dataesr/scientific-paragraphs-categorization huggingface dataset:
25 and activation time 30 ms. The following MS data were analyzed with Proteome Discoverer 1. 4/PhosphoRS 2. 0 [MS tol, 10ppm; MS/MS tol, 0. 8Da, Variable modifications, Oxidation (M) and Phosphorylation (S, T, Y); Fixed modifications, Carbamidomethyl (C); FDR Peptide, 0. 01]. 36.
Amplicon sequencing and analysis The bacterial diversity in the different gut compartments was analyzed by paired-end sequencing of the 16S rRNA genes  [13]  on an Illumina MiSeq platform using the same DNA preparations as for metagenomic sequencing and bacteria-specific primers (V4 region). Amplicon sequencing yielded between 44,000 and 138,000 quality-filtered and trimmed sequences (iTags) per sample (see Table  1  for accession numbers). Reads were classified to the genus level using a curated reference database for the classification of dictyopteran gut microbiota (DictDb)  [14].
Metagenomic sequencing and analysis Metagenomic libraries were prepared, sequenced, quality controlled, and assembled at the Joint Genome Institute (Walnut Creek, CA, USA). DNA was sequenced on an Illumina HiSeq 2000 (Illumina Inc. , San Diego, CA). Quality-controlled reads were assembled and uploaded to the Integrated Microbial Genomes (IMG/M ER) database (https://img. jgi. doe. gov/cgi-bin/mer/main. cgi) for gene identification and annotation by applying the standard operation procedure of IMG  [15]. The metagenomes are publicly available on the IMG/M ER website (see Table  1  for accession numbers). Gene functions of protein-coding genes were identified, and genes were taxonomically assigned using BLASTp (top hit) and RPS-BLAST against the COG database.
Because assembly sizes after dereplication were about tenfold smaller (0. 1-0. 4 Gbp) (Table  1 ), these datasets were not included in the following analyses. A BLASTp analysis of the metagenomes allowed assignment of the majority of the protein-coding genes to the three top-level domains; only 10-38 % of the gene copies remained unclassified (Fig. 2 ). In most libraries, the majority of genes were of bacterial origin. Archaeal genes represented only a small fraction of the gene copies in all libraries, with highest proportions (up to 4 %) in the P4 compartment, which is in agreement with the low abundance of archaeal rRNA in termite hindguts  [20]. However, in most P1 compartments, bacterial genes were outnumbered by genes assigned to eukaryotes. Notable exceptions are Cornitermes sp. and Cubitermes ugandensis, where the P1 is almost as large as the P3  [7, 21].
This discrepancy is explained by the lack of appropriate reference genomes in public databases. The only sequenced genome from Fibrobacteres, the rumen isolate Fibrobacteres succinogenes, is only distantly related Fig. 2  Assignment of protein-coding genes in the metagenomic libraries to the three top-level domains. Taxonomic assignment is based on BLASTp analysis (top hit >30 % identity). The abundance of a gene in a library was estimated using the length and read depth of the gene in the respective assembly (read depth of 1 for unassembled reads). to Fibrobacteres detected in this study  [22] , and the draft genome of Chitinivibrio alkaliphilus, the first isolate of the TG3 phylum  [23] , was not included in public databases at the time of analysis. The high abundance of genes assigned to Proteobacteria, which contrasts strongly with their low proportion in the iTag datasets, is also likely caused by the bias introduced by incorrect assignment due to the lack of reference genomes.
Future directions The results of this preliminary analysis show that microbial structure and function are correlated with both the digestive strategy of the host and corresponding microhabitats. The large metagenomic datasets will allow an in-depth analysis of the microbial functions in the homologous gut compartments and a comparison between hosts with diverging digestive strategies. Of particular interest will be the gene functions related to the digestion of lignocellulose and the putative peptidic substrates in the diet of the humivorous host  [1]. To overcome the bias in the taxonomic assignment of the genes, we are currently using a reference-independent approach to reconstruct population genomes for the major lineages of uncultivated symbionts. S2 ). The shape of the data points differentiates wood and litter feeders (circle) from humus and soil feeders (square); numbers indicate gut compartments P1, P3, and P4.
