dataesr/scientific-paragraphs-categorization huggingface dataset:
16S rRNA gene metabarcoding data processing and analysis 16S rRNA gene reads were processed using the FROGS workflow  [48]  (details in SI). An average number of 13 827 (± 10 779) 16S rRNA gene sequences per sample was obtained after filtration of sequences affiliated to chloroplasts and mitochondria (which represented an average of 37. 5 ± 23. 5% of total number of sequences per sample). α-diversity measures were estimated with Chao1 and Shannon indexes using the rarefied dataset , and concentrations of dissolved trace metals (copper, lead, zinc, cadmium) measured from the seawater at each site (rarefaction performed to the minimum library size, see SI). As recommended by  [49, 50] , all the other analyses were conducted without rarefaction, using the dataset normalized to the total number of sequences per sample.
β-diversity was analyzed with a non-metric multidimensional scaling (NMDS) and distance base redundancy analysis (db-RDA) using Bray-Curtis distance with "phyloseq" and "vegan" packages, respectively  [51, 52]. The use of environmental constraints for db-RDA is detailed in SI. LEfSe analyses were performed to reveal specific taxa using the online tool from the Galaxy environment  [53]. SIMPER analyses were performed at the genus level with the "vegan" package to identify which genera are contributing the most to the dissimilarity between samples  [54]. The algal core community (ACC) of each algal sample was defined as the community spatially and temporally stable in terms of composition, and thus by keeping only OTUs occurring at least in one of each replicate. This definition was used according to  [14]  and prevent to fix an arbitrary percentage.
Metabolomics data processing, annotation, and statistical analyses Raw UPLC-MS data were converted into netCDF files using DataAnalysis software (version 4. 3, Bruker Daltonics) and processed for peak finding, integration, and alignment using the XCMS package  [55]  (details in SI). Data were filtered according to  [56]  by considering signal/noise ratio, coefficient of variation, and correlation. The resulting data matrix was log 10 -transformed, meancentered, and normalized using the sum of the chromatographic peak areas as described in  [44]  and analyzed using PCA followed by partial least-square discriminant analysis (PLS-DA) allowing to reveal discriminant features according to sampling months or sites. The most discriminant features were selected according to their significance and their variable importance in projection (VIP) scores to pay attention on their annotation.
The annotation step was performed as described in  [14, 44]. In brief, annotation was assessed by comparison of m/z value, retention time, and MS/MS spectra with those of our in-house database  [57]  (including purified compounds and commercial standards listed in SI) and public databases (e. g. , MetLin, LipidMaps). To confirm our annotation, elucidation of the fragmentation pathways was performed and, when possible, compared to the literature. The whole dereplication procedure was facilitated using molecular networking (GNPS platform; details in SI).
Integration of metabolomics, metabarcoding, and environmental datasets Environmental parameters, 16S rRNA gene metabarcoding and surface metabolomics datasets were analyzed together into a global integrative approach to assess the correlations existing between each group of variables. To perform this analysis, the DIABLO approach using mixOmics R package  [58]  was used with the block. splsda() function, allowing to build a correlation network with the most correlated and discriminant features of each dataset (details in SI). The main discriminant metabolites (determined from PLS-DA, VIP scores > 2) and biomarker OTUs (determined from LEfSe, LDA scores > 3. 5) found in the resulting correlation network were plotted as a function of the environmental parameters. These plots were overlaid with smoothed conditional means plots using geom_smooth() function (loess regression, span = 0.
Surface metabolome profiling The surface metabolomics dataset (422 m/z features) was first analyzed by PCA (Fig. 4A ) and showed a clear temporal shift according to each month (Table  S8 ). Statistical differences between sites were observed, except between S2 and S4 (Table  S8 ). Component 1 (23% of the total variance) showed a metabolomic shift from May to July whereas component 2 (11% of the total variance) mainly explained the metabolomic shift from February to May. Two PLS-DA models were constructed using either months or sites as supervised groups. The PLS-DA model discriminating months (Fig. 4B ) showed a score plot highly similar to the pattern obtained with the PCA. The second PLS-DA model discriminating sites showed a clear difference between S1 and the other sampling sites (Fig.
