dataesr/scientific-paragraphs-categorization huggingface dataset:
Evaluation datasets and metrics Both satellite-derived estimates of EOs and in situ measurements are used as reference datasets in this study. The LDAS_ERA5 analysis performance is assessed with respect to the open-loop model run (i. e. no assimilation). The two assimilated datasets, CGLS SSM and LAI, are firstly used to verify that the data assimilation is behaving as expected. Then several independent datasets are used for the validation, namely evapotranspiration from the GLEAM project  (Miralles et al. , 2011; Martens et al. , 2017, version 3b , entirely satellite driven), GPP from the FLUXCOM project  (Tramontana et al. , 2016; Jung et al. , 2017) , SIF from the GOME-2 (Global Ozone Monitoring Experiment-2) scanning spectrometer  (Munro et al. , 2006; Joiner et al. , 2016)  and snowcover data from the Interactive Multi-sensor Snow and Ice Mapping System (IMS, https://www.
Besides 11 stations located in four countries of western Africa (Benin, Mali, Sénégal and Niger) and 21 stations in Australia, most of the stations are located in North America and Europe (see Table  S3 ). Evaluation datasets are listed in Table  3  along with the metrics used for the evaluation. For satellite datasets of SWI, LAI, evapotranspiration and GPP, the metrics consist of the correlation coefficient (R), root mean square difference (RMSD) and normalized RMSD (N RMSD , Eq. 1). N RMSD = RMSD (Analysis) -RMSD (Model) RMSD (Model) ×100 (1) Regarding the SIF satellite dataset, fluorescence is not simulated directly in the ISBA LSM. However, photosynthe-sis activity is simulated through the calculation of the GPP, which is driven by plant growth and mortality in the model. Modelled GPP values are expressed in g(C) m -2 d -1 , while SIF is an energy flux emitted by the vegetation (mW m -2 sr -1 nm -1 ).
Hence, GPP and SIF cannot be directly compared as they do not represent the same physical quantities. However, several studies (e. g. Zhang et al. , 2016; Sun et al. , 2017; Leroux et al. , 2018)  have found a high correspondence in both time and space between those two variables, highlighting the potential of SIF products to support the validation of modelled GPP. Therefore, the correlation between modelled GPP and observed SIF is used as an evaluation metric. Concerning the snow-cover dataset, differences between observed and modelled snow cover are considered for the evaluation. For in situ datasets of soil moisture and evapotranspiration, the standard metrics are considered, namely the correlation coefficient, RMSD, unbiased RMSD and bias. Moreover, a normalized information contribution (NIC, Eq.
Gridded datasets In this section, the LDAS-Monde open-loop and analysis are firstly compared against the assimilated observations (SSM and LAI) to demonstrate that the assimilation system is working as intended. Both experiments are also compared with independent sources of information to evaluate the analysis impact (GPP, EVAP and SIF). Figure  3  presents mean LAI RMSD values between the observations and LDAS_ERA5 for the open-loop (Fig. 3a ) and for the analysis (Fig. 3b ) over 2010-2018. Because LAI observations are ingested into the model, the assimilation reduces the LAI RMSD values almost everywhere. It should be noted that rather large LAI RMSD values (>1. 5 m 2 m -2 ) can remain in some areas after the assimilation, especially in densely forested areas. Figure  4  illustrates latitudinal plots of LAI, SSM, GPP and EVAP for LDAS_ERA5 before assimilation (the open-loop) and after assimilation (the analysis) along with observations.
The number of available data and the percentage of positive and negative values are reported. For correlations (RMSD) differences, posi-tive (negative) values indicate an improvement in the analysis relative to the open-loop. Regarding LAI, the analysis improves 96. 9 % of the grid points for correlations and 99. 9 % for N RMSD. As for SSM, correlation values are improved for 92. 8 % of the grid points (92. 4 % for RMSD). The independent GPP and SIF datasets also demonstrate improvements in the analysis relative to the open-loop. Indeed, the GPP correlation (RMSD) is better for 81. 1 % (74. 1 %) of the grid points and the SIF correlation is enhanced for 79. 7 %. Results using the GLEAM dataset for evapotranspiration are more contrasting with 63. 6 % (48. 9 %) of the grid points showing an improvement from the analysis.
