dataesr/scientific-paragraphs-categorization huggingface dataset:
Structural analysis A portion of the borate crystal was analyzed by single crystal X-ray diffraction. The crystal data and the refinement conditions are shown in Table  2. In good agreement with the results reported above, the borate crystal was identified to be an ammonium polyborate salt of formulae (NH4)2B4O5(OH)4⋅2H2O (diammonium tetraborate dihydrate; 263. 39 g mol -1 ). The crystal system is monoclinic, and the space group is P21. The cell constants were found to be such as: a = Diammonium tetraborate dihydrate is structured around the anion B4O5(OH)4 2-(Figure  9 ), as is the case for borax  [33]. They both crystallize in monoclinic system, but they have different space groups (P21 and C2/c respectively). Diammonium tetraborate dihydrate is comparable to the compound reported by Ramachandran and Gagare  [21].
Acknowledgments UBD thanks the  Agence Nationale pour la Recherche (ANR)  for: (i) the  LabEx CheMISyst  project called  A3  funded within the program " Investissement d'Avenir " with the reference  ANR-10-LABX-05-01 ; (ii) the project called  MobiDiC  funded within the program " AAP Générique PRC " with the reference  ANR-16-CE05-0009.
Acknowledgments This project received the funding from the  European Union 's  Horizon 2020 research and innovation program  under  Marie Sklodowska-Curie Grant Agreement 641939  (HypoTRAIN) and 734317 (HiFreq). The work was partly supported by the  German Research Foundation  under the Grant  WO671/11-1. J. D. Gomez-Velez  is funded by the  U. S. National Science Foundation  ( Award EAR 1830172 ) and the  U. S. Department of Energy, Office of Biological and Environmental Research (BER) , as part of  BER 's  Subsurface Biogeochemistry Research Program  (SBR). This contribution originates from the  SBR Scientific Focus Area (SFA)  at the  Pacific Northwest National Laboratory (PNNL).
The biogeochemical model is initialized and forced at the lateral boundaries with dissolved inorganic carbon, dissolved organic carbon, alkalinity, and iron obtained from stabilized climatological 3-D fields of the global standard configuration ORCA2  (Aumont and Bopp, 2006)  and nitrate, phosphate, silicate, and dissolved oxygen from the World Ocean Atlas observation database (WOA;  Garcia et al. , 2010). The model is integrated from 1993 to 2015 and monthly averages for the period 1995 to 2015 are analyzed. Such short spin-up is justified by the fast adjustment of the equatorial dynamics and the main focus of the study which is on the upper ocean variability. The three-dimensional nitrate balance solved in the model reads as follows: ∂NO 3 ∂t = -u ∂NO 3 ∂x -v ∂NO 3 ∂y -w ∂NO 3 ∂z + D l (NO 3 ) + ∂ ∂z K z ∂NO 3 ∂z + SMS, (1) in which NO 3 is the model nitrate concentration, (u, v, w) are the velocity components, D l (NO 3 ) is the lateral diffusion operator, and K z is the vertical diffusion coefficient for tracers.
Overall, eddy advection brings nitrate into the mixed and euphotic layers in June-July and in November-December. To our knowledge, there are no studies on the role of TIWs and other intraseasonal processes on the equatorial Atlantic nitrate budget. This issue should be further investigated. Data availability. PIRATA chemical data sets acquired during cruises are available through https://doi. org/10. 17882/58141 (last access: 17 October 2017)  (Bourlès et al. , 2018a)  and ADCP data are available through https://doi. org/10. 17882/44635 (last access: 23 November 2018)  (Bourlès et al. , 2018c). ADCP mooring data are available through https://doi. org/10. 17882/51557 (last access: 10 December 2018)  (Bourlès et al. , 2018b). The ocean color products of the GlobColour project are available through http://globcolour.
info (last access: 30 January 2017). The TropFlux data are archived at https://www. incois. gov. in/tropflux (last access: 6 June 2017). Model results can be reproduced by using the ocean code nemo_v3_6 (http://forge. ipsl. jussieu. fr/40nemo/ wiki/Users, last access: 11 December 2017). The DFS5. 2 forcing set is available on the server https://sextant. ifremer. fr/record/ c837b2f8-4152-41fd-8592-d4cd887d0b51/ (last access: 11 December 2017). Author contributions. MHR and JJ designed the research study. JJ and CCT performed the numerical simulation with inputs from OA. MHR conducted the analysis with help from JJ. MHR wrote the manuscript with contributions from all coauthors.
