dataesr/scientific-paragraphs-categorization huggingface dataset:
This is in agreement with our previous studies of substitution effects on halogen bonds  59, 60  and the magnitude of charge transfer follows these trends. The halogen bonds included in the X40 dataset represent the common interaction motifs that have relatively small interaction energies. However, in special cases, halogen bonds can be much stronger and it is necessary to study the role of charge transfer also in these systems. One such halogen bond can be obtained by combining the best electron donor from the X40 set, trimethylamine, with the best acceptor, CF 3 I. This results in a smaller DE HOMO-LUMO gap of 0. 42 a. u. and DE CT of À2. 87 kcal mol À1 , almost 50% more than in the strongest halogen bond in the X40 set. The CCSD(T)/CBS interaction energy in this complex is À8. 57 kcal mol À1.
It must be, however, noted that the DFT-SAPT becomes rather inaccurate in these very strong halogen bonds and it overestimates the interaction energies by 25 and 37% in the complexes of CF 3 I and I 2 with trimethylamine. This is because the intermolecular interaction is no longer a small perturbation and the approximations in the method are not sufficient to describe it. Nevertheless, this accuracy is still sufficient to make valid conclusions. When the DE CT in these two complexes is compared to the sum of the attractive terms from DFT-SAPT, it makes only 10 and 13% of it. This is not much more than in the analogous complex of iodobenzene from the X40 dataset, where the contribution of CT is 7%. It is thus clear that even in the very strong halogen bonds, charge transfer does not play any special role, because all the other attractive terms also increase as the intermolecular distance shortens.
Charge transfer is indeed involved in halogen bonding, but its energetic contribution is, in most cases, relatively small. In the set of 14 halogen bonds from the X40 dataset, chargetransfer energy represents on average 10% of the interaction energy. For comparison, charge transfer contributes to hydrogen bonding with 25% in the X40 set and 13% in organic molecules from the S66 set. However, in very strong halogen bonds where there is a good match between the electron donor and the accepting halogen moiety, the charge transfer can become a much larger part of the interaction energy. The DFT-SAPT calculations have made it possible to put the charge-transfer energy into the context of other components of the interaction energy. Putting the Pauli repulsion (approximated by the first-order exchange from DFT-SAPT) aside, we have decomposed the remaining attractive interaction into first-order electrostatics, induction, charge transfer and dispersion.
In all the groups of interactions in the X40 dataset, charge-transfer energy is the smallest component, followed by induction energy. All the interactions are dominated by electrostatics and dispersion, the ratio of which depends on the nature of the interaction. In halogen bonds, these contributions are of very similar magnitude, with electrostatics being slightly stronger. In this analysis, charge transfer in halogen bonds becomes even less significant as it contributes to the total attractive interaction with only 3%. Two main trends can be observed among the halogen bonds studied: first, the charge-transfer energy always increases in the series of Cl, Br and I analogs, and this effect cannot be explained by donor/acceptor frontier orbital energies. Since the other attractive terms (mainly electrostatics and dispersion) dominate the interaction energy, which also increases in this series, the increase in charge transfer is attributable to the shortening of the halogen-bond length (relative to the van der Waals contact distance), which accompanies the increase of interaction energy.
Temperature measurements showed that this procedure does not affect media temperature in the microfluidic device since media reached room temperature before entering the device (data not shown). The H 2 O 2 concentration was measured using a colometric H 2 O 2 assay kit (OxiSelect Hydrogen Peroxide Assay Kit (Colorimetric), STA-343, EUROMEDEX, France) in media samples taken from the outlet of the chip. Dosage experiments were run in triplicates.
Image analysis Raw images were processed using custom software, called phyloCell, based on MATLAB and the image-processing toolbox  (Fehrmann et al. , 2013; Paoletti et al. , 2016). This software features a comprehensive graphical user interface to perform segmentation/tracking and to introduce manual error corrections. The software is available for download on GitHub  (Charvin, 2017). A copy is archived on https://github. com/elifesciences-publications/phyloCell. In this study, the software was used to segment cell contours based on phase-contrast images; to track cells over time; and to measure the fluorescence within the cells, including nuclear localization of fusion proteins.
