dataesr/scientific-paragraphs-categorization huggingface dataset:
After G400, the populations were frozen every 100 generations. Then, the cultures were frozen at 280°C until later use. Automated growth curve measurements. Overnight cultures of the indicated microorganism were diluted 1/1,000 in LB. Bacterial preparations were distributed by triplicate for evolved populations and sextuplicate for the ancestral clones in 96-well microplates. Growth curve experiments were performed using a TECAN Infinite Sunrise microplate reader, with absorbance (620 nm) taken at 5-min intervals for 5 h with agitation at 37°C. The experiments were done at least twice independently. The slopes during the exponential phase were directly obtained using a home-made Python script coupled to a growth rates program  (73). To filter noise due to growth variations among experiments, the obtained m was relativized to the ancestral clones, and the relativized values were multiplied by the average of the ancestral clones throughout all the experiments.
The results are presented as the means of four replicate wells in three independent experiments. Deep sequencing analysis. The quality of Illumina reads was visualized by FastQC v0. 10. 1 Babraham Bioinformatics (http://www. bioinformatics. babraham. ac. uk/projects/fastqc/). All reads of 32 samples were aligned against the two chromosomes of V. cholerae (GenBank NC_002505 and NC_002506, 17 December 2014), using paired end mapping mode of BWA "mem" v0. 7. 4  (108)  with the option "2M. " The reference chromosome I was modified since the reference genome sequence (AE003852) showed an inversion around ori1 flanked by two rRNA operons (rrnB and rrnG-H)  (48). Output SAM files were converted to BAM files using Experimental Evolution on Ribosomal Protein Gene Location mBio Month YYYY Volume XX Issue XX 10.
The coverage around the mutation was checked with a Python script with a minimum depth of 7 reads. The annotation of identified variants was processed by SnpEff v4. 1  (112). Common mutations between all sequenced strains were ignored, indicating differences with the reference sequence. We also looked at possible inversions or alterations in gene order in evolved populations. For this we employed Spades 3. 6. 2 (113)  for assembly of trimmed reads with the kmer options "2k 21,51,71" and "2careful. " Contigs were ordered and oriented using CONTIGuator 2. 7. 4 (114) using the two chromosomes of V. cholera as references. The Artemis comparison tool (ACT)  (115)  was used for synteny visualization and for the search for dynamics of large rearrangements such as translocations and inversions. All data generated and analyzed during this study is included in this published article, its supplementary information files, and publicly available repositories.
The Illumina Deep Sequencing data sets are deposited at the GenBank SRA as BioProject PRJNA816505. Multiplex Genome Editing by Natural Transformation (MuGENT). To introduce the mutations observed throughout the evolution experiment, we employed MuGENT  (76, 77). Briefly, the wild-type strain (Table  S1 ) was grown overnight from a single colony at 37°C in LB with agitation. This overnight culture was diluted 10 23 into fresh medium, and the strain was grown to an OD 600 ;0. 5. The cells were washed by centrifugation and resuspended at the original volume in 1Â instant ocean sea salts (7 g/liter) (Blacksburg) and autoclaved chitin powder (Sigma-Aldrich) and incubated overnight without agitation at 30°C to induce competence. In parallel, DNA fragments harboring the mutations were synthesized using PCR assembly, cloned using a pEASY-Blunt Zero Cloning kit (Transbiotech, China), and verified using Sanger sequencing (Macrogen).
ACKNOWLEDGMENTS We thank  Eduardo P. Rocha ,  José Antonio Escudero ,  Alexandra Nivina ,  Céline Loot ,  Soledad Guidolín ,  Lucas Saposnik , and  Inés Marchesini  for useful discussions. We are grateful for the technical assistance of  Laurence Ma  and  Christiane Bouchier  from the  Institut Pasteur Genomics Platform  for DNA sequencing. We thank  Michaël Timmermans ,  Pierre Faure , and  Julie Lambert  for their technical help. This study was supported by the  Institut Pasteur , the  Centre National de la Recherche Scientifique  ( UMR3525 ),  French sNational Research Agency  grants  ANR-10-BLAN593 131301  ( BMC ) and  ANR-14-CE10-0007  (MAGISBAC),  French government's Investissement d'Avenir Program, Laboratoire d'Excellence  " Integrative Biology of Emerging Infectious Diseases " grant  ANR-10-LABX-62-IBEID  (J.
