dataesr/scientific-paragraphs-categorization huggingface dataset:
The quadratic large-field model, in the past often cited as the simplest inflationary model, now lies at the edge of the 95% CL contours allowed by Planck+WP+high-CMB data. A Bayesian parameter estimation and model comparison analysis of a representative sample of single-field slow-roll models shows that Planck is able to discriminate between these models with results that are robust even when a broad set of entropy generation scenarios are allowed. In addition to confirming the exclusion of the φ 4 potential, the Bayesian evidence computed from the Planck data provides significant odds (logarithms of the Bayes factor of about -5 or lower relative to ΛCDM) against large-field models compatible with previous cosmological data, such as the φ 2 potential, and two-parameter potentials such as natural inflation and the hilltop potential.
As presented in Sect. 5, Planck establishes strong constraints on the parameter values of specific inflationary scenarios. For example, the scale parameter of the natural inflation potential is constrained to be log( f /M pl ) 1. 1 (95% CL), improving upon the WMAP 7-year limit on f by a factor of two. The Planck data limit the possibilities for the unexplored physics between the end of inflation and the beginning of the radiation dominated era. Data-driven constraints are obtained on w int , the effective equation of state in the post-inflationary era. Particularly for the disfavoured models listed above, their parameters are pushed to unnatural values (w int 1/3) in order to become more compatible with the data. Using an essentially exact numerical calculation of the predicted primordial spectrum, we reconstruct the observable window of the inflaton potential, expanding the potential as a Taylor series up to a fixed order.
Acknowledgements. The development of Planck has been supported by:  ESA ;  CNES  and  CNRS/INSU-IN2P3-INP (France) ;  ASI ,  CNR , and  INAF (Italy) ;  NASA and DoE (USA) ;  STFC  and  UKSA (UK) ;  CSIC ,  MICINN, JA  and  RES (Spain) ;  Tekes, AoF  and  CSC (Finland) ;  DLR  and  MPG (Germany) ;  CSA (Canada) ;  DTU Space (Denmark) ;  SER/SSO (Switzerland) ;.
History of the Evaluation of the Substances in the Present FGE The EFSA consideration in FGE. 91 dealt with 45 substances, forty simple aliphatic and aromatic sulphides and thiols evaluated by the JECFA at the 68 th meeting, 2007 and five tertiary thiols evaluated by JECFA at the 53 rd meeting, 1999. For seven tertiary thiols  12. 085, 12. 137, 12. 138, 12. 145, 12. 252 and 12. 259 ] adequate genotoxicity data were needed before the substances can be evaluated through the Procedure. The first revision of FGE. 91, FGE. 91Rev1, included the assessment of two additional substances, benzyl methyl sulphide. 077] and methyl phenyl sulfide ]. These substances were evaluated by the JECFA at the 53 rd meeting in 1999. Furthermore, additional information had been submitted on stereoisomerism for  12.
Subgroup I (Acyclic sulphides) In vitro data are available for the candidate substance, di-(1-propenyl)-sulfide [FL-no: 12. 298]; Ames test: S. typhimurium TA98, TA100, TA102, TA1535, TA1537, 1-100 microg/plate. Result was negative with and without metabolic activation  (Stien, 2005). For supporting substances, only data on diallyl sulfide. 088] are available: diallyl sulfide was negative in a limited bacterial reversion assay using one strain only (TA100) and provided equivocal results in an in vitro cytogenetic test in which increased incidences of cells with chromosomal aberrations and sister chromatid exchanges (SCEs), statistically significant but not dose related, were observed. In vivo diallyl sulfide was evaluated as negative in a micronucleus test in mouse bone marrow, which was, however, not designed to evaluate the genotoxicity of the substance itself as it was tested in a mixture.
Subgroup IV (Dithiols) Equivocal results were reported for the only supporting substance tested. 1,. 066] was evaluated positive for induction of gene mutations and SCEs in vitro in a poorly reported study. However, increased mutation frequencies were associated with unacceptably high toxicity, and the relevance of SCEs for genotoxicity assessment is unclear. Moreover, the validity of the latter data set is questionable, as the distinct effect of S9 on toxicity observed in the other mammalian cell mutation study was not replicated. 1,. 066] was reported in an abstract to be negative in the Ames test.
