dataesr/scientific-paragraphs-categorization huggingface dataset:
A B C Figure  1  Five main phylogenetic hypotheses of chaetognaths relationships. Hypothesis based on morphological data: A, Tokioka  [21] , B, Tokioka  [22] , C, Casanova  [24] , D, Salvini-Plawen  [26]. E, data presented here. A, Aphragmophora; Bi, Biphragmophora; Ch, Chorismogonata; Ct, Ctenodontina; Fl, Flabellodontina; Mo, Monophragmophora; P, Phragmophora; Sy, Syngonata. planktonic species Pterosagitta draco and the benthic Spadellidae. They also questioned the inclusion of Sagitta lyra within the genus Sagitta. In another study, Salvini-Plawen  [26]  proposed the littoral-neritic Pterosagitta draco as the most plesiomorphic species and contradicted the ancestrality of phragms (Figure  1D ). He also omitted the Aphragmophora suborders Ctenodontina and Flabellodontina of Tokioka  [21]  and the Syngonata/Chorismogonata hypothesis of Casanova  [24].
Alignments and erroneous sequences First, we identified erroneous sequences by constructing test trees from LSU and SSU rRNA genes and by then identifying which sequences from public databases came out in suspicious positions when compared with new sequences obtain in the present study. This approach revealed three chaetognath sequences that are erroneous due to contaminant or bad species diagnosis. These sequences that must be excluded from any future phylogenetic analyses were: -The SSU rRNA of Krohnitta pacifica (class I DQ351879 and class II DQ351891) from  [18]  which most likely is a Sagittidae contaminant close to Parasagitta setosa when compared with three new sequences belonging to Krohnitta subtilis. -The SSU rRNA of Sagitta sp (class I AY922316) from  [29]  which belongs to the Eukrohniidae family, close to Eukrohnia hamata and Eukrohnia bathypelagica.
-We also characterised new sequences for three different specimens of Pterosagitta draco that were used instead of the erroneous SSU rRNA sequences (class I DQ351885 and class II DQ351898) from  [18]. After excluding these contaminants, we set up five alignments available upon request (dataset 1, LSU rRNA paralogous genes; dataset 2, SSU rRNA paralogous genes; dataset 3, SSU rRNA paralogy class I; dataset 4, SSU rRNA paralogy class II; dataset 5, concatenated alignment of SSU and LSU rRNA genes).
Molecular phylogenetic analyses Whatever the dataset considered, the Bayesian and approximate likelihood ratio (aLRT) trees obtained were almost identical to the maximum likelihood (ML) bootstrapped tree. Thus their statistical values were reported on the ML topology. However, in the case of the concatenated analysis, because only the aLRT and the Bayesian reconstructions showed a fully congruent topology, statistical values obtained from the three methods were reported on the Bayesian topology. The validity of the Krohnittidae and Heterokrohniidae families will not be discussed because both are only represented here by a single species. Large subunit rRNA. The alignment of 58 sequences of LSU rRNA (49 specimens for the class I and 9 for the class II) was 410 base pairs (bp) long (dataset 1 = LSU rRNA Class I and II).
The best model of evolution estimated with MODELTEST was the K2P + Γ model (with a lnL of -2457. 20). Without the nine class II sequences, the same model was selected (with a lnL of -1483. 09). Using the midpoint rooting, the two classes of LSU rRNA yielded two monophyletic groups (Figure  2 ). The rate of substitution for the unconstrained tree was equal to 8. 16 × 10 -2 ([8. 02 × 10 -2 -8. 30 × 10 -2 ] 95% confidence interval). Only the topology based on class I sequences is discussed. Parametric (approximate likelihood ratio values, aLRTv and posterior probabilities, pp) and non-parametric branch tests (bootstrap values, bv) showed a recurrent lack of resolution for the deep nodes of trees based on LSU rRNA genes. To assess whether a consistent phylogenetic signal could be revealed by reducing the impact of noise, the same phylogenetic analyses were carried out, but this time after treatment of the dataset 1 with the program Aliscore.
