dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. We would like to thank the anonymous referee whose valuables comments have improved this work. We acknowledge support from the  Spanish Programa Nacional  de Astronomía y Astrofísica: projects  AYA2013-46724-P ,  AYA2012-30717 , and  AYA2009-10368. This work has made use of the Rainbow Cosmological Surveys Database, which is operated by the  Universidad Complutense de Madrid (UCM) , partnered with the  University of California Observatories at Santa Cruz  ( UCO/Lick,UCSC ). We are very grateful to Pablo G.
Deep-sea sediments represent the largest but least known ecosystem on earth. With increasing anthropogenic pressure, it is now a matter of urgency to improve our understanding of deep-sea biodiversity. Traditional morpho-taxonomic studies suggest that the ocean floor hosts extraordinarily diverse benthic communities. However, due to both its remoteness and a lack of expert taxonomists, assessing deep-sea diversity is a very challenging task. Environmental DNA (eDNA) metabarcoding offers a powerful tool to complement morpho-taxonomic studies. Here we use eDNA to assess benthic metazoan diversity in 39 deep-sea sediment samples from bathyal and abyssal depths worldwide. The eDNA dataset was dominated by meiobenthic taxa and we identified all animal phyla commonly found in the deep-sea benthos; yet, the diversity within these phyla remains largely unknown.
The large numbers of taxonomically unassigned molecular operational taxonomic units (OTUs) were not equally distributed among phyla, with nematodes and platyhelminthes being the most poorly characterized from a taxonomic perspective. While the data obtained here reveal pronounced heterogeneity and vast amounts of unknown biodiversity in the deep sea, they also expose the difficulties in exploiting metabarcoding datasets resulting from the lack of taxonomic knowledge and appropriate reference databases. Overall, our study demonstrates the promising potential of eDNA metabarcoding to accelerate the assessment of deep-sea biodiversity for pure and applied deep-sea environmental research but also emphasizes the necessity to integrate such new approaches with traditional morphology-based examination of deep-sea organisms.
Although such richness is certainly an underestimate, there is no consensus on how many deep-sea species exist  (Miljutin et al. , 2010) , in part because of uncertainty concerning their distribution patterns in a vast and chronically undersampled environment  (McClain and Hardy, 2010). The lack of publicly available molecular data, with only about one fifth of the inventoried species (4918) being associated with such data in publicly accessible databases (Figure  1 ), coupled with the particularly challenging taxonomic identification of meiofauna  (Herman and Heip, 1988; Giere, 2008) , illustrate clearly the difficulty in assessing metazoan diversity in deep-sea sediments mainly inhabited by small-sized animals (e. g. ,  Thiel, 1975 Thiel, , 1983)). The development of DNA barcoding has substantially improved taxonomic knowledge in some groups that are difficult to identify morphologically  (Blaxter, 2004).
Investigation of the molecular signatures of benthic fauna in environmental samples was therefore the logical development of DNA barcoding approaches  (Markmann and Tautz, 2005). In recent years, en mass sequencing of environmentally derived DNA has expanded rapidly with the availability of highthroughput sequencing technologies, commonly referred to as metabarcoding  (Taberlet et al. , 2012b). We consider environmental DNA (eDNA) to comprise not only DNA from living species, including their eggs and larvae, but also DNA from fragments of dead organisms, gut contents and extracellular DNA  (Taberlet et al. , 2012a). Since marine sediments host a FIGURE 1 | Proportions of deep-sea species with available DNA sequences on GenBank. The numbers are based on valid species described and registered in the World Register of Deep-Sea Species.
PCR Amplification and 454 Sequencing Preparation of amplicon libraries for 454 sequencing of the V1-V2 region of the nuclear small ribosomal subunit (18S) followed the protocols described previously  (Fonseca et al. , 2010). In order to maximize inclusion of metazoans in the primer mix, primer R22  (Blaxter et al. , 1998)  was modified as follows: R22mod 5 ′ CCTGCTGCCTTCCTTRGA3 ′ , the primer F04 was left unmodified. Compared to the original R22 primer and based on sequences of various phyla from GenBank, R22mod was shortened to remove mismatches in some groups (cnidarians, echinoderms, priapulids, and kinorhynchs) and one ambiguity (R) was added to accommodate the presence of a thymidine (T) instead of a cytidine (C) at this position in brachiopods, bryozoan, kinorhynchs, rotifers, and within several other groups.
