dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGMENTS We thank  A. Haouz  and the  Crystallography Facility at the Institut Pasteur (Paris)  for invaluable support in obtaining YukC crystals. We acknowledge the  European Synchrotron Radiation Facility  for provision of synchrotron radiation facilities, and we thank  Gordon Leonard  for assistance at beamline ID30A-3. We also thank the  Synchrotron SOLEIL  and its staff for initial tests. We are grateful to  P. Tavares  ( I2BC ,  Université Paris-Sud ,  Université Paris-Saclay ) and  D. Rudner  ( Harvard University, USA ) for the kind gifts of purified SPP1 phage and pDR111 plasmid, respectively. We acknowledge the  Institut Pasteur platforms of Proteomics and Molecular Biophysics (S. Brûlé and B. Raynal)  for the YukE MS analysis and nanoDSF experiments, respectively. We are very grateful to  A.
This work received financial support from the  Institut Pasteur (Paris) , the  Centre National pour la Recherche Scientifique (CNRS) , and  Aix-Marseille Université. T. D. and E. C. acknowledge the  Fondation Bettencourt-Schueller  for the acquisition of the epifluorescence microscope. M. T. was supported by a scholarship from the  Pasteur-Paris University (PPU)   International PhD Program. Author contributions. M. Tassinari: investigation, carried out experiments, figure preparation, writing -review and editing. T. Doan: investigation, conceptualization, carried out experiments, writing -review and editing. M. Bellinzoni: data collection, structure solving, writing -review and editing. M. Chabalier: carried out experiments, figure preparation. M. Ben-Assaya: carried out experiments. M. Martinez: carried out experiments.
Reviewer BZ [3] 1,2. We have changed our wording to document an acceptable rate of survey completion. We also added the number of survey question items to the Methods section, as this is a known factor for survey noncompletion. We have defined COVID-19 and SARS-CoV-2 the first time they are mentioned in the introduction of the paper. We have reread and edited our paper for punctuation and grammatical errors, and we used Grammarly software to assist with this process as well. 5. Please see our above comments on data analysis. 6. As the subjects of the study were physicians in training, the governing body dictating protocol was the Accreditation Council for Graduate Medical Education, which at the time and currently deferred to local institutional protocols apart from maintaining the need for supervision and providing adequate protective equipment.
Evaluation and data consistency The SSP5-3. 4-OS is a concentration-driven scenario based on the implementation of SSP5 in the REMIND-MAgPIE integrated assessment model (IAM)  (Kriegler et al. , 2017; Meinshausen et al. , 2020). Bauer et al. (2017) ,  Popp et al. (2017), and Riahi et al. (2017)  provided additional details on the changes in energy and land use. Hurtt et al. (2020)  provided the changes in land use in a coherent gridded format required for ESMs in the Harmonization of Global Land-Use Change and Management version 2 (LUH2) project. In LUH2, the historical data (up to the year 2014) based on the History of the Global Environment database (HYDE) and future scenarios (2015-2300) based on IAM are harmonized to minimize the differences between the end of historical reconstruction and IAM initial conditions  (Hurtt et al.
A1 ). We label the given grid of CMIP6 simulation outputs as crop-concentrated if the cropland fraction of the grid is larger than a given threshold derived via a sensitivity analysis (Fig. A1 ). We examined time-invariant cropland fraction thresholds ranging from 25 % to 45 % of the grid box area and selected a range of thresholds that best approximate the change in the total cropland area of each ESM in 2015-2100 (Fig. A2 ). Here we choose the fitting period of 2015-2100 because a shorter period (2040-2100) would result in a lower threshold during the 2050-2060 period with a large global cropland increase. More specifically, we selected a range of thresholds with a 1 % step so that they intersect at least once either the global cropland area estimated by ESM itself or LUH2 data set from 2015 to 2100.
