dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2003; Trimoreau et al. , 2013). The Bay of Vilaine, the outer Loire estuary, and two semi-enclosed bays (Pertuis Breton and Pertuis d'Antioche) were sampled concurrently using a beam trawl and grab in late summer  2008 , 2016 and 2015, respectively (Figure 1, left panel), respectively (Figure 1, left panel). These data were used to meet the first objective of the study (i. e. verify that beam trawl data could be used to estimate an index of benthic prey production). These nurseries differed environmentally in sediment composition and bathymetry (Table  1 ). The Bay of Vilaine is mainly a muddy nursery with sampling stations of varying depths (1-35 m), whereas the two Pertuis are half-muddy and half-sandy shallow nurseries, and the outer Loire estuary is mainly sandy with a gradient of depths (Table  1 ).
Grab data collection Grab samples were collected using a 0. 1 m² Van Veen grab, with three replicates at each sampling station. Once aboard, the grab content was sieved through a 1 mm grid mesh and kept in a 7% formalin solution in plastic zip-top bags. In the laboratory, organisms were rinsed and sieved with fresh water in a column of five successive sieves with square mesh sizes ranging from 16 to 1 mm. Organisms retained in each sieve were then stored separately in a 70% ethanol solution, keeping in mind that handling, fixing samples with formalin and storing with ethanol likely led to underestimate biomasses  (Gaston et al. , 1996; Wetzel et al. , 2005). Invertebrates from each sieve were identified to the lowest taxonomic level, counted and weighed. Biomass per taxa was determined as wet mass (WM) and then converted into ash-free dry mass (AFDM) using specific coefficients from a global database of conversion factors  (Brey et al.
Trawl data collection Bottom trawl samples were collected using a 2. 9 m wide and 0. 5 m high beam trawl with a 20 mm stretched mesh size in the cod end. Trawls were performed during daylight hours at a mean speed of 2. 5 knots for 15 min. Trawled benthic invertebrates were rinsed aboard, identified to the lowest taxonomic level, counted and weighed. Biomass per taxa was determined as WM, which was converted into AFDM as described in section 2. 1. 2. Trawled fish were collected, identified, counted, measured and weighed at the species level, with 494 stations sampled during the 14 surveys (nursery-years).
, 2016)  of R software (R Core Team, 2019). YOY individuals (i. e. the first Gaussian distribution) were selected. Then, a minimum and maximum lengths of 7 and 15 cm, respectively, were set to select individuals that feed almost exclusively on macrobenthic invertebrate prey within the size range of the fish cohort (see Supp. Mat. C). These size thresholds for individual fish assumed that fish smaller than the minimum length (7 cm) prey on pelagic and benthic invertebrates that our sampling device could not capture (e. g. copepods), whereas individuals larger than the maximum threshold (15 cm) have a high proportion of small fish in their diet  (Tableau et al. , 2015). When length data were missing (as for C. lyra before 2008), mean individual mass (total biomass divided by the number of individuals) was converted into mean individual length using the coefficients a and b from the size-weight relationship estimated for each species with all length data available in the data set from 2004-2016.
It was assumed that YOY fish are opportunistic feeders that consume a variety of benthic prey no larger than a certain size  (Besyst et al. , 1999; Griffin et al. , 2012). Hence, a benthic invertebrate was identified as potential prey if it was smaller than the maximum mouth height of a juvenile fish. Data on mouth heights of the three most abundant species (M. merlangus, S. solea and T. luscus) in the studied areas were used to define a mean height of 20 mm (unpublished data). As benthic organisms collected with trawl were not sized, but only counted and weighted, we decided to select potential prey of the YOY fish using a filter based on mean individual mass. To set the threshold under which an invertebrate can be considered as prey for YOY fish, we used the benthic organisms collected with grab and categorised into size classes described previously (section 2.
1. 2). Mean individual body mass of the organisms retained in each mesh size was calculated. A threshold of 0. 66 g WM. ind -1 (i. e. ∼0. 1 g AFDM. ind -1 ), corresponding mainly to benthic organisms smaller than 16 mm (the largest mesh size), was identified as potential prey. This threshold was applied to select potential prey from benthic invertebrates using the grab and the beam trawl. Once applied to the grab data, the rare species and mean individual body mass filters selected 73. 3% of the total biomass in the entire dataset (i. e. from all three nurseries where grab and trawl were deployed concomitantly). Once applied to the trawled benthic species, 10. 3% of the total biomass of the entire dataset was retained. The benthic organisms included in those percentages (respectively 73. 3% and 10.
