dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments The publication of this paper is supported by  CONISMA , the  Italian National Interuniversity Consortium for Marine Sciences , which received funding from the  European Community 's  Seventh Framework Programme  ( FP7/2007-2013 ) for the project VECTORS (http://www. marine-vectors. eu). This paper stems from the  International workshop MOLTOOLS (Molecular Tools for Monitoring Marine Invasive Species ), held in Lecce, Italy, in September 2012. The ideas presented here have also benefited from discussions at meetings of the  International Council for the Exploration of the Seas Working Group on Introductions and Transfers of Marine Organisms (ICES-WGITMO). The  United States Environmental Protection Agency , through its  Office of Research and Development , supported the work described here.
g. meiofauna or microbial communities) Single specimen collection Standard (Sanger) sequencing Bulk DNA or eDNA combined with species-specific probes (e. g. qPCR or other targeted approach) Bulk DNA or eDNA combined with HTS using dedicated (taxa- specific) primers and/or dedicated reference database Bulk DNA or eDNA combined with HTS using "universal" primers and databases early detection of NIS early detection of NIS; Trends in NIS distribution Trends in NIS distribution; Impacts to native biodiversity Trends in NIS distribution; Impacts to native biodiversity; broad shifts in ecosystem structure and function Validate a specific NIS identification first based on morphology Targeted (species-specific) surveys Targeted (taxon-or group-specific inventories (e. g. fish, protists)) Non-targeted global inventories.
Abstract. More than 7000 atmospheric measurements of over 60 C 2 -C 16 volatile organic compounds (VOCs) were conducted at a background site in Cyprus during a 1-month intensive field campaign held in March 2015. This exhaustive dataset consisted of primary anthropogenic and biogenic VOCs, including a wide range of source-specific tracers, and oxygenated VOCs (with various origins) that were measured online by flame ionization detection-gas chromatography and proton transfer mass spectrometry. Online submicron aerosol chemical composition was performed in parallel using an aerosol mass spectrometer. This study presents the high temporal variability in VOCs and their associated sources. A preliminary analysis of their time series was performed on the basis of independent tracers (NO, CO, black carbon), meteorological data and the clustering of air mass trajectories.
Biogenic compounds were mainly attributed to a local origin and showed compound-specific diurnal cycles such as a daily maximum for isoprene and a nighttime maximum for monoterpenes. Anthropogenic VOCs as well as oxygenated VOCs displayed higher mixing ratios under the influence of continental air masses (i. e. , western Asia), indicating that long-range transport significantly contributed to the VOC levels in the area. Source apportionment was then conducted on a database of 20 VOCs (or grouped VOCs) using a source receptor model. The positive matrix factorization and concentration field analyses were hence conducted to identify and characterize covariation factors of VOCs that were representative of primary emissions as well as chemical transformation processes. A six-factor PMF solution was selected, namely two primary biogenic factors (relative contribution of 43 % to the total mass of VOCs) for different types of emitting vegetation; three anthropogenic factors (shortlived combustion source, evaporative sources, industrial and evaporative sources; 21 % all together), identified as being either of local origin or from more distant emission zones (i.
Daily background values were then averaged, normalized, linearly interpolated onto all other measurements and subtracted from the atmospheric data before final conversion to mixing ratios. Three calibrations were performed (before, during and after the intensive field campaign) with the GCU and a standard gas mixture provided by Ionicon and containing 17 VOCs at 1 ppm (parts per million by volume). The instrument response for the species targeted was determined through the measurement of a standard gas previously diluted to mixing ratios in the range of 1-16 ppb. Gas calibrations allowed us to determine the reproducibility of measurements, expressed here as a mean coefficient of variation. This coefficient was less than 5 % for all targeted compounds underlining a good stability of the instrument.
Then, targeted masses were quantified following the expression given in  Taipale et al. (2008). For chemicals not found in the calibration gas, their respective ion count rate was directly converted into concentration unit using transmission curve coefficients (de Gouw and  Warneke, 2007; Taipale et al. , 2008). The latter was determined using the transmission tool of the PTR-MS Viewer software (Version 3. 1. 0. 28, Ionicon Analytik GmbH) and calibration results. Transmission coefficients of masses not calibrated were obtained interpolating known transmission coefficients according to a cubic Hermite spline  (Taipale et al. , 2008). Detection limits of targeted compounds have been determined as 2σ of the blank variation and were all below 0. 1 µg m -3 except methanol (0. 24 µg m -3 ). Identified sources of uncertainties were the variability in the ion count rates in zero-gas and calibration measurements combined with the dilution, linearity and reproducibility errors of the sampling system, the uncertainty in the standard gas mole fraction, and the relative humidity effect on the instrument sensitivity.
