dataesr/scientific-paragraphs-categorization huggingface dataset:
Classification and features Strain HIMB11 was isolated by a high-throughput, dilution-to-extinction approach  [15]  from surface seawater collected near the coast of Oahu, Hawaii, USA, in the tropical North Pacific Ocean. The strain was isolated in seawater sterilized by tangential flow filtration and amended with low concentrations of inorganic nitrogen and phosphorus (1. 0 µM NH4Cl, 1. 0 µM NaNO3, and 0. 1 µM KH2PO4). Comparative analysis of the HIMB11 16S rRNA gene sequence to those from cultured, sequenced roseobacters indicates that HIMB11 occupies a unique lineage that is divergent from the 16S rRNA gene sequences of Roseobacter strains already in culture (Figure  1 ). Based on the National Center for Biotechnology Information (NCBI) non-redundant database, the HIMB11 16S rRNA gene sequence is most similar (~99% nucleotide identity) to a large number of environmental gene clones obtained from various marine environments that exclusively fall in the Roseobacter lineage of Alphaproteobacteria.
Because of the significant sequence variation in 16S rRNA genes (up to 11%) and the prevalence of horizontal gene transfer within the clade, establishing a taxonomic framework for roseobacters remains a challenge  [9]. When genome sequence data is available, it is often more informative to perform a phylogenomic analysis based on shared orthologs versus 16S rRNA phylogenetic analysis alone  [9, 21]. A maximum likelihood tree constructed using 719 shared orthologous protein sequences supported the 16S rRNA gene-based analysis by revealing that HIMB11 formed a unique sublineage of the Roseobacter clade (Figure  2 ). Standards in Genomic Sciences   [16]  using the ARB software package  [17]. The phylogeny was constructed from nearly full-length gene sequences using the RAxML maximum likelihood method  [18]  within ARB, filtered to exclude alig nment positions that contained gaps or ambiguous nucleotides in any of the sequences included in the tree.
Genome project history The genome of strain HIMB11 was selected for sequencing based on its phylogenetic affiliation with the widespread and ecologically important Roseobacter clade of marine bacterioplankton and its periodically high abundance in coastal Hawaii seawater  [14]. The genome sequence was completed on May 25, 2011, and presented for public access on September 15, 2013. The genome project is deposited in the Genomes OnLine Database (GOLD) as project Gi09592. The Whole Genome Shotgun project has been deposited at DDBJ/EMBL/GenBank under the accession number AVDB00000000. The version described in this paper is version AVBD01000000. Table  2  presents the main project information and its association with MIGS version 2. 0 compliance  [29].
Genome annotation Genes were identified using Prodigal 1. 4  [38]  as part of the genome annotation pipeline in the Integrated Microbial Genomes Expert Review (IMG-ER) system  [39, 40]  developed by the Joint Genome Institute (Walnut Creek, CA, USA). Predicted coding sequences were translated and used as queries against the NCBI non-redundant database and UniProt, TIGRFam, Pfam, PRIAM, KEGG, COG, and InterPro databases. The tRNAScanSE tool  [41]  was used to identify tRNA genes, and ribosomal RNAs were identified using RNAmmer  [42]. Other non-coding RNAs were found by searching the genome for corresponding Rfam profiles using INFERNAL  [43]. Additional gene prediction analysis and manual functional annotation was performed within the IMG-ER platform.
Genome comparisons with other members of the Roseobacter clade A regression model was used to estimate the genome size of HIMB11 based on the genomes of 40 Roseobacter strains (Figure  5 ). The model considers the number of nucleotides sequenced versus the ratio of the number of conserved single-copy genes universally present in Roseobacter genomes to the number of predicted protein-encoding genes. These data were fit to an exponential regression model (R 2 =0. 94), which estimates the genome coverage of the draft HIMB11 genome to be 90. 6% and the full genome size to be 3. 42 Mb. This is relatively small compared to most cultured Roseobacter genomes (median 4. 35 Mb) with only one notable exception (Roseobacter member HTCC2255, 2. 21 Mb). At the time of this analysis, 35 other Roseobacter genomes were publically available in the IMG-ER database (Table  5 ).
