dataesr/scientific-paragraphs-categorization huggingface dataset:
25). For variant L107A, the drop was a 2:1 mix of protein (10 mg/ml) and reservoir solution (100 mM sodium acetate, 4 mM CuSO 4 , 18% ethanol, pH 4. 25). X-ray Data Collection and Processing, Structure Determination and Refinement. Prior to data collection, crystals were cryoprotected in the same buffers as reservoir solutions plus 30% glycerol and then flash-cooled in liquid nitrogen. Diffraction data were collected at 100 K on the X06DA beamline at the Swiss Light Source (SLS) (Switzerland) equipped with a Pilatus 2 M detector (Dectris Ltd. , Baden, Switzerland). Indexing, integration and scaling were performed using the HKL2000 suite of programs  24. Five percent of the measured reflections were flagged for cross-validation  25. Relevant statistics of data collection and processing are given in Table  1.
The X-ray structures were determined by molecular replacement (MR) using the coordinates of wild-type EcTRX (PDB ID: 2TRX) for variants E101G and L107A or the double mutant M37L/P40S (PDB ID: 1KEB) for variants L94A and N106A. c) Assigned to an unknown residue ionization that affects Trp emission intensity. implemented in the MrBump pipeline  27  from the CCP4 suite  28  , whereas in the case of the L94A and N106A variants, MR was carried out using Phaser but implemented in the Phenix suite  29. Refinement was performed using REFMAC5  30  and Coot  31. Additionally, a round of refinement using the server PDBREDO  32  resulted in a significant improvement in fitting the model to data for the L94A and L107A structures. Final refinement was carried out using Phenix for all the structures. Hydrogen atoms were modelled at riding positions, and anisotropic B-factors of protein atoms were employed for the L94A, E101G and N106A structures.
For the L107A structure, B-factors were treated using TLS refinement, with one TLS group per protein chain. The stereochemical qualities of models were verified using the Molprobity server  33  and the validation server of the RCSB PDB. The coordinates and structure factors were deposited in the PDB (PDB entries 5HR0, 5HR1, 5HR2 and 5HR3 for E101G, L107A, L94A, and N106A variants, respectively). Determination of the Acidity Constant of the TRX Wild Type and E101G Variants. The proteins were reduced during 1 hour using 10. 0 mM DTT and then the DTT was removed by gel filtration. Each protein (3. 8 μ M) was placed in a 96-well plate at 12 different pH-values in a buffer consisting of 30 mM ACES, 15. 6 mM ethanolamine, 15. 6 mM, TRIS, 120 mM NaCl and 0. 1 mM DTPA, adjusted using HCl or NaOH to obtain the desired pH value (from pH 2.
0, supplemented with 10% D 2 O. The relaxation data R1, R2, and 1 H-15 N steady-state NOEs were measured using standard sequence pulses  37. Structural Analysis. Intramolecular interactions were computed with the PIC server  38. The Bio3d 39 package was used for structure superposition and RMSD calculations. Crystal packing was calculated after generation of symmetry-related molecules in pymol, counting the number of intermolecular contacts using the list_contacts. py command from Robert L. Campbell's script repository: http://pldserver1. biochem. queensu. ca/~rlc/work/pymol/ For RMSD calculations in a set of EcTRX structures, annotated EcTRX structures were retrieved from the PDB, pruned to chain A, and superposed. Very divergent or highly substituted structures were removed, and the A chains of the four variants determined in this work were added to the set.
All possible pairwise structural alignments and corresponding RMSD calculations were generated in Bio3d, and the average and standard deviation of these values were calculated. The PDB IDs of the dataset of previously reported EcTRX structures are: 1KEB, 1TXX, 1ZZY, 2EIO, 2EIR, 2FCH, 2FD3, 2H6X, 2H6Y, 2H6Z, 2H70, 2H71, 2H72, 2H73, 2H74, 2H75, 2H76, 2TIR, 2TRX and 3DYR. Molecular Dynamic Simulations. The atomic coordinates of wild type TRX from E. coli (PDB: 2TRX, chain A) and the A, B a and B b conformations of the E101G variant (PDB: 5HR0) were solvated with the 3-particle TIP3P model 40 in an orthorhombic box, which extended 15 Å farther from the nearest protein atom using the ff14SB force field  41. To neutralize the systems, sodium counter ions were added to balance the charge of the protein.
