dataesr/scientific-paragraphs-categorization huggingface dataset:
LSF We collected data from 49 participants (40 in Paris and 9 in Nantes), out of which we excluded seven. One participant was excluded because they were not French, but Belgian, hence signing French-Belgian SL (LSFB); five were excluded due to their very late AoE to a sign language (after 15 y. o. ), and one participant was excluded based on their low performance on fillers, with an accuracy below the 75% threshold. Among the 42 remaining participants, 13 were native LSF signers, 14 were early LSF signers and 15 were late LSF signers. Participants were aged between 19 and 72 years (mean = 36. 95 y. o. , SD = 9. 31) and there were 23 women and 19 men.
LIS We collected data from 44 participants, out of which we excluded five. One participant was excluded due to their very late AoE to a sign language (after 15 y. o. ); one participant was excluded because they were postlingually deaf; one because of a poor performance in the 12 The battery of tests was longer in Italy, hence the higher compensation. 13 The cognitive test administered for the three language groups was the same, but is made accessible in the SIGN-HUB assessment tools platform as three  general cognitive task and two participants were excluded based on their low performance on fillers, with an accuracy below the 75% threshold. Among the 39 remaining participants (19 women, 20 men), 15 were native LIS signers, 13 were early LIS signers and 11 were late LIS signers. Participants were aged between 30 and 65 years (mean = 46.
LSC We collected data from 42 participants at Pompeu Fabra University, out of which we excluded 8. One participant was excluded due to a low score in the cognitive test; one for technical problems in collecting their metadata; one due to their late AoE to LSC (after 15 y. o. ), and five participants were not considered based on their low performance on fillers, with an accuracy below the 75% threshold. Among the 34 remaining participants (19 women, 15 men), 12 were native LSC signers, 11 were early LSC signers and 11 were late LSC signers. Participants were aged between 23 and 69 years (mean = 47. 67 y. o. , SD = 10. 6). 6 Results and analyses.
Accuracy The analyses were realized on each sign language independently, but following the same scripts. 14 We had a binary dataset in which correct answers were coded as "1" and incorrect answers as "0". We started by conducting an item analysis to detect problematic items on which native signers performed below 50% on average. If the score obtained was due to a technical error, we removed the item in both blocks (the subject RC and object RC version of it). If no technical errors could explain the result, the items were kept. In LSF, this led to the removal of four items, hence leaving 48 items (24 subject RC and 24 object RC) to analyze. In LIS, three items were removed, hence leaving 34 items (17 subject RC and 17 object RC) within the analysis. In LSC, only one item had a technical issue such that we performed the analysis on the remaining 38 items (19 subject RC and 19 object RC).
Accuracy and error results were analyzed with the R software (R Core Team 2020) using generalized linear mixed models from the binomial family via the glmer function (package lme4,  Bates et al. 2015). The independent variables were language group (native, early & late), condition (subject RC & object RC) and chronological age (as a continuous, mean centered corrected variable). Random variables were intercepts for participants and items. 15   Using ANOVA, we started by comparing minimally different models (df = 1) in which one of the independent variables was removed to control for the overall impact of a given parameter in the data distribution. Keeping only relevant independent variables (more details within each language subsection), we then conducted pairwise comparisons using the package emmeans  (Lenth 2020) , to obtain finer grained information regarding differences between natives, early, and late learners in their understanding of subject RCs on one side, and of object RCs on the other side.
LSF Accuracy in LSF is illustrated in Figure  15. In LSF, models including chronological age as a variable did not provide a better fit of the data, so we removed this parameter from the independent variables of the model (2(1) = 3. 13, p = 0. 07699). Language group (2(2) = 8. 06, p = 0. 01775), condition (2(1) = 346. 16, p < 0. 0001) and the interaction between these two factors (2(2) = 8. 96, p = 0. 01134) were significant. All pairwise comparisons using the resulting generalized mixed model across language group (natives, early, late) and condition (SRC, ORC) are presented in   1  presents the estimate, standard error, z-ratio and p-value associated with the use of the model to compare the two datasets referred to in the corresponding row. For example, the first row displays the value of the parameters obtained when comparing the score realized by native signers in subject relative clauses accuracy to their performance in object relative clauses.