Subgroup V (Acyclic and Cyclic dipolysulphides) Dibutyl disulfide ] is reported to be negative in a mouse lymphoma assay  (Dooley et al. , 1987). However, the study is reported only as abstract, and thus, the validity cannot be evaluated. Further data are available for the supporting substances diallyl disulfide. 008], dimethyldisulfide. 026], phenyl disulfide [FL-no: 12. 043] and benzyl disulfide [FL-no: 12. 081]. All substances were negative in the Ames test. In addition, diallyl disulfide was reported to be positive in a chromosomal aberration assay in vitro, with and without metabolic activation, and weakly positive in a SCE assay. However, the validity of these findings is doubtful as chromosomal aberrations were only increased in conditions associated with extensive (> 90 %) lethality, and because of the limitation of SCE in genotoxic hazard identification.
EFSA Considerations The 44 JECFA evaluated simple aliphatic and aromatic sulphides and thiols with and without an additional oxygenated functional group are distributed into six subgroups of structurally related substances. The subgrouping is the same as used in FGE. 08Rev5. See Section 2. 1. 2 and Table  2. A new bacterial gene mutation assay for 2-methyl-4-oxopentane-2-thiol [FL-no: 12. 169] in FGE. 74Rev3 has become available. This substance is considered to be supporting for the seven tertiary thiols  12. 085, 12. 137, 12. 138, 12. 145, 12. 252 and 12. 259]  in FGE. 91. Based on these data the Panel considered that the seven tertiary thiols in FGE. 91Rev2 can be evaluated through the Procedure.
CONCLUSION This consideration deals with 44 simple aliphatic and aromatic sulphides and thiols with and without an additional oxygenated functional group which are in the Register and which were evaluated by the JECFA at its 53 rd and 68 th meetings. The Panel concluded that these 44 candidate substances are structurally related to the aliphatic and alicyclic mono-, di-, tri-, and polysulphides with or without additional oxygenated functional groups evaluated by EFSA in the Flavouring Group Evaluation 08, Revision 5 (FGE. 08Rev5). The 44 JECFA evaluated substances are distributed into six subgroups of structurally related substances. The subgrouping is the same as used in FGE. 08Rev5. This revision is made because additional genotoxicity data have become available for 4-mercapto-4methyl-2-pentanone [FL-no: 12.
Strain EB02 T was obtained after 24 h of incubation. In order to obtain a pure culture, colonies were transferred after microscopic examination to Nutrient Agar (NA) medium (meat extract 1 g/l, peptone 5 g/l, yeast extract 2 g/l, sodium chloride 5 g/l, agar 15 g/l). Paucisalibacillus algeriensis sp. nov. strain EB02 T (Table  1 ) was isolated by cultivation under aerobic conditions at 30°C. When compared to sequences available in GenBank database using the BLAST program through the National Center for Biotechnology Information (NCBI) server, the 16S rRNA gene sequence of Paucisalibacillus algeriensis strain EB02 T (GenBank accession number HG315680) exhibited the highest identity (98. 2%) with.
Paucisalibacillus globulus type strain DSM18846 T (Figure  1 ), the phylogenetically closest validly published Paucisalibacillus species. This value was lower than the 98. 7% 16S rRNA gene sequence threshold recommended by Stackebrandt and Ebers to delineate a new species without carrying out DNA-DNA hybridizidation  [7]. sample, but based on a generally accepted property for the species, or anecdotal evidence). These evidence codes are from the Gene Ontology project  [34]. If the evidence is IDA, then the property was directly observed for a live isolate by one of the authors or an expert mentioned in the acknowledgements. Standards in Genomic Sciences GenBank accession numbers are displayed in parentheses. Sequences were aligned using CLUSTALW, and phylogenetic inferences obtained using the neighbor-joining method  [35]  in the MEGA 5 software package  [36].
When compared to other Paucisalibacillus, Ornithinibacillus, Oceanobacillus and Virgibacillus species  [1, [37] [38] [39] [40] [41] [42] [43] , Paucisalibacillus algeriensis sp. nov. strain EB02 T exhibited the phenotypic differences detailed in (Table  2 ). T , used as reference data in the BioTyper database. A score enabled the identification, or not, from the tested species: a score > 2 with a validated species enabled the identification at the species level, a score > 1. 7 but < 2 enabled the identification at the genus level; and a score < 1. 7 did not enable any identification. For strain EB02 T , the scores obtained ranged from 1. 0 to 1. 4 thus suggesting that our isolate was a new species. We added the spectrum from strain EB02 T (Figure  4 ) to our database. Spectrum differences with those of Ornithinibacillus and Oceanobacillus related species are shown in (Figure  5 ).
