dataesr/scientific-paragraphs-categorization huggingface dataset:
2021 ), with about 2500 new species added each year (www. lifewatch. be/en/worms-stats), most of them within Animalia (i. e. , about 204,000). Here, we provide an updated worldwide estimate of the actual knowledge of COI barcodes related to marine animal species. Moreover, we selected five Large Marine Ecosystems (LMEs, www. lmehub. net) considered as biodiversity hotspots (Mediterranean Sea, Caribbean Sea, North Sea, Indonesian Sea, and Red Sea) (sensu  Myers et al. , 2000 , Spalding et al. , 2007) , to detect possible disparities in COI barcoding coverage of marine animals at regional scale and to underline where more efforts should be enrolled. Our data were gathered from the World Register of Marine Species (WoRMS Editorial Board, 2021), while their presence within the five LMEs was retrieved from the Ocean Biodiversity Information System (OBIS, Intergovernmental Oceanographic Commission of UNESCO; www.
iobis. org accessed on May 2021). The availability of COI barcode records at species level were retrieved from the National Center for Biotechnology Information (NCBI, www. ncbi. nlm. nih. gov, accessed on May 2021) and Barcode of Life Data System (BOLD, www. boldsystems. org, accessed on May 2021) repositories using custom Perl scripts (www. osf. io/qsn5e/). Globally, from the 207,821 known marine animal species, only 14. 5% (NCBI) and 13. 8% (BOLD) possessed COI barcodes, representing increases of 4. 3% and 5%, respectively (see Supplementary material S2) since the estimates by  Bucklin and colleagues (2011). This relative increase could be explained both by (i) progresses in barcoding of species already described until 2011 and (ii) identification of new species with consequent barcoding.
The five LMEs encompass a total of 28,219 nominal marine animal species, with 7775 (27. 6%, NCBI) and 8200 (29. 1%, BOLD) having at least one COI sequence. Evidently, the proportion of barcoded species in these hotspots doubles that at global level. Nevertheless, the current LMEs barcoding data are still not sufficient for comprehensive biodiversity assessments. Furthermore, this barcoding coverage and species richness varies across LMEs (Fig. 1 ). Variable research intensity and disparity in sampling efforts and access to marine biological collections may explain the differences in number and relative occurrences (i. e. , frequency) of detected species, as well as the variable barcoding coverage among the LMEs  (Collins et al. , 2020)  (Fig. 2A ). We can clearly notice this in the North Sea, which is the most intensively studied LME, with species occurrence records starting in 1753 vs.
Chordata clearly shows the highest number of nominal species and barcoding coverage (68. 6% NCBI, 71. 5% BOLD), particularly among Vertebrata (70. 1% NCBI, 73. 1% BOLD). Conversely, non-vertebrate Chordata (ascidians) are highly underrepresented (36. 3% NCBI, 35. 0% BOLD) (see Supplementary material S4). Taxa being underrepresented in terms of barcoding could be understudied and/or undersequenced. For example, looking at Table 2 in  Appeltans et al. (2012) , over the 8700-11100 estimated bryozoan species only 5900 are known/described (thus, 53. 1-67. 8% of bryozoan species are currently known). Compared to well-studied groups such as mammals (94-99% of current knowledge), bryozoans could be considered understudied. However, the 1863 COI bryozoan sequence records in BOLD belong to 245 species that have been sequenced.
Final remarks Our results evidence a clear lack of COI sequences available worldwide in respect to the total described species. This poor COI barcode availability is still maintained when focusing on marine animals, particularly invertebrates. We also clearly observe that the North, Caribbean, and Mediterranean Seas are highly underrepresented in barcode databases, thus requiring further efforts. In addition, giving the higher species richness characterizing the LMEs hotspots, we noticed clear morpho-genetic gaps that could possibly be less evident in non-hotspot geographic areas. The high frequency of non-barcoded species could be also related to overwhelming barcode presences of the most common, widespread or problematic (e. g. invasive) species, as well as data unavailability in certain geographic areas (Supplementary S5).
