dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusions and future work Licences are an important part of the data publishing process, and choosing the right licence may be challenging. By applying the Licence Picker Ontology (LiPiO), this task is reduced to answering an average of three to five questions (five being the height of the class taxonomy in LiPiO) and assessing the best licence from a small set of choices. We showed how our approach reduces significantly the e↵ort of selecting licences in contrast with approaches based on feature exploration. In addition, a bottom-up approach on ontology building in this scenario opens new interesting challenges. The RDF description of licences is an ongoing work, modeling issues are not entirely solved and we expect the data to evolve in time, including eventually new licences and new types of policies.
Time-lapse imaging and track analyses Imaging of cell transplants was either done on a Nikon spinning disk equipped with an Evolve camera (Photometrics), or an Axioplan 2 microscope (Zeiss) equipped with a CoolSNAPcf camera (Photometrics) and MetaVue software (Molecular Devices). For detailed analysis of prechordal plate cells speeds and trajectories, dechorionated embryos were mounted in 0. 2% agarose in embryo medium. Z-stacks were collected at 2 min intervals on a thermostated Nikon spinning disk. Nuclei were first automatically tracked in 3D using Imaris software (Bitplane Scientific). Tracks were then manually corrected and validated. Data were then processed in Matlab (Mathworks) using custom made routines as described in  [35] , to extract, in particular, instantaneous speed and persistence.
Instantaneous speed is the displacement vector between two consecutive time points, divided by the time interval. Persistence was defined as the ratio between net displacement (distance between initial and final position) and total displacement (sum of displacements between each time points), computed over 10-min intervals. For 3D representations, for each embryo, tracks at different times points were registered relative to the position of the front of the plate (for x axis) and, between embryos, tracks were registered relative to the centre of the plate (y and z axes). Outliers were removed according to Chauvenet criterion. Data were then interpolated (TriScatteredInterp), and instant speed (norm and direction) was computed on a 3D mesh grid to estimate its value at all positions of the plate.
Illustrations and statistical analyses Images were processed with ImageJ and Adobe Photoshop. Figs. were assembled with Adobe InDesign. 3d colour coded quiver plots were produced in Matlab, using the quiver3Dpatch (available on Matlab Central). Histograms and statistical analyses were performed in R (http:// www. r-project. org/). T-test was used to compare means when a single measurement was done on each embryo. When multiple measurements where done on the same embryo, linear mixed-effects models were used to take into account resampling of the same statistical unit.
The effect of soft electron precipitation is expected to be stronger during disturbed times, particularly in the dusk quadrant, and to play a more dominant role on the dayside where the precipitating electrons tend to be softer: this explains the higher dayside occurrence frequency at ESR compared with EISCAT at both quiet and disturbed times, and the higher frequency on the dawn side during disturbed times. It also suggests that soft electron-driven electron heating may be more efficient than convection-driven ion heating in driving ion upflow. The long time coverage of the EISCAT data set makes it extremely valuable for studying the influence of solar activity on ion upflows. Figure  3  shows (a) the 12-(black) and 3month (grey) averages of the observed occurrence frequency of nightside (19-05 MLT) between 200 and 550 km, and (b) the field-aligned ion velocity and (c) ion flux at 400 km at low (F10.
Further work has demonstrated that the dual-lobe reconnection process must usually occur in two steps, which are separated in time. This interpretation reconciles contradictions between models proposed based on observations of bi-directional MSBL heated magnetosheath electron populations at high latitudes, which suggested that they should also be seen at low latitudes in the MSBL, with observations at lower latitudes in which heated MSBL electrons are typically uni-directional and bi-directional electrons are only seen inside the magnetopause current layer in the LLBL, as well as corresponding issues regarding observations of low latitude O + ions  (Fuselier et al. 1995). Fuselier et al. (2012)  revisited Cluster magnetopause observations, and analysed a large dataset covering 2001-2009, which included the high latitude crossings studied by  Lavraud et al.
