dataesr/scientific-paragraphs-categorization huggingface dataset:
Interspecific overlap in use of vertical habitats Overlap metrics and cluster analysis were used to compare the global vertical habitat use between species with five or more individuals with time-series datasets (n = 28 species). Specifically, depth data were binned into 10-m intervals for each individual and averaged, summarizing the proportion of time spent in each depth bin to a maximum of 1850 m (the maximum bin observed in time-series data). For each species, these proportions were averaged across individuals to obtain one distribution of proportion time at depth. Then, for each pair of species, the Bhattacharyya coefficient was computed using the proportion of time at depth. This coefficient ranges from 0 to 1 and measures the similarity between two discrete probability distributions, where 0 indicates no overlap between distributions and 1 indicates identical depth distributions  (77).
Diel variation in vertical distributions Patterns of DVM (i. e. , where vertical movements change across a daily cycle) are ubiquitous across elasmobranch species  (19, 40). Variation in quantitative definitions, however, has precluded quantitative comparisons among species. To compare standardized diel variation in vertical distributions, we calculated the median daytime and nighttime depths for each individual from available time-series data. To ensure that estimated medians were not biased by crepuscular activity and/or movements through regions of varying day length, daytime depths were calculated from data between 10:00 and 14:00 local time and for nighttime between 22:00 and 02:00 local time. We note that, for all Arctic skates and some Greenland sharks, deployment of tags in the northern hemisphere summer resulted in almost constant daylight for tagged individuals.
Drivers of elasmobranch depth preferences To investigate drivers of depth preferences of elasmobranchs, we fitted hierarchical Bayesian models with INLA using the "R-INLA" package  (78). The INLA methods determine an approximation to the posterior marginal distribution for parameters, which affords a large reduction in computational time compared to simulationbased (e. g. , Markov chain Monte Carlo) methods  (78). Within our initial full model, our response variable-log(median depth + 1)-was fitted within a normal likelihood. After removing data from the initial 24 hours after deployment, the next 7 days of track data per individual (or available data during this set period if data gaps occurred because of nonreceived transmissions) were used to ensure that the calculated median depths were closely tied to the spatial locations (i.
Only species with time-series data available were used, with data split into local day (10:00 to 14:00) and night (22:00 to 02:00) periods. Percentage values represent the proportion of individuals of a species displaying nDVM, rDVM, or neutral patterns (no difference between day and night). Day and night counts display the number of days of data available for each respective diel period. Downloaded from https://www. science. org at IFREMER-Centre de Documentation de la mer on March 29, 2023.
Acknowledgments: We would like to extend our thanks to  J. Carlson  and  R. Carter  for their data contribution and to  Marilla Lippert  for data collection support. We would also like to acknowledge the contribution of F. H. , both directly to this paper and to the field of elasmobranch research, who sadly passed away during the preparation of this manuscript. F. H. was an outstanding human being. Not only was he brilliant at his work but he also developed to be a second father to his team and students, who had the honor and privilege to take part in his life. Brazil, as much as the whole world, lost a persevering leader in the field of fishery sciences, and a void will inevitably remain among us for a long time. His former students and colleagues can only grant him a loud round of applause while regretting that forthcoming students will no longer be able to experience his teaching and companionship.
You will always remain with us, Fábio. Funding: We would like to thank all the organizations that funded the studies contributing to this work and those who supported the collection of the data used within this manuscript. Data analysis was funded by the  Bertarelli Foundation  through the  Marine Science program  through grants to D. J. C. , B. A. B. , and  S. A. D. J. C. is also funded through  Research England, UK. S. A. and B. A. B. thank the  Moore Foundation  and the  Packard Foundation. F. G. -M. thanks the  Instituto Politecnico Nacional  for fellowships (COFAA, EDI). S. B. W. thanks funding from the  Darwin Initiative  ( DPLUS046 ). A. D. M. D. acknowledges funding from the  Research and Conservation Budget at Georgia Aquarium , including philanthropic gifts from several anonymous donors.
