dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical analysis and data visualization Chromosome and plasmid structures were visualized using CGView 1. 7 package  [78]. Statistical analysis and graph plotting were carried out using in-house scripts written in Python v. 3. 8. Analysis and visualization of the transcription data were conducted using python libraries pandas 1. 0. 3  [79]  and matplotlib 3. 5. 1  [80]. Tetranucleotide-derived z-scores of the MAI and the reference regions were calculated as described in  Teeling et al. (2004)  [81] using pyani (v. 0. 2. 11) package  [82]  and a custom script. Correlation was evaluated using the Pearson correlation test. The magnetosome diameters were measured using imageJ v. 1. 53c, and the violin plots were constructed applying libraries seaborn 0. 11. 2  [83]  and matplotlib. The Kruskal-Wallis analysis was conducted using the library statannot (https://github.
ACKNOWLEDGEMENTS We thank  Corinne Cruaud  and the  Genoscope (CEA, Commissariat à l'Energie Atomique et aux Energies Alternatives) at Evry (France)  for their essential contribution to the efficiency of the platform. We are grateful to  M. Schüler  and  S. Geimer  for their help with electron microscopy. We also thank  J. Kachel  and  U. Brandauer  for technical assistance.
DATA AVAILABILITY Sequencing reads and the annotated complete genome of strain Rhodovastum atsumiense strain G2-11 (DSM 21279) wildtype was deposited to the European Nucleotide Archive database under the BioProject number PRJEB52102. RNA-seq reads and sequencing reads for the genomes of G2-11 mutants generated in this study were deposited to NCBI GenBank database under the BioProject number PRJNA818516.
Study Area Two-year research was conducted at the experimental field of the Mediterranean Agronomic Institute of Bari (IAMB) in Valenzano (41 • 03 N, 16 • 53 E, 77 m above sea level). The experimental site is characterized by typical Mediterranean climate conditions, with mild winters and hot, dry summers. The average annual precipitation is about 550 mm (30 years average), distributed mostly during autumn and winter. The average monthly air temperature ranges from 8 • C in January to 24 • C in July and August. The soil of the study area is silty-clay-loam  [54]. Meteorological data were obtained from the weather station next to the experimental field. Sweet maize (Zea mays var. saccharata L. , hybrid Centurion F1) was cultivated in the 2019 and 2020 growing seasons in rows 0. 5 m apart, with a distance between plants in the row of 0.
Plant Reflectance Plant reflectance was measured by using a high spectral resolution ASD FieldSpec Hand-Held 2 spectro-radiometer (Analytical Spectral Devices, Inc. , Boulder, CO, USA). This instrument measures reflectance with a wavelength range of 325-1075 nm, an accuracy of ±1 nm, and a resolution of <3 nm at 700 nm. The field of view (FOV) of the bare fiber-optic probe was 25 •. The spectrum of a white (BaSO 4 ) reference panel with known reflectance properties was acquired to derive the reflectance of the target. Measurements were acquired on three plants for each plot. The vegetation spectrum was measured from a distance of 10 cm above the crop, with a spot size of approximately 14 cm 2. Gradually, as maize grew and expanded, the distance from the vegetation increased to 60 cm. Thereafter, data were processed by means of View Spec software.
The reflectance data were restricted to the 395-1004 nm interval, which was considered noise-free. Then, statistical analyses were performed. The reflectance measurements were averaged over 10 nm to reduce collinearity and overfitting  [59]. In this way, 61 derived reflectance variables were obtained; the name of the variables indicated the central wavelength. The spectral indices used in this study were computed from narrow bands reflectance measurements and are reported in Table  1.
Statistical Analysis Dependent variables-fresh grain yield (GY), agronomic water productivity (WP), and nitrogen uptake (NUptake)-were preliminarily evaluated for normal distribution and homogeneity of variance according to the Kolmogorov-Smirnov test and Bartlett's test, respectively. Since the normality assumption was violated, factorial nonparametric analysis of variance for mixed designs was used by applying the Aligned Rank Transform analysis. Analysis of variance from the 3 × 2 factorial experiment in a split plot design was then conducted, and significance of differences was tested using Fisher's Least Significant Difference (LSD) at a 5% probability level. This analysis was carried out using the software package agricolae  [60]  and ARTool  [61]  in R studio software  [62]. This package is available via the Comprehensive R Archive Network (CRAN, https://cran.