Harmonic positional restraints at a strength of 20 kcal mol -1 Å -2 on Cα -atoms were applied during minimization and equilibration. Restraints were removed in 5 successive simulation stages (20, 10, 5, 2 and 0 kcal mol -1 Å -2 ) of 5 ns each. Unrestrained molecular dynamic simulations were performed with the pmemd. CUDA engine in the Amber14  44  suite for the GPU code. Data analysis was performed using the cpptraj module of AmberTools15  45.
Acknowledgements This work was supported by the  Agencia Nacional de Promoción Científica y Tecnológica (ANPCyT ,  CABBIO2014 N˚3662 ), the  Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET ,  PIP-11220110100723 ) and the  Universidad de Buenos Aires (UBACyT   20020130100468BA ), the  CNRS , the  INSERM , the  Université de Strasbourg , the  Région Alsace , the  Hôpital Civil de Strasbourg, Instruct  (part of the  European Strategy Forum of Research Infrastructures; ESFRI)  and the  French Infrastructure for Integrated Structural Biology (FRISBI)   ANR-10-INSB-05-01. We specially thank the  IGBMC Structural Genomics platform  staff (in particular,  Pierre Poussin Courmontagne  and  Dr. Alastair Mc Ewen ). X-ray data collections were performed on the X06DA beamline at the  Swiss Light Source ,  Paul Scherrer Institut, Villigen, Switzerland.
Words were of medium and high frequency and extracted from the Lexique. org database  (New et al. , 2004). Half of the words designated plants and animals (living items) and the other half designated objects (non-living items). The control condition was identical for the two runs, PHONO and SEM, and included 30 unreadable words (UW) written in unreadable characters (Karalyn Patterson font). All language stimuli were written in white Courier New font and control (UW) stimuli were written in white Karalyn Patterson font, size 40, and centered on black screen. They were generated by the E-Prime software (Eprime Psychology Software Tools Inc. , Pittsburgh, USA), running on a PC computer. Each stimulus belonged to a trial (2. 5 s duration) composed of 0. 5 s fixation and 2 s stimulus (language or control).
MRI Acquisition The experiment was performed in a whole-body 3T MR scanner (Bruker MedSpec S300) with 40 mT/m gradient strength at the Grenoble MRI facility IRMaGe in France. For functional runs the manufacturer-provided gradient-echo/T2 * -weighted EPI method was used. Thirty-nine adjacent axial slices parallel to the bicommissural plane were acquired in interleaved mode. The slice thickness was 3. 5 mm. During each session run the cerebral volume was measured 150 times. The in-plane voxel size was 3 × 3 mm (216 × 216 mm field of view acquired with a 72 × 72-pixel data matrix, reconstructed with zero filling to 128 × 128 pixels). The main sequence parameters were: TR = 2. 5 s, TE = 40 ms, flip angle = 77 •. To correct images for geometric distortions induced by local B0 inhomogeneity, a B0 field map was obtained from two gradient-echo data sets acquired with a standard 3D FLASH sequence ( TE = 9.
1 ms). The field map was subsequently used during data processing. Finally, a T1-weighted high-resolution three-dimensional anatomical volume was acquired, by using a 3D-modified driven equilibrium Fourier transform (MDEFT) sequence (field of view: 256 × 224 × 176 mm; resolution: 1. 333 × 1. 750 × 1. 375 mm; acquisition matrix: 192 × 128 × 128 pixels; reconstruction matrix: 256 × 128 × 128 pixels).
Data Analyses Functional MRI Processing Spatial preprocessing Data analysis was performed by using the general linear model  (Friston et al. , 1995)  for event-related designs in SPM12 (Wellcome Department of Imaging Neuroscience, London, UK, www. fil. ion. ucl. ac. uk/spm) implemented in MATLAB (Mathworks Inc. , Sherborn, MA, USA). Data analysis started by applying several spatial preprocessing steps. First, functional volumes were time-corrected with the 19th slice as reference (the brain volume was composed of 39 slices), in order to correct effects caused by the different acquisition time of each slice. Subsequently, all volumes were realigned to correct the head motion using rigid body transformations. The first volume of the first ER-fMRI session was taken as reference volume. T1weighted anatomical volume was co-registered to mean images created by the realignment procedure and was normalized to the MNI space.