All the analyses have been realized on each sign language independently, following the same procedure. We have created a subset of the data only containing the wrong answers in the object RCs condition. We have a binary dataset in which answers targeting the middle character were coded as "1" and answers targeting the left/right character were coded as "0". This was our dependent variable. The independent variables were language group (native, early & late), condition (SRC & ORC), and chronological age. We again controlled for relevant parameters and their interactions by conducting ANOVA analysis across minimally different models. Random variables were intercepts for participants and items, except for LSF whose model only had "items" as a random effect (instead of both "items" and "participants").
Acknowledgements Many people participated to the discussions, brain storming sessions, and technical struggles which are behind the results discussed here and need to be acknowledged. Within the  SIGN-HUB network ,  Josep Quer  and  Naama Friedmann  gave a crucial contribution, and we are grateful for this. On the French side, we must thank  Marion Bonnet , who helped us with the platform as part of her internship, and  Chiara Annucci  and  Angélique Jaber , who helped with the administration of the tests. Nothing could have been possible without our consultant,  Valérie Jendoubi , and all the participants to the experiment. On the Italian side, thanks are due to all participants and to the  Instituto dei Sordi of Turin  for hosting us. We are grateful to  Alessandra Checchetto  and  Rita Sala  for many useful discussions and suggestions and to  Cecilia Gazzeri  and  Luca Falbo  who helped with the administration of the tests.
Segmentation For the majority of specimens, TIFF stacks were filtered using either a mean of least variance filter (kernel size one) or a 3D median filter, followed by a mean of least variance filter (each with a kernel size of three), implemented using MIA open source software  (Wollny et al. , 2013). Filtered image stacks were segmented in Avizo 6. 3 (Visualization Sciences Group, 2010) using a seed growing watershed algorithm employed via a custom Avizo plugin to segment enamel from dentine, before being checked manually. In specimens with particularly poor contrast between tissue types, enamel and dentine were segmented using the magic wand tool in Avizo v. 8. 0 (FEI Visualization Sciences Group) with manual corrections and use of the interpolation where enamel and dentine could not be precisely demarcated (For more details, see  Zanolli et al.
ACKNOWLEDGEMENTS For access to specimens and microCT scans, we would like to thank  Bernhard Zipfel ,  Sifelani Jira  ( Evolutionary Studies Institute, University of the Witwatersrand), Miriam Tawane (Ditsong Museum ),  Emma Mbua  and  Job Kibii  ( National Museums of Kenya ),  Audax Mabulla  ( National Museum of Tanzania ), the  Tanzania Commission for Science and Technology ,  Metasebia Endalemaw ,  Yared Assefa  ( Ethiopian Authority for Research and Conservation of Cultural heritage ),  Christine Argot  and  Hervé Lelièvre  ( Muséum national d'Histoire naturelle ),  Roberto Macchiarelli  ( University of Poitiers and Muséum national d'Histoire naturelle ) and  Arnaud Mazurier  ( University of Poitiers ), Friedemann Schrenk  and  Christine Hemm  ( Senckenberg Research Institute and Natural History Museum Frankfurt ).
The solution was finally dispersed in 950 mL of distilled water under magnetic stirring. All samples were analysed using a M7 lens, which allows precise measurement of particles with dimensions ranging from 4. 2 to 8665 µm (ISO). The number of particles analysed varied between hundreds of thousands to a million depending on the samples. For each sample, measurements were carried out in triplicate to ensure the reproducibility of the data. PAQXOS software (SympaTec GmbH, Clausthal, Germany) was used to calculate the particle diameter (DIFI).
Environmental Analysis The purpose of this study was to determine the environmental impacts of two physical treatments applied to flax tows: ultrasound (see Section 2. 2. 1) and gamma irradiation (see Section 2. 2. 2). Here, a simplified environmental analysis was conducted and not a full LCA (ISO 14044). The model was realised with the software Simapro. The Ecoinvent V 3. 5 database was used. The system boundaries are based on the cradle-to-gate approach and are represented in Figures  1  and 2. The locations of the production sites were included. The flax tows data were obtained from the company Depestele, located in Normandy, France as part of the program FARBioTY. The model works by mass allocation, which means that if a process has several products, the impact of each of the products is allocated in proportion to its mass.
database was used. The system boundaries are based on the cradle-to-gate approach and are represented in Figures  1  and 2. The locations of the production sites were included. The flax tows data were obtained from the company Depestele, located in Normandy, France as part of the program FARBioTY. The model works by mass allocation, which means that if a process has several products, the impact of each of the products is allocated in proportion to its mass. The studied impacts are abiotic depletion, global warming, ozone layer depletion, human toxicity, acidification and eutrophication based on the CML 2 baseline 2000 calculation method. The energy consumption, fossil and nuclear, was calculated with the Cumulative Energy Demand V1. 11 method. Evaluations of indicators are available in the literature  [31].