3%) were considered as potential prey and were used to estimate respectively benthic prey production using grab data and benthic prey production index using trawl data. Weatherall et al. , 2015). Mean annual bottom temperature for each nurseryyear was extracted from a multi-decadal hindcast of a physical-biogeochemical model of the Bay of Biscay  (Huret et al. , 2013). Individual mean body mass was calculated by dividing each taxon's mean annual biomass by its mean annual abundance. Benthic prey production was first calculated at the scale of the sampling station (P k , in kJ. m -2. y -  ): P = CR * ∑ [B , * E * ( ) , ] Equation 1 where k is the station, i is the species, CR is a coefficient (unitless) that accounts for seasonality in the macrobenthic biomass (set to 0. 7;  Tableau et al. , 2015) , B is the biomass (in g AFDM) sampled during the survey (grab or trawl), E is the energy density (in kJ.
g AFDM -1 ) obtained from a general database  (Brey et al. , 2010)  and P:B is the production-to-biomass ratio (in y -1 ) detailed at the beginning of the subsection. The distribution of biomass and estimates of production are shown in Supp. Mat. B. The patchy spatial distribution of benthic invertebrates led us to consider multiple sampling stations within a similar habitat. Therefore, benthic prey production was finally estimated at the scale of each stratum, as it is likely that, at this scale, the benthic communities are composed of species with similar environmental requirements. Total prey production per unit area in each stratum (P s , in kJ. m -2. y -  ) equalled the sum of each sampling station: P = ∑ ∑ Equation 2 where, s is the stratum and A is the total area sampled at each station k by the grab (∼0.
Estimate benthic prey production index index index index (from trawl data) To verify whether the beam trawl data could be used as an index of benthic prey production, we filtered the benthic organisms captured by the trawl, as it was done for the grab data, to keep only the potential prey. An index of benthic prey production was then estimated at the scale of the stratum by applying Eq. 1 and 2 to the filtered trawl data. To test the trophic limitation hypothesis in YOY fish, the index was calculated at the scale of the nursery, instead of the stratum, as the objectives of this part of the study was first to explore spatiotemporal variability in prey production among nurseries. Moreover, YOY fish may move among strata during their first year of life and do not necessarily feed where they were caught; thus, the nursery scale was deemed more relevant.
(2016)  for more details on assumptions associated with quantile regressions and trophic limitation). The quantile regressions were performed using the quantreg package  (Koenker, 2018)  of R software at the species, species group (flat or round fish), and YOY community scales. Other quantiles (80 th and 85 th for upper quantiles and 5 th and 15 th for lower ones) were also tested but are not shown, as the results were similar to those presented in here.
After removing T. luscus from the analysis, slopes of all fish groups (flatfish, round fish groups, and YOY community) differed significantly from 0 (Table  3 , Supp. Mat. E1). Conversely, the lower quantiles (10 th ) were non-significant for all species. We hypothesised trophic limitation by macrobenthic prey production on YOY of bentho-demersal fish species. To test this hypothesis, we developed a benthic prey production index and validated it using data from grabs and trawls in four coastal nurseries. This index was then calculated from data collected using trawls in nurseries in the Bay of Biscay to estimate and describe prey production in each nursery, and investigate whether microbenthic prey production limits the YOY fish community. Results indicated that benthic prey production significantly influenced YOY fish density (except that of T.
, 2016; Tableau et al. , 2015)  likely smoothed interspecific differences. We cannot exclude that other prey characteristics could influence the prey selection by fish juveniles  (texture, activity; van der Veer et al. , 2016)  but, to our best knowledge, such data are missing in the literature for the fish studied. The benthic prey production index included taxonomic groups (bivalves, polychaetes, ophiurids, and crustaceans) matching the dominant potential prey found in grabs. The species in the grab and trawl samples were consistent with the YOY diet in the nurseries  (Pasquaud et al. , 2008; Tableau et al. , 2015). For instance, both samples contained P. aperta, which are consumed by YOY S. solea in the Bay of  Vilaine (Kopp et al. , 2013) , and small echinoderms (Ophiura spp. , A. filiformis), which can be grazed by flatfish and round fish species  (Duineveld and Van Noort, 1986; Ntiba and Harding, 1993; van der Veer et al.