ROI construction and time series extraction Consistently with our fMRI group analysis results and based on previous literature and research on PHONO and SEM processing  (Perrone-Bertolotti et al. , 2011; Vigneau et al. , 2011; Price, 2012; Yvert et al. , 2012) , the Language vs. Control contrast allowed the identification of three ROI considered as hubs for our model. Furthermore, the contrast Language vs. Baseline provided a supplementary ROI, the visual cortex (i. e. , which was defined as the input region of our model, see Construction of model section). Overall, with MarsBar software (http://marsbar. sourceforge. net/) we created in the left hemisphere four ROI's. To build them we defined spheres (8 mm radius) centered on the peak of activation of corresponding clusters provided by the group analysis.
Dissociation in the modulation of connectivity by the task demands was not observed between the vOTC and the dIFG (dorsal pathway). However, it should be noted as a limitation of the present results that one fully connected and modulated model is the most complex one, which can result in an over-fit of the data  (Seghier et al. , 2010). Furthermore, because the purpose of the present study was to perform inference on modulatory parameters and not on model structure, BMS was not used here. Therefore, we cannot ascertain that connectivity parameters were estimated within the best model structure: the estimated parameters reflect the most likely parameters given this particular model and dataset. It is thus possible that models with different architecture could result in different connectivity parameters.
ACKNOWLEDGMENTS IRMaGe MRI/Neurophysiology facility was partly funded by the  French program  " Investissement d'Avenir " run by the  "Agence Nationale pour la Recherche" ; grant " Infrastructure d'avenir en Biologie Santé "- ANR-11-INBS-0006. This work was supported by  AGIR  PEPS founding from the  Univeristé Grenoble Alpes.
Ocean δ 13 C observations have been used to test marine ecosystem models, including processes such as phytoplankton growth rate, iron limitation, and grazing  (Schmittner et al. , 2013; Tagliabue and Bopp, 2008)  and may also provide insight into climaterelated ecosystem changes. Past changes in δ 13 C recorded in for the north (blue solid) and south (blue dashes). Isotope records are available at input4mips (https://esgf-node. llnl. gov/search/input4mips/), including tropical 14 C (30 • S-30 • N) (not shown). The CO 2 data are identical to those used for CMIP6  (Meinshausen et al. , 2016)  and the carbon isotope data are common with C4MIP  (Jones et al. , 2016). The CO 2 observations are from NOAA  (Dlugokencky and Tans, 2016). The δ 13 C compilation uses ice-core and atmospheric measurements  (Rubino et al.
When modeling chemical and biogeochemical tracers, it is recommended that OMIP groups use the same formulations for gas exchange and carbonate chemistry as outlined below. Little effort would be needed to modify code that is already consistent with previous phases of OCMIP. For gas exchange, model groups only need to change the value of the gas transfer coefficient, the formulations and coefficients for Schmidt numbers, and the atmospheric gas histories. For carbonate chemistry, groups should strive to use the constants recommended for best practices  (Dickson et al. , 2007)  on the total pH scale and to avoid common modeling assumptions that lead to significant biases, notably an oversimplified alkalinity equation  (Orr and Epitalon, 2015). Fortran 95 code to make these calculations is made available to OMIP-BGC participants.
Biogeochemistry For the other passive tracers, referred to as biogeochemistry, the OMIP-BGC protocols build on those developed for OCMIP. These include the OCMIP2 abiotic and biotic protocols  (Najjar and Orr, 1998, 1999; Orr et al. , 1999b)  and the OCMIP3 protocols for interannually forced simulations  (Aumont et al. , 2004) , all available online with links to code and data (see references) or as one combined PDF (see Supplement). Each model group will implement the OMIP protocol in their own prognostic ocean biogeochemical model as in OCMIP3, unlike the common-model approach of OCMIP2. Each OMIP biogeochemical model will be coupled online to an ocean general circulation model forced by the CORE-II atmospheric state. Geochemical boundary conditions for the atmosphere include an imposed constant atmospheric concentration of O 2 (mole fraction x O 2 of 0.
Fields from the 2013 World Ocean Atlas (WOA2013) will be used to initialize model fields of oxygen  (Garcia et al. , 2014a)  as well as nitrate, total dissolved inorganic phosphorus, and to-tal dissolved inorganic silicon  (Garcia et al. , 2014b). The latter two nutrients are often referred to simply as phosphate and silicate, but other inorganic P and Si species also contribute substantially to each total concentration (Fig. 3 ). Indeed it is the total dissolved concentrations (P T and Si T ) that are both modeled and measured. OMIP will provide all these initial biogeochemical fields by merging WOA2013's means for January, available down to 500 m (for nitrate, phosphate, and silicate), and down to 1500 m for oxygen, with its annual-mean fields below. Model fields for A T and preindustrial C T will be initialized with gridded data from version 2 of the Global Ocean Data Analysis Project (GLODAPv2) from  Lauvset et al.