Sequence Analyses Sequences were analyzed using the QIIME 1. 7  (Caporaso et al. , 2010). Raw reads were assigned to samples based on MID tags and checked for quality using split_libraries. py. All sequences shorter than 200 bp were discarded, minimum quality score was set at 25 and maximum homopolymer run was set at 6 bp. No mismatches in primer or MID tags were tolerated. In order to reduce the potential bias introduced by intragenomic variability and sequencing errors, OTUs were clustered at 97% identities. Cluster seeds were selected as representative sequences for each OTUs using pick_rep_set. py in QIIME 1. 7. Sequences were aligned using align_seqs. py and the aligned s108 SILVA database as template. Chimeric sequences were removed using identify_chimeric_seqs. py and ChimeraSlayer in QIIME 1.
7 and single singletons (i. e. , single sequences present in a single sample) were removed from the dataset. The 18S sequences were then compared, using the BLAST method with an E-value threshold of 1e -100 , against a reference database consisting of a "customized" version of the Silva database s108 release formatted for use within the QIIME pipeline  (Caporaso et al. , 2010) , using assign_taxonomy. py in QIIME 1. 7. The E-value threshold was empirically determined following the observation that the default parameter (E-value threshold of 0. 001) provided an unrealistic number of identified OTUs, which after verification were often only poorly related to the assigned taxa, even at phylum level. Several OTUs were independently compared to the GenBank database and the E-values below 1e -100 did not allow reliable identification at phylum level in many cases.
As a result, we chose to use a strict E-value threshold to limit "folkloric" taxonomic assignments. The database was customized by correcting some obvious misidentification (e. g. , a copepod crustacean sequence labeled as octocoral cnidarian) and adding recent deep-sea sequences obtained from public databases and individual sequencing of deep-sea organisms. All "uncultured (marine) eukaryote" or environmental samples identified above the phylum level were also removed from the reference database. Raw 454 reads and reference database are available on the European Nucleotide Archive (Acc. No. PRJEB 13170) and in the supplementary material. After taxonomic assignment, a phylogenetic tree was built with the unassigned sequences and the branch corresponding to metazoans was identified by independent blasts against the GenBank database.
All the sequences forming the branch identified with confidence as metazoan were isolated and merged to the metazoan dataset with the following taxonomic assignment "Eukaryota; Metazoa; no blast hit. " However, because of the conservative approach chosen, and based on the limited phylogenetic resolution provided by the fragment analyzed, the most basal metazoan OTUs (such as those assigned to sponges) may not have been included in the "metazoan branch. " The decision to restrict the analyses to metazoans reflects the fact that the original primers were specific to metazoans and the observation that one major group of deep-sea protists, the foraminifera, was not found in our dataset, thus providing a biased estimation of deep-sea eukaryotic biodiversity. Alpha diversity was measured using the simple "observed OTU" metric in order to estimate the depth of sequencing for the sediments analyses.
OTU networks, linking the OTUs to the different biogeographic provinces in which they were found, were built using make_otu_network. py in QIIME 1. 7 and drawn in Cytoscape 2. 7. 0  (Shannon et al. , 2003)  using the unweighted spring embedded layout. Beta diversity analyses were conducted using the unweighted UNIFRAC method  (Lozupone and Knight, 2005)  implemented in QIIME 1. 7. This method takes into account the phylogenetic information in the dataset. The unweighted approach allows the use of only qualitative data (i. e. , presence/absence) and reduces bias from quantitative results. While this approach increases the importance of rare taxa, the quantitative bias potentially induced by the biomass  (Bohmann et al. , 2014; Hirai et al. , 2015)  was estimated too high considering the minimal amounts of sediments used.
Unrarefied data allowed us to consider the total diversity recovered from the samples analyzed. However, in order to make more objective comparisons between heterogeneous samples, Principal Coordinates Analyses of beta diversity was performed based on 100 rarefied datasets at 4488 metazoan reads per province. The same beta-diversity distances matrices were used to build UPGMA trees. Bootstrap support was calculated based on the 100 rarefied datasets. Biogeographic comparisons are based on the lower bathyal and abyssal provinces described in  Watling et al. (2013). According to the latter  (Watling et al. , 2013) , the Mediterranean Sea and the North Atlantic both belong to the same bathyal province BY4. However, based on the differences in our sampling locations and data, as well as the suggestion made by  Watling et al.
