dataesr/scientific-paragraphs-categorization huggingface dataset:
This work is supported by the  ERC Advanced Grant "LEAP" , Grant Agreement Number  227947  ( PI M. Kramer ). AV and PRB are supported by the  UK's Science and Technology Facilities Council (STFC ; grant  ST/W000946/1 ). AV also acknowledges the support of the  Royal Society and Wolfson Foundation. JPWV acknowledges support by the  Deutsche Forschungsgemeinschaft (DFG)  through thew  Heisenberg programme  (Project No. 433075039 ) and by the  NSF  through  AccelNet award  # 2114721. NKP is funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) -Projektnummer PO 2758/1-1, through the Walter-Benjamin programme. ASa thanks the Alexander von Humboldt foundation in Germany for a Humboldt fellowship for postdoctoral researchers. APo, DP and MBu acknowledge support from the research grant "iPeska" (P.
I. Andrea Possenti) funded under the INAF national call Prin-SKA/CTA approved with the Presidential Decree 70/2016 (Italy). RNC acknowledges financial support from the Special Account for Research Funds of the Hellenic Open University (ELKE-HOU) under the research programme "GRAVPUL" (K. E. -80383/grant agreement 319/10-10-2022: PI N. A. B. Gizani). EvdW, CGB and GHJ acknowledge support from the Dutch National Science Agenda, NWA Startimpuls -400. 17. 608. BG is supported by the Italian Ministry of Education, University and Research within the PRIN 2017 Research Program Framework, n. 2017SYRTCN. LS acknowledges the use of the HPC system Cobra at the Max Planck Computing and Data Facility. The Indian Pulsar Timing Array (InPTA) is an Indo-Japanese collaboration that routinely employs TIFR's upgraded Giant Metrewave Radio Telescope for monitoring a set of IPTA pulsars.
PSR J1024-0719 (Category 2). DR1 showed a marginal detection of DM variations with a steep spectral index. DR2full and DR2new both find only DM variations consistent with Kolmogorov and incompatible with the DR1 value. We find that the DR2full and DR2new models seem more plausible, but they could indicate the presence of smooth DM structures in the early DR1 data. PSR J1455-3330 (Category 1). The three datasets are largely consistent. DR1 cannot distinguish between DM and achromatic noise, but the better frequency coverage of DR2full and DR2new finds only achromatic noise. The DR2full posteriors are more tightly constrained, particularly in γ red. PSR J1600-3053 (Category 3). DR1 has fairly flat spectrum DM variations, which are split into flat-spectrum scattering delay variations and steep (than Kolmogorov) DM variations in DR2full and DR2new.
+0. 46  -0. 36 respectively. Conversely, we report high DM variations spectral index for PSR J1600-3053 at 4. 51+2. 15  -1. 5 , for which the favoured noise model is DM+SV. More details on the noise properties for this A49, page 7 of 20 EPTA Collaboration and InPTA Collaboration: A&A, 678, A49 (2023) pulsar are described in Sect. 4. 2. Figures for posterior distributions, time-domain realisations, and relevant tables generated for each pulsar are available on Zenodo 5.
Introduction According to the Genome Taxonomy Database Enterococci are composed of 13 genera (Enterococcus, Enterococcus-A to J, Melissococcus, and Tetragenococcus). They belong to the Enteroccocaceae family and to the order Lactobacillales, with other families of medical importance such as Streptococcaceae  (Ludwig et al. , 2009; García-Solache and Rice, 2019). Enterococci are Gram-positive facultative anaerobic bacteria, commonly found in mammal's intestinal microbiota, and also major health careassociated infection pathogens, especially Enterococcus faecalis and Enterococcus faecium  (Hendrickx et al. , 2009; Fiore et al. , 2019). As well-documented pathogens, enterococci are associated with various clinical manifestations including urinary tract infections, bacteremia, or endocarditis and they can also be recovered from cultures of intra-abdominal, pelvic, and soft tissue infections  (Agudelo Higuita and Huycke, 2014).