Schmidt number Besides a, the Schmidt number Sc is also needed to compute the gas transfer velocity (Eq. 12). The Schmidt number is the ratio of the kinematic viscosity of water ν to the diffusion coefficient of the gas D (Sc = ν/D). The coefficients for the fourth-order polynomial fit of Sc to in situ temperature over the temperature range of -2 to 40 • C (Wanninkhof, 2014) are provided in Table  1  for each gas to be modeled in OMIP and CMIP6. Fortran 95 routines using the same formula and coefficients for all gases modeled in OMIP are available for download via the gasx module of the mocsy package (Sect. 2. 6).
(1963). For models without Si T , it is preferred that they use climatological Si T data interpolated to their model grid (i. e. , annual average data from WOA2013). These options offer a better alternative than assuming that nutrient concentrations are zero, which leads to systematic shifts on the order of 10 µatm in calculated surface-water pCO 2. The abiotic portion of the biogeochemical simulation carries only two tracers, C abio T and 14 C abio T , which are not connected to other biogeochemical tracers. Hence to compute corresponding abiotic CO * 2 and 14 CO * 2 concentrations, we also need abiotic alkalinity. Following OCMIP2, the abiotic alkalinity in OMIP will be calculated simply as a normalized linear function of salinity: A abio T = A T S S , (27) where A T is the global mean of surface observations 2297 µmol kg -1  (Lauvset et al.
, 2007). Although phosphorus and silicon alkalinity is included in the carbonate chemistry routines provided for OCMIP2 and OCMIP3  (Orr et al. , 1999b; Aumont et al. , 2004) , those routines focused only on computing surface pCO 2 and are now outdated. They have been replaced by mocsy, a Fortran 95 package for ocean modelers  (Orr and Epitalon, 2015). Relative to the former OCMIP code, mocsy computes derived variables (e. g. , pCO 2 , pH, CO 2- 3 , and CaCO 3 saturation states) throughout the water column, corrects for common errors in pressure corrections, and replaces the solver of the pH-alkalinity equation with the faster and safer SolveSaphe algorithm from  Munhoven (2013). The latter converges under all conditions, even for very low salinity (low C T and A T ), unlike other approaches.
Code and data availability. To facilitate comparison, an OMIP-BGC web page (http://omip-bgc. lsce. ipsl. fr) provides links to these protocols as well as links for OMIP-BGC's common atmospheric gas histories, data fields for initialization, and code to compute all facets of gas exchange and carbonate chemistry. Eventually, all input data files will be available directly from input4mips (https: //esgf-node. llnl. gov/search/input4mips). The code mentioned here is available in the mocsy package, which can be obtained as detailed in Sect. 2. 6. That package contains the carbonate chemistry routines as well as routines in its gasx module to compute Schmidt numbers, solubilities, and air-sea exchange for the gases to be modeled during OMIP (CO 2 , O 2 , CFC-11, CFC-12, and SF 6 ). The 14 C tracer that is adopted for OMIP from OCMIP is fractionation corrected to avoid the need to explicitly compute 13 C fluxes between modeled carbon reservoirs.
Figure2. Annual-mean atmospheric histories for global-mean CO 2 (black dots) and δ 13 C (orange) compared to hemispheric means of 14 C for the north (blue solid) and south (blue dashes). Isotope records are available at input4mips (https://esgf-node. llnl. gov/search/input4mips/), including tropical 14 C (30 • S-30 • N) (not shown). The CO 2 data are identical to those used for CMIP6 (Meinshausen et al. , 2016)  and the carbon isotope data are common with C4MIP(Jones et al. , 2016). The CO 2 observations are from NOAA(Dlugokencky and Tans, 2016). The δ 13 C compilation uses ice-core and atmospheric measurements(Rubino et al. , 2013; Keeling et al. , 2001) , while the 14 C compilation uses tree-ring and atmospheric measurements Levin et al. (2010), extended after 2009 with unpublished data from the University of Heidelberg (I.
