dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments The authors would like to warmly thank  Pr Dianna Bowles ,  university of York (UK) , for English edition and critical reading of the manuscript. We thank  Fabienne Fasani  for help in cell culture,  Dr Véronique Piller  and  Dr Friedrich Piller  for help in cloning,  Dr. Ewa Zuba-Surma  for technical advice with flow cytometry and  David Gosset  for cell sorting. Financial support  G. Collet  is a doctoral fellow sponsored by the  French  ministry of research; fellowship N  °32852-2008 , the Malopolska marshal office and the  LNCC (National League Against Cancer). C. Kieda ,  A. Jozkowicz ,  J. Dulak  were supported by the  French-Polish Grant INCa/CNRS/MNiSW  ( 347/N-INCA/ 2008 ) for cooperation and  C. Kieda  received  ANR  " triple sens  project", the  LNCC  and the  Institut National de la Santé et de la Recherche Médicale (INSERM).
Ceramide and sphingomyelin analysis by liquid chromatography mass spectrometry Ceramide/Sphingomyelin extracts and standards were dissolved in 50 µL of solvent 20%B/80%A. Standards and sample solutions were analyzed using an Agilent 1290 UPLC system coupled to a G6460 triple quadrupole spectrometer (Agilent Technologies) and using MassHunter software for data acquisition and analysis. A UPLC Silica Syncronis column 100 x 2. 1 mm; 1. 7µm (Interchim) was used for LC separation. The column temperature was controlled at 40°C. The mobile phase A was CHCl3/MeOH (80:20, v/v), and B was CHCl3/MeOH/water, 5mM ammonium acetate pH 5. 6 (60:34:6, v/v). The gradient used was: between 0 to 10 min, the proportion in B increased from 20% to 100%; 10-13 min, 100% B; and then back to 20% B at 13. 5 min until 15 min for column re-equilibrium prior to the next injection.
Gene expression studies Total RNA was extracted with Ribozol™ reagent (Amresco, Solon, USA). For real-time quantitative polymerase chain reaction (qPCR), total RNA samples (2 μg) were reverse-transcribed using the High Capacity cDNA Reverse Transcription Kit (Applied Biosystems, Foster City, USA). Primers for SYBR® Green assays are presented in Table  2  (in Online Resource 1). Amplifications were carried out on a Stratagene Mx3005p™ Real Time PCR System (Agilent Technologies, Santa Clara, USA), using SYBR® Premix Ex Taq™-Tli RNase H Plus reagent (Takara Bio Inc. , Shiga, Japan). qPCR data were normalized to Ribosomal protein L32 (Rpl32) messenger RNA (mRNA) levels and analyzed with LinRegPCR.
As a model organism, N. vectensis is very convenient since it is sufficiently small and transparent for use with various microscopy methods, it is easily grown in a petri dish and it can reproduce sexually and asexually in a laboratory environment  [11]. Also in terms of development, N. vectensis is an interesting model organism, as its mode of gastrulation is common among metazoans and many conserved signaling pathways have been identified, while its body plan is relatively simple (Figure  3H )  [12]. Many gene expression images have been published for N. vectensis, and some papers are listed at the Cnidarian Evolutionary Genomics Database  [13]. An increasing number of raw pictures, including unpublished material, is collected in the marine invertebrates database Kahi Kai  [14]. While these images show the spatio-temporal progress of gene expression patterns, they do not give insight into how these complex patterns arise.
The raw signal is edited and interpolated at a fixed number of equidistant points to arrive at a standardized gene expression profile. In the third part, the gene regulatory network is inferred and updated. A set of expression profiles is selected as modeling input. The free parameters of a network model are estimated with an optimization algorithm. The optimized parameters are incorporated in an interaction network that can be analyzed and validated; the modeling cycle is then repeated with new conditions. doi:10. 1371/journal. pone. 0103341. g002 description of the complex signaling between genes and among cells. Therefore, in this study we first apply the above method on microscopy images of RNA hybridizations and protein antibodies, and quantitative PCR (qPCR) measurements, such as obtained from the Kahi Kai database.
