dataesr/scientific-paragraphs-categorization huggingface dataset:
Workflow 5: ExtractMSn/Mascot/MFPaQ/MS Signal analysis. The first steps (peaklist creation, database search, validation) were the same than in workflow 1. Quantification of proteins was then performed using the label-free module implemented in the MFPaQ v4. 0. 0 software, as previously described  [3, 12]. Briefly, the software uses the validated identification results and retrieves the XIC of the identified peptide ions in the corresponding raw nanoLC-MS files, based on their experimentally measured RT and monoisotopic m/z values. Peptide ions identified in all the samples to be compared are used to build a retention time matrix and re-align in time LC-MS runs. For peptides not identified by MS/MS in a particular run, this re-alignment matrix is used to perform cross-assignment and extract their XIC signal starting from a predicted RT.
Normalization across conditions is performed based on the median of XIC area ratios for all the extracted peptide ions. Protein quantification is based on a protein abundance index calculated as the average of XIC area values for at most three intense reference tryptic peptides per protein. Workflow 6 and 7: Andromeda/MaxQuant/MS Signal analysis. The first steps (database search with Andromeda and validation) were the same as in workflow 2. For quantification purposes, either Intensities (workflow 6) or LFQ  [13]  (workflow 7) calculated by MaxQuant were used. The LFQ metric, as described in  [13] , is derived from the raw intensities by the MaxLFQ algorithm, which uses a specific normalization procedure, as well as a particular aggregation method to calculate protein intensities, by taking into account, for each protein, all the peptide ratios measured in all pairwise comparisons of the different quantified samples.
"Match between run" time window was set to 2 min. For LFQ quantification, only protein ratios calculated from at least two unique peptides ratios (min LFQ ratio count ¼2) were considered for calculation of the LFQ protein intensity. Workflow 8: Mascot Distiller/Mascot/Skyline/MS Signal analysis. Peaklist creation was performed with Mascot Distiller as described in workflow 3, then database searches were performed with Mascot and validated with Scaffold as described for workflow 4. XIC signal corresponding to all validated peptides were extracted using the Skyline software (Skyline version v2. 5, daily updates of April 2014, https://skyline. gs. washington. edu). This method was well described by Schilling et al  (Schilling et al. MCP, 2012). Total areas, corresponding to the sum of the 3 extracted isotopes areas, were used for statistical analysis.
Missing protein intensity values were replaced by a constant value calculated independently for each sample as the 5percentile value of the total population. A welch t-test (two-tailed t-test, unequal variances) based on triplicate MS analyzes was then performed on log2 transformed values using the Perseus toolbox (version 1. 4. 0. 11; http://141. 61. 102. 17/perseus_doku). Criteria used to classify the proteins were the Welch t-test difference calculated by Perseus (difference between the two compared conditions of the mean log2 transformed value for triplicate MS/MS analyzes), and the Welch t-test p-value. Construction of the mixed dataset and plot of the ROC curves for each workflow For each workflow, quantitative outputs from the 3 pairwise comparisons described in Fig. 2  were merged in a single Excel table.
This composite table is shown for each of the 8 tested workflows in a separate sheet in Supplementary Table  1. The first column indicates the origin of the quantitative values: comparison A (50Vs0. 5), comparison B (50Vs5) or comparison C (25Vs12. 5). The second column indicates whether the protein originate from the background (yeast) or from the spiked mixture (UPS). These UPS1 proteins are highlighted in green, red, or yellow according to the comparison in which they were quantified (A, B, and C respectively). Following columns indicate: 1. Identification data reported by the different tools after database search and validation (fasta header, protein ID, number of peptides, score, etc…) 2. Raw quantitative data (6 columns: 2 conditions, 3 technical replicates) containing missing values 3.
Transformed data (for MS intensity based workflows 5-8) after imputation of missing values as described above, and logarithmic transformation. 4. Fold change values and statistical test values as calculated by the BetaBinomial package (spectral count) or by Perseus (MS intensity values). In the latter case, a z-score was also calculated for each protein as follows: z-score¼{(Welch t-test difference)-Median [(Welch t-test difference) for all quantified proteins] }/Standard deviation [(Welch t-test difference) for all quantified proteins] To classify proteins as variant and non-variant and plot ROC curves, different combinations of criteria were tested: 1. for spectral count workflows: |log2 fold change| 4x (from 0. 8-3) and p-value oy (from 0. 05 to 0. 0001) 2. for MS intensity based workflows: |welch t-test difference| 4 x (from 0 to 7) and p-value oy (from 0.
