dataesr/scientific-paragraphs-categorization huggingface dataset:
The gross primary productivity (GPP) is influenced by nutrient availability, climate conditions such as sunshine, relative humidity, temperature and CO 2 concentrations in the atmosphere as well as by the maximum rate of carboxylation V cmax , which corresponds to the maximum rate of RuBisCo. The lower productivity in peatlands is represented empirically by reducing V cmax by a factor δ N corresponding to the nitrogen limitation. This limiting factor δ N is chosen such that the amplitude of simulated daily mean GPP is consistent with the observed daily mean GPP. For this study, the observation site chosen to adjust the GPP is the Swedish peatland site of Degero (see site description, Sect. 2. 3. 1), which is a fen covered by herbaceous vegetation  (Schubert et al. , 2010). After applying an appropriate limiting factor, the simulations are forced by meteorological data of Degero using the FLUXNET database  (Baldocchi et al.
Site simulations Firstly, we evaluate the modelled peatland processes with site measurements from the FLUXNET meteorological database  (Baldocchi et al. , 2001)  for the peatland sites of Degero, Fajemyr and Siikaneva  (Lund et al. , 2009; Rinne et al. , 2007; Nilsson et al. , 2008; Sagerfors et al. , 2008). The advantage of using the FLUXNET database is that it provides continuous meteorological data such as precipitation and temperature, which help to compare data of net ecosystem exchange (NEE) or WTD. The site of Fajemyr is located in southern Sweden (56 • 15 N, 13 • 33 E). It is an ombrotrophic bog in a temperate climate at 140 m altitude. The annual mean  temperature and precipitation are 6. 2 • C and 700 mm, respectively. The WTD is generally below the surface, where many mounds can be seen with moss, sphagnum, sedge vegetation and small shrubs  (Lund et al.
, 2004). GRACE provides estimates of mass anomalies by measuring the variations in the geopotential field between the two satellites. These estimates quantify changes in the water mass of land surfaces, ice and oceans. The GRACE data are available from April 2002 until June 2014. They are interpolated in a grid cell of 1 • × 1 • resolution with monthly data of total water mass variations TWS, which is defined as the difference between the total water mass obtained at the time of the TWS measurements and the average total water mass between January 2004 and December 2009. In this study, we use GRACE release 5 spherical harmonic coefficients from the Jet Propulsion Laboratory (JPL-RL05), available from http://podaac. jpl. nasa. gov/grace (last access: 2 August 2018). The impact of peatlands on river discharge has been evaluated for different catchments.
Satellite observations cannot identify frozen flooded areas. Therefore the comparison with satellite flooded area was restricted to the thawing season. Since satellite observations account for all open-water surfaces, these data are counted only when the observed flooded areas coexist with the grid points with a non-zero peatland fraction. Here, a peatland is considered flooded when the monthly av-erage water table position is between the soil surface and 10 cm above the surface. Figure  11  shows the observed and simulated flooded surface areas during the month of June, when the fraction of frozen soil is the lowest with a large extent of flooded area. The geographical distribution of flooded peatlands in the June monthly mean is mainly located in eastern Canada and central Russia. Satellite observations of  Prigent et al.
This is insufficient to influence the seasonal and interannual variations in the TWS in northern latitudes. However, the ORCHIDEE model in the standard version (STD) underestimates the interannual variability in TWS compared to the observations. In boreal regions, the interannual variability in TWS is best explained by the change of precipitation or discharge  (Landerer et al. , 2010). A study with different meteorological forcing data could be made to better understand the bias due to the meteorological forcing used in this study. The seasonal and interannual variations in water mass from the humidity of peat soils are reduced by 65 % when the hydrological processes of peatlands are activated. Changes in snow mass contribute to an average of 74 % of the total variation in TWS. These simulated variations are underestimated compared to the observations of the GRACE satellite  (Tapley et al.