Taxonomic Composition After quality checking, and removal of chimera and single singletons (i. e. , single reads present in a single sample, rather than reads being single in each of a few samples), the resulting dataset contained 530976 reads (from 3819 to 97456 reads per location), 38% of which could be confidently assigned to metazoans using a combination of BLAST and the phylogenetic approach (Table  1 ). The remaining reads were assigned to various other eukaryotic groups (e. g. , Stramenopiles, Fungi). The distribution of reads within the different metazoan groups was highly variable between locations with a large dominance of annelids at nearly half of the sampling sites. Nematodes were also sequenced abundantly in several samples followed by arthropods and a large proportion of reads that could not be confidently assigned by BLAST methods to any specific phylum (Figure  3 ).
OTU richness data showed less variation between locations compared to abundance data (Figure  3 ). At a clustering threshold of 97%, metazoan OTUs represented between 8. 4% (S Brazil Basin, abyssal) and 34. 8% (Antarctic Peninsula) of the total eukaryotic richness for each site. Metazoan diversity was largely dominated by nematodes, which formed the most diverse group in all samples (from 25. 4 to 48. 9%). The next most diverse phyla were arthropods (mainly copepods) and annelids, followed by platyhelminthes (Figure  3A ). These four groups comprised nearly 88% of the total number of assigned OTUs. The remaining OTUs were assigned to 19 other phyla. Among the unassigned OTUs that could not be reliably recognized using BLAST with the strict E-value threshold selected here, individual BLAST of some OTUS against GenBank database suggests that they likely belong to the phyla Mesozoa and Tardigrada.
The Copepoda comprised mainly harpacticoids as shown by a comparison between OTUs assigned to copepods and sequences of harpacticoids available in the database (Figure  S2 ). Several clades were formed by OTUs belonging to different taxonomic groups (Figure  5 ). The annelids grouped with the molluscs, the kinorhynchs grouped with mites and potential tardigrades while the echinoderms clustered with hemichordates and chordates forming a deuterostome clade. Within deuterostomes, detailed observation showed that echinoderms, hemichordates, vertebrates, and tunicates formed independent sub-clusters with tunicates appearing clearly distinct at the base of this group (data not shown). Early metazoans (sponges, placozoans, and cnidarians) also formed a monophyletic group branching between the deuterostomes and the clade comprising loriciferans, aceolomorphs and putative mesozoans.
Nemerteans were either located within the two clades containing annelids or formed an independent cluster nearby. Within this "nemertean only" cluster, it is interesting to note the presence of a few "platyhelminthes" OTUs assigned to the flatworm "Nematoplana sp. " However, most likely the reference sequence from the public database originates from a misidentified specimen (sequence GenBank D85093). Except for this likely artefactual identification, all platyhelminthes clustered together in a monophyletic group. Interestingly, aside from the platyhelminthes cluster, which already includes a significant number of unassigned OTUs, another large clade is composed exclusively of unassigned OTUs. While no reliable BLAST identification could be obtained, these OTUs appear to be related to acoelomorphs.
Principal coordinate analyses (PCoA) on rarefied dataset of all metazoan OTUs provided a more robust comparison of the different locations (Figure  7 ). Unfortunately, the rarefaction threshold of 4488 metazoan reads per location did not allow the inclusion of several locations in the analyses (e. g. , most abyssal locations including all the equatorial and South Atlantic locations, see Table  1 ). Nevertheless, all the locations in  Andaman Sea, the province with the most locations sampled, clustered relatively closely together. The two Mediterranean locations also grouped within this cluster. The situation was different for the lower bathyal Southern Ocean, for which the two sampled locations considered did not group together. One possible reason for the difference observed between the Lazarev Sea and the Maud Rise, may be related to the sediment characteristics, as Maud Rise sediments sampled were sandier than the fine muddy sediments from Lazarev Sea.
, 2014). Moreover, the level of codon saturation provided by COI precludes us from identifying OTUs without an accurate and complete reference dataset. A test study conducted in parallel with this research has shown that <10% of the reads obtained for a standard COI fragment could be identified by BLAST (unpublished data). These results support published data on seagrass meadows where 93% of the COI OTUs recovered remained unassigned  (Cowart et al. , 2015). Therefore, although the high-resolution power of COI for identifying species provides a significant advantage, the substantial inadequacy of available reference sequences is even more acute than for rRNA genes. From a taxonomic perspective, the main challenge for deep-sea biodiversity research is to expand the reference database. Deep-sea diversity remains largely unknown  (Costello et al.
