dataesr/scientific-paragraphs-categorization huggingface dataset:
The first equation can be re-expressed as follows: B i × (P/B) i = ∑ B j × (Q/B) j × DC ij + Y i + E i + BA i + B i × (P/B) i + (1-EE i ) where (P/B) i represents the production per biomass unit and, under steady state, is equivalent to Z, the total mortality  (Allen, 1971) , (Q/B) j is the consumption per biomass unit and DC ij is the portion of i in the diet of predator j (in weight or volume units). For each functional group, Ecopath requires three among the following parameters B, P/B, Q/B and EE. It also takes as input the diet of each group, their captures and discards, the assimilation rate (ratio of unassimilated/consumed food) and net migration rate (here considered as an 'import' part of the diet). After all the input data were entered in the Ecopath software, but prior to balancing the model, special attention was paid to the P/Q ratio, as expected values range from 0.
Functional groups The species to be included in the model were selected mainly from the MEDITS database  (Jadaud and Certain, 1994)  in order the represent 99% of their estimated biomass indices. Then, others were included due to the large volume of capture in the area (total number of species representing 97% of the cumulated captures recorded in the SIH  (Halieutics Information System, http://sih. ifremer. fr) fisheries database). Finally, some essential groups not represented in these data sets have been added, such as the planktonic and benthic groups, Posidonia and macrophytes. Then the diet for each species was completed from the literature (details below). To form the functional groups, the species were grouped in two ways: the invertebrates were grouped according to the major taxa (brachyurids, echinids, etc.
) with subdivisions based on the diet (e. g. carnivorous and detritivorous worms). Vertebrates (except birds and marine mammals) were grouped using hierarchical clustering based on their diet (Appendix 1). This clustering was carried out with STATISTICA software (version 12, Dell Inc. ) using Ward's method and Euclidian distances. This approach led to the constitution of 68 functional pelagic, demersal and benthic groups, the species composition of which is detailed in Table  1. They include 31 groups of fishes, 27 invertebrates, 6 primary producers, a group of sea birds and a group of dolphins. Among them, 19 have been kept monospecific, either for commercial reasons (Homarus gammarus, Mullus barbatus) or for conservation interest (sharks and rays).
Biomasses The main source of information on fish and invertebrates biomass used in this work comes from the MEDITS set of annual scientific bottom trawling campaigns  (Jadaud and Certain, 1994)  (Appendix 2). Their sampling scope has broadened over the years and includes all captured supra and epibenthic invertebrate species since 2012. The MEDITS data set has been processed to produce an average estimate of biomass (in t. km -2 ) for the period 2010-2014 and between 0 and 200 m depth while keeping information about the spatial heterogeneity of the species. The calculations were first carried out according to three depth strata (10-50 m, 50-100 m, 100-200 m) before being rescaled to the entire area. Given the geomorphology of the gulf (influence of the Rhône River in the east, mesoscale gyre in the west), it was decided to apply a correction factor to render the horizontal heterogeneity of the species distribution.
For Engraulis encrasicolus, Sardina pilchardus, Sprattus sprattus, Trachurus Trachurus, T. mediterraneus, Scomber scomber and S. colias the biomass datasets from the annual Mediterranean acoustic survey PELMED  (Bourdeix and Hattab (1985)  were used. The GFCM (General Fisheries Commission for the Mediterranean) biomass data estimated by the stock assessment committees for this area were used for Merluccius merluccius and Mullus barbatus. In order to obtain densities per km 2 , the PELMED data, delivered in total weight, were divided by the surface surveyed by the GFCM, i. e. 11,400 km 2. Benthic endofauna data were provided by Labrune (pers. comm. ) and estimated from  Bonifácio et al. (2018). They are based on REDIT2010 and APPEALMED cruises  (Labrune and Amouroux, 2010; Labrune, 2018). These data were used for the following groups: cnidarians, sessile suspension feeders, depositivorous molluscs, filter feeder molluscs, carnivorous molluscs, depositivorous worms, filter feeder worms, carnivorous worms, sipuncula, suprabenthic and benthic invertebrates, pagurids and carnivorous echinoderms.
