dataesr/scientific-paragraphs-categorization huggingface dataset:
The data of our study consist of film retelling and spatial description produced by 5 participants in each group, 20 participants in total. Thus, the corpus analyzed is composed of 40 discourses, 20-40 utterances each. The data are collected according to the procedure exposed in sections 1. 2 and 1. 3. The learners and the native speakers are young adults aged 25-35, graduated at university and from the same social background (middle class). The learners are studying French in a mixed context. They attend language classes and stay (or have stayed in the past) in France. The beginners' proficiency level (group I) corresponds to the basic variety level, detailed in  Klein/Perdue (1997). These learners produce utterances, built around a non-finite verb, which are organized into a coherent discourse, following some discourse and semantic principles.
21 n spite of this knowledge, systematic reviews and meta-analyses still provide summary data to policymakers, healthcare professionals, patients and their relatives to make decisions about the patients' treatment and well-being. It is interesting to explore whether these systematic reviews and meta-analyses provide the true effect estimates, and if all these groups are making decisions on the basis of spurious (biased) effect estimates. The validity of systematic reviews and meta-analysis is depending on methodological quality and unbiased dissemination of trials. 22 The association between methodological components and the overestimated treatment effects will be determined in this meta-epidemiological study, which will enable us to provide guidance on how to interpret OA trials in systematic reviews based on empirical evidence from Cochrane reviews.
Data collection process and data items We will use a systematic, standardised data extraction approach to gather information from all eligible OA studies. The primary outcome ( pain) will be abstracted from overall pain reported in the Cochrane review's first pain measure forest plot. Outcome will be collected at the time point closest to 12 weeks' follow-up. Two reviewers will independently extract data from the trials. We anticipate that pain will be measured in various ways, and thus different instruments will be applied to monitor the changes during any given trial period. 28 ommonly pain is measured by either a visual analogue scale (VAS) or a numeric rating scale (NRS) and less commonly by more complex item-based instruments (eg, WOMAC 29 KOOS 30 and ICOAP  31  ).
'Trial-level' data extraction At the trial level, we will assign studies a trial ID and extract information on author name, year of publication, type of pain measure, type of intervention and type of OA condition. From the forest plot available in the included review, we will extract the following study-level covariates: mean values (m I and m C ), SDs (SD I and SD C ) and size of the trials. The size of the study will be handled in two ways: (1) trial size according to the meta-analysis (ie, forest plot, n I and n C ), and (2) trial size according to the original (ITT) population according to the trial report (N I and N C ). Further, we will assess the following characteristics from the trials: attrition rate during the trial (a Total , a I and a C ), and trial duration (weeks). Further, we will extract information on the comparator groups applied in each trial, whether placebo/sham or waiting-list/ nothing.
Further, outcome data will be considered adequate if missing data have been imputed using appropriate statistical methods like intention to treat (ITT). Deficient information about the blinding process will be regarded as unclear Other bias Centre status A trial will be considered a multicentre trial if more than one centre is involved. In case of missing information, the trial will be classified as multicentre if it reports both several ethics committees and different affiliations of authors. If the report stated both a single ethics committee and a single author affiliation, the trial will be classified as a single centre. Information about single-and multicentre trials will be extracted from the text, statements, author's affiliations, and acknowledgement in every included trial Trial size To judge whether a trial is small or large, we will use a threshold of 128 participants.
Funding Trials will be specified either as being funded by for-profit funding or non-profit funding. Non-profit funding includes money received from both non-profit organisations (eg, Internal hospital funding and governmental funding) and not funded trials. For-profit organisations will be defined as companies that might acquire financial gain or loss depending on the outcome of the trial. Further, trials with a mix of for-profit and non-profit or if the funding is not reported, will be considered as for-profit funded. Funding is defined as including provision of manpower (authorship, statistical analysis or other assistance), study materials (drug, placebo, assay kits or similar materials), or grants. 27 Sources of funding will be extracted from the text, statements of sources of support, authors' affiliations, acknowledgments and trial registration, if available model.