Acknowledgements This study was supported by the  Max Planck Society , a grant of the  Deutsche Forschungsgemeinschaft (DFG)  in the Collaborative Research Center  SFB 987 , and the  LOEWE program  of the state of Hessen (Synmikro). The work conducted by the  U. S. Department of Energy Joint Genome Institute , a  DOE Office of Science User Facility , is supported by the  Office of Science of the U. S. Department of Energy  under Contract No. DE-AC02-05CH11231. The authors thank all  JGI  staff and particular their project manager  Tijana Glavina del Rio  for their excellent service.
9 83. 4 1303 8 2 275 2834 4594 Termes hospes Th196 KP091693 P1 48. 7 98. 5 1511 12 0 85 2508 4613 (humus) P3 34. 2 82. 2 1212 51 9 177 2469 4614 P4 40. 1 90. 6 1800 50 3 110 2462 4615 Neocapritermes taracua Nt197 P1 43. 9 91. 6 1530 23 5 177 2501 4610 KP091692 (humus) P3 28. 3 67. 5 886 88 12 628 2505 4611 P4 39. 1 88. 0 1537 42 14 232 2504 4612 Cubitermes ugandensis Cu122 C 3. 2 40. 5 111 0 0 12 1474 4595 KP091689 (soil) M 3. 0 43. 0 108 0 0 10 1468 4596 P1 68. 4 84. 7 2060 31 2 176 2185 4597 P3 32. 0 71. 2 1122 49 9 227 2127 4598 P4 31. 8 79. 8 1295 15 2 107 2125 4599 P5 37. 6 96. 8 1992 27 0 90 2175 4600 b Assembly size after dereplication c IMG taxon object ID 330000xxxx d NCBI sequence read archive accession number SAMN0334xxxx [12] op (foregut), M midgut, P1-P5 proctodeal compartments (hindgut) a Accession numbers of mitochondrial genome sequence reconstructed from the metagenomes[12].
miRNA profiling miRNA profiling by miChip (Exiqon LNA Array probes V9. 2, Vedbaek, Denmark) was performed as published  [54]. Data were deposited in the ArrayExpress repository under the accession number E-MTAB-1363. Expression of three survival relevant miRNAs (miR-135a, miR-135b, and miR-596) was validated in ten cell lines and nine primary myeloma cell samples using qRT-PCR (miQPCR). For miQPCR, RNA was extracted from 2. 5x10 5 cells using the Allprep-/RNeasy MinElute Cleanup Kit in 50μl RNase-free water, speedvaced and re-eluted in 10μl RNase-free water. One μl of RNA was reverse transcribed and 2. 5 μl of cDNA were used for RT-PCR. cDNAs were amplified by using gene specific primers and SYBR Green (Applied Biosystems) and run on a ABI 7500 instrument (95°C for 10 min, followed by 40 cycles of 95°C for 15 sec, 60°C for 40 sec) with a dissociation step (ramping from 60°C to 95°C) as published  [55].
Statistical analysis Gene expression data were assessed as previously described  [2] [3] [4] [5] [6]. Signal calls on the miChip were preprocessed using MiChip package selecting only spots associated with human miRNAs for further preprocessing (http://www. bioconductor. org/packages/ devel/bioc/html/MiChip. html). The summarized signal calls on the miChip were log2 transformed after scaling with a constant (114) so that the smallest value was 1 before log transformation. For normalization, an invariant-based normalization method was applied  [50]. For unsupervised analysis, hierarchical clustering using the average linkage method with the centered Pearson correlation method was used. Differential miRNA expression was assessed using empirical Bayes statistics in linear models for microarray data  [57]  and P-values were adjusted for multiple testing controlling the false-discovery-rate as defined by Benjamini and Hochberg at a level of 5%  [58].
