dataesr/scientific-paragraphs-categorization huggingface dataset:
Quantitative Real-Time PCR Analyses Fifteen genes were chosen for verification of RNA-Seq data by qRT-PCR. Total RNAs from the four culture conditions described above were extracted using TRIzol ® reagent (Invitrogen, Waltham, MA, USA) and were then reverse-transcribed into cDNA using PremixScript™ RT reagent Kit (Takara, Otsu, Shiga, Japan). RNA degradation and contamination were monitored on 1% agarose gels. RNA purity and concentration were checked using the NanoPhotometer ® spectrophotometer (IMPLEN, Calabasas, CA, USA). The expression levels of the 15 selected genes were determined using SYBR ® Premix Ex Taq™ (RR420, Takara, Japan) with a Light cycler ® 480 (Roche, Switzerland) according to the manufacturer's recommendations. After the cycling protocol, a melting curve was generated in order to detect single-gene-specific peaks and to check for the presence of primer-dimer peaks.
Sequence and Phylogenetic Analyses The gene clusters encoding proteins involved in polysulfide reductases were identified by a local BLASTP protein similarity search using previously characterized gene sequences (e. g. , polysulfide reductase from W. Succinogenes and Sulfurovum sp. NBC37-1) or by ontology using functional search terms (e. g. , sulfur, polysulfide). For the BLASTP analysis, we used an amino acid similarity cutoff of >30%, alignment coverage > 80% and an e-value cutoff of 1E-5. Sequences were then aligned using ClustalW  [50]  and viewed in an on-site program (http://www. bio-soft. net/sms/index. html, accessed on 17 August 2022). A phylogenetic tree was reconstructed by the maximum-likelihood method using RAxML version 8. 2. 11 with the GTR + CAT model  [51]. PSI-BLAST and DELTA-BLAST  [52]  were used for the domain analysis of outer membrane proteins.
succinogenes  [70]. Furthermore, we detected the distribution of the psr gene cluster in genus Sulfurovum from 5fivepure isolated genomes and 122 metagenomes currently available on NCBI database. The result revealed that the four psr genotypes were present in Sulfurovum spp. from various environmental niches, including hydrothermal vents, terrestrial biofilm, hydrocarbon-rich habitats and marine sediments (Table  S4 ). This provides evidence that the genotype of strain ST-419 is shared by other Sulfurovum spp. , and suggests that this mechanism of sulfur reduction may be environmentally significant. The primary amino acid sequences of the four PsrAs from S. indicum ST-419 were aligned with those of homologous enzymes that were biochemically characterized and shown to reduce S 0 to H2S in vitro (Figure  S4 ).
Primers Design V. rugosum Large Sub Unit (LSU) ribosomal RNA genes sequences were obtained from GenBank using the Basic Local Alignment Search Tool from NCBI (National Centre for Biotechnology Information) (Figure  3 , Table  3 ) and aligned using Multalin  [37]. Specific primers were designed in a conserved region to amplify a 132 bp fragment (Figure  4 ). The two primers were as follows: (VulcaF; 5 TACTGGTTCGAGACCGATAG 3 ) sens and (VulcaR: 5 CAACAATCTTGCCAAGCAAC 3 ) antisens. Toxins 15, x FOR PEER REVIEW 9 of 13 quantification was performed using a spectrophotometer (NanoDrop ND-1000) to check for DNA quantity and quality (A260/A280 ratio).
Primers Design V. rugosum Large Sub Unit (LSU) ribosomal RNA genes sequences were obtained from GenBank using the Basic Local Alignment Search Tool from NCBI (National Centre for Biotechnology Information) (Figure  3 , Table  3 ) and aligned using Multalin  [37]. Specific primers were designed in a conserved region to amplify a 132 bp fragment (Figure  4 ). The two primers were as follows: (VulcaF; 5′ TACTGGTTCGAGACCGATAG 3′) sens and (VulcaR: 5′ CAACAATCTTGCCAAGCAAC 3′) antisens.
