dataesr/scientific-paragraphs-categorization huggingface dataset:
From both, a small piece of tissue was first removed and transferred in 80% ethanol in order to extract DNA and proceed to mitochondrial cytochrome oxidase subunit I (COI) and nuclear 18S rRNA barcoding. By this method, both individuals could be compared at the DNA level, allowing confirmation that the morphology-based identified specimen was of the same species as the frozen one, from which the transcriptome was to be sequenced. When COI and/or 18S rRNA sequences had been deposited in databases by others, morphology-based species determination was confirmed by sequence identity. Before flash-freezing specimens in liquid nitrogen, the tunic and digestive tract were removed in order to decrease the level of contaminating RNA. Concerning colonial species, three zooids or pieces of the same colony were fixed in ethanol, formaldehyde, and liquid nitrogen respectively.
The PCR products were labeled using BigDye Terminator ver. 3. 1 Cycle Sequencing kit chemistry (Applied Biosystems), and sequenced on an Applied Biosystems 3730 DNA sequencer. For French samples, DNA was extracted as follows: tissues was first digested by 200 mg/ml Proteinase K at 37 C for 30 min then mashed with a sterile tip; nucleic acids were purified by phenol/chloroform/isoamyl alcohol (25:24:1), precipitated in sodium chloride 0. 2 M and finally quantified on a NanoDrop 2000 Spectrophotometer (Thermo Scientific). Cycling parameters were the same as in Brazil and France. Successful amplifications were sequenced on an ABI 3730 at Genewiz, Inc. (South Plainfield, NJ, USA). New COI and 18S rRNA sequences have were in GenBank (supplementary table 5, Supplementary Material online for accession numbers).
All sequences were aligned and trimmed using Geneious R7 (Biomatters LTD). We aligned the sequences with those from P erez-Portela et al. (  2009 ) and some available on Genbank (NCBI; see supplementary table 5, Supplementary Material online for accession numbers) using CLUSTALW  (Higgins et al. 1996)  and trimmed and confirmed by eye. The 18S rRNA alignment was 801 bp and the COI alignment was 679 bp. As in P erez-  Portela et al. (2009) , we concatenated the COI and 18S rRNA sequences. Two partitions were specified, one for each gene. We conducted a Maximum Likelihood analysis using the program RAxML v. 8  (Stamatakis 2014 ) with nucleotide substitution model GTR þ G þ I, and run 100 bootstrap replicates on our alignment of 100 sequences.
De Novo Transcriptome Assembly For all the species for which de novo transcriptomes were assembled, we applied the following eight-step pipeline: 1) Illumina reads were clipped and trimmed to eliminate low quality regions using Trimmomatic v. 0. 35  (Bolger et al. 2014)  with the following parameters: PE reads_1. fq reads_2. fq ILLUMINACLIP: adapters/TruSeq3-PE. fa: 2: 30: 10 LEADING: 36 TRAILING: 36 SLIDINGWINDOW: 4: 34 MINLEN: 100 and then sequence quality was assessed using FastQC (http:// www. bioinformatics. babraham. ac. uk/projects/fastqc/; last accessed April 2018); 2) reads were assembled with Trinity v. 2. 4. 0  (Grabherr et al. 2011 ) using default parameters; 3) open reading frames (ORF's) of a minimum of 90 amino acids were kept using Transdecoder v. 3. 0. 1 (https://github. com/ TransDecoder/TransDecoder/wiki; last accessed April 2018); 4) very similar ORF's (e.
g. , putative isoforms or alleles) were collapsed using CD-HIT-EST v4. 6  (Fu et al. 2012 ) with default parameters; 5) reads were then mapped back onto the remaining contigs using Kallisto v. 0. 42. 4  (Bray et al. 2016)  with default parameters; 6) density plots were drawn with R, using hist() and density() functions. On the basis of these plots, contigs with low expression levels were eliminated. The threshold above which contigs were kept corresponds to the lower peak of the density plot; 7) exhaustiveness was assessed by running BUSCO  (Simão et al. 2015)  against metazoan database; 8) cross-contaminations were removed using CroCo  (Simion et al. 2018). Number of contigs at each step for each species is given in supplementary table 1, Supplementary Material online.