miRNA target prediction Target gene prediction was carried out using miRWalk  [60]  integrating the predicted target transcripts of the following prediction tools: DIANA-microT, miRanda, miRWalk, miRDB, PITA, RNAhybrid and TargetScan. To limit the number of false-positives, we only retained those transcripts for further analysis identified by at least 6 out of 7 target prediction algorithms.
miRNA signature A miRNA signature for survival prediction was constructed using principal component analysis on the 53 samples for which survival data were available and 5 survival associated miRNAs. First, taking the first and second principal component, two weighted averages for each sample were computed. Second, multivariate proportional hazards coefficients for the two weighted averages were assessed for EFS an OS each. Third, the score for each sample was computed as the vector product of the mean proportional hazard coefficients for EFS and OS and the samples weighted averages. Fourth, maximal selected rank statistics were used to find within the univariate score an optimal cut-off for defining a high-and low-risk group. miRNA target scores miRNA target scores were generated for the survival relevant miRNAs using 247 mRNA expression samples for which survival data were available.
ACKNOWLEDGMENTS We thank  Tomi Bähr-Ivacevic ,  Maria Dörner ,  Ewelina Nickel  and  Véronique Pantesco  for excellent technical assistance, the  Microarray Core Facility of the Institute of Research in Biotherapy, Montpellier, France , as well as  Prof. Bart Barlogie  and  Prof. John D. Shaughnessy  for providing gene expression and clinical data for validation purposes.
Moreover, there was limited overlap between Brn2 sites in Fgf2-induced NPCs and following ectopic expression, as only 202 (200 using the input as control for the Tet0 data) sites were occupied under all conditions. Analysis of Brn2-bound peaks in the Fgf2-induced NPCs revealed an enrichment in degenerate MORE motifs (supplementary material Fig. S3C ) similar to in the TetO dataset. An additional OCT motif was, however, found in the TetO dataset (supplementary material Fig. S3D ). The 202 sites that were occupied under all conditions were strongly enriched in MORE-type motifs, similar to the retinoic-acid-induced embryoid bodies (supplementary material Fig. S3E ). Lodato et al. reported  an association between Brn2-and Sox2occupied sites. Re-analysis of the Sox2 ChIP-seq data set identified 10,207 sites compared to the 16,685 previously reported, and we clearly detected the Sox2 consensus motif at these sites (supplementary material Fig.
Nuclei were stained with 4,6-diamidino-2-phenylindole (DAPI) diluted at 1:1000 in PBS. The cells were then mounted in antifading medium (Vectashield, Vector laboratories, Inc. Birlingame, CA) and observed with an epifluorescence microscope (Carl Zeiss, Göttingen, Germany). mRNA preparation, quantitative PCR and RNA-seq RNA isolation was performed according to standard procedure using the kit from Sigma. RT-qPCR was carried out with SYBR ® Green I (Qiagen) and Multiscribe reverse transcriptase (Invitrogen) and monitored using a LightCycler ® 480 (Roche). Primer sequences for cDNA were designed using Primer3Plus software. Gapdh expression was used as to normalise the results. For RNA-sequencing, mRNA was isolated from two independent experiments. mRNA-seq was performed essentially as described previously  (Herquel et al.
