dataesr/scientific-paragraphs-categorization huggingface dataset:
Spike trains were analyzed off-line using Spike2 software (CED, Cambridge, UK) and then exported to MATLAB for further processing. Recordings were obtained from four preparations for recording periods ranging between 140 and 300 seconds. Results reported in the text and figures refer to a single data set (#1), unless otherwise noted. Results are qualitatively similar for all four data sets. For predicting changes of coupling strength by pharmacological conditions, data were acquired in a similar way as described above. Specifically, CsCl at 5 mM concentration was applied to the preparation to block h-currents. Recordings include 300 s of data before the application (control) and 300 s after application of CsCl (condition). Visible spike sorting artifacts were removed by visual inspection. The model selection procedure selected a maximal lag of 1 s for the self-history filters and 350 ms for the cross-coupling filters where maximal lags were jointly optimized for both data sets using the BICpenalized likelihood criterion.
For the application of picrotoxin (PTX), the control condition consists of 360 s of recordings before the application and 120 s of stationary activity 6 minutes after application of PTX (Sigma Aldrich, St. Louis, MO) at 10 {5 M added to the saline. Spike trains were acquired as described previously. The model selection procedure selected a maximal lag of 1300 ms for the self-history filters and 100 ms for the cross-coupling filters where maximal lags were jointly optimized for both data sets using the BIC-penalized likelihood criterion. For the generation of stochastic spike trains from the model, maximal lags for the model of the PTX condition were manually chosen to accommodate the long period of the pyloric rhythm (approximately 5 seconds).
Funding: FG is supported by the  Swiss National Science Foundation (SNSF)  under the grant number  200020-132871  and by  EU - FP7 program  ( BrainScaleS  project  269921 ). EM and GJG are supported by  NIH  grant  MH 46742 , and TK is supported by  NIH  grant  F32 NS099590. UE is supported by  NSF  grant  IIS 0643993  and  NINDS  grant  R01 NS073118  and MK and UE acknowledge joint support by  NINDS  grant  R01 NS072023. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
91%) and 73 (1. 13%) contigs identified as significantly differentially expressed by EdgeR and DESeq, respectively). The differences in relative expression between the endo-and ecodormancy libraries and relative read numbers for each contig are displayed as MA plots (smear-plot) in Figure  3. A Venn diagram displaying the number of differentially expressed genes identified by each of the three programs (R statistics, edgeR and DESeq) is shown in Figure  4 , where 75 contigs were identified by all three programs (MN set), and 161 contigs (MX set) were detected in total. For 26 contigs, only R statistics were consistent with differential expression, whereas only 14 contigs were identified as differentially expressed by edgeR and only nine were identified as differentially expressed by DESeq package alone.
Genes upregulated during ecodormancy Ecodormancy is a short-term phenomenon induced by a prolonged period of cold and long-day conditions, which provide an initial signal triggering a shift from endodormancy  [46]. In this section we discuss the genes up-regulated during ecodormancy according to the pathway highlighted through the EAPG and FNSE analysis as well as our qPCR analysis (see Additional file 7: Figure  S3 ). Indeed several pathways known to be regulated during ecodormancy (i. e. response to abscisic acid, response to gibberellin stimulus, response to cold and response to oxidative stress) were differentially regulated in our dataset (see Additional file 3: Table  S3  and Figure  5 ). It should also be borne in mind that because of the low number of contigs, as well as the rather relaxed threshold used to declare a significant differential expressions, the analysis of pathways and networks requires further validation.
Cleaning, assembly and annotation of the dormant bud transcriptome Reads were extracted with the sfffile program of Roche 454 Life Science software (Branford, CT, USA). Each 454-read was screened by cross_match  [86]  for primers and adaptors and was then masked. The longest non masked region was extracted and further cleaned up with SeqClean  [87]. The shorter regions were discarded to eliminate potential chimeras. Cleaned reads were assembled into OakContigV1  [28] , which was constructed with ESTs from 20 Sanger-and 14 454-sequencing libraries, including the four libraries studied here (LC1, LC2, SJ1 and SJ2, Figure  6 ), with the TGICL pipeline (see  [28]  for detailed procedures).