It is worth mentioning that 24. 9 % (39. 6 %) of the grid point shows a decrease in skill. However, GLEAM is an evaporation model designed to be driven by remote sensing observations only. GLEAM only estimates (root-zone) soil moisture and terrestrial evaporation, while the CO 2 -responsive version of ISBA in LDAS_ERA5 is a physically based land surface model, accounting for more processes linked to vegetation (see Sect. 2. 1. 1). It should be noted that the auxiliary datasets used to represent the different land cover types also differ. Within GLEAM, the land cover types are sourced from the Global Vegetation Continuous Fields product (MOD44B), based on observations from the Moderate Resolution Image Spectroradiometer (MODIS). Four land cover types are considered, namely bare soil, low vegetation (e.
Ground-based datasets LDAS_ERA5 analysis and open-loop are also evaluated using independent in situ measurements of evapotranspiration, river discharge and surface soil moisture across the world. Daily in situ measurements of evapotranspiration from the FLUXNET-2015 synthesis dataset (http://fluxnet. fluxdata. org/, last access: June 2019) are first used in this study. The LDAS_ERA5 evapotranspiration performance is evaluated using the correlation coefficient (R), RMSD, ubRMSD and the bias (LDAS_ERA5 minus observations) using the 85 selected FLUXNET-2015 stations. The median R, RMSD, ubRMSD and bias for LDAS_ERA5 analysis (open-loop) are 0. 73 (0. 72), 28. 74 (29. 60) W m -2 , 27. 37 (26. 92) W m -2 and 4. 64 (4. 40) W m -2 respectively. Although these values depict a small advantage of the analysis over the open-loop, it is worth mentioning that these differences are rather small and likely to fall within the uncertainty of the in situ measurements.
Although the ensemble system has coarser spatial resolution (∼0. 20 • ) than the deterministic forecast, it accounts for forcing uncertainty in the LSVs through the en-semble spread and extends to a 15 d lead time. The maximum range of the soil and vegetation forecasts could even be extended to 6 months if seasonal atmospheric forecasts were used as forcing. LDAS-Monde has some limitations, where future developments are needed to improve the representation of LSVs. For instance, it does not consider snow data assimilation yet. It has been shown in this study that if the snow accumulation seems to be represented correctly in the system, the onset of snowmelt is too early in the spring. To overcome this issue, two possibilities will be explored. Firstly, a recently developed ISBA parametrization, MEB (Multiple Energy Budget), is known to lead to a better representation of the snowpack  (Boone et al.
, 2017). This could be particularly useful in the densely forested areas of the Northern Hemisphere, where large differences between LDAS-Monde and the IMS snow cover were found in spring (Fig. S2i , Aaron Boone CNRM, personal communication, June 2019). Another enhancement of LDAS-Monde will be to adapt the current data assimilation scheme to permit the assimilation the IMS snow-cover data, which is implemented at NWP centres such as the ECMWF  (de Rosnay et al. , 2014). The current SEKF data assimilation scheme is also being revisited. Even though it has provided good results, one of its limitations is the computational cost of the Jacobian matrix, which needs one model run for each control variable. As the number of control variables is expected to increase, this approach would require significant computational resources.
Therefore, more flexible ensemble-based data assimilation approaches have recently been implemented in LDAS-Monde, such as the ensemble square root filter  (EnSRF, Fairbain et al. , 2015; Bonan et al. , 2020). Bonan et al. (2020)  have evaluated performances from the EnSRF and the SEKF over the Euro-Mediterranean area. Both data assimilation schemes have a similar behaviour for LAI while for SSM, the EnSRF estimates tend to be closer to observations than those from the SEKF. They have also conducted an independent evaluation of both assimilation approaches using satellite estimates of evapotranspiration and GPP together with river discharge observations from gauging stations. They have found that the EnSRF gives a systematic (moderate) improvement for evapotranspiration and GPP and a highly positive impact on river discharges, while the SEKF leads to more contrasting performance.
Furthermore, a comparison of LDAS-Monde with existing datasets from other centres needs to be considered. Current work at Météo-France has began to compare its quality against state-of-the-art reanalyses such as those from NASA at both the global scale (GLDAS,  Rodell et al. , 2004 , MERRA-2, Reichle et al. , 2017; Draper et al. , 2018)  and regional scale (NCALDAS over the continental USA, FLDAS over Africa). Finally, first work has begun to run LDAS-Monde at kilometric-and sub-kilometric-scale spatial resolutions. Promising results have been obtained by assimilating SSM and LAI over the AROME domain (Applications de la Recherche à l'Opérationnel à Méso-Echelle, https://www. umr-cnrm. fr/spip. php?article120, last access: July 2019) of Météo-France. Code availability. LDAS-Monde is a part of the ISBA land surface model and is available as open source via the surface modelling platform called SURFEX.