Acknowledgements We thank the entire  C-MORE  staff and visiting scientists for support and instruc tion during the 2011 Summer Cours e in Microbial Oceanography. Dr. Mary Ann Moran  at the  University of Georgia  provided invaluable instruction on Roseobacter genomics and bioinformatic analyses. We gratefully acknowledge the support of the  Gordon and Betty Moore Foundation , which funded the sequencing of this genome. Annotation was performed as part of the 2011  C-MORE Summer Course in Microbi al Oceanography  (http://cmore. soest. hawaii. edu/summercourse/2011/ index. htm),with support by the  Agouron Institute , the  Gordon and Betty Moore Foundation , the  University of Hawaii and Manoa School of Ocean and Earth Science and Technology (SOEST) , and the  Center for Microbial Oceanography: Research and Education (C-MORE) , a  National Science Foundation-funded Science and Tec hnology Center  (award No.
Acyrthosiphon pisum MIF (ApMIF) family members The search for predicted MIF gene sequences in the annotated genome of Acyrthosiphon pisum  [6]  (Acyr_2. 0 assembly) revealed 5 MIF members referred to as ApMIF1 to ApMIF5 (Table  1 ). Using BLAST searches  [30] , we found one or several full-length ESTs for each ApMIF member supporting their existence, structural annotation and expression. Complementary searches evidenced an additional hit, corresponding to a 6th MIF gene that was actually annotated in a previous version of the genome assembly (Acyr_1. 0) (Table  1 ). BLAST searches using the originally predicted ApMIF6 cDNA sequence against the 214920 redundant ESTs resulted in a partial and fragmented alignment with only 4 similar ESTs that did not support the existence of a full length transcript from a 6th MIF gene.
In order to further characterize ApMIF members, RT-PCR amplification of the complete coding sequences of the predicted ApMIF genes was performed from RNA of two genetically different lineages of A. pisum (LL01 and YR2). Amplicons were obtained and resequenced for ApMIF1 to -5. Sequence results confirmed the expression of full length transcripts of ApMIF1 to ApMIF5. Amplification of a full length cDNA from ApMIF6 (based on originally predicted sequence) was however not obtained. The five predicted ApMIF proteins showed highly significant hits with Macrophage Migration Inhibitory factor domain (PF01187/IPR001398) (Table  1 ). Interestingly, sequences of the 5 ApMIF predicted proteins differ considerably among each other (Additional file 1: Figure  S1 ) and present 33% to 55% similarity in their sequences.
MIF family members in insects In order to further characterize this multigenic family, we first searched for MIF genes in sequenced insect genomes. Intriguingly, with the exception of two MIF genes in Tribolium castaneum and in Bombyx mori, no MIF genes were identified from the other insect genomes available at the time of the study (Table  2 ). We then performed an extensive search for MIF transcripts from 27 hemipteran species (Table  3 ) with available ESTs in public databases. Interestingly, MIF transcripts were detected, in particular, in phloem-sap feeding species belonging to the Delphacidae, Aphididae, Pemphigidae, Pseudococcidae and Psyllidae families (Table  3 ). MIF genes were identified in all Aphididae with available ESTs with up to 5 members. Note that except for A. pisum the number of MIF members is probably underestimated due to the absence of annotated genomic data and to the modest size of some of the transcriptomic databases.
Phylogenic reconstruction of MIFs from hemipterans Phylogenic reconstruction of complete predicted MIF sequences from hemipterans was performed using the crustacean Daphnia pulex as an outgroup (Figure  1 ). With the exception of A. pisum ApMIF5, all predicted MIF proteins from Delphacidae and Aphididae clustered into five clades including one clade specific of Delphacidae and 4 clades specific of Aphididae (Figure  1 ). Each Between parentheses are indicated the assembly version numbers and the URL used for genomic database searches. Daphnia pulex is added as an outgroup. ApMIF member clustered into one of the four clades, together with MIF members from other aphid species (Figure  1 ), showing that several duplications occurred before the diversification of aphids but after the split of Aphididae from the other hemipterans.
Increased ApMIF expression after immune challenges To explore the expected involvement of ApMIF genes in aphid immune response, we examined their expression levels after immune challenges. We first used Aphidius ervi, which is the main parasitoid of A. pisum in the field  [38]. The two clones of A. pisum showed a significant increase in ApMIF1, ApMIF2 and ApMIF3 transcript levels The total number of redundant ESTs (Num EST) available in public databases at the time of the study is shown for each species, as well as the number of complete cDNA unique sequences predicted to code MIF proteins (presence of PF01187 domain confirmed by pfam_scan). Between parentheses are shown the species identification letters as used for the phylogenetic reconstruction. after exposure to female parasitoid (Figure  3 ).