From our results, 3374 species with at least one barcode are shared between at least two LMEs, with Chordata, Artrhopoda and Mollusca leading this tendency (1211, 626, 571 shared species respectively) (Supplementary S5). However, albeit relying on WoRMS for taxonomy, OBIS database does not include all described marine species, particularly when extracting data from specific geographic areas. A clear example is the Red Sea, which hosts more marine animal species  (Dibattista et al. , 2016 ) than those we found in OBIS (  2734 ). This incompleteness is not due to scarce OBIS database curation, but rather to insufficient efforts in transferring new and archived biogeographic data coming from publications devoted to biological monitoring campaigns. The gaps are most marked in those regions (e.
g. the Red Sea and the Indonesian Sea) where initiatives and regulations aimed at sharing data on public repositories are probably not yet well implemented. There is also a need to consider the accuracy of the barcodes deposited at NCBI and, even, at BOLD. Misidentified sequences hinder the broader utility of DNA barcoding, probably more so than the use of open nomenclature and unspecific identification. NCBI and BOLD DNA barcodes derived from unintentionally misidentified organisms have to be tracked down and reduced to zero, preventing the whole barcoding workflow to be meaningless and not reflecting progress(es) with respect to biodiversity assessments. While low barcoding error rate presences at genus level have only been suggested  (Leray et al. , 2019) , instances of species-level misidentification have been effectively detected  (Meiklejohn et al.
Such an integrated approach would enable physical specimens (accessioned in permanent natural history museum collections) to be linked to molecular, morphological and ecological data, creating meaningful and long-lived reference data repositories. In parallel, an interactive virtual platform able to interface with different database sources and showing upto-date species-specific ecological, morphological and molecular data must be created (such as the work in progress within LifeWatch Species Information Backbone, available at www. LifeWatch. eu). Configuring these public repositories following a top-down layout linking other subset databases based on FAIR (Findable, Accessible, Interoperable and Reusable, www. wilkinsonlab. info/node/FAIR, Wilkinson et al. , 2016)  principles is mandatory to allow efficient data sharing.
Acknowledgements This paper is a result of the CoMBoMed workshop ( Census of Marine Mediterranean Benthic Biodiversity : an integrative metabarcoding approach), held in Ravenna (Italy) on February 11-14, 2019 and organized by the  Department of Biological, Geological and Environmental Sciences (BiGeA) of the University of Bologna (UniBo). The CoMBoMed initiative was supported by the  European Marine Research Network (EUROMARINE Network) , the  Inter-Departmental Research Centre for Environmental Sciences (CIRSA -UniBo) , the  Cultural Heritage Department (DBC -UniBo , www. beniculturali. unibo. it/it), the  Fondazione Flaminia  and the  ERANet Mar-Tera  Project  SEAMoBB (Solutions for sEmi-Automated Monitoring of Benthic Biodiversity ). This paper is also a contribution of DM to the  Research Group on Marine Benthic Ecology (AGAUR ,  2017SGR378 ) and to the  Research Project PopCOmics  ( Agencia Estatal de Investigación of Spain, FEDER ,  CTM2017-88080 ).
Therefore, the main objective of the present study is to develop damage functions from insurance claims data supported by hydrodynamic modelling. A total of 423 reported claims in the area of study were used (Fig. 1 ). The damage ratio (DR) is defined as the ratio of damages claimed by each property to the total insured value of that property. More than 9 % of the structures had a damage ratio (DR) higher Published by Copernicus Publications on behalf of the European Geosciences Union. than 0. 5 (considerable damages), 30 % had DR higher than 0. 2 (medium damages), and 49 % had low damages. This is a typical distribution for damage claims (see for example  Fuchs et al. , 2019). The damage curve is an important tool in risk assessment science related to the vulnerability of structures  (Pistrika and Jonkman, 2010; Englhardt et al.
, 2019). From the structural point of view, damage curves depend on the construction materials that buildings are made of  (Huizinga, et al. , 2017; Postacchini et al. , 2019; Masoomi et al. , 2019). Damage curves also depend on construction methods, codes, and building layout, including the distance between buildings  (Suppasri et al. , 2013; Jansen et al. , 2020; Masoomi et al. , 2019). The current paper focuses on one-two-storey masonry buildings under the effect of storm surge and wave forces produced by an extratropical storm in northwest France. The Xynthia storm provided a rare dataset of empirical measured damage from coastal flooding in a European country. Similar analysis of damage from other storms with different return periods in the same region would help to reduce uncertainty  (Breilh et al.
A spinup time of 5 d was considered in the study to remove spurious effects of the top layer soil moisture adjustment even though most of the analyses here are performed over the ocean. Land surface processes are resolved by using the NOAA land surface model scheme with four soil layers. Numerical schemes used in the WRF simulation to downscale Xynthia data are the multi-scale Kain-Fritsch scheme for convection, the Yonsei University scheme for the planetary boundary layer, the WRF single-moment six-class scheme for microphysics, and the RRTMG scheme for shortwave and longwave radiation. WRF outputs are generated every 3 h.
