dataesr/scientific-paragraphs-categorization huggingface dataset:
of seismogenic sources proposed by an about 20-years long activity of the DISS (Database of Individual Seismogenic Sources) Working Group (2018 ( , Basili  et al. 2008)). The formalization of each seismogenic source takes into account a variety of ingredients such as historical seismicity, geological and geophysical data. The DISS database is thus formed by Individual Seismogenic Sources (ISS), which are simplified representations of mapped or hypothesized fault planes with given rupture length/width and expected magnitude, and Composite Seismogenic Sources (CSS) which are 3D envelopes of undetermined seismogenic sources, with soft constraints on expected rupture location, size and magnitude. Unfortunately, simplifications of the representation of faults, especially in regions where detailed geologic data are available, can Frontiers in Earth Science | www.
ACKNOWLEDGMENTS We would like to thank the reviewers and the editor for their constructive comments that have improved our text. We also thank  Thomas Chartier  for providing the 2. 1 version of the SHERIFS code, that greatly enhanced the computation time,  Paolo Bazzurro  for exchanging on the appropriate fragility function to use for central Italy and finally,  Silvia Pondrelli  for helping with GMT plotting of Figure 2A. Figures were created using GMT (Wessel and Smith, 1991),  SHERIFS  ( V1. 2 ) and Matlab (2018b) codes. Codes and data used in this paper are available online in a dedicated GitHub repository (https://github. com /fault2shaESCWG/CentralApenninesLabFAULT2RISK ).
FUNDING This work was initiated in the framework of the  ESC Fault2SHA Central Apennines Working group  (fault2sha. net) that does not provide any funding. The work was essentially supported by each institute involved. A specific funding was provided by the  Institute of Radioprotection and Nuclear Safety  ( IRSN LS 21173 -CA 32003737 ) to support  Francesco Visini 's visit to Paris from September 2019 until May 2020 in the framework of the Research Agreement in the field of probabilistic seismic hazard methodologies between IRSN and the  National Institute of Geophysics and Volcanology (INGV). This study has also benefited from partial funding provided by the  Italian Presidenza del Consiglio dei Ministri -Dipartimento della Protezione Civile (DPC). This paper does not represent DPC, INGV or IRSN official opinion and policies.
The simulated profiles of acetate production as a function of growth and glucose uptake rates were in excellent agreement with experimental data (Figure  3A ), indicating that the model accurately captures the effects of glucose limitation on growth and acetate fluxes. Second, we evaluated the response of E. coli to changes in acetate concentration. We simulated steady-state acetate, glucose, and growth fluxes under glucose excess over a broad range of acetate concentrations (between 100 mM and 100 mM)  (Enjalbert et al. , 2017). The growth and glucose uptake rates decreased monotonously with increasing acetate concentrations, in agreement with the experimental data (Figure  3B ). The acetate flux profile was also well described by the model, with a progressive decrease of acetate production with increasing acetate concentration, and a reversal of the acetate flux above a threshold concentration of about 10 mM.
Transcriptomics experiments Cells were grown in flasks in M9 minimal media with 15 mM glucose and 0, 10, 50, or 100 mM acetate. In mid-exponential growth phase (OD 600nm = 1), 4 mL of each culture was centrifuged for 90 s at 14,000 rpm, the supernatant was discarded, and the pellets were immediately frozen in liquid nitrogen. Total RNA was extracted using a Qiagen RNAeasy MiniKit and quantified using a Nanodrop spectrophotometer. Double-stranded complementary DNA (cDNA) synthesis and array processing were performed by One-Color Microarray-Based gene Expression Analysis (Agilent Technologies). The images were analysed with the software DEVA (v1. 2. 1). All array procedures were performed using the GeT-Biopuces platform (http://get. genotoul. fr). Four independent biological replicates were analysed for each condition.
For each data set, the log2 intensities obtained in the presence of acetate were divided by the log2 intensities obtained in the absence of acetate. These ratios were then normalised by the log2 median intensity. Genes whose expression level differed by a factor of 2 or more between the two conditions were used for further analysis. Gene ontology analyses were performed using Ecocyc (https://ecocyc. org), p-values were estimated using Fisher exact test with Bonferroni correction. The transcriptomics data can be downloaded from the ArrayExpress database (http://www. ebi. ac. uk/arrayexpress) under accession number E-MTAB-9086. Theoretical expression data at a given growth rate were obtained by extrapolating the data from Esquerreet al. , 2014.
