dataesr/scientific-paragraphs-categorization huggingface dataset:
PlaScope, Plasflow and cBar benchmark PlaScope, Plasflow (5) and cBar  (7)  softwares were run on the reference dataset of 70 genomes containing plasmids. All these methods use different databases and classification approaches to sort contigs as plasmidic or chromosomal. Moreover, PlaScope and Plasflow may assign contigs as unclassified for ambiguous results. For each tool, the predictions were considered as i) true positive (TP) (plasmid assignment of a plasmidic sequence), ii) true negative (TN) (chromosome or unclassified assignment of a non plasmidic sequence), iii) false positive (FP) (plasmid assignment of a non plasmidic sequence), iv) false negative (FN) (chromosome or unclassified assignement of a plasmidic sequence). Then, we calculated recall (TP/(TP+FN)), precision (TP/(TP+FP)), specificity (TN/(FP+TN)), accuracy ((TP+TN)/(TP+TN+FP+FN)) and F1 score (2*(recall*precision)/(recall+precision)).
The results are presented for genomes taken as a whole in Table  1  and individually in Fig. 2. PlaScope achieves the highest recall on the dataset (0. 87), closely followed by Plasflow (0. 85), cBar being reaching the lowest value (0. 74). However, important differences were found for the other assessment criteria. Indeed, we obtained with PlaScope very high precision (0. 96), specificity (0. 99) and accuracy (0. 98) compared to Plasflow (0. 27, 0. 68 and 0. 70, respectively) and cBar (0. 21, 0. 60 and 0. 62, respectively). Obviously, these results are easily explained by the contents of our database, which was built specially for E. coli. Plasflow and cBar performed well in terms of recall, and their strength rely on their capacity to class many diverse taxonomic groups. Such methods can be really useful when working on metagenomes, but when focusing on a particular species a targeted approach like PlaScope drastically limits classification errors.
In addition, PlaScope was run on the 7 finished genomes with no plasmids. As expected, no plasmid was predicted for 6 genomes but, surprisingly, PlaScope predicted two plasmid contigs for E. coli KLY (GCA_000725305. 1). To assess this result, we aligned these contigs against the NCBI database by blastN and obtained perfect alignments with the plasmid F sequence of E. coli K-12 C3026. This result suggests that the original assembly of E. coli KLY is missing this plasmid.
Application to resistance, virulence gene and operon locations In a second step, we evaluated our method on Extended-Spectrum Beta-lactamase (ESBL) carrying E. coli strains sequenced by Falgenhauer et al. (14). This dataset is particularly challenging because of an unusual high rate of chromosomal integration of CTX-M-15 coding genes. Indeed among the 27 isolates of sequence type (ST) 410, 21 carried a blaCTX-M-15 gene on their chromosome. We downloaded short reads of these isolates and run PlaScope to classify the assembled contigs. In parallel, we determined the presence of CTX-M coding genes on the contigs using Resfinder (with a minimal identity of 95% and a minimal alignment coverage of 90%). Using this approach, we accurately identified 20 chromosomally-integrated and 5 plasmid-related CTX-M (Fig.
Conclusion Here, we propose a method, called PlaScope, for plasmid and chromosome classification of E. coli contigs. It is based on Centrifuge (10): a fast metagenomic classifier that uses exact matches and small-sized databases. PlaScope offers a high specificity by selecting a unique assignment of contigs to plasmid, chromosome or unclassified. Indeed, we took advantage of the ever growing number of sequences from databases to build a custom database, which combines many high quality sequences of Enterobacteriaceae plasmids and chromosome sequences of E. coli. We compared the performance of our tool with cBar and Plasflow, as these bioinformatic softwares also enable the segregation of plasmid and chromosome contigs. These two programs rely on genomic signature and have been develop to predict plasmid sequences in metagenomic samples.
to PlaScope, Plasflow achieve roughly the same recall value on our dataset, whereas cBar performed a little bit less well. However when looking at the other criteria such as precision, specificity and accuracy, PlaScope outperformed the other ones due to its highly specific database. cBar and Plasflow are virtually able to identify mobile elements in many bacterial species owing to their very diverse taxonomic database. But when focusing on a species, the targeted approach of PlaScope gave indisputably better results both in terms of recall and precision. Using PlaScope, we were able to recover almost all plasmids from the analysed strains, with very high precision, specificity and accuracy. Furthermore, among 1 of the 7 strains described as nonbearing plasmid strains in the NCBI database we were able to identify a mobile element: a typical plasmid F in a E.