Owen et al. (2004)  reported Cluster observations consistent with Kelvin-Helmholtz waves on the dawn flank magnetopause, during northward IMF, provid-ing information on wavelength and propagation direction and noting a steepened leading (tailward) edge. This observation was consistent with predictions by  Miura (1990)  helping to resolve conflicting conclusions of earlier observational studies using one or two spacecraft datasets. Further studies of the conditions for formation of, and the development of magnetopause Kelvin-Helmholtz waves include  Foullon et al. (2008)  and  Hwang et al. (2011 Hwang et al. ( , 2012) )  which demonstrate that, given suitable IMF conditions, such waves can grow at high latitudes under strong dawnward IMF, and under southern IMF, as well as the more commonly expected low latitude regions under northern IMF.
Hasegawa et al. (2004)  examined a dusk flank magnetopause crossing and used multipoint Cluster data to identify specific plasma and magnetic signatures associated with Kelvin-Helmholtz vortices. They further argued that simultaneous observations of plasma at solar wind and magnetospheric energies, on the magnetosphere side of the magnetopause implied that plasma transport had occurred within the vortices, though they were unable to firmly identify the mechanism. Further work to confirm the result and to identify more examples of Kelvin-Helmholtz vortices followed, including studies by  Hasegawa et al. (2006) , which set out criteria with which to identify such vortices in single spacecraft datasets. The statistics of  Hasegawa et al. (2006)  and  Taylor et al. (2012)  show observations usually at low latitudes, and not only tailward but also sunward of the terminator (suggesting that at least sometimes they may develop very rapidly).
Faganello et al. (2014)  published a case study providing observational evidence for the occurrence of this scenario using THEMIS spacecraft data. There may not yet be a consensus on this scenario, as the same THEMIS data were also used to provide support for an interpretation involving reconnection at low latitudes and sub-vortex scales, according to three-dimensional fully kinetic simulations by  Nakamura et al. (2013). It remains to be firmly demonstrated that reconnection-based processes can fully explain the observed magnetospheric boundary layers, particularly under northward IMF conditions, so it remains relevant to consider the relative effectiveness of other processes that may play a role. Alternative scenarios for plasma transport across the magnetic field at the magnetopause typically invoke a kinetic process that acts on scales smaller than those for which ideal magnetohydrodynamics is applicable.
2009b (Engwall et al. , 2009a;; André et al. 2010). Using the Cluster dataset and this unique method to obtain low-energy ion fluxes in the night-side (X GSM < 0)  André et al. (2015)  showed the occurrence rate of low-energy ions was 60-70 % in the lobes to out to X GSM of about 15 R E during all parts of the solar cycle, as indicated in Fig. 13. The ions' very low energy clearly identified them as ionospheric plasma and their high occurrence rate confirmed both their existence and their prominence in the lobes. Their statistics also showed a decrease in the occurrence rate to <20 % on approach to the plasma sheet (|Z GSM | < 2 R E ) and they suggested that the low occurrence at small Z GSM distances was due to the low-energy ions being energized above 10 eV upon approach and passage through the plasma sheet.
Various reviews transformed this complex history into a path to "rewriting a genome"  (Charpentier and Doudna 2013; Doudna and Charpentier 2014). What emerges from this brief historical sketch is that the CRISPR craze was the result of the rapid convergence of two projects and lines of research that had been fully distinct: the search for a mechanistic understanding of a new immune system discovered in prokaryotes and efforts to design efficient genome editing tools by the construction of specific endonucleases. The encounter was explosive because both fields had reached a similar degree of maturation and expectation at the same time. An additional remark is that these extraordinary results were the fruit of a "traditional" way of doing biological research. Databases were important for the comparison of sequences and "deep sequencing" for the characterization of small RNAs, but the breakthrough came from the progressive analysis of the components, and the in vitro reconstitution of the systems, a strategy used since the early days of biochemistry! A result as important as the demonstration that interference occurred at the level of DNA was provided by the "old" Southern method  (Garneau et al.
For the spleen, one experiment representative of at least four with three mice per group is shown. For the skin and CLNs, one representative experiment out of three with three mice per group is shown. (E and F) Specific depletion and recovery of XCR1 + DCs in Karma mice upon DT administration. Splenocytes of DT-injected mice were analyzed by flow cytometry 24 h (E) or several days after treatment (F). The absolute numbers of the analyzed cell population are represented. In these experiments, XCR1 + DCs were gated using CD8α staining in place of XCR1. Data are shown for one experiment representative of two independent ones, with three mice per group. (G) Antigen cross-presentation is abolished in XCR1 + DC-depleted mice. Data are shown for one experiment representative of two with three mice per group.
