dataesr/scientific-paragraphs-categorization huggingface dataset:
2016). Briefly, fatty acid methyl esters were separated using an Elite 5-MS column and monitored by mass spectrometry (Clarus 500-SQ 8 S, Perkin Elmer, Courtaboeuf, France). A spectral database search was performed using MS Search 2. 0 operated with the Standard Reference Database 1A (NIST, Gaithersburg, USA) and the FAMEs mass spectral database (Wiley, Chichester, UK). Antibiotic susceptibility was evaluated using the disc diffusion method and E-test strips (bioMérieux) according to the EUCAST recommendations (www. eucast. org). E-test strips were used to determine the minimal inhibitory concentration (MIC) of the following antibiotics: benzylpenicillin, oxacillin, ceftazidime, tobramycin, amikacin, amoxicillin, ceftriaxone, imipenem, vancomycin, doxycycline, clindamycin, trimethoprim/sulfamethoxazole, ciprofloxacin, rifampicin, linezolid and colistin.
The library profile was visualised on a High Sensitivity Bioanalyzer LabChip (Agilent Technologies Inc, Santa Clara, CA, USA) and the final concentration library was measured. The genomes of strains Marseille-P2918 T and Marseille-P3646 T and those of their closest phylogenetic relatives were annotated using Prokka (Seemann 2014). A map of the circular genome was also built using CGview to display the genomic features of these new taxa  (Petkau et al. 2010). For genomic comparison, the OAT software  (Lee et al. 2016)  was used to build an OrthoANI heatmap in order to estimate the average nucleotide identity at the genomic level between strain Marseille-P2918 T , Marseille-P3646 T and closely related species. Similarly, OrthoAAI (Average Amino-acid Identity) was determined for each strain using AAI-profiler online tool  (Medlar et al.
2018). Moreover, genomic similarity was further determined using digital DNA-DNA hybridization (dDDH) which was calculated using Type (Strain) Genome Server (TYGS) (https:// tygs. dsmz. de/)  (Auch et al. 2010; Meier-Kolthoff et al. 2013). For the proteomics analyses, we performed a BLASTp analysis for our strains and closely related species genomes against the clusters of orthologous groups (COG) database with a minimum identity of 30%, a minimum coverage of 70%, and a maximum E-value of 1e -03. All sequences shorter than 80 amino acids in size were removed. In addition, for the metabolic pathway determination, we used BLASTp Koala against KEGG prokaryotic database  (Kanehisa et al. 2016). Antimicrobial resistance screening was achieved through the analysis of nucleotide sequences of each genome using ABRicate against ResFinder and Plas-midFinder databases  (Zankari et al.
Strain identification and phylogenetic analysis The MALDI-TOF MS analysis did not allow identification of our strains of interest. However, the 16S rRNA gene sequence of strain Marseille-P2918 T (GenBank accession number LT598554) shared Vol. : (0123456789) 93. 71% identity with that of Tissierella praeacuta strain NCTC 11,158 (Genbank accession number X80832), the phylogenetically closest species with standing in nomenclature which putatively classifies it as a member of the family Tissierellaceae within the phylum Firmicutes. Strain Marseille-P2918 T exhibits a 16S rRNA sequence divergence over 5% with its phylogenetically closest relative with standing in nomenclature (7). As for strain Marseille-P3646 T (Genbank accession number LT725660), its highest sequence similarity, 93. 11%, was shared with the 16S rRNA gene of Anaerotruncus colihominis strain 14565 T (Genbank accession number AJ315980) which represents a sequence divergence over 5%.
The sequences of the 16S rRNA gene of strains Marseille-P2918 T and Marseille-P3646 T were also compared to those of type species with a validly published name within their respective families and exhibited sequence divergences all over 5% (Table  S6 ). The spectra of the described strains (Figure  S1 ) were incremented to the URMITE database (http:// www. medit erran ee-infec tion. com/ artic le. php? laref= 256& titre= urms-datab ase). The position of our strains was also assessed by computing phylogenetic trees highlighting their position relative to closely related species  (Figs. 2, 3 and S2).