Acknowledgements The authors thank the  Parker Institute, Frederiksberg Hospital , and  The Oak Foundation  for creating the possibility and right settings for this research. Additionally, the authors thank the  Cochrane Musculoskeletal Group Editorial  based at The  University of Ottawa , Centre for Global Health. Contributors JBH, RC, CBJ, PT and HL conceived and designed the study. JBH, RC and CBJ contributed to the development of the protocol. All of the authors ( JBH, CBJ, IB, PT, EG, JPP, TR, GAW, AM, LM, HL and RC) assisted in the final protocol and agreed to its final approval before submission.
The scale bar indicates nucleotide substitutions per site. Only bootstrap support of >70% are shown. Positions with less than 95% site coverage were eliminated. There were a total of 317 positions in the final dataset. The phylogenetic positions of groups Lyon I, II and III are shown in relation to representative Seoul strains (identical sequences removed for clarity). GIV726 partial L sequence was identical to GIV733, GIV737 and GIV757 (Lyon II). LYO852 partial L sequence was identical to LYO799, 837, 838, 839, 843, 845, 848, 853, 884 and 871 (Lyon III). Genbank accession numbers are shown next to taxa names. detected at 1 (n = 2 rats), 2 (n = 4 rats) and 3 (n = 12) locations respectively (See Additional file 2A). No cocirculation of variants was observed. Lyon I and II were geographically restricted, whereas Lyon III was the most frequently detected and widespread of the variants (Additional file 1).
Mapping sequence reads revealed complete or near complete (>99%) coverage of the SEOV reference genomes for the virally enriched samples (S3). Complete SEOV genome sequences were recovered from the LYO852 sample and the SEOV consensus sequences of the three samples were identical. The SEOV sequences have been deposited in GenBank under accession numbers KF387723 to KF387725. Hence, we report the significantly improved chances of successfully obtaining complete viral genome sequences by NGS following simple viral enrichment steps. The S3 enrichment approach will be assessed for future NGS analysis on the Roche-454 platform.
Genetic and phylogenetic analysis We report the complete genomic sequence of a SEOV strain isolated from R. norvegicus in France. The S-segment has a total of 1755nt with a deduced coding sequence of 1290nt. The putative encoded nucleoprotein (N) (AGZ59811) is 429 amino acids for a predicted 48KDa protein. The S-segment complete coding sequence shared the highest identity (98%) with the complete coding sequences of Vietnamese strains  [29, 30] , the two Singaporean strains Rn41 and Rn46  [31] , the SEOV Belgian Rn895 strain and the French Replonges strain  [21]  (respective GenBank accession numbers: AB6 18112 to AB618126, GQ274944 and GQ274945, JQ89 8106 and KC902522). All these strains originated from wild R. norvegicus but the Replonges strain was obtained from a patient. The putative N protein was identical to the deduced N protein of strains that originated from South Korea, China, Vietnam, the United Kingdom and the French Replonges strain (NP_942556, ADE34611, BAL467 98, AGB05597 and AGL45258 respectively).
The sequencing data were initially assembled in the GS de novo assembly software (Roche). Subsequently, previously published SEOV sequences were used to map specific reads from the original raw data using GS Reference Mapper (Roche). Subsequently Illumina sequencing was assessed. Total RNA extractions were performed using RNeasy mini kit (Qiagen) according to the manufacturer's instructions. The remaining rRNA was depleted using ScriptSeq complete gold kit Human/Mouse/Rat (Epicentre) following manufacturer instructions and controlled with the 2100 Bioanalyzer using "Eukaryote total RNA Pico Assay" (Agilent Technologies). 3. 5-5 ng of depleted RNA was prepared for NGS using the Illumina protocol where 10 cycles of PCR were performed and standard TruSeq adapters and TruSeq barcoded primers were used.
Genetic and phylogenetic analysis of genomic segments The deduced amino acid sequences of the 3 genomic segments of the LYO852 strain were obtained using the Serial Cloner 2. 6. 1 software. The complete coding sequence of the S, M and L segments and the predicted protein sequences were compared to the NCBI database using the BLAST program (http://blast. ncbi. nlm. nih. gov/). Multiple sequence alignments of coding sequences were carried out using ClustalW algorithm in the MEGA 5. 2. 2 software (default parameters)  [35]. Phylogenetic reconstructions were performed using the Maximum Likelihood statistical method. Bootstrapping (1000 or 500 resamplings) was applied according to the best-fit substitution model recommended. (See figure on previous page. ) Figure  3  Phylogenetic tree of hantaviruses based on the S, M and L segment sequences (complete coding region).
