dataesr/scientific-paragraphs-categorization huggingface dataset:
(2021)  in the Mediterranean area. To prevent misinformation to stakeholders in the future, we suggest here to increase efforts in developing a long-term nahttps://doi. org/10. 5194/bg-20-3803-2023 Biogeosciences, 20, 3803-3825, 2023  tionwide observation system quickly collected and analyzed during emergency events as the 2022 fire season. Regarding biomass, we estimated higher values than other automated global biomass information by 8 % to 32 %, mostly as a consequence of varying spatial resolution among datasets, where coarse resolution tends to underestimate higher biomass values as previously reported  (Yu et al. , 2022). Our AGB-L estimates rely on assumptions on tree allometry and tree density. We derived AGB only from tree height provided by remote sensing data, while standard allometry equations rely on both tree height and diameter at breast height (DBH).
The uncertainty reached in our method includes the tree height uncertainty from lidar  (Schwartz et al. , 2022)  with an MAE varying between 2. 38 and 2. 85 m for tree height varying between 3 and 30 m, leading to a 10 % error. The additional error associated with the conversion of tree height to biomass is a major bottleneck in the current biomass density maps at the European or global level. Saito et al. (2022) , for example, pointed out a 35 % difference between two reference global biomass datasets (GlobBiomass and GEOCAR-BON), and  Avitabile and Camia (2018)  report an error reaching up to 58 % to 67 % in Europe for biomass maps elaborated from multispectral remote sensing between 2000 and 2010 so that harmonized global AGB maps still report a 10 % to 26 % variation over Europe  (Spawn et al.
, 2018; Durante et al. , 2019). Shrub  (Li et al. , 2022)  and grassland  (Graux et al. , 2020)  biomass can be more related to foliage proxy detected by lidar or multispectral images and should be further explored for fire impact assessments. Biomass in the understory vegetation, yet hardly provided by remote sensing  (Ferrara et al. , 2023) , would refine carbon stocks for fire impact or fire hazard assessments from this highly flammable and fire conductive vegetation layer. We also acknowledge that our study only covers a short period from 2020 to 2022, as we chose to analyze the fire impact close to the GEDI image acquisition. Tree height information is actually highly dynamic as a result of tree cover change due to disturbances or management plans  (Hansen et al. , 2013; Senf and Seidl, 2020) , particularly in the highly managed Landes region, preventing the use of our database backward in time without increasing bias.
The 2022 fire season with regard to historical and future fire regimes We focused our study on the 2022 fire season, referenced as extreme for France and the overall European context, and compared this year to the 2 previous years. An extended comparison to the previous 6 years shows that 2022 was an exceptional fire year compared to preceding years, particularly outside the Mediterranean region. The extremely hot and long drought of 2022 can thus be viewed as a "natural experiment" to assess the biomass loss by new fire regimes that never or merely happened in the recent past. This conclusion that the fire season of 2022 never happened before was highly reported in the media. However, this conclusion was based on the raw EFFIS dataset, covering only the last 12 years and including pastoral fires.
If 2022 is part of the most extreme years over the recent decades, it cannot be claimed as "never observed before". In the absence of any reference historical database for France and other European countries covering a longer period than the remote sensing era (a few decades), misleading short-term views may continue to spread out. We encourage historical data gathering, although incomplete  (Pausas, 2004; Koutsias et al. , 2013; Mouillot and Field, 2005) , to better characterize high fire years as exceptional, unique, or novel. Still, the year 2022, with all the information available on biomass affected and precise fire contours (yet hardly available before), could be used as a reference fire year that could potentially more regularly happen in the future. Actually, future climate projections show how warmer temperatures combined with more frequent and more extreme droughts in Europe could potentially increase the risk of fires in the future  (Mouillot et al.
We then used this density D, along with the AGB of each tree calculated with the allodb package, in order to fit the 3/2 self-thinning rule and find the coefficient k, described in  Sect. 3. 3. 3. Code and data availability. Fire polygons and their AGB-L assessments are available through the OSU OREME website (https://doi. org/10. 15148/3DB37FDF-46B1-4E7A-BD86-CA4FB93307E1,  Vallet et al. , 2023). Forest inventory data can be downloaded from the French National Inventory platform (https://inventaire-forestier. ign. fr/ dataifn/,  IFN, 2022). Other Fire polygon data can be downloaded from the Copernicus EFFIS website (https://effis. jrc. ec. europa. eu/applications/ data-and-services, EFFIS, 2022) and from the NASA MCD64A1 website (https://doi. org/10. 5067/MODIS/MCD64A1. 006,  Giglio et al.
