dataesr/scientific-paragraphs-categorization huggingface dataset:
Sensitivity analysis For each SA, the resulting files were then analysed for each indicator with the R ''multisensi'' package  (Bidot et al. , 2018; Lamboni et al. , 2009 Lamboni et al. , , 2011)). It allows to evaluate the share of every parameter in the variability of the output along with time, determining up to second order interactions of parameters. It takes as inputs a merged indicator data file and the design of experiments created by ''planor'' and returns detailed sensitivity indices, as well as several graphs, including one presenting the evolution of the share of every parameter to the variability of the output indicator of the model along time (such as in Figs. 2  and 4 ).
Limitations of the experimental design Even if we used a fractional factorial design of experiments, increasing the number of parameters beyond 9 would result in too many simulations for the duration of the simulations (which ranges from a few dozens of seconds to more than 24 h). Moreover, running six different SAs allowed us to better understand the dynamics of the different evolutionary forces separately. We thus chose to restrict the SAs dedicated to Isolated populations to 5 parameters to study evolutionary forces related to genetic drift and selection, and the SAs dedicated to Colonisation-Extinction and Migration to 9 parameters to study forces related to colonisation and migration respectively, along with the genetic drift and selection. As mentioned in the Section 2. 2. 1, using ''planor'' requires the same number of levels for each parameter chosen.
In order to diversify as much as possible the networks used by CropMetaPop in the analysis, we generated a library of a hundred networks for each combination of network type and density. Therefore, as we run ten replicates, it is quite unlikely to find the same network several times in the same parameter combination. To analyse the results of the simulation more efficiently, home-made Python scripts were created in the team. It allows for an adapted and fast computation of the genetic and demographic indicators.
Conclusion Here we present CropMetaPop, a model that addresses the lack of a suitable model to study the genetic evolution of crop metapopulations. Critical features of crop metapopulations that can usually not be represented in classic metapopulations models, are included in CropMetaPop. Among those features, the distinct migration and colonisation processes, the fact that these processes happen on seeds and not on juveniles and/or adults. Moreover the CropMetaPop allows to model precisely the seed movements that can be realised. This seed circulation network can be defined precisely based on real data or defined randomly. Moreover, CropMetaPop allows to set parameters specific to seed movements, such as the ratio of seeds replaced by migration, or the ratio kept by farmers haring seeds, among other parameters.
Acknowledgements The authors thank  Frédéric Hospital  for his help in designing and developing the software. The authors are very grateful to the MIRES network financially supported by  INRAE  and to  François Massol  for his very helpful advises. This research was funded by the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  633571  ( DIVERSIFOOD  project) for the period 2015-2019 and BR received a grant from the  École Doctorale Frontières de l'Innovation en Recherche et Éducation -Programme Bettencourt  and PhD funding from  INRAE. Appendix. Results of SAs migration See Figs. A. 1 and A. 2.
Highlights  Twenty-seven grain samples of Andean pseudocereals were profiled by LC-HRMS.  Untargeted metabolomics was used to differentiate varieties from the whole metabolome dataset.  Five mycotoxins were quantify using targeted metabolomics.  Sixteen samples of Peru and one sample from France were contaminated with Beauvericin.  An inventory of the fungal community infesting the Andean pseudocereal samples was documented.
Untargeted metabolomics LC-MS data were processed according to the MSCleanR workflow  (Fraisier-Vannier et al. , 2020; Vásquez-Ocmín et al. , 2021). Briefly, batches in PI and NI were processed separately with MS-DIAL version 4. 80  (Tsugawa, Cajka, et al. , 2015). MS1 and MS2 tolerances were set to 0. 01 and 0. 05 Da, respectively, in centroid mode for each dataset. Peaks were aligned on QC reference with an RT tolerance of 0. 2 min, a mass tolerance of 0. 015 Da, and a minimum peak height detection at 1 × 10 6 for both ionization modes. MS-DIAL data (both PI and NI) were deconvoluted together with MS-CleanR by selecting all filters with a minimum blank ratio set to 0. 8 and a maximum relative standard deviation (RSD) set to 40. The maximum mass difference for feature relationships detection was set to 0.