Acknowledgements We are grateful to  Zhou Xu ,  Sophie Quintin ,  Nacho Molina ,  Robert Schneider  and  Julien Vermot  for careful reading of the manuscript. This work was supported by the  French Association pour la Recherche contre le Cancer (YG) , the  ATIP-Avenir program  (GC), and a grant from the  Fondation pour la Recherche Medicale (GC and MBT). This study was supported by the grant  ANR-10-LABX-0030-INRT , a  French State  fund managed by the  Agence Nationale de la Recherche  under the  frame program Investissements d'Avenir   ANR-10-IDEX-0002-02.
Ensemble NEMO parallelization The standard NEMO code is parallelized with MPI (message-passing interface) using a domain decomposition method. The model grid is divided in rectangular subdomains (i = 1,. , n), so that the computations associated with each subdomain can be performed by a different processor of the computer. Spatial finite-difference operators require knowledge of the neighbouring grid points, so that the subdomains must overlap to allow the application of these operators on the discretized model field. Whenever needed, the overlapping regions of each subdomain must be updated using the computations made for the neighbouring subdomains. The NEMO code provides standard routines to perform this update. These routines use MPI to get the missing information from the other processors of the computer.
, 2003; Brankart et al. , 2012)  or a particle filtering approach (e. g. Van Leeuwen, 2009 ) may be needed. One of the purposes of the SANGOMA European project is precisely to develop such more general methods for ocean applications, and to implement them within NEMO-based ocean data assimilation systems (e. g. Candille et al. , 2015). In these methods, the role of ensemble NEMO simulations is even more important since they require a more detailed description of the probability distributions (compared to the Gaussian assumption, which only requires the mean and the covariance). The importance of ensemble simulations in data assimilation certainly explains why the ensemble NEMO parallelization (introduced above) has been first applied within SANGOMA, to assimilate altimetric observations in an eddying NEMO configuration of the North Atlantic  (Candille et al.
A popular measure of these two attributes is, for instance, provided by the continuous rank probability score (CRPS), which is based on the square difference between a cumulative distribution function (CDF) as provided by the ensemble simulation and the corresponding CDF of the observations  (Candille and Talagrand, 2005). In OCCIPUT, such probabilistic scores will be computed from real observations and from the ensemble synthetic observations (along-track Jason-2 altimeter data and ENACT-ENSEMBLES temperature and salinity profile data) generated online using the existing NEMO observation operator (NEMO-OBS module). NEMO-OBS is used exactly as in standard NEMO within each member of the ensemble, thereby providing an ensemble of model equivalents for each observation rather than a single value.
Regional ensemble Global ensemble Ensemble name E-NATL025 E-ORCA025 Spatial domain North Atlantic (21 red line in Fig. 3 ) so that 50 × 128 = 6400 cores are used for the ensemble NEMO system. In order to optimize and to make the I/O data flux management flexible, 40 XIOS servers have been run as independent MPI tasks in detached mode, allowing the overlap of I/O operations with computations. Compared to the 10member regional case, the 50-member global case required a larger XIOS buffer size. For this reason, each of the 40 XIOS instances was run on a dedicated and exclusive Curie-TN, allowing each server to use the entire memory available on each 16-core node (i. e. 64 GB); the 40 XIOS servers thus used 16 × 40 = 640 cores in total. The integration of the 50member global E-ORCA025 ensemble therefore required the use of 6400 + (40 × 16) = 7040 cores.
XIOS makes use of parallel file system capabilities via the Netcdf4-HDF5 format, which allows both online data compression and parallel I/O. Therefore, XIOS is used in "multiple file" mode where each XIOS instance writes a file for one stripe of the global domain, yielding 40 files times 50 members for each variable and each time. At the end of each job, the 40 stripes are recombined on-the-fly into global files. Preliminary tests have shown that the 50-member E-ORCA025 global configuration performs about 20 steps min -1 , including actual I/O fluxes and additional operations (e. g. production of ensemble synthetic observations). Since the numerical stability of this global setup requires a model time step of 720 s, about 2 million time steps, 85 days of elapse time, and about 14. 4 million core hours were needed in theory to perform the 56-year OCCIPUT ensemble simulation.
The final E-ORCA025 global database is saved in Netcdf4-HDF5 format (chunked and compressed, compression ratio in italics below). The primary dataset produced by the model consists of the following: monthly averages for fully 3-D fields (56 years × 12 months × 50 members × 2. 8 GB × 41. 5 % = 39 TB), 5-day averages for 16 2-D fields (56 years × 50 members × 6. 8 GB × 30 % = 6 TB), the Jason-2 and ENACT-ENSEMBLES ensemble synthetic observations (5 TB), and hourly ensemble statistics for key variables (1 TB). One restart file per member and per year is also archived (about 35 TB after compression). We then computed a secondary dataset, consisting in 50-member yearly/decadal averages of the 3-D-fields (2 TB), ensemble deciles of monthly/yearly/decadal 3-D-fields (6 TB), and data associated with on-line monitoring (1 TB).