K. F. acknowledges funding from the  Rolex Awards for Enterprise  and the  Whitley Fund for Nature. Author contributions: Conceptualization: D. J. C. and S. A. Data collection: All authors. Methodology: D. J. C. , S. A. , T. C. D. L. , and M. C. G. Investigation: S. A. and D. J. C. Visualization: S. A. , D. J. C. , T. C. D. L. , and M. C. G. Supervision: D. J. C. and B. A. B. Writing-original draft: S. A. , D. J. C. , N. E. Hus. , T. C. D. L. , M. C. G. , A. J. A. , A. C. , D. M. C. , A. C. G. , C. H. , D. M. P. J. , M. G. M. , J. M. , and L. R. P. Writing-review and editing: All authors. Competing interests: The authors declare that they have no competing interests. Data and materials availability: Processed data and code used in the analysis are accessible from the Zenodo Repository: 10.
Data Availability Statement The climate model hindcasts are available via the Earth System Grid Federation (ESGF) archive of the sixth Coupled Model Intercomparison Project (CMIP6) data (https://esgf-index1. ceda. ac. uk/projects/esgf-ceda/). ERA5 data is available from the ECMWF at https://www. ecmwf. int/en/forecasts/datasets/reanalysis-datasets/ era5.
Estimates of dominant energy bands of ocean currents, in the frequency domain, and of water exchanges will also be given to complement present knowledge. Some figures and tables, herein labelled with an ''S'' prefix, are provided as supplementary material with this manuscript. Data used for and obtained from this study are available at: https://doi. org/10. 6084/m9. figshare. 13526366 and https://doi. org/10. 17882/ 62314.
Data reduction Temperature and salinity data were mined from the Department of Fisheries and Oceans (DFO) Bedford Institute of Oceanography (BIO, http://www. bio. gc. ca/science/data-donnees/base/dat a-donnees/climate-climat-en. php, see also  Gregory (2004)  and Northwest Atlantic Fisheries Center (NAFC) archives to make a monthly climatology. The data consists primarily of XBT (eXpendable BathyThermograph), CTD and bottle profiles collected over the years on the Newfoundland shelves from a variety of platforms and institutions. The dataset was completed using recent CTD profiles collected by NAFC's aquaculture section  (Ratsimandresy et al. , 2014; Donnet et al. , 2018a). All data available within a square defined from 46. 2 to 48 • N and 57. 5 to 54. 5 • W were first extracted from the databases within the largest possible time-period  (01-Jan-1900 to 31-Dec-2018).
The dataset was subsequently reduced to the Fortune Bay area using a polygon (Figure  S1 ). In total, 810 profiles from BIO, 1130 profiles from NAFC and 806 profiles from NAFC's aquaculture section databases were extracted. The data was combined daily and spatially using a radius of 500 m; resulting in a dataset of 1750 averaged casts sampled over 911 stations. The earliest cast occurred the 15th of October 1925 and latest cast the 2nd of June 2018. Most of the sampling occurred after 1950 with a first broad peak from the late 50s to early 80s and a second from the mid-90s onward (Figure  S2 ), however, most of the data prior to the mid-90s consisted of temperature profiles only. An important sampling effort occurred in 1981 and the recent sampling effort led by NAFC's aquaculture section from 2009 to 2017 is noticeable also.
The dataset was split into two sub-polygons (areas): Fortune Bay (FB) and Belle Bay (BB), illustrated in Figure  S1. The separation was based on the Region of Freshwater Influence (ROFI) estimated in  Donnet et al. (2018b) ; that is, a region where the surface density field is likely dominated by freshwater inputs and thus where the surface water structure is likely to differ from the rest of the bay. The profiles were checked visually to remove dubious casts or spikes. Temperature and Salinity values at depth shallower than 1 m and salinity values below 20 were removed due to uncertainty in the data. Practical Salinity values (as provided in the databases) were converted to Absolute Salinity values using TEOS-10 relationships (IOC, SCOR,  IAPSO, 2010; McDougall and Barker, 2011)  and assuming a δS A equal to 0 (as advised in Pawlowicz, 2013 when working with coastal waters).
