dataesr/scientific-paragraphs-categorization huggingface dataset:
Unsupervised Analysis The gene expression matrix (GEO Series GSE63860) was then row-mean-centered. The resulting matrix was used for unsupervised classification of the genes. Genes (n = 21678) were partitioned in ten clusters using the kmeans classification algorithm. The biggest cluster (n = 8896) contained genes showing almost no variation across all samples: it was eliminated from further analysis. Three clusters were found to be highly correlated (centroids correlation >0. 95) and were merged in a unique gene cluster (cluster g, Figure  1B ). We thus remained with seven clusters. For each sample, the mean expression of all the genes of each cluster was calculated, yielding a (seven clusters, 36 samples)-matrix shown in Figure  1B.
( * ) Lists of differentially expressed genes: genes yielding a (moderate T-test) p > 1e-5 were removed from the analysis; remaining genes were ordered based on the fold change; the n genes with highest (respectively lowest) fold change were selected as a separate list; the n/2 genes with highest fold change and the n/2 genes with lowest fold change were merged in another list; this operation was performed for several values of n  (200, 300, 400, 500, 750, and 1000). Principal component analysis (PCA) of the expression profiles was performed using R software. Venn diagrams and pathway interaction schemes were generated applying BioVenn (http://www. cmbi. ru. nl/cdd/biovenn/) and GOrilla-REViGO (http://cbl-gorilla. cs. technion. ac. il/) software packages, respectively. Pathway analysis was completed employing DAVID Bioinformatics Resources 6.
Comparative Analysis of Microarray Data with Published Available Datasets Data normalization was performed with frozenRMA and corrected for batch effect using Combat algorithm  (Johnson WE-Biostatistics-2007). Combined data series were the 36 samples from our study and those from published datasets GSE50821  (Sinha et al. , 2014)  and GSE47177  (Liu et al. , 2013). The three Affymetrix series were used to compared adult vs. old expression profiles (supervised meta-analysis young[2 months] vs. old  [>= 18 months] ). This analysis showed that the combination of the three sets found 32% significantly deregulated genes and in the same direction of deregulation (32% = proportion of the combined test under H1 = Test Stouffer).
Imaging and Statistics Analysis was carried out using a Leica TCS SPE confocal microscope. Images were processed with either Adobe Photoshop CS5 software (Adobe Systems) or ImageJ (version 1. 47v; National Institutes of Health, USA, http://imagej. nih. gov/ij). Infected satellite cells in myofiber cultures were directly counted under a Leica fluorescent microscope at 40x and 100x magnification. Mean ± standard error (SEM) was given. The single ( * ), double ( * * ) and triple ( * * * ) asterisks represent p-values p < 0. 05, p < 0. 01, and p < 0. 001 respectively by Student's unpaired t-test or Mann-Whitney U-test. All experiments have been performed on at least three independent experiments for each condition. Supplementary Movies were performed using a DSD2 Workstation with Imaris software (ANDOR).
5) among other cell types  (Engleka et al. , 2005; Stoller et al. , 2008). To exclude a possible contamination of satellite cells with endothelial cells, we performed Pax3-lineage tracing using Pax3 Cre/+ ; R26 mTmG mice (Figure  S2B ). While adult myogenic cells were mGFP+ (Pax3-Cre recombined), all endothelial cells remained mTOMATO+ (not recombined) (Figure  S2B  and Movie S1). Moreover, all CD31 (PECAM-1) + endothelial cells were included within the mTOMATO+ population (Figure  S2B  and Movie S2). These results demonstrate that the Pax3 lineage does not contribute to skeletal muscle endothelial population, and that skeletal muscle expression of PAX3 is specific to muscle stem cells. Since Pax3 is expressed in a subset of the Pax7-expressing satellite cells, we compared our gene expression data with previously published datasets of adult muscle stem cells where markers different from PAX3 were used to isolate satellite cells (Figure  S3A ;  Liu et al.