Acknowledgements. We thank the  IRD IMAGO team ,  Pierre Rousselot  (ADCP),  François Baurand  (nutrients), and  Sandrine Hillion  (chlorophyll), for collecting, validating, and making available the French PIRATA cruise measurements, along with  Jacques Grelet ,  Fabrice Roubaud , and other engineers and technicians of the  PI-RATA program  for maintaining the ocean-atmosphere interaction buoys and ADCP moorings. We acknowledge the GlobColour and TropFlux projects for sharing the freely available data we use. GlobColour data have been developed, validated, and distributed by  ACRI-ST, France. The TropFlux data are produced under a collaboration between  Laboratoire d'Océanographie: Expérimentation et Approches Numériques (LOCEAN)  from  Institut Pierre Simon Laplace (IPSL, Paris, France ) and the  National Institute of Oceanography/CSIR  ( NIO, Goa, India ) and supported by  Institut de Recherche pour le Développement (IRD, France).
DNA fragment lengths were determined on an ABI 3500xl Genetic Analyzer from 1 µl of 1:99 diluted PCR products mixed with 8. 9 µl of HiDi Formamide (Life Technologies), and 0. 1 µl of Gene Scan 500 LIZ Size Standard (Life Technologies) after 5 min denaturation at 95°C. Alleles were called with the GeneMapper v4. 1 Software (Applied Biosystems, Thermo Fisher Scientific). The shoots were assigned to multi-locus genotypes using the R package "RClone"  (Bailleul et al. , 2016).
Whole Genome Sequencing, SNP Detection, and Genetic Distance in Experimental Samples In order to detect SNPs resulting from somatic mutations in the 10 heat-stressed ramets, genomic DNA libraries were prepared according to the TruSeq DNA PCR-Free (Illumina) protocol, and sequenced on one Illumina HiSeq 3/4000 lane (2 × 150 bp) at the Norwegian Sequencing Centre (University of Oslo, Norway). Raw reads (25. 7 million to 44. 3 million per library, Supplementary Table  S2 , NCBI BioProject PRJNA575339) were quality-checked with FastQC v0. 11. 8 1  to control for aberrant read base content, length distribution, duplication, and overrepresentation. We used TrimGalore! v0. 6. 0 2  to remove adapter sequences with a stringency of 3 bp overlap, and low-quality bases with a Phred score Q < 20 (99% base call accuracy).
0), FisherStrand (FS >12. 0), RMSMappingQuality (MQ < 38), MappingQualityRankSumTest (MQRankSum < -1. 5), ReadPositionRankSumTest (ReadPosRankSumand < -4. 0), and Depth (DP > 4000. 0, in order to remove SNPs potentially caused by genome duplication). Subsequently, we used VCFtools v0. 1. 15 to remove genotypes with genotype quality < 30 (-minGQ 30) or depth < 20 (-minDP 20), and to remove SNPs with more than 2 alleles (-min-alleles 2 -max-alleles 2), with a minor allele frequency of 0. 01 (-maf 0. 01), and with any missing genotype (-max-missing-count 0). From the remaining 15,508 high-quality SNPs (Supplementary File S1) we excluded all that shared the same genotypes among all 10 heat-stressed shoots, as these reflected genetic differences only to the reference genome. The remaining 1,079 SNPs (Supplementary File S2) were used to estimate Euclidean genetic distances among the 10 shoots using the R package vcfR v1.
9. 0  (Knaus and Grünwald, 2017)  and the dist function of the R package "stats" v3. 6. 9 (R Core Team, 2019). We tested for correlation between genetic and physical distance among the 10 heat-stressed shoots with Mantel tests in the R package "vegan" v1. 4-2  (Oksanen et al. , 2019) , using 1,000 permutations, and the Pearson's product moment correlation method.
The sequenced reads were quality-trimmed with TrimGalore! v0. 4. 1 3  by removing the adapter sequences with a stringency of 3 bp overlap, low-quality bases with a Phred score Q < 20, and the terminal 2 bp from both ends in order to eliminate artifacts that might have arisen at the ligation position. Quality was checked with FastQC v0. 11. 8 4  to control for aberrant read base content, length distribution, duplication and over-representation. The high-quality reads were mapped with SOAP v1. 11  (Li et al. , 2008)  to 628,255 in silico predicted MethylRAD tags that were extracted from the Z. marina genome v2. 1 from ORCAE  (Sterck et al. , 2012)  with the custom python script InSilicoTypeIIbDigestion. py 5. For mapping, we allowed for two mismatches, filtered reads with >1 N, and used a seed size of 8 bp.
