dataesr/scientific-paragraphs-categorization huggingface dataset:
Monocytes from three independent donors were differentiated into DC with IFNa1, a2, a8, a21 or v, and expression of VSIG4, CXCL11, CLEC5A and IDO1 was measured. The results obtained confirmed that all IFNa subtypes are equipotent in regulating the expression of these genes and showed that the IFNv-DC is closely related to IFNa-DC group for this gene set (Fig. 2A ). This result was reinforced by qPCR analyses performed on IFNa2, v and b-DC from 5 to 11 independent blood donors and for 9 genes (Fig. 2B ). Indeed, 6 genes (IDO1, CXCL11,  IFNb-DC express higher levels of membrane markers associated with IFN-DC differentiation status and produce greater amounts of chemokines Since Affymetrix and qPCR data strongly supported that IFNv belongs to the IFNa group, subsequent work focused on the two clinically used type I IFN: IFNa2 and IFNb-DC.
Measurement of cell surface markers characteristic of DC differentiation state Expression of cell surface markers characteristic of DC differentiation state was measured by flow cytometry on a FACS CANTO instrument using the following antibodies: CD14-FITC, CD83-FITC, CD80-PE, CD86-APC, HLA-DR-FITC, HLA-ABC-PE, CD40-APC (BD Biosciences). Results were analyzed using BD FACSDiva software.
RNA isolation and Affymetrix GeneChip processing Total RNA was isolated from IFN-DC using the High pure RNA isolation kit according to manufacturer's instructions (Roche Diagnostics, France). RNA quality was assessed on an Agilent 2100 bioanalyzer. 200 ng of total RNA from each sample was prepared for hybridization with Affymetrix HG-U133 Plus 2. 0 GeneChip (Santa Clara, CA) according to manufacturer's instructions. The hybridized probes were scanned at a resolution of 3 mm in an Affymetrix Genechip scanner 3000 7G. Affymetrix microarrays were processed in the Microarray Core Facility   located in the Institute for Research in Biotherapy (Montpellier, France). Data have been deposited on GEO site and are accessible with accession number GSE40268. Hierarchical clustering of genes/samples was performed using three metrics (Pearson correlation centered, uncentered or Euclidian).
Hierarchical clustering was applied to one axis (array) using the weighted pair-group method with centroid average as implemented in the program Cluster  [45]. The results were analysed with Tree View  [45]. Microarray data were analyzed according to a previously described procedure  [20] , which allows on the one hand to normalize the signal variance and on the other hand to calculate the expression ratio between two states. The selection criteria used to determine the genes common to all the IFNa-DC were EV#3. 5 and occurrence = 3. Then, the selection criteria of relevant differentially expressed genes between IFNa-DC and IFNb-DC were such that the selected genes had to meet the two criteria: EV$3. 5 and occurrence = 2.
Multiplex analysis of chemokines Chemokines in the cell culture supernatant were simultaneously measured using Luminex xMAP technology (Austin, TX, USA) and commercially available antibody beads, according to manufacturer's recommendations (Human Chemokine 10-Plex pane-l+IL8 from Invitrogen; Milliplex MAP kit Human I-TAC from Millipore). Briefly, antibody-conjugated beads were incubated with standard solution or diluted samples. Subsequent steps involved washes interspersed by the addition of biotinylated detector antibody and Streptavidin-RPE solution. The fluorescence of the beads was read by the Luminex TM 100 system (Austin, TX, USA). The STarStation software (Applied Cytometry, Sheffield, UK) was used for data acquisition and analysis. The MFI value of the analyte in each well was converted into concentration using the standard curve best fitted by 5 parameter logistics.
Acknowledgments We thank  Sandra Pellegrini ,  Mark Livingstone  and  Frederique Michel  for critical reading of the manuscript,  Maria Ferrantini ,  Stefano Santini ,  Caterina Lapenta ,  Sylvie van der Werf  and  Guillaume Cartron  for helpful discussions,  Christophe Duperray  for his assistance in FACS sorting,  Matthew Albert  and the  Centre of Human Immunology  for support,  Hella Kenneweg  for protein production, Veronique Pantesco and the microarray core facility of IRB, Montpellier.
