dataesr/scientific-paragraphs-categorization huggingface dataset:
sibericum and P. massiliensis, with 107 and 54 more genes, respectively (Table  1 ). No tRNA was detected with the two methods applied. A total of 397 Cedratvirus genes (69. 2%) had at least one homolog in the NCBI Genbank database, whereas 30. 8% (177 proteins) had no significant hit, thus being classified as encoded by ORFan genes with an average length of 556 ± 247 nucleotides. The 397 proteins with known homologs showed the greatest homologies with proteins from 258 viruses, 108 eukaryotes and only 31 prokaryotes. As much as 84. 1% of the best viral hits were with pithoviruses, with 107 proteins (40% of the best viral hits) and 116 proteins (43%) having best matches with P. sibericum and P. massiliensis, respectively. A total of 44 proteins had their best homolog in the genomes of other giant viruses, in particular, 10 with marseilleviruses.
Many classical ecological and evolutionary theoretical frameworks posit that competition between species is an important selective force. For example, in adaptive radiations, resource competition between evolving lineages plays a role in driving phenotypic diversification and exploration of novel ecological space. Nevertheless, current models of trait evolution fit to phylogenies and comparative datasets are not designed to incorporate the effect of competition. The most advanced models in this direction are diversity-dependent models where evolutionary rates depend on lineage diversity. However, these models still treat changes in traits in one branch as independent of the value of traits on other branches, thus ignoring the effect of species similarity on trait evolution. Here, we consider a model where the evolutionary dynamics of traits involved in interspecific interactions are influenced by species similarity in trait values and where we can specify which lineages are in sympatry.
The matching competition model has three free parameters: ! ! , S and the ancestral state ! ! at the root. As with other models of trait evolution, the maximum likelihood estimate for the ancestral state is computed through GLS using the estimated variance-covariance matrix  (Grafen 1989; Martins and Hansen 1997). We used the ode function in the R package deSolve  (Soetaert et al. 2010)  to perform the numerical integration of the differential equations using the "lsoda" solver, and the Nelder-Mead algorithm implemented in the optim function to perform the maximum likelihood optimization. Codes for these analyses are freely available on github (https://github. com/hmorlon/PANDA) and included the R package RPANDA  (Morlon et al. 2016). Figure  1. Illustration of geography matrices (defined for each lineage at every node and after each dispersal event inferred, e.
Simulation-based Analysis of Statistical Properties of the Matching Competition Model To verify that the matching competition model can be reliably fit to empirical data, we simulated trait datasets to estimate its statistical properties (i. e. , parameter estimation and identifiability using AICc). For all simulations, we began by first generating 100 pure-birth trees using TreeSim  (Stadler 2014). To determine the influence of the number of tips in a tree, we ran 1 0 0 1 1 0 0 0 1 1 0 1 1 1 1 0 0 1 1 0 0 0 0 1 1 0 0 1 1 1 1 1 0 0 1 1 1 0 0 1 1 1 0 0 0 0 0 1 1 0 0 0 1 1 Island 1 Island 2 1 1 1 0 1 1 1 0 1 1 1 0 0 0 0 1 simulations on trees of size n = 20, 50, 100, and 150. We then simulated continuous trait datasets by applying the matching competition model recursively from the root to the tip of each tree (Paradis 2012), following Eq.
1, assuming that all lineages evolved in sympatry. For these simulations, we set ! ! = 0. 05 and systematically varied S (-1. 5, -1, -0. 5, -0. 1, or 0). Finally, we fit the matching competition model to these datasets using the ML optimization described above. To determine the ability of the approach to accurately estimate simulated parameter values, we first compared estimated parameters to the known parameters used to simulate datasets under the matching competition model (S and ! ! ). We also quantified the robustness of these estimates in the presence of extinction by estimating parameters for datasets simulated on birth-death trees; in addition, we compared the robustness of the matching competition model to extinction to that of the diversity-dependent models. These two latter sets of analyses are described in detail in the Supplementary Appendix 2.
