dataesr/scientific-paragraphs-categorization huggingface dataset:
Finally, the W/Z+jets events are reweighted to account for differences in the W/Z p T spectrum between data and simulation  [64]. In the searches for H + → tb with a lepton+jets final state, diboson events are generated with the requirement of having at least one boson decaying leptonically. Alpgen v2. 14 is used, with the CTEQ6L1 PDF, and it is interfaced to Herwig v6. 520  [65]  for showering and hadronisation, together with Jimmy v4. 31  [66]  for the underlying-event, using the AUET2 tune  [67]. The diboson backgrounds are normalised to the production cross sections calculated at NLO  [68]. The production of the SM Higgs boson in association with a top-quark pair (t tH) is modelled using NLO matrix elements obtained from the HELAC-Oneloop package  [69]. Powheg-Box is used as an interface to shower simulation programs.
All signal and background events are overlaid with additional minimum-bias events generated using Pythia v8. 1 with the MSTW2008 LO PDF and the AUET2 underlying-event tune, in order to simulate the effect of multiple pp collisions per bunch crossing (pile-up). Finally, all background samples and all-hadronic signal samples are processed through a simulation  [77]  of the detector geometry and response using Geant4  [78]. The signal samples with leptons in the final state are passed through a fast simulation of the calorimeter response  [79]. All samples from simulation are processed through the same reconstruction software as the data.
The 95% confidence level (CL) upper limits are presented in figure  6. The mass hypotheses are tested in 25 GeV steps between 200 and 300 GeV, and in 50 GeV steps up to 600 GeV. At 250 GeV, the local p 0 -value for the observation to be in agreement with the background-only hypothesis reaches its smallest value of 0. 9% (corresponding to 2. 4 standard deviations). At m H + values of 300 and 450 GeV, the excess of the data with respect to the background-only hypothesis corresponds to 2. 3 standard deviations. For comparison, the expected upper limit is computed with a signal injected at m H + = 300 GeV, with a production cross section times branching fraction of 1. 65 pb, corresponding to the best-fit value of the signal strength at this mass point. This results in an excess that is more localised at the injected mass value, i.
The last column shows the event yields in the SR. The uncertainties take into account correlations and constraints of the nuisance parameters. of all backgrounds or (b) the variable H had T. Similar excesses were observed with these two alternative methods. The tested mass points are correlated with each other, since no mass-dependent event selections are applied in the analysis and the dataset is the same regardless of the hypothesised H + mass. The limits in figure  6  are presented together with the signal prediction in the m mod- h benchmark scenario of the MSSM  [95]. Model points with 0. 5 tan β 0. 6 and tan β ≈ 0. 5 are excluded in the H + mass ranges of 200-300 GeV and 350-400 GeV, respectively,  4  while the expected limits in the mass range of 200-400 GeV reach tan β = 0. 7. The m mod- h scenario is chosen as a reference model, but similar exclusions are obtained in other relevant scenarios of the MSSM  [95] , i.
Conclusions This paper presents searches for charged Higgs bosons decaying through H + → tb, produced either in association with a top quark or in the s-channel process qq → H + → tb, using the 20. 3 fb -1 dataset of pp collisions at √ s = 8 TeV collected by the ATLAS experiment at the LHC during Run 1. The search for gb → tH + is performed in the H + mass range of 200-600 GeV. The analysis uses multivariate analysis techniques in the signal-rich region, and it employs control regions to reduce the large uncertainties on the backgrounds. An excess of data with respect to the SM predictions is observed for all H + mass hypotheses, except 600 GeV. The injection of simulated H + events yields a deviation from the expectation that extends less to higher and lower masses than the observed upper limit, indicating that a systematic background mismodelling is more likely to give rise to the observed excess than a signal.
Results We propose a new method, called ProfileNJ, that takes a gene tree with statistical supports on its branches, and corrects its weakly supported parts by using a combination of information from a species tree and a distance matrix. Its low running time enabled us to use it on the whole Ensembl Compara database, for which we propose an alternative, arguably more plausible set of gene trees. This allowed us to perform a genome-wide analysis of duplication and loss patterns on the history of 63 eukaryote species, and predict ancestral gene content and order for all ancestors along the phylogeny.
