dataesr/scientific-paragraphs-categorization huggingface dataset:
Housekeeping data will be extracted from the raw spacecraft telemetry and will be stored in separate files. There will be two versions of SWUSV processed science data: quick-look data produced immediately upon receipt of all necessary telemetry from the spacecraft (Level 1a), and final data incorporating any telemetry packets that may be missing or corrupted in the initial telemetry and that are later recovered (Level 1b). Quick-look data will be available for mission operations verification and planning purposes and will be available immediately for scientific analysis. Final data will replace the quick-look data when they are available and will be suitable for archiving and distribution. Both quick-look and final data will be processed in the same way and will have the same file formats.
The quick-look FITS file will be differentiated from the final data product by the completeness of the header. Interactive Data Language (IDL) procedures will be provided in the SolarSoft library to convert the Level 0. 5 FITS image files into the higher-level calibrated data products described in Table  7. These procedures will permit the user to perform standard corrections such as removal of geometric distortion, vignetting and stray light, and photometric calibration on the fly for the data of interest. All calibration data necessary for these corrections will be included as part of the SolarSoft distribution. This approach has been used successfully for previous missions and ensures that the user has access to the most up-to-date calibrations while avoiding repeated processing and redistribution of large amounts of data.
Software tools for generation of higher-level data products and common analysis tasks such as image visualization, generation of movies and synoptic maps, feature tracking, and structure measurement, will also be provided in SolarSoft. Besides the data products of the SWUSV scientific data archive presented in Table  7 , all other SWUSV-related information will also be served to the community from the missionspecific web site that will contain: -a Mission Log of all the observing programs carried out by the science team; -access to Calibration Data (i. e. , flat-field, photometric calibration, positioning); -access to data analysis routines to allow end users to combine Calibration Data and Level 0. 5 Data to create their own Level 1 calibrated FITS files; -access to Quick-look Summary Images and Movies (in png and mpg formats) including synoptic maps in all wavelengths; -access to the most recent data analysis software and documentation for further analysis of SWUSV observations (software will be written in IDL and will be incorporated in the SolarSoft environment); -access to the SWUSV database (a database to allow users to select observations based on instrument set-up or specific parameters or events).
g. Halisarca  [19, 20, 21] ). At present, Oscarella comprises 17 species, listed in the World Porifera Data Base (http://www. marinespecies. org/porifera/index. php), including seven Mediterranean species. However, this is certainly an underestimate and several new species are currently under description (this study) or have yet to be described. The relationships among these 17 Oscarella species and the phylogenetic position of Pseudocorticium jarrei relative to them are also largely unknown  [6]. In addition, the relationships between the different color morphs of the two putative sibling species O. lobularis and O. tuberculata  [14]  have not yet been fully resolved and more loci from more color morphs are needed to elucidate them. This is crucially important, especially because O. lobularis is being developed as a new model species for evo-devo studies  [13, 22, 23, 24, 25].
Thus the aim of this paper is to investigate the principal uncertainties in the phylogeny of Oscarellidae described above: (i) the position of Pseudocorticium jarrei and the monophyly of genus Oscarella, (ii) the relationships among common Oscarella species, (iii) the relationships between different color morphs of Oscarella lobularis and O. tuberculata. For this purpose, we collected a diverse dataset relating to 22 Oscarellidae specimens from different geographical areas. Our dataset includes four molecular markers (two nuclear (18S rDNA, 28S rDNA) and two mitochondrial (atp6, tatC)), predicted secondary structures features for nuclear rDNAs  [26]  and multiple non-molecular characters (in particular, histological and cytological). We also incorporated two new species of Oscarella from Bergen Fjords in our analysis, and we provide their morphological descriptions and formal diagnoses.
Specimen Collection Specimens of Oscarellidae from the Mediterranean Sea, the Norwegian Fjords, the East Atlantic and the North Pacific were collected using SCUBA diving by members of our team (AVE, EG) or were provided by colleagues (see Acknowledgments). Locations of the collection sites are shown on Figure  1. The samples used in this study and their current taxonomic status are summarized in Table  1. The different color morphs of Oscarella lobularis and O. tuberculata are presented in situ in Figure  S1. We obtained molecular data from 9 of the 17 officially described species of Oscarella. In addition, we formally describe two new species in this paper and discuss two specimens with uncertain systematic positions (Oscarella sp. (pink) and Oscarella sp. (purple)). Despite substantial efforts, we did not succeed in obtaining DNA from museum specimens of O.