Metabolomics experiments Extracellular concentrations of labelled and unlabelled glucose and acetate were quantified during growth by 1D 1 H-NMR on a Bruker Ascend 800 MHz spectrometer equipped with a 5 mm QCI cryoprobe (Bruker, Germany), as detailed previously  (Millard et al. , 2014). Briefly, 150 mL of filtered broth (0. 2 mm, Sartorius, Germany) were mixed with 50 mL of D 2 O containing TSPd4 (3-(trimethylsilyl)-1-propanesulfonic acid-tetra deuterated, used as internal standard) at a concentration of 10 mM. A sequence using presaturation (ZGPR) was used for water signal suppression, with a 30˚pulse and a relaxation delay between scans of 10 s to ensure full signal recovery. A total of 32 scans were accumulated (64 k data points with a spectral width of 16 ppm) after 4 dummy scans. From each spectrum, we quantified glucose and unlabelled and labelled acetate.
Model construction and analysis All models are provided in SBML and COPASI formats in the Supplementary Information (Supplementary file 1) and at https://github. com/MetaSys-LISBP/acetate_regulation; Millard, 2021; copy archived at swh:1:rev:c8bcf8fad3459269df30dba7e52c81e62ca181d0. The calibrated kinetic model has also been deposited in the Biomodels database (https://www. ebi. ac. uk/ biomodels)  (Chelliah et al. , 2015)  with the identifier MODEL2005050001. Model analysis was performed using  COPASI (v4. 27, Hoops et al. , 2006)  with the CoRC package (COPASI R Connector v0. 7. 1, https://github. com/jpahle/CoRC, Fo ¨rster and Bergmann, 2021) in R (v3. 6. 1, https://www. rproject. org). The scripts used to perform the simulations, to analyse the models and to generate the figures are provided in the Supplementary Information (Supplementary file 1) and at https://github.
Model validation A total of 170 experimental data (extracellular fluxes and concentrations) were collected from the literature to evaluate the predictive power of each model. These data, which were not used to train or fit the models, were obtained for E. coli K-12 MG1655 and its close derivative strain BW25113 grown on different concentrations of glucose and/or acetate, as detailed in the Results section and in  Renilla et al. , 2012; Enjalbert et al. , 2017; Pinhal et al. , 2019. The steady-state and dynamic responses of E. coli to the corresponding perturbations were predicted using each model (Figure  3 ). The residual error (SSR) was calculated for each model from the simulated and experimental validation data (Figure  3 -figure supplement 4) to identify the model that yielded the most accurate predictions.
Acknowledgements The authors thank  MetaboHub-MetaToul (Metabolomics and Fluxomics facilities, Toulouse, France , http://www. metatoul. fr),which is part of the  French National Infrastructure for Metabolomics and Fluxomics  (http://www. metabohub. fr/),funded by the  ANR  ( MetaboHUB - ANR-11-INBS-0010 ), for access to NMR facilities. JCP is grateful for funding from  INSERM  for his temporary full-time researcher position. The authors are grateful for  Vincent Pierunek 's help with the isotope labelling experiments, and wish to thank the following  INSA Toulouse  students for help with the transcriptomics experiments:  Leidy Caraballo ,  Xavier Caron ,  Pauline Chanut ,  Sarah Guiziou ,  Ngoc Thu Hang Pham ,  Diane Barbay , Celine  Ben Hassen ,  Thomas Cerutti ,  Cecile Roland ,  Sarah Srour ,  Audrey Baylet ,  Mathilde Beraud ,  Claudie Bosc ,  Lilas Courtot ,  Violaine Dolfo ,  Anna Kaci ,  Manon Chevallot-Beroux ,  Sarah Colom ,  Fanny Leclerc ,  Zihan Liao , Gregoire Quinet and Melina Vaurs.
Excess all-cause mortality during the first COVID-19 pandemic wave in Europe The framework and methodology of the EuroMOMO network has been described earlier. Briefly, participating countries obtain weekly data on the number of all-cause deaths from civil registers or other official sources in nearly real-time. The all-cause excess deaths are defined as the observed minus the expected numbers of deaths, and are estimated by participating countries or subnational areas (federal states/cities) using the EuroMOMO statistical algorithm  [5]. The EuroMOMO hub compiles these outputs then performs a weekly secondary pooled analysis using an age-stratified method  [6] , which generates a timely estimate that determines whether there were more deaths than expected in any one week. The first wave of COVID-19 in spring 2020 was shown to be temporally associated with very high numbers of excess all-cause deaths in several individual European countries, as reported by the EuroMOMO network or its member countries  [7] [8] [9] [10] [11] [12] [13] ; most participating EuroMOMO countries implemented lockdowns during this time.
