dataesr/scientific-paragraphs-categorization huggingface dataset:
Population-level assessment of microbial community We sequenced the prokaryotic and eukaryotic viral communities from 223 individually identifiable and known-age mute swan samples collected during one year of sampling at the Fleet Lagoon, UK. A summary of the 223 collected samples and of their associated metadata (age, sex, sample collection date) is presented in Table  S3  and Fig. S1. After sequencing a total of 6,447,121,538 reads were obtained. After filtering for quality, a total of 6,095,650,474 cleaned reads were assembled de novo into contigs. Following data cleaning 205 samples yielded data of sufficient quality for analysis, each of which yielded between 3,873,397 and 46,437,718 reads (average of 29,015,008 reads). Of these, 193 samples contained putative bird-infecting vOTUs. On average 43.
S4b ). The prokaryotic community that we characterised was therefore very similar to those observed in previous metagenomic studies conducted on other Anatidae species (Fig. 1 ), and comparable to those observed in other domesticated and wild avian species  [66] [67] [68]. We reconstructed 138 eukaryotic viruses contigs corresponding to >90% of the expected full length CDS of each virus based on the whole reference genome of the same virus species or the closest-related species in GenBank. Full contig details, including corresponding GenBank accession numbers, are available in Table  S5. The majority of the contigs (73%, 101/138) could be fully classified to the species level under ICTV criteria, and most (76%, 77/101) represent novel species (Table  1 , Table  S5 ). The other 37 contigs could not be classified to the species level according to existing ICTV definitions, as either they belonged to segmented virus families for which we could not unambiguously determine associated segments (e.
). All of the 11 already known species of bird-infecting virus found in our datasets are known to infect Anatidae species (Table  S7 ). Four of the 16 bird-infecting virus species that have been identified in swans were present in our samples (Table  S8 ) (Anseriform dependoparvovirus 1, Waterbird 1 orthobornavirus, and Cygnus olor circovirus). We also detected influenza A virus at very low abundance (<10 reads that could not be assembled into a contig). Taxa that are present in every sample can be considered the common core intestinal microbiota of a population  [72]. No birdinfecting virus families were detected in all samples, putatively due to a lower virus genetic material enrichment compared to prokaryotes 16S rRNA gene (Fig. 2 ). In contrast, we observed that 24 of 48 detected prokaryotic phyla, and 127 of 302 families were present in every sample.
ACKNOWLEDGEMENTS SCH is supported by the  Wellcome Trust  ( 102427/Z/13/Z  and  220414/Z/20/Z ) (https:// wellcome. org/). For the purpose of open access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission. SF is supported by the  Biotechnology and Biological Sciences Research Council (BBSRC)  grant  BB/T008806/1  (https://bbsrc. ukri. org/). This research was supported by the  John Fell Fund (University of Oxford , grant code  0009179  and  0005172 ) (https://researchsupport. admin. ox. ac. uk/funding/internal/jff). We thank the  High-Throughput Genomics Group at the Wellcome Trust Centre for Human Genetics  (funded by  Wellcome Trust  grant reference  090532/Z/09/Z ) for helping to generate sequence data. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
DATA AVAILABILITY Virus genomes generated here are available from GenBank (accession numbers MW588037 -MW588200), and their respective accession numbers can be found in the Table  S5. Metagenomics datasets have been deposited on the SRA database of NCBI with the accession numbers SRR13307418 -SRR13307623 under the BioProject PRJNA685791. Scripts are available at https://github. com/SarahFrancois/ SwanMetagenomics.
Conclusion: In patients with ARDS under low tidal volume ventilation during prone position, the changes in PPV during a TVC can reliably assess preload responsiveness without the need for cardiac output measurements. Trial registration: ClinicalTrials. gov (NCT04457739). Registered 30 June 2020 -Retrospectively registered, https:// clini caltr ials. gov/ ct2/ show/ record/ NCT04 457739 Keywords: Pulse pressure variation, Fluid responsiveness, ARDS, End-expiratory occlusion test.
