dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements: The authors are grateful to the experimental unit (PEAT, INRAE, 2018. Poultry Experimental Facility, doi:  10. 15454/1. 5572326250887292E12 )for the care of birds, to the  GeT-GenoToul platform Toulouse  for performing real time quantitative PCR with the Biomark Fluidigm system and to the  Centro de Instrumentación Científica de la Universidad de Granada  for electron microscopy and elemental analysis. The authors acknowledge Pierre-Ivan Raynal and  Sonia Georgeault  from the  IBiSA electron microscopy platform of the "Université François Rabelais" of Tours  for their assistance. The authors also acknowledge  Nadine Gérard ,  Cindy Riou  and  Agostinho Alcantara-Neto  from  UMR "Physiologie de la Reproduction et des Comportements"  ( INRAE Centre Val de Loire ) for their help in extracellular vesicle isolation.
https://anr. fr/Projet-ANR-13-BSV6-0007. Table S3. S3 (. xlsx file) Proteins identified by proteomics analysis of uterine fluid extracellular vesicles. List of the twenty-nine non-redundant proteins identified by nanoLC-MS/MS analysis on extracellular vesicle (EVs) fraction of the uterine fluid (9 h p. o. ).
Extracted intact peptides/proteins were subjected to fractionation through chromatographic using two different chromatographic approaches (reversed-phase (RP) high-performance liquid chromatography (HPLC) and gel filtration (GF)). After separation and enrichment, each fraction was analyzed by on-line microflow liquid chromatography tandem high resolution mass spectrometry (µLC-MS/MS). Proteo/peptidoform identification and structural characterization were performed using ProSight PC software v 4. 0 (Thermo Fisher, San Jose). We applied a validation threshold with a E-Value of ≤ 1 × 10 -6 (monisotopic) or ≤ 1 × 10 -8 (average) presenting a C score > 3. From the list of the idenfied proteo/peptidoforms by top-down MS, we annotated MALDI-TOF peak if the average mass [M + H] + was within a ± 0.
MS/MS ion searches were performed using Mascot search engine version 2. 6 (Matrix Science, London, UK) via Proteome Discoverer 2. 1 software (ThermoFisher Scientific, Bremen, Germany) against NCBIprot_mammals database. For comparative analyses between control and electrical shock groups in the "near" and "far" regions, we employed Scaffold Q+ software (version 4. 8. 9, Proteome Software, Portland, USA) to apply two independent label-free quantitative methods (Normalized Weighed Spectra (NWS) and Extracted-ion chromatogram (XIC)). Significance was determined using t test where p < 0. 05 was considered significant. An immunoblot analysis was used to confirm our bottom-up quantification. Each individuals from each group in the far region were compared (i. e. 5 individuals from control group and 5 individuals from the electrical shock group).
MALDI-TOF MS phenotyping of myocardial tissue. A total of 100 mean spectra were collected for each region of the two groups. A representative spectrum is shown in Fig. 2A. A total of 133, 145, 136 and 156 m/z were detected in control "near", control "far", electrical shock "near" and electrical shock "far" groups Figure  2. MALDI profiling dataset. Representative MALDI spectrum of a myocardial section in the "near" lead region after electrical shocks (A). Venn diagram with four main conditions (B). PCA analysis in "near" region (C) and in "far" region (D), electrical shock group (red dots) can be discriminate from control group (green dots). PC principal components, a. u arbitrary unit. Images included have been created using ClinProTools 3. 0 software (Bruker Daltonics, Bremen, Germany, https ://www.
bruke r. com/servi ce/suppo rt-upgra des/softw aredownl oads/mass-spect romet ry. html). respectively. A Venn diagram representing common and specific m/z in the four different conditions is also represented in Fig. 2B. The principal component analysis (PCA) performed with the different peaks was able to clearly discriminate the control and electrical shock groups in both regions (Fig. 2C, D ). Differential analysis between control and electrical shock in the "near" region characterized 32 m/z molecular species with a p value < 0. 01 and a FC > 1. 5 or < 0. 66 including 20 m/z with an AUC > 0. 8. In the "far" region, 34 m/z were different (p value < 0. 01, FC > 1. 5 or < 0. 66), including 27 m/z with an AUC > 0. 8 (Supplemental dataset 1).
