dataesr/scientific-paragraphs-categorization huggingface dataset:
The GLM-based DDH estimate was 21% ± 2. 33. The found value (< 70%) confirmed that K. massiliensis and isolate JC8E T did not belong to the same species. based on a generally accepted property for the species, or anecdotal evidence). These evidence codes are from the Gene Ontology project  [23]. If the evidence is IDA, then the property was directly observed for a live isolate by one of the authors or an expert mentioned in the acknowledgements. Sequences were aligned using CLUSTALX, and phylogenetic inferences obtained using the neighbor joining method as implemented in the MEGA 5 software package  [24]. Numbers at the nodes are percentages of bootstrap values supporting that node using 1,000 bootstrap replicates to generate a majority consensus tree. Solibacillus silvestris was used as the outgroup.
5% trifluoroacetic acid) per spot was applied. MALDI-TOF MS was conducted using the Microflex LT spectrometer (Bruker Daltonics). All spectra were recorded in linear, positive ion mode. The acceleration voltage was 20 kV. Spectra were collected as a sum of 240 shots across a spot. Preprocessing and identification steps were performed using the manufacturer's parameters. The JC8E T spectra were imported into the MALDI BioTyper software (version 3. 0, Bruker) and analysed by standard pattern matching (with default parameter settings) against the main spectra of 6,300 bacteria, including the spectra from K. gibsonii, K. sibirica, K. zopfii and K. massiliensis, in the BioTyper database. A score enabled the identification, or not, from the tested species: a score > 2. 3 with a validated species enabled the identification at the species level, a score > 1.
Genome project history The organism was selected for sequencing on the basis of its phylogenetic position and 16S rDNA similarity to other members of the genus Kurthia, and is part of a "culturomics" study of the human digestive flora aiming at isolating all bacterial species within human feces. It was the second genome of a Kurthia species, Kurthia senegalensis sp. nov. A summary of the project information is shown in Table  3. The EMBL accession number is CAEW01000000 and consists of 46 contigs (≥500 bp) and 17 scaffolds (> 2,575 bp). Table  3  shows the project information and its association with MIGS version 2. 0 compliance.
Genome annotation Open Reading Frames (ORFs) were predicted using Prodigal  [34]  with default parameters but the predicted ORFs were excluded if they were spanning a sequencing GAP region. The predicted bacterial protein sequences were searched against the GenBank database  [35]  and the Clusters of Orthologous Groups (COG) databases  [36]  using BLASTP. The tRNAscan-SE tool  [37]  was used to find tRNA genes, whereas ribosomal RNAs were found by using RNAmmer  [38]. Transmembrane domains and signal peptides were predicted using TMHMM  [39]  and SignalP  [40] , respectively. ORFans were identified if their BLASTp E-value was lower than 1e-03 for alignment length greater than 80 amino acids. If alignment lengths were smaller than 80 amino acids, we used an Evalue of 1e-05. Such parameter thresholds have been used in previous works to define ORFans.
The major fatty acids are C15:0 iso 50. 75% and C15:0 anteiso 24. 05%. The polar lipids present are diphosphatidylglycerol, phosphatidylglycerol, phosphatidylethanolamine, phospholipids 1 and 2, unidentified aminophospholipid and glycolipid. The peptidoglycan type is A4αL-Lys←D-Glu (type A11. 33). Cells are susceptible to penicillin G, amoxicillin, amoxicillin plus clavulanic acid, imipenem, gentamycin, erythromycin, doxycycline, rifampicin, vancomycin and nitrofurantoin. The genome is 2,975,103 bp long with a 38. 21% G+C content. A 36. 3 kb prophage, KS1, was identified. The type strain is JC8E T (= CSUR P138 T = DSM 24641 T ). The 16S rRNA gene sequence was deposited in GenBank with the accession number JF824796. The whole genome shotgun sequence of K. senegalensis strain JC8E T was deposited in GenBank/DDBJ/EMBL under accession number CAEW01000000.
Optimal S nutrition (508. 7 M SO 4 2Ϫ ) was applied in control plants, and mineral S restriction (low-sulfur (LS) treatments) corresponding to 8. 7 M SO 4 2Ϫ was applied at GS32 (bolting stage) for LS32 treatment, GS53 (visible bud to early beginning of flowering) for LS53 treatment, or GS70 (start of pod filling) for LS70 treatment, until the end of the growth cycle. At the mature seed stage, the number of mature seeds per plant was determined for four replicates per treatment. Seeds were then used for all the analyses. The yield component data and amounts of total oil, total proteins, and glucosinolates were previously published by  Dubousset et al. (4). Determination of Total S and S-proteic Contents-Freeze-dried samples were ground to a fine powder, weighed, and placed into tin capsules for determination of the total S content using an elemental analyzer (EA3000, EuroVector, Milan, Italy) connected to a continuous-flow isotope ratio mass spectrometer (Isoprime, GV Instruments, Manchester, UK).
