dataesr/scientific-paragraphs-categorization huggingface dataset:
Main text Metagenomic analysis is a powerful method to detect micro-organisms in their ecosystems. These microorganisms belong to all the components of the tree of life but also to those of the viral world. The commitment to be totally independent from the culture process constitutes an extremely important part of its success  [1, 2]. These techniques have pushed researchers to explore different environments, or microbiota as in the Human Microbiome Project  [3] , and sometimes multiplying samples collection in various environments such as the international space station or as the Permafrost in "omics" study  [4, 5]. Nevertheless, a wide part of these metagenomic results are still unknown in database, and is referred to as "dark matter"  [6]. On the opposite side, the culture tools and notably some culturomics studies allows us to describe and characterize isolates, which is impossible by the use of metagenomic analysis alone  [7].
The gene ORPV_1034 was predicted to encode a 587 amino-acid viral major capsid protein (MCP). With the goal of investigating homologies in database to increase efficiency of our annotation we used, with standard parameter (i. e. e-value inclusion threshold 1e-3), an online software (HHblits) able to detect homologies and structure. The result showed 65 hits but only 2 proteins matched with a special interest (Additional file 1). The probability obtained was at 97,72% with a length of 302 amino-acids between Orpheovirus and Pithovirus sibericum and it was at 97,04% with alignments of 237 amino-acids between Orpheovirus and unknown sequence present in a metagenome. Then, using PSI-BLAST against metagenomes database, we also detected homologies between Orpheovirus and Cedratvirus A11 (55% coverage, 29%identity) as well as with Pithovirus sibericum (41% coverage, 21% identity).
PSI-BLAST against metagenomes database of the Orpheovirus's MCP detected an homology with the same protein detected by HHblits (47% coverage, 24%identity) with the sequence EQD26795. 1 and also, with 2 additional proteins issued from different marine metagenomes  [15, 16]. We observed that the best hit with this viral capsid came from an acid mine drainage metagenome located in Spain. We decided to investigate this pyrite mine using MG-Digger program  [17]  with updated giant viruses database (e. g Pithovirus massiliensis LC8, Pacmanvirus A23, Cedratvirus A11, Cedratvirus lausannensis, Orpheovirus IHUMI-LCC2, Kaumoebavirus and Klosneuvirinae). Projects have been previously registered  [18]  under the IDs PRJNA193663 sample (B1A), PRJNA193664 (sample B1B) and PRJNA193665 (sample B2A) at NCBI.
This Whole Genome Shotgun project has been deposited under the accession AUZX00000000-AUZY00000000-AUZZ00000000. The parameters used for MG-Digger were fixed with an e-value cut-off of 10 -3 and no limit of both coverage and identity percent are defined to detect giant viruses-like sequences in the alignment of the BLAST options. All contigs available in the mine drainage metagenome represent 41,233 contigs. The MG-Digger program accepts protein or nucleic acid sequences in input  [17]  and in this case, we used nucleic acid contigs in input against updated database. We detected and revealed 10 contigs from the series AUZX and AUZY as showing best hit informing the viral presence (samples B1A and B1B) (Table  1 , Additional file 2) with no doubt that their origins are viral. Indeed, they presented a score higher than 60 for all queries with an average coverage of 79,2% (ranging from 53 to 99%), e-value inferior at 10 -7 and identity with an average of about 38% (ranging from 27% to 48%).
Each contig detected encode for one putative protein. To complete the annotation, all proteins corresponding to each viral contig were downloaded and all annotations were confirmed by DELTA-BLASTP program  [19]. Six viral contigs retrieved as best hit proteins from the members of the proposed family Pithoviridae (Pithoviruses and Cedratviruses) and Orpheovirus. Detailed annotation of these 6 proteins encoded by these contigs revealed 1 DNA polymerase B family, 1 DNA topoisomerase IIA, 1 mRNA capping enzyme, 1, ribonucleoside-diphosphate reductase, 1 Very Early Transcription Factor (VETF) and the previously found MCP. Moreover, we found 2 putative Ankyrin-repeat proteins matching with Mimiviruses, and one hypothetical protein and one protein coding for D5 helicase primase protein present best hits with Iridoviruses (Table  1 , Additional file 2).
