dataesr/scientific-paragraphs-categorization huggingface dataset:
Additionally, a pseudogene did not result in a classification of inactivated function if we could identify an intact homolog or analog. For example, if wcaJ_KL1 has a frameshift, but wcaJ_KL2 was found in the genome, the pseudogene was flagged and not used to define non-capsulated mutants. Complete gene deletions were identified by Kaptive among capsular loci encoded on a single contig. We built a dictionary of genes that are essential for capsule production by gathering a list of genes (annotated as the gene name in the Kaptive database) present across all CLTs and which are essential for capsule production according to experimental evidence (Table A in S1 Text). The absence of a functional copy of these essential genes resulted in the classification "non-capsulated" (except wcaJ and wbaP, which are mutually exclusive).
To correlate the pseudogenization frequency with the order in the capsular biosynthesis process, we first sought to identify all glycosyl transferases from the different CLTs and grouped them in one category. To do so, we retrieved the GO molecular functions listed on UniProtKB of the genes within the Kaptive reference database. For the genes that could be ordered in the biosynthesis chain (Table A in S1 Text), we computed their frequency of inactivation by dividing the count of inactivated genes by the total number of times it is present in the dataset. To test that sequencing errors and contig breaks were not leading to an excess of pseudogenes in certain genomes, we correlated the number of pseudogenes (up to 11) and missing genes with 2 indexes of sequence quality, namely, the sequence length of the shortest contig at 50% of the total genome length (N50) and the smallest number of contigs whose length sum makes up 90% of genome size (L90).
Inference of genes ancestral states We inferred the ancestral state of each pangenome family with PastML (v1. 9. 23)  [75]  using the maximum-likelihood algorithm MPPA and the F81 model. We also tried to run Count  [76]  with the ML method to infer gene gains and losses from the pangenome, but this took a prohibitive amount of computing time. To check that PastML was producing reliable results, we split our species tree (cuttree function in R, package stats) in 50 groups and for the groups that took less than a month of computing time with Count (2,500 genomes), we compared the results of Count to those of PastML. The 2 methods were highly correlated in term of number of inferred gains per branch (Spearman correlation test, Rho = 0. 88, p-value < 0. 0001). We used the results of PastML, since it was much faster and could handle the whole tree in a single analysis.
Detection of recombination tracts We detected recombination tracts with Gubbins v2. 4. 0  [34]. Our dataset is too large to build one meaningful whole-genome alignment (WGA). Gubbins is designed to work with closely related strains, so we split the dataset into smaller groups defined by a single ST. We then aligned the genomes of each ST with snippy v4. 3. 8 (https://github. com/tseemann/snippy), as recommended by the authors in the documentation. The reference genome was picked randomly among the complete assemblies of each ST. We analyzed the 25 groups in which a CLT swap happened (see above) and for which a complete genome was available as a reference. We launched Gubbins independently for each WGA, using default parameters. We focused on the terminal branches to identify the recombination tracts resulting in CLT swap.
Handling of draft assemblies Since more than 90% of our genome dataset is composed of draft assemblies, i. e. , genomes composed of several chromosomal contigs, we detail here the steps undertaken to reduce the impact of such fragmentation on our analysis. We only included prophages and conjugative systems that are localized on the same contig (see "Prophage detection" and "Analysis of conjugative systems"). Kaptive is able to handle draft assemblies and adjust the confidence score accordingly when the capsule locus is fragmented, so we relied on the Kaptive confidence score to annotate the CLT, which was treated as missing data in all the analysis when the score was below "Good" (see "Capsule locus typing"). For the detection of missing capsular genes, performed by Kaptive, we verified that only non-fragmented capsular loci are included (see "Identification of capsule pseudogenes and inactive capsule loci").
DNA was extracted from pelleted cells grown overnight in LB supplemented with 0. 7 mM EDTA. Additionally, RNAse A treatment (37˚C, 30 minutes) was performed before DNA precipitation. Each clone (n = 22) was sequenced by Illumina with 150pb paired-end reads, yielding approximately 1 GB of data per clone. The reads were assembled with Unicycler v0. 4. 4  [82] , and the assemblies were checked for pseudogenes (see "Identification of capsule pseudogenes and inactive capsule loci"). We also used breseq  [37]  and snippy (https://github. com/tseemann/snippy) to verify that there were no further undetected mutations in the evolved sequenced clones (https://doi. org/10. 6084/m9. figshare. 14673177).