Image analyses and spot volume quantification were carried out with Progenesis SameSpots v3. 0 software (Nonlinear Dynamics, Newcastle upon Tyne, UK) according to the manufacturer's protocol. Molecular weights (M r ) and isoelectric points (pI) were calculated according to the migration of standard proteins (Bio-Rad). Protein Identification via Electrospray Ionization-Liquid Chromatography-Tandem Mass Spectrometry-Spots of interest were excised, washed several times with water, and dried. Trypsin digestion was performed overnight with a dedicated automated system (MultiPROBE II, PerkinElmer Life Sciences, Courtaboeuf, France). The gel fragments were subsequently incubated twice for 15 min in a 0. 1% CH 3 CN solution to allow extraction of peptides from the gel pieces. Peptide extracts were dried and dissolved in starting buffer for chromatographic elution; the buffer consisted of 3% CH 3 CN and 0.
1% HCOOH. Peptides were enriched and separated using lab-ona-chip technology (Agilent) and fragmented using an on-line XCT mass spectrometer (Agilent). The software used to generate the peak list was Data Analysis for 6300 series Ion Trap LC/MS (v3. 4; Bruker Daltonique, Wissembourg, France). Identification was performed using X!Tandem, developed by the Global Proteome Machine organization, and the NCBI nonredundant protein sequence database filtered for viridiplantae entries (released February 11, 2012; v4; 1,945,867 entries). The parameters used in the X!Tandem search were as follows: enzyme, trypsin; number of missed cleavages, 1; fixed modifications, carbamidomethylation of cysteine; variable modifications, oxidation of methionine; lower window mass tolerance for precursor ions, 1 Da; upper window mass tolerance for precursor ions, ϩ2.
5 Da; mass tolerance for fragment ions, 0. 5 Da. Identified proteins were filtered using the X!TandemPipeline (http://pappso. inra. fr/bioinfo/xtandempipeline/) with the following criteria: (i) two or more different peptides with an E value less than 0. 05 and (ii) a protein log E value (the log of the product of unique peptide E values) less than 4. The results of proteomic analysis and protein identification were incorporated into the PROTICdb database  (27) , a Web-based application that allows storage and querying of information related to proteomic analysis (http://moulon. inra. fr/protic/sms). These results are also available in a synthetic form (supplemental Table  S3 ). The identified proteins for spot 233 were filtered on the basis of only one peptide and subsequently a protein log E value less than 1, because of the small size of the identified protein (79 amino acids; see supplemental Table  S4 ).
E. ). The effects of LS70, LS53, and LS32 treatments relative to the control conditions were subjected to statistical analysis using Mi-crosoft® Excel 2008/XLSTAT©-Pro (Version 7. 2, Addinsoft, Inc. , Brooklyn, NY). With a statistical significance postulated at p Ͻ 0. 05, the Mann-Whitney test was done to compare data obtained for LS and control seeds. Protein spots specifically induced and repressed in LS70, LS53, and LS32 seeds were subsequently analyzed via electrospray ionization-liquid chromatography-tandem mass spectrometry.
05 or **p Ͻ 0. 01. The assigned best-matched protein is listed with the organism in which it was identified and its GenBank protein accession number. The log (E value), the percentage of sequence coverage (CV), and experimental (Exp. ) and theoretical (Theo. ) pI/M r values obtained are also indicated. with the three treatments applied, except for one spot over three in LS70 seeds that was increased (spot 179). This protein, along with a reduced form of glutathione, is involved in methylglyoxal detoxification formed from nonenzymatic phosphate elimination of glyceraldehyde-3-phosphate or dihydroxyacetone phosphate  (34). Modulation of Proteins Involved in Plant Stress Response and Lectins-In LS70 and LS53 seeds, there was decreased accumulation of catalase (spots 53 and 185) and glutathione-S-transferase (spots 122, 169, and 218; Table  VII ).
