dataesr/scientific-paragraphs-categorization huggingface dataset:
Future work should aim to update these methods using newer atmospheric models, including results from chemical reanalyses, and to the forthcoming TOAR-II measurement database (https://toar-data. org/surface-data), which will include new monitoring sites in populated regions such as India. This analysis focuses on a single annual metric relevant for health, but this analysis could be reproduced monthly and could be reproduced for other ozone metrics such as those relevant for plants. These methods are also applicable to other air pollutants. Finally, these methods offer an interesting counterpoint to recent machine learning estimates  (Seltzer et al. , 2020; Kleinert et al. , 2021; Sun et al. , 2021; Betancourt et al. , 2022; Liu et al. , 2022). Machine learning has advantages of incorporating a wide range of datasets as predictors of ozone and in finding nonlinear relationships that can be difficult to capture in the geostatistical methods used here.
The spring also receives inflow from several upstream karst catchments and the Flysch zone, where surface runoff can sink into an estavelle and pass through an underground karst conduit during low-flow periods, as demonstrated by multiple tracer tests  (Goldscheider, 2005). Precipitation and temperature data were obtained from three meteorological stations located outside the catchment. The potential evapotranspiration is calculated using data from one station with the modified Turk-Ivanov approach after  Wendling and Müller (1984) , described in  Conradt et al. (2013).
The discharge is measured at the spring pool and is regularly zero during low-water periods, when the pumping rate exceeds the natural discharge of the spring. Precipitation data are from four meteorological stations. Three are located in the catchment and one is located about 5 km west of the catchment. Potential evapotranspiration is calculated with the Oudin approach  (Oudin et al. , 2005). Temperature data are from the Prades-le-Lez meteorological station.
Precipitation and temperature data are from two meteorological stations. One is located in the catchment at 950 m a. s. l. The other, with a heated rain gauge, is located 22 km north-east of the catchment at 1700 m a. s. l. (Doummar et al. , 2018). Potential evapotranspiration is calculated using data from the 950 m station with the modified Penman-Monteith approach  (Allen et al. , 1998).
, 2019). Low-flow periods are sustained by flows from the karstified limestone aquifer, which reaches heights up to 1800 m a. s. l. and has significant groundwater storage  (Ravbar et al. , 2012). Part of the discharge is lost due to an underground bifurcation  (Kogovšek et al. , 1999). When the discharge exceeds about 60 m 3 s -1 and remains high for a few days, a polje downstream of the springs becomes flooded. When the discharge reaches about 80 m 3 s -1 , the flooding reaches the monitoring station, influencing the measurement. The water from the lake is drained by several ponors downstream of the monitoring station, but their absorption capacity is much lower than the discharges of the springs. Precipitation, snow cover height and height of new snow data were obtained from two meteorological stations located in the catchment.
2 ). Besides a number of filters and number of neurons in the first dense layers, we optimized the training batch size and the length of the input sequence for each simulation step using the Bayesian Optimization library  (Nogueira, 2014). The number of minimum and maximum optimization steps was individually selected for each site and can be found in the provided modelling scripts  (Cinkus and Wunsch, 2022). To ensure proper learning, the models are regularized with several measures. Hence, early stopping with a patience of 20 steps is applied to prevent the model from overfitting. Except for Qachqouch, where few data are available, the size of the according stopset ranges between one and four annual cycles (see the provided scripts for details). This stopset is considered a part of the calibration period mentioned in Sect.
Observed discharge time series were used directly (without further pre-processing) in the ANN and reservoir models. In the case of Lez spring, the models were simultaneously calibrated on the spring discharge (Q) as well as on the water level in the aquifer (Z). Furthermore, the pumped discharge time series in reservoir C (Q pump , Fig. 3c ) was used as an input. Precipitation time series were used differently as there are often several meteorological stations per study site. For ANN models, precipitation time series were used as raw input P raw , except for Lez spring, where the individual raw precipitation data had too many missing values, so we used the same input as the reservoir model (P in ). In the case of Aubach, Qachqouch and Unica, P raw includes all the precipitation time series from the different meteorological stations (Table  A1 ).
Remaining within the KarstMod platform provided the time advantages of a turnkey toolbox (which are widely used in research and by stakeholders) but limited the possibilities of the conceptual models. sider the refreezing processes of liquid water in the snow if snowmelt is interrupted  (Bergström, 1992). The output of the snow routine is a time series of redistributed precipitation. Code and data availability. We provide complete codes for the ANN models and. properties files for reservoir models on Github (https://doi. org/10. 5281/zenodo. 7242077, Cinkus et al. , 2022). Due to redistribution restrictions from several parties, a dataset cannot be provided. However, the data are available from the local authorities upon request. Aubach spring discharge time series and meteorological data from the Diedamskopf and Walmendinger Horn stations are available from the office of the federal state of Vorarlberg -division of water management.