The new LSU rRNA alignment (Dataset 1bis) displayed 155 (37. 8%) 'noisy' sites that were filtered out and did not impact on the selected model of evolution (K2P + Γ with a lnL of -1735. 6). Most of these potential 'noisy' sites were located in the loop regions of the LSU rRNA gene class II due to Xenokrohnia sorbei LSU rRNA gene class I alignment. After removing these positions, the accuracy of the trees did not increase (data not shown). Thus, the lack of resolution for deep branching nodes is more likely linked to a lack of phylogenetic information than to a 'noisy' signal due to mismatches in sequence alignment (i. e. , soft polytomy). Because of this, relationships between orders and families were very difficult to assess. LSU rRNA sequences contained information only relevant to the discussion of the relationships between species of a given genus and, to a lesser extent, between genera of the same family.
98/1). Yet Flaccisagitta (two of the three known species are represented) received low support values from the ML analysis and marginal support values from Bayesian analysis (38/0. 84 respectively) whereas this node was not recovered in the aLRT tree. The remaining genera are either monospecific (Caecosagitta, Pterosagitta) or did not appear monophyletic but were grouped in tight assemblages. It is especially the case of large and heterogeneous genera such as Aidanosagitta and Parasagitta. The Mesosagitta genus, represented here by M. minima and M. decipiens, was invalidated. Small subunit rRNA. Three data sets of SSU rRNA sequences were analysed. First, phylogenetic reconstructions were conducted with dataset 2 comprising all available SSU rRNA sequences from both paralogy classes (dataset 2 = SSU rRNA Class I and II, 138 sequences from 33 species: 80 class I and 58 class II sequences).
However, while transitions and tranversions are each responsible for 50% of the substitutions in class I, transitions are involved for 68% in class II. This explains the selection of two different evolutionary models by MODELTEST. Therefore, the difference in distance estimation between the classes I and II is more likely due to the lower frequency of transversion events in class II than to base composition. According to the topology obtained in the analyses of both classes of SSU rRNA genes (see Additional file 1), separate analyses of each paralogy classes were rooted on the monophyletic assemblage consisting of Eukrohniidae + Heterokrohniidae. This rooting was also observed Concatenated analysis. As it is done for large coding genes that contain considerable phylogenetic signal, rRNA genes were concatenated to increase the accuracy of the phylogenetic reconstructions (Dataset 5 = SSU rRNA Class I, SSU rRNA Class II and LSU rRNA Class I concatenated).
The total length of the concatenation based on the two paralogous SSU rRNA class I and class II (1670 and 1087 pb respectively) and LSU rRNA class I (366 pb) was 3123 bp. We conserved as many taxa as possible in the concatenated approach, e. g. , all species showing at least two genes and two specimens. Eukrohnia bathypelagica and Paraspadella gotoi displayed SSU rRNA sequences only for class I and Eukrohnia fowleri only for class II. The model of evolution was estimated on the full concatenated data set without any partitioning of the data. The selected model was the GTR + Γ + I with a lnL of -15473. 70 with the dataset comprising 55 sequences and lnL of -13443. 12 with the dataset comprising 53 sequences. Trees resulting from concatenation (Figure  5 ) showed a very similar topology to the one obtained from the SSU rRNA genes and, to a certain extent, from the LSU rRNA genes, with a better accuracy not only at the family rank but also at the sub family and genus ranks.
779, a HI of 0. 397, and a g1 of -0. 706. As identical morphological sequences had no impact on tree topology, we analysed the complete data set. The consensus topology rooted on midpoint yielded Phragmophora and Aphragmophora monophyletic (data not shown). On the basis of this topology, we conducted two constraint analyses rooted on Phragmophora for characterstate optimization under ACCTRAN and DELTRAN settings (Figure  6A ). In the following, we will only discuss the synapomorphies with a consistency index equal or higher than 0. 6. Within Phragmophora, the relationships were well resolved with the monophyly of the Eukrohniidae (100, character #20 one row of posterior teeth) and the monophyly of Xenokrohnia sorbei + Spadellidae (100, character #7 heterosarcomeric secondary muscle). Within the latter, Spadella was paraphyletic with Paraspadella gotoi as the most derived genus of Spadellidae.
Validity of Biphragmophora and Monophragmophora The morphological analysis supports the division of Chaetognatha into three monophyletic groups, the Biphragmophora, Monophragmophora and Aphragmophora  [24]. However, although the monophyly of the Biphragmophora (Heterokrohniidae) cannot be assessed because only one species has been included in our analyses, none of our molecular or morphological trees show that Monophragmophora (Spadellidae and Eukrohniidae) could be a natural group. All the tree topologies obtained suggest that the lack of phragms in the tail is a homoplasic state, which is observed independently twice in Eukrohniidae and Spadellidae. Yet, the grouping of Eukrohniidae and Heterokrohniidae is well supported for all molecular datasets and morphological phenetic analyses. These results are in accordance with a recent barcoding analysis showing a close relationship between Heterokrohnia and Eukrohnia species in respect to Sagittidae  [28].
