dataesr/scientific-paragraphs-categorization huggingface dataset:
As discussed in Sect. 2,  Pecaut & Mamajek (2013)  have recently compiled a table of effective temperature vs. spectral type for 5-30 Myr old PMS stars. According to this scale, a K4 star presents an effective temperature of 4330 K. Considering this value, the spot model yields results that are very similar to those obtained for 4500 K: T s = 10 000 K and f = 4% as the best configuration that reproduces the observed amplitudes of V354 Mon, and T s = 3852 K and f = 89% if we restrict the model to cold spots only. A hot spot influences not only the light curve of the star, but also its spectrum because the accretion shock produces a continuum flux excess with respect to the stellar photosphere that veils the observed photospheric lines, reducing their depth. During the process of data reduction, we found no evidence of veiling, as shown in Fig.
Each region is located in one hemisphere, one above the disk and the other below, and rotates with the star. The region located above the disk periodically occults the star for an observer that views the system at medium to high inclination. Therefore, the spectroscopic observations and the CoRoT photometry, along with predictions of numerical simulations, corroborate the idea of stellar occultation by circumstellar material in this system. A possible explanation for the color variation observed in V354 Mon BV(RI) c photometry is extinction by circumstellar dust. The star becomes redder when fainter because the warp may not be totally opaque, different from what was observed in AA Tau. An additional analysis is necessary to confirm this hypothesis as the real cause of V354 Mon color variation, which cannot be done based on the data presented in this study.
Acknowledgements. We thank  Suzanne Aigrain  for pre-processing the CoRoT light curve and the anonymous referee for many useful comments and suggestions. NNJF acknowledges support from  CAPES  (fellowship process n o. 18697-12-7 ) and  CNPq. SHPA acknowledges support from  CAPES ,  CNPq , and Fapemig. JB acknowledges funding from  CNES  and from  ANR Toupie 2011 Blanc   SIMI5-6 020 01. This research was developed within the scope of  CAPES-Cofecub  project and is based on data collected in the CoRoT satellite.
Most circulating strains of Human enterovirus 71 (EV-A71) have been classified primarily into three genogroups (A to C) on the basis of genetic divergence between the 1D gene, which encodes the VP1 capsid protein. The aim of the present study was to provide further insights into the diversity of the EV-A71 genogroups following the recent description of highly divergent isolates, in particular those from African countries, including Madagascar. We classified recent EV-A71 isolates by a large comparison of 3,346 VP1 nucleotidic sequences collected from GenBank. Analysis of genetic distances and phylogenetic investigations indicated that some recently-reported isolates did not fall into the genogroups A-C and clustered into three additional genogroups, including one Indian genogroup (genogroup D) and 2 African ones (E and F).
Construction of the VP1 sequence dataset Three thousand four hundred fifty seven sequences were retrieved from GenBank following a search with the key words 'enterovirus 71', 'VP1', 'capsid' and/or 'complete genome' and were then subjected to manual checking. Partial sequences (,891 nt) and those comprising stop codons and frame shift mutations were rejected. Manual editing and exploratory alignments were done with CLC Main Genomics 6 software (CLC bio). As redundant sequences were not discarded, the final dataset may contain identical sequences from a single viral isolate but reported under different accession numbers. In addition, the partial VP1 sequence of isolate R13223 reported earlier  [14]  was updated after sequencing of the complete gene (GenBank accession number AY179600) and included in our analyses.
Sequence analysis by distance methods Phylograms based on full-length VP1 sequences were constructed with the MEGA 5 software  [17]. The evolutionary distances were computed with different algorithms (Juke-Cantor model, Maximum Composite Likelihood model, Kimura 2-parameter model) and the corresponding trees were drawn with the Neighbor-joining method with 500 bootstrap replicates to estimate node consistency. For the purpose of molecular comparisons and Bayesian evolutionary analyses (see below), a subset of 59 sequences representing all genogroups, subgenogroups and outlier lineages of EV-A71 was selected by exploratory analyses of the complete sequence dataset (Table  S1 ).
