dataesr/scientific-paragraphs-categorization huggingface dataset:
Funding This work was supported by intramural funding from the  French Centre National de la Recherche Scientifique  (to Y. Q. , P. S. , M. C. and G. F. ) and by grants from the  French Agence National de Recherche  ( ANR-12-BSV8-0009-01 ; partners Y. Q. , P. S. , M. C. and G. F. ).
EsxN and SigA detection were performed as positive control for secretion and as lysis and loading control, respectively. Although some SigA was detected in concentrated culture filtrates, this did not vary between strains and could not alternatively explain results. The exclusive detection of PE18/19 by western blot was not possible because of the lack of antibodies able to discern these proteins from their numerous homologs. Results are representative of 2 independent experiments. (E) Various fractions from M. marinum E11 WT assessed for the presence of EsxB or PE18/19, as described for (A)-(C). Error bars are the SDs of biological co-culture duplicates. See also Figure  S3  for the EspC mycobacteria subcellular localization. Principal-component analysis (PCA) of MASSTT data depicted a separation among Mtb variants producing, not producing, or barely producing ESX-1 substrates (Figure  5A ), which was confirmed by individual analyses of these variants (Figure  5B ).
T cell hybridomas were stained with Hoechst before sample acquisition on automated confocal Opera microscopy. Nuclei were segmented by using an intensity threshold of the Hoechst signal collected in the blue channel, and the total nuclei area was quantitated. The surface of the ZsGreen + nuclei area was determined by thresholding the signal intensity on the green channel in the nuclei area. T cell activation was defined and quantitated as an overlap of the ZsGreen + area within the cell nuclei area. (B) Confocal images (203 magnification) of co-cultured Ag85A/B-or EsxA-specific T cells. (C) Percentages of ZsGreen + T cells were calculated as a general activation index indicative of the total amount of antigen presented to T cells. See also Table  S2. Data were pooled from 2 independent experiments, and final results were expressed as the average of 9 images/well in six independent wells.
This effect is likely linked to the non-coding RNA (ncRNA) Mcr7, which dampens translation of the TatC core component in phoP-proficient strains  (Solans et al. , 2014). The MASSTT assay thus supported the biological relevance of intraphagocyte secretion of ESX-1 and Tat substrates, and their positive or negative regulation, and provided experimental evidence that active secretion of these substrates, in contrast to their sole expression inside the bacilli, is required for initiation of host CD4 + T cell activation. Numbers on the top right of each contour plot correspond to the percentage of reporter + T cell hybridomas over the total number of CD3 + CD4 + T cells recovered from the co-culture experiments, with a color code specified at the bottom right of the contour plots. In the bottom right quadrant of plots, the peptides (top) or the mycobacterial strains (bottom) used to pre-condition DCs are indicated.
Cells were acquired in an LSR Fortessa flow cytometer system using BD FACSDiva software (BD Biosciences). Cytometric data were analyzed using FlowJo software (Tree Star, OR, USA) or the viSNE algorithm in Cytobank. PCA was performed by using the R function prcomp (center TRUE; scale. TRUE) on intraphagocyte secretion profiles determined for each bacterial strain as (% reporter + T cells 3 MFI reporter + T cells)/1,000. For hierarchical clustering of Mtb clinical isolates according to intraphagocyte Ag85A/B secretion, Euclidean distances were.
ACKNOWLEDGMENTS The authors thank Francoise  Guinet (Institut Pasteur)  for critical reading of the manuscript,  Philippe Souk  ( Institut Pasteur ) for advice concerning LV vector construction and titration, and  Ida Rosenkrands  ( Statens Serum Institut, Copenhagen, Denmark ) for the gift of the anti-SigA antibody. We acknowledge The  Technology Core of the Center for Translational Science (CRT) at Institut Pasteur  for support in conducting this study, in particular Sophie Novault and  Pierre-Henri Commere of the Flow Cytometry facility. We are grateful to  Carlos Martin  ( University of Zaragoza, Spain ) for sharing Beijing GC1237 and DphoP Mtb strains and to  Jeffery Chen  ( E ´cole Polytechnique Federale de Lausanne, Switzerland; University of Saskatchewan, Canada ) for the DespA strain.