ApMIF expression during both agonistic and antagonistic interactions Because some secondary symbionts can impact important aphid fitness-related traits such as resistance to parasitoids  [15] [16] [17]  or pathogens  [18] , it may be disadvantageous for aphids to mount a complete immune response in   3 ). Genbank accession numbers of the sequences used for the reconstruction are shown next to the species identification letters (2 letters as defined in Table  3 ). Clades that are specific of delphacidae or aphididae (A-D) are highlighted in light and dark blue, respectively. Values at nodes are bootstrap proportions. Bar: 0,2 substitution/site. the presence of both these mutualists and a pathogen or parasitoid. Similarly, if the negative regulation of aphid immune response results from a direct effect of mutualists on host immune system, one may expect that this regulation would be maintained after an immune challenge and would limit the increase in ApMIF expression.
Discussion Widespread lineage-specific gene duplications have been observed in the A. pisum genome  [6]. The number of duplications in this aphid appears greater than that of any other sequenced insect with over 2000 gene families that have undergone gene duplication  [6]. However, these duplications were not reported to particularly affect immune-related genes, and, on the contrary, important and conserved genes of the antibacterial pathways are missing from A. pisum genome  [6]. A specific search for MIF genes in genomes from insects revealed that except for Tribolium castaneum and Bombyx mori, no other genome contained MIF members. Conversely, analysis of hemipteran transcriptomic databases provided evidence for the existence of a MIF multigenic family in Aphididae species as well as for the existence of at least 2 MIF members in Delphacidae species.
Database searches and sequence analyses Protein sequences encoded by MIF genes in A. pisum genome were identified using BLAST programs  [30]  at the Aphidbase (available at http://www. aphidbase. com/) and National Centre for Biotechnology Information (NCBI) servers, providing a 6. 2X-coverage genome assembly (Acyr_2. 0 and Acyr_1. 0) of A. pisum  [6]. Other public databases used included the Reference Sequence (RefSeq) database  [46]  for A. pisum mRNA and protein sequences and the 214,920 A. pisum ESTs available at the time of the study in NCBI-dbEST. An exhaustive search of Hemiptera MIF transcripts in the NCBI's databases (NR, dbEST) was performed using the BLAST program  [30]. The predicted translation of each full-length or partial sequence was checked for the presence of the conserved MIF domain (PFAM reference number PF01187 and/or InterPro reference number IPR001398) using pfam_scan software (available at http://pfam.
Real-time quantitative reverse transcriptase-PCR (RT-qPCR) Total RNA was isolated from homogenized tissues (whole bodies or hemocytes) using the RNeasy micro plus kit (Qiagen) and quantified with a nanodrop (Agilent). Firststrand cDNA was generated from 500 ng RNA using iScript cDNA synthesis kit, according to standard procedures (Biorad, California, USA). Gene specific primers were designed with Primer3 software  [50]. Real-time quantitative PCR was performed on a DNA Engine 2 (MJ Research, Minnesota, USA) with qPCR MasterMix Plus for SYBR green I (Eurogentec, Belgium) using one internal reference transcript (elongation factor 1-α, NCBI RefSeq XM_001951252. 2). Running parameters were 95°C for 10 min, followed by 40 cycles of 95°C for 30 s, 60°C for 30 s, 68°C for 30 s. All amplifications were performed in triplicate assays.
Signal intensity was measured at the end of each elongation phase and results were analyzed using the Opticon 3. 1 software provided by MJ Research. To assess the specificity of the PCR amplification, a melting curve analysis of the amplicon was performed at the end of each reaction and a single peak was always observed. Standard curves were established with four serial dilutions of first-strand cDNAs, ranging from 1/10 to 1/10000. ApMIFs expression levels were normalized to expression of the internal control elongation factor 1-α (EF1) using the comparative CT method (Applied biosystems, USA).
Statistical analyses All experiments were repeated three times independently, and each sample corresponds to pools of 10-15 individuals. Differences in relative ApMIFs gene expression levels were tested for statistical significance by oneway ANOVA and the tukey-Kramer test on row data (Software Prism v. 5. 0, GraphPad). Expression of the target genes are presented either as an expression ratio normalized to EF1 expression or as a relative expression normalized to EF1 expression and to expression in the control sample.
© 2014 Groot et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons. org/licenses/by/4. 0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons. org/publicdomain/zero/1. 0/) applies to the data made available in this article, unless otherwise stated.