3 to 0. 0001) Proteins classified as variant according to these criteria were counted as true-positive (TP) if they were UPS1, and false positive (FP) if they were from yeast. Proteins classified as non-variant according to these criteria were counted as true-negative (TN) if they were from yeast, and false-negative (FN) if they were UPS1. Sensitivity of the workflow was calculated as TP/144 (taking into account 3 Â 48 ¼144 real differentially abundant UPS1 proteins after mixing of the 3 quantitative outputs) and False Discovery Proportion (FDP) was calculated as FP/(TP þFP). Fig. 3  shows ROC curves (sensitivity versus FDP) which illustrate how the dataset can be useful for selecting the most efficient classification filters (Fig. 3A ) or for the comparison of software tools (Fig. 3B ).
Oceanographic and temperature reconstructions GISS temperature analysis  [31, 32]  was used to calculate trends in annual temperature change from 1914 to 2014. We also estimated changes in sea surface temperature (SST) per month during the 1980-2015 period, and the seasonal mean SST anomalies per year at all latitudes from 1988 to 2013 using the database SST-Hadley/Reynols. All estimations were calibrated with a base period from 1951 to 1980  [31]. Climate reconstruction focused on the known reproduction seasons of North Atlantic pilot whales: May-October for G. melas  [33, 34]  and July-August for G. macrorhynchus  [35]. For paleoclimatic reconstructions, temperatures were obtained from Marcott et al. [36]. We used the core MD95-2015 with the proxy UK´37, calibrated with Müller et al. [37]  for marine ages of North Atlantic annual temperatures  [38].
Genetic analysis DNA was extracted employing a Chelex-based protocol  [39]. The mitochondrial control region (D-loop) was amplified following Oremus et al. [15]. Sequences were edited and aligned using ClustalW  [40]  from the BioEdit Sequence Alignment Editor  [41]. NCBI-BLAST  [42]  online software was employed for species identification. The number of haplotypes, haplotypic and nucleotidic diversities were calculated with DNAsp v5  [43]. A median-joining  [44]  haplotype network was constructed to visualize the relationships among the different mitochondrial haplotypes, and their frequencies, in the sampled populations with the program Network 4. 5. 1. 6 [45]. Network software reconstructs all possible, shortest, least complex phylogenetic trees (maximum parsimony) from a given dataset using different algorithms.
Eight microsatellite loci (EV37MN; EV94MN; 199/200; 415/416, 417/418, 409/470; 468/469 and 464/465) were amplified as in Fullard et al. [20]. A multi-tube method  [46]  was employed to validate the allele scores. Each microsatellite locus was individually amplified three times in three different thermal cycler machines (Applied Biosystems 2720 Thermal Cycler). Microsatellites were genotyped employing GeneMapper1 Software Version 4. 0 (Applied Biosystems). Scoring errors, large allele dropout and null alleles were tested employing the program MICROCHECKER  [47]. Samples with positive amplification for all mitochondrial and microsatellite loci were sexed by amplifying the Y-linked SRY gene  [48] , typical of males. PCR was run with positive and negative controls to avoid possible errors. PCR products were run and visualized in 2% agarose gel.
Population genetics Conformity with Hardy-Weinberg equilibrium was calculated using the exact probability test with GENEPOP software  [49]  and Bonferroni corrections. Microsatellite variation (number of alleles per locus, allelic richness and observed and expected heterozygosity) was calculated with the programs GENETIX Version 4. 03  [50]  and FSTAT Version 2. 9. 3. 2  [51]. Genetic divergence between populations was estimated using population pairwise F ST values obtained with Arlequin version 3. 0  [52]. To detect recent bottleneck events we employed the software BOTTLE-NECK version 1. 2. 02  [53]  with default settings.
Evolutionary and demographic inferences Population divergence time estimations were computed under a Bayesian Markov Chain Monte Carlo (MCMC) framework using BEAST version 1. 6. 1  [54]. Following a burn-in of 5 million cycles, rates were sampled once every 1 000 cycles from 50 million MCMC steps for an Extended Bayesian Skyline tree, with a stepwise model for mitochondrial DNA and an evolution rate of 1. 5% per million years  [55] [56] [57]. Bayesian intraspecific phylogenies are based on coalescent theory  [58]  and allow the inference of past population dynamics and parameters from contemporary gene sequences. The best evolution model and its priors (kappa, gammashape, proportion of invariant sites, etc. ) were defined by jModeltest software version 0. 11  [59]  using the Akaike information criterion  [60].