Previous Pathfinder data compared favourably with in-situ data in the region (Galbraith and Larouche, 2013 and references therein) and a more recent comparison using this version also showed good agreement with the most extensive in-situ data available of the region  (Poitevin et al. , 2022) ; providing some comfort to its use here. Data were first extracted for the time period covering 25 August 1981 to 31 December 2018 and for a region defined by an area 46. 5 to 48 • N and 57. 5 to 54. 5 • W. To reduce potential land contamination, a buffer of 2 km, i. e. half a pixel size, was used along the NRCan high resolution coastline (see  Donnet et al. , 2018b  for details of this later dataset). Monthly average series were made from spatially averaging the daily series within a polygon of Fortune Bay, i.
e. covering the whole bay. A total of 138 pixels were selected within the polygon (Figure  S8 ). The monthly series obtained were then averaged per calendar month to get a monthly climate (Figure  S9 ). Standard deviation was calculated from this latter average, representing a measure of interannual variability. Minimum and maximum of each averaged month of the monthly series were retained; representing coldest and warmest months observed within the period analysed. Data coverage is uneven both spatially and temporally, being substantially lower in Fortune Bay than in the domain covered in Fig. 4  (by about a factor 2, on average; Figure  S10 ). On average, the coverage does not exceed 10% at any given point in Fortune Bay and is lower in from November to February (<5% on average) and the highest in August (∼8%).
5 ). Using those data, the total annual mean runoff to Fortune Bay was estimated at 163 m 3 /s with a peak at 280 m 3 /s in April, a low at 80 m 3 /s in July-August and a second peak at 214 m 3 /s in December; about 4 times BDNRI's output. The effect of freshwater inputs on the bay's stratification, i. e. its change in salinity, can be looked at by comparing these inputs with the salinity climatology presented earlier and with near-surface salinity timeseries data recently collected  (Donnet et al. , 2020). We used BDNRI's monthly climate discharge described above and monthly climate of salinity at 5 m depth presented in Fig. 2  to make a 'climatology comparison' (Fig. 5 ) while BDNRI's monthly average timeseries were used for comparison with the recent timeseries collected from oceanographic moorings (Fig.
10  are a composite using the 2016-17 surface and mid-depth observations and 2015-16 bottom currents at all sites but F3B01. Due to data gaps in current direction, we used data from the 2015-16 observations at all depths for the spring, summer and fall periods and used data from the 2016-17 observations to represent the winter period surface and mid-depth layers for this latter site. Except for F3B02 in spring, the results were coherent over the 2 years of observations. At F3B02, surface currents flowed towards the SW in spring 2015 (as opposed to the NW in 2016) and mid-depth currents towards the W (N in 2016); currents were about the same strength at the near-surface level in both years but much weaker at middepth in 2015 (about 3 times less). Mean circulation is rather weak, on the order of 1-5 cm/s except in the Bay du Nord river estuary (F3B12) where mean surface outflow is about 15 cm/s in winter (factor 3 scaling applied in Fig.
041 • C/yr) considered as a world 'hotspot'  (Pisano et al. , 2020). It should be noted that those estimates are sensitive to the period of observation as well as the dataset used/chosen; using the same period as that of  Galbraith and Larouche (2013)  the same trend (0. 06 • C/yr) was found using our dataset. Nevertheless, and even at 0. 04 • C/yr, the trend is substantial and can be expected to have important effects on the ecosystem. With this in mind, a monthly trend analysis was performed and indicated this recent warming to be driven up by summer months (July to November) which have warmed at a rate of up to 0. 07 • C/yr (August) rather than by the winter months which show positive trends, nevertheless (0. 03-0. 04 • C/yr). This suggest that the probability for marine heatwaves may have increased but also that the probably for superchill events (i.
Water exchanges Three main mechanisms of water exchange (flushing) between the inner and outer part of the bay were assessed, that is: barotropic tides, estuarine circulation and baroclinic pumping. Large ranges of flushing rate were found, reflecting both large uncertainties with the approaches as well as a strong seasonal variability (as reflected by the salinity climate). The effect of baroclinic pumping process appears to dominate the exchanges but this result should be tempered by the important limitations and assumptions involving the estimates. Hence, the method used to quantify the effect of baroclinic pumping process on water renewal is based on empirical data collected on the Norwegian shelf which may not be representative of our region. As stated by  Aure et al. (1996) , the empirical coefficient β depends on the distribution of the (baroclinic) forcing frequency which is probably area-specific.
