dataesr/scientific-paragraphs-categorization huggingface dataset:
Properties of the components Individual components were tested and data are reported in Table  1. Density measurements were made with an Accupyc 1330 He pycnometer; the results are consistent with ref. [5]. The fiber average diameter was estimated using microscopic observations on approximately 350 fibers. The matrix thermal properties were obtained using flash thermography (diffusivity) and DSC (heat capacity). The fiber heat capacity was obtained by subtraction between the composite and the matrix values, and its diffusivity by a specific laser excitation method  [10]. The reported values will be used in the numerical computations based on tomographic images. For each composite, the analysis of phase ratios was performed using Hg porosimetry (open porosity, skeletal density, and bulk density), combined to 3D (X-ray tomography) and 2D (optical microscopy) imaging.
[12] , because of non-negligible thermal losses; it has been applied here on IR camera data for both composite samples, in both directions, and for the pure matrix. The average of the camera-recorded thermal field was used as the signal to be processed. It should be noted that the direction perpendicular to the composite plates will be called transverse, while those parallel to the plate will be called longitudinal. The experimental setup is composed of a flashlight device (Elinchrom Style RX1200) acting on the front face of the composite sample and an IR camera (FLIR SC7000, 320 × 256 pixels) recording periodically (25 Hz) thermal images on the rear face. Adiabatic conditions were ensured on the sample sides by inserting them inside a PS foam holder. The flash impulsion is spread on the entire front face (30 × 30 mm 2 ) so as to place the sample in 1D heat transfer conditions.
Acknowledgements GIS " Advanced Materials in Aquitaine " is acknowledged for a 1-year post-doc grant to G. B. The authors would like to thank  R. Huillery  ( Fahrenheit ) for the hot disc experiments;  R. C. Oliveira da Rosa & V. -E. Renaud  ( Arts et Métiers ParisTech -Bordeaux ) for density measurements and help for tomographic acquisition;  G. Caillard & V. Rolland de Chambaudoin d'Erceville  ( Arts et Métiers ParisTech -Bordeaux ) for taking micrographs and running the change of scale software;  A. Chirazi  ( Tomomat -Bordeaux ) for help in acquiring the tomographs.
792, CI 95% : 0. 692 to 0. 889; sum of explained variance ¼ 10. 2%, CI 95% : 8. 6 to 11. 9) are greater than those involved in species' migration (cumulative standardized slope ¼ À 0. 511, CI 95% : À 0. 59 to À 0. 426; sum of explained variance ¼ 6. 3%, CI 95% : 5. 3 to 7. 3; Fig. 2 ). Robustness of the results. We found very consistent results regarding the determinism of the climatic debt captured by the PLS0 model (Fig. 2  and Supplementary Table  1 ) and PLS1 to PLS4 models (Supplementary Data 2 and Supplementary Figs  1 2 3 ). The effects of the abiotic, biotic and anthropogenic factors presented in Fig. 2  have similar directions and magnitude across all the models (Supplementary Tables  1  and 2 , and Supplementary Data 2). Hence, our main findings are robust to both changes in the samples and periods considered (the PLS1 to PLS4 models were fitted on a subset of floristic communities observed after 1993, while the PLS0 model was fitted on all the floristic communities observed after 1987) and to the addition of new factors in the set of explanatory variables.
The underlying assumption of this transfer function is that plants and climate were at equilibrium before the onset of contemporary climate change (that is, 1987) and during the entire period covered by the calibration data set  46. This assumption was verified by the absence of climatic debt observed in the French forest plant communities between 1965 and 1986 (ref. 8 ). The transfer function was validated on an independent data set of 5,136 surveys sampled between 1975 and 1985 (R 2 ¼ 0. 83, RMSD ¼ 0. 97 °C). More details about the computation of the FrT values are provided in the Supplementary Methods. Both CrT and FrT were extracted and predicted, respectively, at the location and year of each of the 67,289 georeferenced and dated floristic surveys recorded during the period 1987-2008 and stored in three French databases (IGN-IFN  47  , which is a forest inventory database, n ¼ 54,618 plots; Sophy  48  , which is a phytosociological database, n ¼ 9,575 plots; and EcoPlant 49 , which is a phytoecological database, n ¼ 3,096 plots; Supplementary Fig.