Acknowledgements. This work is mainly a contribution to the  OCCIPUT  project, which is supported by the  Agence Nationale de la Recherche (ANR)  through contract  ANR-13-BS06-0007-01. We acknowledge that the results of this research have been achieved using the  PRACE Research Infrastructure resource Curie based in France at TGCC. The support of the TGCC-CCRT hotline from  CEA, France , to the technical work is gratefully acknowledged. Some of the computations presented in this study were performed at TGCC under allocations granted by  GENCI. This work also benefited from many interactions with the DRAKKAR ocean-modelling consortium, with the SANGOMA and CHAOCEAN projects. DRAKKAR is the  International Coordination Network (GDRI)  established between the  Centre National de la Recherche Scientifique (CNRS) , the  National Oceanography Centre in Southampton (NOCS) ,  GEOMAR in Kiel , and  IFREMER.
SANGOMA is funded by the  European Community 's  Seventh Framework Programme   FP7/2007-2013  under grant agreement  283580. CHAOCEAN is funded by the  Centre National d'études Spatiales (CNES)  through the  Ocean Surface Topography Science Team (OST/ST). The authors are grateful for useful comments from three anonymous reviewers; they also thank the  NEMO System Team  and  Yann Meurdesoif  for interesting discussions about the development of the probabilistic version of NEMO. Laurent Bessières  and  Stéphanie Leroux  are supported by  ANR. Jean-Michel Brankart ,  Jean-Marc Molines ,  Pierre-Antoine Bouttier ,  Thierry Penduff , and  Bernard Barnier  are supported by  CNRS. Marie-Pierre Moine  and  Laurent Terray  are supported by  CERFACS , and  Guillaume Sérazin  by  CNES  and  Région Midi-Pyrénées.
All isolates with gene combinations characteristic for USA300-NAE and USA300-SAE, such as ACME, COMER, and cap5E mutations, were exclusively found in these two clades, confirming the exclusivity of these genetic combinations within the ST8 lineage (Fig. 1  and Dataset S1). However, partial elements of ACME and COMER were also found in isolates from other phylogenetic clades, albeit in different allelic variants. Nevertheless, we did not find evidence for successful spread of those clones possessing single genes of ACME or COMER. This indicates that, if these elements indeed confer an evolutionary advantage, as it was suggested in previous studies  (15, 41) , it is presumably the specific combination of genes or regulatory mechanisms that facilitate the success of USA300. According to our results, USA300-NAE acquired ACME, SCCmec IVa, and the cap5E mutation Asp75Tyr in the late 1980s (median 1989, HPD 95%, 1987 to 1991) (Fig.
2 ) while USA300-SAE acquired SCCmec IVc and the COMER element some years earlier (median 1980, HPD 95%, 1977 to 1983) (Fig. S1 ). Our Results Suggest a Single Introduction of the PVL-Phage ϕSa2USA and Various Introductions of Different SCCmec Elements to the ST8 Background. First, we determined the sequences of the PVLencoding genes lukF-PV and lukS-PV, which were identical among all isolates (n = 120), except for one nonsynonymous SNP (Tyr208Cys) in lukF-PV in one Swiss isolate (Switzerland_2013_2). Reference mapping was performed for all isolates with available read data (66 of 120 isolates). Considering the results of both assembly methods, ϕSa2USA (5,189 bp, GenBank accession no. KX130855. 1) and its 300 nucleotides 3′ and 5′ flanking regions were identified in all 120 samples. The exact reference sequence was identified in 95 samples, 19 samples possessed allelic variants with one to four point mutations, and five samples possessed allelic variants with frameshift mutations.
