dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments Research funding came from the  European Research Council Stg   ELITE FP7/308074 ,  BELSPO IAP PLANET TOPERS , the  FRS-FNRS CDR J. 0014. 18 , and the  FRS-FNRS-FWO Excellence of Science project ET-HOME  grant  30442502. We thank  N. Butterfield  ( University of Cambridge, UK ) for providing specimens of Cerebrosphaera from the  Svanbergfjellet Formation, Spitsbergen ;  K Grey  ( GSWA ) for access to drill cores in GSWA, Perth, Australia;  Y Callec  and the  BRGM  for providing the Bouenza Formation samples;  J. -Y. Storme  for help with Raman analyses,  M. Giraldo  ( ULiège ) for sample preparation,  Sarah Smeets  ( ULiège ) for the preparation of ultrathin sections,  Stephan Borensztajn  ( IPG-Paris ) for the SEM imagery,  S. Porter  for comments on a previous draft, and two anonymous reviewers for constructive comments on the present manuscript.
This, in turn, necessitated individual software solutions specifically tailored to the analysis of data from the experimental workflow. The most common database search engines used in proteomics are not directly suitable for interpreting mass spectra from cross-linked peptides. Therefore, the majority of computational solutions have emerged from laboratories that pioneered the application of XL-MS and created tools specifically tailored for the analysis of cross-linked peptides. Together with a current lack of formal or even informal reporting standards, the present state of XL-MS may confuse researchers that are interested in interpreting results from XL-MS studies or in adopting the technology. Currently, it is not clear which strategies are most suitable in general or for a particular application, which makes it challenging to objectively compare results obtained by different groups.
Protein System BSA was selected as model protein for this study as it is a globular and stable protein that is readily available at low cost. Moreover, the three-dimensional structure of BSA is wellknown, and we selected the Protein Data Bank entry 4F5S (https://www. rcsb. org/structure/ 4F5S) for further interpretation of the results. As BSA possesses a tendency toward forming dimers, this has to be considered when interpreting the results (see also below).
Three cross-linking workflows however recorded a significantly higher number of cross-links (between 260 and 350). The reason could be a false consideration of cross-links from BSA dimers that in some preparations might have been a dominating species due to inappropriate sample treatment. For in-gel digestion workflows, up to 19 overlength cross-links were reported in one data set, which could represent falsepositives due to partial unfolding as only the monomeric form of BSA was considered in these samples (Figure  6b ). A more detailed inspection of the unique cross-links revealed highly interesting insights: Data sets created from amine-reactive cross-linkers (BS 3 , DSBU, DSS, DSSO, DC4, MC4, CBDPS) using an in-solution digestion workflow yielded a total of 1066 unique cross-links. A complete list of unique cross-links, identified with cross-linkers reacting with nucleophiles (amine and hydroxy groups) and sorted by their reproducibility, is provided as separate file in the Supporting Information.
Cross-Links Identified from In-Gel Digested BSA Monomer Band We mapped cross-links in the monomer band of BSA using in-gel digestion (in total 10 data sets) into the published 3D structure of BSA (PDB entry 4F5S). For this, a statistical analysis could be performed for homobifunctional, amine-reactive linkers considered only for this type of cross-linker. Only cross-links identified in at least two independent experiments are presented (Figure  S3 ). A total of 30 out of 230 cross-links exceeds the maximum length of 30 Å for the cross-linkers employed in this study. These overlength cross-links either originate from a false assignment or by applying nonsuitable experimental conditions. Strikingly, 29 of these overlength cross-links were identified in one single experiment only. Cross-links that were identified in at least two independent experiments show one overlength link, while cross-links found in at least three independent experiments all fall within the given distance limit of 30 Å (Figure  S4 ).
We therefore recommend that appropriate consideration should be given to the method section of all XL-MS publications by providing all necessary experimental and computational details. Our reporting template could serve as a starting point for the "minimum information about a cross-linking experiment" that should be included in research articles containing XL-MS data. This template is included in the Supporting Information for all XL-MS data reports. Sufficient information needs to be provided, irrespective of the relative contribution of the cross-linking experiments to a specific project. This will also facilitate the crossreferencing of XL-MS data in integrative structural biology projects, for example, in the dedicated PDB prototype archive, PDB-Dev. 28 ta deposition to a proteomics repository, such as PRIDE, is encouraged, as the paucity of available data sets do not assist the field in validation, methods evaluation, and workflow quality.