Although the selected ensembles of thresholds are time-invariant, the resultant cropland area increases. We find that for a later period (end of the 21st century), a higher threshold is required because both the spatial coverage (the number of grid boxes that have crops) and cropland concentration (a grid fraction of cropland) increase (Fig. A2 ). We confirmed the spatial distribution of the minimum and maximum selected thresholds of energy-crop-concentrated grids against sub-grid-scale ESM and the LUH2 estimates of cropland area (Fig. A3 ). Data availability. The data from the CMIP6 simulations are available from the CMIP6 archive: https://esgf-node. llnl. gov/search/ cmip6 (WCRP, 2022), the LUH2 data from https://luh. umd. edu/ data. shtml (UoM, 2022), and the IIASA database via https://tntcat.
The peerreview process was guided by an independent editor, and the authors also have no other competing interests to declare. Disclaimer. Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. in ESMs. The IPSL-CM6 experiments were performed using the HPC resources of TGCC under the allocation 2020-A0080107732 (project gencmip6) provided by GENCI (Grand Equipement National de Calcul Intensif). This study benefited from state assistance managed by the National Research Agency in France under the Programme d'Investissements d'Avenir under the reference ANR-19-MPGA-0008. Our study was also supported by the European Union's Horizon 2020 research and innovation programme under grant agreement number 820829 for the "Constraining uncertainty of multi-decadal climate projections (CONSTRAIN)" project, by a grant from the French Ministry of the Ecological Transition as part of the Convention on financial support for climate services.
Roland Séférian and Thomas Gasser acknowledge innovation programme under grant agreement no. 101003536 (ESM2025 -Earth System Models for the Future). Roland Séférian acknowledges the European Union's Horizon 2020 research and the support of the team in charge of the CNRM-CM climate model. Supercomputing time was provided by the Météo-France/DSI supercomputing center. Financial support. This research has been supported by the Agence Nationale de la Recherche (grant no. ANR-19-MPGA-0008), Horizon 2020 (CONSTRAIN (grant no. 820829)), the Agence de la transition écologique (convention on financial support for climate services), the Ministry of Education, Culture, Sports, Science and Technology (grant no. JPMXD0717935715), and the Environmental Restoration and Conservation Agency (grant no. JP-MEERF20192004).
Acknowledgements. We acknowledge the  World Climate Research Programme , which, through its Working Group on Coupled Modelling, coordinated and promoted  CMIP6. We thank the climate modeling groups for producing and making available their model output, the  Earth System Grid Federation (ESGF)  for archiving the data and providing access, and the multiple funding agencies who support  CMIP6  and  ESGF. We thank  Richard Houghton  of  Woodwell Climate Research Center  for providing the regional annual fluxes for  LUC  from  HN2017 ,  Eddy Robertson  of  Met Office, and Vivek Arora of Canadian Centre for Climate Modelling and Analysis  for providing additional information on the LUC implementation.
Species inventories are the building blocks of our assessment of biodiversity patterns and human impact. Yet, historical inventories based on visual observations are often incomplete, impairing subsequent analyses of ecological mechanisms, extinction risk and management success. Environmental DNA (eDNA) metabarcoding is an emerging tool that can provide wider biodiversity assessments than classical visual-based surveys. However, eDNA-based inventories remain limited by sampling effort and reference database incompleteness. In this study, we propose a new framework coupling eDNA surveys and sampling-theory methods to estimate species richness in under-sampled and hyper-diverse regions where some species remain absent from the checklist or undetected by visual surveys. We applied this framework to the coastal fish diversity in the heart of the coral triangle, the richest marine biodiversity hotspot worldwide.
Combining data from 279 underwater visual censuses, 92 eDNA samples and an extensive custom genetic reference database, we show that eDNA metabarcoding recorded 196 putative species not detected by underwater visual census including 37 species absent from the regional checklist. We provide an updated checklist of marine fishes in the 'Raja Ampat Bird's Head Peninsula' ecoregion with 2534 species including 1761 confirmed and 773 highly probable presences. The Chao lower-bound diversity estimator, based on the incidence of rare species, shows that the region potentially hosts an additional 123 fish species, including pelagic, cryptobenthic and vulnerable species. The extended and hidden biodiversity along with their asymptotic estimates highlight the ability of eDNA to expand regional inventories and species distributions to better guide conservation strategies.