We also used two R Bioconductor packages: edgeR  [30]  and DESeq  [31]. We applied a false discovery rate (FDR) of 0. 2 as a type I error for significant differential expression. The genes identified as differentially expressed with the three methods were combined, to define a minimum (MN) and a maximum (MX) set of differentially expressed genes. The MN set was defined as the intersection of the genes identified as differentially expressed by the three programs, whereas the MX set was defined as all the genes identified as differentially expressed by at least one of these techniques. Heat maps were drawn for both the MN and MX sets, for visualization of the relationships between differentially expressed genes by gplots libraries in R  [91]. We assessed the statistical significance of GO slim terms for differentially expressed genes, by using the goseq  [92]  R Bioconductor package to overcome the length bias inherent to RNA-seq data.
The FDR was set at 0. 05. We used both the MN and MX sets for goseq analysis. Homology searches were carried out with BlastX [93] against the SWISS-PROT  [94]  and TAIR9 protein databases. The e-value cutoff for the Blast searches was set at 1 × 10 -5. Gene ontology (GO)  [95]  annotations were based on the top Blast hit against SWISS-PROT database. Blast2GO software  [96]  was used to summarize GO, which were mapped onto plant-specific GO-slim terms.
Enrichment analyses Enrichment Analysis for Pathways and groups (EAPG) and Sub-networks (FNSE) for selected genes were performed using Ariadne Pathway Studio 9 Desktop edition Software and the Resnet Plant Version 4 database (Ariadne Genomics Inc. , Rockville, MD, USA). As described on Ariadne web site (http://www. ariadnegenomics. com) the "Find Pathways/ Groups enriched with selected entities" function (named here as EAPG for simplicity) execute a Fisher's exact test on each pathway or group and returns informations relative to overlapping entities together with the p-value of the statistical test. EAPG was executed against Gene Ontology as well as AraCyc plant metabolic pathway  [97, 98]. The "Find sub-networks Enriched with selected entities" function (FSNE) was used to identify the set of entities (sub-networks) organized by specific relationship using Fisher's Exact Test.
Reverse transcription and quantitative real-time PCR (qPCR) The following criteria was applied to select candidate genes: (1) first, genes displaying the strongest differential expression in previous statistical analyses were selected, and (2) the molecular function of the genes selected in bud development in other species was also investigated using the Arabidopsis database available at the following URL (http://www. arabidopsis. org). Using this strategy 13 candidate genes based on expression and function data (see Additional file 6: Table  S7 ) were identified. Among the 13 genes analyzed, only one encoding an 18. 1 kDa class I heat-shock protein, displayed a multibanding pattern after PCR amplification and was discarded from the analysis. Relative quantification was therefore carried out for the remaining 12 genes.
Primer pairs are listed in Additional file 6: Table  S7. We performed qPCR and data analysis as previously described  [99]. Briefly, data were analyzed with the Excel (Microsoft) macro GENEX v1. 10 (Gene Expression Analysis for iCycle iQ W Real-Time PCR Detection System, v1. 10, 2004, Bio-Rad Laboratories), using the methods derived from published algorithms  [101]. Expression levels were normalized with respect to the stable levels of expression of genes encoding an elongation factor and an 18 S ribosomal protein. GeNorm software was used to assess the stability of the genes used as internal control.
Acknowledgments The study was carried out with financial support from the  European Commission  ( FP6-2004-GLOBAL-3 ,  Network of Excellence EVOLTREE "Evolution of Trees as Drivers of Terrestrial Biodiversity ",  N°016322 ) and  INRA (AIP Bioressource)  to  A. Kremer. SU received postdoctoral support from  INRA. VL and EP were recruited to work on the  EVOLTREE  project. This research was also supported by grants from  Agence Nationale de la Recherche ,  Plateformes Technologiques du Vivant  ( BOOST-SNP  project,  07PFTV002 ).
Seventeen clones were PCR amplified using the primers M13d (CGCCAGGGTTTTCCCAGTCACGAC) and M13r (TCACACAGGAAACAGCTATGAC) and the obtained products were sequenced. The obtained sequences were compared with sequences deposited in the GenBank database by using the BLAST program through the NCBI server. This strain exhibited a 96. 4-97% nucleotide sequence similarity with O. profundus, the phylogenetically closest validated Oceanobacillus species. Gene sequences were aligned using the multisequence alignment program CLUSTAL X (1. 8)  [36]. Phylogenetic relationships with closely related species were determined by using MEGA version 5. 0  [37]. Distance matrices were determined following the assumptions described by Kimura and were used to elaborate a dendrogram using the neighbor-joining method (Figure  1 ).