005 Da, and the maximum RT difference was set to 0. 025 min. The Pearson correlation links were applied for a correlation ≥ 0. 8 and a p-value significance threshold = 0. 05. Two peaks were kept in each cluster for further database requests and the kept features were annotated with MS-FINDER version 3. 52  (Tsugawa et al. , 2016). The MS1 and MS2 tolerances were set to 5 and 15 ppm, respectively. The formula finder was exclusively processed with C, H, O, and N atoms. Two pathways from several sets of data were carried out for the compound's annotation. For metabolite annotation at level 1: experimental LC-MS/MS data for 500 compounds (including five mycotoxins standards), analyzed on the same instrument and with the same method, were used as references using MS-DIAL based on accurate and exact mass, fragmentation, and retention time.
For metabolite annotation level 2: the MS-DIAL, MONA (MassBank of North America), and GNPS (Global Natural Product Social Molecular Networking) mass spectral records were used for spectral match using a dot product score cut-off of 800. The in silico match (annotation level 3) were prioritized according to: 1) compounds identified in the literature for both Chenopodium and Amaranthus genera, and both Amaranthaceae and Chenopodiaceae botanic family (Dictionary of Natural Products version 28. 2, CRC press) mined by MS Finder based on exact mass and in silico fragmentation; 2) generic databases included in MS-FINDER (i. e. , FooDB, PlantCyc, ChEBI, T3DB, Natural Products Atlas, NANPDB, COCONUT, and KNApSAcK); 3)fungal compounds (including mycotoxins) were performed using four "in-house" databases from literature to compile: i) only mycotoxins (n=300) and ii) compounds produced from major mycotoxigenic fungi Fusarium, Aspergillus, and Penicillium.
Mass spectra similarity networking was carried out from PI mode using MetGem  (Olivon et al. , 2018)  on the final annotated. msp file and metadata files compiled for both batches obtained with MSCleanR. Values used were MS2 m/z tolerance = 0. 02 Da, minimum matched peaks = 4 and minimal cosine score value = 0. 7. Visualization of the molecular network (MN) was performed on Cytoscape version 3. 9. 1  (Shannon et al. , 2003). Original LCMS raw data were uploaded in zenodo (DOI:xxxx).
Targeted metabolomics and mycotoxins quantification Dataset acquired in format. wiff was converted in. abf format using Analysis Base File Converter (Reifycs, Tokyo, Japan). Analyses were performed with MRMPROBS program version 2. 60  (Tsugawa et al. , 2013; Tsugawa, Ohta, et al. , 2015) , using as parameters of peak detection: five scans for minimum peak width; 2,000 as minimum peak height; 0. 2 min as retention time tolerance for peak identification; 15% as amplitude tolerance; and 70% as minimum posterior. A list of transitions in. txt format was used as a compound library file. A final list containing the information about the Area above zero for each transition selected in the samples was exported in. txt format. From the final list of information about the mycotoxin standards, a calibration curve was created for each concentration mentioned above.
Validation parameters were determined following the International Conference on Harmonization (ICH) Guidelines  (ICH, 2005) , and the following characteristics were evaluated: 1) linearity, 2) limit of detection (LOD), and 3) limit of quantification (LOQ). Wheat contaminated with enniatins, and beauvericin was used as positive control. Original LC-MS raw data were uploaded in zenodo (DOI:10. 5281/zenodo. 6700223).
Statistical analyses The final annotated metabolome dataset generated by the MS-CleanR workflow was uploaded on the MetaboAnalyst 5. 0 online platform  (Chong et al. , 2019). The data were normalized by sum and scaled by Pareto before statistical analysis. First, principal component analysis (PCA) was applied as an exploratory data analysis to provide an overview of LC-MS fingerprints. Then, a hierarchical cluster analysis (HCA) was performed to obtain a dendrogram of varieties using the untargeted metabolome dataset. For each variable the average value across replicates was calculated; then distance matrices were obtained using either 1-r (Pearson correlation coefficient), followed by HCA on each matrix using Ward's minimum variance criterion using Orange 3. 32. 0 (Demšar et al. , 2013). On the clusters obtained, a partial least squares discriminant analysis (PLS-DA) was conducted using clusters as Yvalue, and the top 50 features were plotted on a heatmap using ANOVA.