Acknowledgments We thank  Rafał Mostowy ,  Jorge Moura de Sousa ,  Nienke Buddlelmeijer ,  Olivier Tenaillon ,  Marie Touchon , and other lab members for fruitful discussions. We thank  Sylvain Brisse  for providing us with Klebsiella strains. We thank  Christiane Forestier  and  Damien Balestrino  for providing the pKNG101 plasmid and  Jean-Marc Ghigo  and  Christophe Beloin  for the gift of pZE12::CFP used to construct pMEG-Mob and E. coli MFD λ-pir.
This work was supported by an  ANR JCJC (Agence national de recherche)  grant [ ANR 18 CE12 0001 01  ENCAPSULATION] awarded to O. R. The laboratory is funded by a  Laboratoire d'Excellence 'Integrative Biology of Emerging Infectious Diseases' grant  [ ANR-10-LABX-62-IBEID] , the  INCEPTION program [ PIA/ANR-16-CONV-0005 ], and the  FRM  [EQU201903007835]. M. H. has received funding from the  FIRE Doctoral School  ( Centre de Recherche Interdisciplinaire, programme Bettencourt ) to attend conferences. The funders had no role in the study design, data collection and interpretation, or the decision to submit the work for publication.
All individual quantitative observations (S1 to S18 Dataset) that underlie the data summarized in the figures and results of the manuscript are available at: https:// figshare. com/projects/Supplementary_Datasets/ 114459 The 3980 genomes assemblies analyzed in this study are publicly available from the RefSeq database (accession numbers in S1 Dataset) All the genomes of strains used for experimental evolution and conjugation assays are publicly available from ENA (accession numbers in S1 Table ).
Data analysis All the data analyses were performed with R version 3. 6 and Rstudio version 1. 2. We used the packages ape v5. 3  [87] , phangorn v2. 5. 5  [88] , and treeio v1. 10 [89]  for the phylogenetic analyses. Statistical tests were performed with the base package stats. For data frame manipulations and simulations, we also used dplyr v0. 8. 3 along with the tidyverse packages  [90]  and data. table v1. 12. 8.
Summing up, Figure  1  shows the overall workflow used to generate the graphite/Si composite microstructures. The structures were equilibrated using a Nose-Hoover barostat and thermostat at 300 K and 1 atm. The simulations were performed using LAMMPS molecular dynamics software [57] using periodic boundary conditions in all directions. The FF parameters used for this work are included in Table  S1. All simulations were performed in the TGCC Joliot-Curie (CEA) supercomputer, in 64 core AMD Rome (Epyc) processors. More details on the generation of CGMD electrode structures can be found in Ref  [58].
Continuum Model The mesh was then imported into COMSOL Multiphysics 5. 4 to build the 4D-resolved model for the electrochemical and mechanical simulations. The Lithium-ion Battery, Transport of Dilute Species and the Solid Mechanics modules were used for the electrochemical and mechanical model. All the equations and parameters that are used are listed in Table  S2. The CBD phase was considered as a partially open porous material with a 50% porosity, which is also considered in the CGMD simulations. This means that the CBD phase is electronically conducting and permeable to the diffusion of Li+ ions. Similarly, due to the fact that Si was meshed as a homogenized domain, the resulting Si domain was approximated as a porous electrode. Within this domain, Si particles were assumed to be close packed, resulting in a domain with a porosity of 26%.
Acknowledgements: A. A. F. , C. L. , T. L. and H. O. acknowledge the  European Union 's  Horizon 2020 research and innovation programme  for the funding support through the  European Research Council  (grant agreement  772873 , " ARTISTIC " project). A. A. F. acknowledges  ALISTORE European Research Institute  for partial funding support. A. A. F. and O. A. acknowledges funding from the  European Union 's  Horizon 2020 research and innovation program  under grant agreement No. 957189  ( BIG-.
