dataesr/scientific-paragraphs-categorization huggingface dataset:
Future works will include the evaluation of these temporal profiles when used for modelling activities. Through close cooperation with air quality modelers, we expect to obtain feedback on the dataset as well as suggestions for future improvements. Besides that, a number of future updates have also been identified during the present work, including: • The extension of the year dependent temporal profiles to be in line with the current timespan considered in the CAMS-  GLOB_ANT (2000 -2018)  and CAMS-REG_AP/GHG (2000-2017) inventories.
Figure1. a illustrates the spatial coverage of the compiled dataset by source of information (i. e. ENTSO-E, US EPA, IEA and MBS). Overall, main emission producers (e. g. China, India, Europe and North America) are covered, while most of the countries with no information available are located in South America and Africa. For those countries with no data, the TNO profiles reported under the energy sector (Denier van derGon et al. , 2011)  are used.
https://doi. org/10. 5194/essd-2020-175 started: 4 August 2020 c Author(s) 2020. CC BY 4. 0 License. inter-annual variability can be negligible. Hence, all profiles developed only as a function of traffic count data were constructed by averaging the values (per month, day of the week or hour of the day) over all the available years. Some of the compiled datasets (e. g. Germany and California) report the traffic counts classified by vehicle type (i. e. light-duty.
Same profiles as the ones reported by the TNO dataset (Denier van derGon et al. , 2011)   Table2: Main characteristics of the CAMS regional temporal profiles (CAMS-REG-TEMPO) reported by sector and temporal resolution (monthly, daily, weekly, hourly). Per country: indicates that the profiles vary per country; per pollutant: indicates that the profiles vary per pollutant; per region: indicates that the profiles vary per grid cell within a country; fixed: indicates that the profiles are spatially invariant. The symbol "-" denotes that no profile is proposed.
( 2 ) 2 Same profiles as the ones reported by the TNO dataset (Denier van derGon et al. , 2011)   Table2: Main characteristics of the CAMS regional temporal profiles (CAMS-REG-TEMPO) reported by sector and temporal resolution (monthly, daily, weekly, hourly). Per country: indicates that the profiles vary per country; per pollutant: indicates that the profiles vary per pollutant; per region: indicates that the profiles vary per grid cell within a country; fixed: indicates that the profiles are spatially invariant. The symbol "-" denotes that no profile is proposed.
3 µg m -3 in the cases of propane, acetylene and benzene. OVOC concentrations were generally high in summertime, mainly due to secondary anthropogenic/biogenic and primary biogenic sources, whereas their lower concentrations during autumn and winter were potentially more influenced by primary/secondary anthropogenic sources. Moreover, an apportionment factorial analysis was applied to a database comprising a selection of 14 individual or grouped VOCs by means of the positive matrix factorization (PMF) technique. A PMF five-factor solution was taken on. It includes an anthropogenic factor (which contributed 39 % to the total concentration of the VOCs selected in the PMF analysis) connected to the regional background pollution, three other anthropogenic factors (namely short-lived anthropogenic sources, evaporative sources, and long-lived combustion sources, which together accounted for 57 %) originating from either nearby or more distant emission areas (such as Italy and south of France), and a local biogenic source (4 %).
Study site Located in the north-western part of the Mediterranean Sea, Corsica is a French territory situated 11 km north of the Sardinian coasts, 90 km east of Tuscany (Italy) and 170 km south of the French Riviera (France). The fourth largest Mediterranean island, its land corresponds to an area of 8681 km 2 encompassed by around 1000 km of coastline  (Encyclopaedia Britannica, 2018). Corsica contrasts to other Mediterranean islands due to the importance of its forest cover (about a fifth of the island). Within the framework of the ChArMEx project, an enhanced observation period was set up at a ground-based station in the north of Corsica (Ersa; 42. 969 • N, 9. 380 • E) over 25 months, from early June 2012 to late June 2014. The aim was to provide a high-quality controlled climatically relevant gas/aerosol database following the recommendations and criteria of international atmospheric chemistry networks, i.
01 µg m -3 for the multi-sorbent cartridges. The uncertainties for each species were evaluated respecting the ACTRIS-2 guidelines for the uncertainty evaluation  (Reimann et al. , 2018) , considering precision, detection limit and systematic errors in the measurements. Relative uncertainties assessed in this study ranged from 7 % to 43 % for the steel canisters, from 7 % to 73 % for the multi-sorbent cartridges and from 6 % to 41 % for the DNPH cartridges. Finally, the VOC dataset was validated following the ACTRIS protocol  (Reimann et al. , 2018). Among the 71 different VOCs monitored at Ersa during the observation period, 35 VOCs were finally selected in this study following the methodology described in Sect. S1 of the Supplement.
