dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2006; Locarnini et al. , 2006)  data. The model was forced with the Drakkar Forcing Dataset Set version 4. 1 (version 5. 1 from 2008 onwards)  (Brodeau et al. , 2010)  which supplies surface winds, air temperature and humidity at 6 h intervals, longwave and shortwave radiative fluxes at daily intervals, and precipitation (rain and snow) at monthly intervals. These datasets are temporally and spatially interpolated to the model grid and time step. A weak relaxation of the surface salinity to climatological values was applied using a piston velocity of -33. 333 mm/ day/psu equivalent to a ∼180 day restoration timescale over a typical 200 m ocean surface mixed layer. The simulation period was 1 January 1978 to 31 December 2010 (33 years). Further details concerning the model configuration can be found in  Marzocchi et al.
This shows that the model simulates the variability of the AMOC very well, but with a mean value of about 15 Sv it underestimates the mean observed AMOC value by a few Sv. Slightly higher AMOC values have been simulated in other high-resolution ocean models  (Xu et al. , 2014; Iovino et al. , 2014; Stepanov et al. 2016). Whereas it is beyond the scope of the present paper to explore the precise reason for such differences is it worth pointing out that the models used in  Xu et al. (2014) ,  Iovino et al. (2014)  and  Stepanov et al. (2016)  differ from our model set up in ways (e. g. choice of surface forcing dataset; use of strong restoring for temperature and salinity; type of vertical coordinate, sea ice model) that inevitably affect the AMOC solution the models will adopt. However, such differences are not a problem for our study.
Acknowledgements We acknowledge funding by the  UK Natural Environment Research Council  through the strategic research projects  RAPID  ( Smeed, McCarthy, Moat, Rayner ) and DYNAMOC, grant reference  NE/ M005097/1  ( Hirschi, Sinha, Blaker ); the  Long Term Science -Multiple National Capability programme ACSIS , grant reference  NE/N018044/1  ( Sinha, Smeed, Moat, Josey, Hirschi, Blaker ); and National Capability funding associated with the  Joint Ocean Modelling Programme (Sinha, Hirschi). We thank  Andrew Coward  for running and providing data from the 1/12°resolution NEMO model.
From 50 refined structures the 20 best, judged on energies, consistency with the experimental data and covalent geometry (Fig. 3A , Table  2 ), were chosen to represent the MoVIB solution structure (PDB ID 6CEG; BMRB ID 30405). The backbone structure of MoVIB (Fig. 3A ) comprised a structural four-loop VI/VII framework cross-linked by disulfides to generate an 'inhibitor cystine knot' (ICK) motif reminiscent of other ω-conotoxins. The ICK, which features the three disulfides C1/C16, C8/C19, and C15/C26, is centered around a β-hairpin, which comprised residues 19-26 in MoVIB (Fig. 3A, B ). The hydroxyl side chains from S6 and T17 form hydrogen bonds to the K7 and R27 backbone carbonyls, respectively. The two hydroxyprolines and one proline residue were all in a trans-conformation, based on  13  C chemical shifts and sequential NOE patterns.
Electrostatic mapping revealed that the MoVIB surface was highly charged (net charge +5. 7) (Fig. 3C ). The structures of GVIA, MVIIA and MVIIC (PDB ID code: GVIA, 1TTL; MVIIA, 1TTK; MVIIC, 1CNN), highlighting secondary structure, key pharmacophore residues and surface features, are shown for comparison (Fig. 3B, C ). 125 I-GVIA Binding Assays. ω-Conotoxin GVIA from C. geographus is a high affinity Ca v 2. 2 ligand  24, 25  that can be radiolabeled for binding assays  26. MoVIA and MoVIB fully displaced 125 I-GVIA from SH-SY5Y cell membranes with similar potency (p < 0. 001) to ω-conotoxins from piscivorous species (Fig. 4A, B , Table  3 ), suggesting they have overlapping binding site. Since MoVIA and MoVIB may have evolved for defense against fish predators, we determined if MoVIA and MoVIB could also displace 125 I-GVIA from fish brain membranes.