DISCUSSION This study clearly demonstrated that the effect of S limitation on components of seed yield depend on the timing of the deficiency during the ontogenetic cycle of B. napus. Previous research emphasized the crucial role of leaves as the major source organ for S in response to S restriction (4). Despite enhancement of the S remobilization processes, the LS70, LS53, and LS32 treatments led to a reduction of the S content Significant analysis of variance was followed by a Mann-Whitney test (p Ͻ 0. 05) carried out on the normalized spot volumes (n ϭ 6 for control; n ϭ 4 for LS70, LS53, and LS32). Values indicate fold changes in protein abundance in LS seeds relative to control seeds in a linear scale. Significant differences from the control were at *p Ͻ 0. 05 or **p Ͻ 0. 01. The assigned best-matched protein is listed with the organism in which it was identified and its GenBank protein accession number.
The log (E value), the percentage of sequence coverage (CV), and experimental (Exp. ) and theoretical (Theo. ) pI/M r values obtained are also indicated. Values indicate fold changes in protein abundance in LS seeds relative to control seeds on a linear scale. Significant differences from the control were at *p Ͻ 0. 05 or **p Ͻ 0. 01. The assigned best-matched protein is listed with the organism in which it was identified and its GenBank protein accession number. The log (E value), the percentage of sequence coverage (CV), and experimental (Exp. ) and theoretical (Theo. ) pI/M r values obtained are also indicated. in mature seeds, which could explain the reduced accumulation of S in proteins, GLS, and glutathione (Tables  I  and III ). Late S deficiency (applied at the start of pod filling (i.
Significant differences from the control were at *p Ͻ 0. 05 or **p Ͻ 0. 01. The assigned best-matched protein is listed with the organism in which it was identified and its GenBank protein accession number. The log (E value), the percentage of sequence coverage (CV), and experimental (Exp. ) and theoretical (Theo. ) pI/M r values obtained are also indicated. example, the significant decreases in phosphoglycerate kinase and enolase (Fig. 7 ) might be a source of disturbance in C flux through glycolysis that might contribute to the reduced fatty acid content observed in LS53 and LS32 seeds. Also, the results suggest a redirection of the glycolic flux toward methylglyoxal metabolism previously shown to be activated by various abiotic stresses  (36). It is worth noting in this context that the abundance of chloroplastidial malic enzyme is up-regulated by S deficiency, suggesting that the PEP carboxylase-MDHmalic enzyme pathway is induced under S-limited conditions and that its contribution to the acetyl-CoA supply for fatty acid synthesis is substantial.
Acknowledgments-We thank  Mrs. Nathalie Nesi  and  Veronique Gautier  ( IGEEP, INRA Rennes, France ) for their help in seed composition analysis,  M. Julien Besnard  ( UMR EVA, Caen, France ) for the obtention of germination data,  Mrs. Delphine Aime ( INRA Dijon, France ) for her assistance in the achievement of two-dimensional gel electrophoresis,  Mr. Laurent Coquet  ( Plateforme de proteomique, Rouen, France ) for LC/MS-MS analyses, and  Mr. Olivier Langella  ( PAPPSO, UMR de Genetique Vegetale, INRA Gif-sur-Yvette, France ) for PROTICdb assistance. * This work was supported by the national  SERAPIS program  ( Optimization of the Sulphur Fertilization: Development of Innovative Fertilizers and Indicators of Sulphur Nutrition in Plants). The SERAPIS program is coordinated by the private company TIMAC AGRO INTERNATIONAL-ROULLIER Group and benefits from financial support from the  Region Basse-Normandie, Region Bretagne, Region Pays-de-la-Loire, FEDER Basse-Normandie, Fonds Unique Interministeriel, Roullier, Angers Loire Metropole.
Since the ever-increasing availability of phylogenetic informative data, the last decade has seen an upsurge of ecological studies incorporating information on evolutionary relationships among species. However, detailed species-level phylogenies are still lacking for many large groups and regions, which are necessary for comprehensive large-scale eco-phylogenetic analyses. Here, we provide a dataset of 100 dated phylogenetic trees for all European tetrapods based on a mixture of supermatrix and supertree approaches. Phylogenetic inference was performed separately for each of the main Tetrapoda groups of Europe except mammals (i. e. amphibians, birds, squamates and turtles) by means of maximum likelihood (ML) analyses of supermatrix applying a tree constraint at the family (amphibians and squamates) or order (birds and turtles) levels based on consensus knowledge.