The poriferan origin of the sequences was checked by a BLAST search  [40]  against the NCBI GenBank collection (http://www. ncbi. nlm. nih. gov/). All new sequences/genomes were deposited in GenBank under accession numbers listed in Table  1. 3. 3. Sequence alignment. To achieve a reasonable trade-off between representativeness of outgroup taxa and ease of alignment, and because our prime interests were relationships within the Oscarellidae, we restricted our sampling to Homoscleromorpha. Several species of Plakinidae (three Corticium and two Plakortis species), the sister group of Oscarellidae, were used as an outgroup. Initial sequence alignment was performed using the software MUSCLE online (http://www. ebi. ac. uk/Tools/muscle/ index. html) [41, 42] , and was subsequently optimized by eye using the Bioedit Sequence Alignment Editor v5.
09  [43]. Ambiguously aligned regions were determined by Gblocks v0. 91 b software  [44]  for nuclear markers only (mitochondrial ones were partitioned by codon position). A relaxed selection of blocks is better for short alignment  [45] , thus the settings were the following for the 18S rDNA [1:13; 2:13; 3:8 4:2; 5: all] and the 28S rDNA [1:13; 2:13; 3:8; 4:2; 5: all]. The treatment by Gblocks resulted in the removal of 1% and 4%, for the 18S rDNA and 28S rDNA alignments, respectively. The character exclusion sets based on Gblocks are available upon request from the corresponding author. 3. 4. Phylogenetic analyses. Phylogenetic analyses were performed using maximum likelihood (ML) and Bayesian inference (BI) methods. For ML analyses, we used the Akaike Information Criterion (AIC) in JModelTest  [46]  to determine the best fitting nucleotide substitution model for each data set.
The following models were chosen for 18S rDNA, 28S rDNA, 18S rDNA +28S rDNA, atp6, tatC, tatC+atp6, 18S rDNA +28S rDNA +tatC+atp6 datasets respectively: TIM2+G; GTR+G; TIM2+G; TPM2uf+I+G; TIM1+I; GTR+G; GTR+G. ML phylogenetic analyses were performed with PhyML software v. 3  [47, 48]  using the previously estimated parameters. Among sites, rate heterogeneity was estimated using a discrete approximation of the gamma distribution with six rate categories. Gaps were treated as missing data and the statistical robustness of the tree topology was assessed by nonparametric bootstrap resampling (1000 replicates)  [49]. Bootstrap values. 80 were considered high enough to support clades in ML reconstructions. BI analyses were performed with MrBayes 3. 2. 1  [50]  under the best-fit evolutionary model estimated for each independent gene or partition under the AIC criterion with MrModeltest 2.
3  [51]. The models selected for 18S rDNA and 28S rDNA were GTR+I+G and GTR+G, respectively. For mitochondrial markers atp6 and tatC, we partitioned the dataset according to codon position and the models selected are as follows; [atp6_1 st position: GTR+I, atp6_2 nd position: GTR+I, atp6_3 rd position: GTR+G]; [tatC_1 st position: GTR+I, tatC_2 nd position: GTR+I, tatC_3 rd position: HKY+I]. Four Markov Chains were run for 2 million generations and sampled every 100 generations. The chains converged significantly and the average standard deviation of split frequencies was ,0. 01 at the end of the run. The trees of the early generations (5000 trees) were discarded until the probabilities reached a stable plateau (burn-in) and the remaining trees were used to generate a 50% majority-rule consensus tree.
Only posterior probabilities. 0. 90 were considered to robustly support clades. All trees except the tatC and tatC+atp6 were rooted on Plakinidae species, the exceptions arising as the tatC gene is specific to Oscarellidae  [6]. Based on the nuclear and atp6 phylogenetic analyses (this study) and previous complete mitochondrial analyses  [6] , these trees were instead rooted on a robust internal clade. The trees were visualized and edited using FigTree v. 1. 3. 1  [52]. 3. 5. Secondary structure analysis of 18S rDNA sequences and their optimization on the 18S rDNA molecular Table  1. List of species/specimens used in this work according to the classification of Systema Porifera  [89]  and the recent updates added to the World Porifera Database  [9]. The collection sites and the GenBank accession numbers of the four markers and of the complete mitochondrial genomes are indicated.