ChIP-seq analysis ChIP-seq experiments were performed on chromatin from embryoid bodies or P19 aggregates dissociated by douncing directly in crosslinking solution (0. 4% PFA). Sonication and ChIP were performed as previously described  (Herquel et al. , 2013). The ChIP-grade anti-Brn2 antibody (Sc 6029-X) and anti-H3K27ac antibody (number 39133; Active Motif ) were used. ChIP-seq libraries were prepared and sequenced as previously described  (Choukrallah et al. , 2012; Herquel et al. , 2013). Peak detection was performed using the MACS software (http://liulab. dfci. harvard. edu/MACS/) (Zhang et al. , 2008)  using the corresponding input as control with default settings. De novo motif search was done using MEME-ChIP software (http:// meme. nbcr. net/meme/cgi-bin/meme-chip. cgi). ChIP-seq peak annotation was performed using Homer, individual motif occurrences were detected by FIMO; both pieces of software are available on IGBMC Galaxy platform (www.
galaxeast. fr). Co-occurrence analysis was performed as follows. The analysis was initiated by detecting known motifs using FIMO within regions of interest given as input. From FIMO results, two PERL scripts computed the frequency of occurrence of each motif and of co-occurring motifs. Two motifs were considered as co-occurring if they were present in the same sequence. This analysis was performed once on the input BED file and n times on randomly selected regions. Region size distribution of the randomly selected regions was the same as for the input BED file. Randomly selected regions were used to compute an expected distribution of motif occurrence or motif co-occurrence. The significance of the motif occurrence and co-occurrence was estimated through the computation of a Z-score computed as: z=(x-µ)/σ, where x is the observed value (number of motif occurrence or co-occurrence), µ is the mean of the number of occurrences or co-occurrences of motifs (computed over controls) and σ is the standard deviation of the number of occurrences or co-occurrences of motifs (computed over controls).
Acknowledgements We thank,  I. Michel  for excellent technical assistance and CAT assays,  Y. Barde  for the gift of J1 ESCs,  C. Keime  and  A. Velt  for RNA-seq analysis,  B. Jost , and all the staff of the IGBMC high-throughput sequencing facility, a member of 'France Geńomique' consortium ( ANR10-INBS-09-08 ),  Y. Davidson  for help with data analysis, and  M. Hestin  and  G. Rossi  for assistance with ESC culture.
Funding This work was supported by grants from the  Centre national de la recherche scientifique (CNRS) ; the  Institut national de la santéet de la recherche medicale (INSERM) ; the  Ligue Nationale et Deṕartementale Reǵion Alsace contre le Cancer ; the  Institut National du Cancer (INCa) ; and the  Agence national de la recherche  [grant number  ANR-10-LABX-0030-INRT ]. All were under the frame programme  Investissements d'Avenir  [grant numbers  ANR-10-IDEX-0002-02  and project grant  ANR-11-BSV8-023 ]. I. D. is an 'equipe labelliseé' of the  Ligue Nationale contre le Cancer.
PMT characterization To characterize the PMT R7378A (gain and output linearity), we used the optical test bunch composed of a $1 m 3 light tight chamber which temperature is maintained at 7 0:1 1C, inside which the detector under test, the calibrated PMT (R7400U-01) and PIN photodiode (S3902-19) are fixed to a 3-D translation stage driven by a LabView program that permits one to place with an accuracy of μm the detectors in front of the light sources. The Contents lists available at ScienceDirect journal homepage: www. elsevier. com/locate/nima measurements of the detectors coordinates are performed with a CCD camera. The measurement of the PMT gain is performed with a pulsed Pilas laser diode (467 nm, pulse duration around 50 ps FWHM) with the unique photo-electron method  [10]. For the linearity measurements (Fig.
Identification of actively expressed CRISPR arrays in C. difficile. Comparative analysis of subtype I-B CRISPR-Cas systems in the C. difficile reference strain 630 and the PCR ribotype 027 epidemic strain R20291 was performed. According to the CRISPRdb database, C. difficile strain 630 carries genes that encode 12 potential CRISPR arrays, 5 of which are located within prophage regions (Fig. 1 ; see Table  S1  in the supplemental material)  (11). For further reference, we used the CRISPRdb numbering for the C. difficile strain 630 CRISPR arrays and numbered spacers within each array according to the identified transcriptional order. All 12 arrays are actively expressed and processed into crRNA under laboratory conditions  (24)  (see Fig. S1  in the supplemental material). The distribution of CRISPR arrays throughout the chromosome and their orientation appear to be nonrandom (Fig.