Top-down HR-MS proteomic analysis. Proteins identified by top-down HR-MS. We were able to identify 171 peptidoforms and proteoforms without redundancy, this represented 92 UniprotKB accession numbers corresponding to 73 unique genes. From this identification subset, we retained 68 peptidoforms and proteoforms automatically validated with high values of scoring with C score > 3 and E-values ranging from 9. 06 × 10 -8 to 2. 35 × 10 -71 in a mass range between 1211 and 18,459 Da (Supplemental dataset 2). Among these, 8. 82% corresponded to whole proteins, 13. 24% to N-terminal fragments of original proteins, 26. 47% to internal fragments and 51. 47% to C-terminal fragments. Protein fragments were predominant and represented 91. 17% of the validated identifications. The most cleaved proteins identified by TD were the myosin light chain II and the alpha-crystallin B chain.
To obtain an overview of the residues engaged in these cleavage sites, the occurrence of the amino acids at the end of the N-ter or internal fragments, and those above the internal or C-ter fragments, were noted. Top 5 most frequent aminoacids are represented in Table  1 , the most frequent residues found at the end and above the fragment being A, D, F, G, L, R and S. These data suggest that the identified biomolecules are products of protease activities, from trypsin, chymotrypsin-like, caspase enzymes or other endopeptidases targeting specific sites and/ or specific substrates. Using CASBAH and MEROPS databases, 2 peptidases were relevant for two identified peptidoforms: the matrix metallopeptidase 9 (MMP-9) which cleaves the alpha-crystallin B chain (CRYAB) in position 54  27  , and the mitochondrial processing peptidase beta-subunit (MPP-I) which cleaves the ATP synthase subunit delta (mitochondrial) in position 22  28.
Annotation of tissue MALDI-TOF MS peaks to identify potential tissular markers of acute shock-induced myocardial tissue injury. Fourteen validated peptidoforms/proteoforms were assigned to m/z previously observed by MALDI-TOF profiling. This represented 14 UniprotKB accession numbers and 11 unique genes in a mass range between 2771 and 18,459 Da. Out of these biomolecules and considering the quantitative MALDI-TOF analysis, 3 differential m/z reached our predefined criteria in the "far" region (p value < 0. 01, FC > 1. 5 or < 0. 66 and AUC > 0. 8) (Table  2 ). The m/z 5443 was assigned to the C-term fragment (47 aminoacids) of the subunit 7C of cytochromec oxidase, mitochondrial (COX7C), which was more abundant after electrical shocks in the "far" region (p value < 0. 000001, FC = 1. 57, AUC = 0.
Bottom-up proteomic analysis. All data are presented and summarized in subsections "final table" for each region in the supplemental dataset 3. Global proteome identified in "near" and "far" regions in control and electrical shock groups. A total of 1257 clusters corresponding to 2706 proteins were identified. A Venn diagram is represented in Fig. 3A  showing the Table  1. Occurrence of amino acid residues involved in protein cleavages generating N-terminal, C-terminal or internal fragments. The top five most frequent residues found at the end and above the 62 peptide fragments generated from original proteins are presented.
05 in either peptide-pattern based on XIC or NWS quantification method, and 513 with a p value < 0. 05 in both methods. After cluster sorting, gene redundancy suppression, elimination of keratins and elimination of redundant proteins, proteins from 48 unique genes were less abundant and proteins from 38 unique genes more abundant after electrical shocks (p value < 0. 05 in NWS or XIC method associated to a fold change/ratio ≤ 0. 5 or ≥ 2) (Supplemental dataset 3). The main decreased proteins after electrical shocks were cytoskeleton and sarcomere constituants (including the tropomyosin family), oxygen transport or stockage proteins and actors of protein metabolism including heatshock proteins. Proteins involved in gluconeogenesis, fatty acid beta-oxidation, respiratory chain, metabolism of reactive species and in calcium regulation (calsequestrin-2, sarcoplasmic reticulum histidine-rich calciumbinding protein) were also less abundant.
4C ). Significantly differential proteins were mainly increased after electrical shocks as seen on the volcano plot (Fig. 4D ). A total of 1743 NCBI reference sequences had an average NWS ≥ 5: 1310 were differential with a p value < 0. 05 in either XIC or NWS method, and 544 with a p value < 0. 05 in both methods. After cluster sorting, gene redundancy suppression, elimination of keratins and elimination of redundant proteins, proteins from 35 unique genes were less abundant after electrical shocks, and proteins from 116 unique genes were more abundant (p value < 0. 05 in NWS or XIC method associated to a fold change/ratio ≤ 0. 5 or ≥ 2) (Supplemental dataset 3). Main decreased proteins in "far" region after electrical shocks were cytoskeleton and sarcomere constituants and proteins involved in oxygen transport and stockage.
