dataesr/scientific-paragraphs-categorization huggingface dataset:
The six components χij for each Ni ion were first refined, on the joint basis of the three datasets, but only those for which the obtained value was larger than 1. 5 times the error bar were included in the final refinement. The refinement of the local susceptibility tensors χ (with l = 1, 4) evidences a strong similarity between the Ni1 and Ni4 susceptibilities on one side and between Ni2 and Ni3 on the other side as showed by the reported components in Table  1. This suggests that the two Ni atoms belonging to a same antiferromagnetic (AF) pair, (Ni1, Ni4) and (Ni2, Ni3), respectively, have similar magnetic behaviors with respect to the applied magnetic field. Furthermore, the refined eigenvalues of each Ni local tensor, reported in Table  2 , show that the Ni1 and Ni4 tensors are more anisotropic than those of Ni2 and Ni3.
The recipe book represents for SNCF the main reference for validation of PSEEL's control command systems since many years. It contains several scenarios that systems engineers execute on PSEEL and analyze its behavior in order to validate or not its control command. An example of a test scenario is presented in Table  1. During an automation project, the recipe book and the PLC programs are automatically generated with software named Odil  (Coupat, 2014). For this, the systems engineers enter all inputs data of the system (like single line diagram of PSEEL, equipment configuration, inputs/outputs. ) and then, Odil generates automatically programs and recipe book. After a proofreading of these latter, they verify online the correctness of whole control command (PLC program and electrical cabinets) during factory tests, by executing all the scenarios of the recipe book on system.
SYSTEM MODELING In this section, we present and illustrate our methodology through its application on a transformer group (PSEEL's sub-system). System modeling requires careful structural analysis of PSEEL and PLC programs because the models must have the same behavior as real system. The transformer group is controlled by one PLC, and is basically composed of circuit breakers, switches, and transformer. Its program's outputs correspond to the orders sent to devices, and inputs are the observed faults on operative part and the devices' states (opened or closed). The programs are designed in STRATON (www. copalp. com) using some of the standardized languages  (IEC 61131-3, 2003)  such as Ladder Diagram (LD), Sequential Function Chart (SFC) and Structured Text (ST). But in our Uppaal models, the whole program needs to be translated into ST because the model-checker cannot interpret the other languages.
AUTOMATIC GENERATION OF UPPAAL MODELS The methodology of formal verification must be applied for any new automation project. Because of heterogeneity of PSEEL and programs, systems engineers would be obliged to adapt Uppaal models according to installation's structure, recipe book and program's content. This additional task goes against our principle in this work because it increases time project. As for programs and recipe book, we propose to generate automatically with Odil the Uppaal file (*. xml) which contains all required inputs data (PSEEL models, programs, and recipe book). The methodology used to generate this xml file is not detailed in this paper, but presented in  (Coupat, 2014). The xml file contains exactly the same information as for program and recipe book, but in a different language.
For further information, contact Patrik Magnusson (Patrik. magnusson@ki. se) or Barbro Sandin (barbro. sandin@ki. se). Genotype data from HRS can be accessed via the database of Genotypes and Phenotypes (dbGaP, http://www. ncbi. nlm. nih. gov/ gap, accession number phs000428. v1. p1). Researchers who wish to link genetic data with other HRS measures that are not in dbGaP, such as educational attainment, must apply for access from HRS. See the HRS website (http://hrsonline. isr. umich. edu/gwas) for details. Researchers who wish to use the RAND HRS data need to register via the RAND website (http://www. rand. org/labor/ aging/dataprod/hrs-data. html). PGS in a meta-analysis of GWAS results from genetically and phenotypically heterogeneous studies, and quantifies the loss in power and predictive accuracy incurred by this crossstudy heterogeneity.