Sequencing data was transferred to Expasy Tools  50  for sequence translation and amino acid sequence prediction. Sequences from other conotoxins belonging to the O-superfamily were retrieved either from GenBank  51  or Conoserver  52  and compared with MoVIA. Sequence alignment was performed using Clustal W  53  and Jalview version 2. 8  54. Chemical Synthesis of MoVIA and MoVIB. Synthesis of MoVIA, MoVIB and analogues were performed using in situ neutralization Boc-SPPS on a Boc-Asn-PAM or Boc-Tyr-PAM resin employing HBTU/DIEA activation, respectively, as described previously  55. Assembled peptidyl-resin was cleaved with hydrogen fluoride for 1 h using p-cresol/p-thio-cresol scavenger (10%) and crude peptide precipitated from ether, filtered and lyophilized from acetonitrile/H 2 O. After HPLC clean up, pure reduced peptides (20 mg each/[0.
2 mg/mL]) were oxidized at pH 7. 8 in a solution of 0. 3 M NH 4 OAc/0. 3 M guanidine-HCl in the presence of GSH/GSSG (100:10 mol eq). Two major peptide isomers were obtained after RP-HPLC in quantities of 1-2 mg, corresponding to MoVIA and MoVIB. Mass Spectrometry. LC-ESI-MS/MS was performed on synthetic MoVIA and MoVIB samples separated on a ZORBAX 300SB-C18 (2. 1 × 100 mm × 1. 8 µm) column, eluted with a Shimadzu 30 series HPLC system at 400 μl/min, with a linear gradient from 1-80% over 25 min. The eluent was analysed on a tripleTOF 5600 mass spectrometer (ABSCIEX, MA, USA) with a quadruple TOF system and a DuoSpray ionisation system. The ion-spray voltage was set to 5300 V, with full scanning over 250 ms, followed by full scan product ion data obtained in the information dependant acquisition (IDA) mode over 20 × 50 ms.
The mass range was set to 300-4000 (m/z) for TOF MS mode and 80-4000 (m/z) for full scan TOF MS/MS mode. Buffer A was 0. 1% FA and buffer B was 90% acetonitrile/0. 1% FA. All data analysis was performed using Analyst 1. 6 (ABSCIEX). 2D NMR Spectroscopy and 3D Structure Calculations. MoVIB and analogue peptides samples were prepared at 2 mg/ml in 90% H 2 O/10% D 2 O or 100% D 2 O (pH 5. 0) for Nuclear Magnetic Resonance (NMR) spectroscopy studies. Two dimensional (2D) homonuclear 1 H-1 H total correlation spectroscopy (TOCSY), nuclear Overhauser effect (NOESY) and exclusive correlation spectroscopy (ECOSY) datasets, and a 2D heteronuclear 1 H-  13  C HSQC were recorded at 900 MHz on a Bruker Avance II spectrometer, equipped with a cryogenically cooled probe and processed using Topsin 3. 0 (Bruker).
Homonuclear data were recorded with 2048 data points in the direct dimension and 512 increments in the indirect dimension over a sweep-width of 12 ppm. The HSQC spectrum was recorded with an indirect dimension sweep-width of 106 ppm. Data analysis were performed using the Computer Aided Resonance Assignment (CARA) software  56. Structural restraints derived from the NMR data included (i) Inter-proton distances derived from NOESY cross-peak intensities in spectra recorded in either H 2 O or D 2 O with a mixing time of 100 ms. (ii) Backbone dihedral angles (Phi and Psi) derived from a TALOS+  57, 58  analysis of Cα, Cβ, Hα and HN chemical shifts. (iii) Side chain dihedral angles (χ1) derived from analysis of 3 J HαHβ coupling constants and intra residual NOE patterns (iv) Hydrogen-bond restraints derived from amide exchange rates and analysis of preliminary structures.
Nuclear Overhauser effect (NOE) cross peaks were manually picked and subsequently calibrated and assigned automatically using the automatic assignment and structure calculation module of CYANA 3. 0  59. For the final structures distance restraint lists from CYANA were used as input for simulated annealing and water minimization within CNS  60  , using protocols from the RECOORD database  61  , modified as described previously  62. In the final round 50 structures were calculated, and the best 20 based on energies and quality of packing and geometry as judged by MOLPROBITY 63 scores were chosen as representative of the solution structure of MoVIB. Figures were prepared using MOLMOL  64. Fluorimetric calcium imaging assays. For assay-guided fractionation and Ca v selectivity characterization of MoVIA, MoVIB and mutant peptides, we used a cell-based calcium-imaging fluorimetric assay and the FLIPR platform.