Enzymatic Assays Culture supernatants were collected after 24 h of incubation and preserved by freezedrying before lyophilization. Lyophilizates were resuspended in 1 mL of saline and used for two-point enzymatic assays. For each enzymatic assay, three independent experiments were realized and all reactions were carried out in duplicate. Protein concentration was determined by fluorimetry using Qubit. The specific activity corresponds to the number of international enzyme units (i. e. , µmoles of substrates converted per unit time) per mg of total proteins. According to the data distribution, statistical analysis was performed using a Student's t-test or Mann-Whitney test.
Genome Mining A literature review was performed to identify lignocellulolytic fungi and then focused on lignin-modifying enzymes. Orthologs of the genes identified by this way were searched in Scedosporium genomes (JOWA01000000 for S. apiospermum, JUDQ01 for S. aurantiacum and PGIR00000000. 1 for S. dehoogii) through tBLASTn analysis (https://blast. ncbi. nlm. nih. gov/Blast. cgi, accessed on 10 January 2023). Only results with an E-value < 1 × 10 -6 on at least 40% of the query sequence were considered. Gene organization (exon/intron) was determined using HMMER program on EnsemblFungi (https://fungi. ensembl. org/ hmmer/index. html, accessed on 10 January 2023) and validated with Augustus (https: //bioinf. uni-greifswald. de/augustus/, accessed on 10 January 2023). For classification analysis, all peroxidase sequences were collected from PeroxiBase (http://peroxibase.
toulouse. inra. fr, accessed on 10 January 2023)  [24] , and multicopper oxidase (MCO) sequences from the NCBI GenBank  [25]. The Geneious software package  [26]  was used for the phylogenetic study. An alignment was created using the default settings for multiple alignments. Phylogenetic trees were constructed by the neighbor-joining method. Bootstrapping was carried out with 500 replications.
2. 5042 software  [38]. Depending on the call category, different acoustic features were measured in both temporal and frequency domains. Only good-quality calls that did not overlap with any other noise or vocalization were analyzed. For harmonic calls, we measured, whenever possible, seven acoustic variables: dur, f0, Fmax, Q25, Q50, Q75, and excF. Total duration (dur, s) was assessed from the oscillogram. All frequency parameters were measured from the average energy spectrum: the fundamental frequency values (f0, Hz) were measured using the harmonic cursor, whereas the highest energy peak (Fmax, Hz) and the various energy quartiles (Q25, Q50, Q75, in Hz) were measured using the spectral characteristics function of Avisoft SAS Lab Pro. When possible, we also calculated the excursion frequency (excF, in Hz) on the first visible frequency band by measuring the absolute difference between the higher and lower frequencies on the spectrogram.
Statistical Analysis To validate the aural-visual classification of our call types, we carried out a Random Forest (RF) analysis  [39, 40]  using the randomForest package in R  [41]. We calculated the global accuracy of the RF classification (defined as accuracy = 1 -OOB, where OOB stands for out-of-bag error, i. e. , the misclassification error rate) and computed the Gini index, which indicates the importance of each respective acoustic variable used in the classification. We included only acoustic variables that were measurable for all call types of a given category (6, 5, and 2 variables for harmonic, noisy, and pulsative calls, respectively). The number of variables randomly selected at each split was set at 2 for both harmonic and noisy calls, and at 1 for pulsative calls. The number of trees grown was set at 500.
Data extraction from the literature We collected rooting depth distribution data from arctic and subarctic tundra systems within the northern permafrost area from New Phytologist (2023) www. newphytologist. com Ó 2023 The Authors New Phytologist Ó 2023 New Phytologist Foundation published literature, extending the studies included in  Schenk & Jackson (2002)  with an extensive search in both Web of Science and Google Scholar, resulting in coverage across the circumpolar Arctic (Fig. 1 ). We included studies when they reported root data from at least three soil depths, permafrost was present, and information on active layer thickness at the site could be found. Most studies report whole-vegetation rooting depth distributions which is why we focused on those. If data from all species present in the community individually were reported (< 5% of profiles), we summed up the individual species data to make wholecommunity root biomass depth profiles for the respective site.