Interspecific genetic relationships The software STRUCTURE v. 2. 3. 1  [61]  was employed for estimating the membership of each individual to each species (K = 2; two expected genetic units) under the "Admixture model" which assumes that individuals may have mixed ancestry. Settings were a burn-in period of 70000 steps followed by 700000 MCMC iterations and seven runs. Since there is no clear consensus about the proportion of membership that indicates introgression  [62] , for conservative interpretation we have considered >25% as a threshold for significant introgression  [29]. Confidence intervals of 95% were calculated for all membership values. The software NewHybrids  [62]  was run for identifying individuals of each pure species, hybrids of first and second generation and backcrosses.
An initial run was implemented for only pure species individuals with >0. 980 membership detected from STRUCTURE, in order to assess the discrimination power of our dataset in NewHybrids. A second run was done for 10 hypothetical F1 hybrids and pure species. Then, the MCMC was run for 500000 iterations after a burn-in period of 50000 iterations for our original data set. Gene flow between species in the North Atlantic Ocean was estimated with MIGRATE 3. 0  [63]  from Ө = xNeμ and M = m/μ (x, inheritance parameter; Ne, effective size; m, immigration rate; μ, mutation rate) in each species based on coalescent theory  [64]  and relaxing the original assumption of Wright  [65]. If Ө and M are multiplied together, the number of immigrants per generation can be calculated as gene flow. We used this formula, employing the inheritance parameter x (x = 4 for nuclear DNA [here microsatellite loci] and 1 for mtDNA sequences), to calculate the effective number of immigrants per generation for nuclear and mitochondrial DNA separately.
Population genetics A total of 120 DNA samples provided positive PCR amplification for all mitochondrial and nuclear genetic markers considered. These samples were genetically sexed. More males than females were analyzed for the two species (Table  1 ): 32 males versus 31 females for G. macrorhynchus and 50 males versus 7 females for G. melas. Twenty eight D-Loop haplotypes of 703 base pairs (bp) were found. They are available in GenBank database (https://www. ncbi. nlm. nih. gov/genbank/) under the accession numbers KJ740360-KJ740387. High haplotype diversity and low nucleotide diversity were found in both species but G. melas had lower haplotype and nucleotide diversities, as well as lower number of haplotypes and lower theta values (Table  1 ) than G. macrorynchus. Two microsatellite loci (EV94MN and 468/469) might exhibit possible null alleles (detected with MICROCHECKER) and were discarded from further analyses.
macrorhynchus female x G. melas male] to G. melas. These findings demonstrate that introgression is asymmetrical, with the genome of G. macrorhynchus entering into G. melas genome and not in the opposite direction. In the studied population, hybrids represented 7. 02% of G. melas individuals with an average of 6. 2% introgressed membership estimated from STRUCTURE (Fig 4  ; Table 2 ). The number of alleles in G. melas was indeed higher when hybrids were included in the dataset (45 alleles; Table  1 ) than when they were not (39 alleles; S1 Table ). A high interspecific genetic interchange between species, measured as effective immigrants, was obtained from two different statistical approaches. Based on private alleles and microsatellite data, the interchange between species was estimated at 1.
Acknowledgments We want to thank  B. Mikkelsen  from the  Feroese Museum of Natural History  for the Feroese samples;  S. Lens  from  IEO Vigo ,  A. Rodriguez-Folgar  from  GREMMAR  and  C. Fernandez Maldonado  from  Agencia de Medio Ambiente y Agua de la Junta de Andalucía  for the Iberian Peninsula samples;  M. Carrillo  from  Canarias Conservación  for the  Canary Island samples ,  A. Benet ,  M. Poole  and  S. Baker  for the Polynesian samples, and project  MAPCET-M2. 1. 2/F/012/2011  [ FEDER , the  Competitiveness Factors Operational (COMPETE) ,  QREN European Social Fund , and  Proconvergencia Açores/EU Program ] for Azorean samples. This is a contribution of the  Marine Observatory of Asturias (OMA). The authors declare that none of the authors have any competing interests.
Strain identification by MALDI-TOF MS MALDI-TOF MS protein analysis was performed using a Microflex spectrometer (Bruker Daltonics, Leipzig, Germany), as previously reported  (17). Each separate colony selected was deposited in duplicate on a MALDI-TOF target to be analyzed. A matrix solution of 1. 5 mL (saturated solution of a-cyano-4-hydroxycinnamic acid diluted in 50% acetonitrile, 2. 5% of trifluoroacetic acid, completed with HPLC water) was deposed on each spot. After reading of the plate, the obtained protein spectra were compared with those of the Bruker database (continuously updated with our recent data) in order to obtain a score, which enables, or not, identification of the strain.
