dataesr/scientific-paragraphs-categorization huggingface dataset:
Transcriptome assemblies In total, 64 transcriptomes of isopod species, including 27 new transcriptomes, were assembled. The resulting assemblies ranged from 28,393 (Jaera hopeana) to 685, 588 (Limnoria tripunctata) transcripts depending on the species (Additional file 1). Assembly completeness was evaluated using the BUSCO pipeline. Except for some Proasellus species, all the transcriptomes displayed a good completeness since more than 80% of the complete genes from the arthropod core genome were present in most of the assemblies (Additional file 2).
A B Fig. 2  Comparative analysis of isopod CAZomes comprising all CAZymes (a) and lignocellulose degrading CAZymes only (b). The PCA was constructed from normalized counts of CAZy modules and the Venn diagram was constructed from numbers of CAZy families identified in the transcriptomes but some of them could have expanded this family for cellulose digestion  [40]. The prediction of enzyme activities with Hotpep showed that cellulases and hemicellulases are well distributed in isopod species (Fig. 4 ). Cellulases were found in almost all transcriptomes, notably endo-β-1,4-glucanases (EC 3. 2. 1. 4) that were predicted in more than 93% of the transcriptomes and β-glucosidases (EC 3. 2. 1. 21) in 76% of the transcriptomes. Similarly, hemicellulases, mannases (EC 3. 2. 1. 22) and xyloglucanases (EC 3.
GH7 phylogeny Protein BLAST searches against the non-redundant protein database of NCBI using GH7 proteins identified in isopod transcriptomes showed a large number of matches with GH7 sequences isolated from fungi (basidiomycetes and ascomycetes), and apart from crustaceans, there was no match with GH7 from any other animals. The comparison of isopod GH7 protein sequences with their homologues in other crustaceans, fungi, oomycetes, protists, amoebozoa and demosponges, showed that they were nested in a clade comprising all the proteins of crustaceans and oomycetes (Chromista), and some proteins of ascomycetes (Fig. 6 ). This clade was separated from the other clades formed by the GH7 from protists, ascomycetes and basidiomycetes.
The presence of hemocyanin genes was investigated using sequence similarity searching, and unsurprisingly all transcriptomes show high numbers of hemocyanins. Nevertheless, our data do not allow us to conclude about their potential role in lignocellulose degradation. Hence, marine isopods seem to have a number of strategies to degrade phenols. The ability to degrade lignin could be an important preadaptation to terrestrial life  [17, 49] , which would explain why the ability to degrade phenols alone may have evolved independently in marine isopods.
Biological samples Transcriptomic data of 48 isopod species were retrieved from SRA archive (Additional file 1). For each of them, we ascertained that all tissues were represented or that data were generated from whole individuals. Additionally, we generated transcriptomes from 27 isopod species sampled from our laboratory rearing or collected from various field sites in 2015 and 2016 (Additional file 1). By combining these newly generated data to already publicly available data, we finally obtained transcriptomes from 64 different isopod species including new transcriptomic data for 16 species.
Transcriptome assembly Read quality was checked with FastQC (version 0. 11. 2; http://www. bioinformatics. babraham. ac. uk/projects/ fastqc). Removal of sequencing adaptors and low quality bases was performed with Trimmomatic (version 0. 32;  [71] ). Reads shorter than 35 bp were discarded. Preprocessed reads from each species were assembled using IDBA-Tran  [72]  with default parameters. Transcript redundancy was removed by clustering with ≥95% identity using CD-HIT-EST (version 4. 6;  [73] ). The completeness of the resulting assemblies was assessed with BUSCO (version 3. 0. 1; [74] ) referring to core arthropod genes.
Carbohydrate-active enZyme annotation CAZymes were identified using the Carbohydrate Active enZymes (CAZy) database  [24]. Prior to identification, all open reading frames (ORFs) were predicted from transcriptomes using Transdecoder (version 3. 0. 1; https://transdecoder. github. io/) with default parameters. Subsequently, dbCAN  [75] , a database which uses hidden Markov Models to define the signature domains for each CAZy family (i. e. Glycoside Hydrolases (GHs), Glycosyl Transferases (GTs), Polysaccharide Lyases (PLs), Carbohydrate Esterases (CEs), Auxiliary Activities (AAs) and Carbohydrate-Binding Modules (CBMs)), was used to identify CAZymes. All predicted ORFs were analysed with dbCAN (October 1, 2017) using HMMER (version 3. 1b2;  [76] ) with an E-value threshold of 0. 0001. To remove CAZymes originating from host microbiota including fungi, prokaryotes and viruses, ORFs identified as CAZymes were compared with the Non-Redundant Protein database (October 1, 2017) using BLASTP  [77]  with an E-value cut-off of 0.