Replication of the in-cis miRNAs in an independent breast cancer cohort In order to validate the 70 identified in-cis miRNAs, miRNA expression and copy number data from an independent breast cancer cohort consisting of 123 breast cancer patients were analyzed. In addition, 26 in-cis miR-NAs were selected for methylation aberration assessment by pyrosequencing. Of the 70 in-cis miRNAs, 41 were consistently associated with similar activating or silencing in-cis effects in the replication cohort as in the discovery (See figure on previous page. ) Figure  1  Genomic localization of the 70 in-cis miRNAs. The 70 in-cis miRNAs were identified using Wilcoxon rank-sum tests to identify differential miRNA expression between patients in different aberration groups. miRNAs are color coded according to main aberration type of the miRNA locus (see legend).
The number in parentheses under aberration type represents the number of in-cis miRNAs in the given aberration category. Inter-chromosomal and intra-chromosomal lines (the latter is seen as independent lines on chromosomes 8 and 13) link in-cis miRNAs to other members of the same miRNA family (see legend). miRNA family annotation is taken from TargetScan (release 6. 2)  [2, 33]. The plot was made using the Circos software package  [34]. Of the 41 replicated in-cis miRNAs, 25 were subject to similar copy number aberration in both the replication and discovery cohort. These 25 copy number-replicated miRNAs were among those with the highest copy numberexpression correlation in both cohorts when considering all in-cis miRNAs (Additional file 4a). miR-186-5p was the only replicated miRNA associated with copy number loss and decreased expression.
miRNA expression profiling The miRNA expression profiling for the discovery and replication cohort was performed using the 8x15k 'Human miRNA Microarray Kit (V2)' with design id 019118 from Agilent (Agilent Technologies, Santa Clara, CA, USA). In brief, 100 ng total RNA was dephosphorylated, labeled and hybridized for 20 hours, following the manufacturer's protocol. Scanning was performed on Agilent Scanner G2565A, signals were extracted using Feature Extraction v9. 5 and the subsequent data processing was performed using the GeneSpring software v12. 0 (Agilent Technologies). As the miRNA expression for the discovery cohort was performed in duplicates per sample (on different arrays and time points), the miRNA signal intensities were averaged for replicate samples. For both the discovery and replication cohort the miRNA signal intensities were log 2 -transformed and for each sample, the 90th percentile was calculated across all miRNAs and subtracted from the miRNA expression.
miRNAs that were detected in less than 10% of the samples were excluded. In the discovery cohort this resulted in 461 unique mature miRNAs. When a mature miRNA is encoded several places in the genome, the genomic origin of a transcript cannot be directly inferred from miRNA expression microarrays (unless the pre-miRNAs are detected). To account for this, we constructed an expanded 575 × 89 miRNA expression matrix that represented all the 575 miRNA genomic loci of the expressed 461 mature miRNAs using an annotation file from Agilent eArray. All subsequent analyses were based on this expanded matrix (Additional file 10). The miRNA expression data for the discovery cohort were published in  [4]  and have been submitted to the Gene Expression Omnibus (GEO) with accession number GSE19536. The miRNA expression data for the replication cohort have been submitted to GEO with accession number GSE46934.
DNA copy number analysis The DNA copy number data for the discovery cohort were generated using Illumina Human-1 109 k BeadChip SNP arrays (Illumina) and are described in  [82]. For the replication cohort, the Comparative Genomic Hybridization 244 k Agilent Microarrays (Agilent Technologies) were used  [83]. Copy number data were log 2 -transformed and centered, and for each sample a segmentation was performed using the Piecewise Constant Fitting (PCF) algorithm implemented in the Bioconductor R package Copynumber  [84]. The average log-transformed copy number was then calculated for each segment and assigned to each probe in the segment. The trade-off between sensitivity and specificity was set to the default value (γ = 40) in the discovery dataset and slightly larger (γ = 50) in the replication dataset to take into account the array resolution (as recommended by the software instructions).
mRNA expression profiling The mRNA expression data for the discovery dataset were measured using Agilent 4x44K one-color oligonucleotide arrays (Agilent Technologies) and have previously been published  [4]  and submitted to GEO with accession number GSE19783. For the replication dataset the mRNA was measured using the Human Genome Survey Microarray version 2. 0 (Applied Biosystems), and the data were submitted to GEO with accession number GSE24117  [75, 83].