, 2015). Other biomass data can be accessed through the GLAD website for Potapov tree height (https://glad. umd. edu/dataset/gedi,  Potapov et al. , 2020a) , the ESA CCI Biomass web page (https://climate. esa. int/en/projects/biomass/, ESA CCI, 2022), and the supplementary materials of  Van Wees et al. (2022). Author contributions. LV and MS performed data curation and analysis and wrote the paper. FM and PC wrote the paper and supervised the project. AT and DvW revised the paper. DvW provided GFED framework data. Competing interests. The contact author has declared that none of the authors has any competing interests. Disclaimer. Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Acknowledgements.
The fine-resolution long-term burned area observation from remote sensing over France is supported by OSU OREME. The fire data and biomass estimation are available at https://doi. org/10. 15148/3DB37FDF-46B1-4E7A-BD86-CA4FB93307E1  (Vallet et al. , 2023). We are also grateful to Cedric Vega (IGN) who made possible the validation of our height map with the French NFI plot data. Financial support. This work was supported by the French Environment and Energy Management Agency (ADEME) and the FirEUrisk H2020 project. The FirEUrisk project has been granted funding from the European Union's Horizon 2020 research and innovation program under grant agreement no. 101003890. This work was also supported by the Climate Change Initiative (CCI) Fire_cci Project (contract no. 4000126706/19/I-NB). Review statement.
In this study, the SVM model was chosen for the prediction of the removal rate of Congo red by the PhsEXT using the Matlab R2020a software. For this purpose, the results of the parameter optimization (part ''2. 4. Experimental procedure'') for the removal of Congo red were compiled into a single database, which has 8 input parameters and one parameter Release. The independent parameters were the slow stirring time (×1), the sedimentation time (×2), the initial concentration of Congo red (×3), the dose of biocoagulant (×4), the pH of the solution (×5), the concentration of NaCl (×6), The slow stirring speed (×7) and temperature (×8). In contrast, the dependent parameter was the removal rate of Congo red (Y). To obtain the optimal result from SVM, the Gray Wolf Optimizer (GWO) algorithm was coupled with SVM (SVM_GWO) in order to optimize the parameters of each kernel function.
As a result of its remarkable capacity to enhance model performance via hyperparameter optimization  (Colombo, 2017; Mirjalili et al. , 2014). To construct the model, the MATLAB software Help guide was used, this design is shown in Fig. 2. Where. • The database was normalized once in the interval [-1, +1] using MATLAB's "mapminmax" function. Next, the database was split into two: 80% of the dataset for training and 20% of the validation samples. • The kernel functions that have been selected in this study are as follows:  (Benimam et al. , 2020). • Poly (polynomial) k ( X i , X j ) = (ax t + c) d (3) • Linear k ( X i , X j ) = x T y + c (4) • Gaussian k ( X i , X j ) = 1 σ ̅̅̅̅̅ 2π √ exp { -X i -X 2 j 2σ 2 } (5) with C = BOxConstraint, d = Polynomial Order, ε = epsilon and σ 2 = sigma where d, c, ε, and σ 2 are the defined kernel parameters.
Congo red removal rate modeling using a support vector machine coupled with Gray Wolf Optimizer In this part, 3 kernel functions were optimized, and each kernel synthesized by the variables must also be optimized: The linear function generated by BOxConstraint, epsilon, and sigma; the Gaussian function synthesized by BOxConstraint, epsilon, and sigma; and the polynomial function generated by BOxConstraint, epsilon, and PolynomialOrder, knowing that PolynomialOrder was optimized from 2 to 5. Using the GWO technique, the variables of each kernel function were optimized. After acquiring the outcome of the learning phase, the result was confirmed using the validation database. In order to calculate R, R 2 , R 2 adj, RMSE, MSE, EPM, ESP, and MAE, the generated results (predicted values) were compared with the experimental values in the two phases (the learning phase and the validation phase).
