dataesr/scientific-paragraphs-categorization huggingface dataset:
Pure sedimentation The first test case corresponds to a pure sedimentation of non-cohesive particles; this test case allows us to validate the implementation of the pressure-velocity coupling algorithm when the flow is induced by the sediment phase. The other component that is tested here is the permanent contact pressure model (Eq. 25) for p ff that allows us to predict a stable deposited sediment bed. The experimental dataset from Pham Van Bang et al. (  2008  = 950 kg m -3. The suspension is initially well-mixed in a cylindrical container (base diameter 50 mm, height 100 mm) with an initial solid volume concentration (α 0 = 0. 5). The averaged concentration profiles are measured using a proton MRI device (Ecole de Ponts ParisTech, Champs-sur-Marne, France) with a vertical resolution of about 1 mm and a temporal frequency of 0.
16 Hz (see Pham  Van Bang et al. , 2008, for details). This test case has been used by  Chauchat et al. (2013)  to validate a 1-D vertical two-phase flow model. A script for computing the solution is provided in the tutorial folder coming with the release 2. 0 of SedFoam-2. 0. The mesh is composed of 200 cells in the vertical direction with a uniform distribution over a height h 0 = 0. 06 m and the time step, t, is set to 10 -3 s. The numerical schemes for temporal and spatial derivatives are listed in Table  8 , and details of these numerical schemes can be found in the user guide of OpenFOAM. The lateral boundaries are set to cyclic while the front and back boundaries are set to empty (i. e. , 2-D problem). At the top boundary, the pressure is fixed with a zero value, and a zero gradient (i.
0; however, it was imposed as constant in our earlier work. The details concerning the analytical solution can be found in  Ouriemi et al. (2009)  and will not be further detailed herein. A script for computing the solution is provided in the tutorial folder included with the release of SedFoam-2. 0. The numerical domain setup is based on the experimental configuration of  Aussillous et al. (2013). The channel height is h 0 = 0. 065 m and the particles are made of PMMA with a density ρ a = 1190 kg m -3 and a diameter d = 2 × 10 -3 m. The fluid density is ρ b = 1070 kg m -3 and the kinematic viscosity is ν b = 2. 52 × 10 -4 m 2 s -1. The pressure gradient is fixed to f x = 100 kg m -2 s -2 using the parameter gradP-MEAN in the file constant/forceProperties. The vertical domain is discretized into 200 uniform cells, and the time step is t = 10 -3 s.
This explains the very good numerical prediction of the velocity profile. In Fig. 2d -f, the exact same problem is solved using the non-linear dense granular-flow rheology µ(I ) (see Table  7 ). This solution is compared with the solution of the 1-D code described in  Aussillous et al. (2013). The very good agreement between the two numerical solutions allows us to validate the implementation of the µ(I ) rheology in SedFoam-2. 0. It should be pointed out that, for this problem, the shear induced pressure contribution is not turned on, explaining why the sediment concentration and the particle pressure profiles are not affected by the change in the granular rheology.
Acknowledgements. Julien Chauchat,  Tim Nagel , and  Cyrille Bonamy  are supported by the  Region Rhones-Alpes  ( COOPERA project and Explora Pro  grant), the  French  national programme  EC2CO-LEFE MODSED. Zhen Cheng  and  Tian-Jian Hsu  are supported by  National Science Foundation  ( OCE-1537231 ;  OCE-1635151 ) and  Office of Naval Research  ( N00014-16-1-2853 ) of  USA. Numerical simulations were carried out on MILLS/FARBER at the  University of Delaware , on HPC resources from  GENCI-CINES  (Grant  2015-x2016017567 ) and on the Froggy platform of the CI-MENT infrastructure (https://ciment. ujf-grenoble. fr), which is supported by the  Rhone-Alpes region  ( GRANT CPER07-13 CIRA ) and the  Equip@Meso project  (reference  ANR-10-EQPX-29-01 ) of the programme Investissements d'Avenir supervised by the  Agence Nationale pour la Recherche.
The authors would also like to acknowledge the support from the program on " Fluid-Mediated Particle Transport in Geophysical Flows " at the  Kavli Institute for Theoretical Physics, Santa Barbara, USA. The laboratory LEGI  is part of the  LabEx Tec 21  ( Investissements d'Avenir -grant  agreement  nANR-11-LABX-0030 ) and  Labex OSUG@2020  ( ANR10 LABX56 ). We are grateful to the developers involved in OpenFOAM who are the foundation of the model presented in this paper. Edited by:  James R. Maddison Reviewed  by: two anonymous referees.