The 70 gene expression images and the derived spatial expression profiles for the cluster analysis are listed in Dataset S1. The correlation matrix and dendrogram are displayed in Figure  6. Applying a similarity cut-off of 0. 6 (one minus the correlation coefficient), the genes are clustered in three groups, for which the profiles are plotted in Figure  7. The largest branch, colored in red, contains 40 profiles characterized by their expression in the endoderm. The green branch contains 28 profiles characterized by expression in the (presumptive) oral region. The two remaining profiles in the blue branch correlate with both clusters, as these consist of sharp peaks at the edges of both regions. Expression domains for a large set of genes in the N. vectensis blastula have recently been analyzed with in situ hybridization  [18].
Standardized profiles from gene expression images Published and raw gene expression images are imported into GenExp, a Matlab interface to quantify gene expression patterns. The expression profile is extracted as described in  [15]. A prototype morphology is overlaid with the image (Figure  5A ). The selected morphology is adapted to the observed embryo's cell layer by dragging the points of the digital morphology over the cell layer boundaries (Figure  5B ). The cell layer is decomposed into segments with edges between the inner and outer cell layer boundaries (Figure  5C ). The average color intensities of the pixels within each segment are plotted as a function of the segment's position on the cell layer (Figure  5D ). This plot is edited to compensate for artifacts from the environment, annotations and decomposition (Figure  5E ).
Both sources of uncertainty are diminished with the increased amount of hybridization images. A new series of hybridization images has recently become available from the Martindale lab in the Kahi Kai database. A repeated study including recent contributions would allow a more balanced clustering, a less biased gene selection and more accurate approximations of selected time points. Because the new measurements are systematic, it can even be sufficient to discard the nonsystematic sources. Robust results generated by the model Some reliable results are obtained from the GRN model, despite its shortcomings. The cluster analysis of gene expression patterns confirms recent findings that genes in endomesoderm formation are mainly expressed in two regions. Moreover, many parameter sets in the gene circuit formalism are capable of simulating the major gene expression features, so the correct interactions that appear in the inferred GRN are probably necessary regulatory interactions to describe the main mechanism of N.
Gene expression quantification in other animals With the spatio-temporal RNA data available for N. vectensis, a general procedure for improving GRNs can be designed. First, in situs from a gene expression database are quantified and a correlation analysis is performed. From this analysis, genes are chosen that represent major correlation clusters. For these genes, spatial distributions at fixed time points are constructed and prepared as input for network inference. Many optimization runs are performed and the resulting models are analyzed for their targeted properties, such as statistical relevance and parameter sensitivities. Based on this information, regulatory interactions among the simulated genes are proposed. These interactions, or their absence, are compared to experiments in literature or evaluated by additional experiments.
Sample collection and clinical data H1N1pdm strains were isolated from nasopharyngeal swab samples obtained from seven patients admitted to the hospital between December 2009 and December 2011 and sent to National Influenza Centre of the South of France as part of the national influenza surveillance scheme. Seven influenza H1N1pdm viruses were selected based on their displaying polymorphisms of the HA protein at position 222 and having been isolated from patients presenting a broad range of clinical features (Table  1 ). Amongst the seven isolates, two displayed HA D222 (Lyon09D, Ste10D), three G222 (Reu10G, Ste09G, Lim10G), one N222 (Lyon10N) and one E222 (Lyon11E). As shown in Table  1 , two patients with no comorbidities died from severe influenza, two patients recovered from severe influenza and three had mild symptoms.
DNA sequencing and analysis Viral RNAs were extracted from the supernatant of infected MDCK cells using the QIAmp viral RNA mini kit (Qiagen) and reverse transcription was performed using Avian Myeloblastosis Virus Reverse Transcriptase (AMV RT, Promega). The cDNA products were amplified by high fidelity PCR (Phusion HotStart High-Fidelity DNA polymerase, Finnzymes) using primers specific for segments of the H1N1pdm virus (Table  S2 )  [15]. The PCR products were purified using the MinElute purification kit (Qiagen) and sequenced (GATC). All viral sequences were analyzed and aligned using the ClustalW BioEdit software (version 7. 1. 83). Sequences are available on GenBank (Accession numbers Table  S3 ).
Kinetic analysis and end-point neuraminidase activities The fluorometric NA activity assay and the half-maximal inhibitory concentration (IC50) determination for oseltamivir (kindly provided by Hoffmann-La Roche) and zanamivir (kindly provided by GSK) were performed twice for each isolate as previously described  [17]. The Michaelis-Menten constant (K m ) was measured from viral supernatants using the MUNANA substrate (Sigma-Aldrich) as previously described  [18, 19]. The 4methylumbelliferone fluorescence was measured using the FLUOstar OPTIMA fluorometer (BMG LABTECH) at 37uC. The initial velocities (Vi) were calculated for each substrate concentration and integrated in a non-linear Michaelis-Menten equation by the MARS program (BMG) for K m and V max determination. V max reflects the activity of the enzyme, but this constant is also dependent on the virus quantity.
