dataesr/scientific-paragraphs-categorization huggingface dataset:
Spectral and coherence analysis Electrophysiological data were analysed with custom-written Matlab (The MathWorks Inc. , Natick, MA, 2000) codes that made use of the Chronux toolbox (http://www. chronux. org). Spectrograms and coherograms were calculated with multi-taper Fourier analysis to diminish finite windowing effects. For all analysis, we applied 5 tapers and a window size of 3s with a 0. 1s shift between bins. Changes in power of specific bands were calculated only after identification of a peak in the power spectrum (3-6Hz for the respiratory rhythm) in at least one condition using the signal to noise ratio defined as the ratio between power in the band of interest to the power in the rest of the spectrum. To quantify the relationship between behaviour (freezing or not freezing) and power in the 3-6Hz band, we used Receiver Operator Characteristic curves(3).
Statistics For each statistical analysis provided in the manuscript, the Kolmogorov-Smirnov normality test was first performed on the data. If the data failed to meet the normality criterion, statistics relied on non-parametric tests. We therefore represent the median and quartiles of data in boxplots in all figures, in accordance with the use of non-parametric tests. For comparison of proportions, the standard chi2 test was used. Two-way mixed repeated anova was performed using GraphPad Prism, GraphPad Software, La Jolla California USA. Ranksum and signed rank : we report the signed rank statistic if the number of replicates is too weak to provide the normal Z statistic Model of brain-body-brain feedback loop of emotional initiation and maintenance. An emotive stimulus evokes responses in the amygdala and Periaqueductal Gray (PAG) which lead to changes in behavior (freezing) and somatic physiology (breathing).
Acknowledgments We thank  L. Roux  and  A. Sirota. for discussions, and  A. Peyrache  for discussion and critical readings of an earlier version of the manuscript. Funding: This work was supported by the  Fondation pour la Recherche sur le Cerveau  ( AP FRC 2016 ), by the  French National Agency for Research   ANR-12-BSV4-0013-02  ( AstroSleep ) and  ANR-16-CE37-0001  (Cocode), by the  CNRS: ATIP-Avenir  ( 2014 ) and by the  city of Paris  (Grant Emergence  2014 ). This work also received support under the program Investissements d'Avenir launched by the  French Government  and implemented by the  ANR , with the references:  ANR-10-LABX-54   MEMO LIFE  and  ANR-11-IDEX-0001-02   PSL* Research University. G. d. L. and M. M. L. were funded by the  Ministere de l'Enseignement Supérieur et de la Recherche, France.
S. B. was funded by the  ENS-Ulm ,  PSL Research University , the  Ministere de l'Enseignement Supérieur et de la Recherche, France  and the  Fondation Pour la Recherche Médicale, France. Author contributions: S. B. and J. M. L. performed the experiments and analysed the data. M. M. L. and G. d. L. did several experiments included in the dataset. C. B. helped for the behavioural experiments. H. G. performed the histology. S. B. and K. B. wrote the manuscript with the help of J. M. L. and C. H.
This new version of ORCHIDEE-CAN was parameterized over Sweden. Subsequently, the performance of the model was tested against data for historical storms in southern Sweden between 1951 and 2010 and south-western France in 2009. In years without big storms, here defined as a storm damaging less than 15 × 10 6 m 3 of wood in Sweden, the model error is 1. 62 × 10 6 m 3 , which is about 100 % of the observed damage. For years with big storms, such as Gudrun in 2005, the model error increased to 5. 05 × 10 6 m 3 , which is between 10 and 50 % of the observed damage. When the same model parameters were used over France, the model reproduced a decrease in leaf area index and an increase in albedo, in accordance with SPOT-VGT and MODIS records following the passing of Cyclone Klaus in 2009. The current version of ORCHIDEE-CAN (revision 4262) is therefore expected to have the capability to capture the dynamics of forest structure due to storm disturbance on both regional and global scales, although the empirical parameters calculating gustiness from the gridded wind fields and storm damage from critical wind speeds may benefit from regional fitting.
Although ORCHIDEE does not enforce a spatial or temporal resolution, the model does use a spatial grid and equidistant time steps. The spatial resolution is an implicit user setting that is determined by the coarsest resolution of the forcing data and the boundary conditions, i. e. the vegetation distribution, climatological forcing data, and the soil map. If higher-resolution drivers are available the model can then be run on that scale. If site-level drivers are available then simulations on the site scale are feasible. ORCHIDEE can run on any temporal resolution; however, this apparent flexibility is rather restricted as the processes are formalized at given time steps: half-hourly (i. e. photosynthesis and energy budget), daily (i. e. net primary production), and annual (i. e. vegetation dynamics).