Code availability. The code is distributed under a GNU General Public License v2. 0 (GNU GPL v2. 0) and is available at https: //github. com/SedFoam/sedfoam/releases/tag/v2. 0 or on Zenodo at https://zenodo. org/record/836643#. Wc47Yoo690s with the following DOI https://doi. org/10. 5281/zenodo. 836643 (Chauchat et al. , 2017). The source code has been compiled and extensively tested with OpenFOAM 2. 4. 0. The input files for all the four test cases presented in the paper are available in the tutorial folder of the distribution together with Python scripts for post-processing of the tutorials. The Python scripts require the fluidfoam package to be installed and it is freely available at http://bitbucket. org/sedfoam/fluidfoam.
Results and Discussion Virtual screening. A structural analysis of all ligand-enzyme interaction in the catalytic pocket of Arg1 was conducted using all structural data available in the Protein Data Bank (PDB). Two distinct domains of the catalytic cavity were distinguished: (a) the region binding to the L-amino acid moiety shared by the substrate, product and all known potent arginase inhibitors (interactions with Asp183 and Ser137) and, (b) the ion cluster stabilized by His101, Asp124, His126, Asp128, Asp232 and Asp234 (Fig. 1 ). A 3D-pharmacophore model describing key interactions identified for the binding of a transition state analogue was constructed (see supporting information for detailed methods). This model was used to screen databases of commercial compounds, leading to the selection of 19 compounds, purchased and tested experimentally in a radiometric assay  36.
Methods Molecular dynamics. Crystal structures with the following code were retrieved from the Protein Data Bank (PDB): 3GMZ for the Arginase1-ornithine complex  29  , 2AEB for the arginase-ABH complex  34  , and 2PHA for the apo form of Arginase1  28. The complex cyclohexyboronic acid-arginase was manually built from the 2AEB crystal structure, replacing the amino acid and aliphatic side chain of ABH by the cyclohexane of BA-25 using Molecular Operating Environment (MOE). All systems were prepared with the Maestro protein preparation tool to add hydrogens, remove the flexible and solvent exposed C-terminal loop 309-HKPIDYL-315, cap termini, and coordinate the ion cluster with zero bond order. Residues/atoms coordinating the first Mn ++ are HIS126/ND1, ASP234/OD1 + 2, ASP124/OD1, ASP232/OD2 and the second Mn ++ are ASP124/OD2, ASP128/OD2 HIS101/ ND1 and also ASP232/OD2.
(c) Sequential information: Occurrence of superfeatures and their interactions are described sequentially in form of bar code series (superfeature occurrence sequence and interaction occurrence sequence) as well as interaction distances in form of distances series (interaction distance sequence). The dynophore program has been implemented within the ilib/LigandScout framework  64. Enzymatic assay. Arginase inhibition was measured using a modified version of the fixed-point radioactive assay developed by Ruegg and Russell  36. Assay mixtures contained 50 µL of 100 mM 2-(cyclohexylamino) ethanesulfonic acid (CHES)-NaOH (pH 9. 0), 100 µM MnCl2, 0. 05 µCi of [guanidino-14C]-L-arginine, 3 mM unlabeled L-arginine, and 5 µL of inhibitors in a 50 µL volume per centrifuge tube. Reactions were started by the addition of 5 µL of a10 µg/mL recombinant human Arginase1 solution.
After 5 min, reactions were quenched by the addition of 400 µL of "stop" solution (0. 25 mM acetic acid (pH 4. 5), 7 M urea, 10 mM L-arginine, and a 1:1 (v/v) slurry of Dowex W-X8 in water). Each reaction mixture was immediately vortexed for 30 s after the addition of "stop" solution, and then centrifuged at 6000 rpm for 10 min. 200 µL of the supernatant was taken and 3 mL of scintillation liquid (Ultima Gold tm , PerkinElmer ® ) was added in preparation for liquid scintillation counting using a PerkinElmer ® counter (model: Tri-Carb 2900TR). The DPM value obtained for the negative control (con- taining no enzyme) was systematically subtracted. Results were expressed as percent of activity compared to the positive control (without inhibitor), after GraphPad Prism software was used to treat the data and to analyze the dose-response curves.
