dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusion and Future Work We have introduced ClaimsKG, a knowledge graph of fact-checked claims, which facilitates structured queries of related metadata, such as their truth values, authors or time of release. ClaimsKG is generated through a semi-automated pipeline, which harvests data from popular fact-checking sites on a regular basis, lifts data into a specifically developed for that purpose model, and annotates claims with related entities from DBpedia. The KG is expected to provide support to research in the areas of fact-checking, stance detection and multiple topics related to the analysis of societal debates, where a quality ground truth of labelled claims is required in order to facilitate supervision, validation or reproducibility of research methods. There are several limitations of the current KG that are the focus of ongoing and short-term efforts.
The development of an advanced claim matching approach and its evaluation is among them. We are working on building a goldstandard dataset of claim-pairs, annotated with respect to different relatedness categories, in order to evaluate the process and to provide training data to finetune state-of-the-art deep language modelling approaches such as BERT  [5]  to our matching task. We also intend to extend the content of our graph to other fact-checking websites and languages, enabling multi-and cross-lingual information retrieval and approaches for fact verification. With respect to augmenting ClaimsKG with additional claims, we also intend to harvest semi-structured schema. org markup of claims from Web pages by exploiting data fusion pipelines developed as part of prior work  [36]. Regarding the exploratory Web interface, in the future we aim to support the execution of federated queries that integrate information from external KGs like DBpedia, as well as the inclusion of a statistical observatory allowing us to extract distributions and correlations of different entities, topics and claims.
Results We retrospectively collected data from 857 mCRC samples including 779 samples with valid NGS results. DNA quality was suitable for 91% of the samples for NGS and uncommon mutational profiles were reported in 22 (2. 7% of total) samples. The histological subtypes were: adenocarcinoma for 11 samples, liberkunhian adenocarcinoma for 9 samples, mucinous carcinoma for 1 sample, and ductal carcinoma for 1 sample. Twenty samples were from primary tumors and 2 were from liver metastases (Table  1 ). The range of coverage for rare KRAS, NRAS or BRAF mutations was 700 to 27 000x. Mutant allele fraction was between 1. 8% and 40. 6% for non-hotspot NRAS mutations, between 4. 0% and 14. 5% for non-hotspot KRAS mutations and between 7. 9% and 46. 2% for non-hotspot BRAF mutations. Thirty-five mutations were missense mutations, 5 were silent mutations and one was a stop mutation.
All observed mutations are described in Table  1. Among 22 samples, 3 carried an isolated non-hotspot KRAS mutation (#3, #4, #5), 1 a NRAS mutation (#6) and 4 a BRAF non-hotspot mutation (#13, #14, #15, #21). Seven concomitant RAS mutations (#1, #2, #7, #8, #9, #10, #19) and 7 concomitant RAS and BRAF mutations (#11, #12, #16, #17, #18, #20, #22) were detected. One sample (#12) bore an HRAS mutation not previously described and no sample carried MAP2K1 mutations. Twelve of these mutations are not yet described as somatic nor as single nucleotide polymorphism (SNP) in public database. For each mutation, the PolyPhen-2 score, SIFT score, their interpretation, and protein domain impacted are described in Table  2. All KRAS and NRAS mutations observed in this study are located in the catalytic domain.
The same is true of the NRAS Asp33Glu mutation, although this is located in the Switch 1 loop and is not directly involved in substrate binding. Examination of structures of RAS proteins in complex with RASGAP and SOS (PDB identifiers 1WQ1 and 1XD2, respectively) showed that most of these residues in KRAS and NRAS were not directly involved in the interaction with RASGAP and SOS. The exception is NRAS Asp33Glu, which may affect the interaction with SOS (Fig. 2 )  15. The BRAF Ser602Phe mutation leads to moderate stabilization relative to WT BRAF. This residue is located on the surface of the protein and is not in the BRAF dimer interface. BRAF non-V600E mutations were detected in 1. 43% of patients tested, which is consistent with previous published studies  16. The predictive value of these mutations in the context of mCRC is indeterminate.