This gene has been detected by 53. 42% similarity and 98. 77% coverage with NimE. The prediction of its function by 3D structure, BLAST CDD (Conserved domain database), and Motif search showed that this hypothetical protein has a Pyridoxamine 5'-phosphate oxidase activity, which is known as a function of Nim genes according to Uniprot. Thus, strain Marseille-P3646 T has a potential new Nitroimidazole resistance gene, present the functional domain necessary for this resistance. Genomic characteristics of our strains were compared to those of closely related species with an available genome. OrthoANI values among closely related species to strain Marseille-P2918 T ranged from 61. 86% between strain Marseille-P2918 T and Gudongella oleilytica; and 78. 20% between strain Marseille-P2918 T and Varibaculum cambriense (Fig.
Urmitella timonensis is motile and spore forming. Colonies are circular, smooth, very small and pale grey, with a diameter of 0. 3-1 mm on blood agar. Strictly anaerobic. Optimum growth occurs at 37 °C in an anaerobic atmosphere. The major fatty acid is hexadecanoic acid (40%). The habitat is the human gut. The genome of strain Marseille-P2918 T is 2. 14 Mbp long with 50. 52% of GC content. The 16S rRNA and genome sequences are available in the EMBL-EBI database under accession numbers LT598554 and FQLW01000000, respectively. The type strain Marseille-P2918 T (= CSUR P2918 = DSM103634) was isolated from the stool sample from a Senegalese girl suffering from marasmus. Marasmitruncus (Ma. ras. mi. trun'cus. N. L. masc. n. marasmus, from Gr. masc. n. marasmos, a causing to die away; N. L. masc.
Cells sizes were 2. 71 ± 0. 6 µm in length and 0. 42 ± 0. 06 µm in diameter. No oxidase and catalase activities were found. Marseille-P3646 T is motile and spore forming. Colonies are circular, smooth, very small with a diameter of 0. 5-2 mm on blood agar. Optimum growth occurs at 37 °C in an anaerobic and microaerophilic atmosphere. The major fatty acid is 12-methyl-tetradecanoic acid. The genome of strain Marseille-P3646 T is 3,761,792 bp long with 50. 04% of G + C content. The 16S rRNA and genome sequences are available in the EMBL-EBI database under accession numbers LT725660. 1and FYDE00000000, respectively. The habitat is the human gut. The type strain Marseille-P3646 T (= CSUR P3646 = CCUG72353) was isolated from the stool sample from a Senegalese girl suffering from marasmus.
Acknowledgements We sincerely thank  Takashi Irie ,  Kyoko Imai ,  Taku Sakazume ,  Yusuke Ominami ,  Hisada Akiko , and the  Hitachi team of Japan  ( Hitachi High-Tech Corporation, Tokyo, Japan ) for the collaborative study conducted together with  IHU Méditerranée Infection , and for the installation of a SU5000 microscope at  IHU Méditerranée Infection facility.
Motivated by the growing attention on climate change and the ethical role that board characteristics and ownership may play in reducing greenhouse gas (GHG) emissions, this paper investigates the relationship between institutional ownership and GHG emissions. Using an extensive dataset from the UK and the USA, we show that institutional ownership is associated with less GHG emissions -a one standard deviation increase in the proportion of institutional ownership reduces carbon emissions by 1. 02 metric tons. Our findings are robust when using alternative measures, econometric specifications and several approaches to address endogeneity. Further, we find no evidence for a stronger effect in the UK compared with the USA, as expected from our discussion of the governance contexts in the two countries.
Sample description Our initial sample contains constituents of the FTSE All-Share index and Russell 3000 index. These indices are market capitalization-based indices and are termed as the benchmark for respective markets in the UK and the USA. We exclude from our sample firms that do not have GHG emissions and institutional ownership data available from the Bloomberg database. We also exclude financial firms due to their different reporting and regulatory standards. 354 US-based and 247 UK-based firms between 2010 and 2019 (4352 firm-years). Further, panel B presents the distribution of firm-year observations by industry and year. Industrial and consumer discretionary industries exhibit the highest participation, followed by materials and technology firms, whereas the year-wise distribution exhibits a normal trend.
Therefore, in line with the purpose of this study, we employ an objective, verifiable and direct measure of corporate environmental footprints. We use total GHG emissions and GHG emissions intensity per sales dollar to denote the GHG emissions of a firm. Total GHG emissions indicates the GHG emissions in thousands of metric tons (as reported in Bloomberg); GHG emissions intensity represents the metric tons of GHG emissions emitted per million of sales revenues. We use GHG emissions intensity along with total GHG emissions as this provides a comparative measure of a firm's environmental impact relative to its sales volume. The GHG emissions data are obtained from the Bloomberg database. We follow prior studies (e. g. Harjoto, Laksmana and Lee, 2015;  Zaman et al. , 2021)  and measure the percentage of institutional ownership as the number of shares held by institutional investors divided by total shares outstanding for a firm.