16-µm frozen sections of spleens were blocked in PB1X containing 0. 2% saponin, 2% BSA, and 2% 2. 4G2 supernatant, and stained in PB1X, 0. 2% saponin. When the staining required, further blocking steps with normal goat or rabbit sera were added. Stained sections were mounted in ProLong Gold antifade reagent (Invitrogen), and acquired on a LSM780 confocal microscope (Carl Zeiss). TdTomato expression was amplified by using an anti-Red Fluorescent Protein (RFP) antibody (Rockland). Antibodies used in immunofluorescence are detailed in Table  S1. Quantification of memory OT-I cell clustering and migration into the T cell zone. OT-I cell clustering and migration into T cell zones were determined computationally using ImageJ and R graphical user interface (GUI) software. We used mosaic images representing a complete section of spleen to perform this analysis.
For OT-I cell clustering: a macro program developed for Image J allowed us to spot individual GFP + OT-I cells present on a frozen spleen section. When several GFP + OT-I cells were too close to be detected as individual cells by the macro program due to strong signal intensity, we manually separated them using the pencil tool from ImageJ. Coordinates of each distinct GFP + OT-I cell were retrieved. OT-I cell locations were then represented graphically with R software after installing the Fixed Point Cluster (FPC) analysis package. The DBS CAN algorithm was set to define a true cluster as a group of three or more proximate cells. Percent of clustered cells were then calculated according to the total number of cells present on the whole section. For OT-I localization in the T cell zone: a macro program developed for Image J allowed us to enumerate all individual GFP + OT-I cells, as well as GFP + OT-I cells found in T cell zones.
As previously, merged GFP + OT-I cells were manually separated. T cell regions were manually delimited according to B220 staining of B cell follicles and nuclear density. Microarray data. The data were downloaded from the BioGPS public database (http ://biogps. gnf. org; Gene Expression Omnibus dataset number GSE10246). Statistical analyses. Statistical analyses were performed using nonparametric Mann-Whitney tests in all experiments. Online supplemental material. Table  S1  lists the antibodies used in this study. Fig. S1  shows the gating strategy used to identify DC subsets in spleen, skin, and CLNs. Fig. S2  shows the gating strategy used to analyze tetramer-positive mCTLs and the early activation of mCTLs in secondary infected animals. Fig. S3  shows IL-12 and CXCL9 expression analysis on DCs, neutrophils and monocytes from secondary infected animals.
ACKNOWLEDgMENTS We thank  Bruno Estebe ,  Frédéric Fiore , and  Bernard Malissen  ( Centre d'Immunophénomique [CIP HE], UM2 Aix-Marseille Université, Institut National de la Santé et de la Recherche Médicale   US012 ,  Centre National de la Recherche Scientifique UMS3367, Marseille, France ) for generating the Karma mice;  Marilyn Boyron , and the cytometry and ImagImm core facilities (CIML,  UMR7280 ,  France ) for technical assistance;  Mathieu Fallet  ( CIML, UMR7280, France ) for programming macros to quantify memory OT-I cell clustering and migration into the T cell zone; and  Toby Lawrence  and  Elena Tomasello  for critical reading of the manuscript. We acknowledge  France-BioImaging  infrastructure supported by the  Agence Nationale de la Recherche  ( ANR-10-INSB-04-01 , call " Investissements d'Avenir ").