Model performance test Interpolation was used to assess the performance of the generated SVM_GWO model. This was performed by using a cached database comprising 20 experimental data points that were not used during model training. Table  4  summarizes the results in terms of coefficients and statistical errors. The coefficients shown in Table  4  demonstrate the effectiveness and performance of our model. This result was represented graphically (Fig. 8 ) in terms of the experimental values and the predicted values.
We considered three relevant levels: individual plants (inside patches), isolated patches and multiple neighbouring patches. At the plant level, we observed significant differences in biomechanical traits (Young's modulus, flexural stiffness), resulting in stem Cauchy numbers ranging from 85. 25 to 325. 84, and leaf Cauchy numbers from 163. 81 to 2003. 97. At the patch level, we found significant relationships between patch length, width and height, showing covariation among different patch characteristics. The relationships among patch dimensions differed significantly among sampling sites for three of the four species, suggesting high intraspecific variability in patch sizes. By providing a first set of guidelines for choosing correct and ecologically relevant plant characteristics, this dataset aims to improve our understanding of the complex processes occurring inside and around submerged vegetated patches.
2008a; Puijalon et al. 2011; Marion et al. 2014)  or changes in plant density with patch size  (Cornacchia et al. 2022) , so that not all combinations of parameter values are possible. Thus, paired data on the same species should be collected at the individual and patch levels (e. g. on single plants sampled within a patch). These findings have major implications for our ability to address scientific questions under realistic conditions and to extend the results of eco-hydraulic studies to natural conditions. There is a need to collect field data on the relationships between plant and patch traits, and on both intra-and interspecies variability that can guide the design of vegetation patches in hydraulic laboratories and that of abstracted patches in numerical studies. This study aims to provide guidelines for designing ecologically relevant vegetation patches in hydraulic studies to achieve a more realistic representation of vegetation and advance understanding of plant-flow interactions.
The following parameters were collected and paired with data on local hydrodynamic conditions (water depth and flow velocity measurements): 1. Plant level: plant height, dimensions of stem cross section, Young's modulus, second moment of area, flexural stiffness, Cauchy number. 2. Patch level: width, length, height and their allometric relationships; density; fresh and dry mass per m 2. 3. Between-patch level: species composition (% cover in the patch), minimum and maximum gap size (distance between consecutive patches), distance between patch centroids. The characteristics of the study sites regarding the species sampled in each channel, sampling years, water depths and depth-averaged velocities are shown in Table  1. The methodology used for measuring water depth and depth-averaged velocities is described in Section 2.
Biomechanical traits Biomechanical traits were measured through bending tests on 10 replicate individuals per species using a universal testing machine (Instron 5942, Canton, MA, USA). The species considered all have a caulescent growth form: the stem bears the leaves (canopy), and the bending of the stem is a key element for plant bending (movement with the flow) and canopy reconfiguration. Therefore, bending tests were carried out on the basal part of the main stem  (Hamann and Puijalon 2013). However, due to the very high flexibility of the stems of the species studied, a three-point Table  2. Data on individual plants within patches for the four macrophyte species: average morphological and biomechanical traits [plant height (cm), dimension of stem cross-section (diameter for species with circular cross-section, major axis length for elliptical cross-sections, in mm), Young's modulus (MPa), second moment of area (Â10 À13 m 4 ), flexural stiffness (Â10 À5 N m 2 )].
The pictures were mosaicked together using Autopano software, and the species were identified (together with the % cover of each species, in the case of mixed patches). After that, polygons were drawn manually to delineate each vegetation patch using ImageJ to measure the maximum patch length, width, and the minimum and maximum gap length between consecutive patches. Furthermore, the centroid of each patch polygon was identified to measure the shortest distance between centroids of adjacent patches. To limit user bias, all measurements were carried out by a single person. A comparison of the patch dimensions measured in the field and those measured from the aerial images (n ¼ 38) showed that patch lengths were measured with an accuracy of 2. 8%, and patch widths with an accuracy of 4. 5%.
