dataesr/scientific-paragraphs-categorization huggingface dataset:
Genome sequencing analysis of meningococcal isolates We first performed a deep genetic analysis on the MSM isolates. The origins of the isolates and their corresponding clinical presentation and typing are shown in the S1 Table. Genome sequencing of several invasive isolates from the outbreak among MSM in 2012-2013 and isolates from cases of urethritis/proctitis isolated in 2006-2012 was performed. We analyzed the phylogenetic relationships between these isolates and other C:P1. 5-1,10-8:F3-6:cc11/ET-15 isolates from children and adolescents, which were identified in Germany in 2003 in four Federal States. The isolates were from sporadic disease and from two clusters (European Nucleotide Archive (ENA) study accession number PRJEB7500)  [28]. A core genome multi-locus sequence typing (cgMLST as designated in the SeqSphere + software used here) approach was chosen for genome comparison  [31, 32].
Proteomic analysis of meningococcal isolates We next used recently published proteomic technology to screen for differentially expressed proteins  [29]. Three invasive cc11/ET-15 isolates each from MSM in Berlin and from one of the adolescent clusters unrelated to the MSM community were compared  [28]. The mass spectrometry based proteomics data have been deposited to the ProteomeXchange Consortium (http://proteomecentral. proteomexchange. org) via the PRIDE partner repository  [33]. The dataset ID is PXD001498 http://proteomecentral. proteomexchange. org/cgi/GetDataset?ID= PXD000181. Among the differentially expressed proteins, copper containing nitrite reductase AniA was studied further, as it was only expressed in MSM isolates, but not in isolates from adolescents. AniA is located in the outer membrane of Neisseria, where it is expressed under low-oxic conditions.
Acknowledgments The French Reference Laboratory is funded by the  Institut de Veille Sanitaire  and the  Institut Pasteur. The German Reference Laboratory is funded by the  Robert Koch Institute, Berlin, Germany. The genomics work was funded by the  European Community 's  Seventh Framework Program  [grant number FP7/2007-2013 to DH] under Grant Agreement  N°278864  in the framework of the  EU   PathoNGenTrace  project. The animal work was also supported by a grant from the  ERA-NET   PathoGenomics  ( ANR-08-PATH-003-01 ). The proteome work was funded by  Bundesministerium für Bildung und Forschung  grant  Medizinische Infektionsgenomik: Proteomics von Meningokokken und Pneumokokken ,  Teilprojekt Würzburg ,  Foerderkennzeichen 0315828D , to U. V. and  0315828A  to D. B. This publication made use of the Neisseria Multilocus Sequence Typing website (http://pubmlst.
GetDataset?ID= PXD000181). Funding: The French Reference Laboratory is funded by the  Institut de Veille Sanitaire  and the  Institut Pasteur. The German Reference Laboratory is funded by the  Robert Koch Institute, Berlin, Germany. The genomics work was funded by the  European Community 's  Seventh Framework Program  [grant number FP7/2007-2013 to DH] under Grant Agreement  N°278864  in the framework of the  EU   PathoNGenTrace  project. The animal work was also supported by a grant from the  ERA-NET   PathoGenomics  ( ANR-08-PATH-003-01 ). The proteome work was funded by  Bundesministerium für Bildung und Forschung  grant  Medizinische Infektionsgenomik: Proteomics von Meningokokken und Pneumokokken ,  Teilprojekt Würzburg ,  Foerderkennzeichen 0315828D , to UV and  0315828A  to DB. This publication made use of the Neisseria Multilocus Sequence Typing website (http:// pubmlst.
Botanical inventory For each plot, plant coverage was assessed using the Brau ¨n-Blanquet abundance index  [46]. This method measures the percentage of plant coverage by using coefficients ranked from + (representing less than 1% of coverage) to 5 (between 75 and 100% of coverage). Plants were identified for most of them at a species level, with the support of the Herbarium of IRD (NOU) (Noumea, New Caledonia). For further analysis, epiphytes and liana species were removed from the dataset to retain only species related to the soil.
Illumina data processing The Illumina sequence reads were processed using Mothur version 1. 34. 0  [51]. The forward and reverse sequences were aligned to generate 'contigs' for each sample. Sequences that contained one or more ambiguous reads and homopolymers greater than 8 bp were removed. Reads shorter than 250 bp and longer than 300 bp for bacteria, and shorter than 150 bp for fungi were discarded. Identical sequences were regrouped in unique sequences for shorter computational time. Then, putative chimeras were checked and removed using UCHIME  [52] , available on Mothur software. The number of sequences for each sample was normalised to have the same number as the smallest number of sequences found in a sample. Such an approach was undertaken to get unbiased estimators of the soil microbial diversity.
