMS  facility ASTER (CEREGE) is supported by the  INSU/CNRS , the  French MESR , and the  CEA institute. We are thankful to  G. Aumaître  and  K. Keddadouche  for the AMS measurements. We thank  Charles Brionne ,  Etienne Brouard  and  Vincent Rinterknecht  who provided helpful comments on a previous draft of the paper.
With this model, antibiotics select for resistant bacteria, both at the between-host level due to preferential clearance of sensitive bacteria and at the within-host level due to increased rates of endogenous acquisition during antibiotic exposure  [16]. See S1 Appendix section 1. 1 for full model description and equations. The complete model is programmed in R, and all code is freely available at https://github. com/drmsmith/covR.
.  Zymography After 14 days of culture with differentiation medium containing steroid hormones, total gelatinase activities from ESCs isolated from obese or nonobese women were analyzed using zymography. Aliquots of CM containing 60 μg of protein were resolved under non-reducing conditions in 10% polyacrylamide gels containing 1 mg/mL gelatin (Gibco/ThermoFisher Scientific). The gels were washed in Triton X-100 (2. 5%) for 30 min (to remove SDS), incubated overnight at 37 • C in a renaturing buffer (50 mM Tris-HCl, pH 7. 5, 5 mM CaCl 2 , 150 mM NaCl, and 0. 02% sodium azide), stained with Coomassie Brilliant Blue, and destained in methanol/acetic acid (20%/5% v/v). Proteolytic activity was identified as a clear band on a blue background. The images were scanned, and the proteolytic activity was quantified using ImageJ software.
.  Extracellular vesicle size and concentration analysis The EVs' size distribution and concentration were measured with nanoparticle tracking analysis (NTA) on a ZetaView system equipped with a 488 nm laser (Particle Metrix GbmH, Inning am Ammersee, Germany). In NTA, a liquid suspension's Brownian motion and light scattering properties provide information on the particle size distribution. For each experiment, dilutions at eleven different positions were analyzed. The particle detection was carried out in the sample cell with following specifications and analysis parameters: sensitivity, 80; shutter, 100; max area, 1000; min area, 10; min brightness, 20. NTA software (version 8. 05. 05, SP2) was used to capture and analyze the data. The samples were diluted as a function of the input sample concentrations.
Bioinformatics: protein identification and quantification The data were converted into mzXML format using MS convert (ProteoWizard, version 3. 0. 8934). Proteins were identified using X!Tandem v. 2017. 2. 1. 4 (Craig & Beavis, 2004 ) by matching peptides against the Swiss-Prot Homo sapiens database (https://www. uniprot. org/, version 2021, containing 20,396 entries). Proteins were filtered and grouped using open-source X!TandemPipeline software (version 0. 4. 37, http://pappso. inrae. fr/bioinfo/xtandempipeline/) (Langella et al. , 2017). To eliminate spectra due to contaminants, the data were compared with a contaminant database. The proteome identification process was run with a precursor mass tolerance of 10 ppm and a fragment mass tolerance of 10 ppm. The trypsin digestion rule ("after Arg and Lys, except before Pro") was applied, and semi-enzymatic cleavage was ruled out.
The fix modification was set to cysteine carbamidomethylation, and methionine oxidation was considered to be a potential modification. In a second pass, N-terminal acetylation was added as another potential modification, whereas all the other abovementioned settings remained unchanged. Identified proteins were filtered as follows: (i) peptide E-value <0. 01 and a minimum of two peptides per protein, and (ii) a protein E-value of 10 -4. The false discovery rate was estimated to be 0. 14% for peptides and 0. 1% for proteins. The mass spectrometry proteomics data have been deposited in the ProteomeXchange Consortium database (http:// proteomecentral. proteomexchange. org) via the PRIDE partner repository, with the dataset identifier PXD039568.
2. 8. 4 The spectral count (SC) and the extracted ion chromatogram (XIC) Two protein quantification methods were used to detect changes in protein abundance. As recommended by Blein-Nicolas and Zivy, we first used the SC to detect semi-quantitative changes and presence/absence variations (Blein-Nicolas & Zivy, 2016). Protein abundances were calculated from the SC of independent biological replicates and from the pool of samples used for functional assays (see the sample design in Supplementary Figure  1 )  (Carvalho et al. , 2008; Usaite et al. , 2008). We then used a more precise XIC-based approach to detect smaller abundance changes. MassChroq software (version 2. 2. 12, http://pappso. inrae. fr/bioinfo/masschroq/) was used for retention time alignment and precursor peak intensity integration of the XIC.
The MassChroqR tool box (https://forgemia. inra. fr/pappso/mcqr version 0. 3. 7, http://pappso. inrae. fr/) was used to check the proteomic data quality (peak width, retention time variation, principal component analysis, etc. ), eliminate dubious samples, and apply statistical tests. In the SC analysis, we removed proteins with less than five spectra in all the injections. The ratio between the minimum and maximum mean abundance values was computed for each factor or combination of factors of interest. The XIC data were normalized in order to remove systemic bias between samples and more complex variations resulting from transient stochastic events during LC-MS/MS run, such as ESI instability. Missing peptide ion XIC data were imputed according to the correlation between peptides from the same protein.