Estimating excess all-cause mortality during second wave of COVID-19 in Europe By the end of December 2020, the following 27 European countries or subnational areas had contributed their weekly mortality data: Austria, Belgium, Cyprus, Denmark, Estonia, Finland, France, Germany (Berlin and Hesse), Greece, Hungary, Ireland, Israel, Italy (19 cities), Luxembourg, Malta, the Netherlands, Norway, Portugal, Slovenia, Spain, Sweden, Switzerland and the United Kingdom (UK) (England, Northern Ireland, Scotland, Wales). Details of participating countries are available on the EuroMOMO website  [14]. Weekly numbers of excess all-cause deaths were estimated for the total population (all ages) and for age groups 0-14, 15-44, 45-64, 65-74, 75-84 and ≥ 85 years. Furthermore, the number of weekly all-cause excess deaths was estimated from week 1 2020 up to and including week 53 2020, and compared with the mortality estimates from the previous 3 years (2017, 2018 and 2019), based on data received in week 1 2021.
Pooled excess mortality for participating countries The pooled estimates of all-cause deaths for the 27 participating European countries or subnational areas during the second wave of COVID-19 show an overall consistent and substantial increase in excess all-cause mortality from week 40 2020 (Figure  2 ), though there was a spike in week 38 2020. Excess mortality was highest among individuals aged ≥ 65 years, but we also a Detailed information about participating countries can be found on the EuroMOMO website  [14]. Data updated on 7 January 2021. National data on COVID-19 cases reported to WHO represent the entire country, not just subnational areas, as is the case for the mortality data reported to EuroMOMO from some countries (e. g. Germany and Italy). observed substantial excess deaths among individuals aged 45-64 years.
The recent commencement of a rolling programme of targeted vaccination against COVID-19 offers the prospect of significant blunting of excess allcause mortality in 2021. EuroMOMO: European monitoring of excess mortality for public health action. a Detailed information about participating countries can be found on the EuroMOMO website  [14]. Data was updated on 7 January 2021. manuscript and they do not necessarily reflect the views, decisions or policies of the institutions with which the authors are affiliated.
Acknowledgements We would like to thank  Laila Skrowny ,  Silviu Ciobanu ,  KaYeung Cheng ,  Lauren McDonald  and  Roman Spataru  of the  WHO Europe Health Information Management team  for the collection and provision of the weekly country case data. Funding statement: The  EuroMOMO network hub at Statens Serum Institut  receives funding from  European Centre for Disease Prevention and Control, Solna, Sweden , through a framework contract  2017-2020.
Notably, tissue specific expression analysis (TSEA) further revealed upregulation of genes not related to the skeletal muscle lineage, including brain-related genes such as Map1a, Nrdg2, and Usp11, and heart-related genes such as cardiac troponin T (Tnnt2), which were confirmed by qRT-PCR (Figures  2F  and S2H ). These observations, together with clustering of altered Gene Ontology (GO) biological process categories via using the database for annotation, visualization and integrated discovery (DAVID) (Figure  2G ), strongly indicate that SCs require Chd4 to retain muscle stem cell identity in regenerating muscles, especially during the proliferative phase after injury. RNA-seq of Chd4-deficient SCs in vivo also identified the downregulation of two important gene clusters: one involved in positive regulation of the cell cycle (e.
ACKNOWLEDGMENTS We thank  C. Keller  for the Pax-CRE-ER mouse line;  V. Lukesova ,  L. Ortet ,  E. Andre  ´s, and  A. Navarro  for technical contributions;  J. Lo  ´pez for bioinformatics;  J. Martı ´n-Caballero  ( PRBB Animal Facilities );  O. Fornas  ( UPF/CRG FACS Facility );  A. Dopazo  ( CNIC-Genomics Facility );  E. Rebollo  ( IBMB-CSIC Molecular Imaging Platform ). The authors acknowledge funding from  MINECO-Spain  (grant no. RTI2018-096068 ),  ERC-AdG-741966 ,  LaCaixa-HEALTH-HR17-00040 ,  MWRF ,  MDA ,  UPGRADE-H2020-825825 ,  AFM , and  DPP-Spain  to P. M. C. P. G. -d. A. was supported by  MINECO-Spain  (grant no. SAF2016-77816-P );  French ANR Labex REVIVE  (grant no. ANR-10-LABX-73 ) to F. R. A. R. was supported by a  DCEXS-UPF, BIST  Fellowship. Fundaciola Maratode TV3 supported A.