NA activity was normalized by a quantitative assessment of the virus titer using endpoint titration in MDCK cells and expressed as V max (nmol/h) per 10 6 viruses. The NA activity was expressed as the mean of three experiments. The K i (affinity of NA for an inhibitor) was measured across a concentration range from 0. 01 nM to 10 nM. The substrate was used at 20 mM. The Vi was calculated for each inhibitor concentration and integrated in a non-linear competitive inhibition equation using the GraphPad software (Prism) for the K i calculation. Endpoint NA activity was measured by MU-NANA assay using a standardized HA titer of viruses at 37uC for 1 hr.
Neuraminidase properties All viruses were sensitive to both zanamivir and oseltamivir carboxylate with similar low IC 50 (Table  5 ) and inhibition constants (K i ) (data not shown). This is consistent with our sequence analysis showing a lack of the amino acid changes known to confer resistance in the NA sequence of the seven isolates  [22]. Kinetic analyses of the sialidase activity of NA were performed using the MUNANA fluorogenic substrate, a non-specific link substrate of sialidase. NA efficiency was determined using the Michaelis-Menten constant (K m ), which reflects the affinity for the substrate, and the V max , which reflects the activity of the enzyme (Table  5 ). The K m value for the MUNANA substrate of Ste10D (23. 8 mM) was reduced compared with those measured for the other viruses (mean K m 35.
2003). Finally, this heterochronous sampling of CHV1 strains was completed with data from two isolates available in Genbank: the complete genomes of the viral strains [EP713]  (Shapira et al. 1991) and [Euro7]   (Chen and Nuss 1999). EP713 is an American C. parasitica isolate (EP155) infected by the hypovirus that was originally obtained in 1966 from southeastern France  (Robin et al. 2010; S. Anagnostakis, personal communication). Euro7 is a naturally virus-infected isolate originating from Italy. Forty-five fungal isolates were previously genotyped with 10 microsatellites markers. They could be assigned to one of the three genetic clusters defined among 105 French genotypes known so far  (Dutech et al. 2010). Nineteen isolates (from southeastern and central France) were grouped into the genetic cluster G1, 20 and 4 (from the Pyr enees area and central France) belonged to cluster G2 and G3, respectively.
Phylogenetic analyses For each ORF, base content, chi-squared test of homogeneity of base frequencies across sequences and distances were computed, and phylogenetic relationships among haplotypes were inferred using maximum-parsimony (MP) method with PAUP ver. 4. b10 for PC  (Swofford 2003). MP trees were generated through heuristic searches with 1000 random stepwise additions, with tree bisectionreconnection branch swapping and saving multiple parsimonious trees (MULTREES turned on). Nonparametric bootstrap support values for each dataset were estimated by performing 1000 heuristic searches with 100 random stepwise additions and all other settings as above. The homoplasy matrices generated in PAUP were visually inspected to locate parts of trees where there was phylogenetic conflict and to identify the haplotypes responsible for this conflict.
Estimation of divergence dates Analyses were conducted on subsets of ORF A and ORF B alignments (ORF A sh and ORF B sh ) including only nonrecombinant sequence blocks to avoid recombination bias. The molecular clock hypothesis was first tested by using the likelihood ratio test (LRT) to compare the null hypothesis of a molecular clock to the alternative hypothesis that lineages evolve at different rates  (Hasegawa et al. 1985). The HKY model was the best model for both data subsets using the Akaike information criteria (AIC) in jModelTest ver. 0. 1. 1  (Posada 2008). Second, 'clock-likeness' of the ORF A sh and ORF B sh phylogenetic signals was assessed with the Path-O-Gen software (http://tree. bio. ed. ac. uk/software/ pathogen/) by calculating the coefficient of variation (CV) of distance (i.