2008b ). However, the regular and compact shape of a patch is also determined by hydrodynamic constraints (constraining length of a plant depending on its location in a patch), which also implies that a plant cannot grow long in a small patch. Therefore, the characteristics measured on a plant growing in standing conditions cannot be applied to running conditions. Our findings may inspire future research by raising the hypothesis that isolated plants are not under the same constraints as plants growing within a patch. In that case, plant and patch characteristics would need to be based as much as possible on paired data collected on a single species at both the individual and patch levels. Our patch sampling strategy did not allow us to make a link between patch dimensions and the local flow regime.
In this study, the leaf Cauchy number calculated using paired data (i. e. data per species) varied between 163. 81 and 2003. 97. If paired data were not considered and the most extreme (minimum and maximum) values across all species were used for each variable needed to calculate the Cauchy number, the results would be different: the leaf Cauchy number would be 31. 24 when the lowest values among all species are used, and it would be 5948. 88 when all the highest values are used. This example illustrates the consequences of respecting the paired data per species as opposed to using a value observed for any of the species. While the covariation between characteristics might appear to increase sampling efforts as there is a need to use paired data, it may also be a way to reduce sampling effort by using proxies.
Conclusions This study aims to provide data to build vegetation patches in ecohydraulic studies. Four patch-forming species of submerged aquatic macrophytes were considered, and their characteristics were studied at three levels relevant for flume and modelling studies: individual plants (inside patches), single vegetation patches and multiple patches. The key findings include the following: 1. The results show that different plant characteristics covary in natural patches, as shown by the relationships between patch length, width, and height and by the changes in dry mass with patch size. The covariation between different patch characteristics should be considered when building mimics and planning experimental conditions because not all combinations of parameter values might occur in nature.
work was funded by the ANR-DFG 2016 project ESCaFlex ('Experiments and simulations for the study of submerged aquatic canopies consisting of long flexible blades', ANR-16-CE92-0020) and by the Research Executive Agency, through the Seventh Framework Programme of the European Union, Support for Training and Career Development of Researchers (Marie Curie -FP7-PEOPLE-2012-ITN), which funded the Initial Training Network (ITN) HYTECH "Hydrodynamic Transport in Ecologically Critical Heterogeneous Interfaces," N. 316546. We acknowledge Baptiste Thevenet, Celine Lott, F elix Vallier, Christelle Boisselet and Sophie Poussineau for their help in field sampling and data collection. We thank Nicolas Rivi ere, Delphine Doppler and J. John Soundar Jerome for valuable discussions on the Cauchy number. We thank the Compagnie Nationale du Rho^ne (CNR) for access to field sites.
Sequencing and Bioinformatics Analyses Amplicons were sequenced by the Plateforme Génome Transcriptome de Bordeaux (Université de Bordeaux, INRAE, France) using Illumina MiSeq 250 bp paired-end technology. Bioinformatics processing of the data was performed using the method described by  Escudié et al. , (2018)   [28]  on the Galaxy FROGS pipeline  [29]. After a preprocessing step (merging, denoising, and dereplications of the reads), the sequences were clustered into operational taxonomic units (OTUs) with an aggregation distance of three bases. OTUs containing less than 0. 0005% of the total sequences were deleted, as were chimeric OTUs. Taxonomic assignments were performed using the Silva database v. 128  [30]. The data were deposited in GenBank under accession number PRJNA924123. For OTUs with no taxo-nomic classification or incomplete classification, sequences were individually processed in BLASTn (NCBI).
To determine statistical differences between NH 4 + , COD, and aromatic compounds remediation, one-way analyses of variance were performed using "Rstudio" software. Then, Tukey's tests were performed to evaluate differences in remediation efficiency between each condition. For statistical tests, a confidence level of 95% (significance α level of 0. 05) was considered. Thus, p-values < 0. 05 were deemed to be statistically significant.
Microbial Growth In order to study the bioremediation of aPW, liquid digestate, and seawater, seven conditions were tested with various microbial populations (Figure  1 ; O: abiotic; Mb: microalgae and associated bacteria; Bd: bacteria from liquid digestate; Ba: aromatics-degrading bacteria; Mb + Bd: microalgae and associated bacteria + bacteria from liquid digestate; Bd + Ba: bacteria from liquid digestate + aromatics-degrading bacteria; Mb + Bd + Ba: microalgae and associated bacteria + bacteria from liquid digestate + aromatics-degrading bacteria). Growth for each condition is shown in Figure  2. For abiotic condition (O), the media stayed clear and OD 600 remained constant at 0. 037 ± 0. 008. This condition is not shown in Figure  2. were deposited in GenBank under accession number PRJNA924123.