, 2005) , in the same conditions as above, except for annealing temperature (58°C instead of 55°C). PCR amplicons were sequenced by Eurofins Genomics (Ebersberg, Germany) according to a Sanger method using ITS5, Bt2a, and CMD5 respectively. Sequences were submitted to the BLASTn program (http://blast. ncbi. nlm. nih. gov/Blast. cgi) to determine the close matches in the GenBank database. All ITS sequences were aligned with MAFFT v6. 814b (Katoh at al 2002) using Geneious®6. 1. 8. The PhyML method  (Guindon & Gascuel, 2003)  was used via the Geneious® platform to generate a maximum-likelihood phylogenetic tree with the following setting: GTR substitution model, 100 bootstraps, estimated transition/transversion ratio, the estimated proportion of invariable sites, estimated gamma distribution, branch length, and optimized substitution rate.
To ensure a comprehensive analysis of medium polar extract, positive ionization mode was investigated using the MS-CleanR workflow, which resulted in a list of 219 features (m/z x RT). Depending on the level of annotation mentioned above, 19 compounds were yielded for the genus (8. 7%), 43 compounds for the family (19. 6%), 128 compounds for the generic level (58. 4 %), and 29 unknowns (13. 2 %). The fitness of compound annotation generally depends on the databases (DB) used. Despite the few studies on pseudocereals, we confirmed an intern DB from family and genera gathered from the literature, responsible for 28. 3 % of annotations (annotation level 3-1). This was complemented with our experimental DB (annotation level 1) and the spectral match with other DB in line (annotation level 2). Using NPClassifire and ClassyFire  (Djoumbou Feunang et al.
However, inside the cluster quinoa, two other subgroups for red and white quinoa can be observed. An exception is white quinoa samples from Ayacucho and Cuzco that were compiled with red quinoa samples, which could infer a putative hybridization intraspecies. Additionally, to validate the HCA model and highlight characteristic biomarkers of each cluster, we performed a supervised Partial Least Squares-Discriminant Analysis (PLS-DA) based on the variable importance in projection (VIP) and a heat map was generated from the top 50 metabolites selected by Anova (supp info 4, 6-8). After our workflow analysis, unanticipated metabolites produced by filamentous fungi were identified in the final metadata. These metabolites were placed in a molecular network (Fig. 2-A ). The molecular network of the whole dataset analyzed in PI mode was constructed to identify possible similarities between molecules based on spectral mass similarity (MS-MS fragmentation patterns) (Fig.
Acknowledges The authors wish to acknowledge a postdoctoral fellowship of PGV-O from the  French National Research Institute for Sustainable Development (IRD)  under contract number  04077858. The authors are grateful to  Pascal Pineau  ( Institut Pasteur de Paris ),  Olivier Puel ,  Sophie Lorber-Pascal (Toxalim, INRAE)  for the provision of mycotoxin standards. The authors are also grateful with  Lina Abdelwahed  ( Université Paul Sabatier -Toulouse 3 ),  Sylvie Fournier ,  Amélie Pérez ,  Justine Chervin ,  Virginie Puech-Pagès  ( MetaToul-Agromix ),  Juan Pablo Cerapio Arroyo  ( Centre de Recherches en Cancérologie de Toulouse ),  David Condori Cruz  ( Universidad Peruana Cayetano Heredia ) for their technical supports; and  Alexandre Maciuk  ( Université Paris-Saclay ) for his critical discussion.
The normalized expression of these genes, quantified by qRT-PCR after cultivation without fungicide induction, varied roughly between 0. 5 and 4 comparatively to that of the ancestral isolates (Supplementary Fig. S2 ). As these weak variations included a susceptible isolate collected from the control line, we assumed that overexpression of these three target genes did not operate in the dataset or was rather weak. Enhanced efflux is a non-target site resistance mechanism associated with low RFs per se but causing cross-resistance to unrelated compounds in Z. tritici  43. In field strains, it is determined by insertions in the promoter region of the MFS1 transporter gene  52. Systematic genotyping of 249 isolates collected in this experiment revealed no changes in the length of the mfs1 promoter, particularly in strains displaying tolnaftate resistance.
This scenario is consistent with the fixation of multiple discrete resistance mechanisms, together with the accumulation of relative fitness costs associated with each resistant trait, as suggested by the wider patterns of cross-resistance (including resistance to new AIs), lower resistance scores, and higher frequency of resistance to tolnaftate (associated with enhanced efflux) observed in isolates collected from lines subjected to alternating treatments. The molecular analysis of a representative subsample of resistant isolates hardly detected mutations in genes encoding the targets of selecting fungicides and indicated that enhanced efflux due to the MFS1 transporter was at work in a few -but not all-resistant strains. Our collection, therefore, provides a good opportunity to make use of the progress in genomics to elucidate unknown genetic and non-genetic bases of adaptation mostly at work in our dataset.