Identification of most probable location state for lineages To determine the probable geographical origin of the different CHV1 lineages detected in the six sampling regions, we used a model of diffusion to discrete states to infer maximum clade credibility trees  (Lemey et al. 2009 ). We first assigned sequences from the same approximate region (based on sampling geographical coordinates) to one of the six discrete sampling regions by using the R-libraries 'geosphere' and 'cluster' (Table  S1  for details). Then, based on the results obtained in the previous clock-model analyses, continuous-time models were calibrated for the ORF A sh and ORF B sh datasets under a constant population size tree prior and an exponential relaxed molecular clock. Individual BEAST runs were performed with 200-million steps in the Markov chain and sampling every 10 000 steps to produce a posterior tree distribution containing 20 000 genealogies.
The maximum clade credibility tree (a point estimate of the tree with the highest cumulative posterior probabilities in the posterior distribution of trees) was annotated with geographical locations using the software Tree-Annotator (available in BEAST package). For each node with a bayesian posterior probability higher than 0. 5, posterior state probabilities (PSP) were assigned to the six sampling populations. To avoid bias arising from unequal sampling size between locations, we homogenized the sample sizes for all locations from which more than eight sequences had been sampled, by randomly sub-sampling eight sequences from each of these. Sequences from Italia and Germany with only one sequence in each were discarded for these analyses. For each of the 25 smaller datasets constructed, we performed the same BEAST-phylogeographic analyses as those described above.
Each time a tree node was found being supported with a bayesian posterior probability ≥0. 5, PSP for each of the four sampling region (central France, Center Pyr en ees, Atlantic Pyr en ees and southeastern France) were collected. This resulted for each node in four distributions of probability values that were compared by using an one-way ANOVA, and a Tukey's HSD test (after) was performed when the sam-pling region effect was significant. Finally, to obtain a greater confidence, the ORF A sh dataset was enriched with additional sequences from Italy (n = 3), south of Switzerland (n = 15) and France (n = 2) previously published  (Gobbin et al. 2003 ). Clock-model and phylogeographic analyses were carried out with this larger dataset as described above.
Positive selection To test for positive selection in ORF A sh and ORF B sh subsets, we estimated the rate of non-synonymous nucleotide substitutions per non-synonymous site (d N ), the rate of synonymous nucleotide substitutions per synonymous site (d S ) and the ratio x = dN/dS, with the method of  (Nielsen and Yang 1998)  implemented in the PAML package of the computer program CODEML  (Yang 1997 ). PhyML ver. 3. 0  (Guindon et al. 2010 ) was used to estimate the branch lengths of the phylogenetic trees of the subset phylogeny, which were used as starting values for CODEML. Two models of codon substitution (M2a and M8) that allow for heterogeneous selection pressures among sites were compared to null models (homogenous rate among sites, models M1a and M7) as described in Data S2.
Sequence analysis We obtained unambiguous sequences for ORF A and ORF B for all but one viral strain ([Doi9]). The ORF A sequence of this strain could not be analyzed because of mixed sequence data. To test the hypothesis of an infection by more than one viral strain, mono-conidial isolates of this fungal culture were grown, and CHV1 strains infecting this progeny were analyzed. Two different viral strains were detected: one related to [EP713] and one related to [Euro7], confirming the double infection hypothesis of the Doi9 isolate. This isolate was then excluded from the analyses. We constructed an alignment of the 55 partial sequences of the ORF A and ORF B genomic regions of CHV1, including sequences retrieved from the NCBI database. In the ORF A region (298 nt), 112 sites were polymorphic giving a rate of 0.
Phylogenetic relationships between CHV1 strains with maximum-parsimony analysis The analysis of nucleotide frequencies over all codon positions suggested a compositional bias toward G+C bases. However, chi-squared tests of homogeneity of base frequencies across taxa were non-significant within 1st, 2nd and 3rd codon positions (Fig. S1 ). Of the three codon positions, third position nucleotides were systematically more variable than those of first and second positions as measured by percentage of sites that were variable and the percentages of sites that were parsimony informative (data not shown). To test the hypothesis of saturation of the phylogenetic signal at third codon positions, we calculated and plotted uncorrected distances and corrected distances between each pair of sequences. For each dataset, high slopes (above 0.
The A5 strains ([Sun9] and [Lab7]) were regrouped with four A1, one A2 and one A4 strains in the B2 cluster. Visual inspection of the homoplasy matrices generated in PAUP for the ORF A and ORF B MP trees helped to identify putative recombinants (Fig. S2 ). In each dataset, the distributions of homoplasy values were trimodal with values around zero, indicating little phylogenetic conflict, and higher values in the range of 10-20 and >45, indicative of more conflict. Similar patterns of phylogenetic conflict were found when the codon positions potentially saturated (third position) were discarded from the analyses (data not shown). In ORF A, conflicts were found between taxa of the A4 and A1 groups (homoplasy values ranged between 10 and 18), A1 and A2 groups and A1 and A4 groups (values between 46 and 56).