Subsequently, phylogenetic analyses were performed at first glance on these predicted proteins (data not shown). Indeed, we retrieved homologous sequences by Blast protein strategy with Orpheovirus, Cedratviruses, Pithoviruses and some of the 9 contigs and also with a Table  1  Resume of 10 best hit obtained in the mine metagenome using our viral database. Contigs in the first column were used to the blastx, equivalent proteins are indicated in the fourth column, in the middle (second and third columns) we added the annotation and the best hit obtained with MG-Digger. Blast results are available in supplementary data Rickettsiales bacterium present in the NCBI database (belonging to another marine metagenome from Atlantic North under the accession number: NVVL00000000. 1). Then, this marine metagenome for which the annotation was performed by automatic pipeline, was further investigated.
On the other hand, true Rickettsiales contigs present a GC% more variable around 40% and we identified 11 contigs that could potentially be a ciliate protist close to Oxytricha spp. (Additional file 5). This ciliate could be the host of the Misidentifiedvirus as previously described for others protists  [12]. For all phylogenetic analyses we used Muscle algorithm  [21]  to perform alignment. Alignments were curated manually and finally, FastTree program was used to build maximum-likelihood trees with standard program (Jones-Taylor-Thornton model). Then, ITolV3 online was used to visualized trees  [22]. Phylogenetic analysis based on the MCP anchors these 2 different viruses from marine and mine metagenomes closely-related to Orpheovirus (Fig. 1 , Additional file 6). DNA topoisomerase II, VETF, and mRNA capping enzyme trees confirmed these results (Additional file 7).
Nevertheless, due to the partial sequences for the mine drainage, their exact position could not definitively be determined. Retrieving best hit virus constitute an unambiguous evidence for viral presence, especially when we found structural gene described like hallmark genes in Nucleocytoplasmic Large DNA viruses  [23, 24]. Altogether, these data confirm a viral presence in this mine pyrite and a Misidentifiedvirus in the marine metagenome as two putative viruses possessing a close relationship with the proposed Pithoviridae and Orpheoviridae families. The rapid expansion of gigantic dsDNA reports and their genomic descriptions permit to evaluate and reevaluate published and novel metagenomes. MG-digger program is a functional and appropriate tool to investigate them. Nevertheless, it is limited by the detection of best hit that it confers its confidence.
Study site, animal collection and husbandry Fieldwork was conducted from shore around Moorea, French Polynesia (17°30′S, 149°51′W), where targeted shark fishing in the country's EEZ has been banned since 2012 (Ward-Paige and Worm, 2017). Newborn C. melanopterus and N. acutidens are abundant during parturition months from September through February  (Mourier and Planes, 2013; Mourier et al. , 2013a, b). Sharks were collected during November and December 2016 using monofilament gill-nets (50. 0 m × 1. 5 m, 5. 0 cm mesh) fished at dusk (17:00-20:00). Captured sharks were immediately identified and removed from the net in under five minutes. Prior to release, biological data (total length, mass, and sex) were collected from all sharks. Individuals were tagged with coloured T-bar anchor tags (Hallprint, Hindmarsh Valley, SA, Australia) to avoid repeatedly sampling recaptured animals for this study.
Only animals in good condition (e. g. without open or healing bite wounds or retained fish hooks) were sampled for this study. Environmental temperatures were recorded every ten minutes with one or two temperature data loggers (UA-002-64, Onset Computer Corporation, Bourne, MA, USA) that were deployed in a transect parallel to the gill-net. A subset of sharks was transported to the Centre de Recherches Insulaires et Observatoire de l'Environnement (CRIOBE) by vehicle in 200. 0 l insulated coolers of aerated seawater  (Chin et al. , 2015). Before transport, sharks were retained in individual flow-through mesh bags (0. 2 m diameter and 1. 0 m long) for no more than 1 h prior to transport. Including transport, sharks were typically confined for under 90 min post-capture before arriving at the CRIOBE's holding facility.