These forces, in turn drive the motion of its waters. Special attention is given to the upper layers (above sill levels) circulation, by extracting and quantifying the main components (mean seasonal circulation, weather band, tidal band and high-frequency band) and describing what appear to be the dominant dynamics (qualified herein as 'pulses'). Previous studies on bottom water renewal are summarized and slightly extended using water profiles collected more recently. Estimates of water exchanges from the main processes identified and corresponding flushing times of the inner part of the bay, where the aquaculture activities occur, are also provided to get a quantitative sense of their importance. Limited attention was given to the long-term and interannual variability, largely due to lack of data to do it but also because the focus of this study was on what is likely to be the dominant cycle (seasonal) and modes of variability (weather band).
India We use monthly emissions estimates for India updated from Andrew (2020b) through July 2022. These estimates are derived from many official monthly energy and other activity data sources to produce direct estimates of national CO 2 emissions without the use of proxies. Emissions from coal are then extended to August using a regression relationship based on power generated from coal, coal dispatches by Coal India Ltd. , the composite Purchasing Managers' Index, time, and days per month. For the last 3-5 months of the year, each series is extrapolated assuming typical trends.
EU We use a refinement to the methods presented by  Andrew (2021) , deriving emissions from monthly energy data reported by Eurostat. Some data gaps are filled using data from the Joint Organizations Data Initiative (JODI, 2022). Subannual cement production data are limited, but data for Germany and Poland, the two largest producers, suggest a small decline. For fossil fuels this provides estimates through July. We extend coal emissions through August using a regression model built from generation of power from hard coal, power from brown coal, total power generation, and the number of working days in Germany and Poland, the two biggest coal consumers in the EU. These are then extended through the end of the year assuming typical trends. We extend oil emissions by building a regression model between our monthly CO 2 estimates and oil consumption reported by the EIA for Europe in its Short-Term Energy Outlook (September edition) and then using this model with EIA's monthly forecasts.
Interannual variability in I FOS is the largest source of uncertainty in the GDP-based emissions projections. We thus use the standard deviation of the annual I FOS for the period 2012-2021 as a measure of uncertainty, reflecting a ±1σ as in the rest of the carbon budget. For restof-world oil emissions growth, we use the global oil demand forecast published by the EIA less our projections for the other four regions and estimate uncertainty as the maximum absolute difference over the period available for such forecasts using the specific monthly edition (e. g. August) compared to the first estimate based on more solid data in the following year (April).
, 2020; Chini et al. , 2021). LUH2-GCB2022 provides a distinction between rangelands and pasture, based on inputs from HYDE. To constrain the models' interpretation on whether rangeland implies the original natural vegetation to be transformed to grassland or not (e. g. browsing on shrubland), a forest mask was provided with LUH2-GCB2021; forest is assumed to be transformed to grasslands, while other natural vegetation remains (in case of secondary vegetation) or is degraded from primary to secondary vegetation  (Ma et al. , 2020). This is implemented in BLUE. OSCAR was run with both LUH2-GCB2022 and FAO/FRA (as used with the updated H&N2017), where the drivers of the latter were linearly extrapolated to 2021 using their 2015-2020 trends. The bestguess OSCAR estimate used in our study is a combination of results for LUH2-GCB2022 and FAO/FRA land-use data and a large number of perturbed parameter simulations weighted against a constraint (the cumulative S LAND over 1960-2020 of last year's GCB).
Similarly, an estimate for the year 2021 is based on the MapBiomas trend 2015-2020. The pre-1985 period is scaled with the per capita numbers from 1985 from MapBiomas, and thus this transition is smooth. HYDE uses satellite imagery from ESA-CCI from 1992-2018 for more detailed yearly allocation of cropland and grazing land, with the ESA area data scaled to match the FAO annual totals at country level. The original 300 m spatial resolution data from ESA were aggregated to a 5 arcmin resolution according to the classification scheme as described in Klein  Goldewijk et al. (2017a). DGVMs that simulate subgrid scale transitions (i. e. gross land-use emissions; see Table  A1 ) use more detailed landuse transition and wood harvest information from the LUH2-GCB2022 data set. LUH2-GCB2022 is an update of the more comprehensive harmonized land-use data set  (Hurtt et al.