Meteorological data from the Oberstdorf station are available on the DWD Open Data Server (https://opendata. dwd. de/, DWD, 2022). The Gato Cave spring discharge time series is available from the Confederación Hidrográfica de las Cuencas Mediterráneas Andaluzas (https://hispagua. cedex. es/instituciones/confederaciones/andalucia, Cuenca Mediterránea Andaluza, 2022) and meteorological data are available in the "Datos a la carta" section in Consejería de Agricultura, Pesa, Agua y Desarrollo rural (http://www. redhidrosurmedioambiente. es/saih/ presentacion, Consejería de Agricultura, Pesa, Agua y Desarrollo rural, 2022). The Lez spring discharge time series is available on the OSU OREME website (https://doi. org/10. 15148/CFD01A5B-B7FD-41AA-8884-84DBDDAC767E, SNO KARST, 2019), water level time series can be requested from Montpellier Méditerranée Métropole, and meteorological data are available on request from Météo-France.
The Qachqouch discharge time series and meteorological data are available on request from the Department of Geology at the American University of Beirut. Unica spring discharge time series and meteorological data are available from ARSO (Slovenian Environment Agency) (http://vode. arso. gov. si/ hidarhiv/, ARSO, 2021a; http://www. meteo. si/, ARSO, 2021b). Acknowledgements. Beirut and Mount Lebanon Water Establishment is thanked for facilitating the installation of instruments and access to field sites. For the data provided, we also thank the French Karst National Observatory Service (SNO KARST, 2019), the French national meteorological service Météo-France, the office of the federal state of Vorarlberg -division of water management, the German Meteorological Service, the Slovenian Environment Agency (ARSO, 2021a, b) and the Meteorological National Agency (AEMET).
, 2011 ), Pandas (McKinney, 2010; Reback et al. , 2021) , Scikit-Learn  (Pedregosa et al. , 2018) , Matplotlib  (Hunter, 2007) , Bayesian Optimization  (Nogueira, 2014) , TensorFlow 2. 7  (Abadi et al. , 2016)  and its Keras application programming interface (API)  (Chollet et al. , 2015). Financial support. This research has been supported by the French Ministry of Higher Education and Research for the thesis scholarship of Guillaume Cinkus and the European Commission for its support through the Partnership for Research and Innovation in the Mediterranean Area (PRIMA) programme under Horizon 2020 (KARMA project, grant agreement no. 01DH19022A). The data collection and instrumentation on the Qachqouch catchment were funded by USAID and the National Academy of Science (Peer Science; 705 project award: 102881; Cycle 3) and the KARMA project (L-CNRS in the framework of the PRIMA programme; award no.
Soil data The soil data in France was extracted from the LUCAS Soil 2018 (Fernandez-Ugalde et al. , 2020) (Fig. 1 ). In LUCAS Soil 2018, soil sampling was carried out in all EU Member States and UK using the same set of 25,947 locations targeted in 2015. In 65% of these locations, samples were to be taken following stratified random sampling according to topography and land use  (Fernandez-Ugalde et al. , 2020; Orgiazzi et al. , 2018) , in which a spade was used to collect a sample from a depth of 20 cm. In the remaining 35% of the locations (approximately 9000 points), metallic rings were used to collect soil cores to determine BD from a depth of 0-10 and 10-20 cm. The soil sample was aggregated for further laboratory analysis  (Fernandez-Ugalde et al. , 2020). The coarse fragment data were from sieve to separate coarse fragments (2-60 mm) from fine earth fraction, and the SOC content was determined by subtracting the inorganic carbon calculated after carbonate content measurement from the total organic carbon obtained by dry combustion  (Fernandez-Ugalde et al.
Environmental covariates Following the Scorpan framework  (McBratney et al. , 2003) , we collected 24 environmental covariates associated with soil formation (Table  S1 ; Fig. S1 ). These environmental covariates provide information on the factors assumed to control SOC, bulk density, coarse fragments, and SOCD. Relief covariates were obtained from 30 m spatial resolution produced by ASTER GDEM V2 (Geospatial Data Cloud, https://www. gs cloud. cn/). We used SAGA (System for Automated Geoscientific Analyses) GIS to calculate the relief factors from elevation, including slope, aspect, specific catchment area, plan curvature, profile curvature, fill, flow accumulation and terrain wetness index. The available water capacity, clay, sand, silt, bulk density (estimated by pedotransfer function), and USDA soil texture class were derived from the European Soil Data Centre using LUCAS Soil 2009  (Ballabio et al.