Because of the scarcity of novelties at the anatomical level, it becomes needed to explore their body plan variations from the tissue level to the cell level. Considering the pivotal phylogenetic position of Chaetognatha within bilaterians, it is of primary importance to reconstruct their ground pattern. Future studies need to focus on a set of new characters based on a broad range of taxa including specimens belonging to meso-bathypelagic and deep benthoplanktonic genera. The use of an expanded taxonomic dataset combined with appropriate observation (i. e. , with transmission electron microscopy, immunohistochemistry combined with confocal laser scanning microscopy and Next Generation Sequencing) will be crucial in improving the understanding of Chaetognatha's diversification processes.
Collection, species diagnosis and taxonomic sampling Specimens were collected from a broad geographical range sampled during the circum-global Tara Ocean expedition and also from other regional missions (Table  1 ). Table  2  shows the sequences used in the present study already available in public databases. Morphological identifications of specimens were performed using Wild M5 and Nikon SMZ 645 stereoscopic microscopes, and combined with ecological and molecular data from previous literature. For morphological data, comparisons with original descriptions were undertaken. When different species exhibited a very close gross morphology and similar number of teeth and hooks, only mature adults were chosen because one of the key features for the diagnosis of chaetognaths in ethanol is the shape and position of seminal vesicles disposed on each side of the tail.
DNA extractions, PCR amplifications and sequencing All specimens were placed in 80% ethanol for preservation. The genomic DNA was then extracted using the DNAeasy kit (Qiagen, Valencia, CA) from pieces or entire individuals dried on filter paper and devoid of alimentary bolus to prevent contamination. Because the whole ribosomal cluster in Chaetognatha is duplicated  [18, 27]  two sets of specific primers for each paralogous SSU rRNA gene were used to amplify sequences of approximately 1800 bp (class I: 18SCI5′ TTGATGAA ACTCTGGATAACTC and 18SCI3′ GGACCTCTCTACA TCGTTCG) and 1200 bp (class II: 18SCII5′ TCGTCGG GGTCTCATCC and 18SCII3′ AGATACCTCGCAAAAT CG). As we concentrated our efforts on the class I of LSU rRNA gene, which is the most represented class in public databases, only one set of primers previously described in  [27]  was used to amplify a fragment of approximately 500 pb: 28S5′ AAAGGATCCGATAGYSRACAAGTACCG and 28S3′ CCCAAGCTTGGTCCGTGTTTCAAGAC.
After amplification, all PCR fragments were purified with Wizard® SV Gel and PCR Clean-Up System (Promega, Madison, WI), cloned into pGemT-easy vector (Promega, Madison, WI) and sequenced in both directions using the T7 and SP6 primers with a ABI 96-capillary 3730XL sequencer at Eurofins genomics. 145 sequences have been deposited in GenBank under the following accession numbers: -SSU rRNA class I: KM519789 -KM519853 -SSU rRNA class II: KM519854 -KM519901 -LSU rRNA class I: KM519902 -KM519931 -LSU rRNA class II: KM519932 -KM519933. The sequence alignments were established using CLUS-TALX  [55]  and Muscle  [56]  implemented in Mega 5 and then further improved manually. The MODELTEST v3. 0b4 program  [57]  was used to identify the best model of DNA evolution for each of our dataset based on maximum likelihood (ML) and using Bayesian information criterion (BIC).
Adequate burn-in was determined by examining a plot of  Qualitative morphological data were analysed using Paup* 4. 0b10 under maximum parsimony (MP) with a heuristic search with 10 random taxon addition replicates followed by tree bisection and reconnection (TBR) branch swapping. All characters were treated as unordered and unweighted. ACCTRAN (accelerated transformation) and DELTRAN (delayed transformation) character optimization were both used to map the character changes and resolve ambiguous nodes. The g1 statistic was obtained using 1 000 000 random trees. Clade frequencies were obtained by 50% majorityrule consensus trees.