In the sequence column, the new sequence accession numbers are written in bold. doi:10. 1371/journal. pone. 0063976. t001 tree. The MFold server (http://mobyle. pasteur. fr/cgi-bin/ MobylePortal/portal. py?form = mfold  [53]  was used to determine secondary structures of V4 variable regions of the 18S rDNA for all species. This region was defined as covering helix 43  [54, 55]. The default settings for all parameters were used as in a previous study  [37]. When multiple secondary structures were found, we chose the structure with the lowest free energy (DG in kcal/mol)  [26]. We decomposed the rDNA secondary structures into elements (framed by different colors) and defined binary characters (i. e. presence/absence of each element). A matrix (Text S2) was constructed and parsimony reconstructions of character evolution were performed using Mesquite software version 2.
72  [32]  on the 18S rDNA molecular tree. 3. 6. Sequences identity and nucleotide diversities. We investigated the percentage of molecular divergence (Table  4 ) on the mitochondrial marker sequences (atp6 and tatC) by using the identity matrix option of the Bioedit software  [43]. Nucleotide diversities (p) between Oscarella tuberculata, O. lobularis, D3 sequences, as well as congeneric species of Oscarella, were estimated using DnaSP 5. 10 software  [56]  for the atp6 marker and compared to available demosponge data  [38]  (Table  5 ). 3. 7. Consensus tree and diagnostic characters. A consensus tree based on tatC+atp6 molecular phylogenies was manually drawn to map the diverse molecular and non-molecular characters that are diagnostic of some Oscarella clades. All robust nodes (BP. 50+ PP.
72, 77/0. 84, 2/0. 65). In all cases, the positions observed here are not congruent with those found using nuclear markers. Furthermore, in contrast to the results obtained with the nuclear genes, both mt markers have partly resolved the relationships between the members of the D clade. The latter can be subdivided into three sub-clades (variably supported): D1, D2 and D3. D1, containing all color morphs of O. lobularis, is moderately supported by the analyses of tatC and the combined dataset, but is not supported by atp6 (2/0. Although the position of the third species from Bergen, O. bergenensis sp. nov. , was not precisely determined, we noted that with both nuclear and mt markers, it is unrelated to Oscarella sp. (pink). The mt combined dataset was more powerful than both nuclear markers and separated mt markers in resolving the relationships among D (Figure  4B ).
Phylogenetic analyses of combined dataset of four markers. The topology obtained from the whole combined dataset is given in Figure  4C. The four main clades (A: 94/1; B: 100/1; C: 100/1; D: 100/1) as well as their interrelationships are retrieved. Among A, as in nuclear topologies, the robust clade [Oscarella nicolae sp. nov. +O. balibaloi] (100/1) is the sister group of the weakly supported clade [Pseudocorticium jarrei+O. kamchatkensis] (55/0. 71). O. microlobata is the sister group of B (95/1) and O. viridis is the sister group of D+C (63/0. 94) as in 18S rDNA and 18S rDNA +28S rDNA topologies. As in the case for tatC marker, the sub-clade D3 is found, its robustness being weakly supported in ML and well supported in BI analyses (56/1). 1. 4. Predicted secondary structures of 18S rDNA.
A contains Oscarella balibaloi Perez et al. , 2011, O. kamchatkensis Ereskovsky et al. , 2009 and O. nicolae sp. nov. (see description hereafter)+Pseudocorticium jarrei. Although we failed to obtain O. balibaloi's 28S rDNA sequence, 18S rDNA and 18S rDNA +28S rDNA datasets highly support the pairs [Oscarella nicolae sp. nov. +O. balibaloi] and [P. jarrei+O. kamchatkensis] as having a sister-group relationship (98/1, 99/1 and 98/1, 83/1). Clade B contains all the other Oscarella species except for Oscarella microlobata Muricy et al. , 1996 which is positioned as the sister group of B but with low support (58/0. 69, 2/0. 53, 88/0. 98). Three sets of non-molecular characters-states for each species/specimen: ecology/geography, external morphology and associated microbes.
71, not found, 84/0. 97). D2, grouping all O. tuberculata color morphs, is recovered in the analyses of atp6 (61/ 0. 90) and tatC+atp6 (83/0. 97) mitochondrial datasets with moderate support. A sub-clade of O. tuberculata -with the exclusion of O. tuberculata yellow -received better support in all analyses (93/0. 73, 75/0. 94, 96/0. 98). Finally, D3 groups together Oscarella sp. (purple), O. sp. (pink) and O. rubra (95/1; not found; 96/1).