Whenever different root types were reported, we used the smallest diameter ones, as those are generally more important for plant-soil interactions  (Pregitzer, 2002; McCormack et al. , 2015). If the original data were not publicly available, we extracted them from figures or tables from the manuscripts using the program PLOTDIGITIZER. As amounts of roots or root mass was reported in different ways and units (see Supporting Information Table  S1 ), and also differed between sites, we further standardized root data for each profile by 'total root mass' for all subsequent analyses and therefore these data represent proportional and not absolute biomass of roots by depth. We added information on organic layer depth, if this was reported in site descriptions, latitude, as well as derived climate and other soil data from databases based on study locations (Table  S1 ).
Grouping of rooting depth profiles The rooting depth profiles were further classified into four vegetation types following the classification of the Circumpolar Arctic Vegetation Map (CAVM,  Walker et al. , 2005) : Graminoid tundra (dominated by e. g. Carex aquatilis Wahlenb. , Carex ensifolia Turcz. ex Gorodkov, Dupontia fisheri R. Br. ), Erect-shrub tundra (dominated by e. g. Betula nana L. , Salix glauca L. , Vaccinium vitis-idaea L. ), Prostrate-shrub tundra (dominated by e. g. Dryas octopetala L. , Salix arctica Pall. , Salix phlebophylla Andersson), and Wetland tundra (dominated by e. g. Carex aquatilis, Dupontia fisheri, Eriophorum angustifolium Honck. ). We classified profiles based on the site description and species presence that were reported in the original papers. As a readily available and spatially explicit product, the CAVM vegetation classes are widely used for upscaling purposes in the northern permafrost region as they allow for a spatial representation of current and future vegetation distribution also in combination with other data products such as soil organic carbon content, gross primary production, and active layer thickness  (Bartsch et al.
, 2016)  and thus are relevant for comparison in this study. Furthermore, to detect possible patterns in root profile shapes across the dataset without reference to aboveground vegetation type, we calculated Fréchet distances  (Alt & Godau, 1995)  for all pairwise combinations of root profiles in the dataset with the SIMILARITYMEASURES package  (Toohey, 2015). The Fréchet distance metric quantifies the similarity in shape between lines defined by sets of points, and thus allows assessment of similarity and dissimilarity between different rooting depth curves. Both cumulative root biomass and root depth were standardized (divided by maximum observed value) before computing distances to remove the influence on the similarity estimation of variation in total biomass or maximum depth. The resulting matrix of Fréchet distances was used as input for a cluster analysis (hierarchical clustering with complete linkage,  Paradis & Schliep, 2019).
Silhouette analysis  (Rousseeuw, 1987)  of clustering arrangements obtained with between 2 and 10 classes identified 2 as the optimal number of clusters, but further graphical inspection revealed no clear discontinuity between rooting profiles (see Fig. S1 ). We therefore proceeded with three representative profile types in order to check how sensitive model estimates of plant-soil interactions are to variation in root profiles on the scale we observe in our dataset. Three clusters were chosen to cover much of the range observed in the field but without focusing on extreme observations, as could have happened with picking the outermost observed profiles instead of summary clusters. These clusters are thus not clearly distinct groups in the field but represent the variation that is present in arctic plant communities.
Statistical analysis Differences in rooting depth distribution between vegetation types were tested by fitting Bayesian multivariate linear models in the brms package  (Bürkner, 2017 (Bürkner, , 2018 (Bürkner, , 2021)) , a front-end to the Stan probabilistic programming language (Stan Development Team, 2023). As the shape of the estimated root profile curves is fully determined by the two parameters c and D50, this was done with D50 and c as (potentially correlated) response variables and vegetation classification as predictor variable. All BRMS models fitted used the default priors (BRMS v. 2. 16. 2) and three chains, each running 3000 iterations (1000 warmup). Convergence of chains was confirmed with the Rhat statistic. Differences between vegetation types in 50% and 90% rooting depth (the latter computed from estimated values of the D50 and c parameters) were tested by generating a posterior distribution of the pairwise differences in parameters between vegetation classification group.