Patient characteristics Patients with ARDS ventilated with a 6 mL/kg tidal volume under prone position were prospectively included from January 2019 to May 2021. In total, we included 69 patients of whom 55 had COVID-19 (76%). The inclusions were conducted after prone positioning for 11  (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14)  hours. Data from eleven patients (13 cases) were excluded from the analysis since only one of the two preload responsiveness tests (Trendelenburg or EEO 8 ) was positive (three cases with only ΔCI TREND ≥ 8% and ten cases with only ΔCI EEO 8 ≥ 5%). Therefore, data from 58 patients were included in the analysis (Table  1 ). Sixteen patients were studied more than once (maximal number: four times), and the delay between two inclusions was 5  (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14)  days.
Also recognizing the added value of networking, it is worth highlighting that this review encompasses the main outcomes of multidisciplinary, international and intersectoral regional coordinated efforts in the framework of the Mediterranean Operational Network for the Global Ocean Observing System (MONGOOS) HFR Task Team. These endeavors are primarily focused on meeting end-user and science-driven requirements, aiming to unlock HFR data potential and deliver greater uptake, use and value from the data for the benefit of ecosystems, services and human activities of the coastal areas of the Mediterranean Sea. This paper constitutes the second part of two complementary contributions, the first one providing a detailed overview of the main achievements, ongoing activities, future challenges and the roadmap towards an integrated, mature HFR network in the Mediterranean Sea  (Lorente et al.
In particular, the nested model showed a better representation of the smallscale coastal flow intensification at the mouth of the Ebro River and a refinement in the characterization of the circulation in the Ibiza Channel. Notice that HFR-Gibraltar, HFR-Ebro and HFR-Ibiza versus model comparisons are updated daily on the SOCIB WMOP web page https://socib. es/ ?seccion=modelling&facility=wmedvalidation (last access: 9 May 2022). Those HFR systems, among others, are also integrated in the IBISAR science-based data downstream service  (Reyes et al. , 2020a ) -freely available under registration at https://www. ibisar. es/ (last access: 9 May 2022) -for visualizing, comparing and evaluating the performance of ocean current predictions in the Iberian-Biscay-Irish regional seas. IBISAR allows the identification of the most accurate ocean current dataset in a specific area and period of interest, thus facilitating decision-making for SAR operators and emergency responders.
e. , satellite altimetry, sea surface temperature -SST, and Argo temperature and salinity profiles). After 48 h, the mean separation distance between virtual buoys and real drifters was reduced by 53 % compared to the simulation without any data assimilation and by 29 % compared with the simulation assimilating traditional observations only (as shown in Fig. 5 ). To the best of the authors' knowledge, SOCIB WMOP (https://www. socib. es/?seccion=modelling&facility= forecast, last access: 9 May 2022) is presently the only system in the Mediterranean Sea including an assimilation scheme of HFR data in its operational chain.
Hardware failures due to power issues, communications or environmental conditions often result in spatiotemporal gaps within HFR datasets. Spatial gaps can be filled on a real-time basis, but the filling of long temporal gaps is not straightforward. Several gap-filling methodologies have been developed for HFR datasets: open modal analysis -OMA (implemented by  Lekien et al. , 2004 , and further optimized by  Kaplan and Lekien, 2007) , data-interpolating EOFs -DINEOF  (Beckers and Rixen, 2003; Alvera-Azcárate et al. , 2005; Hernández-Carrasco et al. , 2018b, Bourg and Molcard, 2021) , self-organizing maps -SOMs  (Kohonen, 1982 (Kohonen, , 2000 (Kohonen, , 2001;; Hernández-Carrasco et al. , 2018b) , reduced-order optimal interpolation -ROII  (Kaplan et al. , 1997) , optimal interpolation -OI  (Kim et al.
, 2013 ). The forecasting system performance was tested in the forecast (hindcast) mode during 2009 and 2010. Unfortunately, the HFR system has had substantial problems since 2010 and the antennas were eventually removed in the following years, resulting in a relatively short dataset that is possibly not sufficient for strong reliability of the forecasting system solutions. However,  Vilibić et al. (2016)  compared this SOM-based surface current forecast system (available online in http://jadran. izor. hr/neural/index. htm, last access: 9 May 2022) with the operational ROMS -Regional Ocean Modelling System  (Shñhepetkin and McWilliams 2003, 2005)  for the Adriatic, with the former lower showing biases and root mean square errors.