0001. The BLAST outputs were then imported into MEGAN6 software (version 6. 9;  [78] ) for taxonomic assignment using the NCBI taxonomy database. All ORFs assigned to fungi, prokaryotes or viruses were discarded. To verify that all nonhost CAZymes were discarded, the 37,481 remaining ORFs were compared again with the latest Non-Redundant Protein database (Avril 25, 2019), using DIA-MOND  [79]  with the "--more-sensitive" mode and an Evalue cut-off of 0. 001. Among them,  35, 194  were assigned to metazoans by MEGAN6 software (version 6. 9;  [78] ), 120 to eukaryotes, 488 to cellular organisms, and 8 were not taxonomically assigned. The 1671 remaining ORFs, most of them being CBMs (1515 including 1068 CBM14), have no hits in the Non-Redundant Protein database. We are aware that this method could exclude sequences that may have been the result of recent horizontal gene transfer.
However, without any further genomic information we cannot identify such transfers. The resulting host CAZymes were then imported into Hotpep  [80]  to predict their enzymatic activity. CAZyme counts (note that "CAZyme" refers to functional modules or domains, not genes) were normalized to compare their diversity across isopod species. This was done to even out the heterogeneity arising from differential transcriptomic sampling and sequencing methods. CAZyme counts for each transcriptome were divided by the number of ORFs in the transcriptome of interest to calculate the relative abundance for each CAZyme family. Then, normalized count of each family in each transcriptome was calculated by multiplying the relative abundance by 15,256, representing the lowest number of ORFs identified in our dataset corresponding to the Proasellus ebrensis transcriptome.
GH9 and AA3 phylogenies Selected sequences which were present in most sampled isopods were examined in depth to infer their phylogeny. Enzymes belonging to GH9 and AA3 families were identified in 97 and 89% of the transcriptomes. All GH9 sequences used for the phylogeny were predicted by Hotpep to function as endo-β-1,4-glucanases (EC 3. 2. 1. 4). Not all the activities of AA3 sequences were predicted and only 54 sequences were predicted to be cellobiose dehydrogenases (EC 1. 1. 99. 18). Consequently, the AA3 sequences were compared to the Pfam database (version 32. 0;  [85] ) using hmm-search (version 3. 1b2;  [76] ) with an E-value cut-off of 0. 00001 to identify conserved domains. In a conservative approach, all sequences lacking the conserved domain GMC oxidoreductase (an enzyme family that includes cellobiose dehydrogenases) were discarded.
All remaining gene sequences (251 AA3 and 128 GH9) were aligned with Muscle  [86]  and the subsets of sites used for the phylogenetic analyses were determined with Gblocks using Less Stringent Selection parameters  [87]. Phylogenies were constructed with the iqTree software (version 1. 6. 2; [88] ) using ModelFinder for model selection  [89]  and performing 1000 ultrafast bootstrap with UFBoot  [90]. For the construction of the phylogenetic trees, Model-Finder selected the GTR + F + R7 and the TIM2 + F + R6 substitution models for AA3 and GH9, respectively. Trees were modified with the iTol online tool  [91] ; branches were collapsed when the ultrafast bootstrap values were less than 50. When very close sequences of a given isopod species branched together in the tree, only one sequence was kept.
GH7 phylogeny Unlike GH9 and AA3, GH7 enzymes were identified in only four transcriptomes (L. tripunctata, P. cavaticus, P. karamani, and S. terebrans). To infer their putative origin, a phylogenetic tree was constructed with GH7 sequences from other organisms. A BLASTP using GH7 proteins identified in isopod transcriptomes as query was applied to identify GH7 homologues in the NR protein database of NCBI. The GH7 protein sequences were also retrieved from GenBank with GH7 Pfam id (pfam00840 and cl21662) as query and from the CAZy database (http://www. cazy. org/). Methods used for alignment and phylogenetic tree reconstruction were the same as described above. The WAG+F + R6 substitution model was selected for phylogenetic tree constructions. Branches were collapsed when the ultrafast bootstrap values were less than 50.