All analyses were performed using the MEGA software  [35]. A. S segment: analysis was performed applying the generalized time reversible model (GTR) using a Gamma distribution with five rate categories and invariant site (+G + I) Only bootstrap percentages ≥70% (from 1000 resamplings) are indicated. B. M segment: analysis was performed applying the generalized time reversible model (GTR) using a Gamma distribution with five rate categories (+G). For clarity purpose, the nodes corresponding to bootstrap percentages ≥70% (from 1000 resamplings) are indicated by dots. C. L segment: analysis was performed applying the generalized time reversible model (GTR) using a Gamma distribution with five rate categories (+G). Only bootstrap percentages ≥70% (from 500 resamplings) are indicated. The scale bars indicate nucleotide substitution per site.
Acknowledgements We acknowledge financial support from the  European Commission  under the Food, Agriculture and Fisheries, and Biotechnology theme of the 7 th  Framework Program for Research and Technological Development ,  FP7 'WildTech'  project grant agreement no. 222633  and  FP7 'European Virus Archive ' projectgrant agreement no 19  228292. We also acknowledge funding from the  UK Defra  surveillance projects  SV3037 & FZ2100. Technical support and assistance was also provided by  Emma Wise ,  Richard Ellis  and  Charlotte Featherstone  (AHVLA). We acknowledge special financial support from  VetagroSup for the post-doctoral grant  ( TD ) and we also acknowledge  Pr Etienne Benoit  ( USC1233 ) for his constant support.
Concentration in cell culture supernatant of infectious virus particles was measured 2-4 days post infection (p. i. ) by immunofocus assay. Cell growth and viability under compound treatment was determined by the 3-(4,5-dimethylthiazol-2-yl)-2,5diphenyl-2H-tetrazoliumbromide (MTT) method as described  [43]. A sigmoidal dose-response curve was fitted to the data using Prism GraphPad 6. 0 (GraphPad Software). The inhibitory concentrations that reduced the virus titer by 50%, 90%, and 99% (IC 50 , IC 90 , and IC 99 , respectively) and the cytotoxic concentrations that reduced cell growth by 50% and 90% (CC 50 and CC 90 , respectively) were calculated from the sigmoidal functions. For analysis of combinations of two drugs, an 868 concentration matrix was tested. Drugs x and y were tested in the.
While ribavirin and arbidol showed some or no beneficial effect, respectively, T-705 was highly efficacious in the animal model. These data hold promise for clinical efficacy of T-705 in human CCHF. concentrations c = 0; IC 90 /8; IC 90 /4; IC 90 /2; IC 90 ; IC 90 N2; IC 90 N4; IC 90 N8 in all possible combinations (c x ,c y ). The IC 90 values were derived from the prior single-drug experiments. The drug combination data were analyzed using the Bliss independence drug interaction model  [44]. This model is defined by the equation E xy = E x +E y 2(E x NE y ) , where E xy is the additive effect of drugs x and y as predicted by their individual effects E x and E y. E x = (-V obs (0,0)2V obs (c x ,0))/V obs (0,0) and E y = (V obs (0,0)2V obs (0,c y ))/V obs (0,0), where V obs (c x ,c y ) is the observed, i.
e. experimentally determined virus titer for (c x ,c y ). In analogy to the MacSynergy II program  [44, 45] , which evaluates antivirus data according to the Bliss independence model, a three-dimensional approach was used to identify areas where observed effects are greater (synergy) or less (antagonism) than those predicted by E xy. To this end, the ratio between predicted virus titer V pred (c x ,c y ) = V obs (0,0)N(12E xy ) and observed virus titer V obs (c x ,c y ) was calculated for each drug combination (c x ,c y ). A ratio. 1 indicates synergy (i. e. for (c x ,c y ) the virus titer predicted for additive effect is higher than the experimentally determined virus titer), a ratio ,1 indicates antagonism (i. e. for (c x ,c y ) the virus titer for predicted additive effect is lower than the experimentally determined virus titer).