Reconstruction of Pre-Industrial ENSO Variations Multiple paleo-proxy reconstructions of ENSO exist in the literature (e. g. ,  Dätwyler et al. , 2020; Emile-Geay et al. , 2013a , 2013b , 2020; Li et al. ,     from each other because they use different ENSO timing, ENSO definitions, instrumental data sets to build predictands, proxy records, and statistical methods to make reconstructions. Here, we decide to update these reconstructions using the latest version of the PAGES 2k database (PAGES 2k Consortium, 2017), therefore including almost all proxy records available to reconstruct the ENSO index. Also, we propose using advanced statistical methods that are accounting for a non-linear relationship between predictor and predictand within the reconstruction phase: the random forest approach.
For this purpose, the Climate Index Reconstruction software  (Michel et al. , 2020)  was used to derive several reconstructed SST-NINO3. 4 time series covering the last millennium. Numerous reconstructions were obtained by combining different methods and sets of proxy records according to their temporal coverage (cf. Michel et al. , 2020; Michel, Swingedouw, Ortega, et al. , 2022). We utilized a large initial set of 554 annually resolved proxy records, essentially composed of proxy records from the PAGES 2k database. The reconstruction models were computed by shuffling: • Four different regression methods: principal component regression, partial least squares, elastic-net and random forest  (Michel et al. , 2020; Michel, Swingedouw, Ortega, et al. , 2022 ). • A total of 31 possible time windows on which to build the model (i.
Contemporary Precipitation and ENSO Variability In this section, we (re-) document Easter Island precipitation's mean and seasonal variability as a background and analyze the "present-day" relationships between precipitation and ENSO. The analysis is performed on precipitation and SST data sets from 1950 to 2021 (Section 3. 1) and 1850-2014 (Section 3. 2) using usual statistics. To have a large-scale view, we further examine basin-wide ENSO-related precipitation changes for CMAP, ERA5, the CMIP5 NorESM1-M GCM outputs from the Norwegian Climate Center  (Bentsen et al. , 2013) , and the CMIP6 CESM2 Community Earth System Model from the National Center for Atmospheric Research  (Danabasoglu et al. , 2020). These two models were chosen for illustrative purposes but also due to their overall good representation of ENSO properties (see  Bellenger et al.
, 2014; Planton et al. , 2021). ERA5, 0. 096 ± 0. 074 for Mataveri). As expected, the two gridded products (CMAP and ERA5), which are more representative of spatial averages over their respective grid mesh, underestimate the value obtained from the Mataveri point measurements. There is no permanent station-based precipitation network that can monitor spatial variations over Easter Island. Occasional precipitation time series indicate large differences in mean precipitation depending on the topography, with changes of the order of +0. 02 m/month per 100 m elevations (Figure  2 ). Thus, there is a potential bias in interpreting Mataveri as representative of the entire island (as also discussed below). The monthly mean years and corresponding standard deviations of precipitation computed from the CMAP, ERA5, and Mataveri data sets are shown in Figure  6.
10. 1029/2022PA004514 9 of 19 SST-NINO3. 4 standard deviation intervals. However, the scatter plot indicates a relatively large spread of precipitation values, especially within -1 and +2 standard deviations of the mean SST-NINO3. 4 (also visible with the ERA5 and Mataveri data sets, not shown here). This underlines, for instance, that some El Niño events (say when SST_NINO3. 4 is above 0. 5 standard deviation) do not necessarily correspond to above-average precipitation. The relationships between SST-NINO3. 4 and precipitation are thus more complex than a simple linear one. Three metrics, D1, D2, and D3, are then computed to specifically clarify La Niña effects on Easter Island precipitation shortage: • D1: The difference of standardized averaged precipitation during La Niña states against those during neutral ENSO states; • D2: The difference of standardized averaged precipitation during La Niña states against those during El Niño states; • D3: The difference of standardized averaged precipitation during La Niña states against those during either El Niño or neutral ENSO states.