, 2013; Sinha et al. , 2014). Pax3expressing satellite cells were not significantly divergent from previously reported datasets, while embryonic and fetal/early postnatal datasets showed different specific profiles (Figure  S3A ). Moreover, we compared available data from adult (3-8 monthold) and old satellite cells (18-24 month-old) with our data. We identified a similar variation in all datasets, demonstrating that Pax3-expressing satellite cells do not define a subpopulation of satellite cells. Our data therefore are likely representative of the whole satellite population. Expression profiles from 11 developmental stages were normalized, generating a GEO (GSE63860) showing the kinetics of each transcript over time (Figures  1A, B ). Transcript variations were divided into seven clusters based on general expression profiles (Figure  1B  and Figure  S3B ), which were determined to be functionally homogeneous and easily aggregated in defined GO pathways (Figure  S3B , Pathways).
ACKNOWLEDGMENTS We thank  Jonathan A. Epstein  for the Pax3 Cre/+ mice. We thank  Bernadette Drayton ,  Bruno Cadot , and  Petra Gimpel  for their input and technical assistance;  Ana Cumano  for her advice in FACS-sorting experiments and  Olivier Poch  and  Julie Thompson  for discussion about gene expression analysis. We thank  Catherine Blanc  and  Bénédicte Hoareau  ( Flow Cytometry Core CyPS, Pierre and Marie Curie University ) for their assistance. We also wish to acknowledge  Serban Morosan  and the animal care facility ( Centre d'Expérimentation Fonctionnelle, School of Medicine Pierre et Marie Curie ). SAM was recipient of a postdoctoral fellowship from the  Basque Community  ( BF106. 177 ). This work was supported by funding to FR from  INSERM Avenir Program ,  Association Française contre les Myopathies (AFM)  via  TRANSLAMUSCLE  ( PROJECT 19507 ),  Association Institut de Myologie (AIM) ,  Labex REVIVE  ( ANR-10-LABX-73 ), the  European Union Sixth and Seventh Framework Program  in the project MYORES and ENDOSTEM (Grant #  241440 ),  Fondation pour la Recherche Médicale  (FRM; Grant  FDT20130928236 ),  Ligue Nationale contre le Cancer, Agence Nationale pour la Recherche (ANR)  grant  Epimuscle  ( ANR 11 BSV2 017 02 ),  RHU CARMMA  ( ANR-15-RHUS-0003 ),  BMP-biomass  ( ANR-12-BSV1-0038-04 ),  Satnet  ( ANR-15-CE13-0011-01 ) and bonemuscle-repair ( ANR-13-BSV1-0011-02 ),  Decrypthon research program.
This work was also funded by the  German Research Foundation  (DFG; Grant  GK1631 ),  French-German University (UFA-DFH ; Grant  CDFA-06-11 ) and the  AFM  as part of the  MyoGrad International Research Training Group for Myology. PSZ wishes to acknowledge support from the  Association Française contre les Myopathies  grant  16050. The lab of PZ is also supported by The  Muscular Dystrophy Campaign , the  Medical Research Council, FSH Society  and  BIODESIGN (262948-2 ) through  EU   FP7. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The authors declare no competing financial interests.
boldsystems. org). The standard PCR reaction protocol of the Canadian Center for DNA Barcoding was used for amplifications (http://www. dnabarcodes2011. org/conference/preconference/CCDB-Amplification-animals. pdf), and products were checked on a 2% E-gel 96Agarose (Invitrogen). Unpurified PCR amplicons were sequenced in both directions using M13 tailed primers, with products subsequently purified using Agencourt CleanSEQ protocol and processed using BigDye version 3. 1 on an ABI 3730 DNA Analyzer (Applied Biosystems). Sequences were assembled and edited with Sequencher 4. 5 (GeneCode Corporation, Ann Arbor, MI, USA). The alignment was obtained using BIOEDIT version 7. 0. 5. 3  (Hall 1999). Sequences are publicly available on GenBank (JN298074-JN298078, JN970909-JN970929, KC900191-KC900205, KR736063-KR736070) and on BOLD at the following doi: 10.