This work was supported by institutional funding from  CNRS  and  Inserm , by the  Innate Immunocytes in Health and Disease  ( I2HD ) collaborative project between CIML, AVI ESAN, and  SAN OFI , and by grants from  Association pour la recherche sur le cancer (ARC) , from  Fondation pour la Recherche Médicale  (label "Equipe FRM 2011", project number  DEQ20110421284 ) and from the  European Research Council  under the European Community's  Seventh Framework Program  ( FP7/2007-2013  grant agreement number  281225 ). Y. O. Alexandre was supported by doctoral fellowships from the  French Ministère de l'Enseignement Supérieur et de la Recherche , and from  ARC. S. Ghilas was laureate from a " University President "  Excellence Ph. D. Fellowship  from  Aix Marseille Université  and from  ARC. C.
ssDNA targets were hybridized onto high-density microarrays (Affymetrix Human Genome 1. 0 ST GeneChip array) according to the Affymetrix Eukaryotic Target manual. The microarrays were then washed and stained using the Affymetrix fluidics station 450/250 and Genechip Operating Software and scanned with an Affymetrix GeneArray scanner. The raw affymetrix datasets (. CEL) are available in the Gene Expression Omnibus database (http://www. ncbi. nlm. nih. gov/geo/) with the accession number (GSE46874).
Microarray analyses Quality controls, including scaling factors, average intensities, background intensities, noise (raw Q) values were within acceptable limits for all the arrays. The twelve datasets obtained were processed and normalized using the plier program in R. Two different statistical analyses were performed: 1) we determined which genes were significantly differently expressed (p≤0. 05 by t-test, >2fold difference) following exposure of cells to TCDD, α-endosulfan or the mixture of the two as compared to the vehicle (DMSO), 2) we used the Focus software  [57] , as described by Garcia-Ortiz  [58]  to select genes that had at least a 1. 2-fold mean difference in expression and a default "interest score" of >5. Principal component analysis (PCA) was carried out using the svd module in R.
Hierarchical clustering analysis was performed with the Genepattern software  [59]  on the statistical scores derived from the Focus analysis rather than on the expression levels in order to reduce data variability. Gene Set Enrichment Analysis (GSEA, http://www. broadinstitute. org/gsea/index. jsp) was used to test (using a metric derived from the Kolmogorov-Smirnov statistic  [60, 61] ) whether the distribution of the genes in our study deviated from random in ranked lists of genes up or down regulated by TCDD or 17β-estradiol. Rank-rank hypergeometric overlap was also used to identify statistically significant overlap between gene expression signatures  [62]. The lists of genes were derived from an analysis of publicly available (literature or public repositories) genome-wide expression data obtained from exposure of primary human hepatocytes  [63, 64] , HepaRG  [42]  or mouse primary hepatocytes  [65]  to TCDD or 17βestradiol.
Raw data were renormalized with the plier module in R and robust averages calculated with Tukey's Bi weight average algorithm  [66]. One way analysis of variance (ANOVA) was calculated for each pair of treatment groups and for all treatment groups. FDR were calculated by the Benjamini-Hochberg approach  [67]. Differentially expressed genes were those having a fold change >2 and p<0. 05. The data from supplementary files 11 and 17 from Forgacs et al. [64]  were filtered as indicated in the files. Murine expression data from Flaveny et al. [65]  were also analyzed with Focus. Genes were ranked according to Focus scores and filtered to retain only those showing a differential expression with t-test p-values less than 0. 20 on log ratios. The genes with the highest Focus scores (165 and 195 genes upand down-regulated, respectively, by TCDD) were selected as the gene sets to be compared with our samples.
Acknowledgments We thank  Mrs D. Glaise  for her help with the HepaRG cell culture,  Dr L. Aggerbeck  and  Dr E. Blanc  for critical reading of the manuscript and  F. Dumont  for help with the bioinformatics. Financial support: This work was supported by  ANR (Agence Nationale pour la Recherche)  [ 06SETS26 , Oncopop],  Association de la Recherche sur le Cancer  [ ARC-3927 ],  INSERM  ( Institut National de la Santé et de la Recherche Médicale ),  Université Paris Descartes, MESR  ( Ministère de l'Enseignement Supérieur et de la Recherche ) (doctoral fellowships,  Ariane Ambolet-Camoit ,  Alix Leblanc ), and the  European Union 's  Seventh Programme for research, technological development and demonstration (HEALS project , grant agreement Number. 603946).