Among the species included to reach 97% of the total captures, some lacked biomass estimates. These missing values were completed from the literature (see Appendix 2). To estimate the biomass of the detritus group, the particulate organic carbon (POC) inventory from  Many et al. (2021)  was used (average between 2011 and 2014, in t C). Their value was converted to wet weight (ww) with a 10× factor  (Dalsgaard and Pauly, 1997)  and divided by the GoL area. The import term was calculated using data from the Rhone sediment observatory  (Thollet et al. , 2018). The daily liquid discharge and daily concentration of particulate organic carbon (POC) from 2010 to 2014 were used to determine an average annual import of POC (t C) from the Rhone River to the GoL. According to  Many et al. (2021) , the Rhone river accounted for 97% of the POC riverine input in the GoL over the period 2011 to 2014 (consistent with the 97.
Catches The landings data recorded from the GoL were provided by the Halieutics Information System (SIH). The raw data were averaged over 2010-2014, by species and fishing gear type and over the entire GoL fishing area. Gear types were then regrouped into: benthic trawls, pelagic trawls, nets, seines, long lines, recreational fishing and other. The 2010-2014 averages of the reported Spanish catches (https://www. fao. org/faostat) in the study area were added. As the gear used were not specified, they were distributed by species in the 7 functional fleet groups according their relative importance in the French catches. The organization of fisheries in the GoL, with a high proportion of small boats (< 10 m) and a small number of auction sale halls, makes it difficult to accurately assess the catches, as part of them are sold directly in small harbours, fish markets and/or are undeclared (CRPMEM PACA, 2016).
The most accurate taxon for the given species was selected among those available and only marine species were included in the computation. For gastropods and bivalvia, the 'with shell' conversion factor was chosen, to remain consistent with the MEDITS data. The Q/B ratio of invertebrates was calculated based on the following empirical relation  (Cammen, 1979; Brey, 2001) : Log (Q) = -0. 42 + 0. 742 × log (BM) where BM is the body mass (mg DW) and Q the consumption (mg DW d -1 ). The average dry weight of each species was obtained as explained above, from the MEDITS dataset and the Brey conversion factor calculator. For vertebrates, the P/B was estimated as follows: P/B = Z = F + M with Z the total mortality (y -1 ), F the fisheries mortality (y -1 ) and M the natural mortality (y -1 ). F is the ratio between the catches of a species and its biomass.
Diet and data quality The diet for each species was obtained from the literature (see Supplementary Table  3  and Appendix 2 ). When multiple data sources were found, the closest geographically was chosen. For multispecies groups, the diets were calculated in proportion to the biomass of each species making up the group. As advised by  Heymans et al. (2016) , the integrated pedigree routine was used to assess the input data and the overall model quality. For each input parameter (biomass, P/B, Q/B, diet and catches), a score was assigned to the source according to the Ecopath default rating scale. The routine is then able to produce an overall score for the model, ranging from 0 to 1, with 1 being the best quality.
Acknowledgements This work was funded by the  Institut de Radioprotection et de Sûreté Nucléaire (IRSN)  and by the  French Investissement d'Avenir program  run by the  National Research Agency  ( AMORAD project , grant  ANR-11-RSNR-0002 ). T. Seyer's thesis is co-funded by the  Provence-Alpes-Côte d'Azur Region and IRSN  ( 2020-03395 ). T. Seyer was hosted for 1 year at the  Mediterranean Institute of Oceanography in  order to build this model based on their most recent data on the GoL food web. This work was also carried out in the framework of collaboration with the consortium of  CONTAMPUMP  ( ANR-19-CE34-0001 ) and  WINDSERV  ( ANR-10-IEED-06-34 ) projects. D. Bȃnaru  benefited from leave within the  ANR CONTAMPUMP. The  REDIT2010  and APPEALMED data used in this work are from samples collected on board the NO Tethys II.
The REDIT2010 data are part of  P. Bonifácio 's doctoral thesis which was funded by the  Agence de l'Eau Corse-Méditerranée  (convention n • 2010 0871),  LECOB (UMR CNRS -UPMC) ,  EPOC  ( UMR CNRS -Université Bordeaux 1 ) and the  Agence des Aires Marines Protégées  (Marché N • 2009-AAMP-16; Lot N • 9; ASCONIT/GIS Posidonie). The  APPEAL  project was funded by  France Energies Marines  and the  National Research Agency  within the  framework of Investissements d'Avenir  ( ANR-10-IEED-0006-25 ). The authors would like to thank  L. Lescure  and  J-M. Amouroux  for sorting and identifying macrofauna, as well as the captains and crew members of N. O. Thetys and Nereis II for technical assistance during samplings for these projects.