Statistical and data analyses Underlying physiological responses were characterized by comparing values of physiological parameters over time after an exhaustive challenge, and against baseline values. The influence of temperature on physiological status (i. e. values of physiological and oxygen uptake parameters) was assessed by including temperature as a covariate in models. Physiological parameters (i. e. blood glucose and lactate concentrations, blood pH, [Hb], Hct and MCHC) were fit with linear models to observe variation in responses with treatment (fixed effect), temperature and mass (covariates) for both species. For C. melanopterus, the factor "treatment" had four levels (i. e. baseline, immediate, three-hour, and respirometry). It was not possible to catch comparable numbers of N.
acutidens, and as a result the factor "treatment" only had three levels (i. e. baseline, immediate and 3-h). All possible interactions (two-way and three-way) were included in these models for C. melanopterus. Samples sizes were too small to include interactions for N. acutidens. Post hoc multiple comparisons were made with Tukey's honest significant difference (HSD) tests. Models were validated with Q-Q plots of model residuals, and by plotting residuals against treatment and fitted values  (Zuur et al. , 2007). For all tests, the acceptable Type I error rate (α) was 0. 05, and all analyses were conducted using the R Stats Package (R Core Team, 2016). Recovery times and costs were estimated for C. melanopterus using respirometry data. The mean value of M ˙O2Min that was derived from the laboratory was used as a baseline for estimating the excess post-exercise oxygen consumption (EPOC, in mg O 2 kg -1 ) of individual sharks from field respirometry.
Acknowledgements The authors wish to thank  Cécile Berthe ,  Eva Jacquesson ,  Lizzie Duncan ,  Tommy Norin  and the staff and volunteers at the CRIOBE for their invaluable support both in the laboratory and field. In addition, we are grateful to  Liz Tynan  for providing comments to improve the quality of this manuscript, and  Rhondda Jones  for guidance with statistical analyses. The quality of this manuscript was greatly improved by the constructive feedback of two anonymous reviewers.
MediTer. The surveys underlying this article were carried out as part of a six-month engineering internship (C. Cardon) and three theses in geography (M. Mourou, H. Benalayate, A. Adam) under supervision of Mr Aderghal, Mr Vaillant and B. Romagny. This research was supported by the National Research Agency (France) as part of the "Med-Inn-Local" project (2013-2017, ANR-12-TMED-0001).
ACKNOWLEDGEMENTS This work was funded by a  University of   Sheffield Prize Scholarship  to MRL, an  ERC  grant (grant number  ERC-2014-STG-638333 ) and a  Royal Society Research Grant  (grant number  RG130448 ). LTD and JKO are supported by a  NERC  grant (grant number  NE/M00208X/1 ) and PAC is supported by a  Royal Society University Research Fellowship  (grant number  URF120119 ). JWB was supported by  301  and  Think Ahead Sheffield Undergraduate Research Experience  grants to MRL. The work on ultrastructure was supported by a  Natural Sciences and Engineering Research Council of Canada  grant (no  2015-04878 ) to TLS. The authors thank  Peter Westhoff ,  Stefanie Schulze  and  Udo Gowik  for use of their GLDH antibody,  Susanne von Caemmerer  for advice about outer bundle sheath cell resistance,  Paul Hattersley  for leaf samples and 13 C isotope data,  John Thompson  for field assistance and sample collection,  Emma Jardine  for discussion of linear discriminant analysis,  Heather Walker  for mass spectrometry assistance,  Gareth Fraser  for the use of his vibratome and  Emanuela Samaritani  for histology assistance.