Acknowledgements. Thanks to two anonymous reviewers and the editor ( G. Munhoven ) for comments. J. C. Orr  and  L. Bopp  were supported by the  EU H2020   CRESCENDO  project (grant  641816 ). J. L. Bullister was supported by the  NOAA Climate Program Office  -this is PMEL contribution no. 476. H. Graven  was supported by an  EU   Marie Curie Career Integration Grant. A. Mouchet  benefited from an  EU   H2020 Marie Curie  project (grant  660893 ). R. G. Najjar was supported by  NASA's Ocean Biology and Biogeochemistry Program  and  NASA 's  Interdisciplinary Science Program. F. Joos was supported by the  Swiss National Science Foundation. The  National Center for Atmospheric Research (NCAR)  is sponsored by the  US National Science Foundation. J. -M. Epitalon  modified mocsy to allow for optional input of conservative temperature and absolute salinity.
In parallel, using a microplate reader, we recorded the growth curves of all the strains (Fig 1A ), to which we fitted the Gompertz function to estimate two population-growth features: the maximal growth rate (α max ) and the saturating density (OD max ) of each culture  (Appendix Fig S1A). High-throughput dataset curation using support vector machine Cells and their contours were detected in an automated fashion using the open-source software Oufti  [46]. The large size (> 1, 500, 000 cells detected) of the dataset precluded the validation of each cell contour by visual inspection. Therefore, we implemented an automated classification method based on support vector machine (SVM)  [20]  to identify and discard incorrectly detected cells  (Fig 1B). To generate a training dataset for the SVM model, we visually scored (positive or negative) 43,774 cell contours from the parental strain and the 419 mutants displaying the greatest deviations in cellular dimensions before data curation.
This inclusion of the most aberrant mutants in the training dataset allowed us to build a versatile model that performed well on the wide range of cell sizes and shapes present in the Keio collection. The quality of the fit of the SVM model to the training dataset was evaluated by a 10-fold cross-validation  [25] , which gave a misclassification error rate under 10%. The model was further validated on an independent dataset of 102,137 visually scored cell contours taken from the same group of WT and mutant strains. We found that our SVM model performed very well on this validation set, as shown by the high AUROC (area under the 'receiver operating characteristic' curve) value of 0. 94  (Appendix Fig S1B). By comparing the model classification with visual scoring (Fig 1C ), we found that only about 3% of cell contours in the validation set were incorrectly 2/40 identified as positive (false positives) by the SVM model.
Importantly, these misclassified cells introduced no biases in the measurement of morphological features  (Appendix Fig S1C) , even when considering the 419 most aberrant strains  (Appendix Fig S1D). This validated SVM model was used to curate the entire dataset, retaining about 1,300,000 identified cells (∼300 cells/strain). In addition, we verified the reproducibility of our experimental approach by separately imaging two independent replicates of 192 strains that include 2 copies of the parental (WT) strain and 190 mutants with severe morphological defects. Even for cell width, the smallest dimension measured, we observed a Pearson correlation (ρ) of 0. 92  (Fig 1D) , indicating high reproducibility.
Quantification of growth and cell cycle features across the genome From the images, we also calculated the degree of constriction for each cell, and inferred the fraction of constricting cells in the population for each strain (see Materials and methods). The latter reflects the timing of initiation of cell constriction relative to the cell cycle. In addition, the analysis of the DAPI staining with the objectDetection module in Oufti  [46]  provided additional parameters, such as the number of nucleoids per cell and the fraction of cells with one versus two nucleoids. From the latter measurement, we estimated the relative timing of nucleoid separation. We also measured the degree of nucleoid constriction in each cell for each strain, and compared it to the degree of cell constriction to obtain the Pearson correlation between these two parameters, as well as the average degree of nucleoid separation at the onset of cell constriction (Appendix Fig  S1E ).
High-dimensional classification of the morphological mutants While the gene deletion annotation of the Keio library is not perfect, our large dataset provided a powerful platform to examine global trends and to identify gene function enrichments in phenotypic classes of mutants with |s| ≥ 3. First, we considered morphological phenotypes. Instead of ranking strains on a feature-by-feature basis, we sought to classify strains based on their combination of features, or 'phenoprints', to better capture the phenotypic complexity of morphology. Each strain in our dataset is characterized by scores for 19 morphological features. We added two growth-related features (OD max and α max ) to this morphological phenoprint because growth rate is often implicitly assumed to control cell size. This assumption derives from the early observation that bacterial cell size (mean cell mass) scales with growth rate when the latter is modulated by varying the composition of the culture medium  [47].