Data analyses Forty-nine specimens of Megalothorax dataset representing 14 morphologically recognized species were selected, 36 from  Schneider et al. 2011, Schneider and D'Haese 2013 and 13  specimens belonging to the two new species (Table  4 ). Distance analyses were performed with  MEGA6 (Tamura et al. 2013) , utilizing a Neighbor-Joining  (Saitou and Nei 1987)  algorithm with the Kimura-2 parameter model  (Kimura 1980)  to estimate genetic distances. The robustness of nodes was evaluated through bootstrap re-analysis of 1000 pseudoreplicates. Molecular Operational Taxonomic Units (MOTUs) were defined with the software 'mothur'  (Schloss et al. 2009).
Ecology and distribution. The species was collected in lowland forest litter, and only found so far in the southern part of Primorye. Etymology. Megalothorax potapovi sp. n. is dedicated to Mikhail Potapov, who led the 2004 field trip in Primorye which allowed to discover the new species. DNA barcode. A 658bp fragment of the COI gene was amplified and sequenced from paratype (MNHN-EA040229, voucher 00620C05) and seven other specimens (type locality, specimens lost). 251 bases in 5' were not readable, thus a final sequence of 407bp is available. The sequences are identical. The sequence is deposited into the GenBank database under accession number KR736069. The base composition of the sequence is A = 26%, C = 23. 1%, G = 13. 5%, T = 37. 4% (A + T = 63. 4%). 5'-TAAGTTTTTGACTTCTTCCACCTTCTCTCACCCTTCTACTTTCAA-GAGGTCTAGCAGAATCAGGTGCTGGAACAGGTTGAACTGTTTATCCTC-CTTTATCTTCAAATATTTCCCATGCAGGAGCCTCTGTCGACTTAAC-TATTTTCAGTTTACATTTAGCTGGTATGTCATCAATTTTAGGAGCTAT-TAATTTTATTACAACTATCTTTAACATACGATCCCCAGGAATAACAT-GAGATCAAACTTCACTATTTATTTGATCTGTTTTAATTACATCAATTT-TACTTCTCTTGTCTCTTCCAGTTCTAGCAGGAGCTATCACCATACTTT-TAACCGACCGAAATTTAAATACTTCATTTTTTGACCCCGCTGGGGGTG-GTGACCCAATTTTATACCAACACCTATTC-3'   Diagnosis.
Specimens were unfortunately lost, sequences identification is based on consistency between: the peculiar pigmentation of the species observed on specimens before destruction, the genetic similarity of the five specimens and the morphological identification of four other specimens with the same pigmentation from the same sample. The sequences are deposited into the GenBank database under accession numbers JN298074-JN298078. Four sequences are identical (JN298074-JN298077, provided below), base composition is A = 29. 6%, C = 17. 5%, G = 15. 8%, T = 37. 1% (A + T = 66. 7%). The fifth sequence (JN298078) differs in 11 sites (= 98. 3% pairwise identity), base composition is A = 29. 5%, C = 17. 6%, G = 15. 7%, T = 37. 2% (A + T = 66. 7%). 5'-AACCTTATATTTAATTTTTGGAGTATGATCTGCTATAGTTG-GAACAGCATTTAGAGTTTTAATTCGGTTAGAATTAGGACACCCAG-GAAGCTTAATTGGAAACGATCAAATCTATAATGTAATAGTTACGGC-CCATGCATTTGTAATAATTTTTTTTATAGTAATACCAATAATAATTG-GAGGCTTTGGTAATTGATTAGTACCTTTAATAATTGGAGCACCTGA-TATAGCATTTCCTCGAATAAACAATTTAAGATTCTGACTTTTACCAC-   34,70 35,89 34,40 26,48 31,97 29,36 27,97 34,84 30,04 32,00 37,62 26,26 29,31 24,15 25,08  Megalothorax willemi  -35,46 28,38 37,99 28,84 30,68 32,34 26,23 30,68 24,34 35,00 34,42 27,54 30,71 25,13 24,26 25,45  Megalothorax willemi  -30,24 31,81 32,52 27,27 33,44 