Within each extracted component, the five bands with the highest loadings (in absolute value) were selected  [66]. The Pearson correlation was used to determine the relationship between GY, WP, NUptake, physiological parameters (A, gs, Tr, WUEi, and LCC), vegetation indices, and the components extracted using PCA. This analysis was carried out using the software package Corrplot  [67]  in R studio software  [62]. The multiple linear regression using stepwise technique was applied to explain GY, WP, NUptake, and physiological variables variation from VIs across different water supplies and nitrogen treatment on both growing seasons, satisfying the criteria of probability-of-F-to-enter ≤0. 05 and probability-to-remove ≥0. 05. The overall model's performance was evaluated by its coefficient of determination (R 2 ), a measure of the proportion of variance in variables estimated that can be predicted by the explanatory variables (VIs).
Phenotype Data Anthracnose severity score data collected at different times during the crop's growth period were converted to AUDPC for quantitative comparison over the years. The area under disease progress curve data was subjected to mixed model analysis using lme4 library package implemented in R  [34]. Y = μ + β + R + G + (β xG ) + e (2) where Yijk = phenotypic value, µ = overall phenotypic mean, βi = effect of year i, Rij = effect of block j in year i, Gk = effect of genotype k, (βi × Gk) = effect of interaction between year i and genotype k, and eijkm = residual. Block within-year effects were added to the model as a random variable to remove the spatial variation within the trial field. Broad sense heritability was estimated from the model to assess the proportion of phenotypic variation in the data set due to genetic effects.
SNP Calling and Quality Assessment Raw data were first filtered using a pipeline described in Scarcelli et al. [35]. Demuladapt (https://github. com/Maillol/demultadapt accessed on 12 March 2020) was used for demultiplexing. Cutadapt 1. 2. 1  [36]  was used to remove the adaptors and low-quality bases read with a mean quality score <30 using a free perl script https://github. com/SouthGreenPlatform/arcad-hts/blob/master/scripts/arcad_hts_2_Fil-ter_Fastq_On_Mean_Quality. pl accessed on 11 March 2020. For the final SNP calling, GATK was used while mapping was performed using default options of Burrows-Wheeler Aligner (BWA)  [37]  using the D. alata reference genome. The SNP quality assessment was performed using vcftools  [38]  and plink  [39]. SNPs with low MAF <0. 05 and low depth sequencing <5 were removed.
Genetic Map Construction Linkage analysis was conducted using MAPpoly package  [40]  in the R environment  [34]. A series of filtering steps were applied using all segregating markers polymorphic in at least one of the parents to construct an integrated genetic map. Chi-square (χ2) test was conducted to calculate the marker segregation ratio and exclude markers showing significant segregation distortion from map construction. The p-value threshold to assess the significant marker segregation distortion was set using the filter_segregation function as implemented in MAPpoly package. To construct linkage groups, the pairwise recombination fraction and LOD matrices between markers retained after the segregation test were calculated using the function est_pairwise_rf in MAPploy package. Linkage grouping was then performed using an initial LOD value of >6 obtained from αthres function in MAPpoly.
QTL Analysis The QTL analysis was performed with mean trait value over years and linkage map constructed from the 159 recombinant clones using the Composite Interval Mapping (CIM) method in R/QTL2 package  [42]. A forward and backward simple stepwise regression was run to select background markers with a significant level of P < 0. 05. The threshold levels to declare significant QTLs were empirically determined through 1000 permutations of the data, which maintained a chromosome-wise Type I error rate of 0. 05  [43]  with a fixed LOD of 4 as a minimum threshold of declaring a SNP marker linked with the YAD. The location of a QTL was described according to its LOD peak location. The proportion of phenotypic variance accounted for by each detected QTL was estimated by a single-factor analysis of variance using the General Linear Model.
