dataesr/scientific-paragraphs-categorization huggingface dataset:
doi:10. 1371/journal. pone. 0076039. g007 pyruvate that blocks the release of HMGB1 (Figure  S4C )  [29]  and glycyrrhizin that interacts with HMGB1 and prevents its binding to its cellular receptors (e. g. TLR2, TLR4, RAGE)  [30, 31]. The drug concentrations used herein did not affect cell viability as measured by MTT assay for SW872 (data not shown). The secretion of IL-6 was significantly decreased by 75% (p,0. 001) after treatment of SW872 cells with low concentrations of ethylpyruvate and in a dose independent manner (319. 52 pg. mL 21 of IL-6 secretion 6 73. 88 after treatment with 10 mmol. L 21 of Ethylpyruvate, 323. 99 pg. mL 21 688. 64 after treatment with 20 mmol. L 21 281. 54 pg. mL 21 6237. 78 after treatment with 30 mmol. L 21 ) compared to untreated cells (1220 pg. mL 21 688.
Screen of MV-H mutants used to select four nonoverlapping epitopes surrounding the top half of the H cuboidal head domain We screened the panel of MV-H# viruses against a panel of mAbs in an in vitro neutralization assay (table  S1 ). A plaque reduction neutralization test (PRNT) was initially performed to determine the neutralizing concentration of each mAb (concentration required to neutralize MV-eGFP by 100%) (data not shown). A neutralizing concentration of mAb was then used for all subsequent neutralization assays to screen MV-H mutants. The neutralizing assay was performed in a 96 well format. We incubated MV-H# in neutralizing concentrations of mAb or media (control) prior to infection. Two days following infection the number of infectious foci were quantified by counting the number of eGFP expressing syncytia per well.
Acknowledgments We would like to thank  Dr. D. Gerlier  and  Dr. T. F. Wild  ( France ) for mAbs c87 and cl48 and  G. Griesmann  and  Dr. M. Ferderspiel  ( Mayo Clinic, Rochester ) for sufficient quantities of purified cl48 for our selection studies. We like to sincerely thank  Dr. M. Ehnlund  ( Sweden ) for providing us with  16DE6 ,  I-44 ,  I-41  and  I-29  (originally from E. Norrby) and  Dr. I. Iankov  ( Mayo Clinic, Rochester ) for technical discussions.
Domain Function Assignment To build a metabolic network of functional units represented by enzyme domains, we have to identify for each enzyme, its structural domains and the elementary actions they provide. To identify the structural domains we perform a systematic molecular modelling of all enzymes of the network, thanks to a pipeline dedicated to protein structure modelling using Python and Perl routines, and use the homology modelling software Modeller version 9v4  [10, 11]. Briefly said, this routine recursively takes the list of target sequences as input file (profile. py) and does a multiple (multalign. pl) or single alignment (salign. py), then generates the model (model. py). The best model of each enzyme is selected based on the lowest modeller objective function score (MOF). After this step, the structural domains of the proteins are assigned to each of the protein models using fastSCOP  [12, 13].
TCA cycle network using domains as enzymatic units To enrich the metabolic network description of the TCA cycle with functional enzyme domains, we applied the same methodology as the one used to describe pyruvate dehydrogenase complex in the previous section. To perform domain identification and functional assignment, we used all the sequences of all TCA enzymes from subtilist database  [23]  and the methodology presented above for the 3D modelling and domain assignments. Our functional domains description allows to emphasize that the PDH complex and the a-ketoglutarate dehydrogenase (ODH) complex have similar mechanisms and similar domains structures. The rectangular nodes represent the enzymes' domains and the oval nodes represent the metabolites. The external nodes are surrounded in a blue compartment.
stagnalis  [15] [16] [17]. Moreover, genomic resources have recently been improved by pyrosequencing of cDNAs from L. stagnalis, expanding the databases for protein identification in genomic and proteomic approaches for this species  [18]. Finally, this species has been proposed as a candidate species for the development of guidelines for the testing and the assessment of endocrine disruptors in molluscs  [19]. Proteomic analyses in ecotoxicology aim to obtain a comprehensive and quantitative description of protein expression and modification (e. g. , abundance, activity, structure, post-translational modification) following exposure to environmental stress conditions  [20]. Temperature fluctuation, parasitism, and exposure to environmental pollutants lead to alterations of the protein expression in stressed animals  [21, 22].
