dataesr/scientific-paragraphs-categorization huggingface dataset:
4 (93. 4) 95. 4 (71. 5) 95. 5 Redundancy 2. 8 (2. 6) 9. 9 (5. 0) 3. 5 (3. 5) I/(I) 12. 1 (2. 5) 19. 0 (3. 9) 8. 0 (1. 9) R merge (%) 0. 042 (0. 416) 0. 062 (0. 73) 0. 099 (0. 635) Refinement Reflections used for refinement 75 444 27 532 34 917 Correlation coefficient 0. 954 0. 953 0. 942 R factor (R work /R free ) (%) 17. 7/19. 9 17. 9/20. 4 0. 19/0. 22 Model contents Protein atoms 5,155 1,295 5,172 Water molecules 652 166 286 Ligand atoms - - 5172 Root mean square deviation Distances ( Å) Bond angles ( • ) 0. 010 1. 01 0. 010 1. 02 0. 010 1. 10 Ramachandran plot Favored 92. 3 91. 2 91. 9 Allowed 7. 7 9. 8 8. 1 Outlier 0 0 0 PDB Code 5GMC 5GMB 5GM8 a R merge = |I obs -I av |/ I av , over all symmetry-related observations. b R work = |F obs -<|F calc |>|/ |F obs |, over all reflections included in the refinement.
Linked directional selection is expected to locally increase the stochasticity of allele frequency evolution, a process sometimes coined genetic draft  [24]. Its effect can therefore be modeled by assuming that the effective population size, N e , which determines the strength of genetic drift, varies among loci  [25]. Multilocus analyses of the process of population divergence have been achieved in various groups of animals  [26, 27]  and plants  [28] [29] [30]  for which genome-wide data are available, revealing a diversity of patterns. These case studies, however, are limited in number and have taken different approaches, such that we still lack a unifying picture of the prevalence of gene flow during early divergence between gene pools. Here, we gathered a dataset of 61 pairs of populations/species of animals occupying a wide continuum of divergence level.
In the ABC framework, the posterior probability of a model corresponds to its relative ability to theoretically produce datasets similar to the observed dataset, compared to a set of alternative models. Before analyzing datasets from the 61 pairs of animal species, we first assessed the power of the adopted ABC approach to correctly distinguish between models involving current isolation (SI + AM) versus ongoing migration (IM + SC + PAN). This was achieved by randomly simulating 116,000 datasets distributed over the 16 compared models and applying our ABC inference method to each of them. Specifically, we investigated which model had the highest posterior probability and assessed significance by estimating the associated robustness-the probability to correctly support a model given its posterior probability.
A robustness greater than 0. 95 can be interpreted as a p-value below 0. 05  [32]. The analysis of simulated datasets allowed us to empirically measure a threshold value of 0. 6419 for the posterior probability P migration (= P IM + P SC + P PAN ), above which the robustness to support ongoing migration is greater than 0. 95. Similarly, a posterior probability P migration below 0. 1304 implied a statistical support for the current isolation model with a robustness greater than 0. 95. Among the 58,000 simulated datasets in which current gene flow was assumed (IM, SC, and PAN; Fig  2A ), 99. 462% were true positives (P migration > P isolation and robustness ! 0. 95), 0. 129% were false positives (P migration < P isolation and robustness ! 0. 95), and 0. 409% were ambiguous cases for which ABC did not provide any robust conclusion (robustness < 0.
95). Among the 58,000 simulated datasets in which current isolation was assumed (SI and AM; Fig 2B ), 99. 649% were true positives (P isolation > P migration and robustness ! 0. 95), 0. 002% were false positives (P isolation < P migration and robustness ! 0. 95), and 0. 34% were ambiguous cases (robustness < 0. 95). When current gene flow was assumed, the rates of false positive and ambiguity were very low at every level of population divergence. When current isolation was assumed, a higher rate of ambiguity, but no elevation of the rate of false inference, was observed at low levels of divergence (D a < 0. 01, Fig  2D ). This contrasts with the recent suggestion that the full-likelihood method developed in the IMa2 software  [33]  might be biased towards supporting current gene flow when isolation is recent  [19, 34] -our approach appears to be immune from this bias.