Data In this study, we used two types of data: reanalyses and seasonal forecast model outputs. We used outputs from the fifth-generation European Center for Medium-Range Weather Forecasts (ECMWF) Reanalysis (ERA5)  (Hersbach et al. , 2020). The ERA5 atmospheric variable studied here is daily T850 with a spatial resolution of 0. 25 • × 0. 25 • downloaded on the climate data store website: https://cds. climate. copernicus. eu/ (last access: 14 January 2020). The Modern-Era Retrospective analysis for Research and Applications (MERRA) dataset was also used. The MERRA data have a spatial resolution of 0. 5 • × 0. 625 • with 42 vertical levels downloaded on the ClimServ database. As for ERA5 reanalysis, we used the MERRA T850 to carry out our analyses. To be coherent with the model outputs, we consider only the daily temperature data at 00:00 and 12:00 UTC.
, 2012). Let F m, h and F o, h be the CDFs at a large and local scale respectively of the modeled data X m, h and the observational data X o, h over a historical period h and T the transformation allowing us to go from F m, h to F o, h. We have the following relation  (Vrac et al. , 2012) : T (F m, h (X m, h )) = F o, h (X o, h ). (5) By applying this relation to the CDF F m, f of the modeled data X m, f in a future period f, it provides an estimation of the local CDF F o, f in the future period f: Fo, f = T (F m, f (X m, f )). (6) Quantile mapping can then be performed between F m, f and Fo, f to obtain bias-corrected values of future simulations. More details about CDF-t can be found in  Vrac et al. (2012). All the computations for the CDF-t method were done with the R package "CDFt". After applying the bias correction methods to the model outputs, the added value of the bias correction compared to the raw re-forecasts will be assessed by the computation of the Cramér-von Mises (hereafter Cramér) score  (Henze and Meintanis, 2005; Michelangeli et al.
6 ) is almost similar over the CSHL location except for the Sahara, where a little shift of MERRA to high temperatures is observed but the patterns in the evolution remain very close to ERA5. The distribution of yearly T850 over the JJAS period (see Fig. 7a , b, g, h) in ERA5 and MERRA is quite similar, suggesting a good correlation between the two reanalyses. The uncertainties between the reanalyses (see Fig. S3  in the Supplement) are much smaller than the biases in the seasonal forecast models with respect to ERA5. We also found large correlation between ERA5 and MERRA (see Fig. S1  in the Supplement) of around 0. 97/0. 92 over the CSHL/Sahara location during the JJAS period. By considering all these results highlighting high similarities between ERA5 and MERRA, we decided to choose ERA5 as our reference dataset for the rest of the study.
7a, c, d  and g, i,  j ). Another specificity of MF7 is its slightly larger ensemble spread. SEAS5 seems to underestimate the warming trends present in ERA5 from 2005-2016, and an overestimation of this trend is observed with MF7 (Fig. S10 ); this behavior in the seasonal forecast models is present over both the central SHL box and the Sahara. By using this type of visualization (heatmap which is a graphical representation of data where values are depicted by color), it is possible to assess the intensity of the climatological trend with respect to the intraseasonal variability (Fig. 8 ). The interannual variability in the SHL anomalies distribution in the seasonal forecast models is too far from ERA5, but some characteristics are captured by the models (e. g. , the increase in the frequency of anomalies in ERA5 during the 2000s).
Conclusions This work assessed the representation of the SHL in two seasonal forecast models (SEAS5 and MF7) using ERA5 reanalyses as reference. The choice of ERA5 as reference for this evaluation was supported by a sensitive study conducted between ERA5 and another reanalysis dataset, namely MERRA. Very high correlations have been found between the two reanalyses data, i. e. , around 0. 97 and 0. 92 over the CSHL and the Sahara location respectively; robust similarities are also observed in the yearly distribution of June. S5_B and S5_C represent respectively the CRPS evaluated using the SEAS5 raw and corrected forecast respectively with the CDF-t method. The same applies for MF7_B and MF7_C with the MF7 model. MS_C represents the CRPS evaluated on the multi-model formed by SEAS5 and MF7 corrected forecasts with the CDF-t method.
The computation was made using the ensemble member for both corrected and raw forecasts. The y axis indicates the CRPS values and x axis the data type used for the computation of the CRPS. T850. The discrepancies between the reanalyses are much smaller than the biases in the seasonal forecast models with respect to ERA5. Through a set of analyses, we have found opposite biases in the seasonal forecast models compared to ERA5. MF7 has a warm bias and tends to overestimate the intensity of the SHL with respect to ERA5. SEAS5 develops a cold bias and tends to underestimate the intensity of the SHL over the Sahara. The models are able to represent the mean seasonal cycle of the SHL and capture some characteristics of its interannual variability like the warming trends observed during the 2010s.