Topography and bathymetry We use two types of topography datasets: a global dataset for the bathymetry/topography (GEBCO, 2020, which is based on SRTM 15+ v2 over land), and a higher-resolution bathymetry (MNT HOMONIM project) and topography (IGN). Additionally, a survey of flood wall height was performed during August 2020 in order to include flood walls as thin weirs inside the Delft3D model and in this way overcome the fact that inside the high-resolution 5 m topography, these structures are not represented, as suggested by  Bertin et al. (2014). Luppichini et al. (2019)  and  Ettritcha et al. (2018)  found that the quality of bathymetry and topography data has a large effect on estimation of the hazard, and Brussee et al. (  2021 ) similarly found topography data quality affects resulting damage estimates.
Wave model validation The wave model was validated against data from the SHOM-Coriolis operational oceanography centre (http://www. coriolis. eu. org/About-Coriolis, last access: 1 May 2021) in Fig. 5. Important to mention is that the data available at the buoys stations do not include the significant wave height; therefore, the swell height was extracted to compare the results from Delft3D-SWAN. The uncertainty produced by the meteorological downscaling by means of the WRF model in the hindcast of the winds can add errors in the results. Unfortunately, no more meteorological information is available. If we again compare the indices from Table  2  to those found in the literature such as  Baron-Hyppolite et al. (2019) , we find comparable goodness of fit between modelled and measured waves.
P (x) = xµ σ , where P (x) is the cumulative probability of the damage ratio with values between 0 and 1, and x is the hydrodynamic variable, is the standardized normal distribution, µ is the median, and σ is the standard deviation  (Tsubaki et al. , 2016). It is also very common to express Eq. (  1 ) as a logarithmic function in order to easily obtain the parameters of the distribution with least squares fitting as proposed by  Suppasri et al. (2013). In the present paper, the parameters are assessed using the Lmoments package within the open-source program R. In this way, it is possible to relate different hydrodynamic variables with the damage ratio. From the 423 claims data within our domain, approximately 185 are from Île de Ré, and the remaining 238 are from the towns of La Rochelle, Aytré, Yves, Châtelaillon-Plage, and Fouras.
Results After determining the model hydrodynamic and wave results (Fig. 6 ) at the location of each claim location, the data were subdivided into 10 categories according to damage ratio level, and box-whisker plots were built to display the entire dataset and analyse the trend of the data (Appendix A). Among the flow-only variables, the unit discharge (hv) appears to have the clearest trend and least scatter. From the variables related to both flow and waves, the total force E f C g + ρhv 2 appears to have the clearest trend and correlation with the damage ratio.
Regarding the first point, there is a trend that indicates that better topography/bathymetry data give hydrodynamic variables that correlate better with the damage ratio. This is because higher-resolution data generally more accurately reproduce the actual flood conditions  (Luppichini et al. , 2019; Ettritcha et al. , 2018). Damage curves developed with a better representation of the topography (IGN + structures) improve the accuracy indicators (Table 3), though scatter in the data themselves (Figs. A1, A2 or A3) is large for all topographies. This first point is also related to the mesh resolution and the roughness coefficients used. The second point deals with the quality of the damage ratio data. It is known that insurance claims can sometimes be subject to fraud or information distortion.
Acknowledgements. This work is funded by the  AXA Joint Research Initiative (JRI)  project  INFRA: Integrated Flood Risk Assessment. A special acknowledgement is due to  Adri Mourits  from  Deltares  for the help provided with the Delft3D debugging and to  Christopher Lashley  for the help during the field trip in Île de Ré and surroundings during August 2020. Financial support. This research has been supported by the  AXA Research Fund (AXA Joint Research Initiative (JRI)  project  IN-FRA: Integrated Flood Risk Assessment). Review statement. This paper was edited by  Sven Fuchs  and reviewed by  Bret Webb  and two anonymous referees.