Genome sequencing information Genome project historyThe organism was selected for sequencing on the basis of its phylogenetic position and 16S rDNA sequence similarity to other members of the genus Paucisalibacillus, and is part of a study of the microbial diversity of the hypersaline lakes in northeastern Algeria. It was the 2 nd genome of a Paucisalibacillus species and the first genome of Paucisalibacillus algeriensis sp. nov. The EMBL accession number is CBYO000000000 and consists of 23 contigs. Table  3  shows the project information and its association with MIGS version 2. 0 compliance  [46]. Table  3.
Genome annotation ) was used with different kmer values (from 67 to 99, interval 4) and the best assembly result, with kmer value (n=95) producing 23 contigs with sizes from 1214-bp to 717,245-bp and average size of 174,207-bp, was retained for genome annotation. Open Reading Frames (ORFs) were predicted using Prodigal  [48]  with default parameters. The predicted bacterial protein sequences were searched against the Clusters of Orthologous Groups (COG) databases and the GenBank database  [49]  using BLASTP. Ribosomal RNAs were found by using RNAmmer 2. 1 server  [50, 51]  and BLASTn against the GenBank database, whereas the tRNAScan-SE tool  [52]  was used to find tRNA genes. Transmembrane helices and lipoprotein signal peptides were predicted using phobius web server  [53]. ORFans were identified if their BLASTP E-value was lower than 1e-03 for alignment length greater than 80 amino acids, if alignment lengths were smaller than 80 amino acids, we used an Evalue of 1e-05.
Artemis  [54]  was used for data management and DNA Plotter [55] was used for visualization of genomic features. To estimate the mean level of nucleotide sequence similarity at the genome level between Paucisalibacillus algeriensis sp nov. strain EB02 T and Paucisalibacillus globulus, Ornithinibacillus scapharcae, Oceanobacillus iheyensis and Virgibacillus halodenitrificans, we used the Average Genomic Identity of Orthologous gene Sequences (AGIOS) home-made software. Briefly, this software uses the Proteinortho software  [56]  to detect orthologous proteins between genomes compared two by two, then retrieves the corresponding genes and determines the mean percentage of nucleotide sequence identity among orthologous ORFs using the Needleman-Wunsch global alignment algorithm.
Paucisalibacillus algeriensis was resistant to nalidixic acid, but susceptible to amoxicillin, nitrofurantoin, erythromycin, doxycycline, rifampicin, vancomycin, gentamicin, imipenem, trimethoprim-sulfamethoxazole, ciproflox-acin, ceftriaxone and amoxicillin-clavulanic acid. The G+C content of the genome is 36%. The 16S rRNA and genome sequences are deposited in GenBank under accession number HG315680 and EMBL database under accession number CBYO000000000, respectively. The type strain EB02 T (= CSUR P858 = DSM 27335) was isolated from a soil sample from the margin of the hypersaline lake Ezzemoul Sabkha in the Oum-El-Bouaghi region of northeastern Algeria.
Figure1. A consensus phylogenetic tree based on 16S rRNA gene sequence comparisons, showing the relationships between strain EB02 T Paucisalibacillus algeriensis and other type strains from the genera Paucisalibacillus, Ornithinibacillus, Oceanobacillus, Virgibacillus and Salirhabdus. GenBank accession numbers are displayed in parentheses. Sequences were aligned using CLUSTALW, and phylogenetic inferences obtained using the neighbor-joining method [35]  in the MEGA 5 software package [36]. Numbers above the nodes are percentages of bootstrap values obtained from 1,000 replicates that support the node. Paenibacillus polymyxa was used as the outgroup. The scale bar represents 0. 01 substitutions per nucleotide position.
Microarray data analyses and annotation of gene function RNA extracts from 3 infected and 3 control mice were selected for array hybridization, corresponding to 30 days, 60 days, 90 days and 180 days after infection. Total RNA was purified with NucleospinH RNA Clean-up Kit (Macherey-Nagel, Germany) and each purified RNA sample isolated from an individual sample was run on a single microarray. All microarray procedures were done according to a previously described procedure  [27]. The original microarray data have been uploaded to Gene Expression Omnibus (GEO) website: http://www. ncbi. nlm. nih. gov/geo/index. cgi (accession number: GSE24376). All data is MIAME compliant.