Bacteria subsampling was performed for 3,025 sequences per sample. Compared to the bacteria, the fungal dataset had to be analysed twice. During the first analysis, all of the samples were normalised to 210 sequences, corresponding to the lowest number of reads found in our samples. The four Rivière Blanche sedge-dominated formations had the lowest number of sequences (between 210 and 300), which may be due to the low fungal richness explained by the particularly low plant cover in this plant formation (see Results and Discussion sections). The second time, to avoid an underestimation of fungal richness present in other plant formations, the four Rivière Blanche sedge-dominated formation samples were removed, and the remaining dataset was subsampled at 1,591 sequences per sample. Once normalised, uncorrected pairwise distances between the DNA sequences were calculated; then, the sequences were clustered to assign sequences to OTUs with 97% homology as commonly admitted  [53] [54] [55]  using the furthest neighbour method  [56].
Each sequence, then each OTU, was taxonomically assigned by using the database UNITE  [57]  for fungi and SILVA v4 for bacteria  [58]. Once all steps were done, all OTU sequences obtained in the first analysis (210 sequences subsampling) for Rivière Blanche sedge-dominated formations were compared one by one with the OTU sequences from the second dataset (1,591 sequences subsampling) and were merged to create the final dataset. A total of 798,073 reads for bacteria and 288,957 reads for fungi were obtained after the first sequence-processing step (S1 Table ). After filtering, the numbers of remaining sequences fell to 322,807 reads for bacteria and 245,137 reads for fungi. Finally, 12,493 OTUs for bacteria and 3,741 OTUs for fungi were identified. After the exclusion of singletons, 6,539 OTUS for bacteria and 2,722 OTUs for fungi were used for further analyses.
The OTUs defined for bacteria and fungi were used to perform rarefaction curves and rank-abundance diagrams. A Bray-Curtis dissimilarity index was calculated and ordinated by Nonmetric Multi-Dimensional Scaling (NMDS)  [62]  to observe the dissimilarities in microbial community composition per plot. Congruence of NMDS was tested by two-way PERMANOVA analyses  [63] , with again site and plant formation as factors. The Edaphic parameters per plot were analysed with a Principal Component Analysis (PCA)  [64]. The relationships between bacterial and fungal communities were studied by plotting the Bray-Curtis distance matrices obtained for the bacterial and fungal datasets, and drawing a trending curve. Finally, to determine the relationships between bacterial or fungal community and environmental parameters (botanical inventory and edaphic parameter), distance-based linear model (distLM) were performed with PRIMER-E v7 software  [65].
Bacterial and fungal composition and diversity The taxonomic assignment for most sequences and OTUs found in this study indicated that members of Proteobacteria, Planctomycetes and Acidobacteria phyla were the most predominant in our bacterial dataset, followed by Actinobacteria and Verrucomicrobia (Table  1 ). Among the fungi, Basidiomycota and Ascomycota were the most dominant phyla, with other phyla representing less than 10% of all sequences and OTUs (e. g. , Zygomycota, Glomeromycota, Chytridiomycota) (Table  1 ). Based on the number of sequences per phylum, the phylum composition for each plant formation per site revealed that the most predominant bacterial phyla were the same regardless of the plant formation or site (Fig 2A. 1 ). For fungi, the barplot per plant formation and site seems to indicate that Basidiomycota was the most abundant phylum in the two type of rainforest in Rivière Blanche and in Nothofagus dominant rainforest in Kopeto, whereas Ascomycota was the most abundant in all other plant formations (Fig 2A.
However, for fungi no genus dominated in all plant formations and at all sites (Fig 2C ). Rarefaction curves were performed for each site on the bacterial and fungal datasets. The bacterial and fungal curves did not reach a plateau (S5 Fig) , but the fungal curves had lower slopes than the bacterial curves and seemed to start to level off. However, the fact that the curves did not reach a plateau clearly indicates that the species diversity has not yet been assessed. To assess the level of diversity within the bacterial and fungal communities, species richness (S) (here, the number of OTUs), Chao index, Simpson index (1-D) and Pielou evenness (J) were calculated by plots (S3 Table ) and by plant formation per site (Table  2 ). ANOVA analyses revealed significant differences in species richness in both bacterial and fungal communities among the formations of the two sites (Table  3 ).