Following synthesis, the samples were hot pressed at 800 • C or 900 • C (V, Nb compositions) and 80 MPa applied pressure inside graphite dies using a homebuilt hot press. The densities of the hot-pressed pellets were 93(1)% of the theoretical x-ray density. S and ρ were measured using a Linseis LSR-3 instrument on bar-shaped specimens cut from the hot-pressed disks. The thermal diffusivity (D) was measured on hot pressed cylindrical disks using a Linseis LFA instrument. The thermal conductivity κ = DC p d was calculated by multiplication with the gravimetric density (d) and heat capacity (C p ) obtained from the literature  [12]. A Maxwell-Eucken porosity correction was applied to the measured κ  [29]. SXRD data was collected on the I11 beamline at Diamond Light Source, Oxford, UK using five MAC arms with a total of 45 individual analyser-detector channels  [52].
The synchrotron wavelength was 0. 49397(1) Å. Heating and cooling was carried out using a Cyberstar hot air blower, with the measurements carried out in the 300 K-848 K temperature range, using a heating rate of 10 • C min -1 , with data collected every 30 • C, with a collection time of 2 s/scan. A Pt standard was used as a temperature reference, calculation of the actual temperatures was derived from the refined lattice parameters and literature  [53]. Prior to data collection, all samples were finely ground using mortar and pestle and loaded into thin-walled 0. 1 mm diameter quartz capillaries to minimise x-ray absorption. All data underwent Rietveld refinement using the GSAS software  [54]  and EXPGUI interface  [55]. Corrections were made for anomalous scattering and x-ray absorption using the XPrime programme.
HH compositions Variable temperature SXRD patterns for TiNiSn collected between 295 and 848 K are shown in figure  1 , whilst equivalent plots for the other compositions can be found in figures S1-S5 (available online at stacks. iop. org/JPENERGY/3/035001/mmedia) in the supplemental information. Inspection after cooling revealed that for all samples, except for Ti 0. 5 Zr 0. 25 HfNiCu y Sn, the thin-walled capillaries had broken, leading to exposure of the fine powders to air. Despite this, all samples show good stability up to 848 K with the dominant peaks originating from the HH phase. An overview of the fitted unit cell and atomic parameters and fit statistics at 295 K, 570 K and 848 K is given in tables S1 and S2 in the SI. The 295 K data were used to determine the experimental compositions of the HH phases.
Variable temperature SXRD data The temperature dependence of the lattice parameters (a) and thermal displacement parameters (U iso ) of the XNiCu y Sn and X'FeSb samples are given in figures 3 and 4, respectively. These data were fitted simultaneously to a Debye model using the following expressions  [62, 63] : Here a 0 is the lattice parameter at 0 K, b is a scaling constant, R is the ideal gas constant, θ D (θ Di ) is the average (atomic site) Debye temperature, h is Plank's constant, m i is the Debye oscillator mass, k B is Boltzmann's constant and σ i 2 is the displacement correlation function. The index (i) refers to the individual crystallographic sites (X, Y or Z positions) or to the average of the sites. In these fits, the average U iso and a(T) were fitted simultaneously. A summary of the obtained θ D , σ 2 , a 0 and b values is given in table  1.
We present theoretical results linking the (unobserved) parameters of the model to various quantities which are more easily measured during the early stages of an epidemic. We then apply these results to estimate the state of the COVID-19 epidemic in France, using available information on the infection fatality ratio and on the distribution of the exposed and infectious periods. Using the hospital data published daily by Santé Publique France, we gather some information on the delay between infection and hospital admission, intensive care unit (ICU) admission and hospital deaths, and on the proportion of people who have been infected up to the end of 2020.
The first basic idea is to estimate an exponential growth rate from the available data (the number of hospital admissions or the number of deaths occurring in hospitals), and then to relate it to the parameters of a model of the evolution of the COVID-19 epidemic. Our approach is modelled upon the approach of Tom Britton in two recent preprints  [2, 3]. Note that the exponential growth rate can be negative, which was the case in particular in France during the two lockdown periods. The exponential growth rate is the most natural parameter which can be extracted from the data. It equals log  (2)  divided by the doubling time, i. e. the number of days necessary for the number of daily new cases (or deaths) to double, a notion of which everyone listening to the news at the time of the rise of the epidemic has heard.