coli K-12. In a second analysis, we challenged our approach on more concrete data by looking at specific genes. Analysing clinical or environmental strains, it could be of great interest to detect specific clones with particular genetic backgrounds. Indeed, plasmid location of a resistance or virulence gene has not the same impact on an epidemiological point of view and on the capacity of transmission of the strain in a particular environment. For example, we can fear plasmid outbreaks when a gene that confers resistance against wide-spectrum antibiotic is carried by such a mobile element. Conversely, if the same gene integrates in the chromosome of an already highly virulent strain, it can lead to the emergence of a well-adapted and dangerous clone. To highlight this, we chose a genome dataset of E.
It can highlight integration events or plasmid transmission between isolates. Nonetheless as it is based on previous knowledge of plasmids found in a specific taxon (e. g. Enterobacteriaceae), it will require the enrichment of the database keeping it up-to-date. At last, it could also be interesting to create other databases for well-known bacteria with many complete genomes available such as Klebsiella pneumoniae, Staphylococcus aureus or Bacillus species.
We provide new astrometric observations of Enceladus from images taken using the Cassini Imaging Science Subsystem (ISS) Narrow Angle Camera. Astrometric reduction was performed using a new fully-automatic camera repointing technique built within the Caviar software package. The new automatic procedure is based on the k-d tree technique, and has been developed as an alternative to the manual technique currently available in Caviar. We describe the new procedure and demonstrate its effectiveness compared to manual processing. Repointing corrections were computed using reference stars from the UCAC4 and Gaia DR1 star catalogues. We compare the observed-minus-computed (O-C) residuals of Enceladus based on UCAC4 with those based on Gaia DR1, relative to the JPL SAT375 ephemeris and the IMCCE NOE ephemeris, respectively.
The Saturnian satellite Enceladus has been a subject of particular interest following the discovery of geysers, an icy outer shell and liquid layer beneath it  (Porco et al. 2006; Thomas et al. 2016) , and astrometric methods have been shown to play a key role in solving the fundamental problems relating to the structure and dynamical evolution of Enceladus and the Saturnian satellite system as a whole  (Lainey et al. 2012; Thomas et al. 2016; Lainey et al. 2017). c 2015 The Authors In addition to making available Cassini ISS astrometric observations of Enceladus from 2013 to 2017, the aims of this paper are (1) to investigate the feasibility of performing fully automatic astrometry within the framework of the Caviar software package, and (2) to evaluate the benefits, if any, of using the Gaia DR1 reference star catalogue in the astrometric reduction, compared to the UCAC4 catalogue.
ASTROMETRY Methods for the astrometric reduction of Cassini ISS images using proprietary software have been described in  Cooper et al. (2006 Cooper et al. ( , 2008) )  and  Tajeddine et al. (2013). The development of these methods over the course of the Cassini mission culminated in the release to the community of the Caviar software package, developed jointly by QMUL (Queen Mary University of London) and IMCCE (Institut de mécanique céleste et de calcul des éphémérides)  (Cooper et al. 2018). In Caviar, the astrometric data reduction pipeline includes two main steps: a correction for errors in the nominal spacecraft camera pointing direction, followed by the astrometric measurement of the centre of the target planetary satellite. In the first step, the correction to the camera pointing direction is estimated by matching reference stars from a star catalogue (currently UCAC4) with imaged stars.
In the second step, the position of the centre of the target satellite is measured using either limb-fitting or centroiding, depending on whether or not the target satellite is resolved in the image. At present, the Caviar package has no capability for the automatic correction of the camera pointing direction  (Cooper et al. 2018). Currently a 'bulk' manual alignment step is required in order to achieve an approximate match between the catalogue and imaged star positions before a final statistical fit is performed. Hence, the only barrier to fully-automatic repointing has been the development of a satisfactory technique to allow the 'bulk' star alignment to be performed fully automatically, removing the need for any manual intervention. Considering the potentially large number of images to be reduced for a given target satellite, including possibly nonclear filter images with stars in the background, and the difficulty of matching stars in some cases, we think that the automatic pointing correction is a necessary upgrade.