MS/MS database search and abundance analysis Compiled MS/MS spectra were searched against the annotated coding domain sequences (CDS) of each strain downloaded from the NCBI (date 20/06/2012). The CDS database of the corresponding co-cultured Synechococcus strain and typical contaminants were included during MS/MS searches in order to avoid false attributions. Experiment 1: searches were carried out with Mascot software (Matrix Science, version 2. 2. 04) using parameters previously established  [15]. Mascot results were parsed and peptides were filtered at a p value below 0. 05. Experiment 2: tandem mass spectra were extracted and analyzed using Mascot (version 2. 4. 1). Mascot was set up assuming the digestion enzyme trypsin and allowing two miss cleavages with a fragment ion mass tolerance of 0.
80 Da and a parent ion tolerance of 20 ppm. Carbamidomethyl of cysteine was specified as a fixed modification and oxidation of methionine as a variable modification. Scaffold (version 4. 3. 4, Proteome Software Inc. ) was used to validate MS/MS-based peptide and protein identifications. Peptide identifications were accepted if they could be established at greater than 95. 0% probability by the Scaffold Local FDR algorithm. Protein identifications with at least two identified peptides were accepted at 95% probability achieving an FDR less than 0. 1%. Protein probabilities were assigned by the Protein Prophet algorithm  [27]. Proteins that contained similar peptides and could not be differentiated based on MS/MS analysis alone were grouped to satisfy the principles of parsimony. Protein quantification by normalized spectral count abundance factor (NSAF) was done as previously described  [28].
Protein sequence in silico analysis The theoretical exoproteomes of the three Roseobacter strains were obtained from Christie-Oleza et al. [15]. Local BLASTp analyses were done with the BioEdit BLAST tool v. 7. 0. 5. 3  [29]  Table  1. Cell counts from each co-culture experiment from which the exoproteome was analyzed using default parameters and an E-value cut-off <10 -20. Conserved protein domains and motifs were determined using the Conserved Domains tool at the NCBI (http://www. ncbi. nlm. nih. gov/Structure/cdd/wrpsb. cgi). The BLASTp tool of Roseobase was used for determining protein conservation throughout the clade (http://www. roseobase. org). 3 Results and discussion   1  and 2 ), respectively. Approximately 40% of the remaining spectra were assigned to the Synechococcus strain present in the co-culture (data not shown) that correlates nicely with the cell ratio obtained at the end of the experiment (Table  1 ).
d. meaning "not detected. " Column "Roseo BLASTp" indicates the number of sequenced Roseobacter strains that encode each protein (currently 57 in the Roseobase). NA is indicated for those proteins for which a BLASTp search did not retrieve a result in the Roseobase website. The detection of each protein in previous proteomic analyses (references  [15, 17] ), where cells were pregrown in a similar way and exoproteomes were processed with the same protocol, is indicated as follows: EP, exoproteome analysis in different culture conditions with easily assimilated organic carbon (i. e. marine broth); SW, exoproteomes achieved from incubating R. pomeroyi DSS-3 in natural seawater; CF, cellular fraction proteomic analyses. Superscripts a and b represent proteins detected in two and one of the three replicate samples, respectively, whereas all other proteins were detected in the three biological replicates analyzed.
(Fig. 2 )  [35]. Looking retrospectively at previously reported datasets we found the major capsid protein was also detected in the milieu of stationary phase marine broth cultures and all natural seawater incubations  [15]. Conversely, they were undetectable in cellular fractions  [17] , confirming their exoproteomic nature. Further work needs to be performed in order to elucidate the role of the GTA in this particular synthetic community.
Other relevant proteins Those proteins involved in dealing with oxidative stress are not abundant in the different exoproteomes but have important biological significance. It has been reported that the "helping" effect heterotrophs have in picocyanobacterial cultures is related to scavenging reactive oxygen species  [43]. In our exoproteomic survey we detected superoxide dismutase and catalase/peroxidase, both suggested to possess a nonclassical secretion by in silico predictions. The prophage-like GTA was also experimentally detected in all three Roseobacter exoproteomes proving commonality of this mechanism for maintaining genetic exchange in this group of bacteria. When considering the entire dataset of homologous clusters we can conclude that the secreted fraction of the three different Roseobacter strains is similar when grown with the same source of DOM.