The relative abundance of proteins was determined by summing the values for at least two specific peptides. After the abundances had been log 10 -transformed, the statistical significance of between-group differences was determined in an analysis of variance. The resulting p-values were Benjamini-Hochberg corrected for multiple testing  (Benjamini & Hochberg, 1995). After adjustment, the threshold for statistical significance was set to p < 0. 05 for the SC data and 0. 01 for the XIC data. Lastly, proteins with a fold-difference (obese vs. normal-weight women) above 1. 5 or below 0. 66 were considered to have been significantly affected.
In the years 2000-2020, the trap was emptied every five days from November to April, and every seven to ten days from May to October. After collection, the samples were sorted in the laboratory at the Port-aux-Français research station, and the numbers of adult flies were counted. In the present work, adult counting was used to examine the local phenology of the species. The temporal trend in annual average air temperature recorded was analysed using generalised additive models (GAMs), which allowed us to capture complex patterns in temperature across the seven-decade time series data  [41]. Individuals of C. vicina were also monitored during general invertebrate surveys on the Kerguelen Islands during the years 2004-2021. Different observation and collection methods were used during the general invertebrate surveys, including pitfall traps (Portaux-Français, Isthme-Bas, Guillou Island, Verte Island, Cochons Island), yellow traps (Portaux-Français), and soil samples and visual observations (over the whole of the Kerguelen Islands).
For each observation, GPS coordinates (WGS 84) and dates were noted and used to describe the spatial and temporal distribution trends of C. vicina at the Kerguelen Islands. As the geographical location of observations of specimens of C. vicina were not consistent across the years, the data did not allow us to show repeated occurrence points on a specific location. Therefore, in the few cases where repeated annual observations of C. vicina were made on the same location, the occurrence points on the map represented the time when the first observation was made. To map the temporal changes in the geographical distribution of C. vicina, we used QGIS version 3. 22. 5  [42]. To assess the interannual variation in the timing of adult activity in C. vicina, we generated phenological curves of capture rates for each breeding year using GAMs assuming a poisson distribution (Figure  1 ) using the GAM implementation in the mgcv package in R version 4.
The research was funded by the ASICS project (ANR-20-EBI5-0004, BiodivERsA, Biodi-vClim call 2019-2020), the French Polar Institute (Project 136-SUBANTECO), Zone Atelier CNRS Antarctique et Terres Australes (ZATA, long-term research on biodiversity in Antarctic and sub-Antarctic ecosystems), and the University of Rennes (Appel à projets Actions incitatives 2022 'Collaborations internationales non formalisées'). Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable.
Funding: The research was funded by the ASICS project (ANR-20-EBI5-0004, BiodivERsA, Biodi-vClim call 2019-2020), the French Polar Institute (Project 136-SUBANTECO), Zone Atelier CNRS Antarctique et Terres Australes (ZATA, long-term research on biodiversity in Antarctic and sub-Antarctic ecosystems), and the University of Rennes (Appel à projets Actions incitatives 2022 'Collaborations internationales non formalisées'). Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable.
OBSERVATION AND DATA REDUCTION The three observing epochs of  Dec. 2018 , Feb. 2020 , and Dec. 2020  are located before, during, and after the GDE of Betelgeuse ( The N band data of the GDE already shown in  Cannon et al. (2023) ). All the MATISSE observations are close to a light curve photometric minimum of variability. We reduced and calibrated the observation data using the MA-TISSE data reduction pipeline and additional python tools provided by the MATISSE consortium 1. The pipeline provides calibrated oifits files of the science target comprising measurements of the spectral distributions of flux, squared visibility, differential and closure phases, in the LM-and N-bands of the mid-infrared. Finally, we reject each night contaminated by adverse instrumental or weather effects, not already identified by the MATISSE pipeline.
IMAGES We use the image reconstruction software IRBIS  (Hofmann et al. 2014)  to produce L-band maps at the three epochs of observation in the combined spectral ranges 3. 942-3. 974 𝜇m and 3. 992-3. 998 𝜇m (pseudo-continuum), and in the spectral range 4. 004-4. 012 𝜇m (SiO 2-0 transition). After a cautious scan of the imaging parameter space, we found the following optimal parameters for the reconstruction: i) initial image: an uniform disk, ii) prior: maximum entropy, and iii) hyper-parameter: 𝜇 = 10. We show the obtained images in Figure  2. Since the Feb. 2020 observation run has a sparse (u,v)-plane coverage, in order to compare the three epochs images together, we convolved them with the Feb. 2020 equivalent circular interferometric beam of an angular diameter of 4 mas. In order to make a quantitative analysis between the reconstructed maps obtained at the 3 epochs, we compute the values of the photometric median, the median absolute deviation (MAD), and the intensity contrast as defined by Wedemeyer-Böhm & Rouppe van   2.
Dust Clump and Cold Spot model In addition we checked the robustness of the cold spot and dust clump model used in  Montargès et al. (2021)  to explain the GDE with respect to our Feb. 2020 VLTI/MATISSE data. Both models are derived from radiative transfer code and own therefore wavelength-dependent structures. Here, we use the exact same models with the same parameters as in  Montargès et al. (2021,  the reader can refer to that paper to get the models parameters values), just in a different wavelength domain. Thus, the dust cloud and the cold spot presented in this paper as seen by MATISSE in L-band (4000 nm), may not exhibit the same dynamic range as in  Montargès et al. (2021)  in V-band (≈650 nm). Indeed, in the top panel of Figure  3  showing the dust clump model, we can see that the dynamic range of the model image (inset) used to compute the L-band visibilities (blue dots) is insufficient to distinguish the models from an uniform disk model (green dashed line).