Aerosol measurements The chemical composition of non-refractory submicron aerosol (NR-PM 1 ) has been continuously monitored using a quadrupole aerosol chemical speciation monitor (Q- ACSM; Aerodyne Research Inc. , Billerica, Massachusetts, USA), which has been described in detail by  Ng et al. (2011). This recent instrument shares the same general structure with the aerosol mass spectrometer (AMS) but has been specifically developed for long-term monitoring. The Q-ACSM instrument operated non-stop with 30 min time resolution during the whole duration of the campaign totalizing 1292 valid data points (corresponding to a time recovery of 95 %). The ACSM dataset was validated against PM 1 chemical composition results provided by integrated daily resolution measurements. Instrument settings, field operation, calibration and data processing are the same as those reported in  Petit et al.
VOC dataset In this present study, EPA PMF 5. 0 using a multilinear engine ME-2  (Paatero, 1999)  and additional guidance on the use of PMF  (Norris et al. , 2014)   2. Additionally, the PMF approach, regarding data processing and the analysis of the quality of the VOC dataset, is based on the recommendations of  Norris et al. (2014)  and is presented in the Supplement (Sect. S1).
Therefore, a six-factor PMF solution has been selected and an F peak parameter of 0. 8 allowed a better decomposition of the chemical dataset with a low change in the Q value, consistent with the recommendations of  Norris et al. (2014). Quality indicators from the PMF application have been summarized in Table  2. The ratio Q(robust)/Q(true) reached a value around 1. 0 indicating that the modeled results were not biased by peak events. The PMF model results explained on average 98 % of the total concentration of the 20 VOCs. Individually, almost all the chemical species also displayed both good determination coefficients and slopes (close to 1) between predicted and observed concentrations, with the exception of species declared as weak and propane, acetylene, toluene and acetonitrile. Therefore, the limitations of the PMF model to simulate these species should be kept in mind when interpreting the PMF results.
With an average contribution estimated to be 6. 1 µg m -3 , this factor, biogenic source 2, is one of the two main VOC factors observed during March 2015 at CAO, explaining 36 % of TVOC (i. e. , the sum of the n species including in the initial dataset of the PMF model -see Sect. S1 in the Supplement). Factor 2 is mainly composed of OVOCs, such as methanol (77 % explained), acetaldehyde (84 % explained), acetone (50 % explained) and MEK (methyl ethyl ketone) (59 % explained). The total contribution of these oxygenated compounds to factor 2 is 79 %, with more than 42 % attributed to methanol. Most of these species are formed by the oxidation of both anthropogenic and biogenic compounds although some of them can also have a primary origin (e. g. , the MEK budget described in Yáñez-Serrano et al.
A source apportionment (PMF) analysis was conducted to better identify covariation factors of VOCs that were representative of aged or local primary emissions as well as secondary photochemical transformations occurring during the transport of air masses. The US EPA PMF v. 5. 0 was applied to the dataset composed of 20 VOCs of anthropogenic and biogenic origins and a total of 1179 atmospheric data points with a 30 min time resolution. The best PMF solution allowed the decomposition of VOCs into six different sources; two factors attributed to biogenic sources (biogenic sources 1 and 2), three anthropogenic factors (shortlived combustion sources, evaporative sources, industrial and evaporative sources) and a last factor associated with regional background pollution. The biogenic sources and the regional background were found to be the largest contributors to the VOC concentrations observed at our background site, with a total contribution of 79 %.
4. 1. 2 Comparison with another PMF study performed at a remote site of the Mediterranean region Michoud et al. (2017) have reported gas and aerosol measurements conducted at a French remote site of the Western Mediterranean region (Cape Corsica) during the ChArMEx SOP2 field campaign (summer 2013). They performed a PMF analysis on a gas database made of 42 VOCs, including primary VOCs with anthropogenic and biogenic origins, and OVOCs and therefore offer a unique opportunity to provide a characterization of VOCs for the entire Mediterranean.