Supplementary Material Refer to Web version on PubMed Central for supplementary material. Overview of data sets provided by the participants of this study: 32 groups participated in this study, yielding 58 separate cross-linking workflows. Nine data sets had to be excluded due to missing replicates and nonuniform conditions, resulting in a total of 49 data sets that were further considered. Several workflows contain both insolution (47 samples) as well as in-gel digestion (10 samples) as processing methods. The samples were considered only once during a workflow analysis.
Proteomics Analysis of Bear Skeletal Muscles We recently analyzed the vastus lateralis proteome for seven of the twenty bears used in the present study  [58]. Here we make use of proteomics data related to oxidative stress that were not detailed by Chazarin et al. [58]. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE  [59]  partner repository with the dataset identifiers PXD004908 (MS-based strategy) and PXD011687 (Gel-based strategy). Samples from these seven bears were also used for measurement of oxidative damages (MDA-protein adducts, protein carbonyls, and 3-nitrotyrosine levels), and samples from the 13 other bears were used to complement the data using other methods (see below).
Funding: This research was funded by the  French Space Agency (CNES ; Grant_No. 480000974and No. 4800001006 ),  CNRS and Strasbourg University  ( H2E  project;  MyoBears  project of the  PEPS ExoMod program ),  French Proteomic Infrastructure (ProFI ;  ANR-10-INSB-08-03 ), and  Agence Nationale de la Recherche  of the  French government through the program  " Investissements d'Avenir " ( 16-IDEX-0001 CAP 20-25 ).
The principal quantitative evaluation of the solving process comes from the final crack paths and lengths at the end of the simulation, directly influenced, as above, by the leaking transient and especially by the capability of the solver to reproduce the water flow through the opening cracks. Secondary validation data are provided by high-speed camera views of the water jet in the visualization chamber below the test specimen. Relevant views are available for Tests E20A5 and FUA5 (see Figure  16 ). Due to the shape of the opening in the specimen in test FXA5, no distinct view of the jet is obtained with the camera. Moreover, as the water cast shadows on the specimen, no direct view of the structural strains and cracks is available either during the tests, so that quantitative comparison for structural behavior can only be made with the final shape of the specimen.
Water Content Analysis The water content of the complex coacervate phase below and above the LCST was determined as follows. Below the LCST, the dilute phase was removed from the tubes containing the samples. After that, a small volume of complex coacervate phase was loaded into an Eppendorf Tube ® and weighed on a Mettler Toledo XS205DU analytical balance. The samples were then freeze-dried for four days. To study the water content above the LCST, the Falcon™ tubes containing both the dilute phase and the complex coacervate phase were left in a water bath at 40 • C for four days. After removing the dilute phase, the same weighting and freeze-drying procedures were performed. The water content was determined by the weight difference before and after the freeze-drying process. Two replicas were conducted to ensure data reproducibility.
Non-Linear Rheology Non-linear rheology was used to monitor the mechanical properties at high deformations above the LCST. The temperature was raised to 50 • C and an equilibration time of 60 min was applied. After that, shear start-up experiments were performed by shearing the samples prepared at 0. 75 M NaCl at constant shear rate (γ, 0. 1 s -1 ) and by monitoring the evolution of the shear stress (σ) as a function of strain (ε). Two replicas were conducted to ensure data reproducibility.
Small Angle X-ray Scattering (SAXS) SAXS experiments were performed at the European Synchrotron Radiation Facility (ESRF) in Grenoble, France, at the Dutch-Belgian Beamline (BM26B, DUBBLE). A Pilatus 1M detector, a fixed energy of 12 keV and a detector distance of 2. 7 m were used for all experiments, covering a total q-range from 0. 0665 nm -1 to 5. 23 nm -1. The two-dimensional images were radially averaged around the centre of the primary beam to obtain the isotropic SAXS profiles. The scattering pattern from silver behenate was used for the calibration of the q-range. Eltex was used as reference sample for the intensity calibration in absolute units (cm -1 ). The data were normalized to the intensity of the incident beam to correct for primary beam intensity decay. The data were corrected for absorption and background scattering.