Construction of a New Set of Orthologous Alignments We selected 15 chordate species for which genomic data were available (i. e. , six tunicates, six vertebrates, one cephalochordate, one echinoderm, and one hemichordate, see supplementary table 3, Supplementary Material online), and we retained only the longest transcript for each gene of their corresponding proteomes. These proteomes were further dereplicated using CD-HIT (-c 1. 0;  Fu et al. 2012 ) and sequences <30 amino acids were removed. These 15 proteomes were clustered using OrthoMCL (-I 1. 3,  Li et al. 2003 ). We later added three new proteomes from tunicate species for which genomic data Nonembryonic Development in Styelid Ascidians. doi:10. 1093/molbev/msy068 MBE became available (supplementary table 3, Supplementary Material online) into the clusters using Forty 2 (https://bitbucket.
Gene Augmentation, Selection, and Concatenation Transcriptomic data from 18 stolidobranchs including 16 styelids (table 1) were added to the 14,552 clusters of orthologous sequences using Forty 2. Clusters that contained apparent paralogy for at least one tunicate species among those that correspond to the genomic data set (i. e. , species belonging to the Ciona, Phallusia, Molgula, and Halocynthia genera) or which contained <10 tunicate species or which contained >80 sequences were discarded. All nontunicate sequences were removed from the remaining 8,187 clusters in order to improve the accuracy of the subsequent aligning step, a. All clusters were aligned using MAFFT (-maxiterate 1000localpair,  Katoh and Standley 2013). For every alignment, nonoverlapping sequences were fused as they most likely correspond to sequence fragments from the same transcript.
We used an extended definition of a "nonoverlap" between two sequences: if actually overlapping, the total overlap is smaller than 200 amino acids and the two sequences differ by less than five amino acid positions. This allowed us to concatenate 64,340 fragmented transcripts (out of the initial 219,099 transcripts) into 28,188 longer ones. We then removed 4,631 low-quality transcripts (i. e. , that had >70% missing data and were shorter than 100 amino acids). Alignments were then checked again for apparent paralogy, this time for all species, and we only kept the 4,908 alignments that showed no paralogy. We also selected alignments containing at least 24 species (out of the 26 total species) to create a smaller and more complete phylogenomic data set of 1,306 alignments. These alignments were concatenated into supermatrices using ScaFos  (Roure et al.
Acknowledgments We would like to thank  Philippe Dru  and  Faisal Bekkouche  for help with bioinformatics resources,  Laurent Gilletta ,  Loann Gissat ,  Regis Lasbleiz ,  Sophie Collet , and  Camille Lejeune  for setting up the aqua-culture systems. Francoise Monniot for advice on taxonomy and sample identification. Sophie Booker ,  Elodie Bourrigaud ,  Laurent L evêque ,  Ronan Garnier ,  Francesco Mastrototaro , and the  Service Mer of Banyuls-surmer, EMBRC-France  for sample collection. We thank  Martine Fioroni  for providing literature and old manuscripts that were difficult to obtain. This work was supported by  FAPESP   Jovem Pesquisador grant  ( JP 2015/50164-5 ) to F. B. ,  ANR  grant ( ANR-14-CE02-0019-01 ) to S. T. ,  FAPESP postdoctoral fellowship  (grant number  2015/14052-8 ) to L.