We performed whole-cell patch clamp recording using a MultiClamp 700B Amplifier (Molecular Devices). Data was digitalized with a Digidata 1322 A (Molecular Devices), filtered at 10 kHz and sampled at 100 kHz using pClamp 9. 2 software. External recording solution contained (in mM): 150 TEA-Cl, 2 BaCl 2 , 10 D-Glucose and 10 HEPES, adjusted to pH 7. 4 with TEA-OH. The pipette electrodes had a final resistance of (1-3 MΩ), with an intracellular solution containing (mM): 140 CsCl, 1 MgCl 2 , 5 BAPTA and 10 HEPES adjusted to pH 7. 2 with CsOH. High voltage-activated (HVA) calcium channel currents were recorded using Ba 2+ as the charge carrier and by measuring peak inward current amplitude elicited by 75 ms voltage steps to 0 mV from a holding potential of -70 mV. After current achieved steady state, we applied toxins to the physiological solution and plotted the peak inward current amplitude every 10 s.
Series resistance were typically compensated at 70-80%, and leak and capacitance currents were subtracted using a -P/4 pulse protocol. We used selective Ca v 2. 1 (P/Q-type) channel inhibitor Agatoxin-IVA (Abcam, Cambridge, United Kingdom) and CVIE, a selective Ca v 2. 2 (N-type) channel inhibitor, to examine the selectivity profile of MoVIA and MoVIB in rDRG. Radioligand Binding Assays. The cell membranes from SH-SY5Y and fish brain preparations and the radioligand binding assays using ω-peptide GVIA radiolabelled at Tyr 22 ([ 125 I]-GVIA, Perkin Elmer) were performed as described previously  26, 40. Partial Nerve Ligation-Induced Neuropathy in Rats. Animal experimentation followed the National Health and Medical Research Council Code of Practice for the Care and Use of Animals for Research in Australia, with the approval of the Royal North Shore Hospital Animal Care and Ethics Committee.
Acknowledgements We acknowledge  Markus Muttenthaler  for collecting Conus moncuri during a dive at 30 m in the Kavieng region off the Papua New Guinea coast. We thank  Manuel Tenorio  for providing a micrograph of a C. moncuri radulae to confirm vermivory. An  NHMRC Program Grant  to R. J. L. , P. F. A. , D. J. A. and M. J. C. supported this study. R. J. L. , P. F. A. and M. J. C. are supported by  NHMRC Fellowships. K. J. R. was supported by an A. R. C. Future Fellowship. S. R. S. was supported by a  University of Queensland International Research Scholarship (UQIRS).
Based on Centrifuge, a metagenomic classifier, and a custom database containing complete sequences of chromosomes and plasmids from various curated databases, PlaScope classifies contigs from an assembly according to their predicted location. Compared to other plasmid classifiers, PlasFlow and cBar, it achieves better recall (0. 87), specificity (0. 99), precision (0. 96) and accuracy (0. 98) on a dataset of 70 genomes of Escherichia coli containing plasmids. In a second part, we identified 20 of the 21 chromosomal integrations of the extended-spectrum beta-lactamase coding gene in a clinical dataset of E. coli strains. In addition, we predicted virulence gene and operon locations in agreement with the literature. We also built a database for Klebsiella and correctly assigned the location for the majority of resistance genes from a collection of 12 Klebsiella pneumoniae strains.
INTRODUCTION Recently, several studies have evaluated the effectiveness of in silico plasmid prediction tools  [1, 2]. In fact, many bioinformatics methods are now available to detect such mobile elements, with different approaches such as read coverage analysis (e. g. PlasmidSPAdes), k-mer-based classification (e. g. cBAR, PlasFlow) and replicon detection (e. g. Plasmid-Finder); some of these are fully automated  [3] [4] [5] [6] [7] , others not  [8]. Some of them achieve high sensitivity: for example, PlasmidSPAdes and cBar enable plasmid recall of 0. 82 and 0. 76 on a dataset of 42 genomes, respectively  [1]. On the other side, some tools display very high precision, for example PlasmidFinder which reaches 100 %  [1]. Unfortunately, none succeeds in finding a good trade-off between sensitivity and specificity, and thus users need to combine different methods to get correct predictions.
Concomitantly, more and more sequences are becoming available in public databases, with various levels of completeness from large sets of contigs to fully circularized genomes and plasmids. Some researchers have made an effort to curate these databases and proposed high-quality datasets. Carattoli et al. and Orlek et al. , for example, have published interesting and exhaustive plasmid datasets for Enterobacteriaceae  [4, 9]. With this in mind, we propose here a workflow, called Pla-Scope, to assess the plasmidome of genome assemblies. We took advantage of available genomic data to create custom databases of plasmids and chromosomes. These are used as input of the Centrifuge software, a tool originally developed as a metagenomics classifier and that is able to assign sequences based on exact matches against the database  [10].