Nevertheless, our data did not enable us to explain that pattern and it would be interesting to use a larger dataset to properly explore the potential drivers of interannual and inter-site variability in benthic prey production. The hypothesis that YOY fish density was higher in years and sites with higher benthic prey production was supported by the quantile regressions. Unlike the mean of a distribution, upper quantiles can indicate that a tested limiting factor (here, available food resources) may be acting on the same processes as other unknown limiting factors  (Cade and Noon, 2003). These models have been used to investigate the influence of food supply on demersal fish abundance around the Balearic Islands in the western Mediterranean  (Johnson et al. , 2012). Significant relationships at the upper quantile in the present study suggest that benthic prey production limits YOY fish density in coastal and estuarine nurseries along the Bay of Biscay.
Other studies, including this one, confirm the importance of trophic limitation mechanisms in the first year of life for fish in coastal nurseries  (Tableau et al. , 2019; van der Veer et al. , 2016). Frequency and intensity of food limitation may differ among nurseries and periods of the year, but this could not be tested due to our unbalanced dataset coming from annual surveys. All YOY fish species showed a positive relationship with the benthic prey production index at the upper quantile, except T. luscus, which may have different feeding or behaviour ecology. This species seems to have an aggregative behaviour that is not exclusive to soft bottoms  (Reubens et al. , 2011) , which could partly explain the highest densities sampled at certain stations in the three nurseries. Moreover, T.
10. 3% of total catches by biomass and 35. 2% by abundance for the entire dataset). The communities of invertebrates sampled with both gears had similarities, with Owenia fusiformis dominating the two communities (from grab and trawl collection) in stratum V1, Abra alba in strata V3 and L3, and Corbula gibba in stratum PA2 (Table2, Supp. Mat. D). Although the proportion and abundance of species caught with each gear differed, the two body mass spectra and the identity of the species support the hypothesis that the two gears sampled complementary parts of the same large community of benthic invertebrates in the nurseries. The part of the body mass spectrum from trawl data not considered as potential prey (i. e. the right side of the vertical line, Figure2B) contained small individuals of motile epibenthic species, such as Crangon crangon and Liocarcinus holsatus, and larger benthic species not consumed by YOY (Supp.
Slopes of quantile regressions of YOY fish density (no. ha -1 ) at three scales as a function of the benthic prey production index (log scale) for lower (10 th ) and upper (90 th ) quantiles. Asterisks indicate slopes that differed significantly (p < 0. 05) from 0. The percentage of non-empty stations (out of 494 stations sampled in the 14 nursery-years) is indicated in the last column. "---" indicates species for which the number of empty stations was greater than 10% and 90% of the dataset for the lower and upper quantile regressions, respectively. "NS" indicates non-significant results.
( 10. 3% of total catches by biomass and 35. 2% by abundance for the entire dataset). The communities of invertebrates sampled with both gears had similarities, with Owenia fusiformis dominating the two communities (from grab and trawl collection) in stratum V1, Abra alba in strata V3 and L3, and Corbula gibba in stratum PA2 (Table2, Supp. Mat. D). Although the proportion and abundance of species caught with each gear differed, the two body mass spectra and the identity of the species support the hypothesis that the two gears sampled complementary parts of the same large community of benthic invertebrates in the nurseries. The part of the body mass spectrum from trawl data not considered as potential prey (i. e. the right side of the vertical line, Figure2B) contained small individuals of motile epibenthic species, such as Crangon crangon and Liocarcinus holsatus, and larger benthic species not consumed by YOY (Supp.
Methods The modeling results presented here are obtained with an improved version of the code presented in  Andrés-Martínez et al. (2019) , MILAMIN-Rifts, which is a Lagrangian code that stems from the MILAMIN finite element solvers  (Dabrowski et al. , 2008). MILAMIN-Rifts is a non-Newtonian viscoelastoplastic code that includes a Winkler bottom boundary condition, a free-surface, strain softening in the brittle and ductile domains, and which is coupled to a surface process solver. We model a 2-D section of the continental lithosphere and upper asthenosphere that is divided in three layers with different thermomechanical properties: the upper crust (UC), the lower crust (LC), and the mantle. Mantle includes an upper lithospheric mantle section consisting of dryer olivine and a deeper asthenospheric section, consisting of wetter olivine, separated by a 5 km transition zone.