Acknowledgements We are deeply grateful to the Tara schooner and crew for collecting plankton samples all over the World Oceans during three years. Our special thanks go to  Gaby Gorsky  and  Christian Sardet  who facilitated our integration in the  Tara Oceans consortium. We are also keen to thank the following people for providing us with planktonic samples:  Jean-Marc Pagano  from the  Institut de Recherche pour le Développement (IRD) ,  John Lamkin  and  Akihiro Shiroza  from the  Southeast Fisheries Science Centre (SEFSC) ,  Cédric Guigand  from the  Rosentiel School of Marine and Atmospheric Science (RSMAS)  and  Fredrika Norrbin  from the  Department of Arctic  and  Marine Biology  (The  Arctic University of Norway ). Our manuscript profited from stimulating discussion with  Daniel Papillon  who also provided English corrections.
28/0. 81 Eukrohnia fowleri Eukrohnia fowleri Eukrohnia fowleri Eukrohnia fowleri Eukrohnia fowleri 80/1/0. 98, class II 19/1/0. 85). Datasets 3 (SSU rRNA Class I, 1679 bp long) and 4 (SSU rRNA Class II, 1119 bp long) consisted of two in- for the class I and the T92 + Γ with a lnL of -4073. 31 for dependent alignments for each paralogy. Here, we only variable sites (34. 48%). This number decreased to 446 the class II. The substitution rates were equal to 1. 14 × 10 -2 present the analyses based on these two datasets in the SSU rRNA class I (26. 56%) and to 252 in the ([4. 19 × 10 -4 -2. 30 × 10 -2 ] 95% confidence interval) for (Figures 3 and 4 respectively) because separate phylogen-SSU rRNA class II (22. 52%). However, the number of the SSU rRNA class I and 1. 03 × 10 -2 ([4. 83 × 10 -4 - etic reconstructions based on one paralogous gene gave highly supported nodes within the ML phylogenies 2.
14 × 10 -2 ] 95% confidence interval) for the SSU rRNA more robust and accurate results than the full data set (bv >75) increases from 44 when analysing both SSU class II. When looking at the overall alignments based comprising both SSU rRNA paralogy classes (dataset 2). rRNA classes together to 56 (32 in class I and 24 in on both SSU rRNA paralogy classes, they included 579 The selected model was the K2P + Γ with a lnL of -7733. 81 class II) when analysing each class separately. In fact,.
Human tissue samples Three different patient cohorts were included in this study. Human breast cancer biopsies were analyzed using the same methods as for the xenograft models. Gene expression data from breast cancer tissue samples from patients (N = 152) in Cohort 1 (from three different hospitals in the Oslo region, Norway) were used solely to mean center global gene expression data from the xenografts before subclassification. Patient Cohort 2 (N = 50, from two different hospitals in the Trondheim region, Norway) was used to evaluate metabolic characteristics in breast cancer subgroups. The MR spectra from the breast cancer tissue samples were selected from our local spectral database based on ER receptor status. The samples were previously classified as ER positive (N = 37) and ER negative (N = 13) measured by immunohistochemistry (IHC).
The ER staining cut-off point was 10% (that is, <10% considered negative). Breast cancer tissue samples from patients (N = 115) in Cohort 3 (patients enrolled at The Norwegian Radium Hospital) were used for comparative analysis of gene expression between human and xenograft samples. This patient material was collected and gene expression analysis performed as previously described in  [26, 27]. Only basal-like (N = 18) and luminal B samples (N = 14) were selected from the data set, and only expression of genes directly involved in Cho metabolism was used in the analyses. An overview of the xenograft samples, patient cohorts and analyses is shown in Figure  1. The use of all patient materials was approved by Regional Committees for Medical and Health Research Ethics (South East and Central Norway), and informed written consent was obtained from all included patients.
The microarrays were scanned using Agilent Technologies Microarray Scanner (G2505C). Data were extracted from the scanned images using Feature Extraction software (Agilent Technologies), version 10. 7 and protocol GE1-107-Sep09 for mRNA. Four xenograft samples (HBCx-11, HBCx-23, HBCx-29, and HBCx-33) were excluded from further analyses due to poor data quality.
Processing, normalization, and molecular subtyping Raw signals were detrended for multiplicative effects using Agilent's GenomeAnalyzer and log2 transformed. Data from control probes were excluded, as well as spots that were defined as feature outliers from Feature Extraction due to quality assessment and non-uniform signal distribution. Data were then quantile normalized (R, limma package) and missing values imputed using LLSimpute (R, pcaMethods package). The dataset was generated by additionally averaging the signal intensity of the multiple unique probes for each gene based on GeneSymbol as provided by Agilent in the annotation file. This set included data for 21,851 unique genes on 30 microarrays from 30 breast cancer xenografts. In order to determine the molecular intrinsic subtype of the xenograft tumors, the gene expression data from the 30 samples were mean centered against a larger, more heterogeneous human breast cancer gene expression dataset of 152 tumors (Cohort 1): for each probe, the mean expression in the reference dataset was subtracted from the probe signal in each of the xenograft samples.