Introduction Several gene tree databases from whole genomes are available, including Ensembl Compara  [1] , Hogenom  [2] , Phog  [3] , MetaPHOrs  [4] , PhylomeDB  [5] , Panther  [6]. However they are known to contain many errors and uncertainties, in particular for unstable families  [7]. Their use for accurate ancestral genome inference, orthology detection, or the study of genome dynamics could lead to erroneous results. For example Ensembl Compara trees, when reconciled with a species tree to annotate gene duplications and losses, systematically and unrealistically overestimate the number of genes in ancestral genomes, and lead to erroneous predictions of ancestral chromosome structures  [8]. It is a known artifact, and a substantial number of nodes in the Ensembl gene trees are labeled as "dubious"  [9].
g. TreeBeST  [14] , TreeFix  [15] , NOTUNG  [16] , PhylDog  [8] , ALE  [17] , GSR  [18, 19] , SPIMAP  [20] , Giga  [21] , MowgliNNI  [22] ). They all report gene trees with better accuracy compared with sequence based methods. But they leave a large space for improvement, both on tree quality and on computing time. In terms of models, they often assume unrealistic loss/retention ratios  [23]. In terms of computation strategy, most of them use tree space exploration based on small modifications or local moves on branches (typically NNI, SPR, TBR), usually proposed at random. Moves are accepted or rejected according to hill-climbing, Metropolis-like criteria, or other statistical or empirical arguments. Such exploration methods are computationally intensive and do not scale well as databases grow in size.
Consequently, database construction pipelines such as TreeBeST (constructing the Ensembl Compara gene trees  [1] ) have to adopt compromises, exploring a limited tree space. Improving local exploration can be done by using some correction techniques, most of them based on the idea of selecting local moves reducing the cost of reconciliation with a species tree (e. g. [16, 22, [24] [25] [26] [27] [28] [29] [30] [31] [32] [33] ). But even with such improvements, it remains that most local search strategies have no guarantee, neither on running time, nor on the quality of the solution. In this paper, we propose a new gene tree correction method, called ProfileNJ, which can be directly used as a fast integrative method, without local search. It is a deterministic approach with a guaranteed time complexity.
Other extensions are considered such as allowing different costs for duplications and losses, as well as non rooted trees as input. These extensions turn an algorithmic principle into a workable method suited for constructing trees from biological data. We compare ProfileNJ with TreeFix  [15]. Indeed, among all correction methods, TreeFix adopts the most similar evaluation strategy, by exploring neighboring trees which are statistically equivalent according to the sequences. Moreover, TreeFix is among the best available integrative methods, according to the quality of the output trees and running time. On simulations, both algorithms achieve results of comparable quality, but ProfileNJ is several times faster. We also ran ProfileNJ on the whole set of gene families from the Ensembl database, which is out of reach for competing methods with comparable quality.
The trees for the whole database were processed by ProfileNJ in less than 48h sequential time. In the same amount of time, 0. 5% of the database was corrected by TreeFix. The trees we propose compare very favorably with the trees stored in Ensembl. We then used the reconstructed trees to study genome evolution across the 63 eukaryotic species from the Ensembl database (release 73). A whole genome analysis of duplication patterns is provided, pointing at certain branches which seem to show acceleration of duplication or loss processes. ProfileNJ is integrated into a modular interface called RefineTree that contains two other gene tree correction tools using information on extant and ancestral synteny  [32, 33]. We could thus evaluate the results of a pipeline taking into account gene sequence, gene content and chromosome structure evolution on the Ensembl database according to several criteria: (1) likelihood ratio based on the Ensembl alignments; (2) ancestral genome sizes based on reconciliation with the species tree; (3) linearity of ancestral chromosomal segments computed with DeCo  [35].
A more radical modification would be to contract all branches, leading to a star tree. In this case, ProfileNJ can be seen as a tool for computing ancestral gene content with Wagner parsimony, minimizing the cost of duplications and losses. If the star tree has all its genes belonging to a single species, ProfileNJ returns an NJ tree. Other kinds of contraction schemes can be imagined, as contracting branches around "Non Apparent Duplications"  [42] , or "Dubious duplications" stored in the Ensembl trees. End For End If Else If m(s)<V [s] Do {Perform V [s] -m(s) losses} Add V [s] -m(s) Notice that the pseudo-code for ProfileNJ has been given for a single count vector. For a full exploration of the tree space, all count vectors should be considered. This is what we do in the simulation section.