Acknowledgements We thank  Fabrice Blanc  for facilitating the administrative organization of this Ph. D. studentship. We would also like to thank  Dr. Stefano Torriani ,  Dr. Stephanie Bedhomme , and  Dr. Mato Lagator  for their sound comments about our findings. We are very grateful to  Alicia Noly  for her help with the analysis of the expression of target genes in evolved isolates of Z. tritici. A. B. was supported by a Ph. D. studentship funded by the  French Ministry of Higher Education, Research and Innovation , and  Syngenta France , through the  CIFRE program , supervised by the  National Association for Research and Technology (ANRT). This work was supported by the  Plant Health division of INRAE  through the  STRATAGEME  project.
05, quasi-likelihood F test). The pri-GdmiR156a and pri-GdmiR156b precursor transcripts have the highest and fourth-highest statistical support, respectively. (E) Northern blotting shows that GdmiR156 expression is high in unspotted florets, but low in spotted Springbok florets. MW denotes an RNA ladder showing single stranded RNA bands of 25 and 21 nt for sizing. GdmiR162, a not differentially expressed miRNA, serves as control for transfer and equal loading (see D), and SybrGold staining of total RNA as loading control (S6, S8, and S20 denote G. diffusa individuals). and (2) all mature miRNAs from the miRbase database  60  and conducted a DE analysis with each. Among the transcripts with the highest statistical support are the two pri-GdmiR156 precursors from the mRNA reference assembly (Figure  5D ), and the mature miRNAs AmamiR156, stu-miR156f-5p, and cca-miR156b from miRbase, respectively.
Demultiplexed mRNA-seq reads were quality filtered using Trimmomatic v. 0. 38  68  with the settings ILLUMINACLIP:TruSeq3-PE-2. fa:2:30:10 LEADING:5 TRAILING:5 SLIDINGWINDOW:4:5 MINLEN:25. A combined reference transcriptome was assembled with Trinity v. 2. 8. 4  69  using trimmed reads from spotted and unspotted ray florets of Springbok plant 8, and from spotted ray florets of Naries plant 13. Coding regions were predicted with TransDecoder v. 5. 5. 0,  70  and annotated with Trinotate v. 3. 1. 1  71 against the SwissProt, Pfam, SignalP, EggNOG, Kegg, and Gene Ontology databases. Finally, transcriptome completeness was estimated with BUSCO v. 3. 1. 0 72 using the lineage database eudicotyledons_odb10.
Gene expression and overrepresentation test Trimmed reads from all samples were mapped to the combined reference transcriptome, and expression levels were estimated using RSEM v. 1. 3. 1  73  with Bowtie v. 2. 3. 4. 3  74  as aligner. Posterior mean counts were extracted in R v. 3. 5. 2  75 using the R library v. 1. 10. 1,  76 and transcripts with fewer than 5 counts per M reads in at least 12 samples were removed. To find genes underlying petal spot development, a negative binomial generalised linear model was fitted for reads with at least a 1. 5 fold expression difference between spotted and unspotted Springbok petals, accounting for the paired experimental design with samples coming from the same individual. Differential expression was then determined with a quasi-likelihood F-test. To further isolate genes underlying Springbok-specific petal spot traits, gene expression differences between spotted Springbok and spotted Naries petals were first determined with a standard negative binomial generalised linear model fitted for reads with at least a 1.
5 fold expression difference, and a quasi-likelihood F-test. Results of both analyses were then filtered for transcripts differentially expressed both between spotted and unspotted Springbok ray florets, and between spotted Springbok and Naries ray florets. All analyses were conducted using the R library edgeR v. 3. 24. 3. 77 Transcripts with a significant expression difference were annotated using the blastx results of the reference transcriptome annotation. In cases where annotation differed between isoforms of a gene, the annotation matching the isoform(s) with the total most mapped reads was chosen for the gene. Lists of both putative general and Springbok-specific petal spot genes were submitted to PANTHER v. 16. 0  78  to retrieve gene ontology (GO) IDs from the GO annotation dataset 'GO biological process complete' (released 18.