Introduction The quantification of magmatic processes represents a long-term endeavor in Earth Sciences. Nowadays, determining magmatic variables such as temperature, pressure, melt water concentration, and oxygen fugacity increasingly makes use of crystallization softwares such as MELTS  (Ghiorso and Sack, 1995)  or COMAGMAT  (Ariskin et al. , 1993). These pieces of software, based on thermodynamic models for multicomponent melts and crystalline phases, have become quite popular and are mostly applied to compositions for which experimental data are nonexistent. However, it is also interesting to test these pieces of software against new experimental data, when they become available. This coming and going between experiments and models allows identification of potential problems and ultimately ensures the development and the extension of crystallization softwares.
The data from this study suggest that the influence of temperature becomes stronger for higher melt H 2 O concentrations. The plagioclaseeliquid hygrometer-thermometer of  Waters and Lange (2015) , when applied to the five experimental charges from the study, returns either melt H 2 O concentrations (8. 6e9. 7 wt%) higher than H 2 O solubilities at 300 MPa (~8 wt%) or temperatures (690e744 C) higher than the experimental temperatures. However, four data points from this study (at 675 and 665 C) are outside the calibrating database and the temperature range recommended for this hygrometer-thermometer  (Waters and Lange, 2015).
Previous studies  (Becker et al. , 1998; Holtz et al. , 1992; Pichavant, 1987; Pichavant et al. , 1992)  have clarified the respective individual influences of pressure and melt H 2 O concentration on minimum liquidus points, as shown by vectors in Fig. 3. The influence of the An component (also represented in Fig. 3 ) can be deciphered from the data at 100  (James and Hamilton, 1969)  and at 300 MPa (this study). Although the results at 200 MPa combine the influences of An, melt H 2 O content, and the presence of FeO t and TiO 2 , the data demonstrate an effect of An consistent with that found at the two other pressures on FeO t -and TiO 2 -free melts. Examination of the dataset for the FeO t -and TiO 2 -bearing rhyolitic liquids at 200, 500 MPa and for various melt H 2 O concentrations  (Wilke et al.
However, both the formulation and calibration of rhyolite-MELTS are in principle inappropriate for simplified compositions (G. Gualda, personal communication, 2018). Nevertheless, it seemed interesting to test the extrapolating capacity of MELTS (i. e. its response when used on compositions and experimental results outside its calibrating database). Detailed calculations were performed at 300 MPa on the same four experimentally investigated compositions (0, 7, 13, 14, Table  s1 ) as above and for variable melt H 2 O concentrations until saturation. For all four compositions and despite the ability.
Dashed lines indicate plagioclase An contents. The large red diamond is the H 2 Osaturated experimental temperature (plagioclase saturation) point (Table  1 ). Notice the marked differences between the calculated (860 C) and the experimental (730 C) temperature of the H 2 O-saturated plagioclase saturation point and between the calculated (>65) and experimental (30) An content of the liquidus plagioclase. melts (e. g. , CaOeNa 2 OeK 2 OeAl 2 O 3 eSiO 2 ,  Gualda et al. , 2012) , small amounts (<0. 5 wt%) of TiO 2 , FeO, and MgO were added to each starting composition. We checked that this additional chemical complexity causes only negligible variations on MELTS results (e. g. , <5 C on computed Qz and feldspar saturation temperatures). For three compositions (0, 7, 14), model liquidus phases and crystallization sequences agreed with experimental data (Table  1 ).
Acknowledgements The experimental work in Nancy benefited from discussions with  P. Barbey ,  W. L. Brown  and  F. Holtz. I. Di Carlo  helped with analytical work in Orl eans. G. Gualda  shared his expertise with the rhyolite-MELTS code. The analytical work has been partly supported by  Equipex PLANEX. The LABEX Voltaire provided a suitable scientific environment as well as the motivation to present the experimental data. Reviews by  F. Holtz ,  E. Christiansen , and  O. Bachmann  were very helpful.
PH0952 crystallization, data collection and structure determination All crystallization trials were carried out using the sittingdrop vapor diffusion method and a Mosquito nanoliterdispensing crystallization robot (TTP Labtech), by mixing 300 nl of protein solution and 300 nl of reservoir solution, equilibrated against 150 l of reservoir solution in Grenier 96-well plates. Crystallization plates were stored at 18 • C in a RockImager 1000 TM (Formulatrix, USA) automated imaging system to monitor crystal growth. The optimized conditions for crystal growth were 10% (v/v) 2-propanol, 0. 1 M 4-(2-hydroxyethyl)-1-piperazine ethanesulfonic acid (HEPES), pH 7. 7, for seleno-methionine (Se-Met)-labeled PH0952 N (10 mg/ml) and 10% (v/v) 2-propanol, 0. 1 M imidazole, pH 8. 0, for native PH0952 N (15 mg/ml).