According to  Zimmerman (1978)  and label data from Philodoria specimens in the collection of the Bernice Pauahi Bishop Museum (BPBM), there appear to be numerous undescribed Philodoria species on Myrsine. In total, 19 Myrsine species are known to be endemic to the Hawaiian Islands  (Wagner et al. 1999) , and two species of Philodoria that feed on Myrsine have been described: P. succedanea Walsingham, 1907 (type species of the genus) and P. auromagnifica Walsingham, 1907, both with similar scale colors and genital characters  (Walsingham 1907 , Zimmerman 1978). In late April 2016, several of the authors collected numerous blotch mines on leaves of Myrsine species at two sites on the island of Hawaii (Big Island). Initially, we believed that these mines were created by a single Philodoria species, but after studying them, we realized that they comprised diverse larval habits (e.
4. 3  (Rambaut 2009). Intra-and interspecific genetic distances were estimated using the Kimura 2-parameter model implemented within the analytical tools available in BOLDv4 (Table  3 ). We also used BOLD to obtain Barcode Index Numbers (BINs)  (Ratnasingham and Hebert 2013). While single-marker COI analyses can be prone to insufficient resolution and error  (Rubinoff and Holland 2005) , we were unable to obtain additional genetic data for these species during the time of this study. We therefore chose to use a gene-tree based approach  (Hebert et al. 2003; Hajibabaei et al. 2007)  as another source of evidence to complement morphology to assess species limits. Sequences, voucher data, images, and trace files are deposited in the Barcode of Life Data Systems (BOLD)  (Ratnasingham and Hebert 2007; www.
kauaulaensis, is 6. 63%. Remarks. We identified two adult moths (Coll ID CJ-144 / GenBank accession no. ID KT982414 and CJ-145) as P. succedanea, based on the presence of a basal black patch on forewing, from which whole bodies were sacrificed for molecular analysis  (Johns et al. 2016; Figs 6O, 12). Zimmerman (1978)  did not recognize Walsingham's (1907) Kauai record of this species because Walsingham had only one specimen at hand, which was in poor condition (specimen data: 1 ♂, Mts [which Mts not further specified], 3-4000 ft. , Kauai, vi. 1894 Perkins. 27297| PARATYPE 17/17 (?)|'NOT succedanea Det. by E. C. Zimmerman|NHMUK010862812). We could not find the specimen from Kauai. However, we found Myrsine knudsenii (Endangered, IUCN) leaves with mines with active larvae from Kokee, Kauai Is.
auromagnifica (Coll. ID CJ-064 / GenBank accession no. KT982404 and CJ-072 / KT982407). Comparison of adult morphology and larval behavior with other species shows that these moths belong to P. kauaulaensis  (Figs 10, 14). Unfortunately these specimens were sacrificed for molecular analysis, so that they cannot be added to the type series. Walsingham, 1907 Figs 2E, F, 5E-H, 6E-H, 7G, 8B, 11, 14C Philodoria auromagnifica Walsingham, 1907: 718, pl. 25, fig. 20; Swezey 1913b: 223  Molokai Is, in BPBM: 1 unsexed,  Kawela, 3700ft, 23. xii. 1925 , O. H. Swezey Collector, "Suttonia"(= Myrsine), 34144;1♂, Kamakou Boardwalk, 24. i. 2014 (stored), C. A. Johns leg. , host: M. lessertiana, 18. xii. 2013, CJ-241, SK768♂ in BPBM. Hawaii Is. , Hawai'i Volcanoes National Park, host: M. lessertiana in BPBM: 2♀, A.