2D-Gel images were analysed using Decyder 2D Differential analysis software (v. 7. 0, GE Healthcare, Piscataway, NJ, United States). Differential In-Gel Analysis (DIA) module was used for spot detection and abundance measurement for each sample. Internal control was used to match the acquired gels. Protein spots intensities in samples are normalized by dividing them by the intensity of the corresponding spot on the internal standard image  [55]. The differentially expressed proteins in treatments compared to water or solvent controls were detected using Biological Variance Analysis (BVA) module. The unpaired student's t-test was used and a p-value <0. 05 was considered statistically significant. 3. 5. Protein Identification. Protein spots that showed a significant fold change of at least 1.
3. 0. and Flex Analysis v. 3. 0. software (Bruker Daltonics, Billerica, MA) in MS mode for peptide mass fingerprint (PMF, spectra acquisition mass range: 70-4000 m/z) followed by the MS-MS mode for peptide sequencing. Peptides identification was managed using Biotools v. 3. 1. software (Bruker Daltonics, Billerica, MA) with an in-house hosted Mascot v2. 2. 2. server. Metazoa taxonomy was used for database search with the following parameters: peptide mass tolerance of 100 ppm precision, charge state of 1+ and a maximum number of missed cleavages of 1. Carbamidomethylation of cysteines was used as a fixed modification and oxidation of methionine as a variable modification. Identification was significant for peptide mass fingerprint with a p<0. 05 and a Mascott protein score ≥75. Further protein identification was performed using genomic resources obtained by pyrosequencing L.
stagnalis individuals from our laboratory cultures  [18]. Open reading frames (ORFs) were searched among all the snail contigs available (Great Pond Snail Contig Browser: http://genotoulcontigbrowser. toulouse. inra. fr:9095/Lymnaea_stagnalis/ index. html) with the EMBOSS getorf free open source software (http://emboss. open-bio. org/wiki/Appdocs). Translation of regions of minimum 30 nucleotides was performed between methionine (start codon) and stop codons. ORFs were also searched in the reverse sequence. The ORFs obtained in Pearson FASTA format were included in our in-house Mascott library and protein identifications were performed using the previously described parameters. Significantly identified contigs were searched in NCBI database for sequence homology with a cut-off e-value of 1 -5 as previously described  [18]  and available on the Great Pond Snail Contig Browser: http:// genotoul-contigbrowser.
4: Western Blot 4. 1. Antibody Design and Production. For antibody production, a peptide was selected in the L. stagnalis yolk ferritin protein sequence (Uniprot accession number: P42578) based on the hydrophobicity, antigen index and on secondary structure using dedicated software (Eurogentec, Liege, Belgium). The peptide selected was searched in the NCBI database to avoid cross detection of other proteins. The synthetic peptide (H2N-LRSFEQGSGNNYKLGC-CONH2) was coupled with ovalbumin carrier protein and was injected in rabbit. Total rabbit serum was recovered after 28 days (Eurogentec, Liege, Belgium).
Ponceau S reversible protein staining, usually applied as quality control of membrane transfer, was used to quantify total amount of protein loaded per condition by scanning membranes prior to antibody incubation. In comparison to the quantification using the "housekeeping" proteins method, which generally uses ß-actin and/or glyceraldehyde 3-phosphate dehydrogenase (GAPDH) amongst other proteins whose level is thought to be stable between conditions, Ponceau staining normalisation avoid the possibility that these housekeeping proteins abundance would also be altered by the experimental conditions  [57, 58]. Total proteins and yolk ferritin bands were quantified using the 1D gel analysis of ImageQuantTL software (GE Healthcare, Piscataway, NJ, United States). The inter-gel normalisation of the results was performed using a positive control (i.
e. , proteins extracted from eggs sampled in our laboratory breeding stock) as 100% reference. Therefore the results of yolk ferritin expression in samples were expressed as percentage of the yolk ferritin expression quantified in the positive control ran in the same gel. Data were checked for normality using the d'Agostino and Pearson test  [59]  and for homoscedasticity using the Bartlett and Kendall test  [60]. Statistical differences between water and solvent controls were analysed using a Mann-Whitney test. Differences between treatments and controls were tested using One-Way ANOVA and followed by a Dunnett's post-hoc test. Statistical analyses were performed using GraphPad Prism 5. 0 software (GraphPad Software, San Diego, CA, USA).