The results are presented as diagrams with a relative scale. Due to the uncertainties, for the same environmental indicator, if the difference between two studied pre-treatments is less than 5%, they are considered equal. Interventionary studies involving animals or humans, and other studies that require ethical approval, must list the authority that provided approval and the corresponding ethical approval code. database was used. The system boundaries are based on the cradle-to-gate approach and are represented in Figures  1  and 2. The locations of the production sites were included. The flax tows data were obtained from the company Depestele, located in Normandy, France as part of the program FARBioTY. The model works by mass allocation, which means that if a process has several products, the impact of each of the products is allocated in proportion to its mass.
The grid was mounted on a tomographic single-tilt sample holder. The tilt series were acquired in HAADF mode between 70° and -70° from the initial position of sample using 2° increments through the tomography plug-in of the Digital Micrograph software, which controls the specimen tilt step-by-step, the defocusing and the specimen drift. The inner radius of the HAADF detector was about 40 mrad. Image alignment was performed through fiducial-less methods, namely a combination of crosscorrelation and center of mass methods using IMOD software. 21 The reconstructed 3D volumes were obtained using the algebraic reconstruction technique (ART)  22  implemented in the TomoJ plugin  23  working in the ImageJ software. Segmented 3D models were constructed using the displaying capabilities and the isosurface rendering method in the Slicer software.
Binding energies were calibrated by referring to the C 1s peak at 284. 4 eV. Shirley-type background subtraction and fitting of the spectra were done with the software package Casa XPS vs. 2. 3. The Pd 3d peak was fitted using two doublets (i. e. Pd 0 and Pd II ) representing metallic and oxidized Pd species. Asymmetric and symmetric peak shapes were used to fit the metallic and oxidized Pd components respectively. For the fitting of C 1s five components were used corresponding to the various chemical states of the carbon support. Gas-chromatographic (GC) measurements were performed on a Varian 3800 CX gas chromatographer equipped with a PONA column and a Flame Ionization Detector (FID). The retention times and GC response in terms of area versus concentration of the reagents and products were calibrated by using pure components diluted in an ethanol solution at various concentrations.
■ ACKNOWLEDGMENTS The  Agence Nationale de la Recherche (ANR)  is gratefully acknowledged for the PhD fellowship of A. A. K. and its financial support during the project ( ANR-16-CE07-0030-01 ). G. G. , C. P. -H. and O. E. would also like to thank the  TRAINER  project  (Catalysts for Transition to Renewable Energy Future ) of the " Make our Planet Great Again" program  (Ref. ANR-17-MPGA-0017 ) for support. Mr. Han PENG  is gratefully acknowledged for his contribution to revisions experiments.
Cryo-EM sample preparation and data collection The MtbRho-ATP-DNA complex sample was prepared by mixing 0. 6 mg/mL of freshly purified WT MtbRho with 10 µM dC20 oligonucleotide and 1mM ATP  26  in cryoEM buffer (150 mM NaCl, 5 mM MgCl2, 10 mM Tris-HCl, pH 7. 6). Three microliters of the mixture were applied to glowdischarged Lacey 300 mesh copper grids (Ted Pella Inc. ), blotted for 3-4 s, and then flash frozen in liquid ethane using the semi-automated plunge freezing device Vitrobot Mark IV (ThermoFisher Scientific) maintained at 100% relative humidity and 22°C. Preliminary images were recorded using a JEOL 2200 FS electron microscope operating at 200 kV in zero-energyloss mode with a slit width of 20 eV and equipped with a 4k x 4k slow-scan CCD camera (Gatan inc. ). High resolution data were collected at the EMBL cryo-EM core facility (Heidelberg, Germany) with a Titan Krios S-FEG transmission electron microscope (ThermoFisher Scientific) operating at 300 kV at a nominal magnification of X 165,000 under low-dose conditions with defocus values ranging from -0.