This work was supported in part by the  Institut Pasteur  ( PTR 441  to L. M. and P. B. ), the  Agence National de Recherche  ( ANR-14-CE08-0017  to L. M. and  P. B  and  ANR-14-JAMR-001-02 ,  ANR-10-LABX-62 - IBEID , and  ANR-16-CE35-0009  to R. B. ), the  European Union 's  Research and Innovation Program  ( MM4TB 260872  to R. B. and P. B. ,  TBVAC2020 643381  to R. B. and P. C. , and  ERC-STG INTRACELLTB 260901  to P. B. ), grants from the  Fondation pour la Recherche Medicale  ( DEQ20130326471  and  SPF20160936136  to R. B. and M. O. ), and contributions from the Feder (12001407  D-AL), Equipex Imaginex BioMed  ( ANR-10-EQPX-04-01 ), and the  Region Nord Pas de Calais (Convention 12000080 ) to P. B.
Designing peptides to generate anti-protein Abs The 'One Specific Peptide Design' section of the PEPOP web site is dedicated to the prediction of one peptide at a time. This section already existed in the previous version of PEPOP but was updated and enriched with new methods. This section allows defining only a small number of peptides. The peptide is progressively built through 4 steps: the reference segment, the method of extension, the area of extension and the peptide length. At each step, a choice is selected by default so that at the end the peptide can be built automatically. Else, the user may control the choices and the parameters (the 5 physicochemical and structural criteria: hyphobicity, accessibility, segment length, β-turn content, WRYP content) at any step. We prepared anti-protein Abs by designing a peptide from the 3D structure of the LMW (low molecular weight) form of adiponectin (PDB code: 1C3H) and using it to immunize mice.
Each method predicts all the possible peptides. For example, in the case of the prime, ALA, SA, and SAS methods, all the segments determined by PEPOP are individually selected as the reference segment. Thus, the method predicts as many peptides as segments. In this way, the entire surface of the protein is explored. Moreover, using several methods allows testing different arrangements of the same segments in peptides. Indeed, as we do not really know what governs the antigenic rules, we do not really know how some peptide characteristics, such as the peptide conformation, the aa position, the aa spacing, or the aa order influence the interaction with the Ab. The predicted peptides can be visualized on the 3D structure of the protein one or several at a time. Using this methodology we map discontinuous epitopes either recognized by a pAbs on Amm8  [38]  or recognized by mAbs on AaH II  [35]  and GM-CSF  [42].
Acknowledgements We warmly thank  Dr P. Lapalud  and  Dr B. Jardin  for hers collaborations and  D. Jean  for his precious technical contribution. This work was supported by grants from the  Ministry of Research (France) ;  University Paris Diderot ,  Sorbonne Paris Cité (France) ; the  National Institute for Blood Transfusion (INTS, France) ; the brazilian instituitions  CAPES ,  FAPEMIG  and CNPQ; the  Institute for Health and Medical Research (INSERM, France) ; and a  Labex GR-Ex grant (France)  to  Alexandre de Brevern. The  Labex GR-Ex , reference  ANR-11-LABX-0051 , is funded by the program " Investissements d'avenir " of the  French National Research Agency , reference  ANR-11-IDEX-0005-02. We are grateful to  A. -M. Madec  and  M. Powell  for providing anti-GAD65 DPC and mAb54 monoclonal antibodies.
RNA extraction and qRT-PCR analysis Total RNAs from root samples were extracted with the Easy Spin RNA kit (Aidlab, Beijing, China) following descriptions by  Dossa et al. (2017d). The quantity and quality of RNA samples were assessed by 1% agarose gel electrophoresis and on the ultraviolet spectrophotometer measurement of the A260/A280 ratio. For cDNA synthesis, 1. 5 µg of RNA was reverse transcribed using the Superscript III reverse transcription kit (Invitrogen, Carlsbad, CA, USA), according to the manufacturer's instructions. We designed primers to amplify the differentially methylated genes using the Primer Premier 5. 0 software  (Lalitha, 2000)  (Table  S3 ). The qRT-PCR was conducted on a Roche Lightcyler® 480 instrument using the SYBR Green Master Mix (Vazyme), according to the manufacturer's protocol.