4 spatial box. Sensitivity studies show that using the location of the peak 5°N-5°S ENSO variability rather than the fixed NINO3. 4 spatial box does not change our conclusions. Two of the models that show a strong correlation between ENSO and Rapa Nui rainfall, the NorESM1-M General Circulation Model (GCM) from CMIP5 and the CESM2 GCM from CMIP6 (Figure  10 ), are also among the best at simulating ENSO  (Bellenger et al. , 2014; Planton et al. , 2021). They show large-scale correlation patterns similar to those obtained using the CMAP and ERA5 data sets (compare Figures S1a-S1d in Supporting Information S1). To conclude, the analysis of historical simulations from 46-GCMs for the 1850-2014 period corroborates results obtained from the last 4-7 decades. Hence, both the observed and simulated precipitation data sets reveal precipitation deficits on Easter Island ranging from -20% to -30% of the total standard deviation in precipitation during La Niña events.
5 centuries, and (c) a new reconstruction of the SST-NINO3. 4 ENSO index covering the last millennium. Results based on in situ, satellite-derived, reanalysis products, and GCM simulations overall document below-normal precipitation during La Niña events and a tendency for above-normal precipitation during El Niño events. We obtained similar conclusions when comparing the Eastern Pacific versus Central Pacific El  Niño events, and Eastern Pacific versus Central Pacific La Niña events. Remarkably, precipitation anomalies are consistently negative for all data types during La Niña events as compared to neutral situations, with a precipitation deficit of the order of -0. 2 to -0. 4 standardized anomalies (see metric D1 in Table  3 ). To put this estimate in perspective, using a mean precipitation of 0.
Acknowledgments We deeply thank three anonymous reviewers for very helpful comments that significantly improved our manuscript. BM benefits from financial support of the LEFE-EVE IMAGO programs BYCEPHAL and VADEMECUM, and the international  BELMONT (PACM-EDY) program. TD and BM thank  C. Ripoll ,  J. Nehe Nahoe , and  L. Kaleta  for logistical support and kindness during one stay on Easter Island, and  Air Tahiti Nui  for facilitating their trips. Dialogs with multidisciplinary experts at the  early stage of our study during the "International Conference on Early Migration and Navigation  in the Pacific and  Rapa Nui ," organized on-site by S. H. Cardinali in 2018, were very motivating. We had valuable discussions with  C. Orliac ,  M. Orliac , and  N. Cauwe. Technical support from  Vincent Hanquiez  was appreciated.
Data Availability Statement The precipitation data we used are from (a) CPC Merged Analysis of Precipitation (CMAP): https://psl. noaa. gov/ data/gridded/data. cmap. html (Xie & Arkin, 1997) , (b) ERA5  (Hersbach et al. , 2019 (Hersbach et al. , , 2020)) , and (c) Mataveri: https://climatologia. meteochile. gob. cl/application/requerimiento/producto/RE1006/270001 (Only available in Spanish). The 1870-2021 sea surface temperature data are from HadISST: https://www. metoffice. gov. uk/hadobs/ hadisst/  (Rayner et al. , 2003). The 1525-2002 ENSO event list is available from  Gergis and Fowler (2009;   their Table  9 ). The PAGES 2k proxy data set (PAGES 2k Consortium, 2017) is available from the NOAA Paleo data search: https://www. ncei. noaa. gov/pub/data/paleo/pages2k/pages2k-temperature-v2-2017/, version 2.
0. 0. The Coupled Model Intercomparison Project phase 5 (CMIP5)  (Taylor et al. , 2012)  and CMIP6  (Eyring et al. , 2016)  models used in this study are listed in Table  S1  in Supporting Information S1; the model outputs are available from the Institut Pierre Simon Laplace (IPSL) CMIP nodes through the Earth System Grid Federation (ESGF) data portal: https://esgf-node. ipsl. upmc. fr/projects/esgf-ipsl/. The 850-1981 reconstructed SST-NINO3. 4 time series generated and analyzed in this study is deposited in a Zenodo repository  (Michel, Swingedouw, & Delcroix, 2022). Codes and analyses were integrally made using the R (R Core Team, 2019) and Bash Unix (https://www. gnu. org/software/bash/) languages.
In support of the global stocktake of the Paris Agreement on climate change, this study presents a comprehensive framework to process the results of an ensemble of atmospheric inversions in order to make their net ecosystem exchange (NEE) carbon dioxide (CO 2 ) flux suitable for evaluating national greenhouse gas inventories (NGHGIs) submitted by countries to the United Nations Framework Convention on Climate Change (UNFCCC). From inversions we also deduced anthropogenic methane (CH 4 ) emissions regrouped into fossil and agriculture and waste emissions, as well as anthropogenic nitrous oxide (N 2 O) emissions. To compare inversion results with national reports, we compiled a new global harmonized database of emissions and removals from periodical UNFCCC inventories by Annex I countries, and from sporadic and less detailed emissions reports by non-Annex I countries, given by national communications and biennial update reports.