5% trifluoroacetic acid) per spot was applied. MALDI-TOF MS was conducted using the Microflex LT spectrometer (Bruker Daltonics). All spectra were recorded in linear, positive ion mode. The acceleration voltage was 20 kV. Spectra were collected as a sum of 240 shots across a spot. Preprocessing and identification steps were performed using the manufacturer's parameters. 3 with a validated species enabled the identification at the species level, a score > 1. 7 but < 2 enabled the identification at the genus level; and a score < 1. 7 did not enable any identification. For strain N'Diop T , none of the obtained scores were > 1. 5, thus suggesting that our isolate was not a member of a known species. We incremented our database with the spectrum from strain N'Diop T (Figure  4 ). The spectrum was made available online in our free-access URMS database  [49].
Genome project history The organism was selected for sequencing on the basis of its phylogenetic position and 16S rRNA similarity to other members of the genus Oceanobacillus, and is part of a "culturomics" study of the human digestive flora aiming at isolating all bacterial species within human feces. It was the second sequenced genome of an Oceanobacillus species, Oceanobacillus massiliensis sp. nov. A summary of the project information is shown in Table  3. The EMBL accession number is CAER01000000 and consists of 95 contigs (≥500 bp) and 9 scaffold (> 2,500 bp). Table  2  shows the project information and its association with MIGS version 2. 0 compliance.
Genome annotation Open Reading Frames (ORFs) were predicted using Prodigal [50] with default parameters but the predicted ORFs were excluded if they were spanning a sequencing GAP region. The predicted bacterial protein sequences were searched against the GenBank database [51] and the Clusters of Orthologous Groups (COG) databases  [52]  using BLASTP. The tRNAscan-SE tool  [53]  was used to find tRNA genes, whereas ribosomal RNAs were found by using RNAmmer  [54]. Transmembrane domains and signal peptides were predicted using TMHMM  [55]  and SignalP  [56] , respectively. ORFans were identified if their BLASTp E-value was lower than 1e-03 for alignment length greater than 80 amino acids. If alignment lengths were smaller than 80 amino acids, we used an E-value of 1e-05. Such parameter thresholds have been used in previous works to define ORFans.
Genome properties The genome is 3,532,675 bp long with 40. 35% GC content (Table  4  and Figure  5 ). It is composed of 95 Contigs (9 Scaffolds). Of the 3,589 predicted genes, 3,519 were protein-coding genes, and 72 were RNAs (1 gene is 16S rRNA, 1 gene is 23S rRNA, 9 genes are 5S rRNA, and 61 are tRNA genes). A total of 2,536 genes (72. 07%) were assigned a putative function (by cogs or by NR blast). In addition, 84 genes were identified as ORFans (2. 39%). The remaining genes were annotated as hypothetical proteins (618 genes (17. 56%)). The distribution of genes into COGs functional categories is presented in Table  5. The properties and the statistics of the genome are summarized in Tables  4  and 5. Two CRISPRs were found using CRISPERfinder program online [58] which included at least 48 predicted spacer regions (contigs 39-41) and 13 predicted spacer regions (contig 92).
Figure1. Phylog enetic tree hig hlig hting the position of Oceanobacillus massiliensis st rain N'Diop T relative to other type strains within the Oceanobacillus, Ornithinibacillus, and Virgibac illus g enera. GenBank accession numbers are indicated in parentheses. Sequences were alig ned using CLUSTALX, and phylog enetic inferences obtained using the neig hbor joining method within the MEGA 5 software [18]. Numbers at the nodes are percentag es of bootstrap values obtained by repeating the analysis 1,000 times to generate a majority consensus tree. Halobacillus litoralis was used as outg roup. The scale bar represents 0. 005 nucleotide chang e per nucleotide position.
The N'Diop T spectra were imported into the MALDI BioTyper software (version 3. 0, Bruker) and analyzed by standard pattern matching (with default parameter settings) against the main spectra of 4,108 bacteria including the spectra from Oceanobacillus profundus CIP 109535 T , Oceanobacillus picturae CIP 108264 T , Oceanobacillus oncorhynchi subsp. oncorhynchi CIP 108867 T and Oceanobacillus oncorhynchi subsp incaldanensis CIP 109235 T which were the most closely related species when 16S rRNA gene sequences were compared and Ornithinibacillus bavariensis DSM 15681 T , Ornithinibacillus californiensis DSM 16628 T and Ornithinibacillus contaminans DSM 22953 T , used as reference data, in the BioTyper database. A score enabled the identification, or not, from the tested species: a score > 2.