Details on living cells as photocatalysts (NPC-systems) Like for any whole-cell biotransformation, the correct identification of the strains/sub-strains and their genetic background is of uttermost importance, as well as the description of the modules utilized for their genetic engineering. [80]  This should include information about the ORFs (open reading frames; e. g. , sequence, database accession number, source organism, IUBMB EC number, protein tag, codon optimization) and details of the regulatory elements used (promoter, RBS, terminator, and any additional elements). Since genetic instability might also be an important issue, the doubling time should be noted. The transformation method to obtain engineered strains should be described and stated, as well as if the modules are on a selfreplicative plasmid or integrated into the genome.
For this, we ectopically expressed fluorescently labeled SYMREM1 in WT M. truncatula plants, a setup that was previously shown to elevate nodulation levels in Lotus japonicus  46. Here, we observed that the frequency of stabilized   stacks (d-f ) can be found in Supplementary Fig. 1. Experiments were performed with three biological replicates with at least 10 nodules being imaged per replicate. g-j Membranes were visualized by expressing the phosphatidylserine (PS) biosensor LactC2. Spatially confined membrane tubes (arrow) were found on wild-type (WT) IT containing rhizobia (magenta, g) but not on ITs in symrem1 mutants (h). Symbiosome membranes are loosely associated with released rhizobia (arrows, i) or appear as empty spheres (arrowheads, i and j) in symrem1 mutants. Data were collected based on three biological replicates.
Homologs of MtSYMREM1 (Medtr8g097320. 2 or Mtru-nA17Chr8g0386521) were retrieved using the tBLASTn v2. 11. 0+  73  against a database of 189 species covering all Viridiplantae lineages with default parameters and e-value threshold of 1e-10. Coding sequences of putative homologs were aligned using MAFFT v7. 471  74  with default parameters. Positions with more than 80% of gaps were removed from the subsequent alignment using trimAl v1. 4rev15  75  and cleaned alignment subjected to phylogenetic analysis using Maximum Likelihood approach. Prior tree reconstruction, best-fitting evolutionary model was tested using ModelFinder 76 according to the Bayesian Information Criteria. Maximum likelihood analysis was conducted using IQ-TREE2 v2. 0. 3  77. Branches support were estimated using 10,000 replicates of both sh-aLRT  78  and UltraFast Bootstraps  79.
Analysis of the main tree revealed that MtSYMREM1 clade derives from Eudicots duplication and then, a subtree corresponding to the Eudicots clade of MtSYMREM1 has been reconstructed based on protein sequences aligned using MUSCLE v3. 8. 1551  80  and phylogenetic procedure described above. Trees were visualized using the iTOL v6 platform  81. To look for specific selective pressure acting on the Caryophyllales clade, which is composed only of species not forming infection threads, we conducted branch and branch site analysis. We estimated relaxation (K < 1) or intensification (K > 1) parameters and also positive selection acting on Caryphyllales (Supplementary Data 1), we used the RELAX and aBSREL programs implemented in the HYHPY software  82, 83. These methods calculate different synonymous and nonsynonymous substitution rates (ω = dN/dS) using the phylogenetic tree topology for both foreground and background branches.
Other AtREM expression vectors were built in a previous study  36. All the used constructs and all primers used are listed in Supplementary Data 3 and Supplementary Data 4, respectively. The sequence data from this article can be found in phytozome (https:// phytozome. jgi. doe. gov/) with gene IDs: SYMREM1 (Medtr8g097320), SYFO1 (Medtr5g036540). All gene IDs are listed in Supplementary Data 3. For promoter activation studies, a sequence 1 kb upstream of the start codon of the Medicago truncatula REM2. 1 gene was amplified via PCR from genomic DNA (A17) and cloned into a pENTR/D-TOPO vector. The ProMtREM2. 1:GUS construct was created by a LR-reaction of pKGWFS7-vector and pENTR/D-TOPO: ProREM2. 1.