However, exon 11 codon 469 BRAF mutations are located on the gene region coding for protein kinase function  11. For exon 11 codon 466 mutations, the amino acid change is located within the glycine-rich loop in the kinase domain  17. For samples #14 and #16, BRAF exon 15 codon 594 mutations are located on the gene region coding for protein kinase function and lead to impaired kinase activity, potentially conferring a favorable prognosis  18, 19. BRAF p. (Lys601Glu) (sample #21) is described as pathogenic but without published data on response to anti-EGFR mAbs therapy. We now turn our attention to the 14 mutational profiles in our dataset with concomitant RAS and/or BRAF mutations. KRAS and BRAF mutations have been frequently described as mutually exclusive in CRC and concomitant KRAS and BRAF mutations are rare, occurring in less than 0.
001% of cases  20. In our study, in 10 samples with uncommon BRAF mutations, 6 are concomitant with a RAS mutation, and 1 BRAF hotspot mutation had a concomitant RAS mutation. The percentage of concomitant KRAS and BRAF mutations in our dataset is higher than previously described  [21] [22] [23] [24] [25] [26] [27]. Since these studies only assessed BRAF codon 600 hotspot mutations, we may infer that hotspot KRAS mutations are more frequently associated with rare BRAF mutations than with hotspot mutations. This inference agrees with a previous study which found that patients with BRAF non-V600E mutations were more likely to have concomitant RAS mutations than patients with the BRAF V600E mutation  16. Due to the infrequent observation of this phenomenon, it is actually not clear whether or not these doubly mutated tumors have a different biology and natural history than KRAS or BRAF mutant tumors.
All methods were performed in accordance with the relevant guidelines and regulations. All data were anonymized prior to analysis. DNA extraction and quality assessment. For all samples, DNA was extracted as previously described using QIAamp DNA FFPE Tissue Kit (Qiagen, Hilden, Germany)  14. After extraction of the 669 new samples, TruSeq FFPE DNA Library Prep QC kit (Illumina, San Diego, USA) and qPCR using Cobas z480 (Roche Diagnostics, Meylan, France) were used for quality DNA assessment. Cycle quantification (Cq) values were calculated using LightCycler ® 480 Software W UDF 2. 0. 0 (Roche Diagnostics). For samples showing a ΔQC score lower than 6, DNA libraries were then prepared using TruSeq ® Custom Amplicon Library Preparation Kit v1. 5 (Illumina). Fifty-three samples failed to yield sufficient DNA quality and 616 samples qualified for DNA library preparation.
Sequencing was performed according to the manufacturer's instructions. All libraries were pooled before sequencing on the MiSeq instrument (Illumina). Sequencing data analysis was performed on Sophia DDM ® software (Sophia genetics, Saint Sulpice, Switzerland). Reference sequences NM_033360. 2 for KRAS, NM_002525. 4 for NRAS and NM_004333. 5 for BRAF were used for alignment and variant calling. Fourteen samples had insufficient coverage to be interpretable and 602 samples had interpretable sequencing data.
PolyPhen-2 score predicts the possible impact of an amino acid substitution on the structure and function of a human protein. PolyPhen combines amino acid composition analysis in multiple sequence alignments with information from solved protein structures (where available). Sequence composition is evaluated using the Position Specific Independent Counts (PSIC) tool, which calculates sequence  profiles  35. Differences in the profiles calculated by PSIC for the different variants at a site are indicative of damaging impact. In cases where structural data are available, the impact of a mutation is assessed by considering physicochemical properties such as residue size and hydrophobicity, and the maintenance of contacts with ligands, metals or other interacting proteins. Sequence-and structure-based features are combined to produce predictions using a naïve Bayes classifier.
PolyPhen-2 produces scores between 0 and 1 along with annotations of whether the mutation is predicted to be benign or damaging. SIFT also predicts whether an amino acid substitution affects protein function. SIFT scores are used to predict the damaging effect of nucleotide substitutions and frame shifts (insertions/deletions) on protein function  based on the maintenance of amino acid composition in alignments of the target sequence with closely related sequences. The SIFT server assigns scores for each residue from 0 to 1, where mutations with a score of ≤0. 05 are predicted to not be tolerated, and mutations with score >0. 05 are predicted to be tolerated  36. SIFT and PolyPhen-2 scores were determined using Sophia DDM ® software (version 5. 0. 7). Structural mod- elling of observed missense mutations was carried out using FoldX version 4  33, 34.