The additive (Add) and dominance (Dom) effects and the proportion of phenotypic variation explained (PVE%) by each QTL were estimated using the "fitqtl" function in R/QTL. The sign of the additive effect of each QTL was used to identify the origin of the favorable alleles. A simple mixed model was implemented in lmer4 package to estimate QTL interaction/environment using the identified QTL by considering the year and the SNP marker as fixed effect, while the genotypes were considered as a random effect. Markers linked with the yam anthracnose disease were then placed in the respective chromosome, and their position was viewed using Qtl/jittermap. For the gene mining, the related putative genes associated with SNP markers were searched within the upstream and downstream locations of the QTL generic feature format (GFF3) of the reference genome of D.
Hydrological and meteorological data are available in the Seanoe database  (DOI 10. 17882/58280 Mostajir et al. , 2018) ; they were also previously published  (Trombetta et al. , 2019; Trombetta et al. , 2021)   ), and silicate (SiO 2 ). Hydrological and meteorological parameters were Chlorophyll-a (Chl-a) fluorescence, air and water temperature, wind speed and direction, photosynthetically active radiation (PAR) (400-700 nm), short wave ultraviolet B radiation (UVBR) (280-320 nm), salinity, turbidity, depth, air pressure, humidity, and precipitation.
Sequence Analysis Raw sequence analysis was performed using FROGS (Find Rapidly OTU with Galaxy Solution) software (https://galaxy-datarmor. ifremer. fr/) used in Galaxy  (Escudiéet al. , 2018). This software analyses large sets of DNA amplicon sequences with precision and speed. It produces an abundance table of operational taxonomic units (OTUs) based on their taxonomic affiliations and is the only tool capable of handling non-overlapping pairs of reads. It was designed to support multiplexed or demultiplexed sequences. The preprocessing step involves matching the reads, cleaning up, and dereplicating the sequences. The sequence clustering step uses Swarm, which works with a local clustering threshold (not global) (often 97% by default), similar to other clustering software. The chimaeric removal step used VSEARCH combined with cross-validation of the original chimaeras.
The filtration step removed most of the remaining artefactual sequences. This last step makes it possible to affiliate each OTU to a taxon (up to the species level) with the possibility of a "multi-affiliation" annotation, which can generate a list of all the taxa with the same sequence. The scripts are available at https://github. com/geraldinepascal/FROGS. We analysed 88 samples for the eukaryotic phytoplankton and bacteria data sets and obtained an average of ca. 6023 and 18030 reads per sample, respectively. Taxonomic assignments were made using the Silva 132_16S database for prokaryotic analysis and PR2 4. 11. 0 for eukaryotic analysis. As with all primers, there can be biases introduced during the amplification because of the amplified preference or the uneven number of rRNA copies. In our case, primers for 18s rRNA covers only 54.
Data and Statistical Analysis As the present study aimed to establish the diversity of planktonic microorganisms, the 18s rRNA genes OTU database was filtered to retain only unicellular organism sequences. Further statistical analyses were performed on the 16s and 18s rRNA genes of unicellular OTU database. All analyses described hereafter were performed using R software (R version 4. 0. 2). The temporal dynamics of alpha diversity, from winter to spring, were assessed by computing the Shannon index  (Shannon, 1948)  at each date for both 16s and 18s rRNA genes in 2015 and 2016. The number of OTUs per date was computed, and an UpSet diagram  (Lex et al. , 2014)  was produced to identify the number of common OTUs between months or periods of consecutive months. UpSet diagrams are a visualisation tool allowing the identification of a number of elements (here OTUs) in intersections between groups (here month), but with more flexibility than classic Venn diagrams.
The temporal dynamics of beta diversity, from winter to spring, were assessed by computing the Bray-Curtis dissimilarity index on consecutive sampling dates for both 16s and 18s rRNA genes data in 2015 and 2016. Non-metric multidimensional scaling (nMDS) ordinations were also applied to OTU Bray-Curtis dissimilarity to classify 16s and 18s rRNA genes OTU community diversity from winter to spring. To identify the environmental factors driving the planktonic assembly, hydrological, meteorological, and nutrient variables were ordinated, and their correlation scores with non-metric multidimensional scaling (nMDS) axes were calculated using the envfit function in R (vegan package, version 2. 4-2). Phylum composition from winter to spring was assessed by computing the relative OTU abundance clustered by Phylum at each date for both 16s and 18s rRNA genes in 2015 and 2016.