These models were fitted using geiger  (Harmon et al. 2008)  when available there (BM, OU, TD exp, TD lin ), or using our own codes, available in RPANDA  (Morlon et al. 2016 ) when they were not available in geiger (DD lin , DD exp ). With the exception of TD exp , which we restricted to have decreasing rates through time since the accelerating rates version of the model is unidentifiable from OU  (Uyeda et al. 2015) , we did not restrict the ML search for the parameters in TD lin or DD models. We assessed the identifiability of other trait models against the matching competition model by calculating the fit of this model to datasets simulated under the same trait models mentioned above. For BM and OU models, we generated datasets from simulations using parameter values from the appendix of  Harmon et al.
2010  scaled to a tree of length 400 (BM, ! ! = 0. 03; OU, ! ! = 0. 3, α = 0. 06). For both the linear and exponential versions of the time-and diversity-dependent models, we simulated datasets with starting rates of ! ! = 0. 6 and ending rates of ! ! = 0. 01, declining with a slope determined by the model and tree (e. g. , for timedependent models, the slope is a function of the total height of the tree; for the TD exp model, these parameters result in a total of 5. 9 half-lives elapsing from the root to the tip of the tree, Slater and Pennell 2014). In another set of simulations, we fixed the tree size at 100 tips and varied parameter values to determine the effect of parameter values on identifiability (see Results). As above, we calculated the AICc for all models for each simulated dataset.
Finally, to understand how removing stabilizing selection from the likelihood of the matching competition model affects our inference in the presence of stabilizing selection, we simulated datasets with both matching competition and stabilizing selection on 100 tip trees, across a range of parameter space (S = -1, -0. 5, and 0, α = 0. 05, 0. 5, and 5, holding ! ! at 0. 05). We fit BM, OU, and matching competition models to these simulated datasets. All simulations were performed using our own codes, available in RPANDA  (Morlon 2016).
Fitting the Matching Competition Model of Trait Evolution to Caribbean Anolis Lizards To determine whether the matching competition model is favored over models that ignore interspecific interactions in an empirical system where competition likely influenced character evolution, we fit the matching competition model to a morphological dataset of adult males from 100 species of Greater Antillean Anolis lizards and the time calibrated, maximum clade credibility tree calculated from a Bayesian sample of molecular phylogenies  (Mahler et al. 2010 (Mahler et al. , 2013;; Mahler and Ingram 2014). We included the first four size-corrected phylogenetic principal components from a set of 11 morphological measurements, collectively accounting for 93% of the cumulative variance explained (see details in  Mahler et al.
2013). Each of these axes is readily interpretable as a suite of specific morphological characters (see Discussion), and together, the shape axes quantified by these principal components describe the morphological variation associated with differences between classical ecomorphs in Caribbean anoles  (Williams 1972). In addition to the matching competition model, we fit the six previously mentioned models (BM, OU, TD exp , TD lin , DD exp , and DD lin ) separately to each phylogenetic PC axis in the Anolis dataset. For the matching competition model and diversity-dependent models, to determine the influence of uncertainty in designating clades as sympatric and allopatric, we fit the model for each trait using 101 sets of geography matrices (i. e. , A in Eq. 1b, 2, & 3, see Fig. 1 ): one where all lineages were set as sympatric, and the remaining 100 with biogeographical reconstructions from the output of the make.
Statistical Properties of the Matching Competition Model Across a range of S values, maximum likelihood optimization returns reliable estimates of parameter values for the matching competition model (Fig. 2 ). As the number of tips increases, so does the reliability of maximum likelihood parameter values (Fig. 2 ). Parameter estimates remain reliable in the presence of extinction, unless the extinction fraction is very large (i. e. , ≥ 0. 6; Supplementary Appendix 2). When datasets are simulated under the matching competition model, model selection using AICc generally picks the matching competition model as the best model (Figs. 3, S1 ); the strength of this discrimination depends on both the S value used to simulate the data and the size of the tree (Figs. 3, S1). For example, when S = -0.
1, the matching competition model often has a higher AICc than Brownian motion, largely due to the fact that the Brownian motion model has one less parameter. Simulating datasets under BM, OU, DD exp , and DD lin generating models, we found that in most scenarios, and in most parameter space, these models are distinguishable from the matching competition model (Fig. 4a, b, e, f , Fig. S2 ). As with the matching competition model, the ability to distinguish between models using AICc generally increases with increasing tree sizes (Fig. 4 ) and with increasing magnitude of parameter values (Fig. S2 ). When character data were simulated under a TD lin model of evolution, the matching competition and/or the diversitydependent models tended to have lower AICc values than the TD lin model, especially among smaller trees (Figure  4d ).