To specifically address this point, we repeated the exact same simulations as in  [34]  and confirmed that our ABC approach has a reduced power (i. e. , more ambiguous cases with robustness <0. 95) when the split is recent but still a very low rate of false positive in these conditions (see S1 text). In addition, the robustness of the ABC inference was only weakly dependent on the sample size when the number of loci was greater than 100: similar results were obtained when we simulated samples of size 2, 3, 25, or 50 diploid individuals (S1 Fig). Finally, and importantly, simulations showed that ABC is not accurate enough to discriminate between the IM and SC models. Datasets simulated under SC were assigned to SC with high confidence only when the period of isolation before secondary contact represents at least a proportion of about 60% of the total divergence time (S2A Fig).
Dataset: Molecular Divergence and Population Differentiation in 61 Taxa The posterior probability of ongoing gene flow was estimated in 61 pairs of species/populations of animals (S1 Data) showing variable levels of molecular divergence (S1 Data). Fifty pairs were taken from a recent transcriptome-based population genomic study  [31] , with two individuals per population/species being analyzed here. The datasets for the other 11 species pairs were downloaded from the NCBI (S1 Data). They correspond to sequences from published studies using either ABC, Ima  [33] , or MIMAR  [35] , for which 3 to 78 diploid individuals were analyzed. We computed various measures of molecular divergence between species/populations: namely, D a , the relative average divergence, corrected for within-species diversity  [36] ; D xy , the absolute average divergence; and F ST , a classical measure of population differentiation.
again in line with published analyses  [19]. Analyses assuming homogeneous N e and M in many cases failed to support either isolation or migration, producing the highest number of ambiguous pairs (S8 Fig). The detected genomic heterogeneity in gene flow increased with D a until 2% of divergence. Finally, across the whole continuum, there was no significant effect of the divergence on the probability of supporting genomic heterogeneity in effective population size in our dataset.
04: 17 vertebrate pairs, 22 invertebrate pairs, p = 0. 32, r 2 = 0. 03). This effect only reflects the paucity of pairs of vertebrate population/species with a high divergence in our dataset. Finally, we tested whether the current geographic distribution of species coincides with the establishment of genetic structure in our data by distinguishing pairs in which the two considered species/populations occur on the same versus distinct continents or oceans. We did not find any significant effect of this variable on the estimated values of P migration in either of the three divergence zones: D a < 0. 5%, t test = -0. 015269, df (degrees of freedom for the t-statistic) = 18. 522, p-value = 0. 988; 0. 5% < D a < 2%, t test = -0. 74229, df = 7. 1996, p-value = 0. 4814; 2% < D a , t test = 0. 35512, df = 22.
Ongoing Gene Flow and Taxonomic Status Finally, we verified whether our inferences confirmed or contradicted the current taxonomy (S1 table). Our dataset comprises 26 pairs of recognized species and 35 pairs of populations (or subspecies) sharing a common binomen. Twenty-one pairs of recognized species belonged to the high-divergence zone (D a > 0. 02). Of these, 16 were inferred to be currently isolated, 4 produced ambiguous results and 1 pair, Eunicella cavolinii versus E. verrucosa (gorgonian), was found to be connected by heterogeneous gene flow. Among the 5 remaining recognized pairs of species (with D a < 0. 02), 2 were inferred as being fully isolated and 3 were inferred to be connected species: 2 pairs of semi-isolated species with heterogeneous gene flow (Mytilus galloprovincialis versus M.
Accounting for Among-Locus Heterogeneity in Drift and Migration Rate Inferring the history of divergence and gene flow, which determines the rate of accumulation of species barriers, is of prime importance to understand the process of speciation  [17]. This can be achieved by various methods, among which ABC approaches have proven particularly flexible and helpful to compare alternative evolutionary models. Our analysis of simulated datasets illustrates that ABC methods have the power to effectively discriminate recent introgression versus current isolation based on datasets of several hundreds of loci and a few individuals per species-typical of population genomic studies. Comparisons of alternative demographic models, however, can be strongly impacted by assumptions regarding the genomic distribution of effective population size (N e ) and introgression rate (M).