Efficiency of ProfileNJ Efficiency of the NJ criterion We ran ProfileNJ twice on the same data set of 20519 trees (the Ensembl Compara gene families), except that once the distance matrix was computed using the Ensembl nucleotide alignments with FastDist from the FastPhylo package  [43] , and once the distance matrix was random. The starting tree was computed for every family using PhyML on the nucleic alignments, and all branches with aLRT support <0. 95 were contracted. In average 55% of the branches were contracted. A histogram of the full distribution is shown in Fig A in S1 File. Then we computed the likelihood of both trees for every family with PhyML. Among the trees for which the likelihood was different (55% of all tested trees), 76% were in favor of the trees built with the FastDist distance matrix.
Efficiency of the tree space exploration strategy on simulated gene trees We compared ProfileNJ with TreeFix, the most closely related tool, on simulated data. The principle of TreeFix is to randomly explore, by local moves, the space of trees that are statistically equivalent to the input tree, and report the one with the best reconciliation cost. Instead, we take a deterministic and more targeted approach by focusing on weakly supported branches of the tree, with a possibly deep modification of the tree. The comparison with TreeFix is therefore intended to compare these two space exploration strategies. In  [15] , TreeFix has been compared with NOTUNG  [16]  and SPIMAP  [20] , showing a better accuracy than NOTUNG, and a higher speed than SPIMAP. We perform a similar comparison on the same simulated dataset of 16 fungi.
This dataset consists of simulated gene families generated under the SPIMAP model and their corresponding nucleotide alignments, for four different rates of duplication and loss (DL) events: (1 × r D , 1 × r L ), (2 × r D , 2 × r L ), (4 × r D , 4 × r L ) and (4 × r D , 1 × r L ), where r D and r L are respectively the estimated duplication and loss rates for fungi. For instance, a (2 × r D , 2 × r L )-simulated gene family is expected to have, on average, two times more duplications and losses than a real gene family in fungi. Comparisons reported in this section are performed on 2575 simulated gene families randomly chosen from the four fungi datasets with different DL rates. An initial maximum likelihood (ML) tree is constructed for each simulated gene family with RAxML v-8. 1. 2 [11] , with the rapid bootstrap algorithm, under the GTR-Γ model and the majority rule consensus tree as bootstopping criterion.
A randomly rooted tree is then provided as input to TreeFix (as TreeFix requires the input tree to be rooted), while a multifurcated non rooted tree obtained by contracting the branches with support lower than 95% is provided as input to ProfileNJ. We used default parameters for both programs. Among the set of all binary trees output by ProfileNJ (one for each count vector), the best statistically supported tree was selected using RAxML under the GTR-Γ model of nucleotide substitution. For RAxML, TreeFix and ProfileNJ trees, we measured the Robinson-Foulds (RF) distance to true trees, compared the reconstructed tree with the true tree using site-wise likelihoods (see It shows that sequence-only does not contain enough signal to lead to the true tree for our simulated dataset, and integrating additional information from the species tree actually improves reconstruction.
Protocol The low running time of ProfileNJ makes it possible to run it on large databases. Here, we exhaustively use ProfileNJ to correct all trees of the Ensembl Compara database, containing 63 eukaryotic whole genomes. Gene trees are constructed for 20519 families. In order to quantify the contribution of ProfileNJ and the contribution of methods using other kinds of information as synteny, we compared three sets of trees on the whole database. • Ensembl trees: Trees stored in the Ensembl gene family database (see Method section); • ProfileNJ trees: Trees output by ProfileNJ with unrooted PhyML trees as input (where branches with aLRT support <0. 95 are contracted) and FastDist distance matrices. A single solution is retained for the rooting leading to a minimum weighted reconciliation cost (see Method section); • Synteny trees: Trees output by either ParalogyCorrector or Unduplicator  [32]  (the two are computed and the most likely according to the sequence is chosen) with ProfileNJ trees as input, using PhylDiag  [45]  and DeCo  [35]  to infer synteny constraints (see Method section and Fig  5 ).