Potential determinants of the climatic debt. As the aim of our study was to assess the main determinants of the climatic debt across the French territory (552,000 km 2 ), we computed several abiotic, biotic and anthropogenic variables, which are known or at least suspected to have an impact on the response of forest herbaceous plant communities to climate change both directly, through baseline conditions and magnitude of changes  15, 16  , and indirectly, through mechanisms involved in species' persistence and migration  16, 24  (Fig. 1 , Table  1  and Supplementary Data 1). First, we computed 14 variables depicting baseline environmental conditions and species exposure to environmental changes. Initial climatic conditions (macroclimate) before the onset of contemporary climate change are likely to have an impact on the composition and size of the regional species pool from which plant communities can be reshuffled over a short timescale  37  and thus are also likely to affect the magnitude of the observed climatic debt.
93 and RMSD ¼ 0. 58 °C for 13,620 independent temperature observations; Supplementary Fig. 13 )  50. It is noteworthy that we did not use this finer-resolution temperature grid when computing the climatic debt because of the 500 m uncertainty in the geographic location of some floristic surveys. Finally, we used five more variables to account for the potential impact of anthropogenic and natural disturbances. Three qualitative variables (coded 1 or 0) informing for the presence/absence of recent silvicultural practices (SILVP; for example, observation of tree cutting, tree extraction, cleaning brush and forestry machine track), the presence/absence of human-mediated and natural disturbances (DISTURB; for example, observation of fire, storm, inundation and agricultural activity encompassing livestock grazing and abandonment of agriculture areas) and the presence/absence of exotic trees (EXOT; considering 24 species defined by the Inventaire National du Patrimoine Naturel and listed in Supplementary Table  6 ) were extracted from floristic database.
We also computed proximity to road as the minimum distance between each floristic inventory plot and the French road network (that is, a spatial layer downloaded from the GeoFabrik website  55  and containing all linear road structures from highways to forest tracks). Finally, we extracted the sum of the human population density (HPD; assessed in 2009; ref. 56) in a radius of 10 km around each floristic inventory plot. Both variables are used to infer the human impact on biodiversity  41  through forest management  40  and/or assisted migration and invasion  57  , as well as the impact of natural disturbances  20, 58. The TBUF, SILVP, DISTURB and EXOT variables were computed or extracted from a subset of 45,806 floristic surveys belonging to the IGN-IFN  47  database over the 1993-2008 period for which the information was available.
thermal-and water-stress tolerances, respectively) for each of the 760 herbaceous plant species mentioned earlier. For a given species, we computed the ranges between minimum and maximum annual mean temperature and annual precipitation where the focal species occurred between 1961 and 1986 (n ¼ 14,954 floristic surveys from Sophy and EcoPlant databases; Supplementary Data 3). The 1961-1986 period was used here for its relative stability in terms of macroclimatic conditions, thus allowing us to compute robust climatic niche breadths considering the equilibrium observed between plants and climate 8 , and to not account for any climatic niche shift in response to climate change due to evolutionary adaptation, acclimation and/or phenotypic plasticity that we want to test explicitly the effect in our framework (see below).
Such sampling strategy allows comparing thermal niches over time with similar accuracy. Without this step, artificial thermal niche shifts due to differences in the number and distribution of species occurrences between both studied time periods can mask the effect of evolutionary adaptation, acclimation and/or phenotypic plasticity. Fifth, we computed the thermal niche overlap (D obs ) between N 0 and N obs using Schoener's D index  61. Finally, we computed an index of species' distribution conservatism over time (DC sp ) as the ratio between D obs and D ref. It is noteworthy that D obs never exceeds D ref and at best equals D ref , and thus DC sp cannot exceed 1. Distribution conservatism for a species is maximum and so is evolutionary adaptation, acclimation and/or phenotypic plasticity, when DC sp reaches 1 (Supplementary Data 3).