Acknowledgments The Authors wish to thank  Gilles Le Godec  and  Serge Deleval  for their extensive support concerning standards and best design practice, with reference to power converters and cooling, respectively. The Authors are grateful to  Attilio Milanese ,  Rosario Principe ,  Vittorio Parma ,  Pierre-Ange Giudici ,  Vitalii Zhiltsov ,  Jakub Kurdej ,  Isabel Bejar Alonso  and  Marco Buzio  for fruitful discussion and useful suggestions. This work is supported by a  Marie Sklodowska-Curie Innovative Training Network Fellowship  of the European Commissions  Horizon 2020 Programme  under contract number  765710 INSIGHTS.
e. , Calpe and Cabo de Gata-Níjar). Altogether, according to the long-term recruitment data series in the Columbretes Islands  (Kersting and García-March, 2017)  and the results presented here, 2017 seems to have been a year of successful P. nobilis larval recruitment. The Lagrangian trajectory model points out to the north African coast as the main area holding unaffected populations that could be acting as a source of larvae for these sites during those years. Mar Menor is also shown here as a potential source by the model, but as already mentioned, there is a major concern about its connectivity potential due to limited seawater exchanges. Regarding the Balearic Islands, sporadic recruitment was detected in 2018 in Son Saura (Menorca). In this case, according to the model, larvae could origin from the north, i.
e. , the coast of France, where in fact several P. nobilis populations were still unaffected in 2018  (Kersting et al. , 2019). It is remarkable that none of the other sites in the Balearic Islands holding historical larval settlement data (i. e. , Cabrera, Pollença, and Magaluf) registered recruitment after the MME. This pattern moved eastwards along the coast as other western Mediterranean sites were sequentially impacted by the mortality. Recruitment was recorded in 2018 in the Embiez Islands (France) before the mortality affected this site but ceased afterward. In Scandola (Corsica), first affected by mortality in 2018, recruitment fell from 18 in 2017 to 3 recruits in 2019. In this last case, larval origin seems more diverse, enclosing both northern sites (Occitania, France) and southern ones (southern Corsica and northern Sardinia), while in Embiez Islands recruitment seems to be more dependent on local and near-by populations and thus ceased when those were impacted by the MME.
ACKNOWLEDGMENTS We want to thank the  Columbretes Islands ,  Cala Rajada , and  Cabo de Gata-Níjar Marine Reserves  staff for logistic support, and  Parc Natural Serra d'Irta, Cabrera National Park and Brijuni National Park  for permissions. We want to thank  J. Moreno  ( Servei de Protecció d'Espècies, Govern Balear ) for his support and  D. Petricioli  and the staff of the  Jaume Ferrer Research Station  ( La Mola ,  Menorca ) for assistance in the field. AB and DM thank the managers of the  Sustainable Management Program  for the  Marine Environment of Andalusia (F. Ortega ,  E. Montes , and  S. Vivas ) and the colleagues who have worked at sea:  J. M. Remón ,  J. De la Rosa ,  M. Fernández-Casado , and  M. Carmen Arroyo.
FUNDING DK and MV-L were supported by a  Juan de la Cierva-Incorporación postdoctoral contract (Spanish Ministry of Science, Innovation and Universities ;  IJCI-2017-31457  and  IJCI-2016-29329 , respectively). SK was partially supported by a postdoctoral contract ( EU Horizon 2020 , Project:  MERCES , No. 689518 ). EÁ was supported by a  Technical Support Staff contract (Spanish Ministry of Economy and Competiveness ,  PTA2015-10829-I ). This study was partially funded by:  EsMarEs (MITECO) ,  SuMaEco  ( RTI2018-095441-B-C21 ,  Spanish Ministry of Science, Innovation and Universities)  and  Prince Albert II of Monaco Foundation  (Project  BF/HEM 15-1662 ). The authors acknowledge the  MEDCLIC  project, funded by  "La Caixa" Foundation , contributing to the development of the WMOP hydrodynamic model.
The TLC plates were exposed to a storage phosphor screen (GE Healthcare) for 16 h, and images were revealed using a Typhoon Trio imager and Image Quant TL software (GE Healthcare). The GTase inhibition assay was carried out by measuring the residual activity (RA) of E. coli PBP1b in the presence of 400 and 800 μM compounds. All the assays were repeated at least three times. The IC50 values were determined for the active compounds using variable concentrations of inhibitor (0-1000 μM) by fitting the data using GraphPad Prism 6. 0 software.
Acknowledgements This work was supported by the " Fonds de la Recherche Scientifique " CDR J. 0030. 18. MT is a research associate of the  FRS_FNRS (Brussels, Belgium ), AB is supported by  FRIA   1. E. 038. 17 ( Fonds pour la formation à la Recherche dans l'Industrie et dans l'Agriculture ) fellowship FRS_FNRS.