Methylation Variation Between the Field Transect Shoots In order to describe the level of intra-and inter-clonal epigenetic distance among the 42 transect shoots (of which shoot 27 belonged to a different genet than the 41 other ramets, Supplementary Table  S1 ), we calculated the Euclidean distance between shoots based on their coordinates in the 2dimensional PCA (Principal Component Analysis) plot using the dist function of the R package "stats" v3. 6. 0 (R Core Team, 2019). PCA was done on reads-per-million for all shoots with the PCA function of the R package "FactoMineR"  (Lêet al. , 2008). We tested for correlations between epigenetic and physical distance among the 41 ramets with Mantel tests using the R package "vegan" v1. 4-2  (Oksanen et al. , 2019)  with 1,000 permutations, and the Pearson's product moment correlation method.
Methylome Shift Under Experimental Conditions The methylome of the 10 heat-stressed shoots was characterized under control, stress, and recovery conditions (3 shoots died from the stress), and compared with the methylome of field transect samples with PCA on reads-per-million data using the PCA function of the R package "FactoMineR" (R Core  Team, 2019). Epigenetic distances between the samples were estimated for each sequence context (gene, intergene, TE, each in CG and CHG regions, respectively) as the Euclidean distances in the 2dimensional PCA plot using the dist function of the R package "stats" v3. 6. 9  (Lêet al. , 2008).
Differential Methylation Analyses To estimate the number of sites that changed in methylation state from control to stress and recovery conditions, we applied differential methylation analyses using the R package "edgeR" 3. 20. 9 (R Core Team, 2019) within the "SARTools" pipeline v1. 6. 6  (Robinson et al. , 2009). Read counts were normalized using a trimmed mean of M-values (TMM) between each pair of samples  (Varet et al. , 2016). All samples taken under control, stress, and recovery served as replicates for the three different conditions. Methylation levels were considered significantly increased (hyper-methylated) or decreased (hypo-methylated) when Benjamini-Hochberg adjusted p-values  (Robinson and Oshlack, 2010 ) fell below 0. 05. To identify how the methylome differed between samples of high and low photosynthetic performance, we used differential methylation analyses using the R package "edgeR" 3.
1 and 17. 1) and the two samples of lowest photosynthetic performance (57. 1 and 59. 2). Methylation levels were considered significantly different between shoots of high and low photosynthetic performance, when Benjamini-Hochberg adjusted p-values (Robinson and Oshlack, 2010) fell below 0. 05. For differentially methylated sites within gene bodies, we tested with Fisher's exact tests for enrichment of gene ontology terms of biological processes with the R package "topgo" (Benjamini and Hochberg, 1995) using Fisher's exacts tests. GO terms were obtained from the v2. 1 Zostera genome annotation from the ORCAE database  (Alexa and Rahnenführer, 2010). To reduce redundancy in the significantly enriched GO terms (p-values < 0. 05), we calculated "sim rel" scores  (Sterck et al. , 2012 ) (Allowed similarity=0.
Methylome Characterization and Variation Among the Clonal Transect Shoots On average, 74 million high-quality reads were obtained per sequencing library (Methylome Characterization section, ranging from 0. 7 to 151 million (Supplementary Table  S3 ). DNA raw reads are accessible from NCBI under BioProject number PRJNA575339. On average, 35% of the high-quality reads mapped to the in silico digested Z. marina genome, and 11% mapped uniquely (annotated reads-per-million in Supplementary File S4). In total, 144,420 sites were methylated (covered by at least two reads) across all transect shoots. Across all transect shoots, 84,640 methylated CG sites and 59,780 methylated CHG sites were detected, which represents 59% and 41% of all methylated sites, respectively (Supplementary Table  S5 ). About 23% of all methylated sites fell in gene bodies (of which 41% in TEs), and 77% in intergenic regions (of which 42% in TEs) (Figure  3 ).