Virulence study on a Galleria mellonella model Infection assays were performed on Galleria mellonella larva as previously described  (Benachour et al. , 2012). Bacteria were inoculated with a dose of 2 × 10 6 CFU to 15 caterpillars per strain for each experiment. At least three experiments were performed for each condition. G. mellonella survival was followed from 16 h post-infection and during 24 h. 2. 12. Phylogenomic analyses 2. 12. 1. Genome samples, annotation, and quality assessment Genomes were retrieved from the NCBI website (https://ftp. ncbi. nlm. nih. gov/genomes/, last accessed November 17,2021). DNA sequences of 2064 E. faecalis genomes were extracted and the GTDB database was used to select a set of 81 genomes of Enterococcaceae species, annotated as representatives (https://gtdb.
ecogenomic. org/; Parks et al. , 2022) , and including the ATCC 19433 strain genome for E. faecalis (Supplementary Table  4 ). All genomes were annotated with Prokka (version 1. 14. 6; Seemann, 2014) , and protein domains were annotated with the hmmscan program of the HMMER suite (version HMMER 3. 1b2;  Eddy, 2011). Three filters were used to ensure the quality of the genomes selected. First, we identified genomes for which the number of coding sequences predicted by Prokka is outlier. Then, the quality assessment of E. faecalis genomes was performed using CheckM  (version 1. 1. 3; Parks et al. , 2022). Finally, the Mash software (version 2. 3;  Ondov et al. , 2016)  was used to identify genomes incorrectly classified as E. faecalis. The intersection of the lists of genomes retained leads to a set of 1,949 E.
NagY, NagE, and HylA orthologs To identify the set of Enterococcaceae proteins that were orthologous to NagY, NagE, and HylA from E. faecalis V583, we used the OG clusters calculated with the PEPPAN pipeline and the genetic context conservation (Supplementary Table  5 ). The genetic context of the genes is obtained by extracting the gene positions from the Prokka GFF files. The annotation files were prepared with in-house scripts and the trees were annotated and visualized with the online tool Interactive Tree Of Life (iTOL v6; https://itol. embl. de; Letunic and Bork, 2019). The Pfam domain annotation of the proteins was predicted with the hmmscan program  (Eddy, 2011). GeneRax (version 2. 0. 4; Morel et al. , 2020)  was then used to infer rooted family tree directly from the multiple sequence alignment and a rooted species tree.
Each dataset was aligned with mafft and the amino acid substitution model that best fit the data were selected with modeltest-ng (v0. 1. 7; Darriba et al. , 2020). In addition to the protein family root tree, the program returns statistics on the events predicted by the reconciliation (speciation, speciation+loss, duplication, and transfer). For the identification of OGs in E. faecalis strains, the V583 protein sequences were used as query with mmseqs2  (Steinegger and Söding, 2017)  on the entire E. faecalis proteomes annotated by Prokka. Next, we identified the Panaroo OGs to which significant hits belong and extracted all proteins from each OG.
Identification of conserved motifs The conserved motifs on Enterococcaceae 5'nagY were identified with MEME 5. 3. 0  (Bailey and Elkan, 1994). A secondary structure search was performed with RNAfold 2. 4. 14 from the Vienna RNA package  (Lorenz et al. , 2011)  and validated with rLocARNA 2. 8. ORC8 software  (Will et al. , 2012)  that simultaneously folds and aligns the input sequences. The alignment obtained with rLocARNA was used to construct a covariance model that combines primary sequence conservation and secondary RNA structure (cmbuild and cmcalibrate from the Infernal package 1. 1. 4; Nawrocki and  Eddy, 2013). The cmsearch (Infernal package 1. 1. 4) was used to search for these patterns in the DNA sequences.