FoldX estimates the impact of point mutations on the folding energy or stability of the protein using rigid-backbone modelling and a classical forcefield whose parameters are trained to reproduce experimental observations of mutational impacts on folding energy. Starting from the wildtype structures for KRAS, BRAF and NRAS (PDB identifiers 4OBE, 5VAM and 5UHV, respectively), the missense mutations observed in these proteins were modeled using the FoldX BuildModel tool, and the structural and energetic impacts of each mutation were assessed as the predicted change in folding energy (∆∆G). Synonymous (silent) and stop mutations were not modeled. All FoldX modelling results are available at https://doi. org/10. 5281/zenodo. 1467311. Protein domain impacted. KRAS and NRAS protein domains impacted by the different mutations described were identified using UCSF Chimera  37.
SIFT, PolyPhen-2 score and protein localization of each identified mutation. a As described in the Catalogue of Somatic Mutations in Cancer (COSMIC), available online at. b Not described as somatic nor as single nucleotide polymorphism (SNP) in databases. c KRAS and NRAS hotspot mutation. d Mutant allele fraction (MAF). e Impacts of observed mutations on protein stability, as predicted by FoldX. Negative values of ∆∆G indicate that a mutation stabilizes the protein structure relative to the WT, and positive values indicate destabilization. f HRAS and MET mutations were not modelled.
For kernel M(y,τ), infinite integrals are given in Constantinescu et al. [9]  which unfortunately contain highly oscillatory integrand if a/h 1 (the contact area over thickness of the first layer) is high. To improve the range of a/h 1 < 100 with sufficient accuracy, we modified the original code in the supplementary material of Constantinescu et al. [9] , by splitting the integration intervals of the infinite integrals (which are obviously already truncated in practice to where the integrand is significantly nonzero) in 10 parts, where on each of them Gauss-Legendre quadrature with 25 points is done.
, in prep). In testing the ORCHIDEE against in situ semi-arid water stores and fluxes, a novel component of our study was to investigate whether some of the site-scale semi-arid LSM hydrology model discrepancies outlined above (e. g. underestimation of C4 grass T, weak dry season ET (and therefore low T/ET ratios), ET issues related to incorrect representation of leaf seasonality, and overestimation of surface runoff) are improved with recent ORCHIDEE model developments. Where the model does not capture observed patterns, we attempted to investigate which model processes and parameterizations are responsible for modeldata discrepancies. First, we evaluated how changing from the conceptual 2LAY bucket model to the physically-based 11LAY soil hydrology scheme -and all associated modifications -influences the high temporal frequency and seasonal variability of semi-arid ecosystem soil moisture, ET (and its component fluxes), runoff, drainage, and snow mass/melt.
General model description The ORCHIDEE land surface model (LSM) forms the terrestrial component of the French IPSL ESM  (Dufresne et al. , 2013) , which contributes climate projections to IPCC Assessment Reports. ORCHIDEE has undergone significant modification since the 'AR5' version  (Krinner et al. , 2005) , which was used to run the CMIP5  (Coupled Model Inter-comparison Project)  simulations included in the IPCC 5 th Assessment Report  (IPCC, 2013). Here, we use ORCHIDEE v2. 0 that is used in the ongoing CMIP6 simulations  (Peylin et al. , in prep). ORCHIDEE simulates fluxes of carbon, water and energy between the atmosphere and land surface (and within the subsurface) on a half-hourly time step. In uncoupled mode, the model is forced with climatological fields derived either from climate reanalyses or site-based meteorological forcing data (2m air temperature, rainfall and snowfall, incoming long and shortwave radiation, wind speed, surface air pressure, and specific humidity).
5x0. 5° resolution for years 2000-2010  (Bastrikov et al. , in prep). The prior background (bare soil) albedo values were retrieved from the MODIS data using the EU Joint Research Center Two Stream Inversion Package (JRC-TIP). Evapotranspiration, ET, in the model is calculated as the sum of four components: 1) evaporation from bare soil; 2) evaporation, E, from water intercepted by the canopy; 3) transpiration, T, (controlled by stomatal conductance); and 4) snow sublimation  (Guimberteau et al. , 2012b). There are two soil hydrology models implemented in ORCHIDEE: one based on a 2-layer conceptual model, the other on a physically based representation of moisture redistribution across 11-layers. These two schemes were described and compared in the Amazon basin by  Guimberteau et al. (2014) , and are described further in the following sections.