To achieve this, we will use simulations of a regional numerical model covering the NGG area, preliminary validated with observations. The rest of the manuscript is organised as follows: the model description, the observation data and the methodology used are described in Section 2. The results including the evaluation of the model, simulated river-induced coastal warming, and the heat budget to identify physical processes that drive this warming are presented in section 3. Finally, discussion and conclusion are given in Section 4. (Sohou et al. , 2020). We also used the monthly version of the Moderate Resolution Imaging Spectroradiometer (MODIS-AQUA, L3, v2019, https:// oceandata. sci. gsfc. nasa. gov ) SST, with a spatial resolution of 4 km over the time period 2007-2017. We prefer to use this infrared SST product rather than blended satellite products that can underestimate SST cooling in coastal upwelling regions  (Meneghesso et al.
52°N) and Lagos in Nigeria (3. 41°E; 6. 42°N) in the framework of the Research Program in Physical Oceanography in West Africa (PROPAO). The in situ ONSET sensor hourly data series are available over the period 2008-2012 2 Data and methods 2. 1 Observations Different in situ and satellite datasets were used to evaluate the model. Sea surface temperature (SST) in situ measurements are based on four coastal stations equipped with ONSET sensors installed along the NGG coast at Sassandra in Co ̂te d'Ivoire (6. 08°W; 4. 95°N ), Takoradi in Ghana (1. 73°W; 4. 88°N), Cotonou in Benin (2. 67°E; 6.
The distributed PDE-based hydrological modeling approach In addition to GR4H, we used the hydrological model ParFlow with its internal land surface module CLM (Common Land Model), hereafter ParFlowCLM  (Kollet and Maxwell, 2006; Kuffour et al. , 2020; Maxwell, 2013)  https://lpdaac. usgs. gov/products/astgtmv003) DEM (digital elevation model) combined with the hydrologically-enhanced DEM MERIT Hydro  (Yamazaki et al. , 2019). Soil and subsoil types are defined from the SoilGrids250m  (Hengl et al. , 2017)  reclassified into 12 USDA (United States Department of Agriculture) texture types. The hydraulic parameters for each soil type (hydraulic conductivity, residual and saturated water content, and van Genuchten parameters) were obtained from the ROSETTA model  (Schaap et al. , 2001). Below the depth to bedrock, given by SoilGrids250m, the typology of the International Hydrogeological Map of Europe IHME1500 (scale 1:1500000) was used  (Duscher et al.
, 2015). Land cover was characterized using the CORINE Land Cover database of the Copernicus Land Monitoring Service for the year 2018  (Langanke et al. , 2016) , whose land cover classification was converted into the 18 IGBP (International Geosphere-Biosphere Programme) categories. To account for the uncertainty in Manning's roughness coefficient, which highly impacts the peakflow simulations  (Lumbroso and Gaume, 2012) , three uniformly distributed values were tested for the whole domain: a default value of 0. 2 s m -1/3 (HMann, i. e. high roughness, from  Schalge et al. , 2019) , and two additional values of 0. 1 s m -1/3 (MMann, medium roughness) and 0. 02 s m -1/3 (LMann, i. e. low roughness). These three values cover the whole range of Manning's coefficient values reported by  Lumbroso and Gaume (2012).
Atmospheric forcing and streamflow data Eight atmospheric variables were needed for the runs of ParFlowCLM, namely 2-m air temperature, precipitation, surface pressure, downward solar and thermal radiation, specific humidity, and eastward and northward components of the 10-m wind. Precipitation was obtained from the operational radar-based RADOLAN product of the DWD (Deutsche Wetterdienst, German Weather Service), which is gauge adjusted and available at 1-km resolution. The remaining atmospheric variables were obtained from the ERA5-Land dataset  (Muñoz-Sabater et al. , 2021) , available at 9-km resolution. All variables were regridded to the model resolution using a bicubic interpolation. For GR4H, data demand is limited to precipitation and 2-m air temperature, which were catchment-averaged using the Thiessen polygon method, and discharge data for model.
Funding. This study is part of the  RealPEP  (Near -Realtime Quantitative Precipitation Estimation and Prediction  https://www2. meteo. uni-bonn. de/realpep/doku. php) P4 project (Evaluation of QPE and QPN improvements in a flash flood  nowcasting framework with data assimilation ), funded by the  Deutsche Forschungsgemeinschaft (German Research Foundation). Computing resources for ParFlowCLM were provided by the  Jülich Supercomputing Center (JSC). Code and data availability. Both ParFlowCLM (https://parflow. org/) and GR4H (https://github. com/cran/airGR) codes are available in public repositories. All original data are public, except for the QPE products generated for the study, which can be made available upon reasonable request from the authors. https://doi. org/10. 5194/nhess-2022-111 Preprint.