Figure11illustrates seasonal cycles of observed LAI (Fig. 11a) and SWI (Fig. 11e), LDAS_ERA5 analysis and open-loop LAI (Fig. 11b) and SSM (Fig. 11f) for the WEUR domain. The 2018 period is compared to the 2010-2017 average. Figure11ashows the heatwave impact with a sharp drop in observed LAI values from June to November 2018 (solid green line). Such low LAI values have never been observed over the 8 previous years (it is below the minimum value in shaded green). A similar behaviour is also visible in the ASCAT SWI dataset in Fig. 11e, with the lowest values recorded in 2018 for the 2010-2018 period. Over WEUR, LDAS_ERA5 open-loop overestimates LAI in the second part of the year, as already highlighted by several studies (e. g. Albergel et al. , 2017 Albergel et al. , , 2019)). The LDAS_ERA5 analysis has a positive impact and reduces LAI values, as seen in Fig.
performed with in siDorigo et al. , , 2015apotranspiration from the FLUXNET 2015 synthesis dataset (http://fluxnet. fluxdata. org/,lastaccess: June 2019), soil moisture from the International Soil Moisture Network (ISMN,Dorigo et al. ,  2011Dorigo et al. , , 2015, https://ismn. geo. tuwien. ac. at/en/, last access: June 2019) and river discharge from several networks across the world.
Acknowledgements. Results were generated using the Copernicus Climate Change Service Information, 2017. The authors would like to thank the  Copernicus Global Land Service  for providing the satellite-derived leaf area index and surface soil moisture. Financial support. This research has been supported by the  RT Antoine de Saint-Exupéry Foundation  ( POMME-V  project, grant no. CDT-R056-L00-T00 ) and the  Climate Change Initiative Programme Extension ,  Phase 1 -Climate Modeling User Group ESA  (grant no. 4000125156/18/I-NB ). Review statement. This paper was edited by  Harrie-Jan Hendricks Franssen  and reviewed by four anonymous referees.
4. 2 2 Figure11illustrates seasonal cycles of observed LAI (Fig. 11a) and SWI (Fig. 11e), LDAS_ERA5 analysis and open-loop LAI (Fig. 11b) and SSM (Fig. 11f) for the WEUR domain. The 2018 period is compared to the 2010-2017 average. Figure11ashows the heatwave impact with a sharp drop in observed LAI values from June to November 2018 (solid green line). Such low LAI values have never been observed over the 8 previous years (it is below the minimum value in shaded green). A similar behaviour is also visible in the ASCAT SWI dataset in Fig. 11e, with the lowest values recorded in 2018 for the 2010-2018 period. Over WEUR, LDAS_ERA5 open-loop overestimates LAI in the second part of the year, as already highlighted by several studies (e. g. Albergel et al. , 2017 Albergel et al. , , 2019)). The LDAS_ERA5 analysis has a positive impact and reduces LAI values, as seen in Fig.
performed with in siDorigo et al. , , 2015apotranspiration from the FLUXNET 2015 synthesis dataset (http://fluxnet. fluxdata. org/,lastaccess: June 2019), soil moisture from the International Soil Moisture Network (ISMN,Dorigo et al. ,  2011Dorigo et al. , , 2015, https://ismn. geo. tuwien. ac. at/en/, last access: June 2019) and river discharge from several networks across the world. and snow-cover data from the Interactive Multisensor Snow and Ice Mapping System (IMS, https:// www. natice. noaa. gov/ims/, last access: June 2019). Ad-ditional validations are.
, 1999; (assimilated Bartalis et al. , 2007) dataset) Satellite-derived leaf area index http://land. copernicus. eu/global/ R, RMSD and N RMSD No (GEOV1, Baret et al. , 2013) (assimilated dataset) Satellite-driven model estimates of http://www. gleam. eu R, RMSD and N land evapotranspiration (GLEAM, Martens et al. , 2017) RMSD YesUpscaled estimates of gross primary production (GPP,Jung et al. , 2017)https://www. bgc-jena. mpg. de/ geodb/projects/Home. php R, RMSD and N RMSD Yes Solar-induced fluorescence (SIF) from GOME-2 (Munro et al. , 2006; Joiner et al. , 2016) See references R Yes Interactive Multi-sensor Snow and Ice Mapping System (or IMS) snow cover https://www. natice. noaa. gov/ ims/ Differences Yes.