Estimating SNP heritability and CGR Using 1000-Genomes imputed data from the RS, STR, and HRS, we estimate SNP-based heritability and CGR respectively by means of univariate and bivariate GREML  [36, 37]  as implemented in GCTA  [36]. In our analyses we consider the subset of HapMap3 SNPs available in the 1000-Genomes imputed data. In S1 Data we report details on the genotype and phenotype data, as well as our quality control (QC) procedure. After QC we have a dataset, consisting of % 1 million SNPs and % 20,000 individuals, from which we infer h 2 SNP and CGR. In S1 Estimation we provide details on the specifications of the models used for GREML estimation.
RS (Rotterdam Study) The generation and management of GWAS genotype data for the Rotterdam Study is supported by the  Netherlands Organisation of Scientific Research NWO Investments  (nr. 175. 010. 2005. 011 , 911-03-012 ). This study is funded by the  Research Institute for Diseases in the Elderly  ( 014-93-015 ;  RIDE2 ), the  Netherlands Genomics Initiative (NGI) /  Netherlands Organisation for Scientific Research (NWO)  project nr. 050-060-810. We thank  Pascal Arp ,  Mila Jhamai ,  Marijn Verkerk ,  Lizbeth Herrera  and  Marjolein Peters  for their help in creating the GWAS database, and  Karol Estrada  and  Maksim V. Struchalin  for their support in creation and analysis of imputed data. The  Rotterdam Study  is funded by  Erasmus Medical Center  and  Erasmus University, Rotterdam ,  Netherlands Organization for the Health Research and Development (ZonMw) , the  Research Institute for Diseases in the Elderly (RIDE) , the  Ministry of Education, Culture and Science, the Ministry for Health, Welfare and Sports , the  European Commission (DG XII) , and the  Municipality of Rotterdam.
Background: In drug design, an efficient structure-based optimization of a ligand needs the precise knowledge of the protein-ligand interactions. In the absence of experimental information, docking programs are necessary for ligand positioning, and the choice of a reliable program is essential for the success of such an optimization. The performances of four popular docking programs, Gold, Glide, Surflex and FlexX, were investigated using 100 crystal structures of complexes taken from the Directory of Useful Decoys-Enhanced database.
Results: The ligand conformational sampling was rather efficient, with a correct pose found for a maximum of 84 complexes, obtained by Surflex. However, the ranking of the correct poses was not as efficient, with a maximum of 68 top-rank or 75 top-4 rank correct poses given by Glidescore. No relationship was found between either the sampling or the scoring performance of the four programs and the properties of either the targets or the small molecules, except for the number of ligand rotatable bonds. As well, no exploitable relationship was found between each program performance in docking and in virtual screening; a wrong top-rank pose may obtain a good score that allows it to be ranked among the most active compounds and vice versa. Also, to improve the results of docking, the strengths of the programs were combined either by using a rescoring procedure or the United Subset Consensus (USC).
We may cite more particularly some semi-rigid docking benchmarks that include Gold, Glide, Surflex or FlexX and that were made on a large number of targets. Usually, benchmarks are used to compare, or simply evaluate, the performance of programs or scoring functions. For example, Liebeschuetz et al. [21]  evaluated the pose prediction and the performance of Gold scoring functions using the Directory of Useful Decoys (DUD) dataset  [22] , which consists of 40 varied protein targets. They found that 81% of the top-rank poses were close to the crystal conformation with a root-mean-square deviation (RMSD) lower than 2 Å. Recently, Murphy et al. [23]  investigated, by an ensemble docking, the performance of various scoring functions in Glide for docking and virtual screening on 22 targets and obtained 92% of the top-rank poses within 2 Å from the crystal pose.
7). They concluded that these programs may be more suitable for these families. However, this assertion seems fragile in the absence of information concerning the degree of identity between the proteins of the same family and the similarities between their ligands. Some conclusions of these docking benchmarks seem to be in contradiction with our observations made on a virtual screening benchmark  [26]. Indeed, we have reported recently the results of the benchmark of Gold, Glide, Surflex and FlexX for virtual screening on the 102 protein targets of the Directory of Useful Decoys-Enhanced database (DUD-E)  [27]. We have shown that the good performance of these programs was mainly due to biases in some properties of the chemical libraries and that in contrast, there was no clear relationship between the performance and the properties of the protein cavities or the protein families.