To compute DC sp at the community level (DC), we averaged DC sp values across all species co-occurring within a given community. Third, we computed three variables depicting important biotic mechanisms involved in species' migration. Poleward and upward range shifts  3, 7  are important biotic responses to climate warming. Climate niche tracking underlies such migrations, whose efficiency is modulated by species' dispersal abilities and habitat fragmentation. A perfect climate niche tracking implies both unlimited seed dispersal (allowing species to migrate towards newly suitable climate conditions as fast as climate warms) and immediate local extinction in the newly unsuitable climate conditions, which lead to niche conservatism over time (NC). As for DC, NC cannot be computed directly due to spatio-temporal variations in our data and thus we also had to assess first N 0 , N exp and N obs (Supplementary Fig.
Again, we computed the observed overlap (D obs ) between N 0 and N obs using Schoener's D index  61. Finally, we computed an index of species' thermal niche conservatism over time (NC sp ) as the ratio between D obs and D ref. It is noteworthy that D obs never exceeds D ref and at best equals D ref , and thus NC sp cannot exceed 1. Thermal niche tracking over time is perfect and thus species' range shifts are synchronous with climate warming, when NC sp reaches 1 (Supplementary Data 3). To compute NC sp at the community level (NC), we averaged NC sp values across all species co-occurring within a given community. To account for the species-specific effect of habitat connectivity on migration processes, we computed at the species level two variables representative of habitat proximity and habitat change.
Information about species longevity for each plant species were collected from the LEDA database  63  (a database of life-history traits of the Northwest European flora; Supplementary Data 3) and were averaged across all species co-occurring in each plant assemblage. Resource competition is also expected to affect the responses of species and communities to climate change  22  , because it is an important driver of community assembly  37. In the absence of resource competition measure in our data, we estimated resource competition among co-occurring species composing forest plant communities. We assumed that species niche differentiation observed in forest plant communities is a good proxy of resource partitioning directly linked to the magnitude of competition  64. We computed two indices of resource competition: one for water competition (C W ), which is directly linked to climate change, and the other for soil nutrient competition (C N ), which can be considered as a factor competing with climate change effects.
,20 are the estimated coefficients, which we used to quantify the magnitude of the effect of each variable (the definition of variables' abbreviation is provided in Table  1  and in Supplementary Data 1) and e denotes the residuals. All variables were centred and reduced to allow parameter comparison. Four other PLS regressions were fitted from a subset of 45,806 floristic observations sampled between 1993 and 2008. We ran these four extra models, to improve our consideration of the impact of anthropogenic and natural forest disturbances on the climatic debt. First, we fitted a model (PLS1) accounting for the exact same set of explanatory variables included in PLS0 (see equation (1) above). By doing so, we tested for the impact of using a different set of floristic observations covering a slightly different period.
To check for potential spatial autocorrelation remaining in the model, we computed the spatial correlograms on residuals of each model using the Moran's I index  66. The numbers of floristic observations selected in each subsample are 2,843, 2,725, 2,725, 2,423 and 2,247 observations for PLS0, PLS1, PLS2, PLS3 and PLS4, respectively, due to the different set of data considered for each of these models. Floristic observations in each of the 5,000 subsamples were weighted in the PLS regressions by the inverse of the total number of observations per year, to correct for an unbalanced temporal distribution of samples. This correction was required in our case, because we wanted to assess the determinism of the climatic debt in a way that is representative of the temporal changes in French forest plant communities between 1987 and 2008.
Acknowledgements We thank  G. Decocq  and  P. De Frenne  for seminal discussions;  D. Legrand  and  S. Jacob  for their comments;  C. Piedallu  for his contribution to the computation of the climate models; the  French National Geographical Institute (IGN)  to provide us with the IGN-IFN database;  I. Seynave  for the management of the EcoPlant database;  H. Brisse  and  F. Morneau  for contributions to the Sophy and IGN-IFN databases, respectively; and all those have participated in the conception of the EcoPlant, Sophy and IGN-IFN databases. EcoPlant database was funded by the  French Institute of Agricultural, Forest and Environmental Engineering (ENGREF, AgroParisTech) , the  National Forest Department (ONF)  and the  French Agency for Environment and Energy Management (ADEME). This study was supported by the  TULIP Laboratory of Excellence  ( ANR-10-LABX-41 ).