Chromosomal neighborhood analysis of the nagY genes reveals, in all cases, the presence of a downstream nagE gene. However, in eight genomes, a nagE gene is present without its nagY partner. Five genomes belong to a subtree composed of E. sp. 10A9_DIV0425 and four E. mundtti genomes (Figure  3 ) suggesting that the nagY gene may have been lost in their LCA. One of the two E. thailandicus DSM 21767 paralogs does not have a nagY gene in its chromosomal neighborhood. Note that nagE genes encode proteins with the EIICBA architecture as PtsG of B. subtilis (and B. cereus) while BglF from E. coli has EIIBCA domain order. In addition to gene losses, GeneRax predicted that horizontal gene transfers (HGT) may have occurred, with a higher frequency for nagE (17 HGTs for 54 genes) than for nagY (7 HGTs for 47 genes; Supplementary Figures  2A, B ).
Identification of a new NagY target gene encoding a polysaccharide lyase As regulator binding on nucleic acid, NagY could potentially regulate other genes expression by recognizing a conserved sequence. To identify potential NagY targets, we searched for RAT/terminator motif with cmsearch software (Infernal package; Nawrocki and  Eddy, 2013)  in the E. faecalis V583 genome. We obtained two hits, the highest upstream of the nagY gene, and the second upstream of the ef3023 (ef_rs14340) monocositronic gene, named hylA, which shares 88% identity with the nagY RAT-like sequence. This suggests that hylA possesses a 5'UTR (named 5'hylA) on which NagY could potentially bind to regulate the expression of this gene. We confirmed the existence of a 5'hylA of 193 pb long by 5'RACE-PCR (Supplementary Figure  4B ), and the interaction between NagY and this RNA was studied by MST (Figure  5 ).
This assay highlighted a binding of the protein on 5'hylA RNA, with a lower affinity than with 5'nagY (7. 46 ± 0. 63 nM). Moreover, RT-qPCR assays showed that the expression of hylA depends on the presence of the nagY gene (Figure  4 ). These results are compliant with the hypothesis that NagY also regulates the expression of hylA, suggesting that nagY, nagE, and hylA belong to the same regulon and potentially the same carbohydrate consumption pathway. HylA was identified as a cell-wall anchored protein, annotated as a polysaccharide lyase 8 [Lyase_8_N (PF08124), Lyase_8 (PF02278), and Lyase_8_C (PF02884. 17) domains, Figure  6 ]: this group of enzymes targets uronic acid-containing polysaccharides such as some GAGs (hyaluronate, chondroitin, or heparin) that are components of the ECM  (Sillanpaa et al.
, 2004 (Sillanpaa et al. , , 2009;; Lombard et al. , 2014). To our knowledge, the function and substrate of E. faecalis HylA are unknown, although it is annotated as hyaluronidase in KEGG database  (Kanehisa et al. , 2017)  and was identified as a MSCRAMM family member mostly extracytoplasmic  (Sillanpaa et al. , 2004 (Sillanpaa et al. , , 2009)). In addition to a signal peptide and the lyase regions, HylA possesses other domains: (i) a F5/8 type C domain (discoidin domain) that is a major domain of many blood coagulation factors (F5_F8_type_C PF00754), (ii) a bacterial Ig-like domain (Big_2 PF02368) found in bacterial cell-adhesion molecule, mediating the intimate bacterial host-cell interaction  (Kelly et al. , 1999) , (iii) FIVAR domains (Found In Various Architectures PF07554) mostly associated to binding domains in cell wall associated proteins, and (iv) a LPXTG cell wall anchor motif (Gram_pos_anchor PF00746) presents in virulence factors which are produced by Gram positive pathogens.