Note that in this study the values for the mean wind ratio are specific for the CRU-NCEP half-degree forcing at a 6 h time step. The mean wind ratio will thus need to be re-parameterized if the wind driver is replaced by any other forcing. The MWR was estimated from 38 European eddycovariance sites covered by forests for which the meteorological data were freely available. For the period 1996 to 2007, 208 site-year combinations were retained for which over 60 % of the half-hourly measurements were available. The remaining data gaps were filled with the ERA-Interim reanalysis data  (Dee et al. , 2011; Vuichard and Papale, 2015). The 208 site-years were extracted from the CRU-NCEP reanalysis. Subsequently, the ratio between the maximum 30 min mean wind speed within a 6 h period and the 6 h mean wind speed obtained for the same location and time frame from the CRU-NCEP reanalysis was calculated.
MWR = U Fluxnet U CRU-NCEP , (10) where the subscripts Fluxnet and CRU-NCEP denote the data source, U Fluxnet is the maximum value of the 30 min mean wind speeds (m s -1 ) within a 6 h time frame, and U CRU-NCEP is the 6 h mean wind speed from the CRU-NCEP dataset. Finally, maximum wind speeds in the time series of each forest site were stratified according to the wind force catalogue in Beaufort wind scale (BWS), an empirical measure that relates wind speed to observed conditions on land (National Meteorological Library and Archive, 2010). Data were then analysed to account for the relationship between the mean wind speed and the ratio between the observed and temporal average mean wind speed. The quality of the fitted relationship was calculated as the root mean square error (RMSE). RMSE is a statistic that is widely applied to quantify the difference between values predicted by a model and the values actually observed: RMSE = [mean(Y i -F i )] 2 , ( 11 ) where Y denotes the observed values, F denotes the modelled values, and the subscription i is the sample index.
Storm damage observations A 60-year-long record of storm damage statistics over Sweden was extracted from the country-level European Forest Institute storm damage database from 1951 to 2010  (Nilsson et al. , 2004; Schlyter et al. , 2006; Bengtsson and Nilsson, 2007; Gardiner et al. , 2010). We refined this dataset with regional information. Following Gudrun in January 2005, the Swedish Forest Agency mapped the spatial distribution of the storm damage for damage classes ranging from 5 to 50 m 3 per ha in steps of 5 m 3 for the first damage class and 10 m 3 for all subsequent classes (see Fig. 7 ). The storm made landfall near the south of Norway, went through the north of Götaland, and resulted in extensive forest damage in the central area of Götaland. The area around the cites of Ljungby and Växjö was reported as having the greatest damage of about 30 m 3 ha -1.
The spatial extent of storm damage was retrieved from ocular inspection of aircraft images processed by the Swedish Forest Agency. In January 2009, Klaus made landfall in south-western France near Les Landes forest and damaged 43. 1 × 10 6 m 3 of wood in France. The dynamics of surface albedo and LAI between 2001 and 2010 were extracted from MODIS and SPOT-VGT satellite images, receptively  (Planque et al. , 2017). These remote sensing time series were overlaid by ORCHIDEE-CAN simulations and compared at two locations between 2001 and 2010, thus resulting in a total of 20 data points (shown as pink arrows in Fig. S2 ).
Critical wind speeds for five tree species Model implementation and parameterization were tested for all five tree species shared between ForestGALES and ORCHIDEE-CAN (see Sect. 2. 3. 2) by running ORCHIDEE-CAN for a test pixel. The Fontainebleau forest, which is the closest large forest to the LSCE research institute, was arbitrary selected as the simulation site and ORCHIDEE-CAN was run for 200 years by cycling over the CRU-NCEP reanalysis data from 1901 to 1930. Subsequently, the canopy structure variables simulated by ORCHIDEE-CAN, including h, D, C w , C d , and LAI, were used as the input data for a stand-alone version of ForestGALES (MathCAD version) to estimate the CWS ov and CWS bk. In total four types of CWSs, CWS ov and CWS bk for the inner and outer forest, were simulated by both models for the same canopy structure.