, 2016). The SOC for the topsoil (0-20 cm) at 1 km resolution for the year of 2009 in Europe was derived from  Aksoy et al. (2016)  using a total of 23,835 soil samples from LUCAS Soil 2009, BioSoil (a research project to use additional data covering the forest land use which was not sampled adequately in LUCAS survey) and SoilTrEC (originated from the Critical Zone Observatories in SoilTrEC Project). The soil type information was extracted from the French national soil type map at 1:5,000,000 scale. Among 916 sampling sites, most of the soil samples were located in Cambisols (421 samples) and Luvisols (322 samples). The SOCD for the great group level of soil type was summarized in Fig. 2. The monthly normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) from the MODIS (https://lpdaac.
Evaluation of model performance The whole soil dataset (916 samples) was randomly divided into the calibration (687 samples) and validation (229 samples) sets at a ratio of 75%-25%. The root mean square error (RMSE) and the determination coefficient (R 2 ) were used to evaluate the model performance. Higher R 2 close to 1 and lower RMSE close to 0 mean better model accuracy. RMSE ¼ ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi 1 n X n i¼1 ðy i À b y i Þ 2 s (2) R 2 ¼ 1À P n i¼1 ðy i À b y i Þ 2 P n i¼1 ðy i À yÞ 2 (3) where y i and b y i are the measured and predicted values for validation sample i, y is the mean of the measured values, and n is sample size of the validation set. We also evaluated the spatial structure of maps by calculating the experimental variograms and the nugget-to-variance ratio.
Descriptive statistics for soil properties The statistical summary of bulk density (BD), SOC, coarse fragments (CF) and SOCD to a depth of 0-20 cm are shown in Table  1. For whole dataset, SCOD ranged from 0. 11 to 26. 9 kg m À2. The mean and coefficient of variation of SOCD were 5. 01 kg m À2 and 55%. The kurtosis of 5. 83 and skewness of 1. 75 indicated that SOCD had a slightly positive and leptokurtic distribution. SOC and BD ranged from 3. 6 to 439 g kg À1 and 0. 02-1. 74 g cm À3. The mean and coefficient of variation (CV) of SOC and BD were 34. 6 g kg À1 and 12%, 1. 11 g cm À3 and 25%. The SOC and BD presented positive skewness distribution (4. 53 and À0. 78). CF ranged from 0 to 79% with mean and CV of 15. 4 and 75%. The CF data presented a positive skewness distribution (1. 51).
That is, the more soil samples are used in the modelling, more accurate model can be obtained. This is reasonable since soil have a great spatial heterogeneity at a broad-scale, especially for regions with very diverse pedo-climatic conditions. In a comparative study over South-American countries,  Guevara et al. (2018)  also showed a rather poor predictive performance for SOCD. They stated that the density of available data and variability of country-specific environmental gradients were among the main reasons for this poor predictive performance. In comparison to the earlier French study by  Martin et al. (2011) , our study yielded a slightly lower model performance. It may result from two main reasons: (1)  Martin et al. (2011)  used a substantially larger dataset (1974 soil samples), whereas we only used 916 soil samples for modelling due to the lack of BD for more than half of the soil samples.
However, when BD and CF measurements are missing for the majority of the soil database, CTM will lead to poor model performance since the soil samples with SOCD observations for spatial modelling are limited due to the absence of BD and CF. In such cases, the MTC approach can be a valuable alternative. In addition, when only digital maps of SOC, BD and CF are available for a given region (no soil samples for spatial modelling), the MTC approach can be useful for determining SOCD.
02 Pg in 0-20 cm and 3. 53 Pg in 0-30 cm in mainland France, which was closer to the previous estimate (3. 75-3. 86 Pg) for upper 30 cm than CTM and MTC approach  (Meersmans et al. , 2012b; Mulder et al. , 2016a). We note that the logarithmic fitting model used to infer 0-30 cm SOCS from 0 to 20 cm SOCS is an approximation which generates some additional uncertainties. Rather smooth exponential or logarithmic gradients have been used to model SOC decrease with depth under more-or-less natural conditions whereas more abrupt changes due to tillage have been modelled by  Meersmans et al. (2016). However, the information on tillage depth is both missing in the LUCAS Soil database and French national soil databases. Therefore, we acknowledge that our estimate of SOCS at 0-30 cm is a rough approximation, but it is the best that we could perform considering the available data.