The new scheme represents peatland hydrology relatively well on a large scale, without disrupting the large-scale hydrology of the surface model. The peatland hydrology has only small effects on the simulation of northern river discharge. This implementation will be further used to estimate the future evolution of the hydrology of peatlands and their associated methane emissions at the end of the century. Code and data availability. The documentation and the code of the trunk version of ORCHIDEE are open source and can be found here: http://forge. ipsl. jussieu. fr/orchidee (last access: 2 August 2018). The branch  MICT-v1 (rev. 1255)  of the code used in this paper with the development of peatland ORC-HL-PEAT (rev. 3058) can be found here: https://github. com/CLargeron/ORC-HL-PEAT/ tree/MICT-PEATLAND (last access: 2 August 2018).
This path also includes the PFT map with peatlands which is needed to run the version ORC-HL-PEAT of the model. Readers who are interested to run this model are encouraged to read the documentation of ORCHIDEE to understand how to use the model and to contact the corresponding author for further details on the peatland scheme. External data set from GRACE land are available at http:// grace. jpl. nasa. gov, supported by the NASA MEaSUREs Program. FLUXNET data collection at ORNL DAAC are available at https: //fluxnet. ornl. gov/. Author contributions. Model development, validation and evaluation was principally carried out by CL. This study has been supervised by the co-authors. Competing interests. The authors declare that they have no conflict of interest.
Acknowledgements. This study has been supported by the  PAGE21  project, funded by the  European Commission   FP7-ENV-2011  (grant agreement no. 282700 ) and the  European Research Council Synergy  grant  ERC-2013-SyG-610028 IMBALANCE-P. Simulations with ORCHIDEE were performed using computational facilities of the  TGCC-Curie (CEA, France). Edited by: Gerd A. Folberth Reviewed by: two anonymous referees.
Mat-CHN software was developed specifically to resolve these difficulties, by delivering the individual mole numberof each constituent assayed in the compound analyzed, leading to an accurate comparison of the theoretical and experimental results of centesimal compositions. Results: Apart its large capabilities for simulations, Mat-CHN increases the interest of the Elemental Analysis technique for scientists working in discovery fields or when new molecular entities are promoted to predevelopment and development stages, either for absolute titration of their reference standards or for direct titrations of the drug substance itself. The software conducts to simple titration determinations, based on Nitrogen "Kjeldhal" assays and/or molecular weight reports, compared to data of the pure substance.
Mat-CHN Software principles Mat-CHN software was specifically developed, to simplify microanalysis interpretations, based on a unique recalculated composition, including all the individual assayed components and/or impurities in the analyzed substance. In a few seconds, Mat-CHN generates the corresponding mole number and composition of the whole redefined raw formula, leading to the possible comparison of the experimental microanalysis results and finally to the titration of the analyzed substance. The principles of calculations of Mat-CHN do not include any approximation, recursive processes with target options (e. g. , percent of convergence), and need nothing more than individual data experimentally determined. This means also that Mat-CHN's recalculated centesimal compositions must be considered as experimental results.
This box is supported by an application (button "organic and mineral assays residues"), to manage a personal library of pre-described and new-described components (including raw formula, molecular weight, charge, number of hydrogen atoms to add or to subtract). This box 3 can integrate up to 36 (18 per set of columns) contents of components. After the calculation process, mole numbers are reported (column "Nb moles, " results in red or gray). -{Box 4}: corresponds to the entry of the experimental results of the Elemental Analysis for the five elements (C, H, N, S, and O). Two sets of experimental data are recordable. -{Box 5}: is the specific place of the water content determination, two experimental results are recordable. On the left, the average content calculation is provided. -{Box 6}: corresponds to the comparisons of the experimental and the recalculated elemental compositions, when the "CALCULATE" button is activated.