, Delft3D calculates non-steady flow phenomena that result from tidal and meteorological forcing on a rectilinear or a curvilinear grid(Deltares, 2021). At the same time, and coupled with Delft3D, a spectral wave model (SWAN) calculates significant wave height and period fields. Delft3D and SWAN were used to hindcast the physical forcing at the locations of all claims in the database. Afterwards, a probability standardized normal distribution function as proposed by Suppasri et al. (2013)  was used to develop damage curves by correlating claimed damage with a variety of hydrodynamic forcing variables. To conserve computational resources and reduce computation time, domain decomposition (two-way hydrodynamic nesting) was implemented with grids of resolution of ∼ 2 km over the open ocean, ∼ 400 m close to the study area, and ∼ 80 m over the area of claims data (Fig.
20, 2010) and the study was approved by the local review board ("Comité d'éthique pour l'Expérimentation Animale Neurosciences Lyon:" CELYNE; CNREEA no. 42; APAFIS agreement no. #4688). Data were reported according to ARRIVE guidelines (Animal Research: Reporting of In Vivo Experiments). Animals were housed in groups of six per cage (except for the two-photon microscopy experiments: one animal per cage, due to the cranial implant), in a temperature and humidity-controlled environment (21 ± 3 °C), with 12:12 h light-dark cycle, and free access to standard chow and tap water. Animal experiments were performed in 8 week-old (24 ± 2 g) C57Bl/6 male mice (Janvier, France), and 8-12 week-old (23 ± 2 g) CX3CR1 eGFP/+ transgenic male mice (originally donated by Serge Nataf in 2012 and bred at the INMG Institute, Claude Bernard University, Lyon).
Except for four mice (group II: n = 2; group III: n = 2) that were imaged only with intravital microscopy, all the mice (n = 23) underwent postcontrast MRI at day 3 (D3) post-pMCAo (48 h following injection). All animals were sacrificed by intracardiac perfusion at the end of the experiment and brains were sampled for histology, transmission electron microscopy, or X-ray phase-contrast tomography for 3D depiction of gadolinium brain distribution. [57] Sample size was determined so as to obtain enough data for a proof-of-concept study. pMCAo Model: Focal cerebral ischemia was induced at D0 in mice anesthetized with isoflurane (induction: 3. 5%; surgery: 2%; ISO-VET, Piramal Healthcare, Morpeth, UK), by permanent occlusion of the distal middle cerebral artery (pMCAo) with iron chloride (FeCl 3 ), as previously described by Karatas et al.
These sites were determined and selected by visual observation of the morphological characteristics of microglial cells and their localization with regard to the lesion on two-photon imaging. Twenty-four to 45 consecutive Z-stacks, with field size ≈420 × 420 µm, were acquired every minute with step size of 1 µm. A typical recording lasted ≈10-15 min (10-15 Z-stacks). To compare progression in CX3CR1-GFP/+ cell density and morphology and NanoGd internalization by CX3CR1-GFP/+ cells, the same areas were imaged at D1 and D2. Anatomical landmarks such as blood vessels were used to locate the same areas between the two days of imaging. Data Analysis: MRI: The volume of hypointense MR signals on T2-WI in the ischemic lesion was used as a biomarker of NanoGd accumulation following pMCAo. [24] For each mouse, hypointense signal volume in the lesion was normalized by ischemic lesion volume.
Analysis was performed manually by a single blinded operator using ImageJ software (National Institute of Mental Health, Bethesda, MD, USA; imagej. nih. gox/ij/). Two-photon microscopy: Image processing and analysis were performed using ImageJ and custom-written MatLab software (ver. R2018b). Prior to analysis, the first Z-stack from each acquisition was adjusted as well as possible between D1 and D2 in all three axes. For CX3CR1-GFP/+ cell density count, the first Z-stack from each acquisition, systematically containing 24 images, was used to generate a maximum intensity projection. After uniformly adjusting the images to reduce background noise, the number of CX3CR1-GFP/+ parenchymal and circulating cells was quantified manually in each field of view. The total number of parenchymal and circulating CX3CR1-GFP/+ cells was then divided by the imaging volume corresponding to the parenchyma and blood vessels, respectively, in order to generate a measure of cell density for each compartment.
An average Z-projection was generated, and the number of cells that had internalized the nanoparticle was counted manually. This number was subtracted from the total number of CX3CR1-GFP/+ cells in the field, providing a percentage value. For nanoparticle quantification in the brain interstitium, four 20 × 20 pixel ROIs were delineated on the average intensity projection in the red channel. Mean fluorescence intensity was measured for each ROI at D1 and D2 and averaged for each imaging session. For 2D time-lapse video generation, images were first corrected for drift in the x, y, and z axes during acquisition, using a custom-written MatLab program. The correction was accomplished by shift estimation from the cross-correlation peak by fast Fourier transform (FFT) between the first stack of each acquisition (reference Z-stack) and the following Z-stacks.