ACKNOWLEDGMENTS We thank  Marvin Choquet  from  Nord University  for help with replanting the shoots in the wet lab. We acknowledge  Steinar Johnsen  from  Nord University  for setting up the aquaria and water flow in the wet lab. We are grateful to the four reviewers, and the editor,  Gidon Winter , whose suggestions and comments significantly improved the article. The content of this manuscript has previously appeared online as a preprint in bioRxiv (Jueterbock et al. , 2019).
OVOC concentrations were generally higher in summertime, mainly due to secondary and biogenic sources, whereas their concentrations during fall and winter were potentially more influenced by anthropogenic primary/secondary sources. Moreover, an apportionment factorial analysis was applied to a database comprising a selection of 14 primary individual or grouped VOCs by means of the positive matrix factorization (PMF) technique. A PMF solution composed of 5 factors was taken on. It includes a biogenic factor (which contributed 4% to the total VOC mass), three 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 remaining one (39%) connected to the regional background pollution.
An extensive chemical composition dataset was collected at a receptor site considered to be representative of the northwestern basin. In this article, we present and discuss ambient levels and variations of a selection of VOCs observed at the Ersa station of the Corsican Observatory for Research and Studies on Climate and Atmosphere-ocean environment (CORSiCA -https://corsica. obs-mip. fr/, last access: 03/04/2020;  Lambert et al. , 2011) , over more than two years as part of the ChArMEx project. Selected species include alkanes, alkenes, alkyne, aromatic compounds and oxygenated VOCs (OVOCs), which were measured using off-line techniques. To reach its objective, this study will (i) quantify concentration levels of the targeted VOCs, (ii) specify their temporal variations at seasonal and interannual scales, (iii) identify and characterize their main sources by statistical modelling, (iv) assess and examine their source contributions on seasonal bases, together with (v) examine the representativeness of the Ersa station in terms of seasonal variations in VOC concentrations impacting continental Europe.
Study site Located in the northwestern part of the Mediterranean Sea, Corsica Island is a French territory situated 11 km northerly from Sardinian coasts, 90 km easterly from Tuscany (Italy) and 170 km southerly from the French Riviera (France). Being the 4th 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 has been 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, with the aim of providing a high quality controlled climatically relevant gas/aerosol database following the recommendations and criteria of the international atmospheric chemistry networks ACTRIS, EMEP, and GAW.
The uncertainties for each species were evaluated respecting the ACTRIS-2 guidelines for uncertainty evaluation  (Reimann et al. , 2018)  considering precision, detection limit and systematic errors in the measurements. Evaluated relative uncertainties ranged from 7% to 43% concerning steel canisters, between 7% and 65% for multi-sorbent cartridges and from 6% to 41% concerning DNPH cartridges. Finally, the VOC dataset was validated following the ACTRIS protocol  (Reimann et al. , 2018).
) and most of them are categorized as GAW regional stations for Europe. More information on these stations can be found on EMEP (https://www. nilu. no/projects/ccc/sitedescriptions/index. html, last access: 03/04/2020) or GAW station information system (https://gawsis. meteoswiss. ch/GAWSIS//index. html#/, last access: 03/04/2020) sites. VOC measurements were realized by different on-line (GC or proton-transfer-reaction mass spectrometer -PTR-MS) or off-line techniques (VOCs collected by steel canisters) and were reported in the EMEP EBAS database (http://ebas. nilu. no/Default. aspx, last access: 03/04/2020).
Identification and contribution of major sources of VOCs In order to characterize VOC concentrations measured at Ersa, we apportioned VOC sources in this study using the positive matrix factorization approach (PMF;  Paatero, 1997; Paatero and Tapper, 1994)  applied to our concentration dataset. The PMF https://doi. org/10. 5194/acp-2020-607 Preprint. Discussion started: 8 July 2020 c Author(s) 2020. CC BY 4. 0 License. mathematical theory has already been presented in  Debevec et al. (2017)  and is therefore reminded in Section S1 of the Supplement. We used the PMF version 5. 0, an enhanced tool developed by the Environmental Protection Agency (EPA) and including a multilinear engine ME-2  (Paatero, 1999) , and followed the guidance on the use of PMF  (Norris et al. , 2014). In order to have sufficient completeness (in terms of observation number), only VOC measurements from bi-weekly ambient air samples collected into steel canisters from 04 June 2012 to 27 June 2014 were retained in this factorial analysis.