Observation residuals relative to the JPL ephemeris SAT375 are shown in Figure  4 , where the residuals in α*cos(δ) and δ have been converted to kilometres. Figure  4 , shows that there is no data coverage in 2014 : the few ISS images of Enceladus taken in 2014 were not suitable for astrometric reduction. Table  3  lists the corresponding mean and standard deviation values for the residuals (in pixels, arcseconds and kilometres respectively). As mentioned above, catalogue stars are used to correct the camera's pointing, so the precision of the catalogue will affect the result of astrometry. Following the release of Gaia DR1, with its expected improvement in precision, we consider whether this provides benefits for the astrometric reduction of ISS images. We therefore compared the effects of using Gaia DR1 with the UCAC4 catalogue.
ACKNOWLEDGEMENTS This work was partly supported by the  Joint Research Fund in Astronomy (U1431227)  under cooperative agreement between the  National Natural Science Foundation of China (NSFC)  and  Chinese Academy of Sciences (CAS) ,  National Natural Science Foundation of China  (Grant No. 11703008 ),  Natural Science Foundation of Guangdong Province, China  (Grant No. 2016A030313092 ,  2014A030313374 ) and the  Fundamental Research Funds for the Central Universities. This work has also been supported by the  European Community 's  Seventh Framework Program  ( FP7/2007-2013)  under grant agreement  263466  for the  FP7-ESPaCE project  and the  International Space Science Institute (ISSI). NC thanks the  UK Science and Technology Facilities Council  (Grant No. ST/P000622/1 ) for financial support.
the manual of the PLUMED code for more examples  [67, 68] ). In the last 15 years, MetaD has seen a considerable progress and many biological applications  [57, 69, 70] : From a method based on heuristic assumptions  [17, 71] , it is now well established in its formalism  [72] [73] [74]  (cf. Ref. [75]  for a review). Running a MetaD simulation requires the choice of few parameters in addition to the choice of the CVs: (1) A target effective free-energy (that is the sum of the system freeenergy along the CVs plus the MetaD bias)  [76] [77] [78] [79] [80]. In the case of well-tempered MetaD (WTMetaD)  [76] , this is the free energy scaled by a constant factor. Therefore, the only parameter to choose is the scaling factor (or bias factor, e. g. a bias factor of 10 means that the effective free energy will be that of the system scaled down by ten times).
Finally, transition rates between kinetic basinstypically much slower than rates between adjacent microstatescan be estimated either by analysing the kinetic matrix or, again, using kinetic Monte Carlo, and associated to experimental observables like folding times or protein-ligand association/dissociation rates. Available software packages for the estimation, validation and analysis of Markov state models include PyEMMA  [153] , HTMD  [154] , MSMBuilder  [155]  and METAGUI  [94, 95]. To date, most Markov state models were targeted to the folding of small proteins  [156, 157]  (and, recently, also conformational changes in disordered proteins  [158]  and in RNA oligonucleotides  [159] ). However, a growing field of application is the binding of small ligands to proteins, a problem of great interest for drug discovery  [120, [160] [161] [162] [163] [164].
Typically, a CV is used to define interfaces, and the computational cost of the protocol can often be large. In principle, even if individual trajectories evolve in an unbiased manner, the specific algorithm used to select their starting configurations might introduce a form of bias in the simulations. Note that in its original formulation, transition path sampling aimed at the direct generation of continuous pathways joining the end states, starting from an initial guess, exploiting a Markov chain Monte Carlo procedure called shooting  [2, 186]. The software packages OpenPathSampling  [187]  and PyRETIS  [188]  have been specifically developed to perform and analyse path sampling simulations. A different approach to kinetics is based on a projection of the highdimensional atomic dynamics on a low-dimensional space of CVs, resulting in Langevin stochastic differential equations  [132, [189] [190] [191].
