dataesr/scientific-paragraphs-categorization huggingface dataset:
3-0. 4 Hz  (Laurendeau et al. , 2017). In April 2016, an Mw 7. 8 earthquake occurred on the subduction interface in the Pedernales area. This event was the first large earthquake in the city of Quito to be well recorded by 13 stations of the permanent accelerometric network  (RENAC). In this study, we take advantage of this dataset (mainshock and large aftershock recordings) to (1) test an empirical Green's function blind simulation approach where the input stress drop is taken from a global catalog of source time functions, (2) compare the synthetic accelerograms and ground motion values we obtain for an Mw 7. 8 earthquake with the actual recordings of the Pedernales earthquake, and then (3) simulate larger earthquakes of Mw 8. 2 and Mw 8. 5 from the subduction zone. For Mw 7. 8 simulations, our approach allows a good reproduction of the ground motions in the whole frequency bands and properly takes into account site effects.
In order to take into account site effects in Quito and to reduce the variability due to numerous input parameters, we use a well-constrained empirical Green's function summation method. In this method, the recordings of relatively small earthquakes are used to account for path and site effects in a realistic way. The main input parameter to constrain, the stress drop of the target earthquake, is chosen from the Source Time Function (STF) duration of the global SCARDEC database  (Vallée et al. 2011; Vallée 2013; Vallée & Douet, 2016). The method is presented in chapter 2 and the data used in chapter 3. In a first step, we simulate the ground motions in Quito generated by several Mw 7. 8 earthquakes from the subduction zone (with variable source time function duration). We locate the target earthquakes at the place where the 2016 Pedernales event occurred, in order to be able to compare the simulation results with actual recordings of the Pedernales earthquake.
Stress drop variability constrained by a global STF database The stress drop ratio between the large and the small earthquake can be expressed as C=Mo. Fc 3 /mo. fc 3. C strongly influences the level of acceleration obtained at the surface as shown by  Honoré et al. (2011)  and analyzed by  Wang et al. (2019). It is then important to correctly estimate its value and variability. For a given EGF, C directly depends on the value of Fc, which is also directly linked, through a rupture model, to the duration T of the STF. We propose to determine the mean value and the variability of T by using the SCARDEC global database of source time functions  (Vallée 2011 (Vallée , 2013;; Vallée & Douet, 2016). SCARDEC is a consistent database of source parameters (seismic moment, focal mechanism and STF) of earthquakes of magnitude larger than 5.
8 since 1992. Courboulex et al. (2016)  analyzed the SCARDEC database and showed that the duration of the STF is a log-normally distributed quantity for a given magnitude. The log of its value can then be represented by a Gaussian curve with a mean value and a standard deviation that represents its variability. For a given value of Mw,  Courboulex et al. (2016)  and  Chounet & Vallée (2018)  found that the STF duration is larger for earthquakes in the subduction zone than outside (Figure  2 ). A simple regression makes it possible to obtain the following mean duration T for earthquakes in and outside the subduction zone  (Courboulex et al. 2016) : O R I G I N A L U N E D I T E D M A N U S C R I P T log10 (T) = 0. 2867 log10 (Mo) -4. 3205 (SUB) (2) log10 (T) = 0. 3101log10 (Mo) -4. 9032 (NOT-SUB) (3) The standard deviation (hereafter called sigma) values are 0.
32 for subduction events and 0. 34 for others in natural log (respectively 0. 138, 0. 147 in log10), without any dependence on the magnitude. These values are smaller than the variability usually reported for stress drop values in global databases  (Cotton et al. 2013; Courboulex et al. 2016 ). We use these results (equations (2) for subduction zone and corresponding sigma value) in our procedure (Figure  3 ) to build a large number of eSTFs that account for the natural variability of the rupture processes. The details of the procedure are given in the Annex section and the corresponding python code is available (see data availability). The eSTFs are then convolved with the EGFs to obtain a large number of synthetic seismograms at each station and each component. From those, we compute PGA, Fourier and response spectra.