The cross-referencing of two databases, namely the compartmentalization into groundwater bodies (GWB) and the quality monitoring (2830 observations including 15 physico-chemical and bacteriological parameters, on 662 collection points and over a period of 27 years) is applied to better understand the diversity of the waters of the island of Corsica (France) and to facilitate the surveillance and quality monitoring of the groundwater resource. Data conditioning (log-transformation), dimensional reduction (PCA), classification (AHC) and then quantification of the information lost during grouping (ANOVA), highlight the need to sub-divide the groundwater bodies in the crystalline part of the island in order to take better account of lithological diversity and other environmental factors (slope, altitude, soil thickness, etc.
The mapping of these GWBs has stimulated considerable research efforts in the EU Member States  [10] [11] [12] [13] [14] [15] [16] [17]. This inventory has been referenced according to the major European watersheds (Rhône, Rhine, Danube, Loire, Seine, etc. ). In order to cover all territories, small coastal rivers and island areas have been attached to the nearest major basin. In France, for example, we refer to the Rhône-Méditerranée-Corse basin, which includes the Rhône basin, the basins of small rivers flowing into the Mediterranean and the island of Corsica. Independently of this inventory, the Regional Health Agencies have been carrying out regular checks on the quality of water intended for human consumption, both surface water and groundwater, throughout France for over 30 years. All this geo-referenced data conveys information acquired during the water's journey from the surface to the aquifer, and has gradually been incorporated into a database called Sise-Eaux.
Sise-Eaux Database Extraction The Sise-Eaux database (https://data. eaufrance. fr/concept/sise-eaux accessed on 15 March 2021) is supplied with the results of health inspections of water points supplying local communities  [24, 25]. It covers both untreated water, taken from catchment areas or boreholes, and treated water, i. e. , disinfected by chlorination or another process and possibly decanted or filtered. In this work, only untreated (bulk) water has been considered. For each water sample taken, several analysis reports could have been realized, namely, complete with several hundreds of parameters, standard with about 30 parameters, or a routine follow-up, with only about ten parameters. Thus, data extraction from the whole island over 27 years (from April 1993 to September 2020) results in a matrix with a total of 5395 water analyses involving 23 parameters, but with some empty cells that need to be conditioned before processing (Figure  1a ).
After the manual correction of errors during data entry in the database and the elimination of empty cells, a full matrix consisting of 2830 observations and 15 parameters was used for this study. The 15 parameters selected are major ions (Ca, Mg, Na, SO 4 , Cl, HCO 3 ), electrical conductivity (EC), bacteriological parameters (total Coliforms (Col. ), revivable aerobic bacteria at 22 • C and 37 • C (Aer. 22, Aer. 37), also including the fecal contamination parameters Enterococci and Escherichia coli (Ent. , E. coli), nitrate ions (NO 3 ), and two trace metals (Fe and Mn). For some sampling points, the geographical coordinates were not entered in the database. These coordinates were retrieved from the Infoterre database of the French Geological Survey (https:// infoterre. brgm. fr/ accessed on 15 March 2021).
After the manual correction of errors during data entry in the database and the elimination of empty cells, a full matrix consisting of 2830 observations and 15 parameters was used for this study. The 15 parameters selected are major ions (Ca, Mg, Na, SO4, Cl, HCO3), electrical conductivity (EC), bacteriological parameters (total Coliforms (Col. ), revivable aerobic bacteria at 22 °C and 37 °C (Aer. 22, Aer. 37), also including the fecal contamination parameters Enterococci and Escherichia coli (Ent. , E. coli), nitrate ions (NO3), and two trace metals (Fe and Mn). For some sampling points, the geographical coordinates were not entered in the database. These coordinates were retrieved from the Infoterre database of the French Geological Survey (https://infoterre. brgm. fr/ accessed on 15 March 2021).