Bayesian phylogenetic analyses The evolutionary rate, divergence time, and demographic history were co-estimated in a Bayesian statistical framework using a Markov Chain Monte Carlo (MCMC) approach implemented in the BEAST program version 1. 7. 5  ([19,20] ; http://beast. bio. ed. ac. uk). Two substitution models were compared: the general-timereversible (GTR) model with an invariant class of nt substitution and a gamma distribution of substitution rates and the SRD06 model  [21]. As coalescent priors, the Bayesian skyline  [22]  and Gaussian Markov random fields (GMRF) Bayesian skyride  [23]  models were compared under relaxed molecular clock models. Models were compared with a Bayesian method of model selection that uses Bayes factors  [24]. The Bayes factor indicates relative superiority of competing models by evaluating the ratio of their marginal likelihoods.
The marginal likelihood distributions were estimated with the method of Suchard et al. [25]  implemented in the tracer v. 1. 5. 0 program  [20]  available at http://tree. bio. ed. ac. uk/software/tracer/. MCMC analyses were run for 40 million generations, sampling a tree every 2,000 steps and the first 10% were discarded as burn-in. MCMC convergence and the effective sample sizes (ESS) estimates were checked with Tracer v1. 5. 0. The trees sampled during the MCMC process were summarized in a maximum clade credibility (MCC) tree, as estimated with the TreeAnnotator program (http://beast. bio. ed. ac. uk/TreeAnnotator), and produced with the FigTree program (http://tree. bio. ed. ac. uk/ software/figtree). Statistical support for the tree nodes was assessed by a posterior probability (pp) value.
Genetic relationships between the EV-A71 VP1 gene sequences A comprehensive phylogenetic analysis was performed to investigate the genetic relationships of the two original Madagascan sequences and to classify them consistently among the current EV-A71 strains. We analyzed a large dataset of complete VP1sequences available on public databases. The final dataset contained 3,346 sequences, including the two original sequences of isolates MAD-72341-04 and MAD-3126-11 from Madagascar and the updated sequence of isolate R13223 from India. All the sequences comprised 891 nt and encoded a protein of 297 aa residues. The nt and aa alignments displayed a substantial proportion of positions conserved among all the sequences, 277/ 891 (31%) and 74/297 (25%) respectively. The genetic relationships between the nt sequences were investigated with the Neighbor-Joining method by using three different distance models.
Sub-clusters B0, B3 and B5 were supported by high bootstrap values ($90%) in contrast to subcluster B4 (75%). The unpublished sequence with the assigned accession number FJ846663 clustered separately from the other B subgenogroups (Figure  1 ). The major clade in the EV-A71 tree (Figure  1 ) comprised 2,563 (75%) sequences assigned to genogroup C and was supported by a bootstrap value of 91%. The sequences exhibited $83. 9% nt similarity and $91. 2% aa identity (Table  1 ). The sequences of this genogroup segregated into distinct clusters corresponding to the reported subgenogroups C1 to C5. Subgenogroups C2, C3, C4 and C5 were supported by high bootstrap values ($94%). The low bootstrap value (23%) that supports the C1 cluster could be related to few phylogenetic features shared by a small number of C1 sequences and sequences of the other subgenogroups C2, C3, and C5, thus resulting in uncertainty in estimation of the C1 root.
This could be due to recombination events between viruses belonging to C1 genogroup and viruses belonging to other C subgenogroups or, most probably, to convergent evolution or parallel nucleotide substitutions at similar sites among the different subgenogroups. One previously reported sequence with the accession number AF119795 had been assigned earlier to the genogroup C  [26]. In our phylogenetic analysis, it was actually more closely related to the genogroup B but clustered separately from the other subgenogroups (Figure  1 ). The AF119795 sequence was compared with sequences selected as representative of the EV-A71 genogroups to determine the pairwise nt similarity by a scanning analysis along the sequence alignment (Figure  S1 ). The AF119795 sequence displayed a high similarity (range 93-98%) with sequences of genogroup B only in a large central part of the VP1 gene (approximate nt positions 120 and 660).