Identification and phylogenetic distribution of the hylA homologs in Enterococcaceae We used the two largest conserved domains of the protein to identify homologous sequences in Enterococcaceae (PF08124,  Study of the interaction of NagY on the 5'UTR RNA target genes. MST dose response curves for interaction between NagY labeled protein and 5'nagY (black circles), 5'hylA (empty circles) RNA (ligand), and SRC65 sRNA (used as negative control; black triangles). Error bars represent the standard error of triplicate measurements. Lyase_8_N and PF02278, Lyase_8) and the simultaneous occurrence of these two domains was found in 27 proteins. These proteins also have the PF02884 Lyase_8_C domain. Three of them are significantly longer and have the same domain architecture as E. faecalis HylA, except for the number of FIVAR domains (Supplementary Figure  2C ).
The formers are referred to as short Lyase_8 and the latter as long Lyase_8. The SignalP program  (Almagro Armenteros et al. , 2019)  predicts a standard secretory signal peptide in all proteins. Two proteins are very partial (155 AA for T. koreensis KCTC 3924 sequence and 131 AA for E. crotali ETRF sequence). Note that both strains of E. faecalis (V583 and OG1RF) have both types of proteins. Prokka does not annotate a hylA gene in the genome of E. faecalis ATCC 19433A, but a search with tblastn reveals the presence of a partial sequence similar to a gene encoding a long Lyase_8 (Figure  3 ). The long Lyase_8 protein found in the genomes of E. faecalis V583 and OG1RF strains is also found in the genomes of E. hirae ATCC 9790 (Figure  3 ). GeneRax predicted that the hylA sequences of E. hirae and E.
While no difference was observed after 24 h, a significant 2. 3 and 3. 2-fold decrease in crystal violet staining was observed after 48 h for the ΔnagY and ΔhylA mutants on microtiter plates coated with hyaluronic acid, respectively (Figure  8B ), and 1. 9/1. 7 and 1. 5/1. 6-fold decrease on plates coated with heparin or chondroitin compared to the WT strain (p < 0. 0001; Supplementary Figure  8 ). To investigate the role of NagY and HylA in virulence in vivo, we monitored G. mellonella larvae survival infected by WT, the two mutant strains and the 5'nagY mutant strain in which NagY is deregulated and overexpressed whatever conditions are HylA protein of Enterococcus faecalis. The HylA protein is composed of a N-terminal signal peptide, a coagulation factor F5/8 domain (F5_F8_ type_C PF00754), an Ig-like domain (Big_2 PF02368), enzymatic family 8 polysaccharide lyase domains (Lyase_8_N PF08124, Lyase_8 PF02278, and Lyase_8_C PF02884.
17), a repetition of four FIVAR domains (Found In Various Architectures PF07554), and a LPXTG cell-wall anchor domain (Gram_pos_anchor PF00746). The C-terminal transmembrane and intracytoplasmic domains are not shown. The short version of Lyase_8 is solely constituted of lyase domains. (Figure  9 ; Supplementary 9). The experiment showed that the deletion of ΔnagY does not affect larval mortality (Supplementary Figure  9 ). However, larvae infected by ΔhylA and ∆5'nagY strains showed a significant increase in survival relative to the parental WT strain (p < 0. 001 and p < 0. 01 respectively, Figure  9 ).
The saturation index SI of a given mineral is defined as: SI ¼ log IAP K s , ( 1 ) where IAP is the ion activity product and K s is the solubility product constant at thermodynamic equilibrium. The mineral is close to equilibrium state with the groundwater when its SI is within the range -0. 5/+0. 5, and may precipitate when SI is positive (supersaturation state), or may dissolve if present in the rock when SI is negative (undersaturation state). The SI values in Table  3  were calculated using the HYTEC geochemical module and the LLNL thermodynamic database  (Wolery and Sutton, 2013). A temperature of 20 °C was assumed as the fluids partly cooled down while rising in the wellbore. The initial groundwaters were close to equilibrium with calcite and disordered dolomite (a proxy for magnesian calcite).