Data Treatments The Kolmogorov-Smirnov normality test, which is adapted to high-dimensional statistical distributions  [26] , was applied to compare the empirical cumulative distribution function to a specified normal distribution, in this case one with the same mean and standard deviation. Following this test, the next step was to perform a logarithmic conditioning of the data as suggested by Jabrane et al. [20] , according to the formula: y = log(x + DL), x being the value of the physico-chemical or bacteriological parameter X, and DL the detection limit of this same parameter. The aim of this transformation was to bring the distributions of each parameter closer to a normal distribution and to limit the weight of extreme values likely to obscure certain processes responsible for the variability within the dataset  [22, 27].
The normality tests revealed the presence of two observations (out of 2830) which clearly showed a data entry error. These two observations were therefore removed from the study. A principal component analysis (PCA) was then performed on the log-transformed data to reduce the dimensionality of the data hyperspace  [19, 28]  and to identify and rank the sources of variability in the dataset  [29]. The PCA was based on the correlation matrix and thus considered the reduced centered variables, which allowed the integration of parameters of very diverse natures and units. Furthermore, it was carried out by diagonalization of the correlation matrix. Under this condition, the factorial axes were perpendicular to each other, and were therefore theoretically associated with independent processes responsible for the variability of water quality.
Principal Component Analysis The distribution of the inertia of the different factorial axes is shown in Table  1. PCA allowed a significant reduction in dimensionality. The first principal component clearly stood out from the others, accounting for 37. 1% of the total variance. About half of the information contained in the database (49. 3%) was concentrated in the first factorial plan, which is substantial compared with the 15 parameters in the dataset. The first five factorial axes had eigenvalues greater than unity, meaning that they concentrated more information than a single parameter  [29]. The first eight axes, which accounted for 94. 7% of the information, were retained for further hierarchical clustering calculations. The remaining 5. 3% was considered as statistical noise and information generated by minor sources of variability.
A multifactorial Information Set The distribution of the PC inertia, and in particular the fact that the first five eigenvalues are greater than unity, underlines that the information contained in the dataset cannot be summed up in one or two parameters, but on the contrary that this information is clearly multifactorial. The contrast between mineralized water and water with low mineral content but subject to fecal contamination (E. coli and Enterococci) has been classically observed in the processing of databases in Mediterranean climates  [18] [19] [20] [21] [22]. During rainy periods, runoff water, which is poorly mineralized but turbid and loaded with bacteria, contaminates vulnerable or poorly protected water catchment structures. In contrast, during low-water periods, the catchment structures only collect groundwater, which is more mineralized, filtered by the soil and less affected by surface bacterial contamination.
• While it now appears that the information analysis method can be extended to other administrative regions, we do not know whether it can be extended to new parameters not taken into account here (pesticides, land use, fractured medium, water-rock contact time, etc. ). The use of databases of much larger dimensions is currently being tested in the Provence-Alpes-Côte d'Azur administrative region (PACA, France); • However, the proposed method has certain limitations. In particular, at this stage, the analysis is carried out parameter by parameter, which does not take into account the interrelations between several parameters. Access to a greater number of parameters over a larger number of observations should make it possible to establish a typology of parameter behaviour, while separating their variability in space (range of each variable) and in time (common variability of parameters during rainy events, more or less marked seasons, or even multi-annual dry or wet periods).
The paper is organized as follows. In the Materials and methods section, a brief review of the existing approaches and tools in the field of CE product-level metrics and product design is conducted. The Results section outlines a methodological background underlying the CPD Toolkit. The section also sets out the basic principles and building blocks of the CPD Toolkit with further discussion of information block as actual product circularity dataset and recommendations block dealing with specific guidelines for a company. The conclusion section sets out the main contributions.
ICED23 and "Reused content" and also additional basic information about a product. As a result of filling out, the Toolkit will generate the Guideline based on actual product circularity dataset with appropriate recommendations on product design in terms of circularity performance. The CPD Toolkit Diagnostic block will include actual product circularity dataset, in particular the following information: (i) product circularity profile corresponding to a circular product category identified (see Table  1 ); (ii) product circularity data quadrant including information about available circularity potential (see Fig. 2 ); (iii) global product circularity index (see formula 1); (iv) additional basic information. The CPD Toolkit will be accompanied by general guidelines corresponding to fifteen circular product categories and other basic dataset listed above.