Phylogenetic analysis of GdEXPA7 Coding sequences of Arabidopsis thaliana EXPA1-18 and 20-25, and Helianthus annuus EXPA1, 4 and 6-11 were downloaded from GenBank and aligned manually at protein level with GdEXPA1, 7 and 15 using MEGA v. 7. 79  Amino acid sequences of the two conserved expansin domains  48  were extracted and loaded into ProtTest3 v. 3. 4. 2  80 to determine the best-fit model of amino acid replacement. A phylogenetic gene tree was constructed with MrBayes v. 3. 2. 6  81  using LG + G as evolutionary model. The run was stopped when apparent stationary of log-likelihood was reached (2 million generations with a sampling frequency of 500 and a diagnosis frequency of 5000).
Detection and folding of pri-GdmiR156 The reverse complement of the miR156 binding site, situated within the 3'-UTR of the GdSPL1 gene (TGACAGAAGAGAGAGAGCAT) was searched against the combined Springbok and Naries reference transcriptome (see above) with blastn v. 2. 4. 0+  83 using the option '-task blastn-short'. Transcripts, which (1) contained fewer than two mismatches to the query sequence, (2) were differentially expressed between spotted and unspotted Springbok petals, and (3) had no annotated ORF were kept for further analysis. Candidate transcripts were pruned to 10 bp upstream and downstream of the miR156 binding site, and minimum free energy folding structures were predicted using RNAfold v. 2. 4. 18 from the ViennaRNA Package 2. 0. 84 fferential expression analysis of small RNA Samples for small RNA sequencing were prepared for spotted and unspotted ray florets of Springbok plant 6, 8, 17 and 20 with the same tissue used for mRNA-seq (see above).
2. 8. 4,  69 and the trimmed small RNA-seq reads were aligned to the SuperTranscripts using Bowtie v. 1. 3. 0 86 with the settings ' -n 0 -l 10 -m 100 -k 1 -best -strata'. In a second step, the trimmed small RNA-seq reads were aligned to the complete mature miRNA database from miRbase v. 22 60 using Bowtie v. 1. 3. 0 86 with the settings '-n 1 -l 10 -m 100 -k 1 -best -strata'. Both alignments were analysed independently as follows: Posterior mean counts were extracted into R v. 3. 5. 2  75 with SAMtools v. 1. 9  87  and custom scripts, and transcripts with fewer than 40 total counts were removed. A negative binomial generalised linear model was fitted for reads with at least a 1. 5 fold expression difference between spotted and unspotted Springbok petals, accounting for the paired experimental design with samples coming from the same individual.
The combined reference transcriptome is deposited on NCBI TSA. Sequences of GdEXPA7 and GdSPL1 are deposited on NCBI Genbank. All other datasets generated in this study are deposited on Figshare. All datasets generated in this study are publicly available as of the date of publication. Accession numbers are listed in the key resources table. This paper does not report original code. Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.
ACKNOWLEDGMENTS We thank  Matthew Dorling  and  Emma Jackson  for plant rearing and technical support, and  Dr. Eftychios Frangedakis ,  Dr. Chiara Airoldi ,  Dr. James Connorton ,  Rose McNelly ,  Dr. Weibing Yang ,  Dr. Raymond Wightman , and  Dr. Hamish Symington  for assistance and material for lab experiments. We also thank  Prof. Daniel Cosgrove ,  Prof. Ottoline Leyser ,  Prof. Philipp Schlu  ¨ter,  Dr. Elinor Jax , and  Farahnoz Khojayori  for helpful discussions, and three anonymous reviewers for constructive feedback. We further thank the  Northern Cape Department of Environment and Nature Conservation  for issuing a collection permit ( FLORA 0057/2018 ). This study was funded by a  Swiss National Science Foundation (SNSF) Early Postdoc. Mobility grant  to  R. T. K. (fellowship P2ZHP3_178043 ), an  Isaac Newton Trust  grant to R.
Plasmid: pGREEN-II-2335S-GdSPL1-truncated This paper N/A Software and algorithms Trimmomatic v. 0. 38 Bolger et al. 68 https://github. com/usadellab/TrimmomaticTrinity v. 2. 8. 4 Grabherr et al. 69 https://github. com/trinityrnaseq/trinityrnaseq/wiki TransDecoder v. 5. 5. 0Haas et al. 70 https://github. com/TransDecoder/TransDecoder/wiki Trinotate v. 3. 1. 1Bryant et al. 71 https://github. com/Trinotate/Trinotate/wikiBUSCO v. 3. 1. 0Sima ˜o et al. 72 https://busco. ezlab. orgRSEM v. 1. 3. 1 Li and Dewey 73 https://github. com/deweylab/RSEM.