The 2016 Mw 7. 8 Pedernales earthquake The largest recent earthquake in Ecuador (Pedernales earthquake, April 16, 2016, Mw 7. 8) caused extensive damage to cities on the coast and the death of 700 persons. This event occurred on the subduction interface at shallow depth on a part of the fault that already broke during the 1942 earthquake. The rupture area has been well identified thanks to seismologic, geodetic and tsunami data  (Nocquet et al. 2016; Ye et al. 2016). It began at the north and then propagated almost unilaterally towards the south with an area of higher slip located 70 km south of the hypocenter  (Nocquet et al. , 2016). Many studies have been undertaken on this earthquake in order to localize the aftershocks  (Agurto-Detzel et al. , 2019; Meltzer et al. , 2019) , specify the structure  (León-Ríos et al.
We see that, for stations CIRC and 24MA, the AB2016 prediction fits rather well the actual Pedernales data and our simulations. For stations ALLO and LILI, which are prone to site effects, we see that our simulations better fit the Pedernales observations than AB2016. This is not surprising as it well known that GMMs may not adequately capture site amplification effects. The results obtained at each station are now represented on Figure  9  for three selected periods: T = 0. 1 s (equivalent to the PGA at 10 Hz), T = 0. 6 s, the period that gives the highest value of the response spectrum on most stations, and the period T = 2. 8 s, which corresponds to the second peak visible on LILI (and the other stations in the southern part of the basin). For comparison, we also plot the lines that correspond to the mean and mean +/-sigma values of AB2016.
The distances used for the equation and indicated in figure  9  are the closest distance to the rupture. First, in Figure  9  we observe For a period T = 2. 8 s, the recorded values of the Pedernales earthquake at four stations in the south QUIB, ZALD LILI and VILF are significantly higher than the value predicted by AB2016 (and it is not the Vs30 value that can change anything at this frequency). Our simulations reproduce this lowfrequency remarkably well. In general, we always obtain an excellent agreement, in a large frequency band, between the results of our simulations and the actual data from the Pedernales earthquake whatever the chosen EGF. These simulations better fit the shape of the Pedernales earthquake than AB2016, especially for the stations situated in the southern part of the Quito basin.
Indeed, earthquakes with a magnitude greater than 8. 5 being rare in the SCARDEC database, it is rather difficult to constrain the mean duration of their STF. Moreover, our EGF method would be difficult to apply to a very large fault, since we do not have any recordings of earthquakes so far north that could be used as EGFs. We choose to present the results of Mw 8. 2 (lowest magnitude estimated for the 1906 earthquake) and Mw 8. 5 (upper limit for the method and the source-city situation) earthquakes that would occur on the subduction interface (see dashed zones on Figure  1 ). Because the impact of the choice of the EGF is rather low, we only present the results obtained with EGF-N1 and EGF-S1. In the first scenario (southscenario), the main slip zone is located in the southern part of the Pedernales rupture area (southern star of Figure  1 ), and then EGF-S1 is selected.
We propose an original way to constrain the input stress drop using an equation giving the STF duration (mean and variability) as a function of the seismic moment from a global STF database, SCARDEC  (Courboulex et al. 2016; Vallée & Douet 2016). These values are used as input for the empirical Green's function summation method and make it possible to obtain a large number of synthetic seismograms O R I G I N A L U N E D I T E D M A N U S C R I P T representative of different source scenarios. This global approach is supported by the relative simplicity of the shapes of the source time functions at different magnitude scales, at least in the subduction zones  (Meier et al. 2019). The method we use is simple and quick to compute. The only part that needs to be undertaken with great care is the selection and analysis of small events used as EGFs.
Conclusion We propose a simple approach where the input variability of the source time function is taken from a global database and used in a stochastic empirical Green's function method. With this method we estimate the ground motion that could be generated in Quito by large subduction earthquakes. Our results show that the high frequencies generated by such earthquakes should be moderate but that, especially in the southern part of the city, we expect large amplitudes at low frequencies (around 0. 3 Hz). We found that the building code of Quito is well adapted to resist to such earthquakes. It is therefore crucial that future constructions, especially tall buildings in the south of the city, strictly follow the imposed code in order not to suffer serious damage. However, the results obtained with this simple approach need to be completed by a large number of extended fault simulations that properly take into account the complexity of large earthquakes in subduction zones.