We compared it with other plasmid classifiers, cBar and PlasFlow, and showed that with our specific knowledgebased approach we were able to recover nearly all plasmids of various Escherichia coli strains without compromising on specificity. Finally, the usefulness of our approach is illustrated on two datasets: (i) one composed of whole genomes of E. coli for which we have sought to identify the location of specific genes involved in virulence or antibiotic resistance, and (ii) the other made up of whole genomes of Klebsiella pneumoniae for which we focused on resistance genes and highlighted putative plasmid transmission between strains.
Workflow description The PlaScope workflow is illustrated in Fig. 1. First, users have to provide paired end fastq files. Assembly is then run using SPAdes 3. 10. 1  [11]  with the 'careful' option and automatic k-mer size selection to obtain contigs. Subsequently, Centrifuge  [10]  predicts the location of these contigs thanks to a custom database and sorts them into three classes: plasmid, chromosome and unclassified. The last includes (i) contigs shared by both categories (i. e. matching with plasmid and chromosome sequences from the database) and which are therefore indistinguishable, (ii) contigs without any hit against the database and (iii) contigs with length, hit length or coverage below the defined thresholds. Finally results are sorted based on those three classes and extracted using awk.
Centrifuge custom database construction We gathered all the complete genome sequences (chromosomes and plasmids) of E. coli from the National Center for Biotechnology Information (NCBI) web site on 10 January 2018. We also added the plasmid sequences that were used to create the PlasmidFinder database  [4]  and those proposed by Orlek et al. [9]. Finally, we added a specific dataset containing E. coli plasmids involved in antibiotic resistance  [12]. Altogether the database includes 347 chromosome and 3127 plasmid sequences (Table  S1  database available at https://doi. org/10. 5281/zenodo. 1311641). We then pooled separately plasmid and chromosome sequences to create a custom database for Centrifuge 1. 0. 3  [10]  with an artificial taxonomy containing only three nodes: 'chromosome', 'plasmid' and 'unclassified' (see README on https://github.
com/GuilhemRoyer/PlaScope). In the same way, we built a Klebsiella database. All complete genomes (326 chromosomes and 985 plasmids) of Klebsiella species were downloaded from the NCBI web site on 4 July 2018. In addition, the three plasmid databases (Plasmid-Finder, Orlek et al. and Branger et al. datasets) were included (Table  S1  database is available at https://doi. org/ 10. 5281/zenodo. 1311647).
Centrifuge classification method Centrifuge has been developed as a classifier for metagenomic reads. It identifies exact matches between the input sequences and a database originally composed of sequences from several species. It then assigns a score to each of the species that match with the reads and go through a taxonomic tree of these species to output a classification. Pla-Scope uses this software with a custom database (centrifuge -f --threads 2 -x custom_database -U example. fasta -k 1 --report-file summary. txt -S extendedresult. txt) to classify contigs as 'chromosome', 'plasmid' or 'unclassified', with the option '-k' set to 1 in order to obtain only one taxonomic assignment. Only contigs longer than 500 bp, with a Centrifuge hit longer than 100 bp and with a SPAdes contig coverage higher than 2 are classified as plasmid-or chromosome-related.
Reference dataset for method evaluation To evaluate our tool, we searched for completely finished genomes of E. coli with Illumina reads available on the NCBI database. All corresponding chromosome and plasmid sequences and Illumina short reads were downloaded from the NCBI on 10 January 2018, and converted into fastq files with fastq-dump from the sra-toolkit (fastq-dump --split-files). For evaluation purposes, these genomes were not included in the centrifuge custom database. The short reads were assembled with SPAdes 3. 10. 1  [11]  with standard parameters and 'careful' option (spades. py --careful -t 8 -1 read_1. fastq. gz -2 read_2. fastq. gz -o out-put_directory). After assembly, rapid identification of 16S rRNA sequences was performed on fasta files using ident-16s  [13]. Twelve assemblies which did not contain Escherichia 16S sequences or with multiple 16S sequences from various organisms were excluded from the subsequent analyses.
Finally, we kept 70 genomes containing 183 plasmids and seven genomes with no plasmid according to the NCBI database (Table  S2 ). We filtered the assemblies based on contig length (!500 bp) and SPAdes coverage (!2). Each assembly was then mapped against the corresponding complete chromosome and plasmid sequences from the NCBI database using Quast 4. 6 with standard parameters  [14]. Contigs that did not align on any sequence (chromosome and plasmid) or aligned on less than 50 % of their length were not considered, as well as contigs that aligned on both sequences.