, 2010; Danovaro et al. , 2010)  and even when identified using molecular taxonomic approaches, high-level assignment cannot be achieved in many cases. Moreover, a complete and reliable reference database is not only needed for taxonomic assignment but is also essential for post-sequencing processing of the data  (Edgar et al. , 2011; Quince et al. , 2011; Fonseca et al. , 2014). As shown in Figure  5 , the level of taxonomic identification depends on the group. For example, the copepod clade, several groups of nematodes, and one group of annelids include only a modest proportion of unassigned OTUs. However, other groups are almost entirely composed of unassigned OTUs, suggesting the absence of reference sequences in the database. Overall, the amount and clustering of the unassigned OTUs observed here suggest the existence of largely uncharacterized taxonomic groups and highlights the potential extent of the unknown diversity in the deep sea.
ACKNOWLEDGMENTS We thank the crews and participants to the various cruises that allowed us to obtain the deep-sea sediments, especially to  Dr. P. Martinez-Arbizu ,  Dr. A. Brandt ,  Dr. T. Pérez ,  Dr. T. Maruyama , and  Dr. M. Tsuchiya. Thanks go also to the  British Antarctic Survey ,  Dr. L. Peck  and all the staff of the  Rothera Antarctic  station during the summer 2010-2011 who made the sampling possible, and to  Mr. B. Vanhoorne  for his precious help with the WoRMS data.
FUNDING This study was funded through the  European Community   FP7 Marie Curie International Incoming Fellowship  project  MARMEDIV (No 253251 ) and a  Japan Society for Promotion of Science  grant in aid for young researcher to FS (no 26870917). Part of this research was funded by the  Japanese Cross-ministerial Strategic Innovation Promotion (SIP) Program for Development of New-generation Research Protocol for Submarine Resources  and part of the sampling was supported by the Carlsberg foundation to TC (No  2010-01-0376 ),  Swiss National Science Foundation  to JP (grant  31003A-140766 ), and a  British Antarctic Survey  collaborative gearing scheme to SC (No  57 ). Mediterranean samples were obtained through funding by the  "Deepsets" Responsive Mode Proposal (RMP)  of the  MarBEF NoE (EU Network of Excellence ).
Acknowledgements Work in the  Algal Genetics group  is supported by the  CNRS , the  ANR  (project  Bi -cycle  ANR-10-BLAN-1727  and project  Idealg   ANR-10-BTBR-04-01 ), the  Interreg program France (Channel)-England  (project  Marinexus ), the  UPMC  and the  European Research Council  (grant agreement  638240 ). LM had a PhD grant (ARED) from the  Region Bretagne.
A 30 cycle PCR with the HotStarTaq Plus Master Mix Kit (Qiagen, Valencia, CA, USA) was conducted under the following conditions: 94 • C for 3 min, followed by 28 cycles of 94 • C for 30 s, 53 • C (bacteria), or 50 • C (archaea) for 40 s and 72 • C for 1 min, after which a final elongation step at 72 • C for 5 min was performed. Pyrosequencing was conducted by a commercial laboratory (Research and Testing Laboratory, Lubbock, TX, USA) on a Roche 454 FLX (Brandford, CT, USA) using commercially prepared Titanium reagents. Sequences have been deposited to the GenBank Sequence Read archive under number SAMN04126915-SAMN04126946. For the metagenomes, libraries for shotgun sequencing were prepared using Nextera DNA Sample Preparation Kit (Illumina, San Diego, CA, USA) with 50 ng of DNA from each sample.
Library insert size was determined by Experion Automated Electrophoresis Station (Bio-Rad, Hercules, CA, USA). The insert size of the libraries ranged from 300 to 850 bp (average 500 bp). Pooled libraries (12 pM) were loaded to a 600 Cycles v3 Reagent cartridge (Illumina) and the sequencing was performed on Miseq (Illumina) by a commercial laboratory (Research and Testing Laboratory, Lubbock, TX, USA). Metagenomes are archived in MG-RAST under accession numbers  4612993. 3, 4612994. 3, 4612995. 3, 4612996. 3, and 4612997. 3.