The apparent molar volume of solutes was calculated using the Helgeson-Kirkham-Flowers equation. The vapor H 2 O(g) was considered in the system. Although the pore space was initially saturated, a small amount of water evaporated when the air was injected. The Henry constants of the LLNL database  (Wolery and Sutton, 2013)  used in this study are given at 30 °C in Table  S2. The temperature was assumed to be constant throughout this study. γ i is the asymmetric activity coefficient of species i, and c i is the molal concentration of species i in the liquid phase. For example, O 2 (aq) for O 2 (g), see Table  S2. Depending on the reaction, the term γ i c i becomes the activity of the dissolved species with respect to the mass action law. The asymmetric approach Eq. (  6 ) is universal: it combines the equations of state and activity models that allows to accurately model the thermodynamic properties of gas mixtures.
Geochemical thermodynamic equilibrium Assuming that the aqueous species are at thermodynamic equilibrium, HYTEC solves the chemical system for independent primary species and derives the secondary concentrations from the linearly dependent reactions (van der  Lee et al. , 2003). The mass balance then stems from the equilibrium reactions and mass action law and it is assembled as the total concentration for each primary species, that is actually a mathematical sum to conserve during the simulation. The LLNL thermodynamic database  (Wolery and Sutton, 2013 ) was used. Table  S3  gives the formation constants of the mineral considered in this study. The activity of the dissolved species and water was calculated by the B-dot and Helgeson formalisms  (Helgeson, 1969). In total, the geochemical model contained about 100 aqueous species, 5 gas species, and 8 solids.
Modeling results 4. 1. 2. 1. Groundwater chemistry and mineralogy changes. Fig. 5  shows the changes with time of the groundwater chemistry, mineralogy and gas composition inside the air plume over 0. 5 years. Three scenarios were assessed: (i) the Base-case scenario with the full dataset described in Section 3, (ii) an extreme scenario without any calcite, to simulate the sandstone layers that are locally impoverished in calcite (especially around Well B) and (iii) a scenario considering a slower kinetic rate for pyrite using the Field factor case to simulate coating or ageing. The geochemical driving force of the full system is the oxidation of pyrite by O 2 (aq) (Fig. 5a ) sustained by O 2 (g) solubilisation in the liquid phase. Eq. (  8 ) clearly indicates that 1 mol of oxidized pyrite and 1 mol of precipitated goethite produces 4 mol of protons, and therefore may significantly acidify the reservoir groundwater.
A unique set of field data To the best of our knowledge, our study presents a unique set of field data acquired from June 1989 to June 1992 on a pilot test of air cushiongas injection in a deep-aquifer natural gas storage facility in the Paris Basin. These data were obtained under conditions representative of an operational facility over a large spatial scale and a duration of 3 years. The initial state of the reservoir (mineralogy, petrographical log, groundwater chemistry, hydrodynamic parameters) before the air injection was fully characterized as well as the operational parameters (wellbore geometry, pressure, injection rate, cushion-gas composition). After the injection period, a consistent set of data on groundwater chemistry, as well as the analysis of the gas phase in CO 2 and O 2 were collected.
Conclusions An alternative cushion gas consisting of air partially depleted in O 2 was injected into an underground natural-gas reservoir, a deep aquifer located in the Paris Basin (France). The gas and groundwater were sampled at several wellbores, before and after air injection, to evaluate the induced geochemical reactions. Multiphase reactive transport models were developed using the HYTEC reactive transport code with an increasing complexity of the reservoir geometry, i. e. 0D-batch, 1D-radial and a more representative 2D-reservoir configuration. The following conclusions can be drawn: 1. The reservoir is a carbonate-cemented sandstone composed by quartz, calcite, clay, feldspar and pyrite. The injected gas contained 8 mol% of O 2. About 6-9 months after injection completion, the gas analysis indicated a full depletion of O 2 (g), meanwhile CO 2 (g) was detected around 1.