On the other hand, differences in the marginal likelihood value between the strict and relaxed molecular clock models were below the significance threshold of 1. 3 generally accepted (log 10 BF = 0. 49, Table  3 ). Three model parameters (i. e. , constant size, exponential size and expansion size) were also tested for each clock model fitted to the data subsets (Table  S2 ). Marginal likelihood analyses showed that no other demographic model significantly fit ORF A sh and ORF B sh data better than the constant size population model [log 10 (BF) always <0. 94], suggesting that there could be insufficient information in the data to fit a more complex demographic model. Finally, using an exponential with constant population size model, average evolution rates were 2. 483 x 10 À4 [95% credibility intervals (CI) of 0.
289 x 10 À4 to 4. 929 x 10 À4 ] and 1. 716 x 10 À4 (95% CI, 0. 303 x 10 À4 to 3. 410 x 10 À4 ) substitutions per site per year for ORF A sh and ORF B sh , respectively (Table  S3 ). Under these rates, the tMRCA obtained for the different virus lineages with the two data subsets were mostly consistent, except for the age of the most recent common CHV1 ancestor [605 years for ORF A sh (1406, 95% CI, À809/+498 years) and 903 years for ORF B sh (11 088, 95% CI, À1332/+735)] (Fig. 4 ). The analysis of the full dataset (i. e. , ORF A + ORF B), including interlineage recombinants with highly divergent CHV1 genes, resulted in an estimation of the time to the most recent common CHV1 ancestor of 110 years before (i. e. , at 998 years; 95% CI, À1353/+816) the date estimated with the ORF B sh dataset (Fig.
Positive selection For both data subsets, LRT-values for comparing models M1a with M2a and M7 with M8 were statistically significant, suggesting that discrete models M2a and M8 P 0 = 0. 99, P = 0. 01, q = 0. 08, P 1 = 0. 005, x = 5. 57 systematically fit these two data subsets better than the neutral models M1a and M7 (Table  4 ). Model M2a indicated that 0. 8% and 0. 4% of the sites were under strong positive selection with x = 6. 08 and x = 5. 75 in ORF A sh and ORF B sh , respectively. Model M8 showed for the two subsets that about 99% of the sites had x from a U-shaped beta distribution, and about 0. 5-1. 0% of sites were under strong diversifying selection with x = 5. 78 for ORF A sh and 5. 58 for ORF B sh. In OFR A sh under models M2a and M8, empirical Bayes' theorem identified one amino acid site under positive selection with >99% confidence.
The occurrence of ancient interlineage recombinants in sequence datasets might have biased the estimated age of the most recent common ancestor by pulling the estimated date deeper in the past  (Awadalla 2003; Duffy and Holmes 2008; Lefeuvre et al. 2010). Using the relaxed evolutionary clock model, we inferred a rate of substitution per site per year of 2. 5 9 10 À4 for ORF A sh and 1. 7 9 10 À4 for ORF B sh. These values are similar to those previously estimated with a relaxed clock model applied on ORF A sequences  (Bryner et al. 2012)  and in the range of substitution rates of other RNA viruses  (Duffy et al. 2008). Our data confirmed that the first divergence of the different lineages had occurred prior to the transcontinental migration of C. parasitica (which did not occur before the end of the 19th century), similarly to what  Gobbin et al.
Figure5Posterior state probabilities of population origin (PSP) inferred from Cryphonectria hypovirus 1 ORF Ash (left panels) and ORF Bsh (right panels) datasets. (A) PSP-values obtained for the original datasets. (B) PSP-values obtained after resampling ORF Ash or ORF Bsh datasets 25 times (see Methods). Letters above columns indicate significant differences among posterior location probabilities means as determined by a Tukey's HSD test after significant one-wayANOVA.
Acknowledgements We thank  Angela Dale  ( TAIGA-Lab. , Vancouver ) for her helpful comments on the manuscript and English editing,  Olivier Fabreguettes  and  Martine Martin-Clotte  for their technical assistance,  Carlos Colinas  for sharing some Cryphonectria parasitica isolates. This work was funded by the  Institut National de la Recherche Agronomique (INRA)  and  R egion Aquitaine.