Xylanase, cellulase, and laccase-like activities were assessed spectrophotometrically, by following azo-xylan (A) and azo-CMC (B), hydrolysis at 590 nm, and ABTS oxidation (C) at 420 nm. For each activity, groups with different letters exhibit significantly different activity at a 95% confidence interval according to a Kruskhal-Wallis rank sum test, followed by a Dunn posthoc pairwise comparison test. The results of Dunn tests are to be found in the supplementary data (Table  S1 ). Enzymatic activity was tested after nine days of growth on either wheat straw or seagrass, in either non-saline (RHI, black bars) or saline (RHI-salt with 3% sea salt, grey bars). Xylanase, cellulase, and laccase-like activities were assessed spectrophotometrically, by following azo-xylan (A) and azo-CMC (B), hydrolysis at 590 nm, and ABTS oxidation (C) at 420 nm.
These characteristics, in fact, point to the potential retrieval of novel microbial and enzymatic features of interest for industrial applications, deriving from S. lucomagnoense adaptation to the marine environment. As no genomic data are available for this fungus, a representative cDNA library was constructed and sequenced using the approach developed for the mangrove fungus, Pestalotiopsis sp. [12]. Briefly, the transcriptome (total RNAs were extracted from the various growth condition sand pooled together before sequencing) was sequenced to prepare the cDNA library (replacing the genome) to identify the proteins obtained from the proteome and not to quantify the expression of the various genes. In addition, the cDNA library was normalized so that the read counts of each transcript could not be determined.
The datasets of this study were deposited under National Center for Biotechnology Information (NCBI) BioSample accession ID: SAMN15897915 with corresponding NCBI BioProject accession ID: PRJNA659110. The corresponding proteins were identified using the CAZy database (www. cazy. org), and the resulting protein database was used to identify, by mass-matching, the proteins obtained from a proteomic mass spectrometry analysis. To capture the largest possible number of transcripts, a normalized cDNA library was prepared from total RNAs isolated from pooled samples taken after 2, 4, 6, 8, and 10 days of culture for each condition. This library was sequenced using the Illumina technology (2 × 150 bp) (Illumina, San Diego, CA, USA) with an average of 300 million paired-end sequences. After the initial quality assessments, de novo transcriptomic assembly was achieved using the de novo assembler suite from the CLCBio Genomic Workbench version 11 (https://digitalinsights.
qiagen. com) (Qiagen, Hilden, Allemagne). In this de novo transcriptomic assembly, a total of 520,000,000 reads were assembled in 350,657 contigs. The N50 for the contigs is 504 bp, and an average length of 316 bp (Table  1 ). The assembled contigs sequences are available at GitHub URL as https://github. com/drabhishekkumar/Stemphyliumlucomagnoense-transcriptomics. In order to obtain an expert and accurate annotation of the lignocellulolytic enzymes present in the transcriptome, translated transcripts were compared to the entries in the CAZy database (www. cazy. org). Using 1D electrophoresis followed by LC-MS/MS analysis, 51 secreted S. lucomagnoense proteins were identified as belonging to one of the CAZy classes. CAZymes, including glycoside hydrolases (GHs), carbohydrate esterases (Ces), polysaccharide lyases (PLs), carbohydrate-binding modules (CBMs), glycosyl transferases (GTs), and auxiliary activities (Aas), are, in fact, microbial enzymes involved in complex carbohydrate breakdown and assembly, and in particular in lignocellulose degradation and transformation.
We first sequenced and assembled the transcriptome of Pestalotiopsis sp. Transcribed genes were identified and functionally annotated using the CAZy database  [54]  and used to identify secreted enzymes by matching LC-MS/MS data. The correlation between the CAZyme repertoire identified in secretomes and growth phenotypes is not always clear, and the absence or incomplete transcriptomic and proteomic data might be claimed responsible in these specific cases, yet comparing proteomes from the four culture conditions provides an interesting insight into the adaptation on fungal secretomes to saline conditions. As an example, higher xylanase activities were found in secretomes from cultures in non-saline conditions, on both wheat straw and seagrass, indicating inhibition by sea salt added to the culture medium.
Two enzymes, APMZ2_prot912 (family 5_1, CRO) and APMZ2_prot4629 (family AA12, pyrroloquinoline-quinone (PQQ)-dependant oxidoreductase), were found in all conditions tested at pretty high yields. Recently, it was shown that a (PQQ)-dependent pyranose dehydrogenase (family AA12) from Coprinopsis cinerea was able to activate a family LPMO from Neurospora crassa  [35]. One candidate LPMO (APMZ2_prot21080, family AA11), was also identified in S. lucomagnoense cultures grown on wheat straw, both in the presence and absence of sea salt. Although further investigation will be necessary to demonstrate the potential synergy between the two S. lucomagnoense enzymes, like for terrestrial fungi, fungal cellulose degradation seems to rely on LMPOs, which are copper oxidases classified among the AA section of the CAZy database.