29,24 26,12 31,69 26,56 35,48 29,72 30,02 26,05 23,31 26,32 27,08 28,17  Megalothorax willemi L6 0,56  30,64 29,85 28,76 25,65 26,88 26,02 22,39 28,10 21,84 30,24 31,64 27,26 25,06 20,80 25,99 24,20 22,98 20,99  Megalothorax willemi  -32,26 33,03 34,25 26,14 30,37 31,42 23,60 31,72 21,83 31,09 29,61 27,70 26,98 23,08 27,64 26,01 26,68 23,68 20,67  Figure  CATCTTTAATCTTATTACTATCCAGAGGGTTAGTTGAAACTGGTGCTG-GCACAGGATGAACAGTATATCCCCCTCTATCGTCTAATATTTCTCATA-GAGGAGCTTCTGTAGATTTAACTATTCTTAGACTTCATTTAGCTGG-GATATCTTCTATTCTTGGGGCAATTAATTTTATTACAACTATTCT-TAATATACGAATACCAGGAATAACATGAGACCAAACTTCTTTATTT-GTATGATCAGTTTTTATTACCTCAATTTTATTACTCCTCTCGCTTC-CAGTGCTTGCTGGAGCAATTACTATACTTTTAACTGACCGTAACCT-GAATACCTCATTTTTTGATCCTGCGGGAGGAGGAGACCCTATTCTA-TATCAACATTTATTT-3'.
09% (range: 19. 36%-31. 92%) and 28. 52% (range: 22. 00%-34. 84%). These ranges of genetic divergences are comparable to those observed among Megalothorax MOTUs included in the dataset as well as with the interspecific variation found among MOTUs corresponding to monophyletic identified species (28. 08% ; range: 19. 36%-36. 47%). This supports further the validity of the specific status for the two new species.
Acknowledgements Special thanks to  Mikhail Potapov  for having organized the field trip to Primorye (Russia), and to  Jacek Pomorski  and  Anne Bedos  for their contribution to specimen collection and extraction. The study on the Bernadouze peat-bog (France) was supported by grants of the LABEX DRIIHM -OHM Vicdessos for field trips and the laboratory work of  Lorène Marchal. We are grateful to  Vladimir Papáč  and to anonymous reviewer for the thorough review of this manuscript and their valuable corrections and recommendations.
CpG sites in islands II & III in the HBV genome As reference sequences, three normal HBV sequences were obtained from GenBank of the National Center for Biotechnology Information (NCBI; accession numbers: EU871997, AY206384 and AY123424). The schematic structure of the HBV genome, including the CpG island distribution in the representative HBV genomic sequence, is shown in Supplementary Figure  1. Island II is located at nucleotides 1170-1669, which encompasses Enh I and the X gene promoter and part of the X gene. Island III is located at nucleotides 2298-2446, which is upstream of the sp1 and sp2 promoters. Primer pairs were designed using MethPrimer  [31]  to amplify a segment of island II (nt1249-1380) containing seven CpG sites and a segment of island III (nt2369-2480) containing eight CpG sites.
Statistical analysis The Spearman's correlation analysis and graphing were performed using GraphPad Prism 7 software to assess the associations between the transcriptional levels of the HBV X and S genes and the methylation percentages of CpG sites in islands II and III in the HBV genome, respectively, in the sperm and spermderived embryos. The Wilcoxon signed rank test for paired samples was performed using SPSS 16. 0 software to identify significant differences in the average methylation percentages of CpG sites in islands II and III between the sperm and sperm-derived embryos. A p-value of <0. 05 was considered significant.