5 million hectares (so 10% of its EEZ) by 2030 (Indonesia Ministry of National Development Planning 2019,  Campbell et al. 2020). However, the evaluation of these management measures currently considers only conspicuous fish species that contribute to small-scale fisheries  (Campbell et al. 2020 ). Yet, many species that are often missed by visual surveys are important for ecosystem functioning like cryptobenthic and pelagic fishes that fuel reef productivity  (Brandl et al. 2019, Morais and Bellwood 2019). Environmental DNA (eDNA) metabarcoding is an emerging tool that can provide wider biodiversity assessments than classical visual surveys particularly for rare and elusive species  (Boussarie et al. 2018 , Garlapati et al. 2019 , Polanco Fernández et al. 2021). This non-invasive method is based on retrieving DNA naturally released by organisms in their environment, amplified by polymerase chain reaction (PCR) using universal primers, sequenced and taxonomically assigned using a genetic reference database  (Ruppert et al.
2019 , Polanco Fernández et al. 2021 ). Yet, species diversity using eDNA metabarcoding is often limited by the incompleteness of reference databases to accurately assign each sequence to a given species  (Marques et al. 2020a , Polanco Fernández et al. 2021). Additionally, the detectability of species using eDNA is sensitive to environmental conditions such as sea current or temperature  (Harrison et al. 2019) , hence some species can be missed in particular habitats or simply by chance. To overcome such sampling incompleteness, a wide range of statistical methods have been proposed to estimate the 'true' species richness or the number of undetected species across samples based on the occurrence of rare species  (Chao et al. 2017). The coupling of eDNA metabarcoding and samplingtheory-based methodology presents a promising approach to estimate biodiversity in under-sampled hyper-diverse regions but has never been tested.
Here we developed a quantitative framework to estimate the extended and hidden species diversity from eDNA samples, enhanced by an augmented reference database, in a region where visual surveys have been carried out and where an extensive species checklist has been assembled. We applied this framework to marine fishes of the Bird's Head Peninsula  (West Papua, Indonesia)  located in the center of the coral triangle, known as the richest marine biodiversity hotspot  (Allen and Erdmann 2012, Mangubhai et al. 2012).
Updated marine fish checklist We constructed an extensive species checklist of the 'Bird's Head Peninsula' (BHP) of West Papua Province' region based on historical fishing records and visual surveys  (Kulbicki et al. 2013)  including the ecoregions of the study area, extended with species occurring within and in the adjacent ecoregions with similar environments  (Allen and Erdmann 2012, Froese and Pauly 2020) , and with the specimen collected and observed during the 2017 survey. Species names were checked and updated using the authoritative reference and searchable on-line database Eschmeyer's Catalog of Fishes (<http:// researcharchive. calacademy. org/research/ichthyology/catalog/fishcatmain. asp>, Fricke et al. 2020). This extensive checklist identifies 2534 marine fish species including 1761 species with confirmed occurrences belonging to 582 genera and 144 families; it also includes 736 species that are present in close regions and similar environments (Supporting information).
Underwater visual census We retrieved data from 186 UVC transects performed during Aug-Sept 2014, Sept 2015 and Mar2018 from the Reef Life Survey initiative (<https://reeflifesurvey. com>). Additionally, we used data from 93 UVC transects performed between 2004 and 2013 in the region  (Cinner et al. 2016, Fig. 1). All surveys used standardized protocols with two divers recording fish identity, abundance and size in 5 × 50 m, or 2. 5 × 50 m for  Cinner et al. (2016) , blocks either side of the transect line. The two transect blocks include independent counts that are averaged to characterized the transect  (Edgar et al. 2020).
2019 , Polanco Fernández et al. 2022). Although alternative fish eDNA primers might cover a larger proportion of fishes in the reference database and hence be more informative on species identification, there is currently no primers located outside the 12S with similar performance  (Zhang et al. 2020). We followed a contamination control protocol during both field and laboratory stages  (Valentini et al. 2016). Water sample processing included the use of disposable gloves and single-use filtration equipment, and the bleaching (50% bleach) of Niskin bottles between samples. Staffs who performed eDNA filtration were not involved in tissue sampling of fish and used a dedicated workspace to avoid both contact and airborne contamination.