Acknowledgments We gratefully acknowledge  Guilherme Muricy ,  Michelle Kelly ,  Wilfried Bay-Nouailhat ,  Rob van Soest and Pascal Lape  ´bie for providing specimens and  Didier Aurelle , an anonymous reviewer and the PloS One Editor for helpful comments on the manuscript. We thank  Dr Emilie Egea  for her help with partitioning dataset. We are indebted to  Daria Tokina  for her assistance with samples preparation to LM and TEM observations. Frederic Zuberer  and  Marcin Adamski  are thanked for diving assistance and the molecular biology staff of the  IMBE laboratory (SCBM)  for technical means provided.
87% (Figure  2 ). In the case of the percentage of OTUs shared between disinfected and non-disinfected samples (8. 78%) (Figure  2 ) it is mostly driven by OTUs that appeared as shared due to a single sequence in the first pool vs thousands of the same OTU in the other, reflecting the very scarce persistence in the surface disinfected samples of some bacteria that are mostly epiphytic. Some of the sampling units from the different locations were amplified for 18S rDNA and sequences were compared to the Genbank database in order to identify the phylogenetic identity of SUs from the different collection sites. Interestingly, the same dichotomy found for the endophytic bacterial communities (Figure  1B ) was reflected by the host phylogeny, where two different clusters separating samples from Tunis, Villefranche and Greece and samples from Malta, Marseille and Mallorca and those from the Australian native range (Figure  3 ).
racemosa sampled in Marseille and one of the locations in Mallorca (Figure  S1 ). Interestingly, phylogenetic results for some of the most ubiquitous lineages belonging to Burkholderiales (Figure  S2 ) were segregated between the two varieties of their algal host with the two clusters (A and B) identified and associated either to the invasive Australian or to the ancient Mediterranean variety (Figure  S2 ), supporting a possible co-evolution of host and bacterial lineages. Within Burkholderiales order the most represented family was Comamonadaceae which phylogenetic tree showed a cluster exclusive of the invasive group B, while the other showed a more complex distribution among groups (data not shown). The Comamonadaceae include nitrogen-fixing and nitrate-reducing bacteria  [30]. Within this family, the most ubiquitous OTU (OTU110, 230 base pair) had highest similarity (99%) with Alicycliphilus denitrificans (GenBank CP002657.
Next Generation sequencing and Metagenomics analysis Extracted DNA was submitted to Biocant (Cantanhede, Portugal) to be analyzed through tag-Pyrosequencing (GS FLX Titanium, 454-Life Sciences-Roche technology ® ) after amplification with modified primers for region V4 of 16S rRNA  [52]. PCR amplification of the hypervariable V4 region of the 16S rRNA gene was performed using the 8bp key-tagged. After sequences' quality control and chimera removal by Chimera Slayer, all analyses were performed using the program QIIME: Quantitative Insights Into Microbial Ecology  [53]. Sampling diversity and specific richness was assessed by calculating Chao1 and Shannon indexes. To assign each OTU to the closest matching described 'species', BLASTN searches were performed against SILVA database and sequences were putatively assigned to a described taxa provided their blast was associated to a minimum e-value threshold of 0.
001 (default value). Beta-diversity was also calculated on Qiime using the weighted Unifrac algorithm which uses qualitative measures the phylogenetic distance between sets of taxa in a phylogenetic tree  [54]  and PCA 2D plots were constructed to visualize data. Statistical differences between OTUs hits of different replicates were assessed by One-way ANOSIM performed using PAST (Ver. 2. 16)  [55]. 16S sequences from the most common order and family to all disinfected samples (Burkholderiales and Comamonadaceae respectively) were used to build phylogenetic trees with Qiime's script make_phylogeny. py and using, by default, FastTree  [56]  and root was chose by the tree method default from Qiime. An OTU table was constructed by pooling the samples from different treatments (disinfected, non-disinfected and sediment) and a Venn diagram was constructed using Venny  [57]  in order to assess percentage of OTUs shared between different treatments.