Furthermore, they also provide an indication of the contribution of different sources of experimental data to the quality of the simulation, and can provide feedbacks about the quality of the experimental data themselves. Last but not least, these techniques make the dependence of the results on the initial choices less important, rendering overall more robust all the simulation process. MaxEnt approaches (with and without replicas) as well as Metainference are available and well documented in PLUMED (cf. EDS, ENSEMBLE, MAXENT and METAINFERENCE in the PLUMED manual), including a large number of methods to calculate experimental observables provided in the PLUMED-ISDB module  [250]. Hybrid methods have been successfully employed in a number of problems to obtain results in quantitative agreement with experimental data, mostly NMR, including the characterization of the dynamics of folded  [240, 251, 252]  and unfolded/disordered proteins  [253] [254] [255] [256] [257] , in molecular recognition  [258, 259] , membrane proteins  [260, 261]  and nucleic acid dynamics  [239, 262].
MSA (Multiple Sequence Alignment). The sequence alignments revealed a number of identical residues with significant conserved motif regions. The bunOBP sequence had 70. 51% matches with bovine lipocalin (PDB id: 1BJ7_A) and 40% matches with bovine nasal mucosal OBP (PDB id: 1OBP). The -GXW-*motif region, a signature of lipocalin superfamily, is functionally conserved and acts as an evolutionarily significant amino acid to authenticate the protein diversity  3. The visualization and representation of MSA was made using ESPript server with bovine, pig and porcine OBP sequences (Fig. 3D ). Phylogenetic Analysis. The un-rooted phylogeny for interspecies OBPs, viz. , pig, porcine, bovine, and buffalo, were constructed. The maximum sequence resemblances of bovine OBP were observed with bunOBP and the clades were separated based on the bootstrap values.
Pig and bovine OBP have the highest similarity and present as a neighbour members. Evolutionary diversity analysis confirmed that the bunOBP and bOBP appeared as co-clusters and so showed several similar conserved identities and motif regions (Figs  3D  and 4A ). bunOBP Modeling and Validation. The PDB model of chain A of 1BJ7 and 1OBP were taken as template for the structural modeling of bunOBP using MODELLER software (Version 9. 8). Interestingly, the bunOBP revealed similar structural resemblances to templates. The bunOBP has continuous beta sheets with interconnecting loops/coils and its 3D structure depicts the TIM barrel shape, which is an outstanding feature of OBP  7  (Fig. 4B ). The Ramachandran plot revealed that 93. 4% residues occur in the most favored regions and 5. 9% residues occur in additional allowed regions.
LTQ-Orbitrap (Discovery) hybrid mass spectrometer LC-MS/MS (ThermoElectron Corporation, San Jose, CA, USA) which couples with a nano-HPLC (Agilent Technologies 1200 Series,Waldbronn, Germany) was used. The Agilent C18 column (100 × 0. 075 mm; 3. 5 µm particle diameter) was used for the fractionation. Formic acid in water (0. 1%) and formic acid in acetonitrile (0. 1%) were the two mobile phases used in the experiment. The flow rate of the pump was 0. 5 µL/min. The MS spectrum (Survey Scan) was acquired over the acquisition range m/z 200-2000 at high resolution (M/∆M, 60,000 full width half maximum). Precursor ions were selected for the MS/MS scan. Further, the MS/MS spectrum was obtained for the fragment ions generated by collision-induced dissociation  32, 47. Sequence Analysis in Database.
The bunOBP sequence was obtained from Mascot, and sequence matches were identified using Basic Local Alignment Search Tool (BLAST) from UniProt database (http://www. uniprot. org/). The highly matched protein ID was Q0IIA2_ BOVINE, similar to odorant-binding protein. Physicochemical Properties and Amino Acid Analysis. The amino acid (AA) composition of bunOBP and the various physico-chemical properties were derived from the primary protein sequence and computed adopting ProtParam tool accessible from EXPASY bioinformatics resource portal (http://web. expasy. org/ protparam/). The tool was used to confirm molecular weight, theoretical pI, atomic composition, extinction coefficient, estimated half-life, instability index, aliphatic index and grand average of hydropathicity (GRAVY)  48. Secondary Structure and Conserved Map.