97 92% Eusynstyela tincta PRJNA422120 São Sebastião (Brazil) 1 adult (no tunic) 33,225,966 39,494 837 694 74% Microcosmus sabatieri PRJNA422120 Banyuls (France) Siphon 29,425,964 22,951 1,471 1,022. 8 93% Polyandrocarpa PRJNA422120 Misaki (Japan) Asexual zooids and buds 39,178,452 52,545 1,194 851. 48 96% misakiensis Polyandrocarpa PRJNA422120 La Spezia (Italy) 2 adults and buds 158,133,887 57,781 1,197 816. 91 96% zorritensis Polycarpa aurata PRJNA422120 Cebu (Philippines) 1 adult (no tunic, 34,876,779 29,348 1,443 982. 17 95% no stomach) Polycarpa mamillaris PRJNA422120 Banyuls (France) Oocytes 12,985,467 25,221 1,599 1,056. 42 96% Polycarpa pomaria PRJNA422120 Brest (France) 1 adult (no tunic) 33,554,939 41,730 1,386 949. 48 95% Pyura dura PRJNA422120 Banyuls (France) Gonads 33,619,672 17,739 1,854 1,216.
9 96% Stolonica socialis PRJNA422120 Roscoff (France) 1 adult 35,870,064 43,474 1,431 934. 34 94% Styela canopus PRJNA422120 São Sebastião (Brazil) 1 adult (mantle) 44,826,592 42,420 1,047 815. 45 87% Styela clava PRJNA422120 Brest (France) 1 adult (no tunic, 47,686,633 26,553 1,647 1,164. 48 89% no stomach) Styela plicata PRJNA422120 Banyuls (France) Embryos 38,186,700 19,288 1,870 1,253. 51 97% Polycarpa sp. PRJNA422120 Roscoff (France) 1 adult (no tunic) 43,522,115 44,672 1,311 918. 30 90% Nonembryonic Development in Styelid Ascidians. doi:10. 1093/molbev/msy068 MBE A Robust Phylogeny of Styelid Ascidians.
). Phenol has an environmental impact, particularly on aquatic biota  (microorganisms, protozoa, invertebrates, and vertebrates; Babich and Davis, 1981; Duan et al. , 2018). Phenol also represents a risk for human beings because it can be rapidly Published by Copernicus Publications on behalf of the European Geosciences Union. absorbed through the skin and by inhalation through the lungs. In particular, it provokes cutaneous exfoliation and cardiac arrhythmias; it is also toxic to the liver and kidneys  (Babich and Davis, 1981; Lober, 1987 ; National Library of Medicine HSDB Database: https://toxnet. nlm. nih. gov/ cgi-bin/sis/search/a?dbs+hsdb:@term+@DOCNO+113, last access: 4 August 2018). Phenol can be found in all environmental compartments (soil, water), including the atmosphere  (Atkinson et al.
All the dichloromethane fractions were then dried with MgSO 4 and evaporated to 1 mL using a rotary evaporator under reduced pressure; the temperature of the water bath was 20 • C. Samples were kept at 4 • C until analysis All analyses related to cloud samples collected on 21 and 26 October 2016 were performed at Saint Joseph, MI, at LECO Corporation (USA). Accurate GC-MS measurements were performed with the high-resolution time-of-flight mass spectrometer Pegasus ® GC-HRT in GC mode (software ChromaTOF-HRT). The obtained EI mass spectra were used for phenol identification by utilizing high mass accuracy data and retention time  (Lebedev et al. , 2013). Phenol concentrations were measured using naphthalene D8 as an internal standard. The response factor (0. 7) was calculated using a standard solution of phenol.
Analysis of metatranscriptomes Transcriptomic analysis. Cloud water samples were collected on 17 November 2014 for three consecutive periods of 5 h. The cloud air mass origin remained stable over the duration of sampling as attested by air mass backward trajectories (Fig. S1 ). The cloud droplets collected by impaction were immediately transferred by gravity into sterile collection bottles (Nalgene, Rochester, USA) through sterile (autoclaved) silicone tubing. Before cloud sampling operations started, control samples were made by pouring 200 mL of sterile water into the collection device and through the tubing and by processing it in parallel with the cloud water samples, including sequencing and data treatment. These controls were clearly distinct from samples: based on their contribution to identified ribosome sequences, these contained mostly Enterobacteriaceae (66 %), Dikarya (9.