The eddy-permitting global ocean model The numerical integrations described in this manuscript are carried out using the GOSI9 global ocean configuration at 1/4 • of horizontal resolution (GOSI9-025) developed and used by the UK Met Office Hadley Centre and the National Oceanography Centre under the umbrella of the Joint Marine Modelling Program (see  Guiavarc'h et al. (2023)  for a detailed description of the model). GOSI9-025 is an eddy-permitting forced ocean configuration based on the Nucleus for European Modelling of the Ocean (NEMO) numerical ocean model at version 4. 0. 4  (Madec & NEMO-team, 2019). The model used in this study differs in a few details from the standard GOSI9-025 of Guiavarc'h et al. (  2023 ): • it is forced with the 1958-2020 JRA-55 atmospheric reanalysis  (Kobayashi et al.
The eddy-permitting global ocean model The numerical integrations described in this manuscript are carried out using the The model used in this study differs in a few details from the standard GOSI9-025 of Guiavarc'h et al. (  2023 ): • it is forced with the 1958-2020 JRA-55 atmospheric reanalysis  (Kobayashi et al. , 2015; Harada et al. , 2016)  instead of the 1948-2006 CORE atmospheric forcing  (Large & Yeager, 2009) , to cover the observational period (see Sec. 5); • it adopts a bottom friction formulation consistent with the "law of the wall" with a bottom roughness z 0 = 3×10 -3 for a better representation of the bottom boundary layer dynamics; • it employs the  Griffies et al. (1998)  triad formulation for the iso-neutral diffusion since it is the only available option for using iso-neutral mixing with inclined GVCs in the current release of NEMO; • it uses the standard NEMO pressure Jacobian scheme  (Madec & NEMO-team, 2019)  for a more accurate calculation of the horizontal pressure gradient force when using sloping model levels.
manuscript submitted to Journal of Advances in Modeling Earth Systems (JAMES) Acknowledgments Funding support for DB, CG and HH was provided by the  Joint DECC/Defra Met Office Hadley Centre Climate Programme  ( GA01101 ),  UK Ministry of Defense  and the  UK Public Weather Service. JH and MA were supported by the  H2020 European Institute of Innovation and Technology (IMMERSE)  grant no. 821926. The authors would like to thank  Mike Bell ,  Dave Storkey ,  Nigel Wood  and  Jérome Chanut  for helpful and constructive discussions, suggestions and advice. Numerical simulations and analysis were carried out on the Cray HPC at the  Met Office, UK. manuscript submitted to Journal of Advances in Modeling Earth Systems (JAMES) Acknowledgments Funding support for DB, CG and HH was provided by the  Joint DECC/Defra Met Office Hadley Centre Climate Programme  ( GA01101 ),  UK Ministry of Defense  and the  UK Public Weather Service.
JH and MA were supported by the  H2020 European Institute of Innovation and Technology (IMMERSE)  grant no. 821926. The authors would like to thank  Mike Bell ,  Dave Storkey ,  Nigel Wood  and  Jérome Chanut  for helpful and constructive discussions, suggestions and advice. Numerical simulations and analysis were carried out on the  Cray HPC  at the  Met Office, UK.
GOSI9 global ocean configuration at 1/4 • of horizontal resolution (GOSI9-025) developed and used by the UK Met Office Hadley Centre and the National Oceanography Centre under the umbrella of the Joint Marine Modelling Program (seeGuiavarc'h et al. (2023)      for a detailed description of the model). GOSI9-025 is an eddy-permitting forced ocean configuration based on the Nucleus for European Modelling of the Ocean (NEMO) numerical ocean model at version 4. 0. 4(Madec & NEMO-team, 2019).
nemo-ocean. eu, last access: 19 June 2023). The code to localise quasi-Eulerian general vertical coordinates used in this study is included in the NEMO v4. 2 trunk. Additional modifications to the NEMO original code are required for running GOSI9-based configurations. The actual NEMO v4. 0. 4 source code and the namelists used to run the integrations presented in this manuscript are available at https://zenodo. org/record/8056285 and https:// zenodo. org/record/8055445.