Results on Ensembl gene trees The PhyML trees for the 20519 gene families were processed by ProfileNJ in less than 48h sequential time. In the same amount of time, 0. 5% of the database was corrected by TreeFix. We evaluated the resulting trees (all made available at the web page indicated in the abstract) according to sequence likelihood, ancestral genome content and ancestral chromosome linearity. The ancestral genome content metric is based on the assumption that the distribution of ancestral gene content sizes should be close to that of extant genomes. Incorrect trees are known to require additional duplications to be reconciled with the species tree, which tends to increase the number of genes in ancestral genomes. The ancestral chromosome linearity metric is based on the assumption that the linearity of ancestral genomes is expected to be as close as possible to that of the extant genomes, with each gene having zero, one or two neighbors, with a peak at two (having genes with zero or one neighbor is usually due to partially assembled genomes).
Results are given in Fig 6. ProfileNJ trees show a better behavior than Ensembl trees according to the three measures: more than 2/3 of the trees have a better likelihood than Ensembl trees, ancestral genome content distribution is much closer to the extant one, and linearity of chromosomes is higher. Therefore this set of trees, achieving better performance according to sequence evolution, gene content evolution and chromosome evolution, is arguably a better dataset than the one stored in the Ensembl database.
Black arrows depict the use we make of RefineTree on the Ensembl gene trees. The green arrow and the green "or" are alternative uses avoiding one or both of the correction tools ParalogyCorrector and Unduplicator. Any framed set of data can be alternatively provided to the pipeline as input. For example, orthology constraints obtained from various sources can be directly provided as input to ParalogyCorrector. The method for inferring orthology constraints from synteny blocks is described in the text. doi:10. 1371/journal. pone. 0159559. g005 with a subset of species. The ability of ProfileNJ to handle the whole Ensembl database allowed us to perform a more exhaustive study. In addition to gene trees, we reconstructed all ancestral gene contents and organizations. Gene content is computed according to reconciliation (see Methods), and genome organization, which consists in sets of links between consecutive genes, is inferred with DeCo.
Genes are not always clustered into full linear genomes. Such non-linearity has diverse causes that we do not wish to mask with an ad-hoc linearization method. An interesting property of DeCo is to highlight genes or groups of genes evolving together in parts of the tree. For example 8488 blocks of co-duplicated genes are inferred by DeCo on the considered eukaryote dataset. Most of them contain only a few number of genes (83% contain 2 Proportion of trees with a significantly better likelihood computed with PhyML. AU tests were computed for the three trees for each family, and if the tree at the first rank was significantly better than the second, it was stored as the best likelihood, and if not, it was stored as "no significant difference at the first rank". (B) Gene content computed with DeCo.
Discussion ProfileNJ is a new gene tree correction method based on exploring a restricted tree space and choosing the most likely tree according to a species tree and a distance matrix on gene sequences. It is shown to be accurate and it outperforms in running time the most comparable existing correction methods. Efficiency in running time allowed us to apply ProfileNJ to the entire Ensembl database. Trees obtained by correcting PhyML trees with ProfileNJ are arguably better than gene trees stored in Ensembl, according to sequence likelihood, ancestral genome content and ancestral chromosome linearity. We also corrected directly the Ensembl trees and the results (not shown) were similar, ProfileNJ giving better ancestral genomes and more likely trees than the starting trees. Based on such accurate trees, we have been able to perform an exhaustive study of the patterns of duplication and loss on the phylogeny of the 63 Eukaryote species included in Ensembl.
Accounting for synteny In addition to sequence and phylogeny, we also used synteny information and orthology relations as correction criteria. As gene trees contain the most complete information about a gene family history, detecting orthologs or studying gene repertoire evolution should be achieved by interpreting trees. But due to the rate of errors in the current trees stored in databases, orthology is often assessed with a series of techniques including synteny  [50]  and Reciprocal Best Hits, while the evolution of gene repertoires is often studied with phyletic profile techniques  [51]. What we presented here is a way of integrating those diverse techniques into a phylogenetic framework. Unfortunately, integrating synteny information results in a drop of gene tree quality in terms of likelihood.