The base of the calculation is summarized in the generic equation Eq. (1): A 100 ¼ nb:of mol x A ð Þ Â m A Mt Considering A as an element (C, H, N,. ) or as a component (H 2 O, impurity, Na + or Cl -ions,. ), the content, (Box 6) {Box 1} {Box 2} {Box 3} {Box 4} {Box 5} {Box 7} Fig. 1  Screenshot of user interface of the MAT-CHN software resulting from the dedicated analysis, expressed in w/w% will follow this equation Eq. (  1 ). The nb. of mol x A is unknown, but can be easily extracted from this equation Eq. (1) if Mt is known. The Mt value, corresponding to the total molecular weight of the analyzed compound, including components, should be determined according to this second equation: Mt ¼ M free ð Þþ X C x Â m x -e Â C x Â m h ð Þ þ X A y Â m y þ e Â A y Â m h À Á þ W z Â m z þ X I t Â m t ð2Þ in which: -M(free) corresponds to the molecular weight of the pure substance -∑C x × m x -(e × C x × m h ), corresponds to the "molecular weight" of the moles number of cations, excluding the same mole number of hydrogen in the analyzed substance, as a result of the neutralization of acidic functions (in the case of alkaline salts).
The mathematical software development presented here was focused on this particular aspect, leading to a unique solution of contents of components to reach only one redefined raw formula of the analyzed compound, which is a critical point in our activities for research and development of NMEs. For reasons of costs and also for having a simple and direct access to centesimal composition calculations, the software proposed here was designed to be supported by a generalized "Office" interface implemented on each computer in the company. The program is written in Visual Basic for Excel® and does not need any knowledge in this language for the user. The objective of this program is not to replace what the other software can provide, but to give a simple and quick access to centesimal compositions, to everyone, through accurate calculations based on the resolution of a system of equations.
Performances of the software As explained in the previous paragraph, the core of Mat-CHN software is a solver of systems of equations based on matrix calculations. A second and non-negligible functionality of this software (not developed here) is the by-user management of its personal library of molecules or ions, for integration of pre-defined and new-defined components in the calculations. Using this library, the user will be unlimited in the choice of components. He will have also the possibilities to add or to delete components in this library every time. It may be important to notice here that no recursive process is used in Mat-CHN for these mathematical resolutions, or convergence calculations, but only one direct calculation. This is also a critical aspect to obtain a quick and unique result.
Uses of Mat-CHN software for pre-development and development purposes Another and non-negligible aspect of the Elemental Analysis technique is the one related to the pre-development and development fields of activities. Currently, titration determinations for batch controls of drug substances are provided against reference standards, for which definition of the strength is based on specific chemistry processes and accuracy of analytical methods. This means that the absolute assay value of the primary standard (governing the assay value of the reference standards batches) should be determined accurately and verified on a regular basis. Of course, the greatest the purity of this particular batch will be, the less the difficulty to define the assay value around 100% will be. This is frequently the first strategy applied by the companies, which make the statement on the absolute purity of the primary standard batch, when the last recycled purification did not lead to a lower impurity profile than the one obtained during the same penultimate operation.
Conclusions The Mat-CHN principles of calculations have been presented in this work in the theoretical aspect and through practical examples in the contexts of the discovery and the development activities for new molecular entities. This software has the advantage of delivering a unique and reliable solution to the system of equations, resulting in individual tests dedicated to determine mineral and organic contents (expressed in percent and in mole numbers) for substances associated to the main compound analyzed. Mat-CHN software can support analytical determinations obtained in the range of ppm and ppb units, mixed with other results delivered at the percent scale. Without recursive process or convergence test, in a very quick time (around 1 s) and implemented on a classical computer, the scientist is directly oriented to the unique redefined structure, to make comparisons of experimental and recalculated Elemental Analysis results.
Titration of the substance analyzed can be easily delivered, based on the ratio of the molecular weight of the pure compound to the one of the refined molecule in the context of discovery and based on the nitrogen content ratio in the case of development activities, as recommended by the regulations. In these two cases, Mat-CHN software plays a central role to deliver the right figures to make the appropriate calculations. And because no recursive process and no other data added than the ones experimentally obtained, the titration calculations delivered by Mat-CHN should be considered also, as experimental values. If the interest in Elemental Analysis was sometimes limited by the missing capabilities to complete the definition of the structure of the analyzed compound, Mat-CHN is probably one of the first software to support and to reinforce this analytical technique.