Specifically, except for SCCmec IVa and IVc, we also identified six SCCmec IVb isolates from the United States (n = 5) and Austria (n = 1), two SCCmec IVd isolates from Australia (n = 1) and the United States (n = 1), eight SCCmec IVg isolates from the United States (n = 5) and Gabon (n = 3), two SCCmec IVi isolates from the United States, and one SCCmec V isolate from Australia (Dataset S1). African USA300 and ST8 Isolates Are Unique. All 10 African isolates previously described as USA300-like (ST8-MRSA, PVL-positive, ACME-positive, spa types t112 or t121)  (17, (33) (34) (35) (36) (37) (38)  were found within the phylogenetic clade of USA300-NAE and could consequently be confirmed to be "true" USA300-NAE isolates, despite their non-t008 spa-types (Figs. 1  and 2 ). Noteworthy, nine of these isolates, collected in Gabon (n = 6), Cameroon (n = 2), and Ghana (n = 1), formed one distinct monophyletic "African USA300" clade, indicating a single introduction event to the African continent, followed by spread in the local population.
The most recent isolate was collected in 2013; the MRCA of all isolates was dated to 1854 (HPD 95%, 1841 to 1865). MSSA, methicillinsusceptible S. aureus; PVL + , Panton-Valentine leukocidin positive; USA300-NAE, North American Epidemic USA300 clone; USA300-SAE, South American Epidemic USA300 clone. Detailed divergence timings and countries for each node are displayed in Fig. S1. 2 ). However, North America does not appear to be the origin of the entire ST8 clone. Instead, our results suggest an emergence of ST8 in Europe during the mid-19th century (Denmark, 93. 2%; median 1854, HPD 95%, 1841 to 1865). Around 1900 (median 1900, HPD 95%, 1892 to 1907), the shortened cap5D allele (frameshift mutation in nucleotide position 994) (14) (Dataset S2) arose and spread in the European ST8 population.
Discussion The results and findings presented here shed light on the evolution of S. aureus ST8 and USA300. Our findings show that direct evolution of USA300 from USA500 is unlikely because their last common ancestor circulated in Europe around 100 y ago (Fig. 2 ). This corroborates previous findings  (16, 27, 43). Similarly, direct evolution from ST8 CA-MRSA isolated from indigenous people in Western Australia, or PVL-positive MSSA from Trinidad and Tobago or Western Africa, was not supported by our data (Figs. 2  and 3  and Fig. S1 ). Instead, the most likely origin of USA300-NAE and USA300-SAE was calculated to be PVL-positive MSSA circulating within the United States. The respective PVL-negative ST8 ancestor appears to have been imported from Europe in the early 20th century during an era of increased emigration of people from Europe to the United States due to war, economic crisis, and political persecution (Fig.
Materials and Methods Selection of Bacterial Isolates. Our dataset includes a diverse subset of all S. aureus ST8 WGS sequences available in public databases until November 2015, without including all available USA300 genomes from the United States. It was completed by in-house Illumina shotgun-sequenced published isolates from diverse countries of sub-Saharan Africa, Western Australia, Trinidad and Tobago, and diverse European countries. Our isolates represent the previously reported diversity of ST8  (49). Even upon request, no ST8 isolates from Asian countries were available. In total, 224 human ST8 S. aureus from the United States (n = 57), South America (n = 28), Australia (n = 14), Europe (n = 43), the Caribbean (n = 18), and Africa (n = 64) were analyzed by whole genome sequencing (WGS) (Table  2 ).
aureus TCH1516 ST8 reference genome (GenBank accession no. CP000730) using the short-read alignment component of the Burrows-Wheeler Aligner (BWA) after removal of duplicated regions identified using NUCmer. Additionally, 103 fasta files containing contigs of the remaining isolates were aligned using MUMmer/Nucmer. Each alignment was analyzed for single nucleotide polymorphisms (SNPs) using NASP (https://github. com/TGenNorth/NASP). If either the minimum coverage of 10 was not met, or if the variant was present in <90% of the base calls, the respective SNPs were excluded. Indications of putative horizontal gene transfer events were defined as a unique SNPs cluster (≥3) in the NASP output and purged from the alignment. The concatenated sequence of SNPs can be found in Dataset S4. Based upon the purged SNP alignment, a phylogenetic tree was constructed using the maximum-likelihood algorithm implemented in PhyML  (61)  with Smart Model Selection (SMS) and the Bayesian Information Criterion (BIC) with 100 bootstrap replicates.