Multi-omics network analysis A multi-omics-based network (Fig. 6 ) revealed correlations between the most discriminant features from each dataset (OTUs from 16S rRNA gene metabarcoding, surface metabolites from metabolomics, and environmental data). The whole network showed two clusters with only positive correlations. Temperature and copper concentrations were the only two environmental parameters showing correlations with features of other datasets, one in each cluster. The first cluster, associated to the seawater temperature, gathered variables involved in the temporal variations. This cluster was composed by characteristic variables of the summer period, such as surface metabolites corresponding to several DGTAs, lyso-DGCC (C16: 0) and DMSP. This latter is one of the most discriminant metabolites increasing with time (Table  S9 ), and its relative concentration was investigated in more details, as a function of the temperature changes, showing a nonparametric temporal increase with this environmental parameter (Fig.
Especially, in the case of trace metal contamination, whose impact has been rarely considered, this work brought new insights to decipher anthropogenic effects on marine coastal ecosystems. Additional file 1: Table  S1  Environmental parameters and sampling dates (Part 1: from February to April 2017). For trace metals, (d) and (t) indicated dissolved and total acid leachable fractions, respectively. n. d. : not determined. Table  S2  Multivariate pairwise results (p values) examining differences between "sample types" for the 16S rRNA gene dataset with all samples (999 permutations). Table  S3  Multivariate pairwise results (p values) examining differences between "months" and between "sites" for the 16S rRNA gene dataset with Taonia atomaria samples (999 permutations). Table  S4  Multivariate pairwise results (p values) examining differences between "months" and between "sites" for the 16S rRNA gene dataset with rocky biofilm samples (999 permutations).
p values were calculated with a permutation test constructed with 999 permutations and corresponded to the probability of getting a larger or equal average contribution in random permutation of the group factor. Cum. sum corresponded to the ordered cumulative contribution. Table  S8  Multivariate pairwise results (p values) examining differences between "months" and between "sites" for the LC-(+)-ESI-MS metabolomics dataset (999 permutations). Table  S9  List of biomarkers (VIP score > 2) identified by LC-HRMS and involved in the discrimination between months within surface extracts of T. atomaria (Part 1). P values corresponded to results of one-way ANOVA tests using "Month" as factor. Color codes corresponded to mean normalized concentrations (see Part2: Table  S12 ). List of biomarkers (VIP score > 2) identified by LC-HRMS and involved in the discrimination between months within surface extracts of T.
atomaria samples and revealing discriminant epibacterial taxa specific to each sampling site (LDA threshold set to 3. 5). Figure  S10  Percentage of core community sequences (A) and algal-enriched community sequences (B). *: "Other" corresponded to unaffiliated families and families below 1%. Figure  S11  Plots from the db-RDA scoring (A) environmental parameters together with algal samples (identical to Fig. 3B ), and (B) environmental parameters together with OTUs represented by dashed circles. Dashed circles size was proportional to their relative percentage within the whole algal dataset. Dashed circles filled in pink corresponded to core OTUs, while those filled in dark blue were not affiliated as core members. The name of the 5 main core OTUs were annotated above their corresponding dashed circles.
Acknowledgements The authors are grateful to  Dr. M. Peirache ,  I. Masinski ,  G. Esposito , and  L. Maxime  ( National Park of Port-Cros, Hyères-les-Palmiers, France ) for the sampling at Porquerolles Island; to  Dr S. Greff  ( Aix-Marseille University, IMBE, France ) for the acquisition of LC-MS profiles; and to  N. Carriot  ( Toulon University, MAPIEM, France ) for the annotation of MS/MS data.
The mean dynamic topography (MDT) is a key reference surface for altimetry. It is needed for the calculation of the ocean absolute dynamic topography, and under the geostrophic approximation, the estimation of surface currents. CNES-CLS mean dynamic topography (MDT) solutions are calculated by merging information from altimeter data, GRACE, and GOCE gravity field and oceanographic in situ measurements (drifting buoy velocities, hydrological profiles). The objective of this paper is to present the newly updated CNES-CLS18 MDT. The main improvement compared to the previous CNES-CLS13 solution is the use of updated input datasets: the GOCO05S geoid model is used based on the complete GOCE mission (November 2009-October 2013) and 10. 5 years of GRACE data, together with all drifting buoy velocities (SVP-type and Argo floats) and hydrological profiles (CORA database) available from 1993 to 2017 (instead of 1993-2012).
