dataesr/scientific-paragraphs-categorization huggingface dataset:
The computation of the significance level is made using an unpaired t test when comparing inversion results that assimilated different data (we assume that changing the assimilated data makes the inversion results independent), and using a paired t test when comparing inversion results that assimilated the same data (we assume that inversion results in which only the transport model varies are dependent). In practice, changing the independency assumption only affects the detail of the significance level results, but not the overall picture. Comparing solid and dotted lines, we see no benefit of LMDz6A vs. LMDz5A, as version 6A increases the absolute bias of SURF for eight programmes (three in Brazil -RBA-B, ALF and TAB, as well as CALNEX2010, DISCOVER-AQ, ACT, THD and LEF) and improves it for four programmes (the fourth Brazilian site -SAN , as well as SEAC4RS, KORUS-AQ and ETL).
If we combine all measurements, the root-mean-square difference for the OCO-2-based and the surface-based inversions only varies between 1. 51 and 1. 56 ppm. Note that 39 % of these data are from CONTRAIL, a programme that spreads over all continents. If we only take CONTRAIL data, the root-mean-square difference for the OCO-2-based and the surface-based inversions still only varies between 1. 60 and 1. 67 ppm, which tends to indicate that this result is not biased towards features that are specific to one region. The standard deviations are comparable between the OCO-2-based inversions and the surface-based inversions. LMDz6A improves the SURF biases for KORUS-AQ and degrades them for three other programmes  (INPE, LEF and ETL). This lack of improvement also appears for OCO-2 (degradation at INPE, KORUS-AQ and ABOVE).
The statistics for four programmes (ORCAS, KORUS-AQ, ACT and SONGNEX2015) are directly comparable between the two periods because the corresponding data are complete in both periods: in all four programmes OCO-2 performs better than GOSAT. Figure  9  reformulates the bias statistics of Fig. 8  on a map of the differences between the absolute biases of the OCO-2 and SURF inversions. As for the programme biases, some points are more robust than others (due to varying amount of data), but there is some large-scale coherence, with better performance of SURF in the Southern Hemisphere (as could already be seen in Figs. 7  and 8 ) and in the central and eastern US, whereas OCO-2 yields smaller biases in the Northern Hemisphere subtropics and Europe. Other parts of the globe are less consistent such as the western Pacific edge or boreal America.
The overall MSE is very similar between both (1. 5 2 ppm 2 ), but the relative performance still varies in space and time  (Figs. 8, 9)  and we hope to extract some further insight into the relative merits of each dataset. In practice, we  compute δc 1 + δc 2 using the LMDz model linearised around the inversion prior simulation in order to respect the underlying hypothesis. However, some inconsistencies for the initial state of CO 2 could not be completely removed between δc 1 and δc 2 due to the different starting date of each inversion. The map of the sum of all contributions of the flux changes δf i (from the surface-based inversion to the OCO-2-based inversion) at a given pixel to the change in MSE (δMSE) is presented in Fig. 10. Positive values occur when the OCO-2based fluxes increase the MSE relative to the surface-based fluxes.
This can be done with atmospheric observations like those listed in Sect. 1: surface measurements, aircraft measurements, TCCON retrievals, AirCore measurements or satellite retrievals. We remove TCCON and the satellite data from the list on the criterion that they are biased (Fig. 9  suggests that we are interested here in signals that are smaller than the TCCON trueness; see also the discussion in  Chevallier, 2018b) , the surface data on the criterion of independence (the surface data in ObsPack-type databases that are well simulated by the transport models are usually assimilated), and the AirCore data because of limited time and space coverage so far. This leaves aircraft data, however scarce they are, as an obvious choice to define objective measures of the quality of the inversion systems, when they are not assimilated.
They have served this role in the past to some extent, starting from  Peylin et al. (2007)  or  Stephens et al. (2007)  (see also  Pickett-Heaps et al. , 2011; Basu et al. , 2014; Houweling et al. , 2015; Frankenberg et al. , 2016; Le Quéré et al. , 2018; Crowell et al. , 2019) , but few aircraft measurement programmes have been used so far and, as a consequence, their use has rarely been formulated in terms of quality assurance or quality control processes for atmospheric inversions. Compared with previous studies, we benefit from a much larger number of aircraft measurements over the globe in the free troposphere (600 000 for the OCO-2 period and twice as many for the GOSAT period) and from more recent satellite retrievals. We have only used data between 2 and 7 km a. s. l. , where the age of air varies significantly  (Krol et al.