The pooled RNA sample was sent to Fasteris SA (Geneva, Switzerland) to prepare cDNA libraries and sequence it. Briefly, mRNAs were purified from total RNA using the Dynabeads mRNA purification kit (Invitrogen, ThermoFisher Scientific, Madison, WI, USA), and the DNA library was prepared using the Illumina TruSeq Stranded mRNA Library Prep kit (Illumina, Evry, France). The resulting library was then normalized using the Duplex Specific Nuclease protocol (Evrogen, Euromedex, Souffelweyersheim, France) and sequenced using the Illumina HiSeq-4000 technology (2 × 150 bp) (Illumina, San Diego, CA, USA). Sequencing reads were assembled using the de novo assembler suite of CLCBio Genomic workbench version 11 (https://digitalinsights. qiagen. com), with default parameters for cDNA. Transcripts were converted into open reading frames and proteins by TransDecoder (https://github.
The pre-column cartridge was connected to the separating column (C18, 0. 075 × 500 mm, Thermo Fisher Scientific, Madison, WI, USA), and the peptides were eluted with a linear gradient from 5 to 35% ACN in 0. 1% HCOOH for 155 min at 300 nL/min. On-line analysis of peptides was performed with a Tribrid orbitrap fusion lumos mass spectrometer (Thermo Fisher Scientific, Madison, WI, USA), using a nanoelectrospray ion source. Peptide ions were analyzed using Xcalibur 2. 1 (Thermo Scientific, Villebon sur Yvette, France) with the following data-dependent acquisition steps: (step 1) full MS scan (mass-to-charge ratio (m/z) 400 to 1400, resolution 70,000) and (step 2) MS/MS (normalized collision energy = 30%, resolution 15,000). The raw mass data were first converted to mzXML format with the MS convert software (ProteoWizard v 3.
0. 8934) (ProteoWizard, Palo Alto, CA, USA). Protein identification was performed querying MS/MS data against databases, together with an in-house contaminant database, using the X!Tandem pipeline software (version 0. 2. 34) with the following parameters: One trypsin missed cleavage allowed the alkylation of cysteine and conditional oxidation of methionine, precursor and fragment ion set at 10 ppm and 5 Da, respectively. A refined search was added with similar parameters, except that semi-tryptic peptides, possible N-term acetylation, and histidine mono-and dimethylations were searched. All peptides matched with an E-value lower than 0. 05 were parsed with X!Tandem pipeline software. Proteins identified with at least two unique peptides and a log (E-value) lower than -2. 6 were validated.
We establish an updated surface velocity field for the URG by processing GNSS data from a dense permanent network over the 2002-2018 period. From GNSS and levelling data,  Furhmann et al. (2013)  conducted a regional analysis of the URG. Furhmann et al. (2015)  focused on the northern part of the URG by combining GNSS, levelling and InSAR data. By computing more recent GNSS data we aim to precisely quantify the surface deformation affecting this region, where horizontal and vertical surface velocities are small and of the same order as their uncertainties. The spatial density determined by the sites of the GNSS Upper Rhine Network (GURN) allows the analysis of geodetic signals over a large area, and the longer period of data recording offers the opportunity to obtain more reliable results.
Data processing The GNSS data were processed using the GPS Analysis at MIT/Global Kalman filter (GAMIT/GLOBK) processing chain  (Herring et al. 2018a). It provides a daily solution of the GURN network from the RINEX (Receiver INdependent EXchange) files and the IGS (International GNSS Service) final orbit products. The GAMIT/GLOBK processing is in two steps. First, a daily loosely constrained solution (no constraint on the GURN network coordinates and only 5 cm on reference network coordinates) with a covariance matrix is provided by GAMIT  (Herring et al. 2018b). Second, the daily solutions are combined in GLOBK with a Kalman filter to obtain the position time-series, the average coordinates and velocities of the stations  (Herring et al. 2015) , with a referencing in ITRF2014  (Altamimi et al.
So, from the corrected position time-series, the uncertainties of the velocities are estimated using the 'Create and Analyze Time Series' software (CATS;  Williams 2008). The CATS software fits a multiparameter model using a least-squares method and jointly analyses the residuals by maximum likelihood estimation. We assume that the position time-series are affected by a combination of white and flicker noises, and by random walk. These amplitudes, estimated by the CATS software, are used in an empirical model given by  Mao et al. (1999) , which gives the uncertainty of the velocity for each component according to eq. (  2 ): In eq. (  2 ), σ is the uncertainty on the velocity, σ w and σ f are the magnitudes of white and flicker noises, σ rw is the magnitude of random walk, g is the number of measurements per year, T is the total time span in years and a and b are empirical constants.