For data generated under a TD exp model, model selection always favored the matching competition model over the TD exp model (Fig. 4c ). Though the current implementation of the maximum likelihood tools for the matching competition do not incorporate stabilizing selection, simulating datasets with both matching competition and stabilizing selection reveals that as the strength of stabilizing selection increases relative to the strength of competition (i. e. , α as increases relative to S), AICc model selection shifts from favoring the matching competition model (under large S, small α scenarios) to favoring the OU model (under small S, large α scenarios) (Fig. S3 ). Likewise, maximum likelihood increasingly underestimates the value of S as the value of α increases (Fig. S4 ).
S3, S4), we would have likely detected an even stronger effect of competition in the Anolis dataset if we had included stabilizing selection. Recently,  Uyeda and colleagues (2015)  demonstrated that the use of principal components can bias inferences of trait evolution. We used BM-based phylogenetic PC axes here, which should reduce this potential bias  (Revell 2009). We recognize that there is some circularity in assuming BM in order to compute phylogenetic PC axes before fitting other trait models to these axes; a general solution to address this circularity problem remains to be found  (Uyeda et al. 2015). Uyeda & colleagues suggested that using phylogenetic PC axes sorts the traits according to specific models. In the Greater Antillean Anolis lizards, the first axes are easily interpretable as specific suites of traits relevant to competitive interactions, and our results suggest that competition played an important role in shaping the evolution of these traits.
Given this formulation of the model, large S values are not to be expected, and we indeed found relatively small S values when fitting the model to the Anolis dataset. Investigators finding high S values should treat them with caution and consider enforcing bounds on the likelihood search. Nevertheless, the developments presented here provide an important new set of tools for investigating the impact of interspecific interactions on trait evolution, and researchers can perform posterior simulations to assess the realism of the resulting inference. Future development of likelihood-free methods, such as Approximate Bayesian Computation  (Slater et al. 2012b; Kutsukake and Innan 2013) , may be possible for fitting the version of the model in which the outcome of competitive interactions depends on distance in trait space.
The effect of incorporating stabilizing selection into trait evolution on model selection. For datasets generated under the matching competition model with stabilizing selection included, as the ratio of the strength competition (S) to the strength of selection toward an optimum (!) varies, so does the model preferred by model selection. The effect of incorporating stabilizing selection into trait evolution on parameter estimation. As the strength of stabilizing selection increases (i. e. , as ! increases), maximum likelihood under the matching competition model underestimates the true S value used to simulate datasets. Positive S values represent selection toward, rather than away, from the clade mean and are thus expected when the ratio of ! to S is large. The horizontal red line represents the simulated S value, and the dashed horizontal line represents S = 0.
3. 4. 34 The effect of incorporating stabilizing selection into trait evolution on model selection. For datasets generated under the matching competition model with stabilizing selection included, as the ratio of the strength competition (S) to the strength of selection toward an optimum (!) varies, so does the model preferred by model selection. The effect of incorporating stabilizing selection into trait evolution on parameter estimation. As the strength of stabilizing selection increases (i. e. , as ! increases), maximum likelihood under the matching competition model underestimates the true S value used to simulate datasets. Positive S values represent selection toward, rather than away, from the clade mean and are thus expected when the ratio of ! to S is large. The horizontal red line represents the simulated S value, and the dashed horizontal line represents S = 0.
To detect the best predictor of PET/CT positivity between PSAt and PSArel, Akaike information criterion (AIC) was used. For multivariate analysis, two logistic regressions were computed with PSA values, one with PSAt and one with PSArel because of their collinearity, as well as with PSA doubling time, PSA velocity, and D' Amico risk group. The best two models were chosen with stepwise algorithm, using the AIC criteria. Moreover, ROC curves of PSAt and PSArel were estimated for their prediction of PET/CT results using the AUC value. Youden's index was used to determine the best cutoff in terms of both sensitivity and specificity. P < 0. 05 was considered significant. All analyses were performed with R, version 3. 1. 2 (R Foundation for Statistical Computing: https://www. r-project. org/) and Graphpad software.