RNA-seq data analysis Raw data of drought and waterlogging RNAseq experiments were retrieved from GeneBank Short Read Archive (SRA) with the accession numbers SAMN06130606 and SRR2886790, respectively. The sequencing reads containing low-quality were cleaned with FastQC (http://www. bioinformatics. babraham. ac. uk/ projects/fastqc/). The clean reads were mapped to the sesame reference genome v2. 0  (Wang et al. , 2016b)  using HISAT2  (Kim et al. , 2015) ; the program StringTie  (Pertea et al. , 2016)  was used for transcript assembly. The Cufflinks 2. 0 software  (Trapnell et al. , 2010)  was used to calculate the gene expression level for each sample expressed as fragments per kilobase of transcript per million fragments mapped (FPKM). The differentially expressed genes (DEG) were detected as described by  Tarazona et al.
(2011)  based on the parameters: Fold change > = 2. 00 and Probability > = 0. 8. The DEGs were identified by comparing: DSvsCK, DRvsDS for drought stress and WSvsCK, WRvsWS for waterlogging stress. The log 2 transformed FPKM values were used to construct heatmap using the MEV software  (Saeed et al. , 2006).
The rate of the plate spreading is 9. 8 mm yr -1 17,47. The shaded gray area is the region covered by the Landsat-8 image in the main figure. To quantify the displacement change, we apply a simple linear regression technique using only the linear part of the dataset prior to the assumed change in behavior (i. e. November 2011 to October 2013) and then we use the resulting slope to estimate the expected displacements at the time of the rifting (i. e. 4 UTC, 16 August 2014 is marked as the start of the major rifting event  22  ). We obtained significant discrepancies of up to -39 mm and 45 mm for the EW and NS directions, respectively (see Fig. 2  and Table  S1 ). An inflating reservoir beneath Bárðarbunga could not explain such discrepancies since displacement contributions toward the south and the east directions are expected instead (see the model displacements expected at GFUM GPS station in Table  S2 ).
Hence, we assume that all the uncertain model parameters are constant except for the basal magma inflow, Q in , which has a tendency to vary in time. The model can be expressed in its analytical solution and differential form, which is convenient to implement for both the inversion and data assimilation. Note that for the inversion and data assimilation, only the radial component of the 2011 post-eruptive dataset is exploited due to uncertain glacial isostatic adjustment (GIA) contribution and low accuracy of the vertical component at GFUM station  17. The actual data are in blue points, the red solid line is the linear fit of the points within the shaded gray area (assumed shift from linear to constant trend), and the black solid line represents the linear fit prior to the shaded gray area.
The latter was extended up to the end of the dataset to estimate the expected displacements after the assumed change of slope (14 October 2013). The red broken lines mark the onset of the May 2011 eruption and the August 2014 rifting event at Grímsvötn and Bárðarbunga, respectively. The horizontal black broken line is the zero-displacement reference. The shaded green area covers the dataset used during the inversion (step 1 of our approach). The insets (orange box) provide a closer look on the data points near the time of the rifting episode. Note that the vertical displacement is not corrected for GIA and seasonal effects. We applied a tectonic correction for the NS and EW components following the estimations of ref. 17. To ensure that the evolution of Q in can be tracked with only using the radial component of one GPS station, we first perform a synthetic test following the EnKF approach of ref.
12. Successful results are obtained provided that non-evolving uncertain model parameters are well-estimated and fixed prior to data assimilation (Figure  S4 ). Given the results of the synthetic case, we then develop a two-step approach suitable for our problem and to follow the behaviour of Q in (see Figure  S5  for the summary and Methods:Bayesian-based Inversion and Data Assimilation for details). Step-1: We apply a Bayesian-based inversion through the Markov Chain Monte Carlo (MCMC) algorithm to first constrain the non-evolving uncertain model parameters and obtain a prior distribution for Q in using only the initial part of the 2011 post-eruptive dataset. Step-2: We then implement the Ensemble Kalman Filter (EnKF)  24  as a data assimilation technique, following the strategy developed by ref.
Results The best-fit values of the uncertain model parameters after step-1 are marked as green lines in Fig. 4  and are also summarized in Table  1. Despite the non-uniqueness, these values are consistent with the data, the physics of the model and the results of previous studies  15, 17, 25  , such that we are able to fix the non-evolving parameters and proceed to step-2 to follow the variation of Q in. If no observation is used to correct the dynamical model, the result is called the "Free-run" (Fig. 5 ) where the model is only propagated forward in time. Obviously, if the model is almost a perfect representation of the observations wherein the model parameters are well-constrained and remain constant in time as in the case of the initial part of our dataset, we expect to have a good fit with the radial dataset.