PGC-1 and ERR-induced regulator in muscle protein 1, cAMP-dependent protein kinase catalytic subunit beta (PKA C-beta), CRP and immunoglobulins were also more abundant after electrical shocks. These proteins have many interactions as shown in Supplemental Fig. 3B. Considering all active interaction sources, including text mining, networks of increased proteins were numerous and mainly focused around mitochondrial energetic metabolism, protein metabolism and cytoskeleton/ sarcomere constituants. Bottom-up quantitative analysis was validated with an immunoblot analysis (Supplemental dataset 4).
Acknowledgements The high resolution mass spectrometer was financed ( SMHART  project  n°35069 ) by the  European Regional Development Fund (ERDF) , the  Conseil Régional du Centre , the  French National Institute for Agricultural Research (INRA)  and the  French National Institute of Health and Medical Research (Inserm). The authors thank  Gilles Gomot ,  Frederic Elleboudt ,  Christian Moussu  and  François Lecompte , in the CIRE platform, for their help in animal experimentation and peri-surgery.
High throughput microscopy U2OS cells (7500) were transfected with 10nM siRNA using INTERFERIN (Ozyme) according to the manufacturer's instructions and seeded in 96 well plates (Perkin Elmer). Two days after transfection cells were exposed to ionizing radiation (8 Gy) then fixed with formaldehyde (4% in PBS). Cells were permeabilized with triton X100 (0. 5% in PBS) for 5 min, stained with stained with γH2AX antibody (1/500) diluted in PBS-BSA 1% and Alexa Fluor 488 anti mouse (Thermofisher). Acquisition was performed on at least 1000 cells per well (3 wells per condition) with 20X objective with Harmony Imaging Software 4. 1 (Perkin Elmer). Image analysis was pursued using Colombus 2. 5. 0 software (Perkin Elmer) to quantify γH2AX spot intensity.
5FUrd incorporation measurement Transcription of rDNA was monitored by revealing the incorporation of 5-Fluoro-Uridine in Nascent RNA (5-FUrd, Sigma). Cells were incubated in presence of 2mM 5FUrd for 20 minutes followed by fixation in 4% FA (Sigma). They were permeabilized with triton X100 (0. 5% in PBS) for 5 min, then stained using an anti BrdU antibody (Sigma, B2531) and Alexa Fluor 488 anti mouse (Thermofisher) diluted 1/500 in PBS-BSA 1% and 0. 4 U. mL -1 RNAsin (Promega). We visualized and quantified 5-FUrd incorporation by using high content imaging device (OPERETTA, Perkin Elmer). All imunofluorescence steps were performed at 4˚C. Image analysis was pursued using colombus software to measure 5-FUrd spot intensity.
Maisch HPLC) and the peptides were eluted with a linear gradient from 5-40% solvent B (A: 0,1% formic acid, B: 80% acetonitrile, 0. 1% formic acid) in 90 minutes, at 300 nL/min. Mass spectra were acquired using a data dependent acquisition mode using Thermo XCalibur software version 3. 0. 63. Full scan mass spectra (350 to 1800m/z) were acquired in the orbitrap using an AGC target of 4e5, a maximum injection time of 50 ms and a resolution of 120 000. Internal calibration using lock mass on the m/z 445. 12003 siloxane ion was used. Each MS scan was followed by acquisition of fragmentation spectra of the most intense ions for a total cycle time of 3 seconds (top speed mode). The selected ions were isolated using the quadrupole analyzer in a window of 1. 6 m/z and fragmented by Higher energy Collision-induced Dissociation (HCD) with 35% of collision energy.
Data Dependent Acquisition MS analysis Mass spectrometry data was stored, searched and analyzed using the ProHits laboratory information management system (LIMS) platform  [41]. Thermo Fisher scientific RAW mass spectrometry files were converted to mzML and mzXML using ProteoWizard (3. 0. 4468;  [42]. The mzML and mzXML files were then searched using Mascot (v2. 3. 02) and Comet (v2012. 02 rev. 0). The spectra were searched with the RefSeq database (version 57, January 30th, 2013) acquired from NCBI against a total of 72,482 human and adenovirus sequences supplemented with "common contaminants" from the Max Planck Institute (http://141. 61. 102. 106:8080/ share. cgi?ssid=0f2gfuB) and the Global Proteome Machine (GPM; http://www. thegpm. org/ crap/index. html). Charges +2, +3 and +4 were allowed and the parent mass tolerance was set at 12 ppm while the fragment bin tolerance was set at 0.