Acknowledgments: We deeply thank the persons in charge of the seismic network and more generally all the researchers, engineers and students working at the  IG-EPN of Quito. We thank  Diego Mercerat  and  Daniel Pacheco  for all the work carried out together on the Quito basin and fruitful discussions on the seismic hazard there. We thank  Allie Hutchinson  and  Anne-Marie Duval  for their readings and comments,  Alain Dujardin  and  Laetitia Honoré  for the previous work on the method. Thanks to  Mario Ordaz , two anonymous reviewers and the associate editor  Victor Cruz  for helping us to strongly improve this manuscript. This work was supported by the  ANR-15-CE04-0004   REMAKE  project, the  UCA/JEDI   ANR-15-IDEX-01  project, the  Geophysical Institute in Quito , the CEREMA and the  Géoazur laboratory.
Data Availability: The source time functions used to calibrate the input parameters can be found on http://scardec. projects. sismo. ipgp. fr/ last access 20/10/2021. The Python code used to build the aSTF can be found at https://gitlab. com/dac_/empirical-greenfunction, last access: 20/10/2021. Seismological data are available on request at https://www. igepn. edu. ec/eq20160416-data (Singaucho et al. 2016 ).
O Figure 5 (top)  shows the log-normal distribution of the duration of 500 eSTF. In a few cases, the source duration is very short (22 seconds), and in a few cases it is very long (80 seconds); most of the eSTFs have a duration between 30 and 60 seconds, following what is obtained on the STF SCARDEC database (Courboulex et al, 2016)  for an Mw 7. 8 also show on Figure5the corresponding stress drop values using the relation Δσ = 7/16 Mo Fc 3 / (k Vs) 3 , with k = 0. 23 and Vs = 3900 m/s. Vs is the shear wave velocity near the source and k a parameter introduced by Madariaga (1976)  that depends on the rupture model and the type of wave. Fc is taken equal to 0. 8/T (T being the STF duration). It is important to remember that absolute values of the stress drop strongly depend on the parameters used in the equation and should not be further interpreted (see figure3of Courboulex et al.
Cells and Infections Vero E6 (ATCC, Vero C1008, clone E6, Manassas, VA, USA) and Huh-7. 5 (Lonza, Cologne, Germany) cells were cultivated in Dulbecco's modified Eagle's medium (DMEM, PanBiotech, Aidenbach, Germany) supplemented with 100 U/mL of Pen/Strep (Gibco, Thermo Fisher Scientific, Paisley, UK) and 5% or 10% of fetal bovine serum (FBS, Gibco, Thermo Fisher Scientific), respectively. All experiments were performed with NiV (Malaysia strain, accession number MK673562. 1), EBOV (Mayinga strain, accession number KR063671. 1), and JUNV (Junín XJ strain, accession number JF799981. 1 and JF799977. 1), under BSL-4 conditions, in the Bernhard Nocht Institute for Tropical Medicine (Hamburg, Germany). All infections were conducted at an MOI of 0. 1. Briefly, 5 × 10 4 Vero E6 or Huh-7. 5 were seeded on glass coverslips in 24-well plates, and the following day, were infected for one hour at 37 • C, before removal of the inoculum and addition of DMEM supplemented with 2.
Quantification of NiV Ordered Structures Size Sizes of the organized structures observed for NiV were estimated by using standard tools and plugins provided in ImageJ (v. 1. 51) software. Briefly, the Straight Line tool in FIJI was used to define the length of one side of NiV ordered structures and subsequently, these lines were added as regions of interest (ROIs) to the ROI Manager. Once one side of each ordered structure was traced, its length was obtained by using the command "measure" and choosing "shape descriptors" as output. The length of one side per ordered structure was determined in 9 fields for Ac:MeOH fixation and PFA fixated samples, respectively, in three independent experiments. Size distribution was then analyzed with GraphPad Prism version 8. 0. 1 for Windows (San Diego, CA, USA).
Protein In Silico Analysis The amino acid sequences of NiV (AAX51852. 1), EBOV (AAD14590. 1), and JUNV (ACS12871. 1) nucleoproteins and NiV matrix protein (NP_112025. 1) were analyzed with ProtParam (https://web. expasy. org/protparam/, accessed on 8 December 2021) software from Expasy to determine the percentage of hydrophobic amino acids contained in their sequences  [21]. In order to estimate their solubility index, Protein-Sol (https://protein-sol. manchester. ac. uk, accessed on 8 December 2021) software from Expasy was used  [22]. The CamSol method from The Chemistry of Health Software (https://www-cohsoftware. ch. cam. ac. uk/index. php/camsolintrinsic, accessed on 29 December 2021) was used for the calculation of the intrinsic solubility profile and the score of each analyzed protein  [22].