Similarly, among models excluding ongoing gene flow, distinguishing between strict isolation and ancient migration was not possible in a substantial number of cases. These are challenges for future methodological research in the field, with important implications regarding the debate about the requirement of geographic isolation to complete speciation  [7, 45]. Only two diploid individuals per population/species were used in this analysis for the sake of comparability between datasets (in many populations, no more than two individuals were available) and because of computational limitation. However, our evaluation of the effect of sample size on ABC-based demographic inference suggested that two diploid individuals per population were largely sufficient to capture the main signal when more than 100 loci are available (S1 Fig).
Implications for Speciation and Conservation Research Our dataset is composed of a large variety of taxa with deep phylogenetic relationships and diverse life history traits. In principle, the propensity to evolve prezygotic barriers might differ between groups of organisms (e. g. , broadcast spawners versus copulating species  [58] ). We did not detect any significant effect of species biological and/or ecological features or taxonomy on the observed pattern. Highly polymorphic broadcast spawners and low-diversity large vertebrates with strong parental investment were equally likely to undergo current gene flow for a given divergence level. Whether the pace of accumulation of genetic barriers, the so-called speciation clock, varies among taxonomic group is a major challenge in speciation research and requires the dissection of the temporal establishment of barriers in many different taxa  [59, 60].
According to this hypothesis, genetic subdivision could have little to do with contemporary connectivity. The width of the grey zone indicates that a number of existing taxonomic debates regarding species definition and delineation are difficult by nature and unlikely to be resolved through the analysis of a limited number of loci. Most of the molecular ecology literature, however, is based on datasets consisting of mitochondrial DNA and rarely more than a dozen microsatellite loci. The time when genome-wide data will be available in most species of interest is approaching, though not yet reached. Since then, we have to accept that knowledge about the existence of gene flow between diverged entities could not be settled from genetic data alone in a substantial fraction of taxa. In addition, our study highlights the commonness of semi-isolated entities, between which gene flow can be demonstrated but only concerns a fraction of loci, further challenging the species concept.
Taxon Sampling A total of 61 pairs of populations/species of animals were analyzed (S1 Data). These include 10 pairs taken from the speciation literature and 51 pairs newly created here based on a recently published RNAseq dataset  [31] , which includes 96 species of animals from 31 distinct families and eight phyla, and 1 to 11 individuals per species. Twenty-nine of the newly created pairs corresponded to distinct populations within a named species. Populations were here defined based on a combination of geographic, ecotypic, and genetic criteria: we contrasted groups of individuals (i) living in allopatry and/or differing in terms of their ecology and (ii) clustering as distinct lineages in a neighbor-joining analysis of genetic distances between individuals. The 2 most covered individuals per population were selected for ABC analysis.
In 4 species, 3 distinct populations were identified, in which case the three possible pairwise comparisons were performed. Results were qualitatively unchanged when we kept a single pair per species. Twenty-two of the newly created pairs consisted of individuals from 2 distinct named species that belonged to the same family. Again, the 2 most covered individuals per species were selected for analysis. In the case of species in which several populations had been identified, we chose to sample 2 individuals from the same population for between-species comparison. When more than 2 species from the same family were available, we selected a single pair based on a combination of sequencing coverage and genetic distance criteria, with comparisons between closely related species being favored. Raw and final datasets are available from the PopPhyl website (http://kimura.
Calling Single Nucleotide Polymorphisms (SNPs) and Genotypes At each position of each locus and for each individual, diploid genotypes were called using the reads2snps program  [64]. This method first estimates the sequencing error rate in the maximum-likelihood framework, calculates the posterior probability of each possible genotype, and retains genotypes supported at >95% if ten reads per position and per individual were detected. Possible hidden paralogs (duplicated genes) were filtered using a likelihood ratio test based on explicit modeling of paralogy. For our demographic inferences, only synonymous positions were retained. Synonymous length and positions were then computed for each loci using polydNdS  [65].