The reasons for these discrepancies should be investigated. Are they solely due to the difference between docking (i. e. , the comparison between poses of the same molecule) and virtual screening (i. e. , the comparison between the top-rank pose of several different molecules)? Since the first is the basis of the second, we may ask if there is a relationship between the performances of these programs in docking and in virtual screening and what would be the influence of the protein and ligand properties on the results of docking. Finally, the weaknesses of the scoring functions were described to be responsible for the docking bad performance of some programs. Is this always the case? And is there a strategy to overcome this bad performance? To answer these questions, we performed docking calculations on the crystal structures of 100 protein-ligand complexes taken from the DUD-E dataset, using Gold, Glide, Surflex and FlexX.
Since only the semi-rigid docking is common to these programs, it was adopted here for the sake of comparability. In what follows, the docking performance of the four programs is first evaluated; then, for each program, the relationship between the performance and the properties of the proteins and the small molecules is investigated and the docking and virtual screening performances are compared; finally, two procedures to improve the chances of obtaining correct poses are explored. But beforehand, the pertinence of the protein-ligand database is investigated to assess its representativeness.
Pertinence of the database for docking The DUD-E database is made of 102 diversified targets. Two of these proteins, aofb and casp3, are covalently bound to their crystal ligand, so they were excluded from the dataset because they cannot be properly handled by the docking programs in their standard usage. The 100 remaining target structures were resolved by X-ray crystallography, at a resolution ranging between 0. 97 and 3. 3 Å (Additional file 1: Fig. S1 ), with a majority around 2 Å, indicating that the crystal poses have a good chance to be well defined, with an uncertainty on their position lower than 1 Å  [28]. Of these proteins, only 58 could be gathered in 9 varied families  [26] : protein kinases, nuclear receptors, proteases, GPCRs, cleaving enzymes, cyclooxygenases, cytochromes P450, ion channels and histone deacetylases.
b a Fig. 1  Evaluation of the performance of the programs. The cumulative number of targets with respect to the RMSD of the best poses (a) and to the top-rank poses (b) obtained by each program of the correct best poses, respectively, are placed beyond the 10 th rank. For Glide and Gold, these numbers fall to 13 and 8%, respectively. For each target, the best poses (or the top-rank ones) obtained with one program were compared to those obtained with another program. This comparison shows the absence of correlation between the results (Figs. 3, 4 ). Indeed, for many targets, while a program finds a best pose with an RMSD lower than 2 Å (or top-rank it), another program would be completely unable to find any correct pose for the same target. For instance, if we consider the two programs with comparable performances, namely Glide and Gold, whose best poses were correct for 79 and 77 targets, respectively, they have in common only 67 targets with correct poses, not 77 as could be expected.
but for the top-rank poses There is no obvious relationship between the protein families and the success of the programs in finding the correct pose or in top-ranking it (Fig. 5 ). Indeed, concerning the top-rank poses obtained with any of the four programs, in a given family there are members with high and others with low RMSD, apart from a few exceptions, like the ion channels (2 targets) for which all programs succeeded (low RMSD). A similar observation can be made concerning the best poses: for most families, the programs succeeded for some targets but not for others, like the nuclear receptors (11 targets), with Surflex, and the proteases (6 targets), with Glide. This is true, despite the existence of some families for which a given program succeeded for all their members, like the proteases (6 targets) with Surflex.
Comparison with virtual screening The poor correlations observed above contrast with the observations based on the results of the virtual screening (VS) that we performed recently with these four programs, using the same procedure (except for Gold, where the scoring function Goldscore was used in the virtual screening study)  [26]. In VS, it was observed that the performances (BEDROC scores) of all four programs are somehow influenced by the small molecule properties. Therefore, one may ask if there is any relationship between the results of the docking presented here and those of the virtual screening. To answer this question, for each target, the BEDROC score obtained with a program is plotted versus the RMSD of its top-rank pose.