For CH 4 , we find good consistency between the inversions assimilating only data from the global in situ network and those using satellite CH 4 retrievals and a tendency for inversions to diagnose higher CH 4 emission estimates than reported by NGHGIs. In particular, oil-and gas-extracting countries in central Asia and the Persian Gulf region tend to systematically report lower emissions compared to those estimated by inversions. For N 2 O, inversions tend to produce higher anthropogenic emissions than inventories for tropical countries, even when attempting to consider only managed land emissions. In the inventories of many non-Annex I countries, this can be tentatively attributed to a lack of reporting indirect N 2 O emissions from atmospheric deposition and from leaching to rivers, to the existence of natural sources intertwined with managed lands, or to an underestimation of N 2 O emission factors for direct agricultural soil emissions.
Introduction Despite the pledges of many countries to limit or decrease their greenhouse gas emissions through the Paris Agreement in 2015, current trends will likely lead to a warming of 3 to 4 • C (Robiou du  Pont and Meinshausen, 2018; UNEP, 2021). Following COP26, many countries have recently announced ambitious plans to become neutral in terms of their net greenhouse gas emissions in the future, with some ambitious near-term reduction targets  (Masood and Tollefson, 2021). The global stocktake coordinated by the secretariat of the United Nations Framework Convention on Climate Change (UNFCCC) aims to use data from national greenhouse gas inventories (NGHGIs) to assess collective climate progress. It is expected there will be differences in the quality of NGHGIs being reported to the UNFCCC  (Perugini et al.
Only by 2024, 1 year after the first global stocktake scheduled in 2023, will non-Annex I party countries move to regular and harmonized reporting of their emissions, following the Paris Agreement's enhanced transparency framework (ETF). The IPCC guidelines for NGHGIs encourage countries to use independent information to check on emissions and removals  (IPCC, 1997 (IPCC, , 2006)) , such as comparisons with independently compiled inventory databases (e. g. , IEA (International Energy Agency), CDIAC (Carbon Dioxide Information Analysis Center), EDGAR (The Emissions Database for Global Atmospheric Research)) or with atmospheric concentration measurements interpreted by atmospheric inversion models (see  Sect. 6. 10. 2 in IPCC, 2019). Such a verification of "bottom-up" national reports against "top-down" atmospheric inversion results is not mandatory, although a few countries have already added inversions as a consistency check of their national reports (specifically  Switzerland -FOEN, 2021 ; the United Kingdom -  Brown et al.
, 2020)  and is being prepared for 14 regions  (Canadell et al. , 2012). Previously, inversion results were compared to inventories only for one greenhouse gas  (Stavert et al. , 2020; Thompson et al. , 2019; Chevallier, 2021)  or for one country  (Kort et al. , 2008; Miller and Michalak, 2017; Miller et al. , 2019; White et al. , 2019; Lunt et al. , 2021). Recently,  Petrescu et al. (2021a, b)  provided a synthesis of the three greenhouse gas emissions over the EU27 (27 member states of the EU) and the UK for all major emitting sectors in two companion papers using global and regional inversions, the latter with higher-resolution transport models. They also compared NGHGIs with bottom-up datasets: global inventories, vegetation models, forestry models, and bookkeeping models analyzing specifically land use change fluxes (as part of the synthesis activity of the VERIFY project) (VERIFY, https://verify.
For Annex I countries, annual common reporting format (CRF) data were downloaded from the UNFCCC website (UNFCCC, 2021c) with complete information about each sub-sector, whereas for non-Annex I countries, information about sub-sectors is not consistently reported and a manual analysis and quality check of NC and BUR reports (UN-FCCC, 2021a, b) had to be carried out for each individual country analyzed here. Our inversion-inventory comparison framework is applicable to countries or groups of countries with an area larger than the spatial resolution of atmospheric transport models typically used for inversions. Further, inversions use a priori information on the spatial patterns of fluxes. Some inversions adjust fluxes at the spatial resolution of their transport models to match atmospheric observations and use spatial error correlations (usually Gaussian length scales) that tie the adjustment of fluxes from one grid cell to its neighbors at distances of hundreds to thousands of kilometers.