ACKNOWLEDGMENTS We could like to thank  Cećile Roques  for sampling and filtration of DNA samples as well as  Benjamin Sembeil ,  Oceane Schenkels  and  Ludovic Pancin  for helping during the sampling. We would also like to acknowledge  Sebastien Mas  and  David Parin , who helped during 2 years of the monitoring performed using facilites of the  Marine Station of the "Observatoire de Recherche Mediterraneen de l'Environnement" (OSU OREME ) in Sete. We would like to thank  Editage  (www. editage. com)for English language editing.
2012 , Resende et al. 2012 , Jarquín et al. 2014) , predicting the genetic component of trait variation across populations or geographical regions of forest trees remains poorly explored. In the present study, we aim to identify the potential drivers of the plastic and genetic components of height growth in distinct maritime pine gene pools (i. e. genetic clusters) and investigate how common garden data can be combined with genomics to e ciently predict height-growth variation across the species range. We compared Bayesian hierarchical mixed models that inferred height-growth variation in maritime pine as a function of climatic and genomic-related variables, using a clonal common garden network (CLONAPIN) consisting of ve sites and 34 provenances (523 genotypes and 12,841 trees). First, we evaluated the relative importance of potential drivers underlying height-growth variation.
We also expected that height-associated alleles selected regionally, i. e. in particular environments, would have a be er predictive ability than globally-selected alleles. Our study is a step towards integrating the recent knowledge brought by large genomic datasets to the modeling of quantitative trait variation in forest trees. Combining common gardens with genomic tools hold great promise for speeding up and improving trait predictions at large scales and for a wide range of species and populations. However, a robust framework is needed to make reliable predictions and to determine , that is genetic clusters that cannot be di erentiated on the basis of neutral genetic markers and that probably derive from a common glacial refuge  (Bucci et al. 2007 , Santos-del-Blanco et al. 2012). Height growth is a key adaptive trait in forest trees, including maritime pine.
Height can be seen as the end-product of multiple ecophysiological processes that are both genetically regulated and a ected by multiple environmental e ects  (Gra apaglia et al. 2009 ). As such, taller trees compete more e ciently for light, water and nutrients, and are also more likely to have high fecundity  (Rehfeldt et al. 1999 , Wu and Ying 2004 , Aitken and Bemmels 2015). We obtained height data from the clonal common garden network CLONAPIN, consisting of ve common gardens located in di erent environments (also referred as test sites; g. 1). ree sites are located in the Atlantic Europe region, with mild winters, high annual rainfall and relatively wet summers: Bordeaux in the French part, and Asturias and Portugal in the Iberian part, the Portugal site experiencing slightly colder winters and half the summer precipitation than the site in Asturias.
Based on the 523 genotypes for which there were both genotypic and phenotypic data, we performed four GWAS following the Bayesian variable selection regression (BVSR) methodology implemented in the piMASS so ware  (Guan and Stephens 2011) , correcting for population structure and using the height BLUPs reported in de  Miguel et al. (2020) , that accounted for site and block e ects. First, a global GWAS was performed to identify SNPs that have an association with height at range-wide geographical scales, thus using the combined phenotypic data from the ve common gardens. Second, three regional GWAS were performed to identify SNPs that have a local association with height in a particular geographical region r (i. e. in a particular environment), thus using separately data from the Iberian Atlantic common gardens (Asturias and Portugal), the French Atlantic common garden (Bordeaux) and the Mediterranean common gardens (Madrid and Cáceres).
Climatic data In forest trees, large-scale pa erns of allele frequencies or quantitative trait variation are known to be associated with climatic variables related to mean temperature and precipitation (e. g. Eckert et al. 2010 , McLane et al. 2011 , Leites et al. 2019 , Fréjaville et al. 2020 , Mahony et al. 2020) , or episodic climatic conditions, such as summer aridity or maximum temperatures  (Rehfeldt et al. 2003 , Grivet et al. 2011 , McLane et al. 2011 , Jaramillo-Correa et al. 2015 , Fréjaville et al. 2020). As climate change will cause major changes in temperature and precipitation in the near future, particularly in the Mediterranean basin, there is a need to understand the complex in uence of climatic variables on quantitative trait variation. We extracted monthly and yearly climatic data from the EuMedClim database with 1 km resolution  (Fréjaville and Benito Garzón 2018).