Data The CNES-CLS18 MDT is calculated from a combination of altimeter and space gravity data, in situ measurements and model winds. The following datasets are used: -MSS. The CNES-CLS15 MSS derived for the 1993-2012 reference time period by  Pujol et al. (2018)  is used. -Geoid model. The satellite-only geoid model GOCO05s  (Mayer-Gürr et al. , 2015)  is used with the CNES-CLS15 MSS in the computation of the MDT first guess. -Altimeter sea level anomalies (SLAs). The DUACS-2018  (Taburet et al. , 2019 ) multimission gridded sea level and derived geostrophic velocity anomaly products distributed by the Sea Level Thematic Assembly Center (SL-TAC) from the CMEMS altimeter are used. -Dynamic heights. These are calculated from temperature and salinity (T /S) profiles from  CORA4. 2 (1993 -2013 ), CORA5.
0 (2014 -2015)   -In situ velocities. Two types of in situ drifting buoy velocities are used, the 6-hourly SVP-type drifter distributed by the Surface Drifter Data Assembly Center (SD-DAC;  Lumpkin et al. , 2013)  and the Argo floats surface velocities from the regularly updated YOM-AHA07 dataset for the period 1993-2016  (Lebedev et al. , 2007). SVP-type drifters consist of a spherical buoy with a drogue attached in order to minimize the direct wind slippage and follow the ocean currents at a nominal 15 m depth. When the drogue gets lost, the drifter is then advected by the surface currents and the direct action of the wind. In this study both the 15 m drogued and the surface undrogued drifter velocities over the 1993-2016 period are considered for the MDT calculation. The year 2017 is used for independent validation of the results.
Another interesting aspect from Fig. 3  is that the latitudinal dependency almost vanishes for deep-MLD values. This is expected from theoretical considerations: from Eqs. (  2 ) and (3) we get β(z = 0 m) = √ 2 √ N √ τ γ 1/4 √ ρ 0 √ f , (5) β(z = 15 m) = √ 2 √ N √ τ γ 1/4 √ ρ 0 √ f e 15 √ f N γ 1/4 w *. (6) In the case of a well mixed upper layer (strong MLD values), the Prandtl number N/f ≈ 1 so that the β value does not depend any more on the f values (i. e. , β becomes independent of latitude). Table  1  shows the percentage of variance explained by the new surface model, compared to the old one  (Rio et al. , 2014a). % VarU = i u w -u insitu w 2 i u in-situ w 2 (7) % VarV = i v w -v insitu w 2 i v insitu w 2 (8) The new surface model is fitted using the entire 2007-2016 period dataset, whereas the old surface model was https://doi.
Note that an upper bound is set at 6 d for the cutting period to avoid overly high values at low latitudes. The Argo floats' surface velocities are corrected from the surface wind-driven model. Argo floats surface velocities being sampled only every 10 d, no further low-pass filtering can be applied on the Argo velocity dataset. Also, as discussed in  Rio et al. (2014a)  and mentioned earlier, no wind slippage correction is applied to Argo floats since, thanks to their design, they are less impacted by wind slippage than undrogued SVP. Temporal variability is then removed by interpolating altimetric geostrophic velocity anomalies along the drifter trajectories and subtracting them from the in situ geostrophic velocities. The obtained mean geostrophic velocities are further averaged into 1/8 • boxes (instead of 1/4 • boxes in Rio et al.
Dynamic heights data processing The method used to calculate the synthetic MDT (MDT synth ) is the same as the one fully described in  Rio et al. (2011)  and also used in  Rio et al. (2014a). We use the temperature (T ) and salinity (S) profiles from the CORA database (Sect. 3) to calculate instantaneous dynamic heights h(t, r) P max relative to a maximum profile depth P max. Following the same philosophy as in the computation of synthetic velocities, the temporal variability is subtracted from h(t, r) /P max using altimetric anomalies (SLA) referenced over the 1993-2012 time period to end up with a mean field < h > (r) /P max also referenced over the 1993-2012 time period. The resulting mean dynamic height < h > represents only the baroclinic processes from the surface down to P max. We need to add the mean contribution of deep baroclinic and barotropic processes.
The energy level is in good agreement with the GLORYS12 energy level (in green) until 50 km wavelength (25 km resolution). At shorter scales a flat response is obtained. This means that the shortest scales (between 12. 5 and 25 km) of the CNES-CLS18 MDT might be contaminated by noise. Figure  7  shows also the power spectra density of the geodetic DTU19 MDT  (Knudsen et al. , 2019a, b)  based on a combined geoid model. At scales shorter than 200 km, DTU19 is less energetic than CNES CLS18 MDT. A visual inspection of the geostrophic current associated with the MDTs confirms that the CNES-CLS18 is more energetic than the DTU19 (not shown). This underlines the value of using oceanic in situ data in the CNES-CLS18 MDT compared with a combined geodetic MDT.