H3K9ac levels follow H3K4me3 at Cfp1-regulated regions independent of gene induction Cfp1 influences H3K4me3 deposition at many genomic locations spread throughout the mouse ES cell genome and particularly at active gene promoters  [28]. We tested whether Cfp1-deficiency affects histone acetylation at nondoxorubicin-inducible promoters by examining H3K4me3 patterns at the most active genes in WT ES cells as identified in our RNA-Seq dataset (n = 2,500; Additional file 6). The results confirm that lack of Cfp1 leads to severely reduced H3K4me3 levels, mainly located downstream of the TSS (Figure  4A, B, E ) and that this is accompanied by a reduction in H3K9,K14ac as determined by ChIP-Seq (Figure  4A, B, E ). Similar results were obtained for this gene set when comparing doxorubicin-treated WT and Cfp1 -/-ES cells (Figure  S5A ,B,E in Additional file 7).
Antisense transcription is also a feature of gene expression thought to be affected by H3K4 methylation  [44] [45] [46]. Active mammalian transcription units produce small sense and antisense RNA molecules at their TSSs and transcription end sites (TESs)  [47, 48]. We investigated levels of these transcripts at genes regulated by doxorubicin in WT and Cfp1 -/-ES cells using strandspecific RNA-Seq datasets. As expected, upon induction sense RNA accumulated both downstream of the TSS and upstream of the TES (Figure  5C, D, G, H ) and profiles were strongly similar when comparing doxorubicintreated WT and Cfp1 -/-ES cells. Induced genes also displayed increased divergent antisense transcription at both TSSs and TESs, albeit at much lower abundance, but this was strikingly elevated in Cfp1 -/-ES cells (Figure  5C, D, G, H ).
RNA expression analysis A GTF file containing ensEMBL mouse transcript information (release 67) was used to generate a count table with HTSeq-count  [65]  with the following parameters: --stranded yes -mode union -type exon. These options summarize unambiguous read counts, on the sense strand, over entire gene units using all exons. Genes differentially expressed upon doxorubicin treatment in WT ES cells were identified using the Bioconductor package DESeq  [66]. Genes with adjusted P-value <0. 05 and fold change ≥2. 5 were identified as differentially expressed. Genes with a base mean count <200 (empirically determined) were removed from the list as they correspond to genes weakly expressed in both conditions. The corresponding differential expression heatmap (Figure  2A ) was generated using Java Treeview  [67].
Average profile and heatmap analysis of ChIP-seq occupancy All read depths were normalized to the total number of uniquely mapped reads in each experiment. ChIP-seq density was determined by calculating the average number of hits per base in 100 bp windows with a 20 bp slide using normalized bigwig files over a 6 kb interval centered on the TSS (for promoters) or a 3 kb interval over the center of each peak (for non-promoter regions) using custom Perl and R scripts interfaced with the Galaxy server  [68, 69] , which are available upon request. Average profiles were generated by plotting the median value in each window for each sample. For heat maps, the sliding window file obtained from WT ES cells was subjected to k-means clustering using Cluster 3. 0  [70]. Results from clustering were used to rank sliding windows files for all other samples accordingly.
Sequencing and data analysis Specific TOSV detection was carried out by sequencing PCR products as described previously  [24]. The PCR products were purified using the ExoSAP cleanup procedure (Amersham Biosciences, France). All nucleotide sequences were obtained using the Big Dye Terminator v. 3. 1 Cycle Sequencing Kit (Applied Biosystems, Foster City, CA, USA) and the 3130 automated sequencer (Applied Biosystems, Foster City, CA, USA). Sequences were edited and aligned using DNA Baser Sequence Aligner v3. 5. 4 software (Heracle BioSoft SRL, www. DnaBaser. com) to obtain optimal sequence alignment files. A BLAST analysis at the NCBI database was made to retrieve sets of homologues exhibiting high scores to the L gene of TOSV. To build phylogenetic tree, the maximum parsimony  [26]  method calculating bootstrap confidence values of 100 bootstrapping trials, using the MEGA 5.
Phylogenetic analysis In a maximum parsimony analysis based on the alignment of the L segment of TOSV, a phylogenetic tree was constructed from sequences of the TOSV isolate obtained from GenBank (Figure  2 ) using a maximum parsimony analysis based on the alignment of the L TOSV segment. The tree showed that the Tunisian sample sequences were clustered in four groups. One group included 7 sequences -5 derived from the CSF sample and 2 from sandly poolsshowed that significant similarities exist among the TOSV-isolated sequences described in France.