Approximate Bayesian Computation The combination of demographic models and genomic settings resulted in a total of 16 distinct models, namely the homoN and heteroN versions of PAN and SI and the homoM_homoN, homoM_heteroN, heteroM_homoN, heteroM_heteroN versions of IM, AM, and SC. Model fit assessment and parameter estimation were performed under the ABC framework. Under each model, 3,000,000 multilocus simulations were conducted using the coalescent simulator msnsam, a modified version of ms allowing variation across loci of the number of sampled individuals  [66, 72]. For each of the 61 pairs of populations/species, the posterior probability of each model was estimated using a feed-forward neural network implementing a nonlinear multivariate regression by considering the model itself as an additional parameter to be inferred under the ABC framework using the R package "abc"  [73].
Robustness Among a set of compared models, ABC returns a best-supported model M and its posterior probability P M. The returned model is validated when P M is above an arbitrary threshold X, corresponding to the posterior probability above which the statistical support for a model is considered as being significant. The robustness of the inference-i. e. , the probability to correctly support model M if true-obviously depends on X. To assess the reliability of our approach, we randomly simulated 116,000 pseudo-observed datasets (PODs) distributed over the 16 compared models. Simulations were independent of the 3,000,000 x 16 reference simulations used for model comparisons in our main analysis, but their parameters share the same boundaries. For each simulated POD, we estimated the posterior probabilities P i of the 16 compared models through ABC.
The probability of correctly supporting M given X was calculated as: PðP M > X j MÞ = ½S m 1 PðP M > X j iÞ , where P(P M > X | i) is the probability that a dataset simulated under m will be supported by ABC as being M with a posterior probability above X  [32]. This is the proportion (among simulated datasets inferred by ABC to correspond to M) of those actually generated under M. For the "ongoing gene flow" versus "current isolation" model comparison, we empirically measured that robustness to support gene flow starts to be above 0. 95 if P migration ! 0. 6419 and the robustness to support isolation is above 0. 95 if P migration 0. 1304. For datasets with P migra- tion between 0. 1304 and 0. 6419, we did not attribute a best model but treated them as "ambiguous cases. ".
Acknowledgments We thank  Aude Darracq ,  Vincent Castric ,  Pierre-Alexandre Gagnaire ,  Xavier Vekemans , and  John Welch  for insightful discussions. The computations were performed at the  Vital-IT  (http://www. vital-it. ch)Center for high-performance computing of the  SIB Swiss Institute of Bioinformatics  and the ISEM computing cluster at the platform Montpellier Bioinformatique et Biodiversite.
European Research Council  (ERC) https://erc. europa. eu/(grant number  ERC  grant  232971 ). PopPhyl project. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. French National Research Agency (ANR)  http://www. agence-nationale-recherche. fr/en/project-based-funding-to-advance-frenchresearch/ (grant number  ANR-12-BSV7-0011 ). HYSEA project. The funder had no role in study.
Gene expression analysis The DNA microarrays used were the Human Genome U133 set (HG-U133 Plus 2. 0, Affymetrix, Santa Clara, CA, USA), containing 54 613 probe sets. Experimental procedures for Gene Chip microarrays were performed according to the Affymetrix Gene Chip Expression Analysis Technical Manual using HG-U133 Plus 2. 0 arrays. All microarray data were simultaneously normalized using the GC Robust Multi-array Average (GCRMA) package version 2. 22. 0 in the R 2. 12. 0 environment to assess the levels of expression of the gene probe sets. Genes were considered significantly deregulated between the two groups (Welch t test) when the p values were ≤0. 05 after Bonferroni-Hochberg (BH) adjustment and the fold change was ≥1. 5 (Additional file 2: Table  S2 ). To identify the most relevant deregulated pathways, analysis was performed using the Database for Annotation, Visualization and Integrated Discovery (DAVID) and results were sorted by p value (p value with BH adjustment ≤0.
05) (Additional file 3: Table  S3 ). The most significant genes identified with the Welch t test were used to perform unsupervised clustering first of the training set, and subsequently of the validation set of tumors (standard Pearson correlation as similarity measure and centroid as linkage criteria). Receiving operating characteristic (ROC) curves were calculated to determine the specificity and sensitivity of the IMPC gene signature (Additional file 4: Figure  S1 ) [GEO: GSE66418].