Bacterial and fungal community structure Floristic and site effects. To compare the OTU composition and abundance of the bacterial and fungal communities, NMDS ordinations, based on Bray-Curtis dissimilarity, were performed (Fig  3 ). The NMDS ordination indicated a distinction between forest and non-forest formations. Moreover, a distinction between monodominant and mixed rainforests was also visible. To corroborate the NMDS result, PERMANOVA were carried out (Table  4 ). The results indicated that the bacterial and fungal communities were significantly partitioned by the interaction between the two variables (plant formation x site) (Table  4 ). This interaction explained 15. 8% and 14. 8% of the variability of bacterial and fungal communities, respectively. PERMANOVA and NMDS were also conducted on the 210 sequences of fungal dataset and exhibited similar results to the 1,591 sequences dataset that were obtained (S6 Fig and  S4  Table ).
Bacteria can also present a host preference  [72, 73]. Indeed, nitrogen-fixing bacteria are associated with the roots of certain plant groups. For instance, Rhizobium is commonly associated with leguminous plants  [72] , or Actinomycetes such as Frankia with Casuarina plants  [73]. In our dataset, ECM fungi and bacteria, such as Bradyrhizobium were present and may subsequently explain the plant formation structure observed in microbial communities. Plant species may also impact the soil microbial community via the type of litter produced  [74, 75]. Moreover, root exudates can affect the soil microorganisms. Each plant species produces root exudates (mainly carbohydrates, organic acids and amino acids), which have been determined to vary in terms of their composition from each other  [76]. These exudates attract some particular microorganisms that can improve plant growth  [4, 77].
These groups of bacteria, called Mycorrhiza Helper Bacteria (MHB), were shown to improve fungal mycorhization  [19] , such as Pseudomonas monteilii which increased ECM and AM colonisation as well as induced the formation of rhizobial nodules on Acacia holoserica  [20]. The effect of MHB was demonstrated to be fungal specific, not plant specific  [20]. In our dataset, bacteria genera known to be MHB were detected, which may partially explain the relationship between bacteria and fungi.
Acknowledgments We thank the  'Direction des Infrastructures, de la Topographie et des Transports Terrestres (DITTT) ' of the  Government of New Caledonia  for providing the orthophotos (Convention n  ˚CS10-3170-0700 ). We thank the  'Plateforme du Vivant'  ( Noumea, New Caledonia ) for access to their facilities and the  'Plateforme Genome-Transcriptome' of Bordeaux University (Bordeaux, France)  for performing Illumina MiSeq sequencing. We thank  Christian Montagnat  and  Joseph Manaute , who allowed us to access to the  Kopeto mine and 'Rivière Bleue' Provincial Park , respectively. Thanks to the field work crew of  IAC ('UniteSARA'): Alexandre Bouarat, ReneGuiglion ,  Anthony Pain  and  Hyppolite Lenoir  for their substantial assistance in the field. Finally, a special thanks to  Laurent Wantiez ,  Laurent Bordez  and  Thomas Ibanez  for their help in data analysis.
5d ). Expression profiles suggest distinct cells of origin. The developmental window comprised between E6 and E10 is characterized by the commitment of stem cells to specified progenitors. We therefore postulated that Smarcb1 inactivation at E6-E10 may target such stem or progenitor cells, as cells of origin of the tumours. To get insights in which cells gave rise to which mouse tumours, we correlated transcriptome profiles from our study with transcriptome profiles from stem or progenitor cells from the cephalic region at E6 to E10 in publicly available databases (embryonic Stem Cell (GSE44175), neural progenitor (GSE44175 and GSE44175), neuron (GSE46150), oligodendrocytes derived from neural stem cells (GSE9566), astrocytes derived from neural stem cells (GSE9566), ectomesodermal tissue from the palate (GSE9566) and neural crest (GSE11149)).
As shown in Fig. 6a , the highest correlation for mIC was obtained with neuron progenitors, while mE/IC best correlated with ectomesenchyme, a cephalic neural crest cell-derived mesoderm. We then similarly compared human tumours with human stem or progenitor cell profiles available in public databases (Fig. 6b ). The most striking correlation was found between hIC2 and neuro-epithelium, neural stem cells and neuron progenitors; hIC1 and hIC3 showed weaker correlations with these early neuronal lineages. Conversely, high correlation was observed between hEC and embryonic stem cells as well as neural crestderived cells. To corroborate these correlations based on genome-wide expression, we looked in more detail at the expression pattern of stem or progenitor cell-specific markers, both in mouse and human tumours.