, Danvers, MA, USA), respectively, followed by exposure to Alexa Fluor 680-tagged secondary antibodies (Invitrogen Inc. ). For total protein measurement, the arrays were stained with Sypro Ruby Blot solution (Invitrogen Inc. ). The slides were scanned with Tecan LS400 (Tecan Inc. , Durham, NC, USA) microarray scanner and Odyssey Licor IR-scanner (LI-COR Biosciences, Lincoln, NE, USA) to detect the Sypro, Ki67, cPARP, and p-AKT signals. Array-Pro Analyzer microarray analysis software (Median Cybernetics Inc. , Bethesda, MD, USA) was used for analyzing the data. The lysate microarray data were log 2 -transformed and converted into z-scores by subtracting the mean of the whole screen and dividing by the standard deviation of the whole screen. Values ±2 × SD were considered as significant, which corresponded to a threshold of |1.
Network analysis using IPA Networks containing mRNAs correlated to candidate miRNAs were generated with IPA (Ingenuity Systems)  [93]. For each candidate miRNA, a dataset containing the list of all the positively correlated (Spearman's rho >0. 3) and negatively correlated (Spearman's rho < -0. 3) mRNAs (overlapping both cohorts) was uploaded into the application. Each identifier was mapped to its corresponding object in the Ingenuity Knowledge Base. These molecules were overlaid onto a global molecular network developed from information contained in the Ingenuity Knowledge Base, and networks were then algorithmically generated based on their connectivity. The confidence level was chosen to include 'experimentally observed' and 'high (predicted)' relations. mRNA co-expression analysis using SEEK SEEK  [94]  is a novel meta-analysis tool that incorporates a large human microarray expression compendium.
It takes a set of gene identifiers as input and finds genes that are co-expressed in the compendium with the input genes. We first utilized SEEK to determine the extent to which the mRNAs that we identified as positively or negatively correlated to each candidate miRNA were supported by other datasets. We input the list of correlated mRNAs as query. The top 50 datasets related to the query were dominated by breast cancer and included the mRNA dataset we used in our analysis. We next retrieved the co-expressed neighbors of the query genes, and performed gene-set enrichment analysis on the neighbor set. By selecting these genes rather than the query genes for enrichment, we utilize the robustness due to the result of integrating many microarray datasets. For the enrichment analysis, the top 1,000 genes surrounding the query were analyzed for overrepresentation in each of 1,371 Gene Ontology biological processes (with human annotations).
Acknowledgements MRA had a PhD fellowship from the  Norwegian Research Council  (grant number  193387/V50 ). This work was supported by grants from the  Norwegian Research Council  (grant numbers  183621/S10  and  175240/S10 ), the  Norwegian Cancer Society  (grant numbers  419616  and  419628 ) and the  KG Jebsen Center for Breast Cancer Research. We thank  Hilde Johnsen  and  Eldri U Due  for performing array experiments. We would like to acknowledge  Dr Pekka Kohonen  for normalizing the CellTiter-Glo data and  Rami Mäkelä  for performing the protein lysate arrays. We would like to thank  Prof. Junichi Kurebayashi  ( Department of Breast and Thyroid Surgery, Kawasaki Medical School, Kurashiki-City, Japan ) for kindly providing the KPL-4 breast cancer cell line. We would also like to thank  Daniel Nebdal  for excellent help in making the figures.
However, with the increasing number of whole cp genome available, many structural rearrangements, large IR expansion and gene loss have been reported  [2, 5, 6]. These events can be used for the reconstruction of plant phylogeny  [7]. Besides, the availability of whole chloroplast genomes or complete sets of cp genes have helped resolving relationships among major clades of angiosperms  [8, 9]  with more accuracy than even well-chosen ''Lucky Genes''  [10]. Most of the reported complete monocotyledons chloroplast genomes are from the Poales group (so far 31 of the 46 complete chloroplast genomes deposited in Genbank). It is thus important to have more representatives of other clades to better understand the evolution of cp genome within monocots. Bananas (genus Musa, family Musaceae) are monocotyledons from the Zingiberales, a sister group of the Poales.