Primary Mice were inoculated i. p. with 10, 100, 1,000, or 10,000 FFU of virus. Organ titers were determined in animals that succumbed to the infection or had to be euthanized due to the severity of the disease. Mean and standard deviation are shown for weight and log-transformed organ titers. Vertical bars in the graphs for AST and ALT (note the log scale of the y-axis) and the log-transformed virus titers in blood represent the mean values. The data for the 100 and 1,000 FFU groups of naı ¨ve animals were pooled with corresponding data from placebo controls shown in Fig. 5  to provide more reliable estimates for the parameters. The range of viremia below the detection limit of the immunofocusassay as well as the normal reference range of AST and ALT in mice  [59]  are shaded in grey. Notes.
5% methylcellulose as a placebo. Treatment was commenced 1 day before infection and continued until death or day 8. Organ titers were determined in animals that succumbed to the infection or had to be euthanized due to the severity of the disease. In addition, three animals from the ribavirin-treated group were randomly euthanized at day 3 p. i. to determine the virus titer in organs (weight, AST, ALT, and viremia data obtained from these mice until day 3 were included in the respective graphs). Mean and standard deviation are shown for weight and log-transformed organ titers. Vertical bars in the graphs for AST and ALT (note the log scale of the y-axis) and the logtransformed virus titers in blood represent the mean values. The duration of treatment in the survival plots, the range of viremia below the detection morphological signs of monocyte/macrophage activation.
Notes. 100 FFU placebo group: The animal, which died at day 6, showed no AST/ALT elevation and viremia at day 4. 100 FFU ribavirin group: The surviving animal did not show detectable viremia, but AST elevation. Viremia was not determined at day 11. 10 FFU arbidol group: The surviving animal had no AST elevation and viremia at day 4. Some values for days 4, 8, and, 11 were not determined due to insufficient amount of blood. doi:10. 1371/journal. pntd. 0002804. g005 antagonistic or synergistic effects were evaluated using the Bliss independence model in analogy to the algorithms of the MacSynergy II program  [44, 45]. This analysis revealed clear synergistic effects when the drugs were combined in concentrations around their IC 90. In this area of the matrix, the experimental virus titer was up to 2 log units lower than the titer The duration of treatment in the survival plots, the range of viremia below the detection limit of the immunofocusassay as well as the normal reference range of AST and ALT in mice  [59]  are shaded in grey.
Figure1. Survival, body weight, AST, ALT, and virus titer in blood and organs of IFNAR 2/2 mice infected with different doses of CCHFV. Mice were inoculated i. p. with 10, 100, 1,000, or 10,000 FFU of virus. Organ titers were determined in animals that succumbed to the infection or had to be euthanized due to the severity of the disease. Mean and standard deviation are shown for weight and log-transformed organ titers. Vertical bars in the graphs for AST and ALT (note the log scale of the y-axis) and the log-transformed virus titers in blood represent the mean values. The data for the 100 and 1,000 FFU groups of naı ¨ve animals were pooled with corresponding data from placebo controls shown in Fig. 5to provide more reliable estimates for the parameters. The range of viremia below the detection limit of the immunofocusassay as well as the normal reference range of AST and ALT in mice [59]  are shaded in grey.
In order to decrease the risks of genotyping errors related to automated or arbitrary decisions in allelic binning  [44] ,  [45] , allelic binning and scoring of genotypes were performed manually by two different persons using the software GeneMapper 4. 1 (Applied Biosystems) and compared to generate the final data set. The genotype dataset for the 10 microsatellites of 399 individuals of A. cilicica, A. cephalonica, A. bornmuelleriana, A. marocana and A. alba is publicly deposited in the DRYAD repository.