[GenBank: KC700343, DQ975233], in Italy [GenBank: X68414], and in Tunisia [GenBank: JX867537]. A second group (18 sequences) shows significant similarities with the TOSV Morocco isolate [GenBank: JN832571]. The third group contains a single sequence [DDJB: AB905362] derived from the sandfly pool and shows significant similarities with the Punique sequences described in Tunisia [GenBank: JF920133]. The fourth group contains a single sequence [DDJB: AB905361] derived from a SFC sample [DDJB: AB905361] and shows similarities with the SFSV isolated in Algeria (Figure 2).
Acknowledgments We are grateful to  Professor Jean-Charles Gantier  and  Adel Rhim  for their help in the entomological work. We are also grateful to all hospital medical staff ( Professors N. Ben Alaya Bouaffif ,  A. Leteif ,  M. Ben Jemmaa ,  M. Chakroun ,  H. Tiouiri ) for their participation in patient recruitment. We thank  Fatma Khrouf , a PhD student, for her technical help. Finally great thank to  Dr. Deborah Glassman  for her constructive comments and English corrections on the early drafts of the manuscript.
Availability of supporting data Links to accession numbers in the DDBJ AB904897 http://getentry. ddbj. nig. ac. jp/getentry/na/AB904897/?for mat=flatfile&filetype=html&trace=true&show_suppressed= false&limit=10 AB904898 http://getentry. ddbj. nig. ac. jp/getentry/na/AB904898/?for mat=flatfile&filetype=html&trace=true&show_suppressed= false&limit=10 AB904899 http://getentry. ddbj. nig. ac. jp/getentry/na/AB904899/?for mat=flatfile&filetype=html&trace=true&show_suppressed= false&limit=10 AB904900 http://getentry. ddbj. nig. ac. jp/getentry/na/AB904900/?for mat=flatfile&filetype=html&trace=true&show_suppressed= false&limit=10 AB904901 http://getentry. ddbj. nig. ac. jp/getentry/na/AB904901/?for mat=flatfile&filetype=html&trace=true&show_suppressed= false&limit=10 AB904902 http://getentry. ddbj.
Conclusions This paper presents the overall features of the fabric data measured in the field, every 10 m along the NEEM ice core, Greenland. The main observations are the following: i. Similar to what was observed along other deep ice cores, the initial fabric, measured from 33 m depth downward, is not isotropic and may therefore be associated with fabric evolution during firnification. ii. Down to about 1500 m depth, the fabric evolves toward a single maximum, with a slight girdle tendency. This is coherent with the position along a ridge, characterized by a vertical compression and a slight asymmetry in the horizontal extension. www. the-cryosphere. net/8/1129/2014/ The Cryosphere, 8, 1129-1138, 2014 iii. A sharp fabric strengthening is measured at the Holocene-Wisconsin transition, with a maximum reached at about 1800 m depth, and maintained down to 2300 m depth.
Both proteins copurified with GFP-NUFIP, when NUFIP was coexpressed in the two-hybrid strain, an interaction could be detected between 15. 5K and ZNHIT3 (Fig. S1 B ). The interaction between NUFIP and ZNHIT3 appeared conserved throughout evolution because the yeast homologues of these proteins, Rsa1p and Hit1p, were also reported to interact in high-throughput interaction datasets  (Ito et al. , 2001) , and we could confirm that this is a direct interaction by GST pull-down with proteins produced in vitro (Fig. S1 C ). To further confirm the interaction between NUFIP and ZNHIT3, we coprecipitated the two proteins (Fig. 1 C ). Extracts from stable clones of 293T cells expressing GST-tagged versions of either NUFIP or ZNHIT3 were immunopurified using glutathione beads and analyzed by Western blots with NUFIP and ZNHIT3 antibodies.
NUFIP copurified with GST-ZNHIT3, and conversely, ZNHIT3 copurified with GST-NUFIP. We then determined the intracellular localization of GFP-ZNHIT3 in transiently transfected cells using fluorescence microscopy. ZNHIT3 localized mainly in the nucleoplasm with some cytoplasmic staining, in a manner similar to the localization of NUFIP  (Bardoni et al. , 1999). Collectively, these data indicate that ZNHIT3 is a partner of NUFIP, and its copurification with hBCD1 suggested a role in C/D snoRNP biogenesis.