, 2017). Since the low number of NMHCs considered in the factorial analysis of this study, PMF result relevance was checked, benefiting from a previous PMF analysis performed with the Ersa VOC summer 2013 dataset  (Michoud et al. , 2017; see Sect. 3. 5. 6). Experimental strategies to characterize VOC concentration variations at a remote background such as Ersa are also discussed in Sect. 3. 5. 6.
Evaporative sources (factor 3) Factor 3 contributions to the selected NMHC concentrations were 1. 2 ± 1. 0 µg m -3 on average (22 % of their sum; Table 5). The profile of this anthropogenic factor displays an important contribution from alkanes, principally i-/n-butanes ) and (b) accumulated relative NMHC contributions. Factor 1 -local biogenic source; factor 2 -short-lived anthropogenic sources; factor 3 -evaporative sources; factor 4 -long-lived combustion sources; factor 5 -regional background. Note that the NMHC dataset used for the PMF analysis included different sampling time hours (09:00-13:00 or 12:00-16:00 UTC) following shifts during the 2-year period (see Table  1 ). (with lifetimes of 10-11 d in winter and ∼ 2 d in summer), with more than 69 % of their variabilities explained by factor 3 (Fig.
Long-lived combustion sources (factor 4) The average contribution of factor 4 to the selected NMHC concentrations is evaluated at 0. 9 ± 0. 7 µg m -3 (corresponding to 16 % of their sum; Table  5 ). Its profile displays an important contribution from acetylene (100 % explained;  2). Note that the NMHC dataset used for the PMF analysis included different sampling time hours (09:00-13:00 or 12:00-16:00 UTC) following shifts during the 2-year period (see Table  1 ). Aromatic compounds and acetylene are generally associated with combustion sources, such as vehicle exhaust (e. g. Badol et al. , 2008; Pang et al. , 2014). However, factor 4 is characterized by a loading of benzene far superior to toluene's (49 % and 3 %, respectively), suggesting it is more related to a residential heating source than a traffic one  (Elbir et al.
Towards the best experimental strategy to characterize variations in VOC concentrations observed at a remote background site In this study, we decided to restrain the number of VOCs in the factorial analysis in order to have a better temporal representativeness of factor contributions (see Sect. S2). This choice has undoubtedly had some consequences for the PMF solution results of this study (in terms of factor number, chemical profiles and variations). Consequently, to support our results, we compared our five-factor PMF solution modelled with the 2-year NMHC dataset (14 variables, 152 4 h resolution observations), with the six-factor PMF solution modelled with the summer 2013 VOC dataset (42 variables, 329 1. 5 h resolution observations;  Michoud et al. , 2017). The comparative study (see Sect.
S6). PMF factorial analyses computed with a limited VOC number therefore show limitations in the investigation of short-lived anthropogenic sources influencing Ersa VOC concentrations given the remote location of the Ersa station. The two studies on Ersa VOC measurements also helped to discuss here the best experimental strategy to characterize variations in VOC concentrations measured at a remote background site surrounded by vegetation such as Ersa (Sect. 2. 1). Firstly, the consideration of monoterpenes in the 2-year PMF analysis would significantly increase the contribution of primary biogenic sources (see Sect. 4. 1). A better temporal resolution and coverage of the VOC measurements would have been beneficial in this study to further assess the contribution of local biogenic sources, considering BVOC day-today variations (as assessed with the summer 2013 dataset; Sect.
The 17 NHMCs selected in the factorial analysis were apportioned to the five modelled NMHC sources. NMHC seasonal measured concentrations which were not modelled by the PMF tool were lower than 0. 09 µg m -3 and are not reported here. Winter: 1 January-31 March periodsspring: 1 April-30 June periods -summer: 1 July-30 September periods -autumn: 1 October-31 December periods. Note that the VOC dataset included different sampling time hours (09:00-13:00 or 12:00-16:00 UTC) following shifts during the 2-year period (see Table  1 ). rative and long-lived combustion sources only contributed 6 % to 16 % in spring and summer, as they likely originated from emission areas relatively distant from the Ersa station (Sects. 3. 5. 3 and 3. 5. 4). Low contributions of longlived combustion sources can also be partly explained by a lower source strength (especially for a residential heating source;  Sect.