Note that although we only use an early subset of the radial displacement data for the inversion (i. e. dotted green box in Fig. 5A ), the inferred best-fit Figure  3. Schematic sketch of the two-chamber model, modified after ref. 12, 17. The uncertain model parameters in this study are highlighted in gray. Except for the bottom magma inflow rate, Q in , which is bounded by a dotted box, the rest are considered as non-evolving uncertain parameters. The GFUM GPS station, with a distance r from the center of the volcanic system, CVS, records the displacement induced by the two reservoirs. = + R r H s s 2 2 and = + R r H d d 2 values of the uncertain model parameters are able to match the radial component of the dataset up to t step = 545 d and appear to be consistent with the vertical component (Figure  S6 ).
It is unclear though if t step = 545 d marks an episode of a true decrease in magma inflow rate or is just a part of some transient noise that affected the dataset. The latter case would require a lower value of Q in at t step = 0 to fit the time series up to our assumed change of slope using the forward model. The assimilation of radial displacement using the strategy that we have developed not only results to a robust fit to the entire dataset but also enables us to follow closely the decreasing trend of Q in (Fig. 5 ). We obtain a minimum rate of 0. 007 km 3 yr -1 from the ensemble of Q in estimates which corresponds to a drop of 0. 039 km 3 yr -1 (85% decrease) relative to its prior value. To ensure that the decrease in Q in is a true episode after our assumed change of slope and is not influenced by the set of values that we fixed, we run two independent cases of data assimilation (see Supplementary Material).
The lack of spatial information in our dataset may also contribute to underestimating the missing magma volume since there is only one GPS time series that we can exploit at Grímsvötn. Indeed, our synthetic case shows the ability of EnKF to detect the sudden change in Q in given a spatially-limited dataset, whilst the convergence to the true value may require some time (Figure  S4 ). As to whether or not the "missing" magma may have reached the Bárðarbunga's magmatic system is yet to be addressed. While the volume of "missing" magma is only ∼3-20% of the estimated 2014 dyke intrusion  22, 23  , it could be enough to trigger a magmatic reservoir rupture beneath Bárðarbunga and initiate magma propagation from the storage zone. It is possible, assuming that the magma reached Bárðarbunga's magmatic system and provided that its storage zone was already in a pressurized stage before this additional magma inflow.
2 ) performing MCMC based on a classical inversion approach/setup (blue), and (3) data assimilation via EnKF (red). The green dotted box covers the dataset used to estimate the non-evolving uncertain parameters (step 1). The robustness of each approach is depicted on how it fits the radial displacement dataset which clearly favors the EnKF method. (B) Estimated value of the magma inflow rate, Q in , as a function of time using: the free-run (green), EnKF (red) and MCMC (blue). Note that the gray and black broken lines (A and B) correspond to the points where a decreasing trend in Q in , tracked via EnKF, are observed. (C) The distribution of Q in used as a prior information for the free-run, the data assimilation and the inversion. One significant argument against the lateral flow hypothesis is that no increase of deep seismicity was recorded during the 10 months preceding the rifting event.
Having GPS data at a distance of 15 km from Grímsvötn could confirm this effect of the deep reservoir (see Figure  S8 ). Data. Data are analyzed using the Bernese 5. 2 software  41  , with absolute antenna phase center offset models, together with precise orbits, earth rotation parameters, ocean tidal loading and atmospheric tidal loading estimates. Velocities and time series were estimated in the ITRF2014 reference frame  41  with discontinuities associated with this reference frame and expressed in terms of the plate boundary reference frame  17. We followed the resolution strategy with (1) an initial ionosphere-free analysis with calculation of the residuals; (2) a residual analysis; (3) code-based wide-lane ambiguity resolution for all baselines  42  , using differential code bias (DCB) files when available and calculation of the ionosphere-free solution with the introduction of resolved Melbourne-Wübbena linear combination ambiguities; (4) phase-based wide-lane (L5) ambiguity resolution for baselines <200 km and computation of the ionosphere-free solution with the introduction of resolved ambiguities; (5) resolution of the previously unresolved ambiguities for baselines <2000 km using the quasi ionosphere-free strategy of resolution; (6) direct L1/L2 ambiguity resolution for baselines <20 km with the introduction of an ionosphere model; (7) calculation of the normal equations; (8) a compatibility test between the daily free solution and ITRF2014 solution, selection of compatible ITRF2014 stations and (  9 ) transformation of the daily normal equation in the ITRF2014 reference frame with a six-parameter Helmert solution (three translation parameters and three rotation parameters) using the ITRF2014 selected stations (BARH, BOGO, EPRT, ESCU, HERT, HLFX, KARL, KHAR, NEWL, REDU, RIGA, SASS, SCH2, SHE2, SKE0, STAS, THU2, THU3, TRDS, VARS, VOL0).