Seasonal forecast models show predictive skills at an intraseasonal timescale for short periods. Bias correction contributes to improving the ensemble forecast score (CRPS), but the forecast skill remains weak for a lead time beyond 1 month. The issue of the lack of correlation in models cannot be solved through a bias correction approach; only model improvements could provide better correlations between forecasts and observations. In a future study, we will investigate the relationship between the SHL and the extreme rainfall in the Sahel region at an intraseasonal timescale. Code availability. The codes to perform the detection and the scores are available on demand; please feel free to contact us. The codes were implemented using CDO, R (CDFt, SpecsVerification, biwavelet packages) and Python (NumPy, pandas, SciPy libraries).
Data availability. The data used for this work are available and open access. ERA5 and MERRA reanalyses can be assessed through the ISPL (Institut Pierre Simon Laplace) server ClimServ or the Copernicus website (https://cds. climate. copernicus. eu/#!/home; Copernicus, 2021). MF7 seasonal forecasts can be found at the Météo-France service, and SEAS5 seasonal forecasts are accessible through the ECMWF website (https://www. ecmwf. int/en/forecasts; ECMWF, 2021). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/wcd-2-893-2021-supplement.
Acknowledgements. We thank  LATMOS  for providing infrastructures and the  IPSL center  for the access to the ClimServ server to download some of the data used in our study. Financial support. This work is supported by the  French National Research Agency  in the framework of the " Investissement d'avenir" program  ( ANR-15-IDEX-02 ) with the project  PREDISAHLIM  ( 2019-2021 ) and under grant  ANR-19-CE03-0012  with the project  STEWARd  ( 2020-2024 ). Review statement. This paper was edited by  Peter Knippertz  and reviewed by two anonymous referees.
Inverse modelling with the MARTHE software has used potentiometric data from the studied well fields  (Bessière et al. 2015). The simulation results show the reversed water pathways from selected starting points (pumping wells) to the probable origin (blue lines on Fig. 1 ). Groundwater-drawdown areas are centred around the groups of exploited wells. For example, four groundwater drawdowns are identified in the Les Ansereuilles well field: the northern part centred around A16, the northwestern part around A26, the southwestern part around A18, and the southern part around A21. In all, 363 current and former industrial sites potentially involved in or identified as responsible for CHC pollution are stored in the BASIAS database (https://www. georisques. gouv. fr/dossiers/basias/) of French well fields.
Physico-chemical conditions in the wells Carbon dioxide, methane, ethane and ethene gases were analysed as they provide insight into the final degradation products of CHCs. In PZ56 and PZ61, only CO 2 was detected at 1900 μmol. L -1 (-19. 5 m) and 2000 μmol. L -1 (-15 m), respectively. In PZ59, at -40 m, CO 2 , methane and ethane were found with 2700, 2. 9 and 0. 09 μmol. L -1 at -40 m and 2100, 2. 1 and 0. 04 μmol. L -1. at -60 m, respectively. The major inflows and redox potential (Eh) were recorded and analysed along the depth of the three wells. The physico-chemical parameters were recorded during three campaigns  (February 2017 , November 2017 and February 2018). Only November 2017 data are shown on Fig. 4 , as there were no significant changes between the three campaigns. In PZ56, redox potential increased from 223 mV -20 m to 237 mV at the bottom of the well.
Acknowledgements This study was supported financially and technically by the  Lille European Metropolis , the  French Geological Survey (BRGM) , the  University of Lille , the  Artois-Picardie Water Agency and the Haut-de-France Region  in the context of projects COHMET ( 2017COHMET ( -2021 )),  CLIMIBIO  ( 2015CLIMIBIO ( -2020 ) ) and  RESEAU  ( 2016RESEAU ( -2019 )). The authors thank  Franck Joublin  (BRGM),  Paul Daudruy ,  Denis Poncin  and  Sébastien Knockaert  (SOURCEO),  Emmanuel Bugner ,  Jean-Noël Ottenwaelder  and  Isabelle Saunier  ( MEL laboratory ) for their technical and scientific support during sampling and water analysis. We thank  H. M. Kluijver  for the English revision of the manuscript. The authors are grateful to the anonymous reviewer for thoughtful revision of the manuscript and constructive comments.