Acknowledgements. This study was supported by  ChArMEx ,  ENVI-MED ,  ACTRIS-2  (European Union's  Horizon 2020 research and innovation programme , grant agreement no. 654109 ),  CEA  and  CNRS. The authors would like to thank  Nikolaos Mihalopoulos  for his help in the establishment of the CAO observatory;  François Dulac  and  Eric Hamonou  for managing the  ChArMEx  project with enthusiasm. The present work is a contribution to the  Labex CaPPA (Chemical and Physical Properties of the Atmosphere)  funded by the  French National Research Agency  ( ANR-11-LABX-005-01 ) and the  European Funds for Regional Economic Development (FEDER). The  Hauts-de-France Regional Council , the  French Ministry for Higher Education and Research , and the  FEDER  are also acknowledged for their financial support through the  CPER  research project  CLIMIBIO  (changement climatique, dynamique de l'atmosphère, impacts sur la biodiversité et la santé humaine).
84 3. 43-4. 73 4. 20 1. 71-3. 61 4. 39 Acetaldehyde 0. 83 0. 45-0. 68 0. 60 0. 42-1. 21 1. 80 Acetone 2. 72 4. 83-6. 44 6. 20 1. 90-3. 74 3. 82 MVK + MACR 0. 09 0. 12-0. 13 0. 27 0. 03-0. 14 Kalogridis (2014)vas (2000)ou et al. ( , 2009))off et al. (2016). cChArMExdatabase. dNavazoetal. (2008). eLoVulloetal. (2016). fLiakakouetal. (2007Liakakouetal. (,2009)). gMoschonasandGlavas(2000). hMichoudetal. (2017);Kalogridis(2014). i Seco et al. (2011). jDavisonet al. (2009).
The second, repeat sequencing run did not experience this issue. We incorporated both sequencing runs for affected libraries into subsequent analyses (including Quality trimming and Error correction, see below). We produced between 23 million and 61 million paired-end 100 bp sequencing reads per library (Supp. Table  1 ). Raw sequence reads are available at the NCBI Short Read Archive with the BioProject identifier PRJNA419411 (accession numbers for each species are listed in Supp. Table  1 ).
Error correction We performed error correction on trimmed reads using Reptile v1. 1  (Yang et al. , 2010)  following the authors' instructions, with the modifications described below. We began by using the 'fastq-converter. pl' script to convert from FASTQ and to discard reads with more than 1 ambiguous character "N" in any window of 13 bases. For reads with one "N", we chose the character "a" as the substitute for "N", as all of the characters in our input reads were in upper case (A, C, G, or T); thus, we could later recognize "N" bases converted in this step. Next, we tuned parameters using the 'seq-analy' utility following the authors' instructions, in four steps: (1) Running 'seq-analy' with default settings. (2) Adjusting the input settings to 'seq-analy' using the results from step 1.
De novo transcriptome assembly We performed de novo transcriptome assembly on trimmed, corrected sequence reads and quality files with Trinity release 2013-02-25  (Grabherr et al. , 2011)  with '--min_contig_length' set to 150 and all other parameters at their default values. We chose a minimum contig length of 150 (rather than the default of 200) so as not to exclude assembly fragments that might encode predicted proteins with lengths between 50 and 66 amino acids, because some domains in the Pfam database are as short as 50 amino acids. Because none of the species we sequenced had an available genome assembly, we did not know whether transcripts might be encoded in overlapping positions within the genome. To test this possibility, we repeated each Trinity assembly with the addition of the '--jaccard-clip' option and compared the estimated number of fusion transcripts predicted by Transdecoder release 2012-08-15  (Haas et al.
Measurement of expression levels and elimination of noise transcripts To estimate expression levels, we mapped sequence reads to decontaminated, non-redundant, Tandem Repeats-masked contigs using the Burroughs-Wheeler Aligner, BWA, version 0. 7. 5a  (Li and Durbin, 2009). We ran BWA 'mem' with the '-a' option to report all equally best hits, with all other parameter values left at their defaults. We converted BWA output to BAM format using SAMtools version 0. 1. 18 (Li et al. , 2009)  and ran eXpress version 1. 4. 0  (Roberts and Pachter, 2012)  with default parameter values in order to produce estimated expression levels, in fragments per kilobase per million reads (FPKM). The distribution of FPKM values (Supp. Figure  18d ) had a peak near 1, with steep decreases in the number of contigs at lower values.