Goldscore The difference is located in a loose or exposed to solvent part of the ligand No Similar to hivpr the best pose was top-ranked by this scoring function. Incidentally, hdac8 was the only target for which the result of rescoring was better with PLP than with Goldscore. 3. For hs90a, in docking, only Glide succeeded in topranking a correct pose (RMSD = 0. 46 Å). But this program was confused during the rescoring process by the presence of poses coming from FlexX, relegating its top-pose to lower ranks. Indeed, FlexX created poses at the edge of the binding site. When these poses were added to the pool, an off-center pose was privileged by Glide, Gold and FlexX itself, with an RMSD of 9. 54 Å (Fig. 7b ). Since visually it was not obvious why the crystal position would be better than this off-center pose, we minimized the energy of all protein-ligand complexes, using the CHARMM36 force field  [36]  for the protein and the CHARMM General Force Field (CGen)  [37]  for the small molecule.
Consequently, there is no particular program or protocol that may be recommended for specific proteins or ligands, although some programs seem to work globally better than the others, like Glide and Gold, with a correct top-rank ligand for more than 60% of the targets. This performance is much better than that observed in virtual screening with the same programs, where a good BEDROC value (over 0. 5) was obtained for less than 30% of the targets. In fact, for each program, the comparison of its performance for docking and virtual screening showed the absence of correlation between these two aspects of the work. A well-positioned and top-ranked ligand in docking does not guarantee its good ranking during VS, and vice versa. Therefore, improving the scoring functions for docking may be necessary but clearly insufficient for improving the virtual screening performance.
Preparation of the DUD-E database Preparation of the DUD-E targets and definition of the binding sites The crystal structure of the proteins were taken from the DUD-E dataset (http://dude. docking. org) and prepared using the Protein Preparation Wizard from the Schrödinger suite (http://www. schrodinger. com) as previously described  [26]. The obtained mol2 files, with the correct atom types, were used for the four docking programs. To define the binding site, all residues of the protein structure with at least one heavy atom within 5 Å from the ligand were selected. Then, this selection was refined upon individual visual inspection, by adding, when necessary, residues beyond 5 Å that are essential for the continuity of the cavity.
Preparation of the crystal ligands The mol2 files of the co-crystallized ligand structures were taken from the DUD-E dataset, in which, for each ligand, the major protonation state was given at pH 7. However, for few ligands, the structures had to be curated to ensure the attribution of the correct bond orders and atom types. This was done on the basis of the information given by the articles that accompany the X-ray structures. In order to randomize the 3D structures of the ligands, all the mol2 files were converted to the 2D SMILES format and the Ligprep module of the Schrödinger suite was used to generate a starting 3D optimized conformation for each ligand, using the force field OPLS2005.
The binding site properties Three properties of the binding site were considered: the total number of atoms at 4 Å from its surface, its hydrophobicity, i. e. , the fraction of carbon atoms among this total number, and its exposure to the solvent. In order to define the surface of the binding site, the protomol generated by Surflex (see above), which closely covers the site, was used. All the heavy atoms of the protein, situated at less than 4 Å from the protomol, were considered as the surface of the cavity. The exposure of the binding site was calculated by using the SiteMap package  [41]  version 3. 3 (Schrödinger). It measures the degree of openness of the site to the solvent. For more details, see  [26].
Energy minimizations All 120 protein-ligand structures of hs90a were energy minimized using CHARMM program  [44] , with the CHARMM36 force field  [36]  for the protein and the CHARMM General Force Field (CGen)  [37]  for the small molecule. This minimization was done with 5000 steps of steepest descent, followed by other 5000 steps of conjugate gradient algorithms. The electrostatics and van der Waals energy terms were truncated using the switching function with a cutoff distance between 6 and 10 Å, and the dielectric constant was equal to 2 times the distance between the interacting atoms.