Related to this, E LUC plus S LAND on non-intact lands is a larger source estimated by ORCHIDEE-MICT compared to NGHGI. We also show FAOSTAT emissions totals (FAO, 2021) as a comparison, which include emissions from net forest conversion and fluxes on forest land  (Tubiello et al. , 2021)  and CO 2 emissions from peat drainage and peat fires. The 2021 data were estimated by including actual 2021 estimates for peatland drainage and fire and a carry forward from 2020 to 2021 for the forest land stock change. The FAO data shows a global source of 0. 24 GtC yr -1 averaged over 2012-2021, in contrast to the sink of -0. 54 GtC yr -1 of the gap-filled NGHGI data. Most of this difference is attributable to different scopes: a focus on carbon fluxes for the NGHGI and a focus on area and biomass for FAO.
HCl (9') and PAβN. Experiments were performed on both E. coli BW25113 and the pyridylpiperazine resistant E. coli isolate (carrying the S450P AcrB mutation) to confirm the on-target activity of BDM88855. HCl (9'). Bacterial viability was determined using the resazurin reduction assay and is expressed as a percentage resazurin reduction compared to the untreated bacteria, with this viability colour coded from yellow (100%) to blue (0%). Data represent the mean bacterial viability of at least four independent replicates. Source data are provided as a Source Data file. unable to fully revert E. coli susceptibility to that of E. coli ΔacrAB (MIC 0. 78 µg/mL), while BDM88855. HCl (9') achieved this.
2 , Fig. S3 ). Analysis of the anomalous signals originating from the iodine atom of BDM88832 (8) or from the brominated pyridylpiperazine inhibitor (compound 11) indicated the location of the halogen atoms (Fig. S3 ) and confirmed the binding of BDM88832 (8) and BDM88855 (9) at this position in the TM domain, with minimal halogen and hydrogen bond length deviations between the interaction of the latter inhibitor in wildtype and the R971A variant (Fig. S3d ). Superimposition of the unliganded AcrB L protomers  31  (PDB: 4DX5) and inhibitor-bound structures reveals a root mean square deviation (RMSD) (C α ) of 0. 89-1. 05 Å over 1024 residues (Table  S9 ), with the most obvious deviations at the TM domain (Fig. 2b , Table  S9 ). Here, the TM helices show subtle differences in their conformation at the cytoplasmic side, leading to an expansion of the narrow channel between the D407, D408, and K940 residues of the proton relay site and the cytoplasm, thus allowing for the accommodation of BDM88855 (9) (Fig.
0004 in cation-adjusted Mueller-Hinton broth (CAMHB). This culture was split in two, and spiked with either DMSO (control for booster activity alone), or pyridomycin (final concentration of 5 µg/mL), and transferred to the destination plates containing the chemical library (50 µL per well). Following culture (5 h, 37 °C), E. coli viability was evaluated using the resazurin reduction assay, and measured by fluorescence (POLARstar Omega, BMG Labtech: Ex: 530 nm, Em: 590 nm). Medicinal chemistry. The chemical synthesis can be found in the supplementary methods section and structural validation of the compounds can be found in the source data files. Liquid MIC determination. The minimum inhibitory concentration (MIC) determination was performed in 96-well format in order to confirm screening results, define EPI activity on bacterial mutants, and evaluate the activity of improved EPIs.
DNA was extracted as per standard phenol-chloroform-isoamyl alcohol (25:24:1) procedure (ThermoFisher scientific). Genomic DNA and purity were checked by DeNovix DS-11 Series (DeNovix, Wilmington, Delaware), and acrB sequenced by Sanger sequencing (Genoscreen, France). Whole-genome sequencing and variant analysis. For whole-genome sequencing, genomic DNA from bacteria was submitted to MicrobesNG (http:// www. microbesng. com, Birmingham, UK) for library preparation (Nextera protocol) and whole-genome sequencing on an Illumina sequencer (NovaSeq600). Trimmed reads (deposited at NCBI BioProject ID: PRJNA764862) were initially processed using PRINSEQ-lite PERL script (PRINSEQ-lite version 0. 20. 4; 42 ) to remove low-quality data with the following parameters (-min_len 50 -min_-qual_mean 30 -trim_qual_right 30 -ns_max_n 0 -noniupac).