Crystals grew after 10 days to a size of ∼100 x 100 x 100 m 3. For cryo-crystallography, crystals were soaked in a cryoprotectant solution composed of crystallization solution mixed with 30% (v/v) ethylene glycol and then flash-frozen in liquid nitrogen for data collection at 100 K. Highly redundant X-ray diffraction data were obtained from eight Se-Met PH0952 crystals on beamline Proxima 1 (Synchrotron Soleil, Saint-Aubin, France). The wavelength for data collection was 0. 97918 Å, corresponding to the peak of the Se K edge, as measured by a fluorescence scan. Datasets from single crystals were indexed and integrated with XDS  (21)  and scaled with XSCALE  (22)  to generate a multicrystal dataset in space group P6 5 22. The seleniumsubstructure determination was performed with SHELXD through HKL2MAP  (23).
Initial SAD phases were calculated by Phaser  (24)  and then subjected to automatic density modification with solvent flattening and histogram matching as implemented in the CCP4 program Parrot  (25). The obtained electron density map was interpretable, allowing us to recognize the topology of two NOD and two TPR domains in the asymmetric unit. Using HHpred  (26) , we retrieved from the PDB the structural models with the highest similarity to PH0952 NOD and TPR domains, respectively the NOD domain of APAF1 (PDB code: 1Z6T, 17% sequence identity, E-value 3. 0 × 10 -16 ) and the TPR domain of protein Pins (PDB code: 4A1S, 14% sequence identity, E-value 1. 9 × 10 -18 ). These models were rigid-body fitted into the experimental electron density using the program Coot  (27). Notably, the position of PH0952 Met residues as predicted by sequence alignments with APAF1 NOD and Pins TPR domains perfectly matched positive peaks in a difference anomalous map calculated with diffraction data from Se-Met PH0952.
Phylogenetic and clustering analyses Protein sequences representing previously defined families and clades of STAND ATPases were collected by BLASTP searches against the nr50 UniProt database. PH0952 homologs were retrieved from the non-redundant protein database at NCBI. Sequences from each clade were aligned separately and the resultant alignments were merged using MAFFT  (31). The uninformative positions were removed using the strict function of the trimAL program  (32). Phylogenetic trees were constructed using PhyML  (33)  with an automatic selection of the best-fit substitution model for a given alignment (LG +G+F). A Bayesian-like transformation of aLRT (aBayes), as implemented in PhyML, was used to estimate branch support. The best-fit substitution model for the PH0952 orthologs from Thermococcales was found to be LG +G+I+F.
Homology modeling of MalT In a first step, the structure of MalT devoid of its C-terminal DNA-binding domain was homology-modeled, using the model-building software Modeller (mod9v18)  (35). The crystal structure of PH0952 solved in this work served as a 3D template to model the NOD-arm segment of MalT (residues 1-442) (16% identity, E-value 6. 8 × 10 -32 as determined by HHpred) and the crystal structure of the TPR sensor domain of MalT (PDB code: 1HZ4, (36)) served as a 3D template for the region encompassing residues 443-803 of the model. The two sequences were merged in the alignment file to generate a MalT model from residue 1 to 803. In the second step of modeling, results of the cysteine pair cross-linking assay reported here and in previous work  (8)  were used to refine the model.
e. APAF1 (PDB code: 3SFZ, (  14 )), NOD2 (PDB code: 5IRM, (39)) and NLRC4 (PDB code: 4KXF, (  13 )), the PH0952 NOD module and arm domain are structurally most closely related to those of APAF1 (Supplementary Figure  S3 ), consistent with the phylogenetic and clustering analyses (Figure  1C  and Supplementary Figure  S1 ). As in all STAND proteins, the NBD of PH0952 is built on a AAA+ scaffold composed of five parallel ␤-strands (␤1-5, Figure  1A  and Supplementary Figure  S4 , strands S1-5, Figure  1D ) alternating with ␣-helices. These secondary structure elements form a core parallel ␤-sheet sandwiched between helix H1 on one side and all the other helices on the other side (Figure  1D ). Note that throughout the text, secondary structure elements will be referred either with respect to the general STAND topology as described in Figure  1D  (with Hn standing for helix n and Sn for ␤-strand n) or according to their succession in the PH0952 structure (Figure  1A ) (with ␣n standing for helix n and ␤n for ␤strand n).