However, the weak overlap of the public datasets suggests that they are far from being complete. The impact of cellular proteins on the influenza virus replication has been extensively studied using RNAi screens  [19] [20] [21] [22] [23] [24]. Although poorly overlapping at the gene level, these screens better converge at the level of biological processes  [25] [26] [27]. Hence, more than the identification of host factors, these studies highlighted major cellular functions that are essential for the virus replication. However, for the majority of identified host factors, the mode of action remains to be determined. Furthermore, comparisons of strain-specific virus-host interactomes are clearly missing, which is required to reveal general principles governing infection mechanisms and to identify common therapeutic targets as well as broad-spectrum antivirals.
In the present study we conducted stringent yeast two-hybrid screens to identify human proteins interacting with NS1 and NS2 from 9 influenza A virus strains representative of the variability in nature. The functional impact of all NS1 and NS2 interactors on viral replication was systematically addressed by RNA interference. In combination with published datasets, our new results offer a comprehensive view of NS1 and NS2 interactomes and corresponding targeted cellular functions. The global analysis of the NS1 and NS2 host cell targets reveals an enrichment of double-stranded RNA binding domain (DRBD) containing proteins for the 9 tested influenza virus strains. A focus was put on ADAR1 since this protein is critical for the replication of other viruses  [28] , is highly expressed in human lung cells  [29] , is induced by type I interferon  [30] , is interfering with interferon signalling production  [31]  and is interacting with all tested NS1 proteins.
From a total of 1422 possible interactions tested (79 cellular proteins tested against 9 NS1 and 9 NS2 proteins), 562 tested positive. In this way, we identified 33 cellular proteins interacting exclusively with NS1, 28 exclusively with NS2, and 18 with both NS1 and NS2. The vast majority (97. 5%) of the NS1 and NS2 interactors are known to be expressed in the respiratory epithelium (Table  S2 ). Twelve out of the 79 host interactors have already been reported (AIMP2, SCRIB, CPSF4, the kinases PIK3R1, PIK3R2, MAPK9, CRK and proteins with a double-stranded RNA-binding domain STAU1, PRKRA, ADAR1, TARBP2, ILF3)  [9, 11, 19, [33] [34] [35] [36] [37] [38]. 21. 5% of host interactors are targeted by all virus strains (Figure  1B ) and 5% appear to be strain specific. 80% of the cellular interactors bind to more than 50% of the tested NS1 and NS2 proteins indicating that the dataset is more appropriate to the identification of common rather than differential interaction profiles.
Together with previously published data available in the VirHostNet database  [39] , we now provide a list of 111 nonredundant cellular proteins interacting exclusively with NS1, 32 exclusively with NS2 and 18 with both proteins (a complete list of influenza virus interactors is given in Table  S3 ). Consistent with observations from previous virus-host interactome studies, NS1 and NS2 proteins tend to interact with highly central proteins in the human interactome  [40] [41] [42] [43]. Indeed, the degree distribution of targeted human proteins was significantly higher than the degree distribution in the human interactome (U-test, pvalue,2. 2610 216 ) (Figure  1C ). Similarly, the betweenness distribution of targeted human proteins was significantly higher than the betweenness distribution in the human interactome (U-test, pvalue,2.
2610 216 ) (Figure  1D ). This suggests that influenza NS1 and NS2 proteins preferentially target pleiotropic cellular proteins  [44]. Finally, an assessment of Gene Ontology categories revealed a significant enrichment (p-value = 3. 3610 214 ) for DRBD-containing proteins (DRBPs) in the interaction dataset. Strikingly, DRBPs were exclusively targeted by NS1 proteins. All virus strains interacted with most of the DRBPs suggesting that the direct targeting of DRBDs is of special importance for influenza A viruses.