2: Protein Identification Among the 144 protein spots excised, the Mascott search within the metazoa database provided 57 significant identifications, among which only 13 proteins were significantly identified by MS-MS. Forty proteins were identified as homologous to invertebrate proteins and 57 % of these proteins were significantly identified in mollusc species (Table  2 ). The DNA contigs obtained after pyrosequencing of the genome of L. stagnalis were used to generate translated open reading frames (ORFs), which were added to the Mascott library, in order to improve the protein identifications. However, only 20 ORFs showed a significant matching score and only 7 proteins were newly identified. Half of these ORFs were identified by MS-MS analyses (Table  S1 ). Most of the spots identified were structural proteins mostly involved in the structure and function of the cytoskeleton (e.
2: Impacts of EDCs on Reproductive Pathways Many studies have reported that identification of specific protein expression signature (PES) is useful to propose new biomarkers of exposure to chemicals  [29, 65]. However, further identification of proteins disrupted by chemicals is essential to understand their mechanisms of action. The recent pyrosequencing of the genome of L. stagnalis  [18]  has helped to increase the number of proteins identified. Thus, seven more proteins were identified with this new genetic information additionally to proteins previously identified using other metazoa databases. Bouétard et al. [18]  have reported that  stagnalis genome produced significant identifications using available databases. Therefore, some protein spots that significantly matched to the contigs could not be identified in this study.
The majority of identified proteins were related to structure and function of the cytoskeleton (i. e. , actin and tropomyosin). The high proportion of cytoskeleton proteins identified was also reported in other studies on mollusc species  [67, 68]. This is probably due to both their high abundance in molluscs databases and to their identification as major target of oxidative stress following exposure to pollutants in molluscs  [23, [68] [69] [70]. Even though L. stagnalis genome was sequenced, our results, along with the low proportion of contigs that produced significant identifications  [18] , support the need for gene annotation in gastropods to improve the results obtained from high throughput methods such as "omics"  [71]. As previously observed on the proteome of different tissues of molluscs exposed to pollutants  [23, 68, 70, 72] , actin and tropomyosin proteins were downregulated in the reproductive organs of L.
Acknowledgements Authors thank the  INRA Experimental Unit of Aquatic Ecology and Ecotoxicology  for providing snails and experimental facilities and its technical staff for their support during toxicity tests. Authors thank  Azedinne Bentaïb  and  Sabrina Labrozzo  ( GIGA-Neuroscience ) for their help in proteomic analysis and  Nicolas Smargiasso  ( Laboratory of Mass Spectrometry ) for his help for protein identification.
Transport studies  [22, 23]  have also been hampered by accuracy of the ion power source due to fast ion slowing down. Using the NUBEAM code  [24] , anomalous fast ion diffusion can be introduced into the modelling. There are many possible ways to include this, as the spatial and energy dependencies can be varied and convection may also be included. In  [22] , using an anomalous diffusion of 2m 2 /s reduced the predicted neutron rate to the measured value, while other parameterisations which maintain the stored energy and Shafranov shift (which depend on fast ion pressure) result in a negative thermal conductivity, indicating the need for improved fast ion diagnostics to support transport analysis. The neutron rate, EFIT  [25]  derived stored energy and Shafranov shift are somewhat blunt instruments in the sense that they are not local quantities.
The first application of a charge exchange diagnostic to infer details of the fast ion distribution used He spectroscopy on JET  [29] , while the first true FIDA experiments were carried out on DIII-D  [30]. Since then the technique has been applied to various machines  [31] [32] [33] [34] [35]. Modelling of FIDA signals has advanced over the years with the development of the FIDASIM code  [36]. In many machines, FIDA measurements have been made by re-configuring the CXRS optics. Pilot experiments to test the FIDA concept on MAST were carried out during late 2009 using both the main CXRS system in MAST  [37] , as well as an auxiliary high resolution spectrometer and fibres dedicated for MSE  [38]. The results were encouraging but not of high quality because of the presence of a very bright D α emission peak and the lack of flexibility of the CXRS spectrometer.