Expression of the data and statistical analysis Immunostaining for IL-17 was semi-quantified by calculating ''expression scores'' that consider both staining intensity and the percentage of cells stained at a specific range of intensities. A score of zero indicated the percentage of positive cells ,5%, 1+ = 5-25%, 2+ = 25-50%, 3+ = 50-75%, 4+. 75%. The staining intensity of each specimen was judged relative to the intensity of a control slide including an adjacent section stained with an irrelevant negative control antibody that was matched by species and isotype to the specimen. Staining of the section labelled with the negative reagent control was considered as background. A score of zero indicated no staining relative to background, 1+ = weak staining, 2+ = moderate staining, and 3+ = strong staining.
According to standard pathology practices, staining intensity was reported at the highest level of intensity observed in all tissue elements, except the distinctive tissue element for which an expanded scoring scheme was reported. The ''expression scores'' were calculated by multiplying the percentage of positive cells (0-4) and the staining intensity scores (0-3). For example: for a specimen with 30% of positive cells(3+), and a moderate staining intensity (2+), the ''expression score'' was362 = 6. Three pathologists read the sections and established the scores, and they were blinded to each other's results. Cells with a positive immunostaining were counted in five random visual fields of 0. 95 square mm each, at initial magnification: 620, for each sample. All the data were analysed by SPSS 17.
Methods Participants Thirty-eight patients and 31 healthy participants gave written informed consent, according to the Declaration of Helsinki. The sociodemographic data of the participants are presented in Table  1. Thirteen patients with FM were referred by the Pain Unit of the Saint-Etienne Hospital, Saint-Priest-en-Jarez, France and met the diagnostic criteria for FM. 20 The mean duration of the disease was 5. 07 years (standard deviation [SD]: ±3. 01 years). Twenty-five patients with MS were selected from the Department of Neurology at the Saint-Etienne University Hospital and the Germaine Revel Center at Saint-Mauricesur-Dargoire, France. All of these patients fulfilled the inclusion criteria for MS as defined by the United States National Multiple Sclerosis Society in 1996. 21 Five patients had primary progressive MS, nine patients had relapsing remitting MS, and nine patients secondary progressive MS.
Data analyses For each group, the mean scores were collected from the QD2A scale, the Anxiety Scale Questionnaire, and the Pain Catastrophinzing scale. Additionally, as mentioned, the P(A) and the B scores were calculated on data collected from the SPQ and STEP tests. Considering the small number of patients, the P(A) and the B scores were each rank-transformed  36  and treated to a separate analysis of variance (ANOVA) and of covariance (ANCOVA) with the group as the between-groups factor; the depression, anxiety, and catastrophizing variables were used as covariates. Post hoc comparisons were carried out using the Bonferroni adjustment.
Acknowledgments We thank  Professor Nicolas Danziger  (Paris) and  Dr Kenneth Prkachin  ( Canada ) who allowed us to use their experimental materials, the SPQ and the STEP. We additionally thank  Dr Kenneth Prkachin  for his valuable help in analyzing some of the results. This work received a 2011 research prize from the  SFETD (French Society of Study and Treatment of Pain)/ APICIL Foundation (Association of Interprofessional Foresight of Executives and Engineers of the Lyon region). This award was given beforehand to fund the study. Finally, we would like to thank  Fannie Carrier-Emond  for the corrections in English.
Intervention trial DiOGenes is a multicentre dietary intervention study. The protocol for this trial and supporting CONSORT checklist are available as supporting information; see Checklist S1 and Protocol S1. The DiOGenes study started January 2005 and enrollment began November 2005 in the first two centres, followed later in the autumn of 2006 of the other 6 centres. In 2006, clinical trials registration became highly recommended and ClinicalTrials. gov registry DiOGenes certification occurred October 2006 (registration no. NCT00390637), before registration became mandatory according to the 59th World Medical Association general assembly (Seoul, Republic of Korea, October 2008). Design and dietary intervention have been described in detail previously  [6]  (Protocol S1 and Checklist S1). Briefly, from January 2006 to August 2007, overweight and obese adults participated in a dietary program with 8-week low calorie (3.
At each clinical investigation day (CID) (i. e. at baseline (CID1), after LCD (CID2) and after WMD (CID3)), bio-clinical data were collected, blood was sampled and a needle biopsy of abdominal subcutaneous AT was performed. Detailed method for sampling and data collection in the DiOGenes trial has previously been published  [8]. IR was assessed using the homeostatic model assessment (HOMA-IR) calculated as [fasting glucose (mM)6fasting insulin (mU/l)/22. 5] [9]. The authors confirm that all ongoing and related trials for this intervention are registered. Figure  1  displays the organizational flowchart through the trial protocol and the individuals' selection from the DiOGenes cohort for the present study.