After heating, the samples were diluted to a final concentration of B0. 2 mM in IC buffer containing 0. 5 mM GDPNP and immediately used for grid preparations. Carbon-coated Quantifoil R2/2 grids (Quantifoil Micro Tools GmbH) were blotted with 4 ml of the purified samples and vitrified in liquid ethane at 100 K on a Vitrobot Mark IV (FEI). Low-dose images were collected at a calibrated magnification of Â 129,629 on an FEI Titan Krios with a Cs corrector operating at 300 kV, using a 4 Â 4 k FEI Falcon II camera (Netherlands Centre for Electron Nanoscopy, Leiden). The pixel size was 1. 12 Å on the camera. De-focus values ranged from À 0. 5 to À 2. 8 mm. All the images were recorded using the EPU software (FEI) for automatic data collection. Image processing. The low-dose images were taken with a total dose of 44 e À Å À 2 summed on 16 frames.
The first frame and the last eight frames were discarded to minimize beam-induced movement and irradiation. The remaining frames (corresponding to a total dose of 19 e À Å À 2 ) were first aligned with DRIFTCORR  47  , averaged and then sorted according to best power spectrum and ice quality. The particles from the selected images (2,154) were automatically picked with the RELION package  21. An initial data set of B192,000 particles was sorted using several rounds of classical two-dimensional classification protocols implemented in RELION. Then, a 3D-classification in RELION using a 70 Å filtered map of the archaeal 30S (extracted from the Pyrococcus furiosus 70S structure  48  ) helped to isolate several well-resolved classes that were used for further 3D classification. The following sorting procedure allowed us to discard classes without or with poorly resolved aIF1, aIF1A or tRNA.
2  and Table  1 ). Such decrease in resolution as distance with the core 30S increases was already observed in other ICs  22. Because of this, for better clarity in the figures showing factors, maps were generally low-pass filtered to 8. 5 Å (IC0-P REMOTE ) or 12 Å (IC1-P IN ). Model building and refinement. IC0-P REMOTE. The structures of the head and body parts of the 30S subunits were extracted from the P. furiosus 70S coordinates  48  (PDB entry 4V6U). The head and body were manually placed independently into the electron density map using Chimera, before automatically refining the position in the same programme. Messenger RNA was clearly visible in the density, including interaction of the SD sequence with the anti-SD (Supplementary Fig. 4a ). The mRNA was modelled in Coot 52 using the structure of the T.
thermophilus 70S ribosome complexed with mRNA and tRNAs (PDB entry 1JGO  53  ) as a guide. The start codon was positioned in the P site. The mRNA was not visible from base þ 5 to base þ 9 (as in yeast 48S complex, PDB entry 3JAQ) at the 3 0 extremity. Bases À 17 and À 16 at the 5 0 end of mRNA were not visible. The isolated PDB models of initiation factors and tRNA were manually placed in the electron density. The crystallographic model of the ternary IC aIF2:GDPNP:tRNA  23  (PDB entry 3V11) could be readily fitted in the electron density with few modifications (see text, Fig. 4d  and Supplementary Fig. 3b ). Finally, a few manual model corrections were performed in Coot  52  to better fit the electron density maps. These corrections concerned the following regions: 16S rRNA connection between the head and body, h44 bulge, aIF1 basic loop, h44 near aIF1A.
As a final step, the basic loop of aIF1 was rebuilt to better fit the density. Because of the modest resolution of IC1-P IN , Refmac refinement systematically led to overfitting and was therefore not applied to the final model. Correlation coefficient between the map simulated from the model (7. 5 Å resolution) and the experimental map was 88. 0%, as determined using Chimera 58. Data availability. The EMDataBank accession numbers for the EM maps reported in this paper are EMD-8148 (IC0-P REMOTE ) and EMD-8149 (IC1-P IN ). The coordinates of the models fitted in the maps have been deposited in the Protein Data Bank (accession numbers 5JB3 for IC0-P REMOTE , 5JBH for IC1-P IN ). The data that support the findings of this study are available from the corresponding author on request. for image acquisition at the National Center for Electron Nanoscopy in Leiden (NeCEN) and helpful advice.