First, thanks to the improvement of the processing, the first guess is improved close to the coast and the new firstguess MDT increases toward the coast, which is not the case for the MDT CNES-CLS13 first guess (Fig. 11 ). Second, the number of available drifters, the improved processing of this dataset and their averaging being performed in 1/8 • boxes instead of 1/4 • boxes (Fig. 12 ) are all key elements leading to the improvement of the CNES-CLS18 in this specific area.
Chilean coast Over the south Chilean coast, the eastward South Pacific Current (SPC) bifurcates to become the Humboldt Current (HC) that flows northward and the Cape Horn Current (CHC) that flows southward. The circulation in this area is fully described by  Strub et al. (2019). The HC and CHC are much better resolved in the MDT CNES-CLS18 compared with the previous MDT CNES-CLS13. The circulation resolved by the MDT CNES-CLS18 is thus in better agreement with what is expected and with numerical models (Fig. 13 ). As in the Gulf of Maine, this improvement of the MDT CNES-CLS18 is due to the new drifters dataset and the improved processing of the first guess. The mean circulation along the Chilean coast is difficult to accurately resolve using geodetic MDT due to the influence of the Andes and the very rugged coastline.
Agulhas Current As first demonstrated by  Chapron et al. (2005) , highly valuable information on ocean surface currents can be retrieved by analyzing the Doppler shift measured between the signal emitted by a synthetic aperture radar (SAR) antenna and the signal backscattered by the sea surface. Within the ESA GlobCurrent project, mean velocities were calculated in the Agulhas Current area from the EN-VISAT SAR Doppler velocities. The ENVISAT data span the period from 2007 to 2012. The main limitation of this  technique is that only the radial component of the velocity is retrieved. To deal with this limitation, the total current velocities are estimated by using the direction given by the altimeter-derived geostrophic currents, whose norm is corrected using a radial component measured by the SAR from either the ascending or descending passes.
14a ). By using the drifter velocities to improve the GOCEderived first guess, much higher velocity speeds are obtained (Fig. 15b ). Maximum values reach 1. 6 m s -1 , which is around 30 % more than the mean velocities obtained in the previous CNES-CLS13 MDT (maximum velocities are around 1. 2 m s -1 ). This is due to the higher resolution of the solution (1/8 • instead of 1/4 • ), an improved processing of the drifters (less filtering is applied) and the removal of a  threshold for a minimum number of data which was applied in the CNES-CLS13 MDT calculation, removing a number of very coastal in situ data for the inversion. In the return current, a discontinuity is observed in the CNES-CLS18 MDT in the speed intensity east and west of around 24 • E. This is in qualitative agreement with both the drifter data and the SAR measurements (Fig.
The RMSD of the independent velocity dataset are then calculated as a function of distance to the coast (Fig. 21 ). Everywhere, the RMSD is reduced with the MDT CNES-CLS18 and especially in coastal areas (distances from the coast less than 100 km). Part of this improvement is due to first-guess improvement, since the RMSD is also reduced when the CNES-CLS18 first guess is used rather than the CNES-CLS13 first guess. This was already pointed out in  the Gulf of Maine and along the Chilean coast. As described in Sect. 8. 1, this improvement is also due to in situ data, especially to processed drifters.
Conclusions The new CNES-CLS18 MDT is presented, focussing on the innovative elements in the processing and datasets used compared to the previous CNES-CLS13 MDT solution, and on validation results at global, regional and coastal scales. Among the main improvements, a more recent GOCE geoid model is used and the calculation of the first guess is improved. More in situ hydrological profiles and drifting buoy data are used, and a new wind slippage correction and wind-driven model are computed to better extract the geostrophic current from the drifters. Grid resolution is enhanced from 1/4 • for earlier solutions to 1/8 •. The effective resolution is also improved as shown by spectral analysis. Thus, this new MDT better retrieves the strongest ocean currents and shows huge improvement in coastal areas, as highlighted by a number of specific coastal and regional examples.