, 2007). It provides complementary information about inversion quality, provided that the minority of measurements above urban areas or in the vicinity of assimilated surface sites are excluded. This has not been explored here. Most of the aircraft measurement programmes here are over North America, but the majority of measurements are provided by the CONTRAIL programme, as noted in the legends of Figs. 7  and 8. CON-TRAIL samples air at our study altitudes above many cities outside North America; it also represents 74 % of all data for the GOSAT period and 39 % for the OCO-2 period. For our ensemble of six Bayesian inversion results, we have seen that large differences in the estimated annual sub-continental fluxes (GOSAT-based vs. surface-based results) are paralleled by a different quality of fit to the aircraft data, with GOSAT-based results not performing as well.
e. simulation ORCHIDEE-trunk in Le  Quéré et al. , 2018) , showing that the OCO-2 performance is not trivial. From our results, we cannot draw general conclusions about OCO-2 retrievals nor about GOSAT retrievals, because our study is limited to two specific retrieval datasets. Takagi et al. (2014)  showed large differences between different retrieval algorithms for GOSAT, and it is still possible that we would get similar results between OCO-2 and GOSAT if we used the same retrieval algorithm. Following  Liu and Bowman (2016) , we attribute the simulation error changes in the free troposphere for the OCO-2 period to flux differences in specific regions of the globe. We find a rather homogeneous geographical distribution of the flux performance with OCO-2-based fluxes and surfacebased fluxes alternating as those with the best performance over continental land masses.
Therefore, we could not identify the distinctive asset of OCO-2 vs. GOSAT in our system: either the data density, the data precision, the data trueness (linked both to the quality of the physical retrieval scheme and to its empirical bias-correction) or a combination of these qualities at once. Further, other GOSAT-based inversions could be more competitive if made differently (e. g. with a different biascorrection), while other OCO-2-based inversions (e. g. with a different transport model or with different retrievals), or our inversion with ACOS v9 retrievals after our study period (e. g. if the empirical bias-correction is less efficient for later months), could still be found deficient for carbon specialists. As we have shown, aircraft data can help with ranking the skill of these alternative inversion configurations among one another and vs.
Zhang et al. (2014)  and  Alden et al. (2016)  presented a different strategy in which aircraft profile measurements are assimilated: a compromise has to be found between exploiting valuable data directly (in particular in areas void of surface measurements), or keeping them for validation. Finally, the evidence provided by aircraft measurements in the free troposphere suggests that the quality of some OCO-2 retrievals over land is now high enough to provide results that are comparable in credibility to the reference (but sparse) surface air sample network, within the above-mentioned limits. For ocean retrievals, this remains unclear as OCO-2 ocean soundings were not tested in this work. The consistency of results from the surface-and OCO-2-driven inversions, in stark contrast to the bottom-up fluxes or to the GOSAT-driven inversion, does not seem to be fortuitous.
Acknowledgements. The authors are very grateful to the many people involved in the surface, aircraft and satellite CO 2 observations and in the archiving of these data that were kindly made available for this study, including  Hartmut Bösch  and  Jasdeep Anand  at the  University of Leicester , the  ObsPack team and LaGEE , and the  Greenhouse Gases Laboratory  from  INPE. The Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE) is an Earth Ventures (EV-1) investigation, under contract with the  National Aeronautics and Space Administration. The Atmospheric Carbon and Transport (ACT) -America  project is a  NASA Earth Venture Suborbital 2  project funded by  NASA's Earth Science Division  (grant no. NNX15AG76G  to  Penn State ). This work was performed using HPC resources from  GENCI-TGCC  (grant no.
2018-A0050102201 ). The authors thank  Hartmut Bösch ,  François-Marie Bréon ,  Grégoire Broquet ,  John B. Miller ,  Britton B. Stephens ,  Jeff Peischl ,  Sander Houweling , the editor and the three anonymous reviewers for constructive discussions about these results and how they are presented. Bianca Baier ,  Kenneth J. Davis ,  Luciana Gatti ,  Kathryn McKain  and  Charles E. Miller  provided useful details regarding some of the measurement programmes. Financial support. This research has been supported by the  Copernicus Atmosphere Monitoring Service , implemented by the  European Centre for Medium-Range Weather Forecasts  on behalf of the  European Commission  (grant no. CAMS73 ), and from  ESA  through contract no. 4000123002/18/I-NB RECCAP-2. Review statement. This paper was edited by  Federico Fierli  and reviewed by three anonymous referees.