This clade was tentatively called 'genogroup F'. It contained no other sequence present in our dataset and BLAST searches conducted in public databases failed to identify any complete or partial VP1 sequence closely related to the two sequences belonging to genogroup F. Interestingly, 20 partial VP1 sequences that were reported recently from viruses collected in India  [32]  did not fall into any of the six genogroups A to F (Figure  S2 ). This strongly points to the existence of additional genogroups. However, as these observations were based on only partial VP1 sequences (some of which were ,400 nt in length), they will need to be confirmed in the future by analyzing longer genomic sequences.
We considered that the phylogenetic data inferred with our sequence dataset were robust because eight phylogenetic reconstructions using different nucleotide substitution, molecular clock models, and tree priors provided estimates for the evolutionary rate parameter with wide overlapping of their Bayesian credibility intervals. Notably, the time origin of the tree root was pushed approximately 20 years back in comparison with the earlier estimate, 1941  [36]. Because the credibility interval of the TMRCA is large, the time origin of the virus still remains uncertain and may be revised in the future with the availability of additional sequences. Nonetheless, the TMRCA of genogroup B estimated in our study is in complete agreement with the previously reported time origin in the 1960s  [37]. Finally, although our estimate of the TMRCA for genogroup C is close to the year 1980, the credibility interval is in line with earlier data  [38].
Overall, this indicates that although the sequence dataset used in our study consists of less than 100 sequences, it is consistent for obtaining reliable data regarding the evolutionary origin of other EV-A71 genogroups. In conclusion, our analyses show a genetic diversity of EV-A71 that is greater than generally indicated in most published reports. First, we identified a sixth genogroup, tentatively called F, that contains sequences of two virus strains sampled in Madagascar. This genogroup, suggested by preliminary Neighbor-Joining analyses performed on a small numbers of EV-A71 VP1 sequences, was confirmed both by Neighbor-Joining analyses conducted on a large dataset of sequences and by Bayesian analyses. Second, we observed an unsuspected intra-genogroup nt divergence within genogroups B and C that was as high as 16.
Selection of an evolutionary model for the EV-A71 VP1 sequence dataset, through comparison of marginal likelihood. The analyses were done with the SRD06 substitution model and a relaxed molecular clock model implementing either an uncorrelated exponential (UCED) or an uncorrelated lognormal (UCLN) distribution of clock rates among the lineages. The molecular clock and demographic models were determined by calculating the marginal likelihoods of the data conditional on all the evolutionary and demographic model parameters. The highest log marginal likelihood values indicate the best fit model. doi:10. 1371/journal. pone. 0090624. t002.
Financial support for this project was provided by the  Agence Interetablissements de Recherche pour le Developpement in Madagascar  and by the  Agence Nationale de la Recherche  ( ANR-09-MIEN-019 ) and the  Fondation pour la Recherche Medicale  ( DMI20091117313 ) in France. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Moreover, PMM is able to estimate a sharedness score that can be used to focus follow-up efforts on generic or specific gene regulators. By fitting a PMM model to our data, we found several novel hit genes for most of the pathogens studied. Conclusions: Our results show parallel RNAi screening can improve the results of individual screens. This is currently particularly interesting when large-scale parallel datasets are becoming more and more publicly available. Our comprehensive siRNA dataset provides a public, freely available resource for further statistical and biological analyses in the high-content, high-throughput siRNA screening field.
Poor reproducibility rates of large-scale RNAi screens are a common concern. They are mostly caused by strong off-target effects from particular siRNAs  [11] [12] [13] [14] [15] [16]. Strategies have been proposed to alleviate the confounding effects of RNAi screens, including experimental  [17, 18]  and statistical approaches  [9, [19] [20] [21] [22]. In this study, we aim to use the parallel screening structure in order to gain statistical power for hit selection in large-scale RNAi screens. We generated high-content siRNA datasets that are uniquely comprehensive in terms of the siRNA libraries and various pathogens used. We employed highly unified protocols for parallel screens and common data analysis pipelines to allow a direct comparison between the readouts of different pathogen screens.
We optimized this procedure for each pathogen separately. (C) For each well in a 384-well assay plate, we calculated the infection index by dividing the number of infected cells (green) by the total number of cells (green and red). (D) The figure shows a schematic representation of the input data for the statistical analysis. Each point represents the average infection index over all its replicate wells (wells with the same siRNA set targeting the same gene and pathogen). (E) The Parallel Mixed Model (PMM) algorithm fits via a normal distribution for an overall effect a g to all data of gene g. The second plot shows the correction of the overall effect a g within every pathogen by an estimate b pg in order to obtain to an pathogen and gene specific effect c pg. The different sizes of the data points refer to weights w s which can be incorporated in the PMM to depict the quality of the siRNA.