IV. FORWARD MODELLING OF FIDA AND BEAM EMISSION Thorough modelling of FIDA and beam emission can be done with the FIDASIM code  [36]. This is based on output from the TRANSP code, which uses the NUBEAM module for the fast ion distribution function. In its basic form, NUBEAM considers only classical collisional slowing down and transport. Additional anomalous diffusion D an coefficients can however be introduced using a variety of parameterisations, which may be utilized to match the experiment due to anomalous activity from fast particle MHD, for example. There will certainly be a broader class of models which would also be consistent with the results, but these simple models are useful for evaluation of the fast ion heat source. For TRANSP simulations, MSE-constrained EFIT equilibria are used.
Thomson scattering data give electron temperature and density; CXRS provides ion temperatures and bulk toroidal rotation velocities; and the Zebra bremsstrahlung diagnostic is used to obtain profiles of Z eff. All diagnostics have spatial resolutions better than a few centimetres at most. Profiles are mapped to the EFIT equilibrium and fitted on both sides of the magnetic axis using adaptive smoothing based on error bars and goodness of fit. For rapid modelling of beam emission measurements incorporating up-to-date ADAS rate coefficients, the NEB-ULA code was used. This is based on the code NEMO  [47] , formerly used for the same purpose  [48]. This code has been validated against other codes, in particular FIDASIM and TRANSP. Though the overall agreement is not perfect here, it is quite reasonable, and serves as a basis for the investigation of anomalous effects.
The FIDA signals are plotted in Figure  (11b ). An attenuation factor of 1. 4, derived from the toroidal beam emission discrepancy, is used in addition to value of 13% due to the in-vessel fibres (n. b. this factor was verified via beam-into-gas shots on the vertical view). Additionally, the background has been multiplied by 0. 4 to account for the coating of the active in-vessel lens. The chosen values of E min here are much lower, simply by virtue of the fact that the higher values contain little or no signal. The lowest value here is just in the tail of the halo. Here, the proper halo calculation was performed using the direct charge exchange module of the FIDASIM code. There are noticeable discrepancies between the measurement and the (classical) theory. It appears that the signal is discrepant by up to a factor of 2 for E min < 25keV.
The authors would like to acknowledge the support of the MAST technical team, as well as  M. Salewski  and  K. McClements  for checking the manuscript,  A. Bortolon  for assistance with the spectrometer,  B. Grierson  for information about atomic rate coefficients, and  D. Liu  and  E. Ruskov  for their assistance with the FIDASIM code. This work was funded partly by the  RCUK Energy Programme  under grant  EP/I501045  and the  European Communities  under the contract of Association between EURATOM and  CCFE. The views and opinions expressed herein do not necessarily reect those of the European Commission or of the  ITER Organization.
Helium was the carrier gas at a flow rate of 1. 2 ml/min with an injection volume of 1 ml. Injector and detector temperatures were set at 250 and 2808C, respectively. Identification of the components of rose geranium volatile oil were made by matching their recorded mass spectra with the mass spectra data bank (Wiley 7N and NIST 2002 libraries) and by comparing their retention indices (RIs) relative to a series of hydrocarbons (C7ÁC28) with literature values  (12).
The difference in footpad thickness was measured by a gauge calliper (Facom, Paris, France). Mean values of treated groups were compared with those of control group (vehicle) and analyzed statistically. The data obtained for the various groups are reported as means9standard deviation (SD) and expressed in mm. The percentage inhibition of the inflammatory reaction determined for each animal by comparison to the controls and calculated by the formula: I (%)0 1( D(PV) t D(PV) c )100 where I (%) 0percent inhibition of edema, (DPV)t0the change in paw volume in the treated mice, and (DPV)c0 the change in paw volume in the control mice.
In brief, 400 ng of RNA was labeled with cyanine-3 CTP using the quick amp labeling kit (Agilent Technologies, Massy, France). The hybridization was performed for 17 hours at 65°C using the gene expression hybridization kit (Agilent Technologies, Massy, France). The microarray slides were scanned with a G2505C DNA Microarray scanner at a resolution of 5 µm. The scanned images were analyzed using the Feature Extraction 10. 5. 1 software.