ADAR1 was co-expressed in HEK293T cells with NS1 or its RBD and with the editing reporter construct. The NS1 effector domain or DLG4, which does not bind to ADAR1 (data not shown), was used as negative control in analogous cotransfection experiments. NS1 RBD and full-length NS1 increased the Firefly luciferase signal by 30% and 60% respectively (Figure  6B ) suggesting that NS1 can cooperatively interact with ADAR1 via its RNA-binding domain to promote ADAR1 editing activity (Figure  6B ). Editing activity was also analyzed in the context of influenza virus infection after expression of the editing reporter construct (Figure  6C ). H1N1 influenza virus infection increased the editing activity of ADAR1 by 70% and this was completely reversed when ADAR1 expression was silenced by RNA interference.
For NS1, there is a strong overlap with hits published by others (11 of the 51 interactors identified in the present study, which is well above the average overlap)  [41, 53] , suggesting that the NS1 interactome dataset is now close to completion. In case of NS2, only 4 cellular interactors have been published and one of them, AIMP2, has been confirmed in our screens. Although 46 new NS2 interactors have been identified, it is difficult at this stage to estimate the completion level of the NS2 interactome due to the lack of published interaction data. Overall, most of the cellular targets interacted with the majority of NS1 or NS2 proteins of the different influenza viruses arguing that we have identified highly relevant and evolutionary conserved interactions. Interestingly, a significant proportion of these proteins is also targeted by other viruses (44.
7%, exact Fisher test, p-value,2. 2610 216 ) indicating that these cellular proteins are likely to be involved in a generic process of viral infection  [39]. Our interaction dataset indicates that NS1 and NS2 proteins are likely to be involved in multiple steps of the viral replication cycle, paving the way for challenging functional explorations. This was largely unexpected for NS2, which is known to be involved in the nuclear export of the vRNPs. Its interaction with the cytoskeleton appears particularly interesting for further studies. Although the pleiotropic nature of NS1 is well established  [54] , our study provides new insights into the breadth of interactions and activities of this regulatory protein. In addition to the 67 new interactors, the current dataset also provides additional information on previously known interactors and related targeted functions.
For instance, the CPSF4 interaction with NS1 has been described as a potential therapeutic target  [55]  and is confirmed in our study. Three NS1 proteins also interacted with CPSF3L, a protein participating in the endonuclease activity of CPSF, suggesting that the corresponding viruses evolved alternative strategies to interfere with the cellular 39end mRNA processing  [56]. The phenotypic analysis of the cellular targets of NS1 and NS2 by RNA interference revealed an enrichment in modulators of influenza virus replication, further validating the interaction dataset. Indeed, out of the 79 cellular interactors of NS1 and NS2 identified in this study, 7 revealed to control positively or negatively the replication of two influenza virus strains. Interaction profiles suggest that the data could be extrapolated to other strains with the noticeable exception of RPL13A, an exclusive target of A/H1N1/Puerto Rico/8/34 NS1.
Functional analysis DAVID database was used for functional annotation  [72]. DAVID functional annotation chart tool was used to perform Gene Ontology categories analysis. Gene Ontology terms with a Benjamini-Hochberg corrected p-value smaller than 5. 10 2 were considered as significantly overrepresented. siRNA screening 5 pmoles of each siRNA (stealth select RNAi, Invitrogen) were arrayed in 96 plates in 10 ml of OptiMEM (2 siRNAs per gene). After 20 minutes of room temperature incubation with a transfection agent (0. 2 ml of lipofectamine RNAiMAX in 10 ml of OptiMEM), siRNA-transfection agent mix was added to 3. 10 4 A549 suspension cells. Cells were incubated for 48 hours at 37uC and 5% CO 2 before influenza A virus infection at MOI 0. 5. At indicated time post-infection, supernatants were titered.
Acknowledgments The authors want to thank  Pauline Radreau-Pierini  and  Patrick Le  ´cine for critical reading of the manuscript,  Bruno Lina  and  Vincent Moules  for  NS1  and  NS2  cDNAs,  Adolfo Garcia-Sastre  and  Juan Ortin  for NS1 mutants and  Amelie Cessieux  for technical assistance.
This work was funded by  ANR ,  ANRS ,  Inserm  and the  FUI  from the  French Ministry of Industry. RB was supported by the  Deutsche Forschungsgemeinschaft  ( FOR1202 ,  TP1 ). RB and VL are also supported by the  European Union 's  7th program  ( FP7/2007-2013)  under grant agreement no  267429  (SysPatho). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.