Phylogenetic analysis of Caulerpa racemosa varieties A 1100 bp amplified region containing the 3' end of the 18S rDNA, including the intron (100 to 108 bases), the ITS1 (112 to 136 bases), 5. 8S rDNA, ITS2 (281 to 315 bases), and the 5' end of the 28S rDNA from C. racemosa, was amplified according to the PCR conditions by  Verlaque et al. 2003  and cloned using pGEM-T Easy. Sequences were aligned using CLUSTAL X  [58]  and Maximum Likelihood analysis performed on PHYML ver. 3. 0  [59]  after evolution model selected using MODELTEST  [60]. Alignments of each cluster were BLAST against Genbank nucleotide database in order to identify the most probable variety. Sequences were made available on GenBank database with the accession numbers KC507512 -KC5077539.
cylindracea compared to those belonging to C. racemosa var. turbinata-uvifera. Alignments were processed using MUSCLE  [61]  alignment in Geneious Pro v 5. 4  [62]. Highly divergent sequences after alignment (>3%) were excluded. Identical sequences were clustered in DNA Sp  [63]  and exported to Roehl format in order to build a network of haplotypes using the software Network  [64]. The preprocessing option implemented in Network was used for Star contraction  [64] , using a radius maximum size of 5. Due the frequent instances of multiple possible characters for numerous sites (i. e. not binary), the median joining procedure was implemented and followed by a MP procedure to remove the unnecessary median vectors and links  [65]  and reduce the complexity of the network to improve its visualization.
Acknowledgements We thank  R. Hovey  and  Geoff Bastian  for field work and all the logistic help;  Prof. C. Atkins  for access to his lab;  F. Zuberer  and  D. Luquet  for their invaluable help in sampling C. racemosa in Villefranche and Marseille;  H. Langar  ( INSTM, Salâmbo, Tunis ) for sampling in Tunis;  R. Martínez  and  N. Marbà  for sampling in Mallorca,  N. Coelho  and  C. Patrão  for helping in the lab and phylogenies and  BIOCANT (Portugal)  for metagenomics analysis and bioinformatics support.
This research was supported by the project  IBISA  ( PTDC / MAR / 64749 / 2006 ) funded by the  Portuguese Foundation for Science and Technology (FCT -Fundação para a Ciência e Tecnologia)  and  FEDER (Fundo Europeu de Desenvolvimento Regional)  and a PhD fellowship (TA) from  FCT  and  FSE (Fundo Social Europeu)  [http://www. fct. pt/]. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
ACKNOWLEDGMENTS The authors thank  Matthew Moscou  for helpful advice and ideas on genetical genomics. This research was supported in part by  National Science Foundation Plant Genome  grants  05-00461  and  09-22746 , and  USDA-ARS   CRIS  project  3625-21000-057-00D. Elsa Ballini  was supported by a  USDA-ARS   Postdoctoral Research Associateship. Mention of trade names or commercial products in this publication is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the  U. S. Department of Agriculture.
Bioinformatics Detection and Selection of SNPs for Array A de novo assembly was performed for the 'Bartlett' sequencing data using AbySS 1. 2. 1 (k = 43). Contigs of 600 bp or larger were used as a reference genome set. The sequencing data from OH and LBJ were mapped to the reference genome set of 'Bartlett' using Soap2. 20 (-p 8 -M 4 -v 5 -c 52 -s 12 -n 5 -r 2 -m 50 -x 600). Soap output files were split into a single file per contig and each contig file sorted by location of the mapped reads. SoapSNP was used for SNP detection and filtering with the same parameters as described in  [9]. The detected SNPs were then subjected to filtering, where calls were discarded when the quality score was less than 20; fewer than two reads per genotype were present; overall coverage depth was greater than the average coverage plus three standard deviations; the site was at least 25 bases away from another SNP call; and the SNPs were not located within regions associated with a set of candidate genes.
SNP Genotyping and Data Analysis Genomic DNA was amplified and hybridized to the apple and pear InfiniumH II 9K SNP array following the InfiniumH HD Assay Ultra protocol (Illumina Inc. , San Diego, USA) and scanned with the Illumina HiScan. Data were analyzed using Illumina's GenomeStudio v 1. 0 software Genotyping Module, setting a GenCall Threshold of 0. 15. The software automatically determines the cluster positions of the AA/AB/BB genotypes for each SNP and displays them in normalized graphs (Figure  2 ). A systematic method was used to evaluate the SNP array data employing quality metrics from GenomeStudio (Illumina): GenTrain score $0. 50, minor allelic frequency (MAF) $0. 15 and call rate. 80%. A Chi-square test at a significance of 0. 01 was performed to determine distortion of markers from the expected segregation.
Linkage Mapping Analysis The genetic maps of both parents of all five populations were constructed using JoinMap v3. 0 and v4. 0 software  [30] , based on the SNP data for each individual population, except for the T0036M population, where both the SNP and SSR data were used. Linkage groups were determined with a LOD score of 5 and higher for grouping and the Kosambi function was used for map calculation. The maps were drawn and aligned using MapChart v2. 2  [31].