Image Processing and high-resolution 3D reconstruction A total of 10,888 movies were recorded. The frames of each movie were computationally dose-weighted and corrected for drift and beam-induced movement in RELION-3. 1. 0 37 own implementation. The contrast transfer function (CTF) of each micrograph was determined using Gctf-v1. 18 program  38. A total of 2,217,252 particles were automatically picked with RELION's autopicking procedure. Briefly, a template-free autopicking procedure based on a Laplacian-of-Gaussian (LoG) filter allowed to select and extract a set of particles from an initial small set of images and to compute corresponding 2D class averages. The best 2D class averages were then used as references in a second round of autopicking in order to optimize parameters. Once determined, the autopicking procedure was applied to all images.
Structure refinement The map coefficients were converted into structure factors using the corresponding Phenix cryo-EM tool  40  , and molecular replacement was performed with Molrep 41 , using one of the monomers of EcRho (chain C of PDB entry 1PV4) as search model. Four of the six chains were immediately placed and the remaining two (the outer ones of the open ring) were added in subsequent runs. The electron density corresponding to these outer chains (A and F) was overall weaker and less clear than that of the inner chains. The homohexameric structure was initially refined by Refmac 42 using the maximum-likelihood method, and subsequently with the Phenix real-space refinement tool  43. Non-crystallographic symmetry, secondary structure, Ramachandran and rotamer restraints were used. The refinement cycles were alternated with extensive manual model-building, fitting and validation in Coot  44.
Acknowledgements We warmly thank  Annie Schwartz  for her help with biochemical experiments. This work benefited from access to the cryo-EM facility of the  European Molecular Biology Laboratory  (EMBL) in Heidelberg with support from the  iNEXT-Discovery program  (project # 871037 ) funded by the  Horizon 2020 framework of the European Commission. The work was supported by grants from the  French Agence Nationale de la Recherche  ( ANR-15-CE11-0024-01  to EM and  ANR-15-CE11-0024-02  to MB), a sabbatical research fellowship from  LE STUDIUM Loire Valley Institute for Advanced Studies  ( Marie Sklodowska-Curie   665790 ) to E. S. , and a doctoral fellowship from  Région Centre-Val de Loire  to I. S. CBS is a member of the  French Infrastructure for Integrated Structural Biology (FRISBI)  supported by  Agence Nationale de la Recherche  ( ANR-10-INBS-05 ).
Experimental design and determination of the optimal isolation conditions Response surface methodology (RSM) was used to analyse the effect of HCl concentration (HCl, M), reaction time (t, min) and microwave power (P, W) on the isolation yield of nanochitin: α-NCH L , α-NCH S and β-NCH SP. A Box-Behnken Design (BBD), which consists of 15 experiments of which 3 corresponds to the replicates in the central point, was selected for the experimental design and optimization. The experimental design was created and the optimum conditions were predicted with the desirability function of Statgraphics Centurion version XV software (StatPoint Technologies INC. , Warrento, VA, USA). Regression analysis function of Microsoft Excel Add-In, USA was employed to fit the experimental data obtained in the experimental design.
Atomic force microscopy (AFM) This technique was used to assess the morphology and size distribution of the obtained nanochitins. AFM pictures were collect using a Dimension 3100 NanoScope IV (Veeco, USA) following  [25]. The images were scanned at room temperature using tapping mode with silicon nitride cantilever and 10 nm tip nominal radius at a frequency of 1 kHz. Ten measurements in random locations of each sample were done and the average length and width values were then calculated with Nano-Scope Analysis 1. 9 software.
Microwave irradiation was chosen because of lower energy consumption, shorter reaction times and higher yields. Table  1  lists the experimental plan including the fixed variable (CH:HCl ratio), the independent variable (concentration of HCl; t and P) and the dependent variables (nanochitin yields: Y L , Y S and Y Sq ). A summary of the set of experiments that were designated by the Statgraphics software, as well as the experimental results obtained for the dependent variables is shown in Table  2. The determination coefficient R 2 indicates the validity of the design by explaining the total variations of the model  [26, 27]. Thus, as can be seen in Table  S1  (Supplementary material), the determination coefficients R 2 obtained for nanochitin from lobster (α-NCH L ) and from squid pen (β-NCH Sp ) were 0.