Cellular kinetics of Cs-131 For all experiments, Cs-131 was added to the cell culture in the form of CsCl (dissolved) and mixed with growth medium (DMEM, 10% FBS, 4 mM L-glutamine and 1% antibiotic, antimycotic). Cs-131 activity was measured by LSC and the data decay corrected to the beginning of measurement. The number of cells and their size/volume was measured using the Sceptor 2. 0 Cell Counter from Millipore (Merck Millipore Darmstadt, Germany), unless otherwise stated.
1% trypsin/0. 5 mM EDTA in PBS for 3-5 min, at 37 C, 5% CO 2 ) and resuspended in growth medium. The number of cells, their size and the Cs-131 activity in each sample were measured. Data were normalized to the cellular activity concentration measured when Cs-131 was removed from the cells. Inhibition of cellular Cs-131 uptake HeLa and V79 cells grown in 48-well plates were washed with PBS and incubated for 5-6 h in Cs-131 containing medium ($20 kBq/ml) with added ouabain octahydrate (Sigma Aldrich; HeLa: 0 nM 10 nM, 50 nM, 0. 1 lM, 0. 5 lM, 1 lM, 5 lM; V79: 0 lM, 10 lM, 50 lM, 0. 1 mM, 0. 5 mM, 1 mM). The cells were washed in PBS, trypsinized (0. 1% trypsin containing 0. 5 mM EDTA in PBS at 37 C, 5% CO 2 for 3-5 min) and resuspended in growth medium. The number of cells, their size and the Cs-131 activity in each sample were measured.
Seventy-one pictures of the HeLa cell culture were taken, with a spacing of 0. 37 lm, covering an axial length of 26. 08 lm. Fifty-nine pictures with a spacing of 0. 37 lm, covering an axial length of 21. 6 lm were taken of the V79 cell culture. The height of the cell layer and the sizes and shapes of the nuclei were determined afterwards using the Zeiss ZEN-lite software package (version 2. 3, Carl Zeiss, Germany) operating directly on the Z-stack images. The top and bottom positions of the cell layer were evident by a sudden drop in the fluorescent intensity and the appearance of plasma membrane in the form of green spots 'overlying' the nuclei. The height was taken as the distance between these two positions given by the inbuilt scale in the ZEN software. The shape of the nucleus was assumed an ellipsoid.
The maximum lengths of the axes in the horizontal plane were measured for each nucleus using the ZEN software. The length of the vertical axis was defined by the 'distance' between the top and bottom position at which the nuclei appeared and disappeared from the images. Of a total of 30 HeLa nuclei and 58 V79 nuclei fully visible in the images, the geometry of 29 HeLa nuclei and 50 V79 nuclei were determined.
A composite dose kernel was made by addition of the individual kernels weighted by their emission probability. Further elaborations on dose kernel computations can be found in  (Siragusa et al. 2017). In order to calculate the S C -values, we need information on the 3D energy deposition pattern within the modeled cell layer. To get that, the composite dose kernel was convoluted with an activity concentration matrix (Bq/pl), representing the cell layer itself. The S C -values were always calculated for an activity concentration of 1 Bq/pl, giving each voxel in the cell layer the value 8 Â 10 À6 pl. Convolution was done in 3D-cartesian coordinates using (0. 2 Â 0. 2 Â 0. 2) lm 3 voxels. The three dimensional 'convn' function in MATLAB (version R2018a, The Mathworks, Inc. , Natick, MA, USA) was used.
To find the rate at which Cs-131 accumulated in the cells (k a ), the data were fitted to the function: AðtÞ ¼ A 0 Á ð1 À e ÀtÁk a Þ (5) using MATLAB Curve Fitting Toolbox (version 3. 5. 7, The Mathworks, Inc. , Natick, MA, USA). A(t), being the normalized activity concentration in the cells at time (t) and A 0 , the normalized activity concentration in cells at equilibrium. The obtained values for A 0 and k a are presented in Table  2.
Release of Cs-131 from the cells The release of Cs-131 from HeLa and V79 cells is presented in Figure  3. Data were normalized to the cellular activity concentration measured when Cs-131 was removed from the cells and fitted using MATLAB (Curve Fitting Toolbox) to the function: AðtÞ ¼ A 0 Á e ÀtÁk out (6) A(t), being the fraction of Cs-131 left in the cells at time (t) and A 0 , the Cs-131 activity concentration in the cells at time point zero. The obtained values for A 0 and k out are presented in Table  2.