2 exons per gene (Supplementary Table  6 ). For these predicted genes, 86. 9% of them were annotated with the SWISS-PROT, KEGG, GO, InterPro and TrEMBL databases (Supplementary Table  7 ). In addition to protein-coding genes, other non-coding RNA including 345 microRNA, 977 tRNA, 278 rRNA, and 299 snRNA were also identified (Supplementary Table  8 ). Genome evolution. 9 , Supplementary Fig. 7 ). A 5-way comparison of the three Musa species and two closely related Arecaceae species P. dactylifera, and E. guineensis showed that 7810 gene families were shared among them, and 2723 uniquely shared gene families between the three Musa genomes (Fig. 1a ). M. itinerans shared more gene families with M. auminata (2727 gene families) than with M. balbisiana (140 gene families), which were suggestive of higher similarity for the latter two species.
Raw reads were subject to the removals of PCR duplicates, adaptor sequences, and contaminants of bacterial or viruses. The processed high-quality reads were assembled into contigs and scaffolds using SOAPdenovo2 package  14  and gaps were filled using Gapcloser (version 1. 12, http://soap. genomics. org. cn/soapdenovo. html). The reference genome size of M. itinerans was estimated based on the K-mer frequency distribution analysis (details for Supplementary Note 2). Two individuals each from the YC and LC populations were sampled and sequenced using a paired-end library size of 500 bp with > 30-fold coverage. Genome annotation. For the draft genome of M. itinerans, interspersed repeats were identified using RepeatMasker v3. 30 (http://www. repeatmasker. org) and homolog research for the Repbase database  16.
Repeat proteins were characterized using RepeatProteinMask v3. 30 with default parameters, de novo interspersed repeats were annotated using RepeatModeler (http://www. repeatmasker. org/RepeatModeler. html). Tandem repeats in the genome of M. itinerans were screened using Tandem Repeat Finder v4. 04  44. Next, a homology searching approach was used to identify protein-coding genes. We obtained the protein sequences of Musa acuminata v1 from the Banana Genome Hub  45  and Phoenix dactylifera DPV01, Oryza sativa IRGSP-1. 0, Sorghum bicolor v3. 1, Zea mays, and Arabidopsis thaliana TAIR10 from Phytozome (https://phytozome. jgi. doe. gov) or related databases (Supplementary Note 5) and then blasted against the repeat-masked genome assembly of M. itinerans using TBLASTN  46  with percentage identity over 70% and coverage not less than 50%.
Gene models were predicted based on these alignments using Genewise  47. De novo gene predictions were performed using Augustus  48  and GENSCAN  49. Both sets of genes were integrated using GLEAN  50. The annotations of the final gene sets were obtained by searching against the protein database KEGG  51  , SwissProt 52 , TrEMBL  53  using an e-value cut-off of 1e-5, and motifs and domains for the gene set were determined using InterProScan  54. Using CEGMA package, the completeness of the gene space for the draft genome assembly was evaluated with the highly conserved 248 core eukaryotic gene (CEGs). For noncoding RNAs, tRNAs were identified using tRNAscan-SE  55  , snRNAs and miRNAs, and rRNAs were obtained by searching the genome assembly against the Rfam database using InFERNAL with default parameters (http://infernal.
Genome-wide duplications and estimation of the insertion times for LTR retrotransposons. Using the web-based tool SynMap on the plant comparative genome platform CoGe (https://genomevolution. org/CoGe/)  56  , the syntenic regions between M. itinerans and M. acuminata were generated, and scaffolds or contigs of M. itinerans were mapped or oriented on the 11 pseudo-chromosomes of M. acuminata. Reciprocal BLASTP between protein sequences of two Musa species with E-value cut-off of 1e-5, were implemented to identify orthologs between species, and self BLASTP for both species were used to obtain paralogs within species. After removing the self-matches of the BLASTP results, the syntenic blocks (≥ 5 genes per block) were determined with the package MCScanX  25. The remaining aligned results were used to generate dot plots.
Phylogenetic analysis. The protein sequences of eight plant species including Arabidopsis thaliana, Vitis vinifera, Oryza sativa, Sorghum bicolor, Phoenix dactylifera, Elaeis guineensis, M. acuminata, and M. balbisiana were downloaded (sources details in Supplementary Note 5). After filtering out protein sequences with amino acid length of less than 50, the longest protein sequence was chosen among spliced variants. All the eight protein datasets together with those of M. itinerans were clustered into paralogous and orthologous groups using the program OrthoMCL v1. 4 with the inflation parameter of 1. 5  58. Protein sequences of each single-copy gene obtained from the nine species were aligned using MUSCLE v3. 8. 31  59  , and corresponding nucleotide sequences alignment of different genes were concatenated into one supergene.