Phylogenetic analysis We performed a phylogenetic analysis based on 16S rRNA of our isolate to identify its phylogenetic affiliations with other isolates of the genus Gracilibacillus. Sequences were aligned using Muscle software  (18)  and phylogenetic inferences were obtained using the approximately maximum likelihood method within the FastTree software  (19). Numbers at the nodes are support local values computed through the ShimodairaÁHasegawa test  (20).
Cellular fatty acid analysis Fatty acid methyl ester (FAME) analysis was performed by Gaz chromatography/mass spectrometry (GC/MS). Two samples were prepared with approximately 40 mg of bacterial biomass, each harvested from several culture plates. FAMEs were prepared as described by Sasser  (21). GC/MS analyses were carried out as described before  (22). Briefly, FAMEs were separated using an Elite 5-MS column and monitored by mass spectrometry (Clarus 500 Á SQ 8 S, Perkin Elmer, Courtaboeuf, France). A spectral database search was performed using MS Search 2. 0,   operated with the Standard Reference Database 1A (NIST, Gaithersburg, MD, USA) and the FAMEs mass spectral database (Wiley, Chichester, UK).
Genome annotation and comparison Prodigal was used for open reading frames (ORFs) prediction  (23)  with default parameters. Predicted ORFs spanning a sequencing gap region (containing N) were excluded. Bacterial protein sequences were predicted using BLASTP (E-value 1e (03 , coverage 0. 7 and identity percent 30%) against the clusters of orthologous groups (COG) database. If no hit was found, a search against the non redundant (NR) database  (24)  was performed using BLASTP with E-value of 1e (03 coverage 0. 7 and an identity percent of 30%. If sequence lengths were smaller than 80 amino acids, we used an E-value of 1e (05. PFAMconserved domains (PFAM-A and PFAM-B domains) were searched on each protein with the hhmscan tools analysis. RNAmmer  (25)  was used to find ribosomal RNAs genes, whereas tRNA genes were found using the tRNAScanSE tool  (26).
We predicted the lipoprotein signal peptides and the number of transmembrane helices using Phobius  (27). ORFans were identified if all the BLASTP performed had negative results (E-value smaller than 1e (03 for ORFs with sequence size greater than 80 aa or E-value smaller than 1e (05 for ORFs with sequence length smaller than 80 aa). Artemis  (28)  and DNA Plotter  (29)  were used for data management and for visualization of genomic features, respectively. We used the MAGI homemade software to estimate the mean level of nucleotide sequence similarity at the genome level. It calculated the average genomic identity of gene sequences (AGIOS) among compared genomes  (30). This software combines the Proteinortho software  (31)  for detecting orthologous proteins in pairwise genomic comparisons, then retrieves the corresponding genes and determines the mean percentage of nucleotide sequence identity among orthologous ORFs using the NeedlemanÁWunsch global alignment algorithm.
Genomes from the genus Gracilibacillus and closely related genera were used for the calculation of AGIOS values. The genome of G. massiliensis strain Awa-1 T (EMBL-EBI accession number CZRP00000000) was compared with that of Halobacillus halophilus type strain DSM 2266 (HE717023), Amphibacillus jilinensis strain Y1 (AMWI00000000), Halobacillus trueperi strain HT-01 (CCDJ000000000), Gracilibacillus halophilus strain YIM-C55. 5 (APML00000000), and Gracilibacillus boraciitolerans strain JCM 21714 (BAVS00000000). Annotation and comparison processes were performed in the Multi-Agent software system DAGOBAH  (32) , which include Figenix  (33)  libraries that provide pipeline analysis. We also performed genome-to-genome distance calculator (GGDC) analysis using the GGDC web server as previously reported  (34).
Description of the table salt microbiota community The cultivable halophilic and halotolerant bacterial consortia isolated from the fleur de sel of Camargue included 18 bacterial species (Table  1 ) from 4,303 colonies. MALDI-TOF MS identified 13 species, whereas 16S rRNA gene sequencing identified five other species including a new species (G. massiliensis sp. nov. ). Among the four culture conditions used, only three conditions yielded colonies. All colonies were isolated from media with a concentration of 75 g/L (standard Chapman medium), 100 g/L and 150 g/L NaCl (house-made media). Conversely, in the culture medium containing 200 g/L NaCl, no bacterial colonies were isolated. Among the 18 cultured species, 10 were halotolerant and 8 were halophilic species (Table  1 ). Identification and phylogenetic analysis of the new species MALDI-TOF score obtained for strain Awa-1 T against our database (Bruker database constantly incremented with new data) suggests that our isolate was not a member of a known species.