33 ± 2. 37% (range of 0. 89 to 8. 04%) and 3. 20% ± 2. 48% (range of 0. 78 to 7. 81%) in the sperm-derived embryos, respectively (Supplementary Figure  4 ; Table  2 ). Table  1. Effects of the methylation statuses of CpG sites on the transcriptional levels of the hepatitis B virus X and S genes in human sperm transfected with an unmethylated and a methylated plasmids and in sperm-derived embryos, respectively. Real-time qRT-PCR and the 2 -DDCT methods were used to evaluate effects of the methylation statuses of CpG sites on the transcriptional levels of the X and S genes in human sperm transfected with an unmethylated or a methylated plasmid and in sperm-derived embryos, respectively. The data were presented as an FCV in transcription of the X and S genes in the M normalized to internal control gene (GAPDH) and relative to those in the Un-M.
Discussion In the current study, two measures were taken to ensure reliable and accurate results. First, combined bioinformatic and experimental analysis was performed. Given that HBV DNA contains three conventional CpG islands (I, II and III)  [23] [24] [25] [26] , we searched Gen-Bank of the NCBI and retrieved three normal HBV sequences (accession numbers: EU871997, AY206384 and AY123424) for use as reference sequences. We then verified the locations and sizes of islands I, II and III in the representative HBV genomic sequences. The relationships between CpG site methylation in island II and III and the transcriptional levels of the X and S genes in sperm and sperm-derived embryos were then investigated using experimental methods. Next, combined in vitro and an in vivo experiment was performed.
baumannii ATCC 17978 cells in order to define the components required for a functional T6SS. These transcriptomic data revealed the genes in the previously predicted T6SS cluster that were upregulated in T6 ϩ cells (Table  1 ). The transcriptomic changes were significant, with up to nearly 50-fold upregulation of some genes. Furthermore, distantly located vgrG gene clusters also showed significant changes in their transcriptional profile. We then confirmed these changes at the protein level by a whole-cell differential proteomic approach (Table 1). Of the 21 genes identified as statistically significantly differentially expressed by RNA-seq in the main T6SS cluster, we identified and quantified, on the basis of at least two unique peptides, 18 of these proteins, all of which were statistically significantly different between T6 ϩ and T6 Ϫ cells.
25; activation time, 10 ms; automated gain control [AGC] at 4 ϫ 10 4 ) and higher-energy collisional dissociation (HCD) MS/MS (resolution, 7,500; NCE, 45%; AGC at 2e5; maximum fill time, 200 ms). MS data analysis. MS data were processed with MaxQuant (v1. 5. 3. 28) (76). Database searching was carried out against the reference A. baumannii ATCC 17978 proteome (downloaded from UniProt on 16 November 2014; 3,799 proteins) and our recent resequencing genome A. baumannii ATCC 17978-mff proteome  (42)  with the following search parameters: carbamidomethylation of cysteine as a fixed modification, oxidation of methionine, acetylation of protein N-terminal trypsin/P cleavage with a maximum of two missed cleavages. TMT-6-based quantitation was enable by using the default TMT-6 settings with an MS tolerance of 6 ppm, an HCD MS/MS tolerance of 20 ppm, a CID MS/MS tolerance of 0.
6 m, 250-by 4. 6-mm reverse-phase column (Phenomenex). The column was equilibrated at 45°C, and separation of individual muropeptides (detection wavelength of 204 nm) was performed in a linear gradient. The mobile-phase (A, 50 mM sodium phosphate [pH 4. 35]; B, 75 mM sodium phosphate, 15% methanol [pH 4. 95]) gradient consisted of elution at 1. 0 ml/min with 100% A for 5 min, followed by a 60-min linear gradient to 0% A-100% B and then 100% B for 5 min. Molecular mass analysis of isolated muropeptides by MS with a model LTQ-Velos linear ion trap from Thermo-Scientific was carried out in the CBMSO Protein Chemistry Facility, which belongs to the ProteoRed network, PRB2-ISCIII. Accession number(s). All of the RNA-seq data obtained in this study are available in the NCBI Sequence Read Archive under the BioProject identifier PRJNA288605.