MSA and Phylogeny. The OBP sequence of bovine, porcine and pig were selected and retrieved from non-redundant database and PDB (protein data bank) website. The bunOBP sequence was aligned with the collected sequences for identification of identical residues and conserved motifs through ClustalW2 (http://www. ebi. ac. uk/Tools/msa/clustalw2/), available in EMBL-EBI webservices  51. Extra indels in the sequence alignment were manually edited adopting MEGA 5. 0 52 and visualized using ESPript server (http://espript. ibcp. fr/ESPript/ ESPript/). The phylogentic tree was constructed for mammalian OBP to identify neighboring members of bunOBP. The generated alignment was followed in the prediction of phylogenetic tree through neighbor joining (NJ) method and it was viewed using MEGA 5. 0. Template Selection.
The bunOBP sequence was subjected to BLASTp against protein sequences from PDB (http://www. rcsb. org) and the maximum matched sequences were identified based on e-value thresholds. The first hit was showed the best template for the construction of the structural model. SWISS-MODEL, an open source fully automated web-based server, was adopted to annotate the bunOBP sequence alignment using three dimensional (3D) structural information of template  53. The template identification was performed adopting SWISS-MODEL workspace, and the first hit exhibited good sequence matches of structural information in template selection. Homology Modeling. The X-ray crystallographic structure of bovine lipocalins was used as the template for construction of homology model. Finally, the alignments were generated between of query (bunOBP), and template structure and the alignment was used to construct the model made by MODELLER software (Version 9.
8)  54. A set of 20 models were generated and retrieved from MODELLER. Among the 20 models, the top five low energy optimized models were selected and subjected to assessment of the stereo-chemical quality of modeled structure by analyzing residue-by-residue geometry with validation using phi/psi value acquired by Ramachandran plot analysis from PROCHECK, accessible in SAVeS server (https://www. ebi. ac. uk/thornton-srv/ software/PROCHECK/)  55. PROCHECK tool was used to calculate the allowed and disallowed regions and also, to determine the helices, strands, coils in the structural model. The model that attained the maximum number of residues in the allowed and additional allowed regions of Ramachandran plot was considered as the best one for molecular docking, and additional validation was executed by QMEAN Server for model quality estimation (http://swissmodel.
expasy. org/qmean/cgi/index. cgi)  56. The final model was energy-minimized using SWISS-PDB viewer to obtain stable structure  57. Further, molecular visualization of the constructed model was performed using PyMol software (The PyMOL Molecular Graphics System, Version 0. 99rc6, Schrödinger) and UCSF Chimera (http://www. cgl. ucsf. edu/chimera/). Q-SiteFinder server was used to predict the possible binding sites of modeled protein (http://www. modelling. leeds. ac. uk/qsitefinder/)  58. Structural Superimposition of bunOBP. The homology modeled bunOBP was superimposed with the corresponding template for prediction of folding pattern and comparison of structures based on similarity and backbone conformation. The superimposition was performed by iPBA, a webserver (http://www. dsimb. inserm. fr/dsimb_tools/ipba/).
The proteins were superimposed by structural alignment and dynamic programming of PBs substitution matrix. Formulation and Optimization of Ligands. The available mammalian chemical cues were analysed from PDB and research articles adopting data mining approach. Thus, the ligands were downloaded as 3D structures (. sdf file format) and retrieved from PubChem database (http://pubchem. ncbi. nlm. nih. gov/)  59. Further, the ligands were submitted for energy minimization using ACD/ChemSketch (Version 10. 0) (Advanced Chemistry Development, Toronto, ON, Canada, www. acdlabs. com). Molecular Docking. The molecular docking simulation was done using one of the most suitable methods for performing receptor-ligands docking, the commercialized GLIDE software package (Grid-based Ligand Docking with Energetics-Schrödinger-Maestro, version 9.
Molecular Dynamics Simulations. Protein and ligand structures used in GROMACS 5. 1. 4 package simulation must be complete for all-atom 3D structures with a reasonable geometry. The topology files for the protein were generated using the automated topology builder in the framework of GROMOS96 53a6 force field for protein-ligand complex  63, 64. The ligand topologies were generated by the PRODRG server  65. The complexes were immersed in a cubic box containing simple point charge (SPC) water molecules, and appropriate counter ions, Na + and Cl -, were added in order to neutralize the net charge of the system  66. The long-range electrostatic interactions were calculated with the Particle Mesh Ewald (PME) method  67, 68. Bond lengths involving hydrogen atoms were constrained by using the Linear Constraint Solver (LINCS) algorithm  69.