The chemical dataset includes 14 selected single or grouped VOCs, i. e. those showing significant concentration levels during the study period (see  Sect. 3. 3). They were divided into five compound families: alkanes (ethane, propane, i-butane, n-butane, i-pentane, n-pentane and n-hexane), alkenes (ethylene and propene), alkyne (acetylene), diene (isoprene) and aromatics (benzene, toluene, and EX, the sum of ethylbenzene, m,p-xylenes and o-xylene). The final VOC dataset encompassed 152 atmospheric data points having a time resolution of 4 hours. Input information are detailed in Table  2. Moreover, the data processing and quality analysis of the VOC dataset are presented in the supplement material of  Debevec et al. (2017). In order to optimize the PMF solution, we followed the same procedure as in  Debevec et al.
(2017)  (see Sect. S1 in the Supplement). As a result, a five-factor PMF solution has been chosen in this study considering a Fpeak parameter fixed at 0. 8 which allowed a finer decomposition of the VOC dataset following an acceptable change of the Q-value  (Norris et al. , 2014). Quality indicators provided by the EPA PMF application have been indicated in Table  2. The PMF model results reconstruct on average 99% of the total concentration of the 14 selected compounds of this study. Individually, almost all chemical species also showed both good determination coefficients and slopes (close to 1) between reconstructed and measured concentrations, apart from propene, n-pentane, n-hexane, toluene and EX. Therefore, PMF model limitations to explain these species should be kept in mind when examining PMF results.
Towards the best experimental strategy to characterize variation in VOC concentrations The 5-factor PMF solution, modelled with a two year VOC dataset (from June 2012 to June 2014) was compared with the PMF solution modelled with the short summer SOP-1b VOC dataset (from 15 July to 5 August 2013), composed of 6 factors, namely primary biogenic factor, secondary biogenic factor, short-lived anthropogenic factor, medium-lived anthropogenic factor, long-lived anthropogenic factor and oxygenated factor  (Michoud et al. , 2017). Note that the SOP-1b PMF source apportionment was performed considering a dataset composed of 42 VOCs, comprising six oxygenated compounds and https://doi. org/10. 5194/acp-2020-607 Preprint. Discussion started: 8 July 2020 c Author(s) 2020. CC BY 4. 0 License. collected with three different on-line techniques (see Sect.
As a result, within the framework of the ChArMEx project, a background monitoring station has been set up and operated from June 2012 to June 2014 at a remote background site of Corsica Island (Ersa) in the northwestern part of the Mediterranean. Around 300 atmospheric measurements of a wide range of VOCs (primary anthropogenic and biogenic species as well as oxygenated compounds) were conducted at Ersa with different off-line techniques. This study presents seasonal variabilities of 25 selected VOCs, which showed significant concentration levels during the study period, and their various associated sources. Particular attention in this study was brought to identifying and evaluating the respective contributions of the various potential sources influencing VOC observations at Ersa. Apportionment factorial analysis was hence conducted on the database composed of 14 primary VOCs (or grouped VOCs) using the positive matrix factorization technique.
Moreover, four oxygenated VOCs (such as acetone) largely prevailed in the VOC speciation during the 2-yr monitoring period. OVOC sources can include primary emissions from both biogenic and anthropogenic origins and OVOCs may also be induced by secondary (biogenic/anthropogenic) sources from oxidation of various VOCs. OVOC concentrations measured at Ersa were generally higher in summer, which could be the result of a high contribution of secondary and biogenic sources, whereas their concentrations during fall and winter were potentially more influenced by anthropogenic primary/secondary sources, more specifically for MEK. Concurrent datasets of VOC concentrations from 3 summer campaigns performed at Ersa help to comfort the representativeness of the 2-yr monitoring period in terms of summer concentration levels, variations and source apportionment.
For these latter, VOC measurements did not cover winter 2014 period, that's why accumulated concentrations were only indicated https://doi. org/10. 5194/acp-2020-607 Preprint. Discussion started: 8 July 2020 c Author(s) 2020. CC BY 4. 0 License. for winter 2013 period. For AUC, HRL, BIR and SMR stations, represented by grey markers, VOC measurements were not realized both during winters 2013 and 2014, or were conducted with a PTR-MS and hence we considered accumulated concentrations only of aromatic compounds are not sufficiently representative for interannual VOC concentration variations. Ersa location and results are highlighted. Map provided by Google Earth Pro software (v. 7. 3. 3 image Landsat/Copernicus -IBCAO; data SIO, NOOA, U. S, Navy, NGA, GEBCO; © Google Earth).