Here we present examples of application of HFR data to investigate ecological questions in three Mediterranean coastal areas: the Gulf of Naples (GoN, Tyrrhenian Sea), the Gulf of Manfredonia (GoM, western Adriatic Sea) and the Malta-Sicily Channel (mid-Mediterranean Sea). In the GoN, continuous observations of the HFR current fields highlighted several characteristics of the surface circulation and water exchange between the interior of the gulf and the neighboring open Tyrrhenian Sea  (Cianelli et al. , 2013 ). An oscillating plankton population dynamic has also been frequently observed in the GoN, at Long-Term Ecological Research station MareChiara (LTER-MC), where plankton abundance has been monitored weekly since 1984. A proof-of-concept study  (Cianelli et al. , 2017)  was thus conducted in order to characterize the spatial scales and the provenance of phytoplankton assemblages detected at LTER-MC and to dissect processes regulating plankton dynamics.
The phytoplankton community detected at LTER-MC generally originated from the coast, whereas the offshore inflow marginally changed the main traits of phytoplankton assemblages. Backtracking simulations and biological data strictly agree, highlighting that the plankton community at LTER-MC during 2009 was affected by the alternation of coastal and offshore influence. Biological autogenic factors drive the modifications of coastal phytoplankton communities during coastal "green" phases, thus suggesting that the GoN tends to retain the same communities via coastward circulation, especially during summer. Physical allogenic factors are important in driving dilution and species advection of coastal phytoplankton during the offshore "blue" phase. This marked alternation between coastal and offshore water masses acts to promote phytoplankton diversity because the dilution in the phytoplankton density may decrease the impact of the dominating species over the available resources.
2. 3. 3). These practical applications have fostered the use of HFR data in supporting coastal zone management (CZM) and assessing the variability in the dynamics of marine ecosystems, becoming a valuable asset for contributing to the achievement of Good Environmental Status (GES) of the Mediterranean waters. However, it has been found that the number of detected eddies depends on the HFR data availability, highlighting the need to combine HFR data with numerical model outputs to bridge the spatiotemporal gap of the observed data and to https://doi. org/10. 5194/os-18-797-2022 Ocean Sci. , 18, 797-837, 2022 improve the reliability of the simulations. It should also be noted that the limited coverage of HFR reduces the potential of larger-scale applications and connectivity studies, thus requiring their integration with other in situ and satellite observations as well as models, which in turn will benefit from the future expansion of the HFR network.
The combination of such observations is challenging mainly for two reasons: the surface and ocean interior are prone to different processes and forcings with different spatiotemporal scales, and, at the same time, the capabilities to resolve and characterize the diverse processes may be different for observing platforms at the sea surface and in the water column. Despite the promising results obtained by  Berta et al. (2018)  and  Guihou et al. (2013)  in the Mediterranean Sea, necessary efforts must continue towards the further development of methodologies to combine HFR data with water column measurements and models.
, 2021)  of building a database of current and potential stakeholders with the input of some Mediterranean institutions. However, stronger involvement is needed to avoid imbalances between countries and greater efforts are required to move it forward by means of programs that promote networking and coordination. Tight interactions with stakeholders on a fitfor-purpose basis and the enhancement of the societal impact of HFR data are major elements of the strategy, especially towards ensuring long-term sustainability. Boosting the regional involvement in this strategy will result in the spreading of end-user applications at the regional and/or local level, also stimulating the development of new ones in response to user feedback. d. Strengthen public-private partnerships (i) at the regional level by enhancing inter-institutional collaboration to exchange expertise, to build consortia and to share job opportunities in this field.
g. , Copernicus Marine Service, EMODnet, SeaDataNet), which will avoid duplication of efforts. (iii) At the global level, this could be achieved by collaborating with international institutions that are world leaders in HFR and ocean observation as well as with the global HFR network to ensure consistency in data standards and best practices. (iv) With the private sector, partnerships could be strengthened by transferring https://doi. org/10. 5194/os-18-797-2022 Ocean Sci. , 18, 797-837, 2022 knowledge and applications from academia to the operational oceanography industry to turn them into commercial services, improving the links between research and new technologies. The development of existing and potential applications will also assist HFR manufacturers in marketing their technologies.