They also pointed out that the PMF-derived factors controlling VOC concentration variations at remote sites may be mainly controlled by the meteorological conditions that occurred during the study period when issued from short observation periods (i. e. up to 2 months). Concurrent datasets of VOC concentrations from three summer campaigns performed at Ersa helped to support the representativeness of the 2-year monitoring period in terms of summer concentration levels, variations, and source apportionment. They also highlight limitations of this study in the investigation of seasonal variations in Ersa VOC concentrations, in connection with the VOC range and the temporal resolution of their measurements considered in the PMF analysis. After this work, some questions remain regarding the contribution of local BVOC and OVOC sources at seasonal and interannual scales.
It would be interesting to conduct additional long-term VOC measurements at Ersa, including OVOCs and tracers of various primary sources, at a finer time resolution and a higher temporal coverage. This would help to complete the understanding of determinants governing VOC concentration variations initiated by both  Michoud et al. (2017)  and this study. son, Vincent Gaudion, Laurence Depelchin and Isabelle Fronval for their contributions to obtaining VOC datasets, from the organization and the setting of instruments at Ersa to the analysis of VOC samples at the laboratory, but also Thierry Bourianne and the Qualitair Corse team for their involvement on site concerning VOC sample routines as well as the associated logistic procedure. The authors finally thank the ChArMEx project manager Eric Hamonou for his logistical help.
Financial support. This study received financial support from the MISTRALS/ChArMEx programme funded by CNRS/INSU, CEA, and Météo-France, from ADEME (grant nos. 1262c0009, 1362c0031, and 1462c0031), the French Environmental Ministry, the CaPPA projects, and the Communauté Territoriale de Corse (CORSiCA project). This research has also been supported by the CaPPA project (Chemical and Physical Properties of the Atmosphere) funded by the French National Research Agency (ANR) through the PIA (Programme d'Investissement d'Avenir, grant no. ANR-11-LABX-0005-01) and by the Regional Council Nord-Pas de Calais and the European Funds for Regional Economic  Development (FEDER). This research was also funded by the European Union Seventh Framework Programme under the DEFI-VOC project (grant agreement no.
Figure9. NMHC factor contributions (µg m -3 ) as a function of air-mass origin represented by box plots; the blue solid line, the red marker, and the box represent the median, the mean, and the interquartile range of the values, respectively. The bottom and top of the box depict the first and third quartiles and the ends of the whiskers correspond to the first and ninth deciles. Factor 2 -short-lived anthropogenic sources; factor 3 -evaporative sources; factor 4 -long-lived combustion sources; factor 5 -regional background. Air masses originating from France and Europe are subdivided into short and long trajectories to highlight local and more distant contributions (see Sect. 3. 2). Note that the NMHC dataset used for the PMF analysis included different sampling time hours (09:00-13:00 or 12:00-16:00 UTC) following shifts during the 2-year period (see Table1).
Acknowledgements. The authors are thankful for the O 3 and CO datasets made available, respectively, by  François Gheusi  from  Laboratoire d'Aérologie  and by the  ICOS France monitoring network. The authors also want to thank  Thierry Leonardis , Emmanuel Tihttps://doi. org/10. 5194/acp-21-1449-2021Atmos. Chem. Phys. , 21,  1449-1484 , 2021.
sedoo. fr/?editDatsId= 1265&datsId=1265&project_name=ChArMEx, Sauvage, 2021f) VOC datasets used for this publication is open to registered users following the data and publication policy of the ChArMEx programme (http://mistrals. sedoo. fr/ChArMEx/Data-Policy/ ChArMEx_DataPolicy. pdf, last access: 11 October 2020). VOC datasets from the Ersa summer field campaigns of 2012 and 2014 are available upon request. Please contact Stéphane Sauvage (stephane. sauvage@imt-lille-douai. fr) for further information. Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/acp-21-1449-2021-supplement. Author contributions. SS, NL, JS and FD designed the research and were involved in the logistics and the collection of VOC samples in the field. TS calculated uncertainties related to VOC measurements conducted with DNPH cartridges and steel canisters and validated them following the ACTRIS protocol.