Metagenomics facilitates the analysis of bacterial biodiversity and the identification of novel taxa because it bypasses the need for microbial cultivation or isolation  (Handelsman, 2004; Rodriguez-R and Konstantinidis, 2014). The recent development of these techniques has greatly expanded the nucleotide sequence databanks, which can be queried to identify bacterial taxa. We have used the Acinetobacter genus as a model to our study. Its members are thought to colonize a wide variety of environments  (Doughari et al. , 2011) , and their phylogenetic relationships have been recently resolved through genome-wide comparative analyses  (Sahl et al. , 2013; Touchon et al. , 2014). Acinetobacter are intrinsically resistant to many toxics, including antibiotics, which gives them an adaptive advantage in the hospital.
Overview of the method and assessment of its quality We developed a pipeline to identify clade-specific sequences in metagenomic data and place them on a reference phylogenetic tree in four major steps (see Experimental procedures, Supporting Information Fig. S1 ). First, we built protein profiles for every protein family of the coregenomes of the focal clade, a close outgroup and a distant outgroup. Second, we retrieved, curated and annotated a large set of metagenomes from different environments. We integrated the two datasets by searching in the metagenomic data for proteins (or peptides) matching the profiles of the core-genomes. Third, we used linear discriminant analysis (LDA) and self-organizing maps (SOM) to remove the distantly related sequences from the hits. Finally, the remaining peptides were placed in the focal clade tree by maximum likelihood using EPA.
The first three steps ascertain that few non-pertinent sequences are subject to EPA. This makes the procedure much faster than if all peptides were subject to the EPA, because this latter step is very time-consuming. We used this procedure to study Acinetobacter, using Moraxella and Psychrobacter as close outgroups and Pseudomonas as a distant outgroup. The choice of these outgroups was based on the phylogenetic distance: the first group is the sister-clade of Acinetobacter in our dataset, whereas the Pseudomonas is the subsequent one (among clades with several completely sequenced genomes). Although the coregenome of Acinetobacter was composed of 923 genes, only 647 of them where shared with the outgroups. To avoid possible misassignments due to the lack of orthologous genes in the outgroups, only the latter were used.
95, see Experimental procedures). To validate the procedure, we selected random parts of proteins (small peptides) from these genomes with sizes representative of the metagenomics datasets and placed them on the reference tree of Acinetobacter. We also built a new phylogenetic tree of the core genome of the genus including the novel genomes (Supporting Information Fig. S5 ). For each of the six taxa, we computed the differences between the observed placement of the peptides in the reference phylogenetic tree and the expected one (given by the new core genome tree). The differences were small: 97% of the peptides were placed less than 4% away from the expected position (the percentage is the distance between the positions, divided by the maximal tip to root distance in the tree, Supporting Information Fig.
Abundance of Acinetobacter in microbial communities Our method allows to identify the microbial communities whose metagenomes contain sequences from Acinetobacter. To evaluate its accuracy, we checked if the six novel Acinetobacter genomes used to validate the EPA procedure were placed in branches of the tree overrepresenting the environments where they were isolated. This was indeed the case for the six taxa (see 'Validation of the EPA using novel Acinetobacter genomes' in Supporting Information), showing that EPA helps identifying the environments where novel taxa can be found. We then retrieved 2568 metagenomic datasets from 126 independent locations and classed them in types of environments (see Experimental procedures). We identified Acinetobacter in 817 out of 2568 datasets. We placed 274 890 of the peptides of these sets (0.
To detail the association of Acinetobacter with humans, we queried specifically the data of the Fig. 1. Results of the evolutionary placement analysis. We computed for each branch the distribution of the environmental categories associated with the fragments placed in the branch. The colour boxes indicate the branches in which the representation of fragments from certain environments was significantly higher than the average abundance for each environment across the tree (one-way Kruskal-Wallis test, P-value < 0. 001). White boxes represent branches without any significant over-representation. The pale backgrounds represent the three large clades with similar broad environmental distribution. The distribution of dissimilarities between ancestral and descendant branches was calculated. Branches showing the top 95% of the ecological shifts towards their descendants are marked in red (see Experimental procedures).