Using 4DTv sites of the supergene with the nucleotide substitution model HKY85 + gamma, a maximum likelihood phylogenetic tree of the nine species was constructed using the package PhyML with NNI (Nearest Neighbor Interchange) tree improvement  60. Bootstrap supports of the internal branches were evaluated by 1000 full-heuristic searches. Divergence time estimations between species were determined using MCMCtree in PAML 4. 7  61  , and the "correlated molecular clock" and "REV" substitution model were used in the approximate likelihood calculation. The MCMC process was run for 2,000,000 steps and sampled every 20,000 steps. A calibrated divergence time of 124~248. 4 Ma between Arabidopsis and Sorghum was used  23. Gene family expansions and contractions of extant species and ancient lineages were estimated using CAFE v2.
Gene family evolution. Using HMMER V3 (http://hmmer. janelia. org/software), protein sequences of M. acuminata, M. itinerans, and M. balbisiana were aligned against the raw hidden Markov model (HMM) corresponding to the Pfam NBS (NB-ARC) family (PF00931, downloaded from website http://pfam. sanger. ac. uk/). Following the method described in D' Hont et al. 8  , the hit NBS-domain sequences were aligned and used to construct a new NBS HMM profile using the module 'hmmbuild'. Using this Musa specific model, we identified NBS-candidate proteins in the three Musa species respectively. TIR and LRR domains of these NBS-encoding amino acid sequences were identified with Pfam_scan. Coiled-Coil (CC) motif was identified using MARCOIL  63  with a threshold probability of 90 and double-checked using paircoil  64.
Demographic history inference. One individual from each of the three populations was sequenced in high depth (Supplementary Note 1), and qualified reads from the short insert size library (500 bp or 180 bp) were realigned to the assembly with BWA with default parameters  65. Consensus sequences were called using samtools  66  , and converted into the Fastq format using bcftools and vcfutils in the package pairwise sequentially Markovian coalescent model  38  (PMSC). The consensus sequences were split into non-overlapping 100 bp bins marked as homozygous or heterozygous and these split sequences were used to reconstruct the demographic history with the PSMC model with the following parameters: "N30 -t15 -r3 -b -p "4 + 5 * 3 + 4". The variance of the simulation results was assessed with 100 bootstrap replicates.
Acknowledgements The authors thank  Dr. Jian-Bo Jian ,  Dr. Chi Zhang ,  Dr. Shan-Ceng Zhao , and  Dr. Xiao-Dong Fang  for their helpful suggestions on data analysis. The authors appreciate  Dr Vincent Johnson  at  CGIAR Research Program on Roots, Tubers and Bananas in Bioversity-France , who has polished the manuscripts. Thanks are also given the people in BGI who are not in the author list but performed the genome sequencing experiments as business service. The study was financially supported by  National Natural Science Foundation of China  (No. 31261140366 ).
A. Experimental setup All the computations were performed offline on a Intel Xeon(R) CPU E3-1240 v3 @ 3. 40GHz. The contact planner is open-source and available at https://github. com/humanoid-path-planner. The OCP is solved using the proprietary software MUSCOD-II provided by the Interdisciplinary Center for Scientific Computing (IWR) of Heidelberg University. This software offers an OCP toolkit  (e. g. integration and numerical-differentiation routines) along with an efficient sparse sequential-quadratic-program solver. The whole-body trajectory is obtained from the contact sequence and the COM and angular-momentum trajectory using a second-order inverse kinematics. The typical tasks where the tracking of the contact placements, the tracking of the COM position and an additional posture task to keep the configuration close to the planned postures.
ACKNOWLEGMENT This work is supported by the  European Research Council  through the  Actanthrope  project ( ERC Grant Agreement 340050 ), the  European  project  KOROIBOT  ( FP7 Grant Agreement 611909 ), the  French National Research Agency  project  ENTRACTE  ( ANR Grant Agreement 13-CORD-002-01 ) and the national  PSPC-Romeo 2 project.