Bare soil evaporation and additional resistance term The computation of bare soil evaporation, Eg, in both versions is implicitly based on a supply and demand scheme. In the 2LAY version, Eg decreases when the upper layer gets drier, owing to a resistance term that depends on the height of the dry soil in the bare soil PFT column  (Ducoudré et al. , 1993). In the 11LAY version, Eg, proceeds at the potential rate Epot unless the water supply via upward diffusion from the water column is limiting, in which case Eg is reduced to correspond to the situation in which the soil moisture of the upper 4 layers is at wilting point. However, since ORCHIDEE v2. 0  (Ducharne et al. , in prep. ) , Eg can also be reduced by including an optional bare soil evaporation resistance term, rsoil, which depends on the relative water content and is based on a parameterization fitted at the FIFE grassland experimental site at Konza Prairie Field Station in Kansas  (Sellers et al.
Site-level meteorological and eddy covariance data and processing Meteorological forcing and eddy covariance flux data for each site were downloaded from the AmeriFlux data portal (http://ameriflux. lbl. gov). Meteorological forcing data included 2m air temperature and surface pressure, precipitation, incoming long and shortwave radiation, wind speed, and specific humidity. To run the ORCHIDEE model, we partitioned the in situ precipitation into rain and snowfall using a temperature threshold of 0 o C. The meteorological forcing data were gapfilled following the approach of  Vuichard and Papale (2015) , which uses downscaled and corrected ERA-Interim data to fill gaps in the site-level data. Eddy covariance flux data were processed to provide ET from estimates of latent energy fluxes. ET gaps were filled using a modified look-up table approach based on Falge et al.
Soil moisture data and processing Daily mean volumetric soil moisture content (VWC, m 3 m -3 ) measurements at several depths were obtained directly from the site PIs. For each site, Table  2  details the depths at which soil moisture was measured. Soil moisture measurement uncertainty is highly site and instrument specific, but tests have shown that average errors are generally below 0. 04 m 3 m -3 if site specific calibrations are made. We only used these measurements to evaluate the 11LAY model because, unlike the 2LAY model, with this version of the model we have model estimates of soil moisture at discrete soil depths. However, several factors mean that we cannot directly compare absolute values of measured versus modelled soil VWC, even though the 11LAY has discrete depths. First, site-specific values for soil saturated and residual water content were not available to parameterize the model; instead, these soil hydrology parameters are either fixed (in the 2LAY) or derived from prescribed soil texture properties (in the 11LAY -see Section 2.
Simulation set-up and post-processing All simulations were run for the period of available site data (including meteorological forcing and eddy covariance flux data -see Section 2. 3. 1 and Table  1 ). Table  1  also lists: i) the main species for each site and the fractional cover of each model PFT that corresponds to those species; iii) the maximum LAI for each PFT; and iii) the percent of each model soil texture class that corresponds to descriptions of soil characteristics for each site -all of which were derived from associated site literature detailed in references in Tables 1. The PFT fractional cover and the fraction of each soil texture class are defined in ORCHIDEE by the user. The maximum LAI has a default setting in ORCHIDEE that has not been used here; instead, values based on the site literature have been prescribed in the model (Table  1 ).
Acknowledgements Funding for AmeriFlux data resources and data collection at  US-SRM, US-SRG, US-Wkg , and  US-Whs  was provided by the  U. S. Department of Energy's Office of Science  and the  USDA. Data collection at  US-Fuf  supported by grants from the  North American Carbon Program/USDA CREES NRI  ( 2004-3511115057 ), the  U. S. National Science Foundation MRI Program, Science Foundation Arizona  ( CAA 0-203-08 ), the  Arizona Water Institute , and the  Mission Research Program, School of Forestry ,  Northern Arizona University (McIntire-Stennis/ Arizona Bureau of Forestry). The US-Vcp site funded by  U. S. DOE Office of Science  through the  AmeriFlux Management Project (AMP)  at  Lawrence Berkeley National Laboratory  (Award # 7074628 ) and  Catalina-Jemez Critical Zone Observatory  ( NSF EAR 1331408 ).