e. Foster HFR data integration with other in situ and satellite data. Exploiting the nature of their measurements, HFRs are currently being used for filling the gaps in other sparse or lower-spatiotemporal-resolution observations in coastal areas, as well as for improving and assessing satellite observations. In this context, the opportunity offered by the launch of the wide-swath Surface Water and Ocean Topography (SWOT) altimeter expected in November 2022 should be considered. The SWOT altimeter should be complemented with other remote and in situ sensors to fully resolve the typical Mediterranean mesoscale structures of 10-100 km  (Gómez-Navarro et al. , 2018). Additional complementarities might be fostered with the monitoring of surface currents worldwide using information from the Automatic Identification System (AIS) data streams  (Benaïchoucheet al.
, 2021) , from which HFR measurements can be used as a consistent ground-truth dataset for validation purposes and for increasing the spatial resolution of the AIS-reconstructed fields at coastal areas. The integration of HFR measurements with other multi-platform observations from gliders or acoustic Doppler current profilers (ADCPs) (Manso-Narvarte et al. , 2020) has already been implemented and tested, particularly under the umbrella of the JERICO-NEXT project  (Griffa et al. , 2019). This multi-platform combination underpins a sound understanding of the threedimensional coastal circulation, allowing the broadening of HFR applications (i. e. , below the surface). Data fusion and integration will contribute to increasing the societal and scientific value of all observations, not only the HFR ones.
However, additional technical training courses provided by manufacturers at the HFR operator level are recommended. Furthermore, the participation of Mediterranean institutions and companies in the creation of an international, intersectoral and interdisciplinary qualified supporting training network will contribute to boosting knowledge and know-how exchanges involving all actors (e. g. , academia, operators, manufacturers, private sector). In addition to all abovementioned recommendations, seeking funding is a compulsory task aiming to support, together with stakeholder engagement and the training of new generations, the long-term sustainability of the HFR network, data and applications at national, regional and European levels. To this end, the EuroGOOS HFR Task Team has taken early steps to prepare a competence matrix that will facilitate the building of effective, interdisciplinary, intersectoral and well-balanced consortia, grounded in shared research interests and goals, aiming to prepare competitive bids and applications for funding, taking advantage of the expertise of the team in diverse grant calls  (Rubio et al.
e. , new HFR systems) and time (i. e. , longer time series). This will increase the availability of multiyear surface current datasets, thus contributing to boosting the application of HFR-derived STPs that use selflearning algorithms to other Mediterranean locations. For such sites, the stability of SOM solutions in time may be tested as well, or the self-learning and training of SOM solutions might change in time to properly reflect long-term changes in oceanographic conditions in a coastal area. The further development of short-term predictive systems based upon HFR surface current fields and their adaptation to the Mediterranean HFR network by incorporating non-tidal components of currents will enhance STP integration into operational maritime safety applications, which has demonstrated their capacity to reduce the searching area  (Roarty et al.
Their detailed and comprehensive reviews have been very helpful to improve the structure and reading as well as to finalize the paper. Financial support. Publication fees are provided by EuroSea (EU Horizon 2020 research and innovation program, grant agreement ID no. 862626) and JERICO-S3 (EU Horizon 2020 research and innovation program, grant agreement ID no. 871153). Research was supported by the following projects: CMEMS-INSTAC phase II, which provides the context of the activities for HFR data harmonization, standardization and distribution; the IBISAR CMEMS User-Uptake project (67-UU-DO-CMEMS-DEM4_LOT7); MEDCLIC project (LCF/PR/PR14/11090002, supported by La Caixa Foundation) that contributed to the development of the WMOP model; the CARTHE III project (Prime Award no. SA 18-14, subcontract agreement SPC-000649) and CALYPSO Departmental Research Initiative (grant no.