The presence and frequency of null alleles were examined using FreeNA  [50]  following the Expectation Maximization (EM) algorithm  [51]  which was found to provide unbiased and low variance estimates of null allele frequencies  [50]. Given that the presence of null alleles result in an overestimation of population genetic differentiation  [50] , FreeNA was used to compute the F ST statistic  [52]  both using and without using the ENA (Excluding Null Alleles) correction method, i. e. by excluding or not excluding null allelic   [105]  while the geographical position of Abies cilicica in Lebanon corresponds to our sampling data. Map was prepared using QGIS version 1. 8. 0  [106]  and the GMT package  [107]  within R  [108]. doi:10. 1371/journal. pone. 0090086. g001 states from the F ST computation  [50].
Among-Population Genetic Differentiation The differentiation between populations was computed in SPAGeDi  [62]  based on allele identity and allele size by respectively using F ST  [59]  and R ST  [63] ,  [64] , and their significance was determined by 10000 permutations. The presence of a phylogeographic pattern was also assessed, using the same software, after 10000 randomizations of allele sizes among alleles within loci  [65] , by testing whether R ST computed before randomization was larger than R ST obtained after allele size permutation (pR ST ). The randomization test also infers whether or not strict stepwise-like mutations contributed to population differentiation  [65]. Population genetic structure was also assessed using two Bayesian clustering approaches with different underlying assumptions concerning the units of clustering (individual, population), the types of clustering (non-spatial, spatial), and the computation algorithm (Markov chain Monte Carlo (MCMC), stochastic optimization).
The first Bayesian clustering approach, implemented in the software STRUCTURE 2. 3. 3  [66] , identifies the number of distinct genetic clusters (K) and probabilistically assigns individual multilocus genotypes to these clusters. To infer posterior probabilities of K, we ran a series of independent runs with different user-defined values of K clusters ranging from 1 to 15. Assuming that individuals may have mixed ancestry and that the different populations have correlated allele frequency  [67] , analysis was run ten times for 10 6 iterations of MCMC after a burn-in period of 30000. No prior information on the sampling locations of each individual was used as recommended in the software documentation  [68]. STRUCTURE results were then processed in a web-based program, STRUCTURE HARVESTER  [69] , which implements the Evanno method  [70]  to estimate a mode at the true K.
The second Bayesian approach, implemented in the software BAPS 6  [71] , estimates the hidden population substructure by clustering sampled populations into panmictic groups  [72]. Analysis was performed at the population level by using a spatial mixture model with prior information on the geographical position of the sampled populations, to prevent both over-smoothing in the case of a weak spatial structure and the emergence of spurious genetic structure as a result of weak stochastic fluctuations in the allele frequencies  [73] ,  [74]. Spatial genetic mixture analysis was run ten times after choosing a set of possible values of K clusters ranging from 1 to 15. To find the optimal partition, the program uses a stochastic optimization algorithm, which is less computationally intensive than the MCMC algorithm used in STRUCTURE  [75].
A joint posterior distribution of partitions of the sampled populations into genetically divergent groups was produced and then plotted onto the map of Lebanon. FSTAT version 2. 9. 3. 2  [54]  was used to compute, for the identified clusters, the observed (H O ) and expected (H E ) proportion of heterozygotes and the overall gene diversity (H T ) according to  [76] , F IS and F ST according to  [59] , and rarefied allelic richness (A R ) for a sample size of n = 102 gene copies or 51 diploid individuals  [57] ,  [58]. Statistical significance at the 5% significance level was obtained after 10000 permutations of populations among demes. R ST  [63] ,  [64]  was also computed among populations of each of the identified clusters using the software SPAGeDi  [62] , as well as the occurrence of a phylogeographic pattern as described above.
Direct estimates of recent migration rates within and between the current clusters were generated in the software GENECLASS2  [77]  through assignment tests allowing the detection of firstgeneration (F 0 ) migrants. The likelihood (L) that an individual originates from a given population sample was computed using the statistical criterion L hom e L max , i. e. the ratio of the likelihood of finding a given individual in the population in which it was sampled to the greatest likelihood among all sampled populations  [78]. Given that all the populations of A. cilicica in Lebanon have been extensively sampled, this criterion was considered to be informative on recent dispersal  [77] ,  [78]. The likelihood estimation was computed using the likelihood-based Bayesian method of  [79] , which has been shown to perform better than frequency-based and distance-based methods  [80].