Simulation set-up ORCHIDEE-GM v2. 2 is integrated over Europe using the harmonized climate dataset from the ERA-WATCH reanalysis at a spatial resolution of 0. 25° by 0. 25° for the period 1901-2010  [49]. A harmonized climate forcing and dynamical downscaling was used for future climate simulations based on the general circulation model (GCM) ECHAM5 (2010-2100) using 1970-2010 as the reference period to correct the GCM bias. Namely, results of the regional climate model (RCM) REMO driven by ECHAM5 GCM climate for the SRES A1B scenario provided by the ENSEMBLE project (http:// www. ensembles-eu. org/) are bias-corrected  [49]  and used in this study. This forcing is described in Beer et al. [49] , and hereafter referred to as bias-corrected REMO + ECHAM5 climate. The SRES A1B scenario has a rapid increase in fossil CO 2 emissions until 2050 and a decrease afterwards, and the projected atmospheric CO 2 concentration reaches 527.
Based on these results, we estimated the relationship between utility and %BTcP avoided to assess the corresponding utility values calculated at 15, 30, 45 and 60 minutes. The utility was estimated with the following formula: Utility ¼ ðð0:1765 Ã e ð0:0073Ã%BTcPÞ Þ Ã 1:0082Þ Ã 2 The details of the methods used to estimate this formula are reported in the data in S1 File.
The reliability of the base case results obtained with our model was tested with an extended number of sensitivity analyses (Table  5 ). In these analyses we assessed different scenarios, changing the treatment duration, the BTcP duration and the health care resources consumed. In all the analyses, FCSL resulted as the cost-effective option compared to the other formulation. The major impact on the results were reported in the scenario with a BTcP duration of 60 minutes and in the scenario with the health care resource estimated as function of %BTcP avoided. This study had some issues that should be discussed. First, the impact of fentanyl formulation on utility values was derived by Vissers et al. that estimated this data with the TTO method in a UK general population  [17]. More reliable data could be collected using a recommended generic quality of life questionnaire as the EQ-5D  [38] ; however, at the moment of the analysis, the data provided by Vissers et al.
No funding sources were provided. PAC received consultation fee by  Angelini  spa. LSD received speaking honoraria by Angelini spa. RV had intellectual scientific activities, participation to scientific boards, invitations to Meetings as speaker. MA received research grants by  European Community  ( FP7 Project ) and  Italian Health Ministry , he also received consultation fees by Grunenthal, Mundipharma,  MSD ,  Angelini  and  CareFusion.
Imperfect models need to be used to help guide future eld and laboratory studies and future model development. We believe we have been honest about the performance of our model against observations and identifying its failings. We do not believe that suppressing modelling until they are able to "match" observations is going to help drive our understanding of atmospheric processes forwards in a useful way. Modelling and measurement need to go hand in hand, informing one-another and supporting the different scientic methodologies. However, much of the impact that we see in the model comes from the global response of ozone to halogens and the regional impact. This is much more driven by iodine and bromine chemistry. Dwayne Heard commented: In the paper several datasets of radical species from eld campaigns are mentioned, in the context of comparison with the output from the GEOS-CHEM model.
It is recognised that the paper is only looking to give a rough comparison with measurements, but it is worth pointing out that the footprint of the measurement technique can make quite a difference. For example, for IO radicals, datasets at Mace Head and Brittany are discussed with a range of values of the mixing ratio of IO given. It should be stressed that the range of values quoted comprises both long-path DOAS measurements of IO and in situ measurements of IO using laser-induced uorescence. It is known that for the same site these values can differ signicantly owing to the heterogeneity of the distribution of iodine sources. For example, at Mace Head, DOAS and LIF values vary by up to a factor of 10, as the LIF instrument is very close to the exposed macroalgae (so gives higher mixing ratios) whereas the long path of the DOAS over several km is mostly over the ocean surface rather than exposed macroalgae so the average mixing ratio along the long-path is much less, with the ratio depending on the depth of the tide and the wind direction.
Tomás Sherwen replied: As you point out there are a range of issues when comparing the concentration of short lived species both with other observations and with models. The current observational datasets for species such as IO have focussed on coastal locations with substantial spatial gradients which leads to these differences between local and spatially integrated techniques. Future observations in open ocean locations where these gradients are less dened would probably offer a more critical and useful assessment of model performance. Dwayne Heard asked: The paper reports an average reduction in surface OH mixing ratio of 16%. I wondered what the equivalent change was for HO 2 radicals? One could imagine this would be larger, owing to the direct reaction of HO 2 with IO and BrO. Can the change in OH and HO 2 when halogens are included be compared with existing eld datasets of OH and HO 2 ? There are some in the Mediterranean, for example during the MINOS campaign in Crete.