The alignment of expressed CRISPR loci in strain 630 revealed conserved leader elements immediately upstream of the first repeat (Fig. 2 ). In addition, upstream of the TSS, consensus elements of sigma A-dependent promoters could be identified (Fig. 2 ). These functional elements are also conserved for the expressed CRISPR arrays from strain R20291 (data not shown). A total of 119 29-bp repeat sequences were collected from the expressed CRISPR loci of strain 630, and aligning these sequences allowed us to establish a consensus sequence shown in Fig. 3A. A total of 105 29-bp repeat sequences were collected from the CRISPR loci expressed in strain R20291 with a consensus sequence similar to that for the strain 630 CRISPR repeats. For some CRISPR arrays in both strains, the leader-distal repeats had a variant sequence that was different from the rest of repeats.
The number of spacers ranged from four in CRISPR 12 to twenty-six in CRISPR 19 (Table  S1 ). In both the 630 and R20291 strains, the complete cas operons (CD2982-CD2975 and CDR20291_2817-CDR20291_2810) are associated with a CRISPR array containing the largest number of spacers. This may suggest that the longest arrays created by numerous spacer acquisition events and coexpressed with a complete functional set of cas genes might constitute an active CRISPR functional unit within the bacterial genome. We can hypothesize that other CRISPR arrays could use the same set of cas genes for functioning. In silico analysis of C. difficile CRISPR system targeting. (i) Phage genome sequencing. To identify sequences targeted by C. difficile CRISPR spacers, we performed a systematic search for sequence similarities in the NCBI database using BLASTN  (27)  and the CRISPRTarget program  (28).
A single mismatch at position 6 and up to five mismatches outside the "seed" are tolerated without an apparent decrease in the interference efficiency  (26, 34, 35). We analyzed the locations of mismatches between the C. difficile CRISPR spacers and the targeted protospacers. Spacers without mismatches in the potential "seed" region (first 8 nt of the protospacer except for position 6) and carrying up to five mismatches outside the "seed" region were considered functional. In this way, six additional   (17, 22, 23, 26). The RNA secondary structure was predicted using the Mfold software  (66). The proposed position of pre-crRNA cleavage by the Cas6 protein that generates the 8-nt 5= tag of crRNA during processing is indicated. spacers with permissive mismatches were identified in CRISPR arrays from each strain (Table  1  and Table  S3 ), raising the proportion of potentially functional CRISPR spacers matching the foreign DNA sequences to 59% and 42% in strains 630 and R20291, respectively.
All primers used in this study are listed in Table  S6. RNA-seq analysis. Total RNA was isolated from late-exponentialgrowth-phase cultures (grown for 6 h) of C. difficile R20291 grown in TY medium as previously described  (55) , and mRNA was enriched from total RNA using MicrobExpress kit (Ambion). Nonoriented RNA-seq library construction was performed with the TruSeq RNA sample prep kit from Illumina as previously described  (56)  and subjected to Illumina HiSeq 2000 sequencing. In silico analysis of CRISPR array spacer content and cas locus. CRISPRdb tools  (11) , the CRISPRTarget program  (28) , or BLASTN  (27)  were used for spacer homology search in the available sequences (April 2015). Several potential CRISPR arrays predicted by CRISPRdb within the tcdA coding region were excluded from further analysis for the following reasons: (i) their location within the toxin-encoding genes in all analyzed C.