A formal error is given as an output of the objective analysis. It highly depends on the parameters prescribed in input and it is known to be underestimated. We plan to use new method to estimate this field, for instance an ensemble method. Data availability. The MDT CNES-CLS18 is available on the AVISO+ website (https://www. aviso. altimetry. fr/en/data/ products/auxiliary-products/mdt. html, last access: 1 June 2021) (AVISO + MDT, 2021). Note that the CNES-CLS18 as described in this paper does not estimate MDT in the Mediterranean Sea, though we have merged it with the SOCIB Mediterranean MDT  (Rio et al. , 2014b). Author contributions. SM, MHR and HE processed the data and computed the MDT CNES-CLS18. RH computed the SAR currents in the Agulhas Current area. CA, MC, HF, CP and PTS analyzed and validated the MDT in their area of expertise (i.
Acknowledgements. We thank the beta users for their valuable feedback. Funding for the development of HYCOM has been provided by the  National Ocean Partnership Program  and the  Office of Naval Research. Data assimilative products using HYCOM are funded by the  US Navy. Computer time was made available by the  DoD High Performance Computing Modernization Program. The output is publicly available at http://hycom. org(last access: 1 June 2020). Financial support. This study has been funded by the  French space agency CNES. Review statement. This paper was edited by  Anne Marie Tréguier  and reviewed by two anonymous referees.
Cabanes et al. , 2013; Szekely et al. , 2019), processed by the In Situ Thematic Assembly Center (INS-TAC) of the Copernicus Marine Environment and Monitoring Service (CMEMS). and CORA5. 1 (2016) Ocean Sci. , 17, 789-808, 2021 https://doi. org/10. 5194/os-17-789-2021 datasets (.
Background Mesenchymal stem/stromal cells (MSCs) were widely described for several decades of years, with 3840 reviews referenced on the Medline/PubMed databases on November, 2020 (reviews for: mesenchymal stem cell OR mesenchymal stromal cell). Only 90 reviews concerned UC-MSCs (reviews for: mesenchymal stem cell OR mesenchymal stromal cell AND umbilical cord OR Wharton jelly). These articles can be classified into five types: (i) describing UC-MSCs isolation, characterization, and biological properties, (ii) comparing UC-MSCs with MSCs from other tissues, (iii) describing UC-MSCs clinical potential in one specific disease or (iv) a systemic analysis of all clinical trials, and (v) describing exosomes derived from UC-MSCs. However, no review has addressed the pharmaceutical development of UC-MSC-based medicines regarding regulatory aspects, manufacturing challenges and financial limits.
Introduction The 2000s have been marked by the emergence of a new drug category, named advanced therapy medicinal products (ATMPs). The active substance of these products can be composed of genes, cells or tissues, which differs completely from conventional medicines. Mesenchymal stem/stromal cells (MSCs) are one of the most used cellular sources, with almost 1200 studies as reported on the ClinicalTrials. gov database on August 2020. The ease of manufacturing, absence of ethical constraints, and sufficient safety perspectives position MSCs as promising candidates to develop ATMPs. Their first therapeutic use was realized in 1995 by Arnold Caplan team  [1]. Since then, there has been a significant increase of clinical trials, with only few marketing authorizations (MA). In 2012, Prochymal® (remestemcel-L) from Osiris Therapeutics, composed of allogeneic culture-expanded adult MSCs, was authorized in Canada to treat acute graft versus host disease (aGVHD)  [2].
UC-MSCs clinical applications UC-MSCs have been tested in multiple clinical trials, with about 100 on-going studies reported on the ClinicalTrials. gov database as of September 2020. As any cell therapy, UC-MSCs present several potential risks including a short-term infectious, embolic, or acute immunogenic risks and a longer-term chronic immunogenicity or tumorigenicity  [25]. In 2012, a meta-analysis of 8 clinical trials including 369 patients showed a good tolerance of MSCs  [26]. In 2019, the same research group confirmed this observation with a larger meta-analysis, of 55 randomized studies, encompassing 2696 patients  [27]. No study has been suspended because of serious adverse effects, confirming that MSCs are well tolerated. Even if reviewed studies differ in their indication and tissue sources, they all demonstrated MSCs safety including UC-MSCs.
Temcell, another MSCbased product, was approved for reimbursement in Japan at a maximum price of 104,000$ per treatment  [3]. Mastrolia et al. estimated the manufacturing of allogeneic MSC-based medicines from 15,000 to 30,000€ per patient  [54]. These data are in accordance with our own estimation of a UC-MSC-based-ATMP manufacturing as part of the clinical trial NCT04333368.