N00014-18-1-2782), which supported the development of the methodology for extreme event monitoring in the Ligurian Sea (Sect. 2. 2. 1); the IMPACT project (EU funded, PC Interreg VA IFM 2014-2020, Prot. ISMAR no. 0002269) that funded the HFR network in the Ligurian Sea; the JERICO-NEXT project (EU Horizon 2020, grant agreement no. 654410) under which the assimilation of HFR data in WMOP (Sect. 2. 1. 2) and the biological connectivity application in the Gulf of Manfredonia (Sect. 2. 3. 3) have been developed; the COCONET project (EU FP7, grant agreement no. 287844) and the Italian national projects SSDPESCA and RIT-MARE; and the SICOMAR-PLUS EU Interreg Marittimo project, which funded the recent upgrades to the HFR installations of the MIO in Toulon. Review statement. This paper was edited by Anna Rubio and reviewed by two anonymous referees.
Code availability. The European HFR node shares the code to process native HFR data for quality control and to convert them to the standard format for distribution for the workflows of both near-realtime and reprocessed data, as defined in  Corgnati et al. (2020 Corgnati et al. ( , 2019)) , respectively. Data availability. SOCIB HFR-Ibiza data, used in Sect. 2. 1. 1 (Fig. 2 ), Sect. 2. 1. 2 (Fig. 5 ) and Sect. 2. 3. 1 (Figs. 10 and 11), are available at http://apps. socib. es/data-catalog/#/data-products/ hf-radar-ibiza  (Tintoré et al. , 2020). SOCIB drifter data, used in Sect. 2. 1. 1, are available at http://apps. socib. es/data-catalog/#/ data-products/socib-exp-radar-sep2014  (Tintoré et al. , 2014)  for 2014 (they have also been used in Sect. 2. 1. 2, Fig. 5 ), http://apps.
Reactions were then incubated at 37°C for 30 min. The reactions were loaded on the gel upon the addition of 3. 5 ll of DNA loading dye (10 mM Tris-HCl pH 8, 30% glycerol, bromophenol blue). The products were separated by electrophoresis in 1% native agarose gels in the presence of GelRed (1:20,000, Biotium). Images of gels were acquired at InGe-nius3 (GeneSys). The results were quantified using ImageJ and expressed as % of cruciform unfolding; any linear DNA (no cruciform) present in control (no protein) reactions was referred to as 0% cruciform unfolding, and subtracted as a background from all other samples. Graphs were plotted using Prism software (Prism 9, GraphPad). Reactions with T7 endonuclease were carried out similarly as described above. The samples were incubated at 37°C for 10 min upon WRN addition, and subsequently T7-Endonuclease I (New England Biolabs) together with the respective restriction endonucleases were added to the reactions, and further incubated at 37°C for 60 min.
The products were separated by 10% polyacrylamide gel electrophoresis, dried on 17 CHR chromatography paper (Whatman), exposed to storage phosphor screens (GE Healthcare), and scanned by a Typhoon 9500 phosphorimager (GE Healthcare). Helicase assays with the 2. 2 kb long substrate were analyzed by running the samples by electrophoresis in 1% agarose. Gels were dried on DE81 chromatography paper (Whatman), exposed to storage phosphor screens (GE Healthcare), and scanned using the Typhoon Phosphor Imager FLA 9500 (GE Healthcare). The bands were quantified using ImageJ and data were plotted using Prism software (Prism 9, GraphPad).
The samples were analyzed by electrophoresis on 1% native agarose gels, unstained. The gels were post-stained with water containing GelRed (1:20,000, Biotium) with gentle agitation for 45 min. Images of gels were acquired at InGenius3 (GeneSys). The results were quantified using ImageJ, and graphs were plotted using Prism software (Prism 9, GraphPad).
Acknowledgements Valentina Mengoli  is a recipient of a  Roche Postdoctoral Fellowship. The  Cejka laboratory  is further supported by grants from the  Swiss National Science Foundation  ( 310030_205199 ),  European Research Council  ( 681630  and  101018257 ), and  Swiss Cancer League  ( KFS-5397-08-2021 ). Related work in the laboratory of PHG was supported by  Institut National du Cancer  ( INCA PLBIO 2019-152 ). We thank members of the  Cejka laboratory  for comments on the manuscript. The plasmids pFB-HisPMS2, pFB-His-hMSH6, pFB-His-hMSH3, and pFB-HisPMS2-E41A were a kind gift of  Prof. Josef Jiricny.