Molecular analysis We obtained DNA barcode data for 16 individual specimens (http://dx. doi. org/10. 5883/DS-PHDRIA). All species have their own unique cluster or Barcode Index Number (BIN) allowing their unequivocal identification (Table  2 ). The lowest interspecific distance (4. 41%) was observed between Philodoria sp. nr. splendida and P. basalis (Table  3 ). Sequences of Myrsine-feeding Philodoria species, when compared pairwise, formed distinct clusters, with a maximum intraspecific divergence which varied between 0. 17-0. 88% and a NN distance which varied from 5. 85-8. 91% (Table  3 ). The minimum interspecific distance was smaller between P. auromagnifica and P. kauaukaensis (5. 85%) than between P. auromagnifica and P. succedanea (6. 71%) (Table 3). Identifying species using DNA barcodes appears to be useful for the Myrsinefeeding Philodoria.
, Steve Perlman (Hawaii Plant Extinction Prevention Program), Lono Dunn, Daniel Tanaka, Joe Ward, Pomaikai Kanaiaupio-Crozier (Puu Kukui Watershed Preserve), Hank Oppenheimer (Hawaii Plant Extinction Prevention Program), Chris Brosius (West Maui Mountain Watershed Partnership), and Keahi M. Bustamente (Leeward Haleakala Watershed Restoration Project) for their help coordinating fieldwork and collection. Chris Hamilton (Florida Museum of Natural History) provided useful feedback on the text. The project was funded by the National Science Foundation (Graduate Research Fellowship to CAJ; DEB #1354585 to AYK), the National Geographic Society (#C283-14 to CAJ; #9686-15 to AYK), the Entomological Society of America (2014 SysEB Travel Award to CAJ), the University of Florida's Tropical Conservation and Development Program (2014 Field Research Grant to CAJ), the Florida Museum of Natural History (Lockhart Fellowship to CAJ), and an Society of Systematic Biologists SSB (2012 Mini-ARTS grant to AYK).
Acknowledgements We thank  Cynthia King  ( Hawaii Department of Land and Natural Resources, Division of Forestry and Wildlife ) for collecting and forest access permits,  Rhonda Loa  ( Hawaii Volcanoes National Park ) and  Pat Bily  (The  Nature Conservancy ) for land access, permitting assistance, logistical and field support,  Daniel Rubinoff  ( University of Hawai'i at Mänoa ) for conception of the project, and the participants and organizers of the leaf miner workshop conducted on Puna, HI in 2016. Klaus Sattler  is thanked for extensive discussion about.
name Col- lection site Host plant species Host plant family Collection ID BOLD ID BOLD BIN GenBank accession no. Institu- tion of DNA extrac- tion and sequenc- ing of COI P. succeda-nea Hawaii Myrsine les-sertiana Primulaceae RMNH. INS. 30669 WOGRA451-17 ADF5435 MF804823 RMNH, Nether-lands P. succeda-nea West Maui M. lessertiana Primulaceae CJ-144 WOGRA489-17 ADF5435 KT982414 FLMNH, USA P. kauau-laensis West Maui M. lessertiana Primulaceae CJ-064 WOGRA487-17 ADI5327 KT982404 FLMNH, USA P. kauau-laensis West Maui M. sandwi-censis Primulaceae CJ-072 WOGRA488-17 ADI5327 KT982407 FLMNH, USA P. auro-magnifica Hawaii M. lessertiana Primulaceae RMNH. 5013750 WOGRA444-17 ADD6965 MF804828 RMNH, Nether-lands P. auro-magnifica Hawaii M. lessertiana Primulaceae CLV6240 LEPPC2422-16 ADD6965 MF804824 CCDB, Canada KPU & P.
Phylogenetic analysis For ND4 analysis, we aligned our ND4 sequences from the 21 specimens sampled by the authors (Table  1 ) with Genbank sequences obtained from previous studies  (Malone et al. 2000; Malone & Davis 2004; Stephen et al. 2013; Martin et al. 2015). Phylogenetic trees were constructed using the Maximum Likelihood (ML) and Bayesian Inference (BI): The best-fit evolutionary model was calculated using the Bayesian Information Criterion in jModelTest2  (version 2. 1. 6; Darriba et al. 2012 ) while the ML analysis was conducted in MEGA6  (Tamura et al. 2013)  based on the best-model obtained in jModeltest. Initial tree(s) for the heuristic search were obtained by applying the Neighbour-Joining method to a matrix of pairwise distances estimated using the Maximum Composite Likelihood (MCL) approach.