Probes from the xenograft dataset were matched to the CloneIDs (CLID) representing the intrinsic genes by corresponding GeneSymbol, obtaining Gen-eSymbols corresponding to each CLID from the Source tool  [28]. For each CLID, if there was more than one matching probe, their expression values were averaged. The molecular subclassification of the xenografts was determined based on distinct variation in gene expression pattern of 500 'intrinsic' genes, characteristic for five major molecular breast cancer subtypes. Five expression centroids, calculated for the intrinsic core members of each of the five subclasses  [29]  were used to determine correlation coefficients between the centroids and each of the 30 xenografts, estimating the xenograft molecular subtype by assigning the subtype which had the highest correlation coefficient to each of the xenografts.
Quantification of metabolites Pulse length based concentration determination (PUL-CON) was used for quantification of Cho, PCho and GPC in the xenograft tissue samples. PULCON can be used to measure metabolite concentrations without the use of an internal reference  [30]. For quantification of the metabolites in the human tissue samples in Cohort 2, metabolite peak areas of Cho, PCho and GPC were related to the internal standard TSP and sample wet weight. The areas under the Cho, PCho and GPC peaks were determined by curve fitting (PeakFit v 4, Systat Software Inc) using a combination of Gaussian and Lorentzian line-shapes (Voigt function). Two-sample ttests were performed to assess differences in the metabolite concentrations and ratios between the breast cancer subgroups. The level of significance was set at P <0.
Multivariate statistical analysis of MR spectra For multivariate analysis, the MR spectra from the xenograft samples were converted to ASCII-files, baseline corrected  [31]  and peak aligned  [32]. The spectral area from 3. 0 to 4. 7 ppm was selected for multivariate analysis. Regions in the spectra with high signals from fatty acids, water and ethanol contaminations were excluded. The MR spectra were normalized and mean centered prior to analysis. Principal component analysis (PCA) was performed (PLS toolbox, Matlab) to visualize the spectral characteristics and to compare the metabolic differences between samples.
Statistical analysis of gene expression profiles A total of 55 genes coding for proteins assumed to be directly associated with Cho metabolism were selected. The selection criteria were: 1) a selection of genes involved in the KEGG Homo sapiens glycerophospholipid pathway hsa:00564 which have a possible catalytic specificity in this pathway  [33] ; and 2) genes coding for proteins reported to be involved in Cho transport  [34] , or 3) involved in degradation of GPC  [35]. One gene (PLA2G4B) was excluded because it was not found in the gene array. PCA (PLS toolbox, Matlab) was performed on the gene expression data from the 54 selected Cho genes to compare gene expression characteristics between the xenograft samples. The gene data were mean normalized and mean centered prior to analysis.
Correlation analysis between choline genes in xenografts and human tissue In order to assess if the panel of xenograft models was representative for human disease, the average difference in expression of the 54 Cho-related genes between basal-like and luminal B subtype samples was computed. One gene (ASPG) was excluded because it was not found in the gene array from the patient tissue samples. Fifty three of the genes were present in the dataset both for the patient samples (Cohort 3) and the xenografts. In order to validate the relevance of the xenograft models, a Pearson correlation test was performed between genes from the patient and the xenograft subtypes. For all these analyses, the level of statistical significance was defined at fdr <0. 1.
Multivariate analysis reveals differences in gene expression of genes involved in choline metabolism between subtypes of breast cancer In order to map the gene expression characteristics of the Cho metabolism pathway in the xenograft models, a PCA was performed. Figure  3b  shows the bi-plot from PCA based on the expression data of the 54 genes in the Cho pathway. Most of the luminal B samples are clustered in the bottom left corner of the score plot with a low PC1 and a low PC2 score. These samples are characterized with a high expression of glycerol-3-phosphate dehydrogenase 1-like (GPD1L), phospholipase C, delta 4 (PLCD4) and glycerophosphodiester phosphodiesterase domain containing 3 (GDPD3). Most of the basal-like samples are clustered to the right of the score plot with a high PC1 score.