For each group, we inferred 100 ML trees to be able to provide a phylogenetic dataset that accounts for phylogenetic uncertainty, and assessed node support with bootstrap analyses. Each tree was dated using penalized-likelihood and fossil calibration. The trees obtained were well-supported by existing knowledge and previous phylogenetic studies. For mammals, we modified the most complete supertree dataset available on the literature to include a recent update of the Carnivora clade. As a final step, we merged the phylogenetic trees of all groups to obtain a set of 100 phylogenetic trees for all European Tetrapoda species for which data was available (91%). We provide this phylogenetic dataset (100 chronograms) for the purpose of comparative analyses, macro-ecological or community ecology studies aiming to incorporate phylogenetic information while accounting for phylogenetic uncertainty.
Squamata and Testudinae The list of European Squamata species was extracted from Maiorano et al  21. DNA sequences of 7 nuclear (BDNF, c-mos, NT3, PDC, R35, RAG-1, RAG-2) and 6 mitochondrial loci (12S, 16S, COI, cytB, ND2, ND4) were downloaded from Genbank with PHLAWD 22. These regions have been shown to be useful for phylogenetic inference in previous studies of squamates according to Pyron et al  14. Only 16 species of a total of 239 had no molecular data available in Genbank. In addition to Squamata species, we included 3 levels of outgroup taxa: Sphenodon punctata (closest living relative to Squamata); all 10 species of European turtles, two crocodilians (Alligator and Crocodylus) and two birds (Dromaius and Gallus); and finally two mammals (Mus and Pan).
Genbank accession numbers are detailed in Table S1 (Appendix 1). For each region, DNA sequences were aligned with MAFFT  23  and checked by eye with Seaview 24. Ambiguous alignment positions were trimmed with trimAl  25. All the regions were concatenated in a supermatrix with FASConCAT  26. The phylogenetic inference analysis was conducted with RaxML v. 7. 8. 1 27 using the GTRGAMMA model and employing the rapid hill-climbing algorithm  28  ; we searched for 100 Maximum Likelihood trees applying a family tree constraint for squamates based on Pyron et al  14. Bootstrapping was conducted with Since the pioneering work of Dan Faith  7  , conservation biology has long recognized the importance of considering phylogenetic diversity as a relevant feature for conservation  8 , 9. The EDGE framework is, in this regard, an important initiative that combines the evolutionary distinctiveness of species (i.
For instance, incorporating phylogenetic diversity in reserve design or gap analysis only require a complete phylogenetic tree for the entire group with the region of interest (see for example  19 , 85  ). It should however be noted that since the complete coverage only concerns Europe, estimates of phylogenetic uniqueness are therefore biased and should be accounted for in the analysis of the data (e. g. 86 ). For that purpose, we here construct and provide a phylogenetic dataset for all Tetrapoda species that occur in the entire European sub-continent (including Turkey) built on relevant phylogenetic data in Genbank and consensus tree knowledge, based on a supermatrix-supertree mixed approach  20. We also check the congruence of the phylogenies obtained with previous evolutionary studies.
Genbank accession numbers are detailed in Table S1 (Appendix 1). For each region, DNA sequences were aligned with MAFFT  23  and checked by eye with Seaview 24. Ambiguous alignment positions were trimmed with trimAl  25. All the regions were concatenated in a supermatrix with FASConCAT  26. The phylogenetic inference analysis was conducted with RaxML v. 7. 8. 1 27 using the GTRGAMMA model and employing the rapid hill-climbing algorithm  28  ; we searched for 100 Maximum Likelihood trees applying a family tree constraint for squamates based on Pyron et al  14. Bootstrapping was conducted with 2 PLOS Currents Tree of Life replicates to assess clade support. The 100 ML trees were dated with penalized-likelihood as implemented in r8s 29 ; we constrained 5 nodes based on fossil information extracted from Mulcahy et al.
30  : we set a minimum and a maximum age of 256 and 300 mya respectively for the most recent common ancestor (mrca) of all Reptilia 31 , 32 , a minimum and a maximum age of 239 and 250 mya respectively for the mrca of Birds and crocodilians 32 , a minimum age for the mrca of Lepidosauria of 223 mya  33 , 34  , a minimum age of 111 mya for the stem branch of Amphisbaenidae  34 , 35  , and a minimum age of 93 mya for the stem branch of Alethinophidia 36. The best smoothing value was determined by a cross-validation procedure, following  29. The data matrix and the phylogenetic tree with the highest likelihood are available in Treebase (accession number: S15708).