Data availability. The aircraft measurements are available from https://www. esrl. noaa. gov/gmd/ccgg/obspack/ (last access: 21 November 2019). The OCO-2 data can be obtained from http://co2. jpl. nasa. gov (last access: 21 November 2019) and were produced by the OCO-2 project at the Jet Propulsion Laboratory, California Institute of Technology. The CAMS v18r1 product can be obtained from http://atmosphere. copernicus. eu/ (last access: 21 November 2019), and the GOSAT retrievals are available from http://climate. copernicus. eu/ (last access: 21 November 2019). All inversion results can be obtained upon request to copernicussupport@ecmwf. int.
3334/ In situ: K. J. Davis, J. P. DiGangi, and America (NASA Airborne Science) ORNLDAAC/1593 M. Yang, https://daac. ornl. gov/cgi-bin/dataset_lister. Flasks: C. Sweeney, B. Baier, and pl?p=37 (last access: 21 November 2019) P. Lang ALF Aircraft Observation of Atmospheric GHG at Alta Floresta, Mato Grosso by LaGEE/INPE CALNEX2010 California Nexus 2010 (NASA Airborne T. B. Ryerson, J. Peischl, and Science) K. C. Aikin CAR Briggsdale, Colorado (NOAA/ESRL C. Sweeney and E. J. Dlugokencky Global Greenhouse Gas Reference Network aircraft program) CMA Offshore Cape May, New Jersey C. Sweeney and E. J. Dlugokencky (NOAA/ESRL Global Greenhouse Gas Reference Network aircraft program) CON CONTRAIL (Comprehensive Observation https://doi. org/10. 17595/ T. Machida, H. Matsueda, Y. Sawa, Network for TRace gases by AIrLiner) 20180208.
Randomization and Blinding Randomization was performed using either an interactive voice response system (IVRS) or interactive web response system (IWRS), whereby a unique identification code was generated for each eligible patient. Batches of ambroxol or placebo lozenges were identified with matching unique identification codes and provided to patients accordingly. Lozenges were otherwise indistinguishable by any other characteristics (color, shape, packaging, and labeling). Everyone involved in the conduct or analysis or with any other interest in the study remained blinded with regard to the randomized treatment assignments until after database lock.
Simon Hitier provided statistical input during the design phase of the study. Disclosures. Simon Hitier is an employee of Sanofi-Aventis Group. Rita Sousa is an employee of Sanofi-Aventis Group. Sandrine Brette is employed by Aixial, contracted by Sanofi. Deepak Ramjee Lakha received funds from Sanofi-Aventis Group. Compliance with Ethics Guidelines. The study was conducted in accordance with the Declaration of Helsinki and Good Clinical Practice Guidelines as defined by the International Conference on Harmonization, and approved by the institutional review boards and independent ethics committees for all participating centers (additional information can be found in the Supplementary Appendix). This study is registered on ClinicalTrials. gov (NCT03583658), and written informed consent was obtained from all patients prior to study enrollment.
Data Availability. Qualified researchers may request access to patient-level data and related study documents including the clinical study report, study protocol with any amendments, blank case report form, statistical analysis plan, and data set specifications. Patientlevel data will be anonymized and study documents will be redacted to protect the privacy of trial participants. Further details on Sanofi's data-sharing criteria, eligible studies, and process for requesting access can be found at: https://www. clinicalstudydatarequest. com Open Access. This article is distributed under the terms of Creative Commons Attribution-NonCommercial 4. 0 International License (http://creativecommons. org/licenses/ by-nc/4. 0/), which permits any non-commercial use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
For remote data collection at Diamond Light Source (UK, Didgot) at beamline I03 the crystals were cryo-protected by soaking in mother liquid containing 25% glycerol and snapfrozen in N 2 for shipping to the beamline. The data were collected at 100 K using 0. 9763 Å wavelength, Pilatus3 6M detector, 30% transmission power, 0. 05 s exposure and 0. 1°oscillation angle as a total of 2400 frames. The diffraction data were integrated and scaled with X-ray Detector Software (XDS)  54. The initial solution was obtained with molecular replacement using PHASER  55  and PDB = 1s0p as a search model. A solution with four HFD domains present in an asymmetric unit was found, after which multiple rounds of refinement with BUSTER  56  and manual building in COOT  57  yielded a good fit to the data (see Table  1 ).