We would A probable example of ILS visible on a subtree of an Ensembl gene family. The monophyly of the chimpanzee and gorilla genes (ENSPTRP00000033018 and ENSGGOP00000011432) is well supported by the sequences (left tree, constructed by PhyML, with aLRT supports), while synteny argues for orthology of both with the human genes (ENSP00000414208 and ENSP00000378687) (right tree, constructed by ProfileNJ followed by ParalogyCorrector), so that a scenario of duplications and losses compatible with the left tree is unlikely. doi:10. 1371/journal. pone. 0159559. g008 need better exploration schemes with integrated models to really trust gene trees on a whole genome database within a deep phylogeny.
In case (3), we are in a situation where the loci are orthologous but not the sequences. In that case we construct the "locus tree"  [52]  and trust syntenic information over gene sequence information. First we ran PhylDiag as follows, for each pair of genomes. Files genome_1, genome_2 and ancestral_genes respectively contain the ordered list of genes from each genome, and the list of families clustering the genes as in the Ensembl database. phylDiag. py genome_1 genome_2 ancestral_genes \\ -gapMax = 2 -pThreshold = 0. 00000005 \\ -filterType = InBothSpecies -multiprocess \\ -minChromLength = 2 >syntenyblocks_1_2 The statistical threshold is calculated in order to minimize the number of false positives, taking into account the number (2211) of comparisons between pairs of species and an expected number (500, as an approximate average of the number of found synteny blocks) of synteny blocks for each comparison (0.
Funding: MS, LG, BB and ET were supported by the  French Agence Nationale de la Recherche (ANR)  Grant  ANR-10-BINF-01-01  " Ancestrome ". EN, ML, JS and NEM were supported by the  Natural Sciences and Engineering Research Council of Canada (NSERC) , and the  "Fonds de recherche du Québec Nature et technologies" (FRQNT) of Quebec. Computations were made on the supercomputer "Briarée" from  Université de Montréal , managed by  Calcul Québec and Compute Canada.
The code of Proï¬ leNJ is available at: https://github. com/UdeM-LBIT/profileNJ and a web interface called Reï¬ neTree, http://www-ens. iro. umontreal. ca/*adbit/polytomysolver. html. The set of gene trees corrected by ProfileNJ from Ensembl Compara version 73 families is available at: ftp://pbil. univ-lyon1. fr/pub/datasets/PlosOne2016/ and is also provided as a Supporting Information file.
Availability A web interface called RefineTree, including ProfileNJ as well as a other gene tree correction methods, which we also test on the Ensembl gene families, is available at: http://wwwens. iro. umontreal. ca/*adbit/polytomysolver. html. The code of ProfileNJ as well as the set of gene trees corrected by ProfileNJ from Ensembl Compara version 73 families are also made available.
39 μ m. To reconstruct the outer and inner surfaces of the head, the CT image stacks were processed with the 3D software package Amira 4 (FEI Visualization Sciences Group, Merignac Cedex, France). The output surfaces from Amira were first converted into a single, watertight surface using Geomagic Qualify 10 software (Geomagic Inc. , Morrisville, North Carolina). Mimicking the in-vivo shape and position of the labial lobes during feeding (Fig. 1 ), surface connections were added in Geomagic between the upper and lower jaws to represent the labial lobes (Fig. 2e ). Both the original and labial-lobed model surfaces were transformed into NURBS surface patches using VRMesh Studio 5. 0 (VirtualGrid, Seattle, WA). Next, the NURBS model of the salamander head was centered in a spherical boundary of the flow domain with a radius of 50 mm (ANSYS DesignModeler 14.
2c ). The three polynomial equations were applied to specific nodes of the mesh by using a factor between 0 (following the motion equation for 0%) to 1 (following the motion equation for 100%) that is stored in the user-defined node memory of ANSYS Fluent (Fig. 2a-c ). The gradual decrease of these factors at the borders of the targeted region prevents mesh intersections from forming. The software was set to move all internal mesh nodes (spring-based smoothing algorithm; spring constant factor = 1; Laplace node relaxation = 1) and re-mesh (make new mesh for triangles smaller than 1 × 10 -5 m and larger than 5 × 10 -3 m) after every time step in response to the motion of the boundaries prescribed in the three polynomial equations included in the mesh-motion user-defined function. To normalise the power input, the total power requirement of the model to overcome hydrodynamic resistance by the two morphs under the input of aquatic feeding kinematics (Fig.