We fitted the linear mixed model by using the "lmer" function from the "lme4" R-package  [34]. The sum of two random effects a g and b pg describes the total effect of the siRNAs within pathogen p. We define the estimated effect c pg for gene g within pathogen p as c pg ¼ a g þ b pg : A positive estimated c pg effect means that the infection level was enhanced if the corresponding gene g is knocked down. A negative effect means that the infection level was reduced. To distinguish hit genes, PMM provides as second step an estimate q pg of the local False Discovery Rate (FDR). We computed the local False Discovery Rate using the approach presented in  [59]  and the "locfdr" function in the R-package of the same name  [35]. We assigned the local False Discovery Rate to every gene and interpreted it as the probability describing how likely the corresponding gene is a false discovery (see Methods for more details).
The PMM method is published as "PMM" R-package on the InfectX dataaccess page. As a first verification for the increase in power by simultaneously using the parallel screening structure, we resampled datasets, each consisting of a fixed number of siRNAs and pathogens, and fitted the PMM, respectively the Moderated T-Test (MTT)  [36]  for the case of one pathogen (see Methods for details). We evaluated the mean and variation (i. e. stability) of the ranks in the ordered lists of genes based on their estimated c pg values over 1000 resampling runs for MET (a known effector gene for L. monocytogenes  [37] ), MTOR (a role of MTOR in the infection pathways of several pathogens has already been established  [6, 15, 38] ) and a non-hit ALK as control (Figure  3 ). The results showed, in particular in the case of MTOR, that the rank and its stability improved by simultaneously using more siRNAs and pathogens.
05) between Dharmacon pooled and Ambion single, and Dharmacon single and Qiagen single siRNA data reproducibility. In addition, the data showed that the averaged single siRNAs of Dharmacon performed at most as good as the single pooled siRNA consisting of the same siR-NAs. This indicated that for most screening purposes, it is more practical to use the pooled library instead of several unpooled libraries. This result of better performance of pooled libraries compared to averaged single siRNA libraries is in contradiction with what has been reported in  [19]. However, good quality single siRNA libraries (such as The network shows the hit genes (FDR < 0. 4 for at least one pathogen) and their direct neighbors that had connections between kinases in STRING database (version 9. 0). The edges are functional interactions in the STRING database with edge threshold 850.
Result comparison to existing hit ranking methods In order to validate the PMM approach and its results we compared it to other existing hit ranking methods and performed different kind of statistical tests. As reference methods we selected the Moderated T-Test (MTT)  [36]  and Redundant SiRNA Activity (RSA)  [39]  which are commonly used in high-throughput RNAi screening. We could not apply other widely used hit ranking methods, such as Strictly Standardized Mean Difference (SSMD)  [40]  or percent inhibition  [29]  since many of our pathogen screens did not have effective and reliable positive and negative control wells. As a first test, we analyzed the stability of the obtained gene rankings with respect to the estimated c pg values  [30, 41]. We resampled with replacement 1000 datasets (12 siRNAs randomly selected with replacement for each gene) and calculated the number of genes that appear with high probability (prob > 0.
We then applied PMM, MTT, and RSA to the simulated data and evaluated them by Receiver Operating Characteristic (ROC) -curves (with false positive versus true positive rates plotted for each FDR-threshold; Figure  6C-E ). The results showed that PMM performed the best especially in the case of shared hits. For the case of unique hits PMM and MTT exhibited about the same performance while RSA performed the worst. As expected, with a higher shift of the hit genes the ROC curves got better for all methods. We also studied how simultaneous modeling affects the ranking of genes in individual screens using PMM. We performed a test where we selected a pathogen and created two datasets. The first dataset was the full data without any changes and the second dataset had the original data for the selected pathogen and randomized data for the 7 other pathogens.