; shotgun libraries were sequenced on Illumina MiSeq paired-end 2 × 300 bp. Sequencing reads were quality checked (FastQC;  Andrews, 2010)  and trimmed (PRINSEQ-Lite; Schmieder and Edwards, 2011) before assembling the mate pairs using PANDA-SEQ  (Masella et al. , 2012). Annotations were made against the UNIPROTKB database (  Leinonen et al. , 2006) , including protein sequences for bacteria, archaea, and fungi using BLASTX software (best hits with e value < 10 -4 ). All steps were performed using custom scripts. The sequence files have been deposited to the European Nucleotide Archive (ENA) under the study accession number PRJEB25802. Bioinformatics treatment. Known enzymes involved in phenol degradation were found in the KEGG database (see Fig. 1 ). We only focused on aerobic metabolism as the cloud environment is highly oxidative.
Four nucleotide sequence databases were created from NCBI corresponding to phenol hydroxylases (69 sequences), phenol monooxygenases (29 sequences), catechol (regrouping catechol 1,2-dioxygenases and catechol 2,3-dioxygenases; 145 sequences), and a fourth database including genes coding for putative phenol degradation enzymes (38 sequences). The sequences from the cloud metatranscriptomes corresponding to the different created databases were then ex-tracted using bowtie2 (very sensitive option;  Langmead and Salzberg, 2012). The affiliation of the extracted sequences was determined using BLASTN on a local server (e value = 0. 00001;  Camacho et al. , 2009).
Before sampling, the evaporation of water has been compensated for by adding Volvic ® mineral water. A control experiment was performed by incubating phenol without bacteria; the phenol concentration remained stable with time (0. 1 mM of phenol was obtained at the end of the experiment). For phenol quantification over time in the incubation experiments, 600 µL samples were centrifuged at 12 500 rpm for 3 min and the supernatants were kept frozen until HPLC analysis. Phenol HPLC analysis. Before analysis, all the samples were filtered on an H-PTFE filter (pore size at 0. 2 µm and diameter of 13 mm from Macherey-Nagel, Germany). Phenol detection was done on an HPLC VWR Hitachi Chromaster apparatus fitted with a DAD detector and driven by Chromaster software. Isocratic mode was used with a reverse-phase end-capped column (LiChrospher ® RP-18, 150 mm × 4.
Possibility of in-cloud phenol degradation by the cloud microbiome using a metatranscriptomic analysis The presence of transcripts involved in the biodegradation of phenol (Fig. 1 ) was investigated from prokaryotic messenger RNA-enriched metatranscriptomes obtained from three cloud water samples. Sequence data were searched for the presence of transcripts of genes involved in phenol  biodegradation among the 281 sequences included in our database (more details about the affiliation of the sequences are given in Table  S2 ). Gene transcripts were detected for all the enzymes, except the catechol 2,3-dioxygenase, showing a possible implication of the microorganisms in the degradation of phenol in cloud (Fig. 2 ). However, the number of hits and the relative abundance of the transcripts coding for the different enzymes varied according to the cloud samples.
This very low diversity was unexpected considering that sequences from 50 bacterial genera, including 109 species, were used for our search in databases. In addition, the relative abundance of sequences affiliated with a bacterial species varied a lot with the considered enzymes and clouds (Fig. 3 ). Gammaproteobacteria were found to contribute up to 21 % of the ribosome sequences identified in bacteria in targeted sequencing investigations. Pseudomonas in particular was highlighted as one of the most represented genera (contributing alone up to 2 % of the ribosome sequences) and the most active genus based on its representation in transcriptomes and consecutively high ribosomal cDNA : DNA ratio  (Amato et al. , 2017a ; Fig. S4 ). Acinetobacter and Rhodococcus were much less represented (< 0.