Sequence Data A reference nuclear genome sequence of the doubled-haploid Pahang accession (DH-Pahang) was produced based on DNA extraction enriched in nuclear content. A total of 27,495,411 reads were generated using Roche/454 GSFLX pyrosequencing platform. An addition of 1,069,954 paired-Sanger 10 kb insert-size reads and 49,216 paired-Sanger BAC-ends sequenced on two BAC libraries generated with HindIII and BamHI restriction enzymes were produced  [11]. The plastid reads were extracted from the total using blast similarity search against Phoenix dactylifera whole chloroplast genome (NC_013991). The 454 filtered reads were then assembled into sequence contigs using de novo assembly with Newbler. A total of six contigs were obtained. Using a python script, an iterative elongation for both ends of each contig using the total 454 reads was applied to ensure that contribution of Musa specific sequences was taken into account.
Genome Annotation The genome was annotated by using DOGMA  [20] , followed with manual corrections for start codons. Intron positions were determined based on those of P. dactylifera  [21]  and Elaeis guineensis  [22]. The transfer RNA genes were annotated using DOGMA and tRNAscan-SE (version1. 23) [23]. Some intron-containing genes in which exons are too short to be detected were identified based on comparisons to corresponding exons in P. dactylifera and E. guineensis. The resulting annotated sequence has been deposited at the European Nucleotide Archive under accession number HF677508.
Phylogenetic Analysis The phylogeny was performed using 79 plastid protein-coding genes derived from 48 plant species (Table  S1 ) with complete chloroplast sequence, most belonging to monocotyledons. A codon based alignment was performed for each gene using homemade scripts that grouped together homologous genes and then converted them into proteins. An alignment was then applied to the protein sequence using MAFFT  [26]  and this protein alignment was then used to make the codon based aligment. Each aligned gene was then concatenated into a single matrix. Missing genes were replaced by Ns. A nucleotide matrix of 76,524 sites was then constituted. Evolutionary model choice was performed using jModelTest 2. 0. 2 software  [27]. A maximum likelihood (ML) phylogenetic analysis was then performed using GTR+G+I model of sequence evolution using PhyML v3.
Musa Chloroplast Structure Comparison with others Whole cp Genomes Gene positions of the different cp genomes were collected from the Genbank file and ordered based on their positions within the genome. Gene order and composition were then compared between the different species. Large events, e. g. gene loss, IR gene gain/loss, large structural rearrangement, relative to the basal angiosperm Amborella trichopoda  [29]  were recorded and used to infer scenarios in the different monocot lineages.
This is why, in this manuscript, data from the surface and data along the water column are addressed separately (e. g. for principal component analysis). From station 5, the sampling depths were de- cided based on the chlorophyll a profile measured with an in situ fluorometer during the downward cast. Due to the maximal depth sampled in this study (200 m), we did not correct the temperature for adiabatic heating as a result of compression. The light parameters have been monitored at five stations (5, 7, 9, 11, 12) using a Profiling Reflectance Radiometer (PRR 600 Biospherical Instrument ® ). The euphotic layer was measured as the depth of 1 % of photosynthetically active radiation at noon. The mixed layer depths were calculated according to the σ O = 0. 15 kg m -3 (relative to z = 10 m,  Thomson and Fine, 2003).
Altimetry and large scale climatic condition The altimeter data (sea level anomaly) were produced by Ssalto/Duacs and distributed by Aviso, with support from CNES (http://www. aviso. oceanobs. com/duacs/). The sea level anomaly map centred on the 18/01/2011 (the process generates maps compiling data collected six weeks before and after the chosen date) (Fig. 1 ) was plotted using the Panoply software from the NASA (http://www. giss. nasa. gov/tools/panoply/). The multivariate ENSO index (MEI) data were used to describe the large scale climatic condition. The data were available from the NOAA website (http: //www. esrl. noaa. gov/psd/enso/mei/) (Fig. 2 ).
The flow cytometer is equipped with a blue (488 nm) air-cooled argon laser and a red (634 nm) diode laser. For each particle (cell), five optical parameters were recorded: two light scatter signals, namely forward and right angle light scatter, and three fluorescences corresponding to emissions in green (515- 545 nm), orange (564-606 nm) and red (653-669 nm) wavelength ranges. Data were collected using the CellQuest software (BD Biosciences ® ). The analysis and identification of ultraphytoplankton groups were performed a posteriori using SUMMIT v4. 3 software (Beckman Coulter). For each sample the runtime of the flow cytometer was set up at 3 and 2 min for ultraphytoplankton and heterotrophic prokaryote analyses, respectively. The sample flow rate was about 50 µL min -1 (corresponding to the "Med" flow rate of the flow cytometer).