Also, the product circularity data quadrant model only focuses on slowing and closing the loop; it is designed to capture other information regarding the sustainability of the products or companies producing the assessed product. For examples, some actions proposed by the company producing model No2 or model No3 are contributing to sustainability (for instance model No2: "metal trims don't use electrical plating process", or less use of water to produce the jeans thanks to a innovative technology; or model No3 is produced and sold in France, limiting the impacts due to transports) are note considered with this tool. Therefore, the CPD Toolkit may be complemented with other evaluation tools focused for example on environmental or social impacts. In the next months, the authors plan to illustrate and test the different steps of the methodology with multiples examples from different sectors, and to collaborate with a French start-up in the forthcoming months to develop and apply the full CPD Toolkit.
CONCLUSIONS A brief review of existing approaches and tools in the field of product-level circularity metrics revealed that there is no quick-and-easy Toolkit to assess a product circularity performance in terms of circular strategy configurations and also provide guidelines for a company on how to progress in the circularity of a product produced. The present paper announces a Circular Product Design Toolkit (CPD Toolkit) under development now which is intended for guiding a company on how to improve a product circularity performance. The paper outlined the methodological background underlying the CPD Toolkit and also elucidated basic principles of its work, originality, significance, and practical implication. The CPD Toolkit was discussed in terms of possible presentation of information block as actual product circularity dataset and recommendations block dealing with specific guidelines for a company.
We also report an isotopic ratio 129 I/ 127 I measured in the marine environment in the vicinity of the outlet of the ORANO reprocessing plant in La Hague. Iodine-129 activity and stable iodine concentrations were determined in total dissolved iodine in seawater and as total iodine extracted from two species of brown seaweed, L. digitata and Fucus serratus collected in Goury (6 km from the plant outlet). Finally, laboratory experiment results and data from the natural environment are put into perspective to attempt to better understand radioactive iodine transfers between seawater and brown seaweed.
, 1998). The Michaelis-Menten model was fitted using Microsoft Excel™ Solver function. A longer time intake experiment was carried out over 14 days with a 0. 1 g plantlet kept in a 10 L tank to estimate iodine turnover by the algae. The procedure was the same as for tissue disks with the plantlet patted dry and lying in a plastic dish for gamma counting. The activity of the seawater (30 Bq. mL -1 seawater) was maintained constant by adding 131 I to compensate for intake by the plantlet as well as 131 I decay. The very large volume of the tank compared to the small size of the plantlet ensured that no total iodine depletion occurred in seawater. The biological half-life of iodine in L. digitata plantlet was estimated as tb 1/2 = Ln(2)/k by fitting the data to equation Act t = Act 0. [1-exp (- k.
Seasonal changes of iodine in seawater and in brown seaweed Seawater samples were periodically collected in Goury and analyzed for iodine chemical speciation. Additional samples were collected in different locations around the Cap of La Hague and along the coast of Brittany (Bretagne, France). The data are presented in the supplementary material (S2-2). Since brown seaweed iodine content may vary during the seasonal Fig. 3. Incubating Laminaria digitata plantlets in seawater with increasing levels of IO 3 -and I -at the same concentration (same symbols as in Fig. 1 , uncertainties ± 5%). Fig. 4. Long-term kinetics experiment on net iodine flux using plantlets of Laminaria digitata (same symbols as in Fig. 1 ). cycle, it was crucial to take these changes into account when looking at radioactive iodine transfer to seaweed.
digitata  (Küpper et al. , 1998). The Michaelis-Menten kinetics confirmed the enzymatic mediation of I -uptake but our values of Km and Vm parameters were quite different. Our Km value of 59 μM was more than seven times lower and our Vm value of 219 μM was more than three times higher. We have no explanation for this discrepancy except maybe the physiological condition of the disks punched out from the blade of L. digitata. On the basis of our Km value of 59 μM, we may speculate that at concentrations more than two orders of magnitude lower, like in our flux experiments (Fig. 4 ), the uptake mechanism reached a steady state. A biological half-life of 5 days for iodine was estimated based on the long-term 131 I -uptake experiment on L. digitata plantlets. Although the values are consistent, this was below our previous estimate of 14 days in brown seaweed based on modeling using 129 I environmental data (L.