The molecular allowed for the detection of transcripts belonging to Pseudomonas and Acinetobacter strains but not for the sequences of the other strains present in Table  S2. It was surprising not to find Rhodococcus sequences as this genus is well known to degrade phenol as reported in the literature. In parallel, Rhodococcus strains isolated from clouds were very active phenol degraders but no Acinetobacter have been isolated from clouds. This difference reflects the complementarity but also the bias of each approach (molecular vs. cultural). Metatranscriptomic analysis can be biased by technical issues (extraction, sequencing, etc. ) or by the creation of an incomplete database. In the future, the database for phenol degradation could be improved by integrating more sequences, especially considering data banks other than NCBI.
For instance, the catechol operon sequences of Pseudomonas synringae  (Berge et al. , 2014)  could be added to the database. We recently published the genome sequence of Pseudomonas syringae 32b-74, Pseudomonas graminis 13b-3, and Rhodococcus enclensis 23b-28, which are degrading phenol (Table  S1 ;  Besaury et al. , 2017a, b; Lallement et al. , 2017) ; they could be used to implement the database. Finally, in the future the genome of many phenol degraders (Table  S1 ) could be also sequenced and integrated. On the other hand, it is well known that culturable microorganisms only represent 1 % or less than the total community, notably in clouds  (Amann et al. , 1995; Vaïtilingom et al. , 2012). Strains of Acinetobacter, Pseudomonas, and Rhodococcus genera are known to degrade phenol in other environments  (Basha et al.
b The CDIAC database has about 250 countries, but we show data for 213 countries since we aggregate and disaggregate some countries to be consistent with current country definitions (see Sect. 2. 1. 1 for more details). c E LUC is still estimated based on bookkeeping models as in 2017, but the number of DGVMs used to characterise the uncertainty has changed. ment emissions. This is greater than the sum of emissions from all countries. This is largely attributable to emissions that occur in international territory, in particular, the combustion of fuels used in international shipping and aviation (bunker fuels). The emissions from international bunker fuels are calculated based on where the fuels were loaded, but we do not include them in the national emission estimates. Other differences occur (1) because the sum of imports in all countries is not equal to the sum of exports, and (2) because of inconsistent national reporting, differing treatment of oxidation of non-fuel uses of hydrocarbons (e.
We combine this with our estimate of emissions from cement production using the monthly US cement data from the U. S. Geological Survey (USGS) for January-August, assuming changes in cement production over the first part of the year apply throughout the year. While the EIA's forecasts for current full-year emissions have on average been revised downwards, only 10 such forecasts are available, so we conwww. earth-syst-sci-data. net/10/2141/2018/ Earth Syst. Sci. Data, 10, 2141-2194, 2018 servatively use the full range of adjustments following revision and additionally assume symmetrical uncertainty to give ±2. 5 % around the central forecast. For India, we use (1) monthly coal production and sales data from the Ministry of Mines (2018), Coal India Limited  (CIL, 2018), and Singareni Collieries Company Limited (SCCL, 2018) , combined with import data from the Ministry of Commerce and Industry (MCI, 2018) and power station stocks data from the Central Electricity Authority (CEA, 2018); (2) monthly oil production and consumption data from the Ministry of Petroleum and Natural Gas (PPAC, 2018a); (3) monthly natural gas production and import data from the Ministry of Petroleum and Natural Gas (PPAC, 2018b); and (4) monthly cement production data from the Office of the Economic Advisor  (OEA, 2018).
Global ocean biogeochemistry models (GOBMs) The ocean CO 2 sink for 1959-2017 is estimated using seven GOBMs (Table  A2 ). The GOBMs represent the physical, chemical, and biological processes that influence the surface ocean concentration of CO 2 and thus the air-sea CO 2 flux. The GOBMs are forced by meteorological reanalysis and atmospheric CO 2 concentration data available for the entire time period. They mostly differ in the source of the atwww. earth-syst-sci-data. net/10/2141/2018/ Earth Syst. Sci. Data, 10, 2141-2194, 2018 mospheric forcing data (meteorological reanalysis), spin-up strategies, and their horizontal and vertical resolutions (Table  A2 ). GOBMs do not include the effects of anthropogenic changes in nutrient supply, which could lead to an increase in the ocean sink of up to about 0.