4 GHz and measures the antenna impedance modification through an oscillator connected to the antenna. The system is quite simple and can use an autoregressive spectral estimation method  [7]  or a PLL  [8]  to retrieve the frequency shift which occurs when antenna impedance is modified. In this study, we propose to investigate the use of an oscillator based sensor working around 30 GHz for which the dielectric behaviour of the skin is significantly modified by the sweat (+1. 55% for conductivity and +14. 4% for permittivity). The frequency deviation estimator is not studied here. The paper focuses on the design of the oscillator and especially the design of a cross-coupled pair (CCP) and its connection to a resonator designed and simulated using HFSS software. As the resonating frequency is different between the two environments (wet and dry skin), the oscillation frequency is modified according to the skin state.
B. Sensing resonator The sensing resonator is based on two-planar resonating patches, which act as a unique differential near field sensor for sweat detection. One patch is presented in Fig. 2. It consists of a metallic square shape deposited on a grounded Teflon layer with a thickness equal to 200 𝜇𝑚 and dielectric permittivity equal to 2. 1. Indeed, a substrate with a low relative permittivity allows to maximize the variation of the antenna impedance. The sensor is placed at 1 mm on top of the skin to ensure near-field radiation. Two access lines (not represented in Fig. 2 ) are used to feed the resonator (one access line per patch). The resonator is optimized to maximize ∆𝑓 at 30 GHz, which leads to a patch size of 2. 2 mm by 1. 3 mm. The resonator has been simulated and optimized with HFSS software.
Genomic analyses of new Burkholderia and Paraburkholderia species The newly isolated strains described in this study (Table  1 ) were compared to genomic databases of Burkholderia sensu lato (s. l. ) and the genomic distance to their closest relative was computed (Figure  1 ). Whole genome comparisons using average nucleotide identity (ANI) estimations showed a 98. 52% score (80. 14% sequence coverage) when comparing strain ABIP441 (hereafter BrABIP441) to the proposed species Burkholderia reimsis sp. nov. BE51 T (Esmaeel et al. , 2018), lying above the species threshold (> 95% identity on more than 70% cover). We had previously proposed that ABIP444 (hereafter BoABIP444) belongs to a novel species, B. servocepacia sp. nov. , closely related to Burkholderia cenocepacia  (Wallner et al. , 2019).
The global transcriptomic response to root exudates is strain-specific In order to unravel bacterial genes involved in the preliminary steps of plant interaction, we stimulated the six Burkholderia s. l. strains with Nipponbare rice RE and compared their transcriptomic response to that of bacteria grown in VSG medium using three replicates by strain and condition (see Methods). A preliminary validation of the sequencing procedure confirmed that ribodepletion efficiency was > 70% for every strain and condition except for BvLMG10929 which only attained ~50% efficiency (Supplementary Table  S1 ). Nonetheless, high enough read numbers mapping against a database of CDSs were obtained for robust bioinformatic analyses (Supplementary Table  S1 ). Furthermore, we verified that the exposition to RE significantly and consistently affected the bacteria's transcriptomic responses throughout the replicates (Supplementary Figure  S2 ).
Statistics were performed in R using packages rstatix, multcompView and ggplot2  (Wickham, 2016; Kassambara, 2020; Graves et al. , 2022)  and differences were estimated with a pairwise Wilcoxon non-parametric test. R Script used to do statistics and produce Supplementary Figure  S1  is accessible on Zenodo (https://doi. org/10. 5281/zenodo. 7447236).
ebi. ac. uk/ena/browser/view/PRJEB42574). Reads were mapped to the reference genomes using the CLC workbench and differential gene expression was inferred using the DESeq2 package  (Love et al. , 2014). Low-count genes were removed at a threshold of 5 reads and an independent hypothesis weighting was applied. One replicate had to be removed from the analysis for B. reimsis ABIP441 as the sequencing depth was insufficient to allow robust analyses (Supplementary Figure  S2 ). Genes that had a positive or negative differential expression superior to 1. 5-fold at pval < 0. 05 were taken into consideration.