Frozen samples were provided by French tumour banks from consenting patients. Approval for this study was received from the Institutional CEST review board (Comite d'Evaluation et de Suivi de recherche Translationnelle) form Curie Institute. Quantitative RT-PCR. Reverse transcription was carried out on 0. 5 mg total RNA from human and mouse tumours with oligo(dT) primers following the manufacturer's instructions (High capacity cDNA Reverse Transcription Kit, Applied Biosystems). Expression levels of genes were analysed using real-time, quantitative PCR. All amplifications were done using the SYBR Green PCR Master Mix (Applied Biosystems) and the CFX384 machine (Bio-Rad). The relative quantification for gene expression was determined using the 2 À DDCt method. The TBP gene was used as internal control for data normalization.
Primers used in this publication are available in Supplementary Table  2. Microarray analysis. Micro-array data have been deposited in the GEO database under accession code GSE64019. In Smarcb1 flox/flox ;Rosa26-Cre ERT2 mice, transcriptomes of seven Smarcb1deficient lymphomas, nine Smarcb1-deficient intracranial tumours and one soft-tissue tumour were analysed. From the Smarcb1 delx_ex2/wt mouse model, only one face tumour was analysed. The transcriptomes of seven Ctnnb del_ex3 lymphomas (GSE7050), three neuroblastomas (previously published, GSE46583)  14  and four medulloblastomas were analysed. In humans, 20 extra-CNS RTs, 30 intra-CNS RTs, 8 SHH medulloblastomas and 8 MYCN amplified neuroblastomas were profiled. cDNA from murine tumours were hybridized to Affymetrix Murine MOE430 2. 0 arrays, while human samples were hybridized to Affymetrix U133Plus2.
0 arrays. Gene expression data was normalized using gcRMA algorithm on custom Brainarray CDF  49. Differential subgroups were defined using different methods: unsupervised hierarchical clustering (average linkage and Pearson correlation distance), consensus clustering (using ConsensusClusterPlus R package v1. 18. 0, with 1,000 resamplings, average linkage hierarchical clustering and Pearson correlation) and NMF (using NMF R package, with standard NMF algorithm method performed on 30 runs). The optimal number of subgroups have been defined on the lower proportion of ambiguous clustering value (PACk ¼ CDFk(valueindex(max)) À CDFk(valueindex(min))) and biological knowledge for consensus clustering method; and on the better cophenetic correlation score and biological knowledge for NMF method. All these methods are based on the expressed and variable genes obtained after elimination of background (threshold: 1.
2) and invariant genes using RIQR (threshold: 0. 75; max(Q3 À Q2,Q1 À Q3)/Q2). Differentially expressed genes between two groups were defined using Welch t-test (Pr0. 05 adjusted with Benjamini and Hochberg) and fold change (|FC|Z1. 2). Concordance of orthologous genes between human and mouse was made with HomoloGene. For intraspecies comparisons, Pearson correlation score on transcriptome profilings were calculated by correlating the means of each gene expression value, after filtering those below the background and unvariant genes. The agreement between human and mouse gene expression tissues was evaluated using AGDEX (Agreement of differential expression) R package v. 1. 6. 0, based on a statistical procedure described in Johnson et al. 18. The reference tissues were prefrontal cortex-downloaded from the GEO database (GSE11512 for human, GSE16660 for mice).
Comparisons between human MYCN-amplified neuroblastomas (previously published, GSE12460) and murine neuroblastomas from the Th-Mycn model (previously published, GSE46583) on one hand, and SHH medulloblastomas in humans  50  (previously published, GSE12992) and shh medulloblastomas from Ptch1 þ / À models, on the other hand, served as controls. All bioinformatic analyses were performed with R software environment.