ACKNOWLEDGMENTS We thank  Luis Actis  for supplying the anti-OmpA antibody. phen I. Morse Fellowship. Work in the lab of M. D. A. is supported by  JCVI 's  Genome Center for Infectious Diseases program  of the  National Institute of Allergy and Infectious Diseases of the NIH  through award number  U19AI110819. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Image analysis for characterization of microglial cell population An automatic image analysis was performed consecutively on the three maximum projection mosaics described above, using a custom-designed script developed with the Acapella™ image analysis software (version 2. 7, PerkinElmer Technologies, Waltham, USA). This script was subdivided into two subroutines: the first, for automated detection of processes (neurite detection module from Acapella™  [34] ), generating morphological characteristics per cell, and the second, for the 2D in situ morphological cartography. The data workflow is illustrated step by step in Additional file 1.
Microglial morphological criteria Using the custom-designed script cited above, the following morphological criteria could be extracted for each microglial cell (Fig. 1b, c ): a set of measured criteria as cell body and cytoplasm area, defined as the cell body area associated with the cytoplasmic area of the primary ramifications, expressed in μm 2 ; branching characteristics such as the total number and length (μm) of ramifications and the number of primary, secondary and tertiary ramifications; and roundness (ratio between surface and perimeter squared of the cell body) and GFP intensity by whole cell. A second set of calculated criteria extrapolated from the previous ones yielded the complexity index (CI) and the covered environment area (CEA). First, we defined the CI using two different criteria extracted from the Acapella™ script: the number of segments of each cell, a segment being defined as the length of process between two nodes, and the number of its primary ramifications.
Elimination of outliers and redundant cells We proceeded to the filtration of outliers by size (inferior to 10 μm 2 or superior to 500 μm 2 ) and roundness (inferior to 0. 7 considered as the limit of a noisy form) to eliminate artefacts due to tissue noise. Using the property of our Acapella™ script to generate in situ 2D cartographies (Additional file 1), cell duplicates on two adjacent sub-volumes were rejected for analysis to avoid data duplication. This 2D localization in situ allowed us also to remove the cells located at the edges of the mosaic reasoning that they may be truncated.
Manual and semi-automated method workflow To assess the accuracy of the 2D reconstruction of the custom-designed script cited, we compared the criteria measured by the cell using our automated method with the analysis realized manually by three independent experimenters, as a technical benchmark: the cell body area, the number of segments, the number of primary ramifications and the measure of the CEA. The manual The scale bar equals 10 μm. d Two-dimensional cartography at a single cell resolution. Colours correspond to the range of complexity and CEA with a gradient from a low level of complexity and CEA (yellow) to a high level of complexity and CEA (red). The scale bar equals 50 μm. For illustration, the images are contrast adjusted to aid in visualizing the GFP expression method has been performed using Fiji environment (Additional file 3).
In parallel, to assess that the criteria analysis is not software specific, a free semi-automated method has been implemented in a Fiji macro using Skeletonize (2D/3D) and Sholl Analysis plugins  [35, 36]. The cell body area, the number of segments and the number of primary ramifications have been extracted by this customdesigned macro. These two methods were tested and compared with our automated method in two regions of interest, the hippocampus and the cerebellum, and in the two conditions, LPS and control.
Data analysis and statistics All the data extracted from Acapella™ after elimination of outliers and redundant cells were exploited following two levels: (i) by the brain and (ii) by specific region. We conducted this study using two different statistical methods. The first conventional method consisted in an approach considering only the median or the average for each morphological criterion by animal. The second approach, more powerful and original, presented results using k-means clustering by cell population. To realize the conventional approach, Prism 6. 0 (GraphPad Software Inc. © , USA) was used for statistical analysis by animal and regions of interest. Data were analysed via Mann-Whitney test or Student's t test after being assessed for normality of sample distribution. Intersample/inter-region variability was tested by ANOVA Kruskal-Wallis method.
Data storage and annotations by in situ 2D cartography All acquired and analysed mosaics have been imported into the OMERO ("OME Remote Objects technology") image database  [38]  including visual results in 2D cartographies by phenotyped cell (Fig. 1d  and Additional  file 1 ). An open-source script (OMERO. csv) has been used to annotate automatically by textual information (i. e. sex, condition or clinical observation by sample or region appurtenance) our large set of imported images in the database.