PlaScope, PlasFlow and cBar benchmark The PlaScope, PlasFlow  [5]  and cBar  [7]  programs with default parameters were run on the reference dataset of 70 E. coli genomes containing plasmids. These three 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 prediction for each contig was considered as (i) true positive (TP) (plasmid assignment of a plasmidic contig), (ii) true negative (TN) (chromosome or unclassified assignment of a non-plasmidic contig), (iii) false positive (FP) (plasmid assignment of a non-plasmidic contig) or (iv) false negative (FN) (chromosome or unclassified assignment of a plasmidic contig). Detailed counts of these metrics for each assignment type are provided in Table  S3.
We then calculated recall [TP/(TP+FN)], precision [TP/(TP+FP)], specificity [TN/(FP+TN)], accuracy [(TP+TN)/(TP+TN+-FP+FN)] and F1 score [2Â(recallÂprecision)/(recall+preciision)]. 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) and is closely followed by PlasFlow (0. 85), cBar having the lowest value (0. 74) (Table  1 ). At the strain level (Fig. 2 ), recall values range from 0. 50 to 1. 00 for the three methods with the lowest median being observed with cBar (0. 76) and the highest value with PlaScope (0. 90). However, important differences were found for the other assessment criteria. Indeed, with PlaScope we obtained 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). Moreover at the strain level, the dispersion of these metrics is high for PlasFlow and cBar compared to PlaScope, in particular for precision (Fig. 2 ). Clearly, these results are easily explained by the contents of our database, which was built specifically for E. coli. Plas-Flow and cBar performed well in terms of recall, and their strength relies on their capacity to class many diverse taxonomic groups. Such methods can be particularly useful when working on metagenomes or on single genomes without any prior knowledge of the species, but when focusing on a particular species a targeted approach such as Pla-Scope drastically limits classification errors. However, 377 contigs from the 10 510 that were analysed remain unclassified with PlaScope.
Among them, 248 share hits on both chromosome and plasmid, 117 have no hit and 12 have hits shorter than 100 bp. In addition, PlaScope was run on the seven E. coli genomes with no plasmids. As expected, no plasmid was predicted for six 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 BLAST N and obtained perfect alignments with the plasmid F sequence of E. coli K-12 C3026 (GenBank accession: CP014273. 1). This result suggests that the original assembly of E. coli KLY is missing this plasmid.
In Fig. 3 , strains are classified in a neighbour-joining tree (module Phylo from biopython 1. 68  [17] ) rooted on strain ECOR70  [18]  based on genomic similarity distances computed with Mash 2. 0 (default parameters)  [19]. The tree has been annotated via the Interactive Tree Of Life  [20]. We only had a discrepancy with the two isolates of Clade E (strains RS254 and RS371). Indeed, we found a plasmid location of the CTX-M coding gene in strain RS254 whereas it was described as chromosome-related, probably because of an uncommon structure formed by the gene and its adjacent sequences. For the second strain, RS371, the location of the CTX-M coding gene is predicted as unclassified (i. e. hits on both chromosome and plasmid reference sequences) whereas it was stated as plasmid-located. Indeed, BLAST N alignment of the contig carrying this resistance gene against the GenBank database gave perfect hits on plasmid (e.
Klebsiella pneumoniae We applied PlaScope with the Klebsiella custom database on a dataset of 12 K. pneumoniae strains recovered from a patient and his hospital room environment  [30]. Plasmid-Finder and ResFinder were then used to identify replicon sequences and resistance genes, respectively. Among the 12 strains, the authors originally described (i) four related strains with one plasmid and no associated resistance genes, (ii) seven extensively drug-resistant (XDR) strains with many plasmids bearing resistance genes and particularly blaOXA-181 (a carbapenemase-coding gene) and (iii) a strain close to the four non-XDR strains but with the plasmid carrying blaOXA-181. Using PlaScope, we were able to find the correct location of several genes on chromosomes (blaSHV-11, oqxA, oqxB, fosA) and plasmids (APH(3 †)Ib, APH(6)Id, blaOXA-181, blaTEM-1B, catA2) (Table  S4 ).
Conclusion Here, we propose a workflow, called PlaScope, for plasmid and chromosome classification from genome assemblies at the species level. It is based on the assembler SPAdes  [11] , and Centrifuge  [10] , a fast metagenomic classifier that uses exact matches between input sequences and a small-sized database to sort these sequences. PlaScope offers 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 software packages also enable the segregation of plasmid and chromosome contigs.