Band intensities were quantified using the Imagelab software (BIORAD). Only the amount of full-length fusion protein was taken into account to calculate the amount of input CRD in histonebinding assays. For the expression of MBP-PgmD401A, the GAC codon encoding Asp401 was replaced by an alanine codon (GCC) in plasmid pVL1392-MBP-PGM  [61]. To produce recombinant baculoviruses, the resulting plasmid (File S2) and those constructed previously to express MBP-Pgm, MBP-Pgm ΔCR and MBP-Pgm ΔCC  [34]  were each transfected into High Five insect cells together with BD BaculoGold Linearized Baculovirus DNA (BD Biosciences). The purification of MBP-tagged Pgm derivatives from recombinant baculovirus-infected cells was carried out as described  [34] , with a final elution step in buffer A supplemented with 10 mM maltose.
NMR spectroscopy High-quality NMR data were obtained for Pgm(692-768)*. NMR protein samples were concentrated to 0. 15-0. 5 mM in 90% H 2 O/10% D 2 O containing 5 mM HEPES, 25 mM NaCl, pH 6. 8, or lyophilized and resuspended in 99. 99% D 2 O. NMR experiments were performed at 293 K on 800 MHz or 950 MHz AVANCE III HD Bruker spectrometers equipped with TCI cryoprobes. 1 H-15 N HSQC spectra of Pgm(692-768)* were acquired at 800 MHz in 90% H 2 O/10% D 2 O with 15 N carrier set to 120 ppm in order to observe the backbone amide groups. The INEPT half delay was set to 2. 8 ms for observation of single-bond coupling ( 1 J HN ≈ 90 Hz). Optimized long-range 1 H-15 N HSQC spectrum was recorded in 90% H 2 O/10% D 2 O with a 21 ms delay for the selective observation of the long-range proton (Hδ2 and Hε1)-nitrogen (Nδ1 and Nε2) correlations of histidines ( 2 J HN = 6-12 Hz) and the 15 N carrier set to 200 ppm.
Sidechain assignments were completed using a lyophilized sample redissolved into 99. 99% D 2 O. 15 N-and 13 C-edited 3D NOESY experiments were acquired on samples in 90% H 2 O/10% D 2 O or 99. 99% D 2 O with a mixing time of 150 ms to obtain nuclear Overhauser effect crosspeaks (NOEs) for structure determination. NMR data were collected and processed using TOPSPIN 3. 5 software (Bruker) and analyzed using the CcpNmr software  [41]. Backbone amide resonances were assigned for all non-proline amino acids except Lys740, for which no cross-peak was detected in the 1 H-15 N HSQC. We were able to assign 96 and 81% of the backbone and sidechain carbon atoms, respectively, 97 and 84% of the backbone and sidechain hydrogens, respectively, and 96% of the nitrogen atoms composing Pgm(692-768). Pgm(685-768)* assignments were deposited into BMRB (BMRB ID: 34527).
For the determination of R 1 relaxation rate constants, 13 total datasets were collected at relaxation delay times of 10, 50, 100, 200, 300, 400, 600, 800, 1100, 1500, 2000, 2500, 3000 ms. For the determination of R 2 rate constants, 13 datasets were collected at delay times of  16. 96, 33. 92, 50. 88, 67. 84, 84. 8, 101. 76, 118. 72, 135. 68, 152. 64, 169. 60, 220. 48, 271. 36, 323. 24 ms. R 1 and R 2 spectra were recorded as 128 × 2126 complex data points. For the backbone { 1 H}- 15 N heteronuclear NOE two different spectra were recorded as 512 × 2048 complex data points in an interleaved manner with and without a 5 s proton saturation pulse. The R 1 and R 2 rates and heteronuclear NOE values and their associated errors were determined from the peak intensities using the CcpNmr software  [41].
Relaxation parameters were analyzed with the model-free formalism of Lipari and Szabo  [42, 43] , using the TEN-SOR2 program  [62]  to extract internal dynamical parameters: order parameter S 2 describing the amplitude of the motions; internal correlation time τ ε on the ps-ns timescale and R ex reflecting exchange contribution on the μs-ms timescale. The isotropic tumbling model was selected since no improvement was found with the anisotropic model.
NMR structure calculation Inter-proton distance restraints were derived from the NOESY spectra (two-dimensional 1 H NOESY and threedimensional 15 N-and 13 C-NOESY). The Pgm(692-768)* structure was calculated in a semi-automated iterative manner using CYANA 2. 1  [63]. Intra-and inter-residue NOEs were manually picked from the 3D NOESY experiments. The backbone dihedral angle restraints ( Φ and Ψ angles) were generated using the chemical shift analysis software TALOS+  [40]. Hydrogen bond restraints were determined by hydrogen-deuterium exchange experiments and observation of NOE cross-peaks characteristic of α-helices and β-sheets. NOE peak lists, dihedral angle restraints, hydrogen bond restraints, and chemical shift assignments were used as input for CYANA 2. 1. We used the standard CYANA protocol of seven iterative cycles of NOE assignment and structure calculation, followed by a final structure calculation.