We visualized the resulting tree with Archaeopteryx version 0. 9813  (Han and Zmasek, 2009). To measure phylogenetic diversity, we selected a set of 49 gene families for which each species had exactly one protein representative and no more than 5 species were missing (or roughly 10% of the 59 species in our data set). We aligned the protein sequences in each gene family separately using MAFFT version 7. 130b  (Katoh and Standley, 2013)  with the parameters '--maxiterate 1000 --localpair'. We performed two rounds of alignment trimming with trimAl version 1. 2rev59  (Capella-Gutierrez et al. , 2009) : in the first round, we used the parameter '-automated1', and we supplied the output of the first round to the second round, with the parameter '-gt 0. 5'. We constructed phylogenetic trees from the tworound trimmed alignments separately for each gene family with RAxML version 8.
2. 0  (Stamatakis, 2014) , with the options '-m PROTGAMMALGF' and '-f a -N 100'. We measured cophenetic distances between pairs of species on the resulting trees using the ape package version 4. 1  (Paradis et al. , 2004)  and plotted pairwise distances averaged across all 49 gene families using the ggplot2 package version 2. 2. 1  (Wickham, 2009) , both with R version 3. 4. 1 (R Core Team, 2017) in the RStudio development platform version 1. 0. 143 (RStudio Team, 2016).
05. We accepted gene family GO annotations present in at least half of the proteins in the family. We assessed the enrichment of animal-specific gene families by comparing against the set of gene families present in the Ureukaryote. We considered the Ureukaryote set to be the most appropriate comparison since gene families that evolved in Holozoa, Choanozoa or Opisthokonts are themselves enriched for GO terms associated with animal gene families (Supp. Fig. 6 ). We compared pathways present in the Urholozoan, Urchoanozoan and Urmetazoan using MAPLE version 2. 3. 0  (Takami et al. , 2016). As input, we provided protein sequences only for species descending from each ancestral node. We supplied the following parameters to MAPLE: NCBI BLAST; singledirectional best hit (bi-directional best hit would not have been appropriate, since our input database consisted of gene families containing closely-related proteins from multiple species); KEGG version 20161101; and organism list "ea" (all eukaryotes in KEGG).
We compared completeness based on the WC (whole community) module completion ratio. We listed modules which differed by 25% or greater in completeness between the Urchoanozoan and Urmetazoan in Supp. Table  9 , and between the Urholozoan and Urchoanozoan in Supp. Table  10. To focus on amino acid biosynthesis pathways (Supp. Figure  12 ), we exported KEGG results from MAPLE for the Urchoanozoan and Urmetazoan gene sets and compared them using the KEGG Mapper Reconstruct Pathway tool, pathway ID 01230  (Kanehisa et al. , 2012).
Evidence for gene presence based on protein domain architecture We predicted protein domains with the Pfam version 27. 0 database and pfam_scan. pl revision 2010-06-08  (Punta et al. , 2012) , which used hmmscan from the HMMER 3. 0 package  (Eddy, 2011). We performed all Pfam searches with default parameter values. We predicted signal peptides and transmembrane domains using Phobius version 1. 01  (Käll et al. , 2004)  with default parameter values. Domains for all input proteins (including those not placed into gene families) are listed in Supp. To determine which animal-specific gene families lacked a Pfam domain of known function, we calculated the proportion of proteins in each animal gene family that were annotated with a given Pfam domain (Supp. Figure  19 ). We accepted Pfam domains present in at least 10% of proteins in a gene family.
Domains of unknown function in the Pfam database had names beginning with "DUF". To ensure that these domain names were not assigned a function in a more recent version of the Pfam database published after our initial annotation, we checked against Pfam version 31. 0 and considered all domains whose names no longer began with "DUF" as having an assigned function. We established a set of criteria for the presence of genes of interest, based on domain content and OrthoMCL gene families (Supp. Table  6 ). In all cases, 'strong' evidence for conservation consisted of the canonical domain architecture (a diagnostic domain or set of domains in a specific order). Because Pfam domains are constructed from a set of aligned protein sequences selected from sequenced species, they are often strongly enriched for animals (and, in particular, for animal models such as D.
For genes in the canonical M. musculus TLR/ NF-κB signaling pathway, we collected information on domain architectures in the Pfam 31. 0 database (which did not include the choanoflagellates sequenced in this study) and on the species present in the same gene family as the M. musculus protein (Supp. Table  14 ). With the exception of adapters and kinases containing the Death domain, there were no architectures that were both specific to animals and present across animal diversity, two features required of diagnostic architectures for a gene family. Similarly, we did not identify any gene families restricted to holozoans (as would be expected for genes participating in the TLR/ NF-κB pathway) and whose choanoflagellate members we could unequivocally identify as orthologous to the M. musculus proteins in the family.