Acknowledgements INSERM  U830  is supported by the  Institut National de la Santeet de la Recherche Medicale , the  Institut Curie  and the  Ligue National Contre Le Cancer. This study was supported by a grant from the  French Ministry of Health (Direction Generale de l'Organisation des Soins)  and the  French National Cancer Institute  ( RTK-2012 , no. 2012-1-RT-02-IC-1 ). We thank the Associations who support research on RTs and brain tumours: Abigae ¨l,  Marabout de Ficelle ,  Franck un Rayon de Soleil ,  Les Torocinelles ,  Couleur Jade ,  Au  nom d'Andrea and ADAM. We are also indebted to the SFCE, Enfants et Santeand  Etoile de Martin  for their financial support,  I. Grandjean  for kind help in animal facilities,  M. Yaniv  and  A. Klochedley-Yeivin  who kindly provided the Smarcb1 flox/flox strain, and  A.
Experimental design Two months before the race, subjects came to the laboratory for a medical examination, familiarization to neuromuscular evaluations and a maximal incremental running test. The familiarization to neuromuscular measurements consisted of submaximal and maximal voluntary contractions of KE and PF with and without electrical nerve stimulation. The incremental running test was performed on a treadmill with a 10% slope to identify maximal aerobic speed. The initial speed was set at 4 to 6 kmÁh -1 depending on the performance level of the runner and running speed was increased by 1 kmÁh -1 every 2. 5 min until exhaustion. Respiratory exchange data were collected throughout the running test. The maximal aerobic speed was used to standardize the speed of each subject's treadmill measurement trials.
Video data were sampled at 120 Hz using a camera (Basler scA640-120gc, Basler AG, Germany) mounted on a tripod placed ~1. 5 m from the treadmill. Marker trajectories were tracked and analyzed in Simi Motion 2D (Simi Reality Motion Systems GmbH, Unterschleissheim, Germany). Accelerometer and video data were systematically calibrated for each subject. Accelerometers and markers were placed at identical locations pre-and post-MUM. Impact-related variables. From a time domain analysis, peak tibial acceleration (PTA) was measured. Then a Fast Fourier Transform was performed on 10 consecutive stance phases recorded during the 20-s acquisition for the frequency domain analysis. The beginning of the stance phase was identified as the deflection before PTA  [32]. The end was identified as the local minimum on the tibial acceleration signal.
Data analysis and statistics For each subject, percent changes (Δ) from pre-to post-MUM were calculated for kinematic, impact-related and neuromuscular variables, and reported as mean ± standard deviation. Normal distribution was tested by Shapiro-Wilk normality tests and variance homogeneity by Fisher F-tests for all variables pre-and post-MUM. Student t-tests were performed on pre-and post-MUM kinematic, impact-related and neuromuscular variables, except VA and 10:100 for which Wilcoxon tests were used because normality tests failed for these variables. Bravais-Pearson correlations were computed from the percent changes in kinematic, impact-related and neuromuscular variables. In view of the results obtained from the Student and Wilcoxon tests', two-way within subjects ANOVAs were computed a posteriori to test main effects of FSP and fatigue (TIME) as well as the interaction effect (FSP × TIME) on kinematics and impact-related variables.
Acknowledgments We declare that we have no conflict of interest. Neuromuscular data have been previously presented in Temesi et al. [27]. The authors warmly thank the subjects for their participation and forbearance, especially after a 110-km race, the organizers of the race and the  "Ecole Nationale de Ski et d'Alpinisme" (Chamonix, France)  for its hospitality. We also thank  Dr Roger Ouillon  and  Dr Pascal Edouard  for conducting medical inclusions. This study was funded by  John Temesi's doctoral research grant  from the  Rhône-Alpes Region.
Determining line strengths The strengths of different spectral features depend on the variety of stars being studied. Thus, it is necessary to include an estimate of the strength of all spectral features. For example, in the case of a stellar spectroscopic study, synthetic spectra could be computed for the types of stars that will be analyzed. Using these spectra, theoretical equivalent widths can be measured to determine the strengths of the lines in the spectral feature line list. Another option is to use the VALD database  (Kupka et al. 1999 (Kupka et al. , 2000) )  to extract line depths for a given stellar type. Both equivalent widths and line depths are suitable measures of line strength. In order to measure the strength of the lines, we calculated theoretical equivalent widths (EWs) for the lines in our list.
The tubes were centrifuged at 125 g for 8 min to remove free beads. Then, supernatant was removed and hemocytes resuspended in artificial sea salt solution containing 0. 5% of paraformaldehyde and 0. 2% of sodium azide. The phagocytic capacity (1 bead and more) and efficacy (3 beads and more) were measured by flow cytometry. A total of 5000 events were recorded for each hemolymph sample. Data collection and analysis were performed with Cell Quest Pro software.