Amphibians For Amphibians, we include here the phylogenetic tree constructed for a previous study  19. The list of European Amphibian species was extracted from Maiorano et al  21. We retrieved from GenBank sequences of phylogenetic informative regions that were available for at least 30% of the species: 9 mitochondrial (12S, 16S, COI, cytb, ND1, ND2, ND4, tRNA-Leu, tRNA-Val) and 2 nuclear (RAG-1, rho) regions. We found relevant molecular data for all species, but we excluded the two hybrid species Pelophylax grafi and Pelophylax hispanicus. We included Xenopeltis unicolor, Gallus gallus and Mus musculus as outgroups to root the tree. For each region, alignment was conducted with four programs (Clustal 37 , Kalign38, MAFFT  23  , MUSCLE  39  ). The best resulting alignment was selected based on Mumsa 38 , and checked visually.
Birds We include here 100 dated phylogenetic trees for 430 species of European breeding birds from Roquet et al  20. This phylogenetic dataset was built upon sequences retrieved from GenBank for 10 mitochondrial gene regions (12S , ATP6 , ATP8 , COII , COIII , ND1 , ND3 , ND4 , ND5 , ND6) and six nuclear ones (28S , c-mos, c-myc , RAG-1 , RAG-2 , ZENK). The alignment procedure was the same as for Amphibians. We also performed 100 ML phylogenetic inference searches and standard bootstrapping (1000 replicates) with RaxML, applying a tree constraint at the ordinal level based on Hackett et al  15. The 100 trees were dated with penalized likelihood (r8s) applying fossil calibrations for 14 clades (Table  S2 , Appendix 2). The best ML tree can be found in Treebase (study number 10770).
Mammals The phylogenetic data here included for mammals is based on the super-tree of Fritz and colleagues  46  ; concretely, we extracted the resampled dataset of 100 fully resolved phylogenetic trees from Kuhn et al. 47  , replicates to assess clade support. The 100 ML trees were dated with penalized-likelihood as implemented in r8s  29  ; we constrained 5 nodes based on fossil information extracted from Mulcahy et al. 30  : we set a minimum and a maximum age of 256 and 300 mya respectively for the most recent common ancestor (mrca) of all Reptilia 31 , 32 , a minimum and a maximum age of 239 and 250 mya respectively for the mrca of Birds and crocodilians 32 , a minimum age for the mrca of Lepidosauria of 223 mya  33 , 34  , a minimum age of 111 mya for the stem branch of Amphisbaenidae  34 , 35  , and a minimum age of 93 mya for the stem branch of Alethinophidia 36.
Supertree construction The trees cited above were combined, after pruning the outgroups, by joining them with the R package ape; to do so we set divergence ages between these main groups based on the information retrieved in the webpage Timetree  49  : the divergence age between mammals and sauropsids (i. e. birds, turtles and squamates) was set to 324 mya, and the divergence age between amphibians and the rest of the groups was set to 361 mya. To build the final tetrapod tree, we randomly selected one tree from each of 100 trees available for each group. We repeated this approach 100 times to get 100 realisations of the tetrapod tree. These combinations were done randomly since the likelihoods of the trees of each group were not significantly different according to the SH test. The 100 dated trees of each group and the 100 dated supertrees for all European Tetrapoda are available from the Dryad digital repository (DOI: X).
Conclusion We provide here a phylogenetic dataset constituted of 100 chronograms of European Tetrapoda species as a tool for ecological studies that aim to incorporate an evolutionary perspective, and for phylogenetic conservation assessment. This phylogenetic dataset is in general agreement with previous studies, and we expect it to be coarsely approximate with the "true" Tetrapoda evolutionary tree. Instead of providing the best ML tree for every group, we provide 100 trees (available on Dryad repository), as computing analyses with several trees allows taking in account phylogenetic uncertainty. Regarding the taxonomic sampling, the big majority of species are included (91%). On the other side, some molecular regions have low sampling, thus, this dataset will be useful until substantial amount of molecular data becomes available for a considerable number of species.
As one of the referees noticed, it may be possible to get the same solution as SICL by including the illustrative variables in the model, as it is the case for example for the Iris data set. But not including them in the model provides a stronger evidence of the link between the illustrative variables and the clustering for the very reason that the illustrative variables are not involved in the design of the clustering. A possible limitation of SICL is the conditional independence assumption on which its heuristics rely (see Section 3). The simulation study described at the end of Section 4. 2 illustrates the situation where u is not conditionally independent on y given z for the number of clusters selected by BIC or ICL. In such a case SICL could highlight clusterings for which u and y are conditionally independent and tends to select a model with a higher number of clusters than BIC and ICL.