We considered vegetation types to be significantly different when the 95% credible interval for the pairwise difference in parameters excluded zero. The matrix resulting of Fréchet distances from the cluster analysis (as mentioned in the previous section) was used as input for ordination analysis with the VEGAN package  (Oksanen et al. , 2020). Principle components analysis (PCoA) was used to produce unconstrained ordinations, and distancebased redundancy analysis (dbRDA) was performed in combination with permutational ANOVA to test the correlation of the variation in rooting profile shapes with the abiotic factors detailed above. Graphical illustrations were done with R packages GGPLOT2  (Wickham et al. , 2016)  and COWPLOT  (Wilke, 2020).
, 2020 for details). In our modified version, we focused on the tundra area alone, leaving out the boreal permafrost zone. We assessed differences in rhizosphere priming effect between the different CAVM vegetation types and between the different Root Profile Types representing the variation in published rooting depth profiles as identified in the cluster analysis (i. e. a belowground classification, as mentioned in the previous section). We did this by assuming that the entire tundra biome would be rooted according to either a single vegetation type or New Phytologist (2023) www. newphytologist. com Ó 2023 The Authors New Phytologist Ó 2023 New Phytologist Foundation single Root Profile Type and comparing the carbon losses between these rooting depth distribution types. The resulting analysis therefore gives an indication of the potential sensitivity of arctic soil carbon emissions to variation in rooting depth distributions on the scale of that observed in our dataset.
Root Profile Types While we did not find any obvious grouping according to the (aboveground) CAVM classes, rooting depth profiles present in the dataset did show variation. We thus summarized these different rooting strategies into three cluster types (Fig. 5 ) to cover much of the range observed in the field but without focusing on extreme observations. These Root Profile Types were determined without references to CAVM class and obtained through a cluster analysis. While we arbitrarily chose the amount of clusters, they do represent the rooting variation present in arctic tundra and clearly illustrate different rooting strategies, with (1) a Root Profile Type where the majority of root biomass is in the uppermost part of the active layer and then sharply declines toward the permafrost ('shallow', 50% of root biomass in the upper 19% of the Fig.
Influence of rooting depth distribution on root-soil interactions and soil carbon losses When we tested whether estimates of plant-soil interactions are sensitive to rooting depth distribution, the PrimeSCale model showed that even assuming such extreme scenarios as the whole tundra being rooted according to each of the individual CAVM vegetation classes resulted in remarkable similar amounts of carbon emissions due to the rhizosphere priming effect. Emissions ranged from 0. 07 to 0. 08 Pg C in different vegetation types under present-day conditions (Table  1a ). This similarity did also not change in the future when active layer thickness increases, neither in the 'stay-where-they-are'-scenario (0. 10-0. 11 in 2100 Fig. 3  Rooting depth distribution over depth. Rooting depth distribution is grouped as Graminoid, Prostrate-shrub, Erect-shrub, and Wetland tundra (according to the Circumpolar Arctic Vegetation Map), plotted (a) using absolute observed depth for creating the profiles, and (b) using the depth relative to active layer thickness (calculated here for average active layer thickness ALT observed in the dataset: 35 cm).
In our illustration of the rhizosphere priming effect, for example, the strength of the priming effect may very well differ between species even if they have the same rooting depth distribution due to different exudation quantities and qualities  (Vries et al. , 2019) , or mycorrhizal symbionts  (Parker et al. , 2021). There is a growing awareness among the scientific community that root dynamics are important but at the moment; the (spatial) data to provide robust belowground traits are often missing, especially from cold biomes. For example, < 1% of trait entries in the global repository for plant traits, the TRY database, are root traits but 10% of all requests from TRY are for maximum rooting depth  (Iversen et al. , 2017; Kattge et al. , 2019). In conclusion, though rooting depth distributions of circumpolar vegetation varied, they were not related to aboveground vegetation types nor driven by most of the tested abiotic factors.