We thank Thomas Simonson for critical reading of the manuscript. This work was supported by the Centre National de la Recherche Scientifique, Ecole polytechnique, the Physique et Chimie du Vivant program of Agence Nationale de la Recherche (MASTIC project) and by the French Infrastructure For Integrated Structural Biology (FRISBI) ANR-10-INSB-05-01. A. M. was supported by a 'Bourse Monge' doctoral fellowship from Ecole polytechnique.
thermophilus complex (PDB entry 4V51; ref. 34) are represented on the right for reference. The mRNA (magenta) is from PDB entry 1JG0 (ref. 53). The relative positions of aIF2g-tRNA in IC0-P REMOTE , in IC1-P IN and in py48S-closed were deduced from superimposition of the body of the small ribosomal subunit of each complex on that of the T. thermophilus 70S. The aIF2g or eIF2g are coloured in green. The view illustrates that the TC is constrained in IC1-P IN , whereas it is unconstrained in IC0-P REMOTE and py48S-closed complex.
Acknowledgements This paper is dedicated to the memory of our friend and colleague  Nicolas Boisset. We thank  F. Weis ,  R. Gillet ,  S. Jonic ,  A. Myasnikov  for valuable discussions on electron microscopy and  J. F. Menetret  for help with data collection at the preliminary stages of this study. We also gratefully acknowledge  R. Matadeen  and  S. de Carlo  ( FEI Company ).
In addition to possible indications concerning MRI screening, the participants were also asked if and when a normal MRI should be repeated, whether contrast medium should be used, and if extreme ages would influence their recommendations. Finally, data concerning the participant's location, practice setting, year of medical degree and number of ADPKD patients in their active files were collected. For each of these variables, several classes were created to determine whether the answers to the questions varied depending on the medical experience of the nephrologist (medical degree before 1989; 1990 to 2004; 2005 to 2017), their practice setting (private practice/hospital setting), and their experience with polycystic kidney disease (0; 1 to 9; 10 to 49; 50 to 99; more than 100 PKD patients in their active files).
Several physician-dependent covariates were evaluated in regards to the attitudes towards ICA screening. Our data mainly indicate that the most experienced physicians in ADPKD, as well as physicians working in teaching hospitals, were less likely to prescribe systematic MRI screening. This finding could reflect a better knowledge of the recommendations and a different perception of the cost and the expected benefits of screening given the incidence of ICA complications in the ADPKD population. Our study has several limitations. Although most of the licensed nephrologists were contacted, we obtained responses from only approximately one-fourth of the nephrologists in our country. We managed to obtain a geographically representative panel, although physicians from hospital-based settings were slightly over-represented.
Photographic images were resized, organized, and labeled using Adobe Photoshop software. Optimized automated quantification of GFP-LC3 dots by high throughput microscopy GFP-LC3 HeLa cells were transfected with empty vector, IRS1 or TRS1 and fixed after 4 h of starvation. GFP-LC3 dots were quantified using a Thermo Cellomics ArrayScan VTI HCS Reader (Thermo Fisher, Pittsburgh, PA, USA). The analysis program detected DAPI-labeled nuclei, cell outline, GFP-LC3 dots and nontransfected rejected cells. The number of GFP-LC3 dots per cell, the total intensity of GFP-LC3 dots per cell, the total area covered by GFP-LC3 dots per cell and the average area covered by GFP-LC3 dots per well were determined by the ID view software.
Acknowledgments We would like to thank  Michael Crawford  for assistance in constructing IRS1-deletion plasmids. We are also very grateful to  Beth Levine  for providing us with the BECN1 constructs. We thank the  FHCRC Genomics Core  for technical assistance. We thank  Val erie Nicolas  from cellular imaging MIPSIT facility and  Claudine Delomenie  from the  Transprot  facility for technical assistance. We thank  Patrice Codogno  for insightful discussions and help for shRNA knockdown.
Methods All of the patients included in this study were informed of the use of their data for clinical research. All patient information was anonymized and de-identified prior to analysis. The institutional review board approved this study. The president of the Ethical Committee of the French Society of Thoracic and Cardio-Vascular Surgery certified that the project had been assessed according to the current regulations framing clinical research in France. This single-center retrospective study included 147 patients who underwent carotid angioplasty stenting at our institution from July 1997 to November 2013 to treat restenosis following CEA. Patients who were treated with carotid angioplasty for radiation-induced carotid stenosis or who had primary CAS were excluded from the study. Clinical and demographic data and information on comorbidities and treatment were collected at admission.