Growth rates of the epidemic We measured the growth rate of the cumulative number of infected individuals during the early phase of the epidemic by fitting an exponential curve to the cumulative number of deaths in each patch, between 19 and 26 March (earlier data were only available at the national level). Despite the fact that very strict lockdown measures were already effective at the time, hospital deaths continued to increase exponentially until at least 26 March, mainly due to the fact that infected individuals who died of COVID-19 during this period had been infected before national lockdown, hence during the exponential growth phase. We can see that the initial growth rate in the Grand Est region is the slowest, despite the fact that this is where the epidemic first started. This can be attributed to the fact that some public gatherings were banned in this region about 10 days before the national lockdown.
1. We estimated the probability of being admitted to hospital given infection. This obviously depends on the infection fatality ratio, but, on the other hand, the death to admission ratio (i. e. the probability of dying given that one is admitted to a hospital) is constant. We estimated that the death to admission ratio was 0. 19 in Île-de-France, 0. 20 in the Grand Est region, 0. 14 in PACA and 0. 17 in Auvergne-Rhône-Alpes. Note that we consider that these are constant throughout the epidemic, which may not be the case, especially if hospitals become overwhelmed by the influx of patients. This could for example explain the discrepancy between the death to admission ratios in the different regions, which are computed using the data from the first weeks of lockdown. Using the resulting estimates on the delay distributions and the probabilities of hospital admission and ICU admission, we can simulate the deterministic SEIR model from definition 2.
Of course it remains possible to choose the joint distribution of (E, I ) such that the model with memory decreases more rapidly than the ODE model after the start of lockdown measures (for example by choosing Gamma distributions with k < 1 for E and I , see appendix C), but such distributions seem even less realistic than the exponential distribution for infectious disease modelling. As a result, we expect that using ODE models to fit COVID-19 hospital data forces one to compensate for this in some way. One way to do this would be to make the change in the contact rate more gradual, but we do not believe that this would reflect what took place in France. Alternatively, one could artificially increase the mean exposed time in the model, which results in a longer delay between the change in the contact rate and the change of slope in the daily new infections.
IMPORTANCE Polyethylene terephthalate (PET) has accumulated in our environment throughout the last six decades, presumably with no major degradation paths identified. While several PET hydrolases have been described, their abundance and activity in nature remains unconstrained. In this study, homology-based mining of metagenomic datasets demonstrated for the first time that functional PET hydrolases are affiliated with the Bacteroidetes phylum. We prove that these enzymes have significant capability to hydrolyze PET at temperatures ranging from 4 °C -30°. Moreover, they are distributed globally and, in several instances, expressed at high levels. While these enzymes have relatively low turnover rates, they may significantly contribute to PET removal because of their global distribution and expression.
Further, the leaf compost-derived cutinase LCC is closely related to the Actinobacterial enzymes and is currently one of the best described and most active PETases  16, 17. More recently, the complete degradation of amorphous PET materials was described for the Gramnegative Betaproteobacterium Ideonella sakaiensis 201-F6, which is capable of using PET as a major energy and carbon source  18. I. sakaiensis' genome also encodes a tannase that appears to be unique, and which is designated MHETase as it is capable to degrade MHET. Besides these, a number of other PETases affiliated with the Proteobacterial phylum have been identified  [19] [20] [21] [22]. In a previous study, we identified potential PET esterases affiliated with the Bacteroidetes phylum using HMM profile database searches  9, 19.
Profile Hidden Markov Model (HMM) searches identify potential bacteroidetal PETases Previously, we identified PET-active genes and enzyme candidates affiliated with phylum Bacteroidetes  19. In this study, we initiated work to enrich the diversity of these genes encoding PET-active enzymes and to validate their catalytic function using experimental approaches. To achieve these goals, we performed global database searches using publicly available data from single bacterial genomes and different metagenomes available through NCBI GenBank. In addition, we searched several private datasets harboring human-and environmental-affiliated Bacteroidetes sequences (TABLE  1 ). Searches were done from January until March 2019. This global search initially resulted in the identification of nine novel potential PETases affiliated with Bacteroidetes including candidates sourced from either Seaweed  28  , an Antarctic moss  28  , river sediment  29  , an aquaculture (unpublished data from our lab) and the human gut microbiome  30, 31  (TABLE  1 ).