Daniel Knopf answered: When considering implementation of our type of data into models two aspects have to be addressed. First, representation of the phase state of organic aerosol (OA) and secondary organic aerosol (SOA) particles under typical atmospheric conditions. Second, parameterization of ice nucleation rates for representative OA and SOA particles. Both aspects depend on the scale of the model, e. g. a climate model, cloud resolving, and/or atmospheric chemistry model. Shiraiwa et al. 1  very recently developed a parameterization scheme implemented in the global chemistry climate model EMAC 2 that includes the organic aerosol module ORACLE 3 to predict the phase state of SOA particles. Since the time steps of climate models are usually large, detailed microphysical parameterizations, as derived from our experiments, cannot be directly implemented except when employing great simplications (e.
Andreas Wahner said: If, as you stated, an "easy" parameterization for SOA formation dependent on biogenic precursors works better in climate runs for CMIP compared to a now available detailed chemical mechanism, what chance do you estimate for process oriented understanding of atmospheric chemistry to be used in global models ? How do we achieve a process based understanding and modelling of the atmosphere? Nadine Unger answered: I refer back to my response to Ravi's earlier question. What is the simulation goal/objective of the global model exactly? Are you using a global CTM to understand SOA formation and processes in different regions with aircra measurement datasets, or are you using a global CCM to calculate the radiative effects of SOA in the present and future worlds? Process-orientated understanding of atmospheric chemistry and SOA formation is already available in many global CTMs, e.
Rare phyla also identified for the first time in this process included Acidobacteria, CKC4, Chlorobi, Fibrobacteres, Gemmatimonadetes, Nitrospirae and TM6 (bacteria), and Chytridiomycota (fungi). No differences in microbial communities and colonization dynamics were observed between early and standard flax harvests. In contrast, the common agricultural practice of swath turning affects both bacterial and fungal community membership and structure in straw samples and may contribute to a more uniform retting. Prediction of community function using PICRUSt indicated the presence of a large collection of potential bacterial enzymes capable of hydrolyzing backbones and side-chains of cell wall polysaccharides. Assignment of functional guild (functional group) using FUNGuild software highlighted a change from parasitic to saprophytic trophic modes in fungi during retting.
Experimental Design-Study Site-Sampling Flax plants (Linum usitatissimum L. , Cultivar Lorea) were sown on 14 March 2014 near Martainneville (F-27210 Region Hautsde-France) in the north of France (50 • 00 ′ 03 ′′ N and 1 • 42 ′ 27 ′′ E). Plants were cultivated and retted on a typical silt loam soil with a neutral/slightly acid pH (INRA Soil Analysis Laboratory, LAS, Arras, France, http://www. lille. inra. fr/las) (Supplementary Table  1 ). Climatic data during the retting period was obtained from the Abbeville meteorological station at 10 km from Martainneville (infoclimat: http://www. infoclimat. fr/observations-meteo/ temps-reel/abbeville/07005. html) (Supplementary Figure  1 ). "Early" and "standard" flax cultures were pulled (up-rooted) on the  16. 07. 2014 and 24. 07. 2014, respectively and dew-retted  in the field until the 25.
Sequence Processing A bioinformatic pipeline based on mothur v. 1. 37. 4 (https:// github. com/mothur/mothur/releases) (Schloss et al. , 2009)  was configured to process the bacterial 16S rRNA gene sequences. This pipeline uses the standard Schloss lab operating procedure (http://www. mothur. org/wiki/MiSeq_SOP). Pair-End (PE) FASTQ files were overlapped to form contiguous reads in a single FASTA file with zero differences to the primer sequence and a quality score threshold of 30. Sequences with the following characteristics were removed: ambiguous bases and mismatches, <300/> 500 bp, homopolymers >8 bp, overlap <30 bp. Bacterial sequences were aligned against both SILVA (SSU SILVA 123) and Greengenes (August 2013 release, for input PICRUSt) reference databases. A pre-clustering was done to reduce noise as recommended  (Pruesse et al.
, 2007; Huse et al. , 2010)  allowing for up to four differences between sequences. Chimeras were detected and removed de novo with the UCHIME (version 4. 2) algorithm  (Edgar et al. , 2011). The clustering of the non-chimeric sequences to Operational Taxonomic Units (OTUs) was done by de novo clustering at 0. 03 cut-off of dissimilarity using neighbor based on genomic distance matrix. Finally, a general count sequence table for each OTU of all samples was generated to obtain the consensus taxonomy based on the Ribosomal Database Project's naïve bayesian classifier method  (Wang et al. , 2007)  and for the future OTU-based analysis. For processing ITS2 from fungal ribosomal ITS sequences, the recently described PIPITS v. 1. 3. 3 pipeline (https://github. com/hsgweon/pipits/releases) was used  (Gweon et al.