[59]  IR = Perimeter∕Area 2×(∕Area) 1∕2 (1) Confocal microscopy: Confocal microscopy was used to investigate NanoGd internalization by cultured microglial cells in vitro. Five representative areas of 246 × 246 µm 2 were acquired (6-9 Z-stacks) for each slide for each condition (control, NanoGd 0. 5 × 10 -3 m, and NanoGd 1. 5 × 10 -3 m). NanoGd internalization by microglial cells in these areas was quantified on ImageJ using the method described above (Two-photon microscopy in the "Data Analysis" section under the Experimental Section). Histology: Mice were euthanized by intracardiac perfusion with PBS followed by perfusion with 4% PFA. Brains were then removed, postfixed with 4% PFA for 24 h, and frozen in methylbutane with dry ice. Finally, tissues were cut into 12 µm sections by cryostat.
In-line phase contrast tomography was performed on beamline ID17 of the European Synchrotron Radiation Facility (ESRF) in Grenoble at 35 keV. An indirect detection-based detector with a LuAg scintillator, standard microscope optics, and 2560 × 2160 pixel CMOS camera was positioned 11 m from the sample to obtain phase contrast. The whole-brain data set was acquired at 6. 1 µm isotropic pixel size. The acquisition time of the 2800 projections lasted <5 min per brain. Reconstruction was performed with Paganin's algorithm by setting / to 1000, following Marinescu et al. [57]  Transmission Electronic Microscopy: Mice were euthanized by intracardiac perfusion with PBS and then with a specific fixation solution (4% PFA, 4% glutaraldehyde, 0. 3 mol L -1 cacodylate, 0. 3 mol L -1 CaCl 2 ). Brains were removed, and three samples of 1 mm 3 were collected and stored in the fixation solution: one in the extralesional area, one in the border zone, and one in the ischemic core, each from the ipsilateral hemisphere.
Finally, sample preparation for TEM imaging session was performed by the CIQLE platform (Medicine Faculty, Lyon, France) as described in the Experimental Section in the Supporting Information. Sections were examined with a Jeol 1400JEM (Tokyo, Japan) 120 kV transmission electron microscope equipped with a Gatan Orius 600 camera on wide-field position and Digital Micrograph software (Gatan Inc. ). EDX analysis was used to determine the presence of gadolinium within the TEM samples. Statistical Analysis: Data were not preprocessed and were presented either as box plots (median (25% percentile; 75% percentile)) or as histograms (mean ± standard deviation (SD)). One-way, two-way, and threeway analyses of variance (ANOVA; R statistical software version 3. 3. 3 (2017-03-06) "Another Canoe") were performed to evaluate the effects of: i) experimental condition; or ii) imaging day, imaged brain area, and (as appropriate) imaged mice.
Acknowledgements V. H. , I. H. , O. P. , and M. W. contributed equally to this work. The authors thank  Radu Bolbos  and  Jean-Baptiste Langlois  of the imaging platform Animage (CERMEP, Lyon), and  Elisabeth Errazuriz-Cerda  and  Denis Ressnikoff  from the  CIQLE platform  ( UCBL1, Lyon ) for technical assistance, and the  ALECS animal facility (Lyon, France)  for CX3CR1 eGFP/+ mouse breeding and housing. Yann Bretonnière  from the  ENS chemical laboratory (UCBL1, Lyon)  is also acknowledged for his help with the nanoparticle fluorophore. The authors thank the whole  NanoBrain consortium  for critically reviewing the results at each step. This research was funded by the  French national research agency (ANR)  project  NanoBrain  (grant #  ANR-15-CE18-0026-01 ) and was performed as part of the  RHU MARVELOUS  ( ANR16-RHUS-0009 ) of  University Claude Bernard Lyon 1 (UCBL) , within the  "Investissements d'Avenir" program.
Data Availability Statement The processed data required to reproduce these findings and perform the statistical analyses are available to download at the figshare repositoryhttps://figshare. com (https://doi. org/10. 6084/m9. figshare. 13046291). The raw data required to reproduce these findings cannot be shared at this time due to the large size of confocal, MRI, and two-photon microscopy data, but can be available from the corresponding author on reasonable request.