Genetic reference database completion During the same survey along the south-western coast of the BHP in West Papua, we collected 1466 individuals from 413 species, 180 genera and 69 families of fishes along the shore. The specimens were mainly collected by hand or with 4-8 m long bottom gillnets deployed by open-circuit and closed-circuit divers in the 0-100 m depth range  (Hocdé et al. 2020). Some brackish and estuarine fishes were also collected with 10 m beach purse seines and pelagic fish with line fishing and spearfishing. We used morphological features and 652 bp CO1 (cytochrome oxidase 1) targeted genetic sequencing to identify the specimens. Then we amplified and sequenced the individuals on a large fraction of the 12S mitochondrial rDNA region (480 bp) with two distinct pairs of primers respectively designed for teleosts and elasmobranchs to improve sequencing results.
Finally, the 12S teleo region defined in  Valentini et al. (2016)  was extracted from the obtained sequences to complement the EMBL genetic reference database (European Molecular Biology Laboratory, <www. ebi. ac. uk>, ver. 141, downloaded on January 2020,  Baker et al. 2000)  and improve taxonomic assignments (see Supporting information for the reference database and the methodological details of its completion). To evaluate the completeness of the online database for the teleo region of the 12S mitochondrial DNA, we performed an in silico PCR on the EMBL database with ecoPCR  (Ficetola et al. 2010 ) using the teleo primer sequences, allowing up to three mismatches. We compared the generated list of sequenced species to the extensive species checklist of the BHP ecoregion. Among the 1761 species of the Bird's Head Peninsula checklist for which presence is confirmed, only 496 species (28%) were sequenced in EMBL for the teleo region.
Taxonomic assignments The metabarcoding workflow was based on the VSEARCH toolkit and the clustering algorithm SWARM that groups multiple sequence variants into MOTUs (molecular operational taxonomic units,  Mahé et al. 2014)  to clean PCR and sequencing errors. We performed taxonomic assignments using the ecotag program (lowest common ancestor algorithm) from the OBITOOLS toolkit  (Boyer et al. 2016)  against our custom reference database and the global public EMBL genetic database (release 141, downloaded on January 2020). For each MOTU, we chose the taxonomic assignment with the highest similarity from either the custom reference database or EMBL. We only retained the assignments with 100% similarity to either reference database so matching perfectly over the full length of the sequence (Supporting information).
Fish traits The extended fish diversity may be characterized by certain traits or behaviors which may limit the detection by classical (fishing or visual records) and eDNA surveys  (Thalinger et al. 2021). To investigate this bias, we retrieved available data on habitat (reef or pelagic), diet, circadian activity, maximum body length, and IUCN (International Union for Conservation of Nature) conservation status for all the species detected by eDNA from Fishbase (<www. fishbase. org>) and compared them among the different sets of species.
This result is also consistent with many studies comparing sampling methods  (Polanco Fernández et al. 2021 , Marques et al. 2021 ). However, the incompleteness of reference databases limits the taxonomic assignment of eDNA sequences  (Marques et al. 2020a , Polanco Fernández et al. 2021). In our study, the custom genetic reference database increased the percentage of species sequenced in the checklist from 28. 4% to 43. 2%. However, a large part of MOTUs obtained by eDNA metabarcoding still could not be assigned suggesting a greater species detection potential  (Juhel et al. 2020). The completion of reference genetic databases is thus crucial to provide extensive biodiversity assessment with eDNA. Given these limitations in the use of eDNA, well established survey methods such as UVC remain paramount to provide baseline monitoring information  (Edgar et al.
2020). Additionally, traditional fishing techniques allow to collect tissue samples to fill the gaps in genetic reference databases. Thus, monitoring methods remain complementary and should be applied considering the aim of the sampling and methodological tradeoffs. In our study, eDNA revealed 37 fish species absent from the historical checklist with a high proportion of pelagic piscivorous species. Such mobile and elusive species are often missed by traditional methods that involve divers and are dependent on water visibility. This result confirms the significant and valuable contribution eDNA metabarcoding can provide for the monitoring of such species  (Mathon et al. 2022). Additionally, a large number of species were recorded with our fishing effort alone and added to the checklist during the development of the reference database.