1 cm light path length with the following parameters: continues scanning mode with scanning speed of 50 nm/min, bandwidth 0. 5 nm, wave range 190-260 nm, data pitch 0. 5 nm. All the proteins were diluted to 15 µM with 10% PBS buffer. Accumulation of ten scans for each protein has been plotted as a single curve. Models for atomistic MD simulations. The cofilin-decorated actin models were based on the 3. 8 Å resolution cryo-electron microscopy structure (PDBID: 5YU8)  41. The missing loops were built using RosettaCM  59  and RosettaScripts 60 in the presence of the electron density map  41  imposing the associated helical symmetry. To match the experimental constructs in this work, actin and cofilin sequences were, in this stage, changed from chicken's to rabbit's and mouse's, respectively. Over 1000 models were generated.
They were, then, sorted based on their total score and the ones that contained structural artifacts (e. g. , cis peptide bonds) were filtered out. Molecular dynamics (MD) simulations were initiated from the top scoring three models (Supplementary Table  1 , simulations F1-3). The HFD-actin complex was isolated from the crystallized HFD domain/actin/ twinfilin ADF-H domain complex and separately docked on to the selected cofilindecorated actin filament models described above. For this purpose, the isolated HFD-actin was superposed onto each pointed end actin monomer, which was then replaced with the HFD-actin. This way, the crystal structure conformation of actin-HFD dimer is transferred to the tip of the cofilin-decorated actin. The docked models were first locally refined using the docking protocol  61  followed by restrained relaxation with the fast relax  62  protocol distributed with the Rosetta Software Suite.
Each actin H73 residue was methylated (N τ -Methyl-L-histidine, HIC), and the N-termini of actin, cofilin, and HFD were acetylated. The topologies for each molecule in the systems was prepared with the LeAP program distributed with ambertools18 64 , which were then converted to the GROMACS format using the ParmEd tool. Each pointed end slice created from the selected models was placed in a hexagonal prism simulation box with its long-axis aligned with the z-axis. The box dimensions were chosen to have a minimum distance of about 17 Å between the filament and each face of the box (Supplementary Table  1 ). Each system was solvated with 0. 15 M NaCl solution with the numbers of ions adjusted to neutralize the system. Before production runs, steepest descent minimization and successive short equilibration simulations (in total ~7 ns) in the NVT and NpT ensembles using the Berendsen thermostat and barostat  65  were performed.
All quantum-chemical calculations were performed using the Gaussian09 program suite  73  at the b3LYP/cc-pVTZ level of theory. Charge calculations were performed with the IEFPCM model in a polarizable continuum with a dielectric constant of 4 by setting the solvent as ether. MD simulation protocols. All simulations were carried out using GROMACS 2018  74. The equations of motion were integrated using a leap-frog algorithm with a 2 fs time step. All bonds were constrained using the LINCS algorithm  75. The simulation protocols were chosen according to ref. 66. Long-range electrostatic interactions were treated by the smooth particle mesh Ewald scheme  76  with a realspace cutoff of 0. 8 nm, a Fourier spacing of 0. 12 nm, and a fourth-order interpolation. Lennard-Jones potential with a cutoff of 0.
8 nm was used for van der Waals interactions. Long-range dispersion corrections were applied for energy and pressure  77. All production simulations were performed in the NpT ensemble. The pointed end slice, the platform, and solvent (water and 0. 15 M NaCl) were coupled to separate temperature baths at 310 °K using the Nosé-Hoover thermostat  78, 79  with a time constant of 1. 0 ps. Isotropic pressure coupling was performed using the Parrinello-Rahman barostat  80  with a reference pressure of 1 atm, a time constant of 5 ps, and a compressibility of 4. 5 × 10 -5 bar -1. Analysis of the MD simulations. All analyses were performed using VMD  81  and in-house scripts. The MMGBSA calculations were performed using the MMPBSA. py software  82  distributed with ambertools18  64  employing the modified GB model developed by Onufriev et al.