7 kbp for ABIP659. The genome quality was assessed using BUSCO v3 for the detection of mandatory singlecopy orthologues  (Simão et al. , 2015). Genome completeness was estimated at 97. 0%, 99. 0% and 97. 8% for ABIP441, ABIP630 and ABIP659 respectively. Genbank accession numbers for the three new genomes are GCA_914492715 (ABIP441), GCA_914484815 (ABIP630),  The tree was constructed using the Genome clustering tool of the Microscope plateform (https://mage. genoscope. cns. fr/microscope/). Distances between whole genomes was computed using Mash  (Ondov et al. , 2016)  and plotted using the rapid Neighbor-Joining method  (Simonsen et al. , 2008). The displayed distances are correlated to average nucleotide identity (ANI) such as D ≈ 1-ANI. Genbank genome accession numbers of strains under study are indicated in Table  1.
We aimed to set an easy-to-use clustering analysis protocol adaptable to different types of data. Here, we introduce Diinamic, which combines different density-based analyses and optional thresholding to facilitate the detection of clusters. On simulated or real SMLM data, Diinamic correctly identified clusters of different sizes and densities, being performant even in noisy datasets with multiple detections per fluorophore. It also detected subdomains ("nanodomains") in clusters with non-homogeneous distribution of detections.
In contrast to the images obtained with optical microscopy, SMLM techniques generate datasets of molecular coordinates in 2D or 3D. Several approaches have been proposed to analyze the spatial organization and morphology of molecular clusters from this kind of data. Clustering analyses typically employ two different strategies, based on spatial statistics or based on density analysis. Concerning the methods based on spatial statistics, the most popular ones are the adaptation to SMLM of pair-correlation analysis  (10)  and Ripley's L-function  (11, 12). As they do not provide information about the number, spatial position, and morphology of clusters, they serve to detect the presence of clustered distributions but not to describe them from a mechanistic point of view. An improvement is the analysis of topological prominence  (13)  that informs about the shape of the cloud of points.
However, these statistical approaches perform well only in the case of small clusters of relatively homogeneous size. Density-based data mining algorithms perform better in detecting clusters in variable biological data due to their capacity to describe the spatial characteristics of individual clusters. The most extensively used is DBSCAN  (14)  , which calculates the neighboring density around each detection by counting the number of surrounding detections in a given radius. Those that are mutually reachable are considered as belonging to a cluster, and those that do not belong to clusters are considered background. Several software packages adapted this algorithm to SMLM data such as LAMA  (15)  , Clus-DoC  (16)  , and FOCAL  (17)  (a grid-based version of DBSCAN). Unfortunately, DBSCAN performs weakly when the detections' density in the clusters is variable among clusters, a limitation that restricts its use to datasets with homogeneous clustering.
The alternatives that have been proposed introduce mesh representations to perform the density analysis. The most common strategy to create the mesh is Voronoi tessellation (SR-Tesseler  (18)  ; ClusterVisu  (19)  ), in which each point is on the face of a Voronoi polygon and edges are the equidistant bisectors between points. In an SMLM data set, regions with a high density of points have small polygons, so dense structures can be segmented by setting a threshold for the area of these polygons. Another improvement that was recently proposed is the application of persistence-based clustering analysis, which improves the individualization of clusters when they are close to each other  (20). This variety of analytical approaches can be overwhelming for users that usually struggle to find an algorithm well adapted to their data.
Moreover, SMLM techniques are affected by several technical issues that may induce various artifacts in clustering analysis; namely background noise, multiple detections of one single fluorophore over time, stage drift, or dependency on the quality of the manipulation used to prepare the sample. Some analysis strategies may be more appropriate than others depending on how the experiment was conducted and the characteristics of the targeted molecule. Our aim was to set an easy-touse analytical protocol able to detect clusters of different characteristics, in noisy datasets with multiple detections per fluorophore. Hence, we have developed Diinamic (Density and Image INtensity based Analysis Method for Identification of Clusters), a modular sequence of cluster analysis that combines different density-based analyses to simplify the adaptation to different datasets.