Acknowledgements This work was initiated for editing certificates of analysis in our R&D environment. Consequently, without help of chemists and analysts involved respectively in batch syntheses and analytical results, this work would have never been put in place. I thank all these teams for all the different and complicated cases, they submitted to me, to validate the robustness of this software. I will never forget the involvement of the analytical team of  Nathalie Hasel , in charge of the analytical support to the medicinal chemistry, for structural analysis purposes with  Armèle de Guinée ,  Stéphanie Lessertois , and  Valérie Bègue  also in charge of the elemental analysis activities and for purity determinations with  Christine Arnal ,  Daisy Leguevaques , and  Fabien Millac  also in charge of ion analysis activities.
Our aim here was to provide to researchers interested in drug discovery, regardless of their scientific backgrounds (biologists, chemists, clinicians, computer scientists…), a user-friendly structure-based virtual screening protocol combined with prepared and purchasable approved drug and natural compound collections. We indeed decided to focus here only on purchasable molecules such as to facilitate the discovery process. We thus constructed a compound collection named Drugs-lib, by compiling and filtering four databases of approved drugs to produce a final library that includes only purchasable approved drugs presenting structures suitable for docking computations. We proceeded the same way to obtain two other electronic libraries of natural compounds, one is named FOOD-lib that includes food constituents and the NP-lib dedicated to natural products.
Purchasable approved drugs, food and natural compounds libraries Compounds libraries generation. Drugs-lib, FOOD-lib and NP-lib are compounds libraries that contain approved drugs, food constituents and natural products, respectively, optimized for structure-based virtual screening studies. The three compounds libraries are implemented in the MTiOpenScreen web server  [23]  that allows user to perform structure-based virtual screening computations on a selected protein target. The three compounds libraries were built using the same protocol (see Methods section and Figure  1 ) that includes the use of FAF-Drugs4  [24]  physico-chemical and toxicophore filtering (Figures 2A and Supplementary Figure  1 ), a visual inspection step to remove compounds not suitable for docking (e. g. , compounds with long aliphatic flexible side chains, compounds with very complex structures, …) and the assessment of their purchasability according to the ZINC15 database  [25].
The Drugs-lib was constructed by using as a starting point four databases of approved drugs, the "drug" subset of the ChEMBL database  [26] , the "approved" subset of DrugBank  [27]  version 5. 0. 10, the DrugCentral online compendium  [28]  and the "approved" SuperDrug2 database version 2. 0  [29]. A total of 21,276 compounds were initially included in the Drugs-lib construction process, but only 7173 stereoisomers corresponding to 4574 single isomer drugs were kept after removal of the duplicates and the filtering protocol mentioned in the method section (Table  1 ). The 26,941 food constituents of the FooDB (http://www. foodb. ca/) with available smiles strings were used as input for the FOOD-lib generation. After removal of the duplicates and the filtering steps, the FOOD-lib included 10997 stereoisomers corresponding to 3015 single isomer food constituents (Table  1 ).
A purchasable diverse library of natural products of 1237 compounds  [30]  was processed to construct the NP-lib that gathers, after the filtering steps, 1228 stereoisomers corresponding to 653 single isomer natural products (Table  1 ). Compounds libraries diversity. For each compound, all stereoisomers tagged as purchasable in the ZINC15 database were included in the corresponding compounds library. Up to 38, 42 and 12 stereoisomers per unique compound are counted in respectively the Drugs-lib, the FOOD-lib and the NP-lib. The average number of stereoisomers per molecule (Supplementary Figure  2 ) is www. oncotarget. com  of 1. 6 for the Drugs-lib (median value equal to 1), 3. 6 for the FOOD-lib (median value equal to 2), and 1. 9 and for the NP-lib (median value equal to 1). The structural diversity of the compounds included in each data set was investigated using atom pair fingerprints (Figures 2B Supplementary Figure  3 ).