Amino acid sequence and structural analyses identify unique traits of bacteroidetal PETases. To further resolve candidate enzymes properties, all predicted PETases were subject to more detailed bioinformatic inspection. The predicted molecular weights ranged from 36 to 48 kDa with an average of 330 aa. Remarkably, the predicted PETases PET27-PET38 showed a type IX secretion system (T9SS)/PorC type sorting domain-containing part at the C-terminus. This C-terminal domain had been described earlier by a profile HMM from the TIGRFAM database (TIGR04183). T9SS sorting domains are involved in protein transport across the bacterial outer membrane and have so far been described as a bacteroidetal-specific secretion system  [32] [33] [34]. The predicted domain encompassed 62-64 aa in the cases of PET27-38.
PET57 is the only exception with a Trp-Met-Tyr binding site. For a more detailed structural inspection, we modeled the structures of all predicted PETases using the IsPETase (PDB code 6QGC) as backbone. These modeling experiments suggested that catalytic parts of the predicted PETases have minor differences in their 3D structures (FIGURE  1  and FIGURE  S2 ). However, the C-terminal part affiliated with the T9SS domain differed largely. It is not present in the IsPETase and was in some cases 100 aa in length (TABLE  2 ) and consisted of up to seven predicted βsheets and, occasionally, a few α-helices. differ in some of the amino acids associated with PET binding. The residues of IsPETase are indicated in black. They also lack a disulfide bridge in the proximity of a catalytic loop. 3D structures were modeled using the Robetta server 37 using the IsPETase crystal structure (6QGC) as a backbone.
Recombinant PET27 and PET30 hydrolyze PET foil. To expand on our bioinformatic analysis, we cloned and expressed the predicted PETases in Escherichia coli for functional testing. The nine candidate genes were synthesized and cloned into the expression vector pET21a(+) (Biomatik, Wilmington, DA, USA) and expressed in E. coli BL21 and T7-SHuffle (TABLE  S1 ). The resulting clones were verified by PCR for carrying the respective insert using the primers indicated in TABLE  S2. Finally, all obtained clones were sequenced using commercial services to verify the correctness of the inserts (data not shown). Initial tests using recombinant purified proteins and tributyrin (TBT)-containing agar plates indicated that the genes PET27-30 coded for active esterases. The remaining enzymes PET38, PET53, PET57 and PET58 were inactive and were either produced as insoluble proteins and/or only at very low amounts (TABLE  1 ).
GenBank entries of the putative and verified PETases affiliated with the Bacteroidetes phylum are listed in TABLE 1. GenBank entries and identifiers of all other PETases included in the tree are indicated in (TABLE  S1 ). The diversity of Bacteroidetal PETases raised the question to what extent these enzymes could impact plastic degradation in the environment. To address this question in part, we analyzed the global distribution of PET27 and PET30 and their homologues. For this purpose, we used the protein sequences of PET27 and PET30 and analyzed their occurrence and frequency in global databases available in IMG/M ER  29, 40. Using both enzymes for a BLASTp-based search (cutoffs 50% identity; 80% coverage), we were initially able to identify very few (<10) possible homologs in the global databases analyzed and affiliated with the genera Aequorivita and Chryseobacterium (FIGURE  5a ).
Bacteroidetal PETases are possibly transcribed in nature Based on these observations, we asked if expression of PET27 and PET30 homologous could be detected in metatranscriptomic data sets. Because one of our database hits was associated with a Bacteroidetes SAG from the Baltic Sea, we started searching in the Baltic Sea by mapping a selection of the recently established Baltic Sea Reference Metagenome (BARM) metagenomic sequence assemblies, or contigs, from discrete depths over the redoxcline from October 2014  42, 43. While the majority of hits were not affiliated with the PET27-30 subcluster, one hit was recovered with high sequence similarity to the Bacteroidetes SAG (FIGURE  S5a ). Based on this observation we expanded our HMM search to the entire BARM data set  43  (TABLE  S3 ). Thereby, we identified at least 9 hits of potential bacteroidetal PETase genes with a bit score of above 200 and an e-value smaller than 1.