We then compared the gene rankings obtained by PMM performed using both datasets for the selected pathogen. The results for L. monocytogenes are illustrated in Figure  7A  (see Additional file 1: Figure  S6  for all the other pathogens). The correlation graph shows that the addition of parallel screens had only a mild effect on the overall gene ranking. However, when considering the number of significant genes (FDR < 0. 4), PMM mainly added genes to the list of significant genes (7 novel significant genes for L. monocytogenes) and only few genes (1 for L. monocytogenes) were dropped off the list. In general, we concluded that using parallelism added novel significant genes while losing almost none. Moreover, the few lost hit genes had high FDR values, just slightly below the selected threshold FDR < 0.
We selected all pathways that were significant (GSEA pathway enrichment FDR score < 0. 2) for at least one pathogen and method pair. We used the ranking of infection indices as the input for GSEA and focused on hits that reduce infection levels. By assuming that most pathways in the used database are biologically valid, we would expect that better hit detection methods give a higher number of enriched pathways than less powerful hit detection methods. However, we only screened kinases and the applicable pathways are limited to those that are highly enriched in phosphorylation events and it may be that some pathogens do not show strong enrichments within this set of pathways. Moreover, differences in pathway  monocytogenes. X-axis is the same, but we randomized the other 7 parallel assays. The colors correspond to hit genes (FDR < 0.
Conclusions We produced a uniquely wide high-content siRNA dataset, in terms of used siRNA libraries (11 single siRNAs and one pool) and eight different pathogens. Our highly unified protocols and common image analysis as well as similar data analysis pipelines enabled a direct comparison between the phenotypic readouts of the different pathogen screens. The unified structures of the datasets also aided discovering shared mechanisms between the studied pathogens. Using our novel statistical approach PMM we detected several interesting and new hits from our kinome-wide pathogen screens. The hits will require further follow-up work in order to understand the exact biological mechanisms of the genes. In addition, we discovered shared effector genes between the studied pathogens including MTOR, TGFBR1 and TGFBR2 that were strong hits for almost all studied pathogens.
Parallel Mixed Model (PMM) We denote the readout of siRNA s silencing gene g for a pathogen g as y pgs. The linear mixed model of PMM is defined as the following linear model y pgs ¼ μ p þ a g þ b pg þ ε pgs ; a g e N 0; σ 2 a À Á ; b pg e N 0; σ 2 b À Á ; ε pgs e N 0; σ 2 ε À Á ; where μ p is the fixed effect for pathogen p (typically close to 0 because of data Z-Scoring), a g is the gene effect overall pathogens, b pg is the gene effect within a pathogen and ε pgs denotes the error term. The parameters are estimated by maximizing the restricted maximum likelihood using the Newton-Raphson algorithm  [33]. We used the implemented version in the "lmer" function from the "lme4" R-package  [34]. This implementation allows also the use of weights, which are incorporated by a weighted maximum likelihood formulation.
Data resampling to show that parallel screens add power We chose gene g and pathogen p for which we wanted to show the increase in power by simultaneously using the parallel screening structure. In our case, we repeated the analysis for three different cases, consisting of a unique hit (g: MET, p: L. monocytogenes), a shared hit (g: MTOR, p: Vaccinia virus) and a non-hit (g: ALK, p: B. abortus). Each time we resampled data for a fixed number of siRNAs (n s = 2,…,11) and a fixed number of pathogens (n p = 2,…,8) from the full dataset. In detail, we chose randomly (n p -1) pathogens and added additionally pathogen p. In the next step, we sampled n s siRNA sets from the full available set of siRNAs for every gene within all sampled pathogens. We applied PMM on the sampled data and we reported the rank of gene g within pathogen p.
Stability analysis We resampled with replacement 1000 datasets from the full data, taking for each gene the same number of siR-NAs as in the full dataset. For each resampled dataset, PMM, MTT and RSA were applied and the corresponding ranking saved. For PMM the ranking was done according to the absolute value of the estimated c pg effects, for MTT we used the absolute values of the estimated mean and for RSA the ranking based on the log(p) values. We took absolute values to take into account down and up hits simultaneously. From the 1000 rankings we calculated the number of genes that appear with high probability (prob > 0. 9 and prob > 0. 7) in the top k (k = 1,…,50) of the ranking.