Modeling of MalT after PH0952 Using homology modeling, we built a model of MalT containing the NOD, arm and sensor domains (residues 1-803). The templates used for the modeling were the NOD-arm part of the crystal structure of PH0952 (corresponding to residues 1-442 in the MalT model) and residues 6-366 of the crystal structure of the sensor domain of MalT (PDB code: 1HZ4, (36) corresponding to residues 443-803 in the MalT model). Models with the best score and stereochemistry (checked by Molprobity  (30) ) consistently displayed contacts between the NBD and the sensor domain involving the ISM helix in the NBD and the turn between repeats 3 and 4 in the TPR module (␣6 and ␣7 of the MalT sensor). The slight shift between the NBD-sensor interface regions predicted for MalT and observed in PH0952 is explained by the more pronounced curvature of the TPR-like sensor in MalT  (36).
Opening of the NOD allows for nucleotide exchange and subsequent multimerization. regulon), inhibits MalT through direct protein-protein interaction in the absence of transport, thus preventing activation of MalT by endogenously produced inducer. The transporter component involved in MalT sequestration is the MalK 2 ATPase dimer located at the inner face of the cytoplasmic membrane. The MalT-MalK interaction was shown to depend on at least two determinants on MalT, one on the NBD (the T38 patch (  56 )), the other on the sensor (the N637 patch (  37 )) and two determinants on the Cterminal domain of MalK (the W267 and D297 patches,  (57, 58) ). Up to now, it was unclear how the MalT and MalK determinants interacted with each other. Analysis of our structural model of MalT juxtaposed with the structure of MalK in the resting transporter (PDB code: 3FH6 (59)) reveals that the two MalT determinants (i) lie on the same face of the protein, like the four patches of the MalK dimer and (ii) are separated by ∼45-66 Å (depending on the atoms considered), consistent with their interaction with the two MalK W267 patches (span: ∼47-73 Å) or with one protomer W267 patch and the other protomer D297 patch (span: ∼31-55 Å) (Figure  7B ).
ACKNOWLEDGEMENTS We are grateful to the staff of the crystallography platform at the  Institut Pasteur  for robot-driven crystallization screening. We thank  Hannu Myllykallio  for the gift of P. horikoshii DNA,  Pierre Legrand  and  William Shepard  for their assistance in the collection of diffraction data at beamlines Proxima 1 and Proxima 2, respectively,  Isabelle Martin-Verstraete  for the gift of pKYB1,  Tony Pugsley ,  Olivera Francetic  and  Ivo Boneca  for their support.
FUNDING Agence Nationale pour la Recherche (France)  [ ANR-08-BLAN-0204-01 ];  European Molecular Biology Organization  [ EMBO ALTF 374-2010  to M. N. L. ];  Fondation pour la Recherche Médicale (France)  [ FRM SPF20130526701 ]. Funding for open access charge:  Institut Pasteur.
CFigure 3. 3 Figure 3. NBD interactions with non-adjacent domains in the PH0952 crystal structure. The protein is depicted in cartoon representation (PDB code: 6MFV). Highlighted residues are shown in sticks in the zoomed panels. (A) Overall view of PH0952 N. Squares highlight regions enlarged in the other panels. (B) NBD-WHD interface, (C) NBD-arm interface, (D) NBD-arm interface, rear view and (E) NBD-sensor.
22 resulted in R work /R free values of 0. 42/0. 38, supporting the choice of space group P6 5 for data. Additionally, model refinement in space group P6 5 with no twin law gave R work /R free values of 0. 35/0. 31. The final model was validated through the Molprobity server (http://molprobity. biochem. duke. edu) (30). It contains more than 96% of residues within favored re- gions of Ramachandran plot and <0. 05% of Ramachan- dran outliers. Figures were generated and rendered with Py- mol 1. 5. 0. 2. (Schr ödinger, LLC). Atomic coordinates and structure factors have been deposited in the Protein Data Bank under the accession code 6MFV.