The LUCAS Soil aims to monitor the soil information in the topsoil, therefore deep soil is not sampled for the EU and UK in current stage. In addition, due to the limited soil samples with BD measurements, we only used less than 1000 soil samples of surface soils in SOCD spatial modelling in this study. Therefore, SOCD prediction can be further improved by augmenting the LUCAS 2018 Soil without BD measurements and/or by adding national data from the French Soil Monitoring Grid. If we stay to adding LUCAS 2018 data, pedotransfer functions (PTFs) could be applied to predict the missing BD and thus increase the soil sample size for spatial predictive modelling and potentially increase its accuracy and robustness  (Chen et al. , 2018b; Palladino et al. , 2022). However, this improvement requires that the PTFs do not propagate large uncertainties.
Nevertheless, when a soil profile database with complete SOC, BD and CF data are available for the EU in the near future, the spatial distribution of SOCS in deep soil can be investigated. Though our study is limited to topsoil SOC, we must stress that topsoil SOC itself is a very important property for many soil health-related issues. Topsoil SOC is one of the controlling factors of aggregate stability and thus of soil sensibility to slaking, run-off and erosion. Therefore, maintaining and increasing topsoil SOC is essential for mitigating this major threat to soil health. Topsoil SOC is one of the main sources of nutrients for numerous soil organisms and contributes to maintaining soil biodiversity and microbial activity. Topsoil SOC is essential to enable the growth of vegetation and biomass-derived carbon inputs, which, in turn, may contribute to maintain and/or increase topsoil or deep soil SOC.
Acknowledgments This study is funded by the  National Natural Science Foundation of China  (No. 42201054 ). Dominique Arrouays  is coordinator and  Anne C. Richer-de-Forges  is the member of the research consortium GLADSOIL-MAP supported by the  Loire Valley Institute for Advanced Studies  through its  LE STUDIUM Research Consortium Programme. The authors would like to thank the  Joint Research Centre of the European Commission  for sharing the LUCAS 2018 Topsoil data.
Together with structural data and primary structure analysis (Supplementary Fig. 2 ), this suggests that AIP56 and AIP56-like toxins arose from the fusion of a gene homologous to nleC  24, 25  , encoding the catalytic domain, with a gene homologous to the one encoding Protein D from bacteriophage APSE2  [27] [28] [29]  , which codes for the receptor-binding domain  18  , with the middle domain likely evolving from a gene/intergenic region of the recipient genome, where those genes were incorporated. Alternatively, the middle domain evolved also as an independent gene, but homologous genes are not yet available in the databases or are difficult to detect due to high sequence divergence.
Detailed analysis of AIP56 structure The catalytic domain of AIP56 has an overall Zincin fold similar to that of NleC (PDB entry 4Q3J; rmsd of 1. 4 Å for 257 aligned Cα atoms), with three β-strands forming a modified Ψ-loop β-sheet motif (β1-β3) overlaid on 9 α-helices (Fig. 1e )  24. The active-site zinc ion of AIP56 is coordinated by H165 and H169 from helix α6 and by D176 located at the loop between helices α6 and α7. In two of the AIP56 monomers of the asymmetric unit, Y207 from the loop between helices α7 and α8 is the fourth zinc ligand, similar to what is observed in NleC  24. The conserved E166 is ideally placed to promote the nucleophilic attack. As described before for NleC  24  , the dimensions and shape of the activesite cleft of AIP56, as well as the pattern of glutamate and aspartate side chains along the ridges of the cleft, mimic the DNA backbone phosphates to which NF-kB p65 binds  [39] [40] [41]  (Supplementary Fig.
d Complete SREFLEX simulated movement after the refinement stage. Vectors (red arrows) are drawn connecting the equivalent residues from the crystal structure (colored as in a) to the SREFLEX model (gray). e Cartoon representation of the superposed catalytic domains of AIP56 (wheat) and NleC (gray; PDB: 4Q3J). The modified Ψ-loop β-sheet motif (β1-β3) of AIP56 is colored blue. The N-(Nter) and C-termini (C-ter) and secondary structure elements are labeled. The inset shows a close-up of the active sites, including the zinc ion (Zn; black sphere) and the coordinating residues from AIP56 (red) and NleC (gray). f The linker peptide contacts both the catalytic and the receptor-binding domains. Top, cartoon representation highlighting the contact regions; Bottom, C-alpha trace of the contact region with contacts within a range of 4 Å is represented.