Acknowledgements The authors would like to thank  Virginie Mieulet ,  Franck Tirode  and  Maria-Carla Parrini  for helpful discussions in the interpretation of the data and bio-informatical analysis, and  Sergio Roman-Roman  at the  Institut Curie Translational Research Department. Anne Vincent-Salomon  was supported by an  INTERFACE grant  from  INSERM , the award Grand Prix de la recherche -Association "Le cancer du sein, parlons-en !-Estée Lauder". Financial support for experiments was provided by the  Institut Curie Translational Department  and the  Association pour la Recherche contre le Cancer (ARC)   AAP 2010 , number  SFI20101201734.
Model A P2P network consists of a set of asynchronous processors, henceforth referred to as peers. By asynchronous, we mean that there is no bound on message delay, clock drift, or execution speed rate. Peers are endowed with distinct IDs. They communicate by exchanging messages. Any peer P 1 can communicate with another peer P 2 provided P 1 knows the id of P 2. We abstract out the details of the actual physical routing, as done in most peer-to-peer systems. The distributed structure considered, used as an indexing system to store the information is a prefix tree. Nodes of this tree are mapped onto the peers of the network. Henceforth, the word node refers to a node of the tree, i. e. , a logical entity. Each peer maintains a part of the indexing system, i. e. , some (logical ) nodes of the prefix tree.
SST Scalability We tested the scalability of the protocol facing a randomly created initial configuration. The script developed creates this initial configuration. More precisely, a high level of randomness is used, ensuring the topology created suffers from many problems with high probability (w. h. p. ). In the following, random refers to uniformly random. Nodes are constructed sequentially. Firstly, to create one node, a label is constructed, with a size randomly chosen in the range  [1, 20] , and its composing characters are randomly picked in the Latin alphabet. Secondly, some nodes are randomly chosen from the set of already created nodes, to be its parent and children, in the sense that this node considers them as parent and children, but this relation is not symmetric w. h. p. , since these last nodes have also chosen their relatives in a random manner.
Controlling photon-pairs propagation using the TTM The TTM matrix is calculated from the measured TM. By analogy to the method described in  (36) , the transpose conjugate of the TTM is used as an inverse operator to determine the two-photon input field that allows focusing photon-pairs into output state 0 ˆ3       , as a function of the path length difference between input photons δ. The observed effects are consistent with the initial indistinguishability of the photons evaluated with a HOM experiment (see Supplementary Materials). Data are acquired for 290s with a coincidence window of 2. 5ns.
, 2010; Betzel et al. , 2012; Chu et al. , 2012; Hutchison et al. , 2013; Leonardi et al. , 2013; Messé et al. , 2014; Hansen et al. , 2015; Yu et al. , 2015). In these networks in resting state, noise-driven fluctuations far from equilibrium provide a rich repertoire of characteristic system trajectories  (Hansen et al. , 2015). Here, we present EEG data obtained from 58 electrodes at rest with eyes closed (REC) and open (REO), and during an auditory oddball task under attended (AOT) and unattended (UOT) conditions. To determine WFC and CFC between different electrodes, we use phase synchronization algorithms described in previous studies that measure directed and undirected phaseto-phase coupling  (Müller and Lindenberger, 2011, 2014; Müller et al. , 2013). These coupling measures were used to construct a connectivity matrix or a graph representing the network properties.
The standard and deviant stimuli were presented binaurally (with a probability of 0. 8 and 0. 2 for the standard and deviant stimuli, respectively) through headphones (Sony DJ MDR-V300) at 70 dB SPL for a duration of 70 ms (including a 10-ms rise and fall period). The stimuli were generated using the Audacity 1. 2. 4 software. The inter-stimulus interval (ISI) was uniformly chosen at random between 1200 and 1500 ms. Two different experimental conditions were used: passive listening (UOT) and active counting (AOT). For the first condition, the subjects were simply asked to listen to the tone pips without any response, whereas, for the second condition, the subjects were asked to listen to the stimuli and count the number of deviant tones. They only had to report back the number of tones counted once the session was complete.