DNA FISH combing on ribosomal DNA Cells were transfected with control siRNA (siCtrl), or two different siRNAs directed against JMJD6 (siJMJD6-1 and siJMJD6-2) and 48h later induced or not for DSB by treatment with OHTam during 24h. At the end of the treatment cells were harvested and included in low melting agarose and sent to the Genomic Vision Company for combing and FISH. DNA fibers stained as represented in Fig  4  were then detected using web interface provided by Genomic Vision company. Identification and counting of rearrangement events were estimated using a script designed to count the total number of rDNA units and assessing the the occurence of changes when comparing one unit to the next. GitHub https://github. com/ LegubeDNAREPAIR/rDNA/blob/master/get_break_event. py.
Acknowledgments We thank  Thomas Mangeat  for his assistance with the laser track experiments and  Magali Suzanne  for assistance with the confocal experiments. We are grateful to the  Non-Invasive Exploration service-US006/CREFRE INSERM/UPS  for giving us the access to the irradiator Biobeam 8000 (Rangueil, Toulouse). We acknowledge the  Toulouse Regional Imaging-Light Imaging Toulouse CBI Platform  for technical assistance in high content microscopy. We thank  Florence Larminat  for her critical reading of the manuscript.
(2017)  for CO 2 , N 2 O, CH 4 , CFC12 and a CFC11equivalent species that includes the effects of all the other GHGs of the original data set (39 species). As in the previous version of the model, the total solar irradiance forcing is also taken into account with yearly averages following  Matthes et al. (2017). Ozone radiative forcing is included using ozone concentrations coming from historical simulations carried out with the global Earth system model CNRM-ESM2-1  (Séférian et al. , 2019). No land use land cover change is taken into account in CNRM-ALADIN63. A spectral nudging method  (Radu et al. , 2008)  has been included in both simulations in order to better constrain the synoptic chronology of the model's large scales. The nudging is maximal between the top of the model and 700 hPa and nil below 850 hPa (with a linear transition between 700 and 850 hPa).
, 2005)  aboard CloudSat, both flying in tandem in the A-Train constellation combined in the radar-lidar geometrical profile product  (Mace and Zhang, 2014). This combination is used in order to benefit from the ability of the CALIOP lidar to detect thin clouds and the CPR in penetrating deeper into opaque clouds as may be encountered in the Euro-Mediterranean area. Because of its increased horizontal resolution (0. 33 km for CALIOP against 3. 5 km for CPR), several lidar samples are present in a radar profile and the radar lidar product pro-vides then a lidar cloud fraction in each radar bin. For model comparison purposes, a cloud fraction is computed from this observational data set in each ALADIN model grid point as the fraction of the grid covered by a cloud detected in radar geometrical profile where the cloud mask is higher than 20 (corresponding to less than 16 % of false detection) or when the lidar cloud fraction exceeds 10 % in a given bin.
Ground-based measurements Ground-based observations are also used to evaluate the CNRM-ALADIN63 simulations. The Aerosol Robotic Network (AERONET)  (Holben et al. , 1998)  provides measurements of aerosol optical depth from automatic Sun/sky radiometers, with an uncertainty of 0. 01  (Eck et al. , 1999). For this study, daily average quality-assured data (level 2. 0, version 3) have been used from 72 stations (see Table  2 ) distributed over the whole domain of the simulation (shown in Fig. 1 ). These stations have been selected because of their temporal period (at least 4 years of data) and their location (in order to have a spatially equally distributed selection). For each of them, AERONET AOD at 550 nm is computed from wavelengths at 440 or 500 nm (depending on availability) and the Ångström exponent at daily frequency.
Daily AOD at 550 nm can then be compared to daily AOD averages of ALADIN simulations at the same wavelength. The Baseline Surface Radiation Network (BSRN) provides surface radiation measurements as well as other parameters such as temperature and humidity over different stations around the world. These measurements from 10 stations over Europe and Africa (see Table  2 ) have been used in this study (shown in Fig. 1 ). They are known for the quality of their data over long periods of time  (Ohmura et al. , 1998). Land near-surface temperature and precipitation are compared to version 4. 0 of the Climatic Research Unit (CRU) data set  (Harris et al. , 2014) , provided by the University of East Anglia. This gridded product, whose horizontal resolution is 0. 5 • , is based on land weather stations around the world.
Mean climate Mean climate is first evaluated for different essential parameters of regional climate (pressure, wind, temperature, precipitation, cloud cover and radiation). Figures 2 to 5 present the average biases of ALD-AER against different observation data sets. In parallel, the averages of these biases have been calculated on the six subregions of Europe and the Mediterranean (see Fig. 2a ), defined in the frame of the PRUDENCE project  (Christensen and Christensen, 2007)  and already used in several studies (e. g. Kotlarski et al. , 2014). These averages are presented in Tables  3  and 4 , together with the equivalent range found in  Kotlarski et al. (2014)  for the Euro-CORDEX ensemble of regional simulations over Europe for temperature and precipitation. This evaluation of the new version (6.