Genetic diversity We tested departures from Hardy-Weinberg expectations and linkage disequilibria using exact tests based on the Markov chain (1,000 permutations) with the software FSTAT v. 2. 9. 3. 2  (Goudet 2001). We adjusted the levels of significances for multiple tests using the standard Bonferroni correction  (Rice 1989). We assessed the polymorphism over all loci for each population, computing allelic richness (AR), expected heterozygosity (He), allelic frequencies and inbreeding coefficient (Fis)  (Weir & Cockerham 1984)  using FSTAT v. 2. 9. 3. 2  (Goudet 2001 ) with 1,200 permutations. The allelic frequencies allowed us to deduce private alleles for each population.
Genetic structure We estimated pairwise fixation index (F ST ) values between populations  (Weir & Cockerham 1984)  using FSTAT v. 2. 9. 3. 2  (Goudet 2001). Their associated significance was computed and tested using global tests implemented in FSTAT v. 2. 9. 3. 2  (Goudet 2001 ) with a level of significance adjusted for multiple tests using the standard Bonferroni correction. In addition, relationships among populations were evaluated with a Factorial Correspondence Analysis (FCA) based on individual genotypes and using the FCA procedure implemented in GENETIX v. 4. 05. 2  (Belkhir et al. 2004 ). We also accessed the genetic structure using the individualbased approach implemented by the software STRUCTURE  (Pritchard et al. 2000). This Bayesian clustering approach estimated both the number (K) of genetic cluster(s) and the admixture coefficient of individuals to be assigned to the inferred clusters.
We choose the admixture model and the option of correlated allele frequencies among populations. As recommended by  Evanno et al. (2005) , we replicated 20 independent runs for each value of K (with K varying from 1 to 10) with a total of 1 million iterations and a burn-in of 10,000. To determine the number of genetic clusters from STRUCTURE analyses, we used the STRUCTURE HARVESTER program  (Earl & VonHoldt 2011)  to compare the mean likelihood and variance per K values computed from the 20 independent runs.
Genetic structure Results revealed significant genetic differentiation between populations. After applying the Bonferroni correction (adjusted P-value threshold = 0. 0083), significant F ST values were found between several pairwise populations (mean F ST value = 0. 495) (Table  3 ). This significant variation was corroborated by both FCA (Fig. 14 ) and the Bayesian individuals-based approach. Indeed, based on the individuals'genotypes, FCA clearly distinguishes four different populations and, furthermore, shows the low genetic diversity within the native Saint Lucia population (only 7 circles are shown because many individuals from Louvet and one from Grand Anse had the same genotypes). The STRUCTURE and STRUCTURE HARVESTER software revealed a highest delta K value of 3  (Figs 15, 16). According to these results, individuals from introduced iguana populations in Saint Lucia and those from French Guiana were mainly assigned to the first and second genetic cluster respectively (clades 3 and 2 from the phylogram, Fig.
Acknowledgements This project was financed mainly by the  Direction Régionale de l'Environnement et du Logement de Martinique  to study the origin of invasive common green iguanas in Martinique and their relationships to the  Saint Lucia iguana. The genetic study formed part of  Barbara Vuillaume 's Master's degree. The genetic analyses were initially conducted at  Genindexe in La Rochelle (France ) and completed by  David Schikorski in Genindexe Labofarm (France) , which financed part of this study. Specimens were collected from  Palm Island and Union Island , in the Grenadines, by  JD  and GG with kind permission from the  Palm Island Resort  and the  St Vincent & the Grenadines Forestry Department. Fieldwork was supported with funding from the  FFI Species Fund, Disney Conservation Fund ,  National Geographic, US Fish & Wildlife Service  (# F18AP00796 ), and the  St Vincent & the Grenadines Preservation Fund.