In each cycle, the structure calculation started from 200 randomized conformers, and the standard CYANA simulated annealing schedule was used with 10,000 torsion angle dynamics steps per conformer. The first calculations with only NOE restraints defined the general fold of the domain and revealed the two Zn 2+ coordination modes. In the final refinement stage, distance restraints were added for Zn-Sγ (2. 25-2. 30 Å), Zn-Nδ1 (2. 35-2. 40 Å) and Zn-Nε2 (2. 35-2. 40 Å) and for the other bonds between the four height coordinating atoms, to ensure tetrahedral Zn 2+ coordination geometry. Graphic representations were prepared with PyMOL  [64]. The structures were deposited into the wwPDB (PDB ID 6ZOP).
FigureS10. Comparison of the long bent hairpin present in Pgm and in typical and atypical C1 domains. The Pgm(706-720) hairpin is displayed in a1 and a2, the typical C1 domain of the cys2 activator-binding domain of protein kinase C delta (PDB ID:1PTR) in b1 and b2 and the atypical C1 domain of the Rho kinase II (PDB ID 2ROW) in c1 and c2. Acidic residues are colored in red, basic residues in blue, hydrophobic residues in grey and all other residues are in green.
Acknowledgments We would like to thank  Sébastien Thomine  for sharing with us his expertise in microwave plasma atomic emission spectroscopy. We are grateful to  Cindy Mathon  and  Pascaline Tirand  for excellent technical assistance and  Loïc Escoriza  and  Coralie Zangarelli  for their help in the handling of Paramecium cultures. Many thanks to  Emeline Dubois  and all members of the  Bétermier and Duharcourt laboratories  for stimulating discussions, and to  Joël Acker ,  Olivier Arnaiz  and  Linda Sperling  for critical reading of the manuscript.
Funding This work was supported by the  Centre National de la Recherche Scientifique (CNRS)  [intramural funding to M. B. ] and the  French National Research Agency  [grants  ANR-14-CE10-0005  to E. G. and M. B. and  ANR-18-CE12-005  to M. B. ]. S. M. was granted by the  Institut de Chimie des Substances Naturelles. Financial support from the  IR-RMN-THC Fr3050 CNRS  for conducting the research is gratefully acknowledged.
The two-step flux calculation in this phase takes advantage of the accuracy of the Method of Characteristics (MOC) on a refined geometry for an affordable calculation cost over the collision probability method  (Hébert, 2020). In order to validate the calculation scheme on an industrial reactor geometry with asymmetrical assemblies, the modeling was applied to the reactor described in the BEAVRS benchmark  (Horelik et al. , 2013). The results presented relate to the fission dis tribution of the Hot Zero Power (HZP) configuration of the first operating cycle. The comparison of the differences between the response of the detectors modeled in DONJON5 and the data available in the benchmark was made for different reflector models as well as for several different homogenization of asymmetrical assemblies.
The module idet recently developed in DONJON5, and further described in Section 5. 3, allows the calculation of fis sion rates at precise points in the core, for a cartesian configuration. The results were compared with the detector responses given in the BEAVRS benchmark, as well as with the corrected data (removing the azimuthal tilt) presented in version 2. 0. 2 of the benchmark, released on April 11, 2018, by the Massachusetts Institute of Tech nology (MIT). The calculated fission rates allowed the construction of two-dimensional radial maps of the DONJON5/BEAVRS deviations and two-dimensional mean values for the same deviations.
Discussion While the two approaches may seem quite different in their forms, a closer look at the Koebke and Lefebvre-Lebigot equations reveals that We strive to participate in reproducible research, so we've released our reflector, assemblies and full-core datasets in  Salino (2020) and Frohlicher (2020). Our results were obtained with ver sion 5. 0. 5 (beta, revision 1761) of DRAGON5 and DONJON5 available from  Marleau et al. (2018). In this section we present the axially integrated fission rates obtained for the core calculation. In particular, we will compare: • the maximum relative error, • the Root Mean Square error (RMS2D), • DONJON5/BEAVRS relative error radial maps.