During these steps, site-specific troposphere parameters are estimated every two hours. Normal equations are analyzed together to determine accurate velocities in the ITRF2014 reference frame with the introduction of ITRF2014 coordinates and velocities. For time series of stations supposed to be linear (not GFUM), outliers and new discontinuities were detected using the "Find Outliers and Discontinuities in Time Series" tool in the Bernese 5. 2 software that reduces, step-by-step, the discrepancy between the functional model and the time series due to statistical adjustment  43  , taking annual and seasonal fluctuations into account. As the Bernese 5. 2 software underestimates the daily errors given that systematic errors or mismodeled parameters are not included in the formal error  41  , we rescaled the formal errors by multiplying them by a factor of 10 to obtain a more realistic estimated error.
Bayesian-based inversion. To constrain the uncertain parameters of our model that remain constant in time (e. g. a H C P , , , , d d d t 0 ρ Δ Δ ) and to obtain a prior distribution for the evolving one (Q in ) before data assimilation, we perform a Bayesian-based inversion using the Markov Chain Monte Carlo (MCMC) algorithm. We follow the step-by-step procedure presented in Figure  S5  (i. e. Step 1). In MCMC, uncertain model parameters independently drawn from given a priori distributions are constrained by accepting model predictions that better fit observations and by randomly accepting those that do not fit to avoid being trapped to a local minima  44. In particular, we built our approach using the PyMC2 python module. The classic linear inverse problem is described as: ε = + D G m ( ) (3) where D is the vector of observation or data, G is the forward model, m is a vector of the uncertain model parameters and ε is the observation error.
In our case, m a H C P Q [ , , , , , ] d d d i n t 0 ρ = Δ Δ , D is the 2011 post-eruptive GPS dataset (we only use the radial displacement component up to t step = 409 d) and G is the analytical solution (obtained by combining equations 3, 11 and 12 of ref. 12  ) to the forward model described in equations (  1 ) and (2). In the Bayesian framework, the posterior probability associated with m is sampled based on a likelihood function, P(D|m), that calculates how well the data fits the model and a prior knowledge about the uncertain model parameters, P(m): | ∝ | P m D P D m P m ( ) ( ) ( ) (4) Note that the errors in the resulting posterior distributions are only related to the observation error and uncertainties of the prior distribution of m. Any error related to how the model, G, represents the reality is not taken into account.
From the constructed posterior distribution, we pick the set of best-fit parameters by computing the misfit relative to the data points within the dotted green box in Fig. 5. The results are summarized in Table  1  and are marked using green vertical lines in the marginal distribution shown in Fig. 4. We perform similar inversion procedure to obtain the two other set of priors that we tested: case (1) using the entire 2004 radial displacement dataset and case (2) adopting the values of a d , H d and Δρ from case 1 and then re-estimating the uncertain parameters that could vary from one eruption to another (e. g. Q in , C, and ΔP d t 0 ). Data assimilation. We closely followed the strategy developed by ref. 12  to assimilate geodetic data into a forward dynamical model. A step-by-step data assimilation strategy using EnKF is presented in Figure  S5  (i.
Though Global Navigation Satellite System (GNSS) data processing has been significantly improved over the years, it is still commonly observed that zenith tropospheric delay (ZTD) estimates contain many outliers which are detrimental to meteorological and climatological applications. In this paper, we show that ZTD outliers in double-difference processing are mostly caused by sub-daily data gaps at reference stations, which cause disconnections of clusters of stations from the reference network and common mode biases due to the strong correlation between stations in short baselines. They can reach a few centimetres in ZTD and usually coincide with a jump in formal errors. The magnitude and sign of these biases are impossible to predict because they depend on different errors in the observations and on the geometry of the baselines.