Code availability The ORCHIDEE model code and documentation are publicly available via the ORCHIDEE wiki page (https://forge. ipsl. jussieu. fr/orchidee/browser) under the CeCILL license (http://www. cecill. info/index. en. html). ORCHIDEE model code is written in Fortran 90 and is maintained and developed under an SVN version control system at the Institute Pierre Simon Laplace (IPSL) in France.
1 ). Other data products in this comparison include the fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis soil moisture product (ERA5; Copernicus Climate Change Service (C3S), 2017), the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA2;  Gelaro et al. , 2017) , and the Global Land Evaporation Amsterdam Model (GLEAM)  (Miralles et al. , 2011; Martens et al. , 2017). All three datasets have a spatial resolution of 0. 25 •. To avoid the integration of results from different climate zones, which are likely to show a dissimilar soil moisture response, we targeted only GLDAS pixels considered to be part of the humid tropics by creating a mask using data from the Köppen-Geiger climate classification system  (Kottek et al. , 2006)  obtained from the Spatial Data Access Tool (SDAT; ORNL DAAC, 2017a).
Each subsurface layer represented in GLDAS varies in depth up to an aggregated, multilayer maximum depth of 3. 5 m among the four models. We only used data from the uppermost group of soil layers within each model closest to a depth of 0-10 cm. This was done to target the near-surface soil moisture response to El Niño, as the El Niño   1  for multiple data products including the GLDAS multimodel mean (black, solid), MERRA2 (red, solid), ERA5 (blue, solid) and GLEAM (green, solid), as well as the individual land surface models that make up GLDAS NOAH (black, short dash), MOSAIC (black, dot), VIC (black, dash dot) and CLM (black, long dash). Note that the GLEAM time series starts in 1980. 2018 ). 13 Bonal et al. (2008), and see Acknowledgements. 14 Meijide et al. (2018), and see Acknowledgements.
The GLDAS data were compared to in situ data using the linear relationship shown in Eq. (  1 ). SM I = β 0 + β 1 • SM G , (1) where SM I is the in situ soil moisture observation (%), β 0 is the y intercept (%), β 1 is the slope and SM G is the GLDAS ensemble soil moisture estimate (%). The coefficients of the linear relationship in Eq. (  1 ) were used to provide a biascorrected estimate of soil moisture from GLDAS that was more representative of the near-surface in situ soil moisture observations. The bias-corrected estimates are compared to in situ observations to assess how the application of the biascorrection method improves the representation of soil moisture at the point scale. In situ soil moisture observations were compared to corresponding GLDAS soil moisture estimates at co-located depths for pixels that encompassed the in situ observation.
In some situations, adjacent pixels were used if data from the co-located GLDAS pixels were missing, e. g. , over lands adjacent to inland water bodies or oceans, due to the coarse resolution of the GLDAS dataset. The same data comparison was made after removing data from one site in Ecuador and another from Australia. In situ observations from these sites were not likely to be representative of the GLDAS data at 1 • resolution given that the sites where data were collected are either located at a high elevation of 3780 m or seasonally flooded wetland where the subsurface soil is frequently saturated. Observations from one site in Brazil were also removed due to poor agreement between observations and GLDAS data relative to other sites. The comparison of soil moisture from GLDAS to in situ point-based measurements does have an inherent scale mismatch.
For example, measurements at an individual site may not necessarily represent soil moisture conditions at the scale of a GLDAS pixel due to heterogeneities in land cover, soil or topography. However, given the previously noted challenges regarding the dearth of large-scale moisture measurements in the tropics, the site-based data represent the best available source of actual soil moisture content in this region. Scale mismatch effects are also moderated by use of multiple sites spanning multiple continents. Site-based measurements of soil moisture considered to be outliers in terms of how they compare to the co-located GLDAS pixel soil moisture estimate are examined further in the discussion section. The soil moisture response to El Niño for the three super El Niño events of  1982-1983, 1997-1998 and 2015-2016  was calculated by taking the difference in the GLDAS soil moisture during the El Niño mature phase of October to December (OND) and January to March (JFM) from the longterm 1979-2016 climatological monthly mean (Eqs.