Microarray analysis Analysis of the microarray data was performed, and the figures were created using the R (v. 2. 13) and Bioconductor software suite as recently described  [30]. The raw data were filtered and normalized using the Agi4x44PreProcess library. Two microarrays were discarded because they did not pass the quality control check. Unsupervised and supervised analyses were performed using hierarchical clustering, principal component analysis (made4 library)  [31]  and the Linear Models for Microarray Analysis (limma library)  [32]. We considered genes to be differentially expressed when the adjusted p value was below 0. 05, and the absolute fold change (FC) was higher than 1. 5. The average linkage was based on the Pearson correlation distance. Functional enrichment analysis was performed on selected genes with the DAVID bioinformatics tool using Gene Ontology (GO) pathways.
In our study, we included a larger cohort of patients and matched controls. Despite a relative heterogeneity in the PE patients, we identified a disease-related signature in which 184 genes were significantly modulated, and the functional categories were related to cell proliferation, differentiation and apoptosis. Genes from the cell proliferation and apoptosis categories were also found in PE patients from Rajakumar's study  [25]. Importantly, a transcriptional signature of severe PE was also identified in the current study. This signature included the overexpression of the ribosomal genes RPL26, RPL34, RPS15A and RPS26, along with genes encoding proteins involved in ribosome  The genes that were specifically upmodulated in PE compared to NT controls were annotated using GO database. The number of modulated genes associated with a particular term is indicated.
VSIG4 belongs to the complement pathway due to its ability to bind C3b, C3i  [49]  and C1q (interactome databases). Because the VSIG4 gene is the only gene found to be modulated in PE in the transcriptional studies published to date  [25, 26] , it represents a superior candidate biomarker to assess PE severity. Given the large variation in expression value and the overlap between the three groups in our dataset, this biomarker requires validation in an independent cohort of patients. Finally, as PAXgene tubes were used in this study, the transcriptome we analyzed comes from heterogeneous origins (mainly leukocytes). Therefore, despite interesting pathophysiological hypothesis were raised, they should be confirmed by further studies. In the present study, we evaluated a large panel of PE biomarkers.
Exposure to ILI in healthcare settings The model was applied to assess the effects of exposure on ILI risk in healthcare settings. The data originated from 3 sources. The first source was a prospective observational study carried out between November 15, 2004 and April 15, 2007 at Edouard Herriot Hospital in Lyon, France  [8]. During the study, each participating ward was followed-up daily to detect ILI cases. A case was defined as an adult patient or HCW presenting with fever (≥37. 8°C) and cough or sore throat. A non-case was defined as a patient or a HCW free from ILI during the study period. As secondary source, all hospital data (admission/discharge dates and ward) on cases and noncases were extracted from the hospital's information system. Data on HCW work periods were included. To quantify exposure to ILI and to define lags, the maximum incubation period was set at I = 5 days  [9].
RNA extraction, reverse transcription and PCR Total RNA was extracted from PBMC using TRIZOL reagent (Invitrogen) according to the manufacturer's instruction. Reverse transcription using random primers and oligo dT was performed on 1 mg of total RNA of each sample using the superscript II transcriptase TM (Invitrogen) for cDNA synthesis. The RT-PCR reactions were performed as described in Burtey et al  [16]. A nested PCR strategy was used in order to assess the specificity of the splicing variant. We designed two oligonucleotides [AB21F: 59-CAGCTCCGCCAACTCCGCCAACT (nt 8875 of L33234 in GenBank), AB36B: 59-GGACAGGAGCCACGCCAACACT-CAC (nt 10981)] which specifically amplify a region of the PKD 1 transcript encompassing exons 23-35. This primer pair was used for the first-round amplifications. Products of this first-round amplification were then diluted by 1/10 000, and the internal region encompassing exons 29-31 was amplified using the following primer pair: [SAB21F6: 59-CTGCCTCTTCCTGGGCGCC (nt 10075), SAB21B6: 59-GCGTGGAGGCCTGAGAACG (nt 10374)].