Numerous CDK2 inhibitors have already been reported in the literature, and the CDK2 binding site is well-characterized. In the Protein Data Bank (PDB), 358 human CDK2 structures co-crystallized with a ligand (holo structures) are available. We selected the 4EK4 and 4KFL PDB structures to perform the virtual screening computations, as they are holo PDB structures, i. e. cocrystallized with a ligand, that present the best resolution. In the 4EK4 and 4KFL MTiOpenScreen service outcomes, fluspirilene was ranked respectively at position 186 and   1 ). We repeated four times the screening protocol using the 4EK4 structure to ensure the repeatability and the reliability of the results (Supplementary Table  1 ). The AutoDock Vina scores and ranks associated to fluspirilene in these four runs were highly similar to those obtained in the first screening run with 4EK4, with scores varying between -9.
4 to -9. 3 and ranks ranging from 149 to 190. To reduce the computational time associated with MTiOpenScreen service virtual screening of the Drugslib on each of the 358 holo CDK2 PDB structures, we selected a representative dataset of 44 CDK2 proteins (Supplementary Table  1 ) that was used to identify fluspirilene as a CDK2 inhibitor  [35]. The best result was obtained using the 1PXO structure, for which fluspirilene was ranked 48 with a predicted AutoDock vina score of -11. 1 (Supplementary Table  1  and Figure  4 ). For all the other structures, except 1E1V, fluspirilene was ranked in the top 1500 best compounds, with ranks ranging from 83 to 1048 and AutoDock Vina scores between -12. 2 to -8. 4 according to the PDB structure used (Supplementary Table  1  and Figure  4 ). To get some insights about the diversity of molecules identified as hits by our virtual screening protocol, we used the 1500 best scored compounds associated with the 4EK4 PDB structure and we computed in DataWarrior  [31]  the similarity between each pair of compounds according to the FragFp descriptors (Supplementary Figure  5 ).
The predicted binding site of raloxifene is located at the surface of the GP130 protein, in the GP130/IL-6 D1 domain interface. In the PDB, three crystal structures of the human GP130 protein including this D1 domain are available (PDB IDs: 1I1R, 1P9M, 3L5H). We used the MTiOpenScreen service to screen the Drugs-lib against the 1P9M structure as it was the only PDB structure of the trimer IL-6/IL-6R/GP130. Raloxifene was ranked 1088 with a predicted AutoDock Vina score of -6. 5 kcal/mol (Supplementary Table  3 ). We repeated the screening protocol five times using the 1P9M structure to ensure the repeatability and the reliability of the results (Supplementary Table  3 ). Similar raloxifene's AutoDock Vina scores (from -6. 4 to -6. 5) and ranks (from Figure  4 : Graphical representation of the retrospective virtual screening results for each example of repositioned drug -oncogenic target system: fluspirilene -CDK2, mebendazole -VEGFR2, raloxifene -IL6/GP130, sulindac -AKR1C3 and thalidomide -cereblon (each system is represented by one line).
Purchasable approved drugs, food and natural compounds libraries Compounds libraries generation. The three virtual libraries of compounds were constructed by gathering and/or filtering available databases of approved drugs and natural products. To construct the Drugs-lib, four databases of approved drugs were used as initial sets of compounds: the "drug" subset of the ChEMBL database  [26] , the "approved" subset of DrugBank  [27]  version 5. 0. 10, the DrugCentral online compendium  [28]  and the "approved" SuperDrug2 database version 2. 0  [29]. We assumed that selecting these four databases should ensure an exhaustive inclusion of approved drugs in the Drugs-lib. Even if precisely quantify the overlaps among these four databases is impaired by drugs name and stereochemistry variations between the databases, we could obtain an approximation using the babel "remove duplicates" utility (Supplementary Figure  12 ).
Around 3000 compounds are common to all databases and the majority of compounds are found in the ChEMBL 'drug' subset, but each of the three remaining databases provides around 100 additional unique compounds. The FOODB-lib is dedicated to food constituent molecules and initially included the compounds of the FooDB (http://www. foodb. ca/) with available smiles strings. Finally, several databases of natural products are available  [54] , but we chose to use as a starting point for the NP-lib, a purchasable diverse library of natural products of 1237 compounds  [30]. The compounds of this diverse library were extracted from the UNPD database  [55]  and the Dictionary of Natural Products  [56]  according to a protocol developed to select a small but representative subset of natural compounds.