Acknowledgements We thank  Mikko Frilander ,  Markku Hakala ,  Ville Paavilainen ,  Minna Poukkula , and  Juha Saarikangas  for critical reading of the manuscript. Emma Sairanen  and the  Helsinki Crystallization  facility are acknowledged for technical assistance. This work was supported by grants from  Academy of Finland  ( 320161  and  307415 ) to P. L. and I. V. ,  Cancer Society Finland (4705949)  to P. L. ,  French ANR  (Grant Muscactin) to G. R. -L. ,  European Research Council  ( StG-679116 ) to A. J. , and  Fondation ARC pour la Recherche sur le Cancer (Postdoctoral Fellowship)  to H. W.
Custom software was designed in Python, integrating a Qt interface (See Video S1 Software demo in Supplementary materials), dedicated to fitting at best NMM parameters on an injected LFP along short sliding windows on the time axis (i. e. , NMM gain parameters are identified once per sliding window). Excitatory ( EXC) and inhibitory ( INH) gains could therefore be tracked during the LFP time course, along with the excitation to inhibition ratio ( EIR, computed as the EXC/ INH ratio). Obviously, the NMM provides full access at any time to the intermediate states for each sub-population, such as the average firing rate of action potentials and average postsynaptic potentials at the level of pyramidal cells and interneurons. Such signals are challenging to extract from LFPs with standard signal processing methods, especially since LFPs originate from the summation of PSPs onto pyramidal cells, and are not directly related with APs at the soma level.
In clinico recordings In the context of pre-surgical planning, epileptic patients candidate to surgery undergo stereoencephalography (sEEG), involving the implantation of multiple intracranial electrodes aiming at improving the identification of epileptogenic zones. sEEG recordings from N=5 patients in the context of clinical evaluation were obtained from the Epileptology Unit of Rennes University Hospital (CHU Pontchaillou, Rennes, France). sEEG recordings were sampled at 2048 Hz with a Deltamed EEG acquisition system. Seizure epochs, involving several minutes pre-and post-seizures, were extracted for each patient to enable LFP reconstruction pre-, per-and post-seizure. All the data are available on the following public link https://zenodo. org/deposit/1285630 and with the DOI: 10. 5281/zenodo.
The increase of EIR is mainly explained by the major drop of GABAergic inhibition, already observed in computational modeling study  (Wendling et al. 2002) , animal model of epilepsy  (Trevelyan et al. 2007 , Wenzel et al. 2017 ) and in human epilepsy  (Schevon et al. 2012). In addition, it is worth noting that the proposed method consistently pointed at a counter-intuitive decrease of excitation (minor as compared to the decrease of inhibition) in all in vivo recordings (PTZ-induced seizure, epileptiform events in the kainate model). At least the PTZ-model directly affects the function of type A GABAergic receptors and is likely to interfere with the inhibitory synaptic barrage. The slight decrease of excitation, predicted by the model, was not found during human seizures from our available database.
Gene ontology annotation and enrichment analysis. To work with current functional annotations of the C. gigas gene set, we performed a functional annotation (Additional file 6). Blastx comparison against the NR database was performed for the 28,027 genes annotated in the genome, with a maximum number of target hits of 20 and a minimum evalue of 0. 001. From these 20 hits, a percentage of mean similarity was calculated and we retained only results with mean similarity >40%. XML blast result files were loaded onto Blast2GO  [42]  for GO mapping and annotation with the b2g_sep13 version of the B2G database. These results were used as inputs for GO enrichment analysis, which was.
performed using adaptive clustering and a rank-based statistical test (Mann-Whitney Utest combined with adaptive clustering). The R and Perl scripts used[43]  can be downloaded [https://github. com/z0on/GO_MWU]. The following parameters were used for adaptive clustering: largest = 0. 5; smallest = 5; clusterCutHeight = 0. 25. For the continuous value characterization of each gene in the dataset, we used a strategy aiming to take into account both the level of expression and the significance of the differential expression. To combine these two factors, the log2 fold change was attributed to genes 20 that were significantly differentially expressed (adjusted p < 0. 05), while a zero was attributed to the others. A category was considered enriched with a FDR < 1%. DNA extraction and quantification of OsHV-1 and total Vibrio.