Corresponding biases for Euro-CORDEX (ECx, minimum/maximum) models  (Kotlarski et al. , 2014)  have been added for temperature and precipitation. The same averages have been added for the previous ALADIN version (ALD5) used in  Nabat et al. (2015b). All these biases are calculated over the PRUDENCE boxes, as well as on land domains of Europe and the Mediterranean Sea shown in Fig. 2  the Mediterranean represents less than 5 % of the total upward radiation (Fig. S1 ). At the surface, a similar evaluation is shown for downwelling SW and LW radiation (Fig. 4c  and d ). It is worth mentioning that SARAH data have been used for SW radiation instead of CERES, to benefit from its finer resolution. This comparison with satellite data is completed by an evaluation against 10 BSRN stations throughout the domain of the simulation (Fig.
Satellite data (MODIS and MISR), which provide only total AOD, have similar AOD spatial distribution. The spatial correlation between ALADIN and MODIS is 0. 75, and 0. 84 with MISR. However, discrepancies have been found locally, for example, in the Benelux region and in the Po Valley (see the biases in Fig. S2 ), where ALADIN AOD is overestimated compared to MODIS (up to 0. 1 in the Po Valley) and especially MISR (up to 0. 2). This bias is much smaller than the negative bias in the previous version of the model which did not include nitrate aerosols  (Drugé et al. , 2019). Annual AOD average over Europe in ALADIN is now similar to MODIS (0. 17 for ALADIN and MODIS) but higher than MISR (0. 13). Besides, sea-salt aerosols are also probably overestimated over the northern Atlantic Ocean compared to MODIS and MISR, as AOD reaches 0.
7 ). This maximum is overestimated in winter, especially in December in the Azores, probably in relation to the overestimation of surface wind in the Atlantic Ocean. Both ALADIN and AERONET data show a decrease in AOD between winter and summer, but the dehttps://doi. org/10. 5194/acp-20-8315-2020 Atmos. Chem. Phys. , 20, 8315-8349, 2020 crease in AOD is too strong in the model, yielding an underestimation of AOD in summer. This could be due to an underestimated transport of dust aerosols from the Sahara in this season. With regards to the AOD daily distribution in this region, the 90 % interval (between the 5th and 95th percentiles) in ALD-AER is close that found in the observations for most stations (Fig. 8 ). In addition, ALD-AER also represents the average AOD values higher than the median as in the observations, due to the contribution of days with very high AOD, even if the model overestimates the most extreme AOD values in several stations.
For that purpose, monthly NAO index provided by the National Oceanic and Atmospheric Administration (NOAA; https://www. cpc. ncep. noaa. gov/products/ precip/CWlink/pna/nao. shtml, last access: 16 July 2020) has been used  (Barnston and Livezey, 1987). Since the ERA-Interim reanalysis has been shown to be consistent in time with observations and atmospheric circulation  (Dee et al. , 2011) , this NAO index data must be consistent with the ERA-Interim reanalysis and therefore the ALADIN simulations.
82, 0. 75 and 0. 76. On the contrary, a negative correlation is noted over the eastern Mediterranean area both in ALADIN and in satellite data, as shown in the EURSE domain. For example, the period 1999-2008, associated with the positive NAO index, is affected by negative AOD anomalies in EURSE, and vice versa for the period 2009-2011. However, this is not the case for the whole period , and even if the sign of the correlation is in agreement in the three data sets, it is not significant at the 0. 05 level except for MISR (-0. 65). Moreover, the anomalies shown in Fig. 12  are generally weaker than in AFRW. This absence of statistic significance is consistent with the results of  Ginoux et al. (2004) , who have not shown a relation between Mediterranean AOD and NAO, but not with those of  Papadimas et al.
(2008)  which have probably used a too-short period of MODIS data. The same conclusion can be drawn for the ALPS region, where the correlation is negative but not significant in two out of the three data sets. In the two regions located above 45 • N (EURNW and EURN), the correlation is largely positive in ALADIN (respectively, 0. 44 and 0. 31) but weaker in MODIS and MISR where the threshold of significance is not reached. However, in these regions where clouds prevail in winter, the lack of AOD data retrieved by MODIS and MISR could hamper the estimation of this correlation with NAO. Finally, in EURSW, no significant correlation in ALADIN nor in satellite products has been found. https://doi. org/10. 5194/acp-20-8315-2020 Atmos. Chem. Phys. , 20, 8315-8349, 2020.