Statistical analysis R software (version 2. 14. 0) was used for hierarchical clustering, Random Forests (RF), sparse Partial Least Square Discriminant Analysis (sPLS-DA) and partial correlations, along with different R packages. Conventional statistical analyses (Kolmogorov-Smirnov, ANOVA, Kruskal-Wallis, Wilcoxon, Mann and Whitney and x 2 tests) were carried out with SPSS Statistics version 17. 0 for windows (SPSS Inc. , Chicago, Ill). Individuals' grouping was performed using hierarchical clustering analysis on a matrix composed of two synthetic variables, V1 and V2. V1 was calculated as the difference in HOMA-IR between end of LCD and baseline, and V2 as the difference in HOMA-IR between the end of WMD and the end of LCD. The Euclidean distance was used as a measure of similarity and the Ward's method was chosen as agglomeration criteria.
The dendrogram obtained was cut in 3 groups. Groups were then hand-cleaned from border line individuals and wrong group assignation (based on HOMA-IR trajectories) to get more homogeneous clusters resulting in a final analysis of 216 individuals. Gaussian distribution of data was tested using the Kolmogorov-Smirnov test. Group comparisons were made using the x 2 , one- way ANOVA (normally distributed data), Kruskal-Wallis, Wilcoxon (paired data) and Mann and Whitney (unpaired data) tests (non-normal data) with Bonferroni correction. Significance cut-off was set at 0. 01 which is lower than the Bonferroni adjusted p value (0. 0167). Two multivariate discriminant analyses were used to identify the important variables (i. e. mRNA levels) for group classification: RF (R package : randomForest) and sPLS-DA (R package : mixOmics).
Then, the use of discriminant analyses to find the transcripts most related to subject's grouping showed CIDEA as the most consistent transcript related to groups. The changes in CIDEA mRNA levels mirrored changes in insulin sensitivity. To further extend and validate the relationship of AT CIDEA expression to insulin sensitivity the whole data set of 311 individuals was investigated according to change in HOMA-IR among the entire dietary intervention (from baseline to the end of WMD), independently of groups. CIDEA encodes a member of the cell death-inducing DNA fragmentation factor 45-like effector (CIDE) proteins, which is expressed in both brown and white fat  [21]. Originally described as involved in apoptosis, CIDEA plays important roles in the development of metabolic disorders  [22]  and lipid metabolism  [23].
The study was supported by funding from the  European Communities (EMIF-MET ,  FP7-115372 ,  DiOGenes ,  FP6-513946 ,  HEALTH-F2-2008-2011 00 ),  Fondation pour la Recherche Medicale ,  Agence Nationale de la Recherche  (LIPOB and OBELIP) and  Region Midi-Pyrenees. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
2013). The climatic differences were calculated for five variables that have already proven to determine Iberian tree species distributions (more details in Appendix S1). Prediction of the population suitability for 2050 was performed using the A1F1 HadCM3 scenario of the IPCC, as in the case of the virtual species. The original growth and survival data was split into training and validation datasets to independently validate the models. The goodness of fit was measured by the generalisation power of the model on the validation dataset (R 2 ) and by the percentage of the variance explained by the algorithm (see Appendix S1). The model was calibrated with the training dataset using the machine learning technique random forest algorithm  (Breiman 2001 ; see Appendix S1). The random effect of the block structure in average increase in DDBH was implemented by comparing a null model (DDBH ~climate data) with a model including the block structure (DDBH ~block, climate data) and comparing the performance of both.
ACKNOWLEDGEMENTS This work was supported by the  Spanish Ministry for Innovation and Science  with the grant  Consolider Montes  [ CSD2008 00040 ], the  Community of Madrid  grant  REMEDINAL 2 [CM S2009 AMB 1783 ], and  CYTED network ECONS  ( 410RT0406 ). The Pinus sylvestris provenance tests used in this research are part of the  Spanish Network of Genetic Trials (GENFORED). We thank all those involved in the establishment, maintenance and measurement of the trials. MBA also thanks the  Portuguese IC&DT Call   N°1/SAE-SCTN/ALENT-07-0224-FEDER-001755  and  CGL2011-26852  project of the  Spanish Ministry of Economy and Competitiveness  for support of his research.