Sshared Ŝhidden Ŝvisible Figure  5. Venn diagrams showing the number of species found in environmental DNA (eDNA) samples and underwater visual censuses (UVC) with the estimates of the visible Ŝvisible and hidden diversity Ŝhidden but also the diversity recorded by both methods S shared (a, b); and the number of species found by eDNA inside/outside the checklist and the extended diversity estimates Ŝext. ( c, d). The number of species in the sequenced checklist not detected by eDNA is indicated in grey. Despite the potential increase in species diversity detected using eDNA metabarcoding, some caution is needed before adding them to regional inventories. Sequences present in the online reference database that were used for taxonomic assignment may have been collected from individuals outside the region of interest.
This can induce assignment errors due to biogeographical related genetic variation  (Wadrop et al. 2016). Additionally, the robustness of taxonomic assignment is dependent on the taxonomic resolution of the barcode. For example, two phylogenetically close species can share a similar sequence on the 12S gene leading to incorrect species assignment if one of them is absent from the reference database  (Jackman et al. 2021). This bias can induce uncertainty on some species detection, which needs to be considered as 'putative' until all species from the same genus are sequenced. In this study, we defined a robust method to consolidate the identification of specimens and improve the quality of the sequences deposited in the genetic reference database (Supporting information).
The potential of eDNA to reveal hidden biodiversity Visual-and video-based sampling methods have long been used for monitoring underwater biota. However, these traditional methods sample only conspicuous species, overlooking cryptic and elusive species that can constitute a large portion of overall fish diversity  (Boussarie et al. 2018 , Brandl et al. 2018). Environmental DNA can reveal this hidden diversity and thus holds great potential for the evaluation of human impacts and the success of restoration and protection measures  (Zinger et al. 2020 , Boulanger et al. 2021) , to ultimately optimize and monitor conservation strategies. Beyond taxonomic assignment, impaired by the actual incompleteness of genetic reference databases and the unperfect taxonomic resolution of genetic markers, eDNA metabarcoding can be analyzed by generating MOTUs that can be defined as distinct taxonomic entities and act as a proxy of taxonomic diversity following adequate curation  (Marques et al.
Acknowledgements -We thank the  National Research and Innovation Agency (BRIN)  for promoting our collaboration and the  Sorong Polytechnic of Marine and Fisheries (Politeknik KP Sorong, West Papua)  for providing the vessel Airaha 02 that we used in this campaign. We thank  Julien Leblond ,  Ghofir Abdul  and  Amir Suruwaki  for contributing to the reference database completion and the fish sampling. We thank the crew of the  Aihara 02  for assisting us during the operations and  SPYGEN  staff for the technical support in the laboratory. Fieldwork and laboratory activities were supported by the  Lengguru 2017  Project (<www. lengguru. org>),that is was based on a joint collaboration between Indonesian and European scientists and was covered by a MoU signed between LIPI and IRD on 5 Apr 2017.
Data availability statement The data supporting this study, the custom reference database and the updated checklist are given in the Supporting information. The sequencing run is available from the Dryad Digital Repository <https://doi. org/10. 5061/dryad. sqv9s4n6f> and the metabarcoding pipelines are available in GitLab (<https://gitlab. mbb. univ-montp2. fr/edna/snake-make_rapidrun_swarm>). Sequence data and associated collection information, corresponding to the custom 12S reference database (Supporting information), have been made available on the GenBank (NCBI).
Aim: To report prespecified and post hoc analyses of the SoliMix dataset exploring the impact of baseline participant characteristics on the original SoliMix study outcomes, to enable informed treatment choices for people with different biomedical characteristics. Methods: SoliMix (EudraCT 2017-003370-13) compared once-daily iGlarLixi (a fixedratio combination of insulin glargine 100 U/mL and the glucagon-like peptide-1 receptor agonist lixisenatide) with twice-daily BIAsp 30 (30% insulin aspart and 70% insulin aspart protamine). In this analysis, the original primary outcomes of noninferiority of iGlarLixi versus BIAsp 30 in terms of glycated haemoglobin (HbA1c) change and superiority in terms of body weight change, together with change in basal insulin dose and hypoglycaemia outcomes, were investigated by baseline age, duration of diabetes, insulin dose, HbA1c level, body mass index (BMI), and renal function.