, 2015). Raw reads were prepared for ITS extraction and the chosen sub-regions extracted with the ITSx software tool  (Bengtsson-Palme et al. , 2013)  before clustering and taxonomic assignation using the UNITE database (version 31. 01. 2016) (Abarenkov et al. , 2010). All parameters, algorithms and tools for the bioinformatic steps used in the two pipelines are given in Supplementary Table  3. The microbial DNA sequencing data sets supporting the results in this article are available at the EBI ENA with accession number PRJEB20299.
For population level analyses, several tools were used: Metastats  (White et al. , 2009)   (White et al. , 2009) , LEfSe (Linear discriminant analysis Effect Size  (Segata et al. , 2011) , and Indicator (from Mothur software). The PICRUSt v. 1. 0. 0 (https://github. com/picrust/picrust/ releases) pipeline (http://picrust. github. io/picrust/) (Langille et al. , 2013)  was used to predict the functional composition of bacterial enzymatic activity abundance using 16S rDNA datasets. An OTUs table (input file) in BIOM format was generated using Mothur and then reference picked against the Greengenes database. Accuracy of metagenome predictions was controlled by measuring the weighted Nearest Sequenced Taxon Index (NSTI) scores that reflect the availability of reference genomes closely related to the most abundant microorganisms for each sample.
To analyze the Carbohydrate Active enZymes (CAZymes) prediction, a pre-calculated table was used (https:// sourceforge. net/projects/picrust/files/precalculated_files/). The FUNGuild v1. 0 database (https://github. com/UMNFuN/ FUNGuild) was used to assign ecological functions (trophic modes) to each OTUs  (Nguyen et al. , 2016). Graphic representations were produced using handmade scripts and based on Highcharts facilities (http://www. highcharts. com/) and jvenn plug-in  (Bardou et al. , 2014).
Metabarcoding and Sequencing 16S rDNA (bacterial) and ribosomal ITS (fungal) amplicons were sequenced using the Illumina MiSeq system. Redesigned primers (Supplementary Table  2 ) were used in order to avoid potential amplification of plant chloroplastic/mitochondrial DNA. Sequencing generated a very large data set ranging between 103,859 and 279,553 (average = 162,390 ± 33,164) bacterial raw sequences and between 187,055 and 483,703 (average = 285,070 ± 62,420) fungal raw sequences (Supplementary Tables  4A, B ). OTU tables listing all OTUs detected and their abundance normalized by a subsampling are given in Supplementary Table  5  (bacteria) and Supplementary Table  6  (fungi).
Hydrolytic Enzyme Potential and Trophic Mode Prediction Dew-retting of flax straw occurs via the action of hydrolytic enzymes produced by microorganisms and we therefore used PICRUSt software followed by expert curation to predict the bacterial Carbohydrate Active enZyme (CAZy) families potentially present during dew-retting and playing a role in the degradation of cell wall polymers. For all stem samples the NSTI scores were around the 0. 15 level considered as acceptable according to PICRUSt instructions. Our results (Figure  6 , Supplementary Figure  7 ) show that a wide range of different enzymes targeting both the backbones and side chains of the major polysaccharide cell wall polymers (cellulose, hemicelluloses, pectins) are present. Altogether, 22, 32, and 6 CAZy families targeting pectin, hemicelluloses and cellulose polymers were identified (Supplementary Figure  7 ).
Generally,  the hydrolytic enzyme potential (all polymers) was greater during the first stages of retting (R0-R2/R3) compared to latter stages (R3/R4-R6) for both "early" and "standard" cultures. The drop in hydrolytic potential observed for R1 and R5 stages in "early" cultures is most likely related to the corresponding decrease in the most abundant bacterial OTU (e. g. , Sphingomonas OTU00001, Figure  5 ). PICRUSt prediction does not exist for fungal OTUs and so hydrolytic enzyme potential cannot be directly predicted. Nevertheless, we were able to gain a relative idea of the overall hydrolytic enzyme potential by using the FUNGuild software that describes fungal trophic mode. Our results (Figure  7 ) show a progressive decrease in relative abundance of pathotrophs associated with a steady increase in saprotrophs and saprotrophspathotrophs as retting progresses.