Water sampling Seawater samples were collected during the noon casts at different depths using 12 L Niskin bottles for the determination of various parameters. For the determination of Chl a and particulate (phytoplankton + NAP) absorption coefficient (a P (λ)), samples were collected at depths corresponding to different percents of  PAR (i. e. , 75, 54, 36, 10 , 1, 0. 1 %) and filtered (288 mL for Chl a determination by fluorometry, and 2. 25 L for a P (λ)) through 25 mm Whatman GF/F filters. Then, the filters were immediately stored in liquid N 2  (-196  • C) in Nunc ® cryogenic vials until analysis. Samples were also collected at all depths for liposoluble HPLC pigment analyses (see LOV laboratory data, OUT-PACE database, J. Ras). In addition, samples for HPLC pigments were taken in duplicate at surface and deep chlorophyll maximum (DCM) as part of a NASA satellite validation program.
0001), also indicating that the freezing effect remained proportional to the concentration for our dataset. The a P (330) / a P (676) ratio showed relatively high values (> 80) from 0 to 25 m; then it abruptly fell to 20 below 30 m depth (Fig. 10b ). When considering the surface layer only (Fig. 10c ), the MAA index was tightly related to Trichodesmium, except at some stations (SD10). Indeed, MAA pigments are also produced by other phytoplankton groups  (Carreto and Carignan, 2011)  when exposed to high nL w (UV) values. MAAs of other groups show generally only one peak at 320 nm as in the southeastern Pacific  (Bricaud et al. , 2010)  or at 330 nm (large phytoplankton in Argentina's continental shelf;  Ferreira et al. , 2013). At OUTPACE, phytoplankton counts indicate that the contribution of other large phytoplankton (shown by the size index from HPLC pigment ratios) was low.
g. , size, type) might affect nL w (565) at the stations with sizeable PC2 values. In comparison, the first two PCs for the southeastern Pacific dataset (BIOSOPE) represent 96 % of the total variance, with 89 % for PC1 and only 7 % for PC2 (Fig. 12c ). The main difference is that nL w (565) is no longer linked to PC2, only to PC1, and that for PC2 nL w (490) has an opposite behavior to that in the southwestern Pacific (correlation is negative instead of positive). At 490 nm, Chl a appears to explain most of the nL w variability. This could reflect the absence of Trichodesmium in the east-ern Pacific. Except for a few stations, the PC2 contribution is much lower; that is, variability is mostly described by PC1.
Acknowledgements. We thank  Joséphine Ras ,  Laboratoire d'Océanologie de Villefranche , and  Crystal Thomas ,  NASA Goddard Space Flight Center , for performing the HPLC analysis,  Mireille Pujo-Pay  for scientific advice during the cruise,  Benjamin Blanc  for sorting and validating UPV-5 images,  David Varillon ,  IRD US IMAGO  for invaluable technical support,  Philippe Gérard  ( MIO ) for Chl a analyses, and the administrative staff of the  IRD Center of Nouméa. We thank  Rüdiger Röttgers  ( Helmholtz-Centrum Geesthacht ) for helpful discussions during the elaboration of the manuscript. This is a contribution of the  OUTPACE (Oligotrophy from Ultra-oligoTrophy PACific Experiment ) project (https://OUTPACE. mio. univ-amu. fr/,last access:  20 August 2018 ) funded by the  French National Research Agency  ( ANR-14-CE01-0007-01 ), the  LEFECyBER program (CNRS-INSU) , the  GOPS program (IRD)  and the  Centre National d'Etudes Spatiales  ( BC T23 ,  ZBC 4500048836 ).
The OUTPACE cruise (https://doi. org/10. 17600/15000900) was managed by  MIO Institute from Marseille (France). The  National Science Foundation  supported  Sophie Duhamel  under grant  OCE-1434916. The  National Aeronautics and Space Administration  supported  Robert Frouin  under various grants. Finally, we thank the two anonymous reviewers for their useful and constructive comments. Edited by: Thierry Moutin Reviewed by: two anonymous referees.