All models were inferred in a Bayesian framework as this approach be er handles unbalanced and multilevel designs  (Clark 2005 ) and also to be er propagate sources of uncertainty from data and parameter values into the estimates (de Villemereuil 2019). Priors used in the models were weakly informative and are provided in section 4. 2 of the Supplementary Information. To build the models, we used the brms package  (Bürkner 2017) , based on the no-U-turn sampler algorithm. Models were run with four chains and between 2,000 and 3,000 iterations per chain depending on the models (including 1,000 warm-up samples not used for the inference). All analyses were undertaken in R version 3. 6. 3 (R Core Team 2020) and scripts are available at https: //github. com/JulietteArchambeau/HeightPinpinClonapin.
S10). Model di erences in their predictive ability on new observations (observations not used to t the models; test dataset of the P1 partition) showed similar pa erns than for the goodness-of-t (Table  4 ), which was expected as the new observations were sampled among the same provenances and genotypes. However, importantly, models combining genomic and climatic data provided much be er predictions of height-growth on new provenances (provenances not used to t the models; test datasets of the P2 and P3 partitions) than did M2, with M8 having a be er predictive ability than M7 in the Mediterranean sites in the P2 partition and in the Atlantic sites in the P3 partition (Table  4 ). Models including each driver of height-growth variation separately had also a higher predictive ability on new provenances than M2, albeit lower than models combining genomic and climatic data, except model M12 that showed a higher predictive ability than M7 in the Mediterranean sites in the P2 partition (Table  4 ).
Importantly, we showed that models combining climatic drivers of adaptation, gene pool assignment and counts of height-associated positive-e ect alleles (PEAs) captured well the genetic component underlying height-growth variation. ey also be er predicted height growth of new provenances than models relying exclusively on the common garden design or models including separately climatic and genomic information (e. g. the widely used climate-based population response functions). Interestingly, PEAs that show a regional association with height growth (rPEAs) had a higher predictive ability than PEAs identi ed globally across the species range (gPEAs). ese results pave the way towards integrating genomics into large-scale predictive models of quantitative trait variation. In the P1 partition, the training dataset was obtained by randomly sampling 75% of the observations and the test dataset contains the remaining 25% observations.
In the P2 partition, the training dataset was obtained by randomly sampling 28 provenances and the test dataset contains the remaining 6 provenances. e P3 partition corresponds to a non-random split between a training dataset of 28 provenances and a test dataset containing 6 provenances with at least one provenance from each under-represented gene pool. e exact values of the prediction R 2 ms |age estimates and their associated credible intervals can be found in Tables S4 (P1 partition), S9 (P2 partition) and S12 (P3 partition).
2010 , Browne et al. 2019 , Fuller et al. 2020). Unlike polygenic scores, PEAs do not account for allele e ect sizes, thus minimizing the circularity of the analysis (i. e. e ect sizes that are estimated based on the same dataset as the one used for the models, only serve for PEAs identi cation) and potentially enhancing the prediction accuracy across genetic groups compared to polygenic scores. Indeed, low observed transferability of polygenic scores across genetic groups  (Martin et al. 2017 , N. Barton et al. 2019 , Martin et al. 2019 ) may stem from varying e ect sizes of "peripheral" alleles (i. e. alleles indirectly a ecting the phenotype), as suggested in Mathieson 2021). Although combining climatic and genomic information allowed us to capture most of the genetic component of height-growth variation ( g.
g. genetic dri in small populations, extreme selection events, etc. ) into the predictions would be necessary to anticipate the response of future forest tree generations to changing climatic conditions and thus provide a much-needed longer-term vision (Waldvogel et al. 2020) (IdEx) de l'Université de Bordeaux -Chaires d'installation 2015' (EcoGenPin) and the European Union's Horizon 2020 research and innovation programme under grant agreement No 773383 (B4EST). 7 Author contributions SCG-M and CP designed the experiment and supervised the curation of eld data. MdM cleaned and forma ed the phenotypic and genomic data, and produced the BLUPs used in GWAS. SCG-M and MdM ran the GWAS to identify the positive-e ect alleles. SCG-M, MBG, JA and FB conceived the paper methodology. JA conducted the data analyses.