Monomeric 3D model structures of NiV N, NiV P, and NiV M were calculated with Alphafold software (https://alphafold. ebi. ac. uk, accessed on 8 December 2021,  [23, 24] ) using the associated pro-tein sequences with the accession numbers AY858110. 1, HM545087. 1, and NP_112025. 1, respectively. The Swiss-Model server (https://swissmodel. expasy. org/interactive, accessed on 8 December 2021) was used for modeling of the 3D structures of the multimeric forms of NiV N, NiV P, and NiV M. Among the 8 templates available for NiV N in the Protein Data Bank (PDB; www. rcsb. org, accessed on 8 December 2021), the selected template, PDB 7NT5 obtained by cryoEM at 3. 5 Å resolution, showed an identity of 98. 68%, coverage of 1. 00, QMEAN: 0. 81 ± 0. 05, global model quality estimation (GMQE) of 0. 76, and quaternary structure quality estimation (QSQE) of 0.
78. For structural modeling of NiV P, 28 templates are available in the PDB. The selected template 4GJW was obtained by X-ray crystallography at 3. 0 Å resolution; it showed an identity of 99. 08%, coverage of 0. 15, QMEAN: 0. 72 ± 0. 05, GMQE: 0. 06, and QSQE: 0. 40. Among the 27 templates available for NiV M, the selected sample 6BK6 was obtained with X-ray crystallography at 2. 5Å resolution, showing an identity of 91. 81%, coverage of 0. 97, QMEAN: 0. 86 ± 0. 05, GMQE: 0. 84, and QSQE: 0. 82. Pymol software was used to generate 3D protein structure images and videos  [25].
The percentage of hydrophobic amino acids was shown to be higher in NiV N and NiV M than in the other viral proteins, resulting in a predicted scaled solubility lower that the average of proteins. This is calculated with Protein-Sol software, where the population average for the dataset is 0. 45. Thus, proteins with values greater than 0. 45 are predicted to have a higher solubility (and less aggregation probability) than the average proteins, and proteins with values lower than 0. 45 are predicted to be less soluble and therefore, are more prone to form crystals. Consequently, the predicted scaled solubility of NiV N.
The percentage of hydrophobic amino acids was shown to be higher in NiV N and NiV M than in the other viral proteins, resulting in a predicted scaled solubility lower that the average of proteins. This is calculated with Protein-Sol software, where the population average for the dataset 0. 45. Thus, proteins with values greater than 0. 45 are predicted to have a higher solubility (and less aggregation probability) than the average proteins, and proteins with values lower than 0. 45 are predicted to be less soluble and therefore, are more prone to form crystals. Consequently, the predicted scaled solubility of NiV N and NiV M, which was found to be lower than 0. 45, hints towards an increased tendency for crystallization. In contrast, JUNV and EBOV nucleoproteins show a higher predicted solubility compared to NIV N and M.
Sma is ubiquitous in nature, being found in both water and soil, and it is associated with plant surfaces, insects and animals  [13, 14]. Starting from the first whole-genome sequence (WGS) determination and detailed genomic comparison of two Sma strains (Db11, an isolate from an insect source; and SM39, a multidrug-resistant clinical isolate)  [15] , both of which were used as representative strains of Sma in this study, several comparative genome analyses have been reported. They included a large-scale genomic analysis of multidrug-resistant (MDR) clones in the UK and Ireland  [9]  and more recently published studies that analysed many publicly available WGSs  [16] [17] [18]. The results of three recent studies indicated the presence of clinical isolate-enriched lineages, but WGSs of environmental isolates were limited in the database.
Analyses of the type strains of genus Serratia We downloaded the genome sequences of the type strains of 16 species and one subspecies belonging to genus Serratia from the NCBI Assembly database (last access: 21 January 2021). These sequences and those of two completely sequenced Sma strains, SM39 and Db11  [15] , were used for core-gene-based phylogenetic analysis, with the type strain of Y. enterocolitica (NCTC 12982 T ) as an outgroup (see Table  S1 , available in the online version of this article, for the list of type strains used). The core genes, which were defined as those present in all 20 strains (n=801), were identified using Roary version 3. 7. 0 [25]  with a 60 % blastp identity cutoff, and a core-gene alignment was generated using mafft  [26]. Using the 296 627 SNPs identified in the alignment, a maximum-likelihood (ML) phylogenetic tree was constructed using RAxML  [27]  with the GTR-GAMMA model of nucleotide substitution and 500 bootstrap replicates.