For these 10 years the climate data for 1941 to 1950 were used. Within the study domain, this approaches reduced the standing biomass mainly in western Norway (not shown). Subsequently, the simulation experiment continued from 1951 to 2010, the period for which damage reports are available  (Nilsson et al. , 2004; Schlyter et al. , 2006; Bengtsson and Nilsson, 2007; Gardiner et al. , 2010). The years 1981 to 2000, which were used for parameter tuning, were excluded from the evaluation.
The first 10 years of the simulation experiment were intended to reach equilibrium between the vegetation and the mean wind speed. For these 10 years the climate data for 1991 to 2000 were used. Within the study domain, this approach had very little effect on the standing biomass (not shown). Subsequently, the simulation experiment continued from 2001 to 2010, the period for which the MODIS albedo product was analysed  (Planque et al. , 2017). Two ORCHIDEE-CAN simulation pixels, one located at the centre of Les Landes forest with storm disturbance and another located in the east of Les Landes forest without the storm disturbance, were selected for analysing the biophysical responses to the storm damage for the summertime during the 10-year simulation. Geosci. Model Dev. , 11, 771-791, 2018  www.
In January 2005, Gudrun caused the biggest recorded storm damage in Swedish history. We extracted the simulation result from the evaluation experiment and calculated the total simulated storm damage by Gudrun. Simulated storm damage was 76. 6 × 10 6 m 3 , which compares well to the reported 75. 0 × 10 6 m 3  (Gardiner et al. , 2010). Moreover, the high-level damage pixels (> 30 m 3 per ha) are located in central Götaland, which demonstrates that when the driver data locate the storm in the right location, the model has the capability to reproduce the spatial distribution for the event (see Fig. 8 ). The model, however, underestimated the storm damage in 2007 (Fig. 5 ). Given the reconstructed wind speed in the driver data, the observed storm damage appears high. The failure of the model to simulate the right order of magnitude in storm damage in 2007 may be for two reasons: (1) the CRU-NCEP reconstruction underestimated the wind speed and/or (2) ORCHIDEE-CAN overestimated the critical wind speed by not accounting for the legacy effects of Gudrun, such as decreased tree stability owing to root damage or increased gap sizes following Gudrun.
Code availability. The ORCHIDEE-CAN code and the Li-bIGCM run environment are open source and distributed under the CeCILL licence (http://www. cecill. info/index. en. html). The ORCHIDEE-CAN branch is available via the follow web links: https://forge. ipsl. jussieu. fr/orchidee/browser/branches/ ORCHIDEE-DOFOCO/ORCHIDEE?rev=4262 or https://doi. org/ 10. 5281/zenodo. 1109750 (Chen, 2017). Readers interested in running ORCHIDEE-CAN (revision 4262) are encouraged to contact the corresponding author for full details and the latest bug fixes. The Supplement related to this article is available online at https://doi. org/10. 5194/gmd-11-771-2018-supplement.
Acknowledgements. This work was funded through a bilateral agreement between the  Swedish Research Council (VR)  and the  French Alternative Energies and Atomic Energy Commission  (LSCE-IPSL,  CEA ,  CNRS , and UVSQ). Part of the computational resources was provided by the  Research Center for Environmental Changes (RCEC) at Academia Sinica (RCEC, AS)  and the  National Central University (NCU) in Taiwan. Yi-Ying Chen  received funding through the  Ministry of Science and Technology, R. O. C. (MOST   106-2111-M-001-001-MY3 ). Sebastiaan Luyssaert was funded in part through an  Amsterdam Academic Alliance fellowship. We thank the two anonymous reviewers and the editor,  Hisashi Sato , for their very helpful observations and suggestions. Edited by: Hisashi Sato Reviewed by: two anonymous referees.
Therefore, we increased the number of manually collected rosettes and, as expected, identified more  13  C proteins each time (Fig. 1c ). Once we manually collected 10,000 rosettes, the task of microdissection became too difficult and time consuming. To speed up the process, we have developed a strategy that benefits from ImageJ software's automated processing. As a first step, under the Zeiss PALM software, the user located a field of interest. An image was taken, which was automatically imported under the ImageJ software (Supplementary Fig. 1b ). Using a homemade plugin, metadata were extracted. This information allowed accessing the precise acquisition stage location, expressed as a calibrated set of coordinates. A homemade ImageJ toolset used those coordinates to calculate a matrix of points placed at the center of adjacent field.
