dataesr/scientific-paragraphs-categorization huggingface dataset:
Genotyping All Collect-1 and