Simulations of SMLM data Monte-Carlo simulations of SMLM data consisted of generating x and y coordinates for a given number of detections and were produced by a program written in MATLAB (MathWorks). Non-overlapping clusters were created by selecting the position of the first detection randomly within the simulation area (100 μm 2 ). Subsequently, the other detections belonging to the cluster were positioned arbitrarily into a circular or ovoid area of the desired size. Non-clustered detections ("noise") were introduced at random positions in the simulation area. Multiple detections of the same molecule were simulated by adding 1-50 detections around the position of the molecule. The position of these extra detections was randomly chosen following a Gaussian distribution with a mean zero and variance similar to the localization precision of our setup (20-50 nm).
Clustering analysis All data analyses were performed using custom routines in MATLAB (MathWorks) and executed typically in a PC equipped with an IntelCore i7 processor (1. 69 GHz) and 16 Gb of RAM. The DBSCAN program was sourced from Pageon et al. (16)  and adapted to be run on MATLAB. The calculation of the adjusted rand index (ARI) and intersection over union (IoU) scores was implemented in MATLAB following the algorithms described in Nieves et al. (23). Clustering detection was carried out on regions of interest (ROIs) drawn on top of pointillistic images constructed from the coordinates of detections. A package with all the tools (ROI selection and clustering analysis) for executing Diinamic-R and Diinamic-V is freely available at https://github. com/mlrennerfr/Diinamic. This package includes Graphic User Interfaces (GUIs), a user manual, and a tool helping the optimization of parameters.
Diinamic-R and Diinamic-V Detections were initially sorted into belonging to candidate clusters or not depending on their localization with respect to a segmentation mask. This mask was created by either thresholding the pixel intensity of the corresponding rendered image (Diinamic-R) or by evaluating the size of polygons resulting from a Voronoi tessellation (Diinamic-V) calculated using the "voronoi" function of MATLAB. The clusters' borders were defined by the "boundary" function of MATLAB. Candidate clusters were retained if: (a) their density, calculated in a pixel-wise manner, exceeded a threshold; (b) their sizes were between given thresholds (minimum and maximum). Thresholds were chosen considering the expected number of detections per molecule, the size of the molecule and the expected size of clusters given other microscopy data already published.
However, as the execution time could be 20 times longer for Diinamic-V in the case of large datasets, we privileged Diinamic-R to run this test. To set the minimum thresholds for Diinamic-R, we considered the structure of Kv2. 1, the labeling method, and the expected size of clusters. As we could not know how many chimeric subunits were present per cluster (tetramers could contain both endogenous and chimeric subunits), we chose to use a low threshold for the number of detections (150). We calculated this number using the strategy described in Patrizio et al. (25)  to assess the number of detections expected for one molecule. Given the worst localization precision in this experiment (~30 nm for Dendra2 detection), and the possible enlargement of clusters due to the use of antibodies, the minimum size of the cluster was 50 grid units (5000 nm 2 , a square of ~70 nm in side) for PALM data and 70 grid units for STORM data.
Given all the above considerations, the minimum density inside clusters was set to 2 detections/grid unit. The most important difference between the parameters for PALM and STORM data was the intensity threshold to segment the rendered image (PALM: 0. 5% of the maximum intensity; STORM: 15%) and the use of a minimum density per pixel (2) in case of STORM data. These differences were due to the different detection density ratios in and out clusters (Figure  5a, b ). The number of clusters detected was different (PALM: 17; STORM: 15), in agreement with the fact that some clusters were not observable in both datasets. However, we obtained a distribution of the number of detections per cluster and cluster surfaces that were not significantly different (Figure  5d ). Therefore, even if the labeling method had a strong influence on the characteristics of the dataset, it is possible to set parameters that provide concordant results for the same molecule.
Improving cluster detection by combining Diinamic with DBSCAN In the case of dense datasets with variable density of detections, applying the same thresholds to different regions of the cell may provide results that are not satisfactory. Figure  6a  shows an example of dense labeling of Kv2. 1WT-Dendra2. Clusters recognizable by eye were merged by the analysis (dark blue in Figure  6a2 ). To improve the analysis in these conditions, we performed two rounds of selection. In the first round, we selected clusters as described before (Figure  6a2 ) and in the second round, we looked inside these clusters to identify areas with higher density (subdomains). We obtained the best results using DBSCAN for the second round. To cope with variable density from one cluster to the other, we calculated the search distance epsilon from the mean distance between detections in the cluster.