Using the plugin, this matrix was exported as an "Element file", a proprietary file format from Zeiss, and manually imported into the PALM software. The microdissection system was then operated to move the stage at each of the coordinates, and to acquire an image for each visited field. The dissection process was finally completed based on the automatically segmented regions (Supplementary Fig. 1b ). When performed semi-automatically, an experienced person could dissect on an average 900 structures per hour, which is a fourfold improvement on the manual procedure. Moreover, after automatic selection of the regions of interest (ROI), the operator can control the selection. Indeed, the operator can deselect the aberrant and not specific structures to improve the quality control of the process.
This point can affect the throughput of the method. However, using pictures extracted from the microdissection process, we can evaluate that the criteria used to select invadosomes are very strict. Thus, the automation system allowed us to save time and collect 40,000 rosettes more comfortably. Over the experiments, a database search analysis with 13 C(6) K or  13  C(6) R labeling as variable modification showed that a large majority (67-97%) of identified peptides in the first experiments were not  13  C labeled and thus came from contaminations. The amount of contaminating proteins decreased with more cellular material and became a minority (5% for 40,000 rosettes collected) as soon as we exceeded the sensitivity threshold of the mass spectrometer (Fig. 1c ). This result demonstrates the relevance of isotopic labeling prior to laser microdissection to guarantee the specificity of protein identifications.
The closest point of the total protein quantity range (100 ng) was chosen to compare the MS relative abundances after normalisation. b Among the 366 proteins identified with at least 2 peptides in the rosettes sample, 312 proteins were enriched with a rosette/total proteome abundance ratio ≥ 1. 5 (log ratio ≥ 0. 6 in red). c Manual sorting of the proteins according to their characterization in the literature. Among the 312 enriched proteins, 42% (95 proteins) were associated to cancer invasion including 37 proteins already described in the invadosomes. d The bart chart represents the classification of the 312 enriched proteins according to the GO categories ("Protein class" classification from the PANTHER database). Among the 70 nucleic acid proteins, 60 proteins were sub-classified as RNA-binding proteins whose functional assignments distribution is represented into the pie chart Next, we generated a functional network of proteins identified and enriched in invadosomes compared with the whole cellular proteome (Supplementary Fig.
3 ). Annotations were attributed manually for the following functions: cancer invasion, invadosomes, and matrix degradation. Involvement in actin reorganization, cell adhesion, chemotaxis, or protein translation was extracted from the Ingenuity® Pathway Analysis Database (Qiagen) (Supplementary Fig. 3 ). According to this analysis, the two major emerging groups are cancer invasion and translation. The identification of several ribosomal proteins (27), ribonucleoproteins (8), mRNA processing factors (14), and translation factors (12) in the invadosome proteome (Fig. 2d ) suggests that a dedicated protein synthesis machinery is associated with these structures. A translation machinery associated with invadosomes. The spatio-temporal control of protein translation is necessaryto ensure protein production at the right time and place  28.
In situ zymography assay. Coverslips were coated with Oregon green gelatin (Invitrogen), fixed with 0. 5% glutaraldehyde (GA) (Electron Microscopy Sciences), and washed with phosphate-buffered saline (PBS) (Invitrogen). Cells were seeded on coated coverslips and incubated 24 h before fixation and staining. Immunofluorescence. Cells were fixed with 4% PFA, pH 7. 2, for 10 min, permeabilized with 0. 2% Triton X-100 for 10 min, and incubated with various antibodies, as described earlier in Juin et al  4. Cells were imaged with a SP5 confocal microscope (Leica, Leica Microsystems GmbH, Wetzlar, Germany) by using a × 63/ numerical aperture (NA) 1. 4 Plan Neofluor objective. To prevent contamination between fluorochromes, each channel was imaged sequentially using the multitrack recording module before merging.
Statistical analysis. Statistical analyses were performed using GraphPad Prism 6. 0 software. Regular one-way analysis of variance (ANOVA) was used for the comparison of multiple means. Means were considered significantly different when P < 0. 05. The ANOVA test was followed by a Bonferroni's multiple-comparison post test, each condition was compared with the control. SILAC labeling. NIH-3T3-Src cells were grown in DMEM medium without lysine and arginine (Gibco) supplemented with 10% dialyzed fetal bovine serum (Gibco), 200 mg/L L-proline (Sigma-Aldrich), and 84 mg/L 13 C6 L-Arginine (R) and 146 mg/ L 13 C6 L-Lysine (K) (both from Eurisotop) at 37 °C in a humidified incubator in a 5% CO 2 atmosphere. The total incorporation of the labeling was checked by MS after six cycles of cellular doubling.