The tree was displayed and annotated using ggtree version 2. 2. 4  [28]  in the R package. ANI was calculated using PYANI version 0. 2. 7  [29]  in ANIb mode. The 16S rRNA sequences of each strain were identified by blastn searches using the consensus 16S rRNA reference sequence of Sma SM39, which was obtained by aligning the sequences of seven copies using Clustal Omega  [30]  and created with the cons command in the emboss package  [31]. The 16S rRNA consensus sequences of other strains were obtained via the same procedure. As the finished genome sequence of the Sma-type strain was not available, we used the sequence of strain SM39 as a reference in this analysis.
Collection of publicly available genome sequences We searched the NCBI database (https://www. ncbi. nlm. nih. gov) for the genomes registered as Sma, Sma_sak, Sne and Sur and found 844 Sma, 4 Sma_sak, 8 Sne and 2 Sur genomes (last access: 23 January 2019). From this dataset, we selected 644 genomes with records on country information and isolation sources. In particular, we included the genomes of type strains even if their strain information was not available.
Genome assembly, quality filtering and annotation The genomes of 171 Japanese and 54 French strains sequenced in this study were assembled using Platanus_B assembler version 1. 1. 0 [33]. Of the 644 genome sequences obtained from the NCBI database, only assembled sequences were available for 176 genomes. As raw-read sequences were available for the remaining 468 genomes, these sequences were also assembled using the Platanus_B assembler. To remove low-quality sequences from those assembled from short reads, we first removed the sequences if the total number of scaffolds was greater than 300 or the read depth was less than 20 (n=13). Then, 79 genomes judged to be of low quality by CheckM  [34]  with a cutoff of <99 % completeness or >3 % contamination were removed (see Fig. S1  for the entire process of genome assembly and quality filtering).
Strain clustering based on SNP distances (determination of SNP 10 clusters) The strain set used in this study included many subsets comprising very closely related strains, which could affect statistical analyses. To reduce this potential effect of strain bias, we performed strain clustering based on the SNP distances between each strain. The SNP distances in the core-gene alignment used for phylogenetic analysis were calculated using snp-dists version 0. 4 (https://github. com/tseemann/snp-dists). Based on the SNP distances, the strains were clustered by Ward's method, and a dendrogram was generated with the hclust command in R. The maximum SNP distance in each cluster was identified, and clusters with a maximum within-cluster SNP distance of ≤10, ≤20 or ≤30 were defined as SNP 10 , SNP 20 and SNP 30 clusters, respectively.
Statistical analyses Statistical analyses other than the above-mentioned analysis performed by Scoary were performed in R version 4. 1. 0 [42]. One-way analysis of variance (ANOVA) followed by the Tukey-Kramer multiple comparison test was performed for the comparison of genome sizes between clades and that of GC contents between clades. Simple linear regression analysis was performed to analyse the relationship between the genome sizes and GC contents of each strain. Fisher's exact test  [43]  with Benjamini-Hochberg correction  [44]  was performed to determine the difference in the ratio of clinical/hospital environment strains between the entire dataset and each clade.
Identification of AMR genes, FQR mutations and MDR strains AMR genes were identified by SRST2  [45]  using ARG-ANNOT_v3 (https://www. mediterranee-infection. com/wp-content/ uploads/2019/03/arg-annot-nt-v3-march2017. txt) as a database. To analyse the genome sequences for which short-read sequences were not available, we used the Illumina MiSeq v3 100 bp paired-end simulated short-read data of each genome (insert size; 300 bp with a standard deviation of ±10 bp, read depth; 100×) generated by art version 2. 5. 8  [46]. When a gene was predicted as '-?-' by SRST2, the presence of the gene was further analysed by a blastn search  [47]  against the assembled genome sequence, and the presence/absence decision was made with the thresholds of sequence identity ≥90 % and coverage ≥80 %. To search for FQR mutations, the amino acid sequences of the gyrA, gyrB, parC and parE gene products of each strain were aligned with those of Escherichia coli strain K-12 (NC_000913.