We investigated these hits further by running blastp of the S. helianthica sequence against the NCBI nr database. In each case, we found that the NCBI nr BLAST hit did not match the function of the original human query protein. For the human dermatan sulfate synthesis gene D4ST (NP_569735. 1), the S. helianthica top hit m. 244703 matched the chondroitin synthesis gene C4ST in the nr database; thus, we considered m. 244703 to be a homolog of C4ST instead of D4ST. For the human hyaluronan synthesis genes HAS1 and HAS2 (NP_001514. 2 and NP_005319. 1), the S. helianthica top hits m. 93480 and m. 227356 both matched to chitin synthesis genes in the nr database, and not to hyaluronan synthesis genes; we thus discarded both of these hits. We note that neither S. rosetta nor S. helianthica had a match to the C6ST gene from the chondroitin sulfate synthesis pathway.
However, S. rosetta was shown experimentally to produce chondroitin sulfate in  Woznica et al. ;  it is possible that another gene present in both species, such as C4,6ST, may substitute for the function of C6ST. To add M. musculus, M. leidyi and O. pearsei to the table of GAG synthesis genes, we used the same blastp-based process as for S. helianthica. M. leidyi and O. pearsei both had hits to the D4ST gene. In both cases, the best blastp matches of these proteins to the NCBI nr database were D4ST genes in other animals, indicating that both species are likely to encode this gene, and that it may have been present in the  Urmetazoan and lost in H. vulgaris, D. melanogaster and C. elegans. Four of the GAG synthesis genes (XYLT1, B4GT7, B3GT6 and B3GA3) participate in the synthesis of a linker region for both chondroitin sulfate and dermatan sulfate, but not hyaluronan  (Kjellén and Lindahl, 1991; Mizumoto et al.
GAG cleavage genes: hydrolases and lyases The hydrolase and lyase GAG cleavage enzymes are frequently referred to as hyaluronidase and chondroitinase, respectively, although nearly all enzymes of both classes have some level of activity on both substrates  (Yamagata et al. , 1968; Stern and Jedrzejas, 2006; Kaneiwa et al. , 2008). We therefore use the term GAG hydrolase in lieu of hyaluronidase, and GAG lyase in lieu of chondroitinase. To build phylogenetic trees for GAG hydrolase and GAG lyase, we downloaded the sequences used to build the 'Full' alignments for their diagnostic domains from the Pfam 31. 0 database  (Finn et al. , 2016) : Glyco_hydro_56 for GAG hydrolase and Lyase_8 for GAG lyase. We appended sequences from gene families in our data set with at least one member encoding a diagnostic domain.
Acknowledgements We would like to thank  Monika Abedin ,  Rosie Alegado ,  Hartmut Arndt ,  Roman Barbalat ,  Greg Barton ,  Cédric Berney ,  David Booth ,  Lauren Booth ,  Bastien Boussau ,  Candace Britton ,  Thibaut Brunet ,  Pawel Burkhardt ,  Minyong Chung ,  Matthew Davis ,  Mark Dayel ,  Stephen Fairclough ,  Xavier Grau-Bové ,  Nicolas Henry ,  Ella Ireland ,  Tommy Kaplan ,  Barry Leadbeater ,  Tera Levin ,  Matthew MacManes ,  Kent McDonald ,  Alan Marron ,  Scott Nichols ,  Frank Nitsche ,  Ryan Null ,  Mathilde Paris ,  Iñaki Ruiz-Trillo , S. punica S. urceolata S. kvevrii S. diplocostata M. fluctuans H. nana S. helianthica A. spectabilis M. brevicollis C. perplexa D. grandis D. costata S. dolichothecata S. macrocollata C. hollandica S. infusionum S. rosetta M. roanoka S. parva H.
Methods We have used ozonesonde measurements from nine stations in Antarctica (as available from the World Ozone and UV data center A careful examination of the data is carried out by looking individually at each profile from each station. Suspected data with unreasonable values such as spurious spikes or erroneous balloon temperatures are exempted. The profiles terminating (bursting altitude) below 450 K (∼15 km) are also discarded. In addition, the correction factor either given in the ozonesonde metadata or calculated using the satellite measurements over the stations is applied to correct the individual ozone profiles. To calculate the missing correction factors we have used the overpass data from the Total Ozone Mapping Spectrometer (TOMS) in 1979-2003  37  and the Ozone Monitoring Instrument (OMI) in 2004-2013  38  , where the missing years (1994-1995) are filled with the Multi Sensor Reanalysis (MSR) data  39.