Sequence Analysis All reads that had a mismatch with the 16S rRNA primers, contained ambiguous nucleotides (N) or were longer than 390 after the forward primer, were removed. Then, reads that had ≥3% of bases with Phred values <27 (0. 2% per-base error probability) were removed. These steps are recommended to ensure that when clustering at 97%, the influence of erroneous reads is minimized  (Huse et al. , 2010). Sequences were clustered at a 97% threshold using Uclust  (Edgar, 2010). Representative sequences of each operational taxonomic unit (OTU) were classified by comparison with Blast to the Greengenes database  (DeSantis et al. , 2006). Sequence analyses were conducted with Pyrotagger  (Kunin and Hugenholtz, 2010). To compare bacterial communities for diversity analysis, all samples were randomly resampled to the size of the sample containing the fewest sequences (n = 950 for Bacteria and n = 424 for Archaea) using Daisy Chopper  (Gilbert et al.
, 2009). For metagenomes, sequence ends were cut to remove adapters. Sequences were considered low quality when >5 bases had a phred score <15 and were subsequently removed. FastqJoin was used to merge paired end reads when possible with a minimum overlap setting of 8 bp and a maximum difference of 10%. Both paired and unpaired sequences were kept for automated annotation in MG-RAST using Hierarchical Classification subsystems against the KEGG Orthology database. Annotations were conducted with a maximum e-value cutoff of 1e -10 , a minimum 60% identity cutoff and 15 amino acids as the minimum alignment length cutoff. Identified microbial gene families (specified by KEGG Orthology groups) were grouped into metabolic pathways (Supplementary Table  S2 ). Metagenome reads were normalized by evenly resampling at random to 1,254,500 annotated reads per sample to allow comparison.
Data Analysis Bacterial diversity was estimated for both the RNA and DNA fractions by calculating the Shannon diversity index (H ) and by the standardized effect size (SES;  Kembel et al. , 2010) , which is a standardized measure of phylogenetic diversity. Phylogenetic diversity was computed using the Picante package  (Kembel et al. , 2010)  in R. To calculate SES, 300-bp-long representative sequences for each OTU were aligned using MUSCLE  (Edgar, 2004)  and the alignment was then cleaned to remove non-overlapping sequence regions. A phylogenetic tree was constructed using FASTTREE  (Price et al. , 2010). The observed phylogenetic diversity was compared to the average phylogenetic diversity in a randomly generated community (null model) and divided by the standard deviation of phylogenetic distances in the null model  (Webb et al.
The effects of disturbance and time on community composition were tested with a permutational multivariate analysis of variance using distance matrices as implemented in the adonis function in the Vegan package (v. 2. 3-0) in the R software (v. 3. 1. 2). To identify OTUs that had a significant difference in abundance between groups we used an ANOVA with a Tukey-Kramer post hoc test as implemented in STAMP  (Parks et al. , 2014).
(2016h). All events identified by the GstLAL or mbta online analyses with a false-alarm rate of less than 3200 yr -1 are uploaded to an internal database known as the gravitationalwave candidate event database (GraCEDb;  Moe et al. 2016). In total, 486 events were uploaded from mbta and 868 from GstLAL. We can measure the latency of the online pipelines from the time between the inferred arrival time of each event at the Earth and the time at which the event is uploaded to GraCEDb. This latency is illustrated in Figure  2 , where it can be seen that both online pipelines achieved median latencies on the order of one minute. We note that GstLAL uploaded twice as many events as mbta because of a difference in how the FAR was defined. The FAR reported by mbta was defined relative to the rate of coincident data such that an event with an FAR of 1 yr -1 is expected to occur once in a year of coincident data.
Source of tissue samples LV samples were obtained from 13 ICM patients undergoing cardiac transplantation and 10 non-diseased donor hearts were used as CNT samples. To improve the numerical base to a higher number of patients for protein analysis, we increased the ICM samples to 21. The clinical history, ECG, doppler echocardiography, hemodynamic studies, and coronary angiography data were available on patients. Patients with primary valvular disease were excluded from the study. All subjects were functionally classified based on the New York Heart Association (NYHA) criteria and received medical treatment in accordance with the guidelines of the European Society of Cardiology  [2]. Table  1  shows the clinical characteristics of patients included in this study. LV samples were collected from near the apex of the left ventricle and maintained in 0.