This work was funded by the 2015-2020 State-Region Planning Contracts (CPER), European Regional Development Fund (FEDER) (BiodivUP project, coordinator DB), and intramural funds from the Centre National de la Recherche Scientifique and the University of Poitiers. The funding agencies were not involved in designing the study nor in collecting, analysing, and interpreting the data or in writing the manuscript. MB was supported by a PhD grant from the French Ministère de lʼEnseignement supérieur, de la Recherche et de lʼInnovation.
Combining AERONET (AErosol RObotic NETwork) sunphotometric data with ACSM observations, we also show a long persistence over weeks of pollution in volcanic sulfate aerosols, while SO 2 pollution disappears in a few days at most. Finally, gathering 6-month long datasets from 27 sulfur monitoring stations of the EMEP (European Monitoring and Evaluation Programme) network allows us to demonstrate a much broader large-scale European pollution, in both SO 2 and SO 4 , associated with the Holuhraun Published by Copernicus Publications on behalf of the European Geosciences Union. eruption, from Scandinavia to France. While widespread SO 2 anomalies, with ground-level mass concentrations far exceeding background values, almost entirely result from the volcanic source, the origin of sulfate aerosols is more complex.
In a third stage, the joint analysis of in situ ACSM measurements with sunphotometry column-integrated observations from co-located stations of the AERONET AErosol RObotic NETwork allows us to evaluate the temporal persistence of particulate pollution in sulfur  (Sect. 4. 4). Fourthly, to provide a broader picture, we explore 6month long sulfur monitoring datasets (September 2014-February 2015) from 27 stations of the EMEP (European Monitoring and Evaluation Programme) network. Using a multi-site concentration-weighted trajectory analysis for selected EMEP stations, we evaluate the intensity of the largescale chemical fingerprint of the Holuhraun eruption on gaseous SO 2 and particulate sulfate in Europe, compared to other anthropogenic industrial sources (Sect. 4. 5). Finally, we assess the range of variability of SO 2 -to-SO 4 mass concentration ratios according to the volcanic cloud history and derive for the first time an estimation of the oxidation rate from the eruption site to stations located a few thousands of kilometres away (Sect.
Flash vaporized molecules are then ionized at 70 eV electron impact before being detected and quantified by the mass spectrometer. Raw data are corrected for aerosol collection efficiency following the protocol defined by  Middlebrook et al. (2012). A specific ionization efficiency (relative to nitrate, RIE) should also be defined for each species. For the Dunkirk ACSM, a constant 0. 55 SO 4 RIE has been used, based on results obtained from calibrations performed regularly (typically, every 2 months) during the campaign. By the time of the measurement, a default SO 4 RIE value was preferably taken as equal to 1. 20 for the SIRTA ACSM  (Ng et al. , 2011; Crenn et al. , 2015). Therefore, figures hereafter display ACSM data processed using these values of 0. 55 and 1. 20 for the Dunkirk and SIRTA datasets, respectively.
, 2014; Crenn et al. , 2015). Standard diagnostics were used to clean up the ACSM data, such as spikes in the air beam and/or water signals, drop of inlet pressures indicative of clogging. No averaging was needed to compare the species obtained with the same instrument and therefore the original time resolution was kept. Dunkirk located in northern France (latitude 51. 041 • N, longitude 2. 312 • E, map in the inset of Fig. 1c ) hosts a large harbour, ranking seventh in Europe, with a developed manufacturing industry (map in Fig. A1 ) accounting for more than 80 % of total particulate matter (PM) emitted locally over  2009 -2011 (Clerc et al. , 2012)). About 97 % of primary PM 1 is emitted by metallurgy, steel and smelter activities (Figs. 1-7 of  Zhang, 2016). A remarkable 14-month long 30 min resolved ACSM dataset was collected at the Port-East site (map in Fig.
ACSM data have been routinely collected there since the end of 2011  (Petit et al. , 2015). A 2-month hourly resolved dataset (September-October 2014) has been used for the purpose of the present study to investigate the speciation of volcanic sulfate aerosols, especially during the largest event of volcanogenic air pollution affecting France in late September 2014  (Boichu et al. , 2016).
Results and discussion First, we evaluate the intensity of air pollution in sulfur-rich particles induced by the Holuhraun eruption in France. We also propose to explore whether the chemical signature of sulfate aerosols is specific or not within volcanic plumes, by comparison with sulfate aerosols of industrial origin. We then define a methodology to discriminate volcanic vs. local industrial sulfur-rich compounds. To do so, we study several events of air pollution observed in France in September 2014 at two locations nearby (Dunkirk) and distant (SIRTA) from industrial activities. We show the volcanogenic origin of these episodes of atmospheric pollution that are characterized by elevated ground-level mass concentrations of both SO 2 and SO 4. Then, we investigate whether similar events of air pollution are also detected more broadly, at the European scale, by exploiting in situ data from the EMEP ground network.