DNA sequences encoding AIP56 P210-N497 or AIP56 L258-N497 were then amplified using pET28AIP56H+ as template and ligated into the SacI/ XhoI restriction sites of pET28Bla L19-W286 in frame with a C-terminal 6xHis-tag. To generate chimera AIP56 N1-E307 DTR S406-S560 , AIP56 N1-E307 encoding sequence was amplified from pET28AIP56H+ and cloned into pET28 NcoI/SacI restriction sites, yielding plasmid pET28AIP56 N1-E307. DTR S406-S560 coding sequence (accession number WP_072564851. 1) was then amplified from pET-22b DT 51E/148K (gifted by Dr. John Collier; Addgene plasmid # 11081; RRID:Addgene_11081) and ligated into the SacI/XhoI restriction sites of pET28AIP56 N1-E307 in frame with a C-terminal 6xHis-tag. Constructs coding for AIP56 E214K , AIP56 E218K , AIP56 H222K , AIP56 H231K , AIP56 E234K , AIP56 E214K/E218K/H222K , AIP56 H231K/E234K , AIP56 D274S/D276-278S and AIP56 D274N/D276-278N with or without V5-tag were generated with the QuickChange Site-Directed Mutagenesis Kit (Stratagene, 200518) following the manufacturer's instructions, using pET28AIP56V5H+  19  and pET28AIP56H+  16  as template, respectively.
Crystallization Initial crystallization hits for AIP56 were identified by high-throughput screening at the HTX Lab of the EMBL Grenoble Outstation (Grenoble, France). AIP56 crystals were obtained by mixing equal volumes of protein (7 mg mL -1 ) and crystallization solution (400 mM sodium acetate, 100 mM Tris pH 8. 5, 15% (w/v) PEG 4 K, 10 mM taurine), which was optimized using an additive screen kit (HR2-428; Hampton Research). Prior to data collection, crystals were successively transferred into the crystallization solution supplemented with 15% and 30% (v/v) glycerol, followed by flash-cooling in liquid nitrogen.
Structure determination Several X-ray diffraction datasets were collected from AIP56 crystals at Synchrotron SOLEIL (Saint-Aubin, France; beamlines Proxima-1 and Proxima-2) and at the European Synchrotron Radiation Facility (ESRF, Grenoble, France; beamlines ID23, ID29 and ID30). The best dataset was collected on beamline ID29 on a Pilatus 6 M (Dectris) detector (2700 images, 0. 1°rotation, 0. 02 s exposure, wavelength 0. 972 Å), indexed and integrated with XDS  92  and analyzed using the STARANISO server as diffraction was highly anisotropic  93. Statistics for data processing are summarized in Supplementary Table  1. The structure of AIP56 was solved by molecular replacement with PHASER  94  using an AIP56 model generated by the artificial intelligence program AlphaFold2_advanced  35, 36.
Several cycles of model building and refinement were performed using COOT, Phenix. refine and BUSTER  [95] [96] [97] [98] [99]. All illustrations of macromolecular models were produced with PyMOL 100. The final refined coordinates and structure factors were deposited at the Protein Data Bank under PDB entry 7ZPF and the corresponding diffraction images at the SBGrid Data Bank (https://doi. org/10. 15785/SBGRID/911). Small-angle X-ray scattering (SAXS) data acquisition, processing and analysis X-ray scattering data were collected at the SWING beamline of the SOLEIL Synchrotron (Saint-Aubin, France). Measurements were performed using a HPLC Bio Sec3 (Agilent) size-exclusion column, online with the SAXS measuring cell, a 1. 5 mm diameter quartz capillary contained in an evacuated vessel. The sample-to-detector (Dectris Eiger 4 M) distance was set to 2000 mm and the wavelength λ to 1.
0 Å, allowing useful data collection over the momentum transfer range of 0. 005 Å -1 < q < 0. 5 Å -1 (q = 4π sin(θ)/λ). SAXS data were collected directly after elution of the AIP56 protein through the HPLC column equilibrated in 50 mM HEPES buffer pH 7. 5, 500 mM NaCl. Fifty µl of protein sample were injected at 15 °C and two different initial concentrations, 13. 4 and 3. 2 mg mL -1 , in order to obtain a curve with a good statistic and devoid of interparticle effects. The flow rate was 0. 2 mL min -1 , frame duration was 0. 99 sec and the dead time between frames was 0. 01 sec. Scattering of the elution buffer before void volume was recorded and used as buffer scattering for subtraction from all protein patterns. The scattered intensities were displayed on an absolute scale using the scattering by water.