For rooting, the chromosome of S. aureus isolates MW2 (ST1) (GenBank accession no. BA000033) was used as it represents a closely related outgroup to ST8 (62) (Fig. S2 ). The ML phylogeny, including each sample's association with USA300characteristic genes, was visualized using itol. embl. de/. Ancestral Dating and Phylogeographic Analysis. To explore the timing of diversification events, we used coalescence-based analyses to investigate evolutionary rates and timing of common ancestors using duplicate runs in BEAST v1. 8. 2 (63) at CIPRES Science Gateway (64,) (https://www. phylo. org). We compared the results of different models, including the general-time reversible (GTR) and the Hasegawa-Kishino-Yano (HKY) substitution models, under the assumption of both strict and relaxed molecular clock and different demographic models: i.
e. , Bayesian skyline, constant population, and exponentially growing population tree priors. For each analysis, 200 million steps were performed, and the chain was sampled every 20,000th step. The first 10% of each chain was discarded as burn-in. A Bayesian analysis was also conducted to determine the most likely geographical origin of the most recent common ancestors (MRCAs) using an HKY, strict clock, constant population size model with uniform clock rate ([0,1], initial = 0. 001) and a Markov chain Monte Carlo (MCMC) length of 120 million steps, with chain sampling every 10,000th step and burn-in at 10%. Taxa groups were defined for monophyletic groups of interest: e. g. , USA300-NAE, USA300-SAE, or basal-African based on the ML data. Detailed parameters are provided in Dataset S5. Again, the analysis was run in BEAST v1.
8. 2 at CIPRES Science Gateway. The geographic origin of the entire dataset based on the. root BEAST output file was determined according to the instructions provided at bedford. io/projects/dynamics-practical/examinethe-phylogeographic-output. html (accessed April 2016). The maximum clade credibility (mcc) tree was calculated with TreeAnnotator v. 1. 8. 2 and used for graphical representation of the data. A. kml file to visualize the phylogeographic spread using Google Earth was created from the mcc tree using the phylogeo. jar script developed by Trevor Bedford (https://github. com/trvrb/ dynamics-practical/tree/master/scripts, accessed April 2016) (Dataset S6). Identification of Genetic Markers. We searched for the presence or absence of genes and unique nonsynonymous SNPs that might be associated with the evolutionary success of the two USA300 lineages (USA300-NAE and USA300-SAE) and the African USA300 clade.
Therefore, we screened the genomes of all USA300-NAE isolates present in our dataset (n = 74) and the genomes of two closely related ST8 outgroup isolates (USA_2009_11 and USA_2009_12) for the presence of USA300-NAE core genome genes, including all single copy genes (genes with fragments that occur in multiple copies with an identity of ≥90% and >100-bp overlap were excluded) with a minimal length of 50 bp of NCBI RefSeq USA300 genome FPR3757 (NC_007793. 1; 2,804 targets in total) (Dataset S7). Similarly, we screened the genomes of all USA300-SAE isolates of our dataset (n = 35) and the two outgroup strains for the presence of USA300-SAEspecific core genome genes derived from strain M121 (CP007670. 1; 2,550 targets in total) (Dataset S7). Moreover, to identify the absence of genes in USA300, we screened both USA300-NAE and USA300-SAE isolates for the presence of core genome genes of one of the outgroup strains (strain C1768, GenBank accession no.
LAMT01000000, syn. USA_2009_11; 2,678 targets in total) (Dataset S7). Candidate genes for USA300-specific yet unknown genetic markers were identified by either being present in all USA300-NAE or USA300-SAE isolates from our collection and absent in the outgroup, or being absent in all USA300-NAE or USA300-SAE isolates and present in both outgroup strains. The resulting candidate genes were subsequently queried against all other ST8 sequences of our dataset (n = 113). For the African USA300 clade, we performed the same procedures using isolate Gabon_2009_1 (this study, 2,643 targets in total) (Dataset S7) as representative for the African USA300 clade. The analyses were conducted with the aid of the SeqSphere + software.
(57,58)nonsynonymous SNPs within the 15 African USA300 clade isolates Whole Genome Sequencing and Genome Analysis. Available WGS data from previous studies (n = 102) were retrieved in the form of assembled sequences (contigs) from NCBI GenBank or as reads from the NCBI Sequence Read Archive (SRA) or European Nucleotide Archive (ENA). They were included into our dataset provided they passed our internal WGS quality controls: i. e. , confirmed affiliation to ST8 by in silico MLST and identification of ≥95% of 1,861 core genome MLST (cgMLST) targets(54). The remaining isolates (n = 122) were sequenced using Illumina technology and Nextera XT version 2 chemistry, either with 150-bp or 250-bp paired-end protocols on a NextSeq 500 or MiSeq sequencer (Illumina). All newly generated reads were deposited at ENA under study accession no.