The raw sequencing read data of published genome sequences from 2,207 C. difficile strains (57-62) were used to search for the presence of cas loci homologous to the CD2982-CD2975 and CD2455-CD2451 cas operons from strain 630 and the CDR20291_2998-2994 operon from strain R20291. For each strain, the sequencing reads were mapped on the sequence of corresponding cas locus using Bowtie  (63). Coverage values of Ն80% were considered positive hits for the presence of corresponding cas loci in a given strain. The multilocus sequence typing (MLST) scheme of Lemee et al. (64)  has also been inferred from raw sequencing read data. Phage infection assays. Phage host range determination was done as previously described (29) using spot tests with diluted phage lysates and exponentially grown C. difficile cultures.
difficile cas genes under the control of T7 RNAP promoter. Cas protein and crRNA production was induced with 0. 5 to 1 mM L-arabinose and 1 mM isopropyl-␤-Dthiogalactopyranoside (IPTG), and electrocompetent cells were prepared under these inducing conditions. Plasmids were then introduced by electroporation, and undiluted and serially diluted aliquots of the transformation mixture were spotted on LB agar plates containing ampicillin and either streptomycin or kanamycin/streptomycin to measure the transformation efficiency. Data access. The complete genome sequences of phiCD24-1, phiCD111, phiCD146, phiCD211, phiCD481-1, phiCD505, phiCD506, phiMMP01, phiMMP03, and phiCD52 were deposited in EMBL-EBI database under accession no. LN681534, LN681535, LN681536, LN681537, LN681538, LN681539, LN681540, LN681541, LN681542, and PRJEB7856, respectively.
ACKNOWLEDGMENTS We thank  Melinda Mayer  for providing us with phiCD27 phage lysate. We are grateful to  Odile Sismeiro  and  Jean-Yves Coppée  for help with the RNA-seq experiment and to  Laurence Ma  and  Christiane Bouchier  for phage genome sequencing. This work was supported by grants from the  Institut Pasteur ,  University Paris Diderot ,  Agence Nationale de la Recherche  (" CloSTARn ,"  ANR-13-JSV3-0005-01 ),  Pasteur-Weizmann Council ,  National Institutes of Health  ( NIH R01 GM10407 ),  Natural Sciences and Engineering Research Council of Canada  ( Discovery grant , no. 341450-2010 ), and  Fonds de la Recherche du Québec -Santé (FRQS)  ( Junior 2 salary award ). O. S. is an assistant professor at the  University Paris Diderot. P. B. has a Ph. D. fellowship from the  University Paris Diderot.
Data used in preparation of this article were obtained from the Diagnostic Molecular Imaging (DiMI) network and the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (adni. loni. usc. edu). As such, the investigators within DiMI and ADNI contributed to the design and implementation of DiMI and ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni. loni. usc. edu/wp-content/ uploads/how_to_apply/ADNI_Acknowledgement_List. pdf Electronic supplementary material The online version of this article (doi:10. 1007/s00259-015-3115-5) contains supplementary material, which is available to authorized users.
DiMI consortium Some of the data used in the preparation of this article were obtained from the European Network of Excellence on DiMI, which was funded by the European Union (EC-FP6), launched in 2005 and ended in 2010. The goal of the DiMI consortium, which comprised six groups from five European countries, was to retrospectively collect PET data from five European centres in order to determine the role of amyloid imaging (using PIB as tracer) in a large population of mild cognitive impairment (MCI) and AD patients  [26].
ADNI The remaining data used in the preparation of this article were obtained from the ADNI database (adni. loni. usc. edu). The ADNI was launched in 2003 by the National Institute on Aging (NIA), the National Institute of Biomedical Imaging and Bioengineering (NIBIB), the FDA, private pharmaceutical companies and non-profit organisations, as a $60 million, 5year public-private partnership. The primary goal of ADNI has been to test whether serial magnetic resonance imaging (MRI), PET, other biological markers, and clinical and neuropsychological assessment can be combined to measure the progression of MCI and early AD. Determination of sensitive and specific markers of very early AD progression is intended to aid researchers and clinicians to develop new treatments and monitor their effectiveness, as well as lessen the time and cost of clinical trials.