7 objective, a Lumencor spectra 7 illumination device (Lumencor, Beaverton, USA) and a HQ2 charge-coupled device (CCD) camera (Photometrics, Tucson, USA). This system was under the control of the Meta-Morph software (Molecular Devices, Sunnyvale, USA), under which a series of journal has been created to automate the acquisitions over a multi-well plate (24 wells/plate, 25 fields imaged/well). Automated image analysis has been performed using an in-house developed workflow, using the CellProfiler software  42. Briefly, for both the invadosomes and nuclei images a correction for uneven illumination has been performed. Invadosomes were first enhanced by top-hat filtering. Candidate structures were isolated using the "Identify primary objects" function, then submitted to a refined identification by filtering based on morphological parameters (area, major/minor axis length, and solidity).
Nuclei identification was performed using only the primary object detection. For both channels, an image of the objects outlines was saved to visual assess the segmentation efficiency. All morphological parameters were extracted and saved as a SQLite database for further reviewing and analysis using the Cell Profiler Analyst software  43. Range of protein quantity of total cellular extract. SILAC labeled NIH-3T3-Src cells were lysed in RIPA buffer (Sigma) supplemented with protease inhibitor cocktail (Roche). Protein concentrations were measured using the Bio-Rad protein assay. Sample preparation for MS. Micro-dissected invadosomes and whole cells extracts were incubated in a Tris-HCl pH 6. 8 solution for 2 h at 95 °C. Samples were loaded on a 10% acrylamide SDS-polyacrylamide gel electropjoresis (PAGE) gel.
After 5 min desalting, peptides were separated on a 75 µm ID × 25 cm C18 Acclaim Pep-Map® RSLC column (Thermo Scientific) with a 4-40% linear gradient of solvent B (0. 1% formic acid in 80% ACN) in 108 min. The separation flow rate was set at 300 nL/min. The mass spectrometer operated in positive ion mode at a 1. 8 kV needle voltage. Data were acquired using Xcalibur 3. 1 software in a data-dependent mode. MS scans (m/z 350-1600) were recorded at the resolution of R = 70,000 (@ m/z 200) and an AGC target of 3 × 106 ions collected within 100 ms. Dynamic exclusion was set to 30 s and top 12 ions were selected from fragmentation in HCD mode. MS/MS scans with a target value of 1 × 105 ions were collected with a maximum fill time of 100 ms and a resolution of R = 17,500. In addition, only + 2 and + 3 charged ions were selected for fragmentation.
The other settings were as follows: no sheath and no auxiliary gas flow, heated capillary temperature 200 °C, normalized HCD collision energy of 27%, and an isolation width of 2 m/z. MS data processing and quantification. For protein identification, we used the Mascot 2. 5 algorithm available with Proteome Discoverer 1. 4 Software (Thermo Fisher Scientific, Inc. ). It was used in batch mode by searching against the UniProt Mus musculus database (45,172 entries, Reference Proteome Set, release 2016_03) from [http://www. uniprot. org/website]. Two missed enzyme cleavages were allowed. Mass tolerances in MS and MS/MS were set to 10 p. p. m. and 0. 02 Da. Oxidation of methionine, acetylation of lysine, and deamidation of asparagine and glutamine were looked for dynamic modifications. Carbamidomethylation on cysteine was searched as static modification.
13 C(6) (K) or 13 C(6) (R) labeling was searched as variable modification for contaminating analyses and as fixed modification for invadosomal proteome analyses. For the enrichment analysis compared with the total cellular proteome, raw LC-MS/MS data were imported in Proline Studio (http://proline. profiproteomics. fr/) for feature detection, alignment, and quantification. Protein identification was only accepted with at least 2 specific peptides with a pretty rank = 1 and with a protein false discovery rate value < 1. 0% calculated using the "decoy" option in Mascot  44. Label-free quantification of MS1 level by extracted ion chromatograms was carried out using the parameters indicated in Supplementary Table  5. Protein ratios were normalized by sum of peak intensities. Proteins with invadosomes/total ratios ≥ 1.