Statistical analyses Statistical analyses were performed using the R software (vegan package) and Biplot macro for Excel ®  (Lipkovich and Smith, 2002). Kruskal-Wallis tests were performed in order to compare the abundances of ultraphytoplankton at the various sampling stations. To analyse the multivariate dataset, principal component analyses (PCA) and redundancy analysis (RDA) were also performed. PCA reduces the data dimensionality by performing a covariance analysis between factors. The procedure transforms a number of possibly correlated variables into a smaller number of uncorrelated factors called principal components  (Pearson, 1901). Due to the severe weather conditions at the beginning of the sampling cruise, two PCAs were performed in this study: (i) using the data collected at the surface only and (ii) using the data collected at various depths from stations 5 to 11.
Choice of two different PCAs was motivated by the lack of sampling point below the seawater surface from stations 1 to 4. Station 12 was not included in the PCA performed on the vertical profiles because several variables were missing. In addition to the PCA, the relationships between phytoplankton communities and environmental factors were investigated by using RDA. Data were log 10 (x + 1)-transformed to correct for the large differences in scale among the original variables. Then, Monte-Carlo tests were used to test the significance of the contribution of the various environmental variables to explain the microorganism distribution by the RDA. Partial RDA was also used to evaluate the effects of each explanatory variable set on the organism composition  (Liu, 1997). Two RDA were performed using (i) the surface observations only and (ii) the vertical profiles (Sta.
The first axe is negatively correlated with temperature and salinity representing the mesoscale circulation. The second axe is negatively correlated with nutrient concentrations which represent the different conditions between the samples collected at surface or deeper in the water column. Partial redundancy analysis (partial RDA) was also applied to the profile dataset to estimate the effects of each environmental variable on the various ultraplanktonic clusters. Among the six environmental variables measured during the cruise, temperature and salinity statistically contribute for 28. 9 % and 19. 2 % of the variation in the composition of phytoplankton and heterotrophic prokaryotes, respectively. The sample depth solely explained 8. 3 % whereas nitrate, silicic acid and phosphate explained 4.
Acknowledgements. We thank  Captain Akira Noda , crew members, and scientists of the  RT/V Shinyo Maru of Tokyo University of Marine Science and Technology, (TUMSAT)  for their cooperation at sea. We are grateful to  Yuta Nakagawa  for his help during the cruise. We are also grateful to the  Mediterranean Institute of Oceanography (MIO)  for its support and to the  PRECYM flow Cytometry Platform (MIO)  for the flow cytometry analyses. We thank  Michel Denis  and  Melilotus Thyssen  for their helpful comments on the manuscript, and  Claude Manté  and  Bastien Mérigot  for helpful advices in statistics. We thank the  Société franco-japonaise d'Océanographie  for its support to ship the samples from Japan to France. We also thank  Chris Norman  for the proofreading. Edited by:  E. Marañón.
There are many ways to build effective networks based on observed spiking activity. A commonly used network inference algorithm is Granger causality analysis  [11, 12]. The strength of a causal link between two network nodes is measured by how well the knowledge of past activity of one node helps to predict the activity of the other node. Granger causality analysis has been applied to a variety of different imaging data at different spatial scales of brain activity  [13] [14] [15] [16] [17] [18] [19] , including spiking activity  [20] [21] [22] [23]. However, an inherent difficulty exists in validating these inference techniques because the underlying, true synaptic connectivity is typically not known. Usually, connectivity inference algorithms are validated on simulated data sets  [15, 20, [24] [25] [26] [27] [28] , and it remains largely unknown how well their predictions match the underlying structural connectivity.
Steel electrodes were placed into these wells with reference electrodes in the bath to record electrical signals. These signals were recorded with an AM Systems Model 1700 AC Amplifier and digitized with an Axon Instruments Digidata 1440A (Axon Instruments, Sunnyvale, CA). pClamp software (Molecular Devices, Sunnyvale, CA), running on a PC computer, was used to record extracellular signals continuously. During recordings, saline was continuously perfused and recording temperature was kept as close to 11 0 C as possible with a Peltier cooling system (Warner Instruments, Hamden, CT; Harvard Apparatus, Holliston, MA). Spikes were extracted for three different neurons (PD, LP, and PY) from three different nerves (pdn, lvn, and pyn), respectively. Single spikes were extracted by a threshold criterion.