Acknowledgements We thank the  PrimeSCale team  for laying the foundation of this research. We further thank  Colleen Iversen  for being up to date with root representation in models. BW acknowledges  ERC StG PRIMETIME  (grant no. 101039588 ). MK acknowledges  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (grant agreement no. 819202 ).
Funding This research has been supported by the  French National Research Agency (ANR)  through the projects MOBIDIC (grant no. ANR-17-MPGA-0013 ) and  METACLOUD  (grant no. ANR-19-CE01-0004-02 ). The authors also acknowledge funding from the  Fédération des Recherches en Environnement of Clermont-Ferrand ,  Clermont Auvergne University , and CNRS.
However, sampling the geometric data during the MC calculation, as required here, is not straightforward with the available tools, because they are usually designed to generate images from fixed scenes (and animations are constructed as sequences of such images). For this reason, most ray-tracing acceleration structures have been developed for fixed geometric data, and generating such a structure at each MC sample would be highly inefficient. We therefore developed a specific approach in collaboration with computer graphics experts  [18] : the geometry is specified by statistical distributions of the parameters and, Fig. 2. Calculation time using Schiff software  [18]  for an increasing number of (a) fins in a shape described by 10 6 facets (zero fins for a sphere) and (b) facets describing the shape with 16 fins.
Schiff approximation is also solved by deterministic method (DM) in Table  1  (Schiff-DM), providing the same results as with MC. Results. We fully implemented this probabilistic approach for Schiff's approximation in a free and open-source software application  [18]  whose features with respect to geometric complexity are presented hereafter. First, we benefit from well identified features of path-tracking tools developed by computer graphics research  [17, 20] : calculation time is only multiplied by two when the number of facets describing a geometry is multiplied by 10 3 [Fig. 2(b) ]. This enables us to solve Schiff approximation with calculation times that are quasi-insensitive to geometric complexity [see Fig. 2 (a) and compare cases 4, 5, 9, and 10 in Table  1 ]. Furthermore, thanks to double randomization, additional calculation time for averaging over scatterer orientation, size, and shape distributions is negligible  [19] , while deterministic methods require us to solve the scattering problem for each quadrature node corresponding to one shape, one orientation, and one size.
Computation of Total Cross Sections (Extinction, Scattering, Absorption) and Differential Cross Section at θ s = 1 •a a Comparison between Schiff software  [18]  (Schiff-MC); adaptative recursive mesh provided by Newton-Cotes rule (Schiff-DM), both on a laptop Intel Core i7-3720QM@2. 60 GHz; and ADDA software  [21]  (discrete dipole approximation reference solution) using 64 processors Intel Xeon Gold 6154@3. 00 GHz. Number of MC samples N and wall clock calculation time t required to achieve standard error <1% for various shapes. "Monodisperse" indicates a unique geometry; "Polydisperse" a lognormal size distribution with ln(σ) = 0. 18; and "Distributed" a distribution of several parameters in the shape parametric equation. Orientation is isotropically distributed, except for 2 and 7, where ⊥ indicates normal incidence.
In our examples, the number of samples required to simulate size distribution is also sufficient to cover aspect ratio and shape distribution (N is the same in cases 5 and 6, and in cases 10 and 11; see also Figs. S5 and S6 in the Supplement 1). However, the computation time t is increased because the loading time is higher for distributed objects [see Fig. 2 (b)], while in the case of size distribution we load just one geometry and rather use a scaling of the wavelength that preserves x. Despite this, simulating distributed helixes with Schiff-MC (case 11) is 50 times faster than straight cylinders of fixed elongation using Schiff-DM (case 9). Finally, our software also takes advantage of the well-identified capability of MC to estimate several quantities simultaneously with the same path samples  [7, 13].
For example, wavelength only affects the contribution of a path, but not the path sampling itself. Hence, each time a path is sampled we may compute N λ contributions corresponding to N λ wavelength. As a result, evaluating a 40 wavelengths spectrum with Schiff software, as in Ref. [4] , only multiplies calculation time by four when a deterministic method would require to solve the scattering problem one time per wavelength. Overall, we are now able to produce spectral and angular radiative properties of helicalshaped microalgae Arthrospira platensis in 20 min, whereas this required several months with a straight cylinder model using Schiff-DM  [4].