Optimization of isolation conditions and validation of the model The aim of the optimisation was to determine the optimal conditions for maximising the isolation yield of nanochitin from different chitin sources used in this study. Therefore, the dimensionless and dimensional values of the independent variables obtained through Statgraphics Centurion XV software are summarized in Table  3. A triplicate of the experiments was performed to validate the model under optimal conditions. A comparison was made between the obtained results and the theoretical ones (Table  3 ). The experimental results are in agreement with those predicted by the software, which validates the selected design (Box-Behnken). Authors such as Yuan et al. , who studied the isolation of nanocrystals from crab shells, obtained similar yields.
Acknowledgments The authors would like to thank the  Basque Government  (scholarship of young researchers training and project  IT1008-16 ) for supporting financially this research and their gratitude for technical and human support provided by  SGIker  ( UPV/EHU/ERDF, EU ). S. C. M. F. is the recipient of an  E2S   UPPA Research Partnership Chair  (MANTA:  Marine Materials ) supported by the " Investissements d'Avenir" French program  managed by  ANR  ( ANR-16-IDEX-0002 ), the  Région Nouvelle-Aquitaine  and the  Communauté d'Agglomération du Pays Basque, France.
■ INTRODUCTION ] [3] [4] [5] [6] [7] [8] [9] [10] [11]  The trinuclear μ 3 -oxido cluster with μ 2 -carboxylate bridges is a well-known structural type within the broader class of complexes with a [M 3 (μ 3 -O)] n+ core. A ConQuest search in the Cambridge Structural Database resulted in 220 hits of unique compounds with structures of [Fe  III  3 (μ 3 -O)(μ 2 -O 2 CR) 6 X 3 ] + (X = neutral coligand; none with five-coordinate iron(III)) and 55 hits of unique structures that are [Ru  III  3 (μ 3 -O)(μ 2 -O 2 CR) 6 X 3 ] + (Chart 1). Mixed-valence μ 3 -oxido transition metal carboxylates have attracted much attention as well: e. g. , iron(III,III,II) complexes were found to be suitable for an investigation of intramolecular electron transfer in the solid state. 12, 13 5] [16]  Even though heterotrinuclear 3d metal complexes with haloacetate ligands of the type [M  III  2 M II (μ 3 -O)(μ 2 -O 2 CR) 6 (H 2 O) 3 ], where M III = Fe, Ru, M II = VO, Mn, Fe, Co, Ni, Cu, Zn, and R = CH 2 Cl, CH 2 Br, CH 2 I, CCl 3 , were reported,  17, 18  there were no hits in a CSD search for heteronuclear 1Fe,2Ru or 2Fe,1Ru clusters.
The first isolated portion of the violet precipitate (ca. 400 mg) was suspended in dimethylformamide (1 mL) and filtered, the violet filtrate was concentrated to 0. 5 mL. Methanol (200 mL) was added to produce violet crystals, which were isolated after 72 h and dried in air (110 mg) , and 10 s over 1, 0. 5, -0. 36, 0. 5, -0. 36, and 0. 36°scan widths, respectively. The structures were solved by the SHELXS program and refined by fullmatrix least squares on F 2 with SHELXL. 89 Non-H atoms were refined with anisotropic displacement parameters. H atoms were inserted in calculated positions and refined using a riding model. Crystal data, data collection parameters, and structure refinement details are given in Table  S14 , while pertinent geometric parameters are quoted in the legends to Figures  1 2 3 4 and Figures  S8  and S23.
Crystallographic data for the structures have been deposited with the Cambridge Crystallographic Data Centre, CCDC: 2107595 and 2098989-2098993. Mass Spectrometric Measurements. Mass spectrometry data were obtained from a Thermo LTQ-FT ion cyclotron resonance mass spectrometer equipped with a 7 T superconducting magnet. Electrospray ionization (ESI) measurements were carried out in a mass range between m/z 100 and 2000 using a transient of 1. 5 s, resulting in a resolution of 200. 000 at m/z 400. Magnetic Susceptibility Studies. Magnetic measurements were carried out on microcrystalline samples with a Quantum Design SQUID magnetometer (MPMS-XL). The variable-temperature (2-300 K) direct current (dc) magnetic susceptibility was measured under an applied magnetic field of 0. 1 T. All data were corrected for the contribution of the sample holder and diamagnetism of the samples estimated from Pascal's constants.