We added the spectrum from strain Awa-1 T to our database (Fig. 1 ). PCR-based identification of the 16S rRNA of G. massiliensis (EMBL-EBI accession number LN626645) yielded 96. 9% 16S rRNA gene sequence similarity with the reference Gracilibacillus thailandensis (GenBank accession number NR116568), the phylogenetically closest validated Gracilibacillus species (Fig. 2 ). This value was lower than the 98. 7% 16S rRNA gene sequence threshold advised by Meier-Kolthoff et al. (35)  to delineate a new species without carrying out DNAÁDNA hybridization. The gel view demonstrated the spectral differences with other members of the genus Gracilibacillus (Fig. 3 ).
jilinensis, H. trueperi, and G. boraciitolerans, respectively (Table  7 ). The average percentage of nucleotide sequence identity ranged from 72. 17 to 78. 29% at the intraspecies level between G. massiliensis and the two Gracilibacillus species, but it ranged from 52. 49 to 68. 02% at interspecies level between G. massiliensis and other species. Similar results were obtained for the analysis of the digital DNAÁDNA hybridization (dDDH) using GGDC software (Table  8 ). The Awa-1 T strain, moderate halophilic bacterium, was isolated from a sample of cooking salt (Sel de Camargue) when studying salt-tolerant bacteria in salty food in the context of the culturomics project. On the basis of the phenotypic characteristics, phylogenetic and genomic analysis, Awa-1 T strain is proposed to represent a novel species named G.
0Á8. 0 with 75 g/L of NaCl. Using API identification strips, catalase, urease, esterase, lipase, a-galactosidase, b-glucuronidase, b-glucosidase, N-acetyl-b-glucosaminidase, and 4-nitrophenyl-bDgalactopyranoside activities are found positive. Oxidase, nitrate reductase, and indole tests are negative. The isolate is susceptible to doxycyclin, rifampicin, vancomycin, erythromycin, ciprofloxacin, gentamicin, trimethoprim/ sulfamethoxazole, and imipenem, but resistant to metronidazole, amoxicillin, ceftriaxone, and penicillin G. The G'C% content of the genome is 36. 05%. The 16S rRNA and genome sequences are deposited in EMBL-EBI under accession numbers LN626645 and CZR P00000000, respectively. The type strain of G. massiliensis is strain Awa-1 T ( 0CSUR P1441 0DSM 29726) and was isolated from Salt specimen (Salt of Camargue).
( 6 ) Blank levels were below 1 % of the main signal and were subtracted from each sample and standard analysis. All measurements were carried out in a matrix composed of HNO 3 0. 5 mol L -1 , HCl 0. 5 mol L -1 , H 2 SO 4 1 mmol L -1 , and medium-resolution mode (M/ M > 6000) to optimize the separation of 30 Si peak and 14 N 16 O interference and were performed on the interference-free left side of the peak  (Abraham et al. , 2008). δ 29 Si and δ 30 Si were compared to the mass-dependent fractionation line (Fig. S3 ) and samples falling outside of its analytical error were excluded from the final dataset. Typical analytical conditions are provided in Table  S3. Numerous analyses of a secondary reference material such as diatomite (δ 30 Si = 1. 26 ‰,  Reynolds et al. , 2007)  generated over the entire procedure indicated an average precision and a long-term analytical reproducibility (24 months) on δ 30 Si values of 1.
, 2016). We have compared the deep KEOPS-2 samples, measured using a Neptune + MC-ICP-MS (this study) after a chemical purification adapted from  Hughes et al. (2011)  with data from KEOPS-1, measured using a Nu Instrument MC-ICP-MS  (Fripiat et al. , 2011a)  after a chemical purification adapted from De La Rocha et al. (1996). Despite the two different sample processing methods and instrumentation, the two datasets show an offset of only 0. 08 ‰ (Fig. S4 ), which is not analytically significant and allows the direct comparison between KEOPS-1 and KEOPS-2 delta values. In general, δ 30 Si DSi signatures in deep waters were homogeneous and did not show large changes with depth or between stations (on average 1. 28 ± 0. 08 and 1. 05 ± 0. 06 ‰ for UCDW and LCDW respectively). There was much higher variability in δ 30 Si DSi in the ML and WW between stations (1.