(2016). Nevertheless, despite these high SO 2 ground-level mass concentrations measured regionally on 22-24 September (Fig. 2d ), it is interesting to point out that, on 24 September, neither OMPS nor OMI satellite observations are sensitive enough to detect any SO 2 over the northern part of France encompassing the Paris region (see the animation of OMPS observations in the Supplement, OMI satellite data not shown here). This demonstrates the necessity to combine both space and ground observations, especially when SO 2 is confined in the boundary layer. Note that the two simultaneous anomalies observed on 9 and 10 September 2014 in both SO 4 at SIRTA and SO 2 mass concentrations at Airparif stations may also be volcanogenic. Nevertheless, this 2 d long episode of air pollution is not selected for further analysis as it is of lower intensity compared to the three other volcanogenic events already selected.
4 ). At SIRTA, unlike nitrate and organics, the highest mass concentrations in ammonium aerosols are recorded between 21 and 25 September 2014, a period corresponding to the largest volcanogenic event of air pollution in sulfur-rich gas and particulate species in France (Sect. 4. 1). Scatter plots of the mass concentrations of gaseous SO 2 , measured by air quality stations, and of the various aerosol species (NH 4 , NO 3 , Org) measured with ACSM, vs. the mass concentration of sulfate aerosols, at the two sites of SIRTA and Dunkirk, display a wide dispersion of data (top of Figs. 5  and 6 ). As described previously in Sect. 4. 1, three episodes of volcanogenic air pollution in SO 2 have been highlighted at Dunkirk and SIRTA in September 2014. The ACSM data collected during the time period of occurrence of these volcanic events are marked specifically in the bottom of Figs.
Specific signature of freshly emitted industrial sulfate-rich aerosols Particle mass concentrations at Dunkirk display a more complex behaviour with widely scattered values compared to SIRTA. We are especially intrigued by a group of ACSM data at Dunkirk that are associated with very low mass concentrations of NO 3 , hence presenting a signature close to the one of the largest volcanic event recorded at SIRTA (red squares) but showing a larger spread of SO 4 mass concentration values up to 30 µg m -3 (Fig. 6c ). For this reason, we colour in cyan these specific data associated with mass concentrations of NO 3 < 1 and SO 4 > 4 µg m -3 in the various scatter plots of Figs. 5c, d  and 6c, d. Polar plots in Dunkirk (Fig. A3 ) cover four sectors defined as follows: marine (271-70 • ), urban (71-140 • ), industrialurban (141-225 • ), and industrial (226-270 • ).
In the Dunkirk area, we expect that local emissions -50 % originating from the "'manufacturing industries, waste treatment and construction" according to the latest available inventory of AtmoHDF (  2012 ), compared to 96 % from the agricultural sector when considering the entire Hauts-de-France region -will even increase this background level by a few µg m -3. Dunkirk atmosphere can consequently be considered to be sufficiently rich in NH 3 to produce the concentration of ammonium required to neutralize the concentrations of industrial sulfate the most commonly measured. Local NH 3 may generally not be lacking, but rather short residence times between the plume emission points and the sampling site are responsible for the acidity of these observed aerosols. To summarize, we show that the group of ACSM data very poor in particulate nitrate, while rich in sulfate, originates from the industrial sector, is acidic and displays short residence time.
However, the NO 3 : SO 4 mass concentration ratio seems the most powerful to also distinguish the chemical pattern of volcanic aerosols from those of freshly emitted industrial aerosols as shown in Dunkirk. Nonetheless, Fig. 9  (as well as Figs. 5, 6  and 8 ) illustrates much more data scatter for background aerosols in Dunkirk (in yellow) compared to SIRTA (in blue), independently of the ratio of interest (NO 3 : SO 4 or Org : SO 4 ). It has to be recalled that the Dunkirk dataset covers a much longer time period (more than a year) than the SIRTA one (2 months), which may partly explain this observation. In addition to its coastal location implying the presence of sulfur-rich aerosols from marine or ship emissions  (Zhang, 2016) , that are naturally absent at SIRTA, Dunkirk hosts both intense harbor and industrial activities as previously mentioned (Sect.
3 in Dunkirk (given a mean AOD value of 0. 175 ± 0. 047 for September months between the start of AERONET measurements in  Dunkirk in 2006 Dunkirk in and 2017 Dunkirk in , excluding 2014)). Most importantly, we find a remarkable correlation between time series of SO 4 ground-level mass concentration and of aerosol optical depth at SIRTA (Fig. 12a ) and also at Dunkirk though to a lesser extent due to a shorter ACSM dataset (Fig. 12b ). This result demonstrates that the aerosol optical depth, a columnintegrated property, is mainly impacted by ground-level sulfate aerosols in these occasions. As observed on 1 September at Dunkirk (Sect. 4. 1), industrial activities can only trigger short-term peaks, lasting a few hours, in both SO 2 and SO 4 ground-level mass concentrations (Fig. 1c ). Therefore, we suggest that the persisting excess anomaly in both SO 4 ground-level mass concentration and aerosol optical depth observed in September 2014 at a regional scale in France may result from the broad dispersion of sulfur-rich emissions, likely originating here from the Holuhraun eruption.
Large-scale volcanogenic pollution in gas and particulate sulfur recorded by the EMEP network To put into perspective our results showing a persistent atmospheric pollution in sulfate particles in France and assess more broadly the geographical impact of Holuhraun emissions on air quality, we explore daily and hourly datasets of sulfur monitoring by filter pack and online ion chromatography measurements from ground stations of the European EMEP network (map in Fig. 13 ) over the complete 6-month long eruption  (September 2014 -February 2015). We also examine the partitioning of volcanic sulfur species (e. g. the SO 2 : SO 4 mass concentration ratio here) to see whether the one observed with ACSM and SO 2 measurements in France is similarly found elsewhere. Unfortunately, the number of EMEP stations in Europe performing the monitoring, at the same temporal resolution, of ground-level mass concentrations of both SO 2 and SO 4 has significantly decreased in the last decade, and only 27 stations, listed in Table  1 , are of interest for our study (time series covering the September 2014-February 2015 period of the eruption at each of these stations are displayed in the Supplement).
On the other hand, lower SO 2 : SO 4 mass ratios may be associated with more aged and diluted volcanic clouds, hence providing more time for SO 2 oxidation. These aged volcanic clouds have also probably resided a longer time at lower altitude thus meeting drastically different atmospheric conditions and more likely mixing with other types of aerosols. To our knowledge, this dataset of SO 2 -to-SO 4 mass ratios at a very long distance (a few thousand kilometres) from the volcanic source is unique. The significant variability in mass ratios that we observe attests of the complex atmospheric history and processes that control the oxidation of SO 2 within a volcanic cloud. Nevertheless, despite this apparent complexity and the vast geographical area over which the volcanic plume is sampled, we show in Fig.
Acknowledgements. Marie Boichu ,  Isabelle Chiapello  and  Colette Brogniez  gratefully acknowledge support from the  French National Research Agency (ANR)  for funding the  VOLCPLUME  project ( ANR-15-CE04-0003-01 ) and the  Chantier Arctique  for funding the  PARCS  project (Pollution in the ARCtic System). This work is a contribution to the  CaPPA project (Chemical and Physical Properties of the Atmosphere) , funded by the  ANR  through the  PIA (Programme d'Investissement d'Avenir)  under contract  ANR-11-LABX-0005-01  and by the  Regional Council of Hautsde-France  and the  European Funds for Regional Economic Development (FEDER)  and to the  CPER Climibio program. Aerosol measurements at SIRTA have been performed in the framework of the  EU-FP7  and  H2020   ACTRIS  projects (grant agreement nos.
Centre ) for  Auchencorth Moss and Harwell stations in Great Britain. We thank  Hervé Delbarre  and  Philippe Goloub  for their efforts in establishing and maintaining  Dunkirk and Palaiseau AERONET sites. NASA Goddard Earth Sciences Data and Information Services Center (GES DISC ) are acknowledged for providing OMPS satellite SO 2 total column data. The  French Government Geoportail  is acknowledged for providing an aerial image of Dunkirk through its public website (https://www. geoportail. gouv. fr,last access: 14 November 2019). We thank the three anonymous reviewers for their detailed and useful reviews as well as co-editor  Anja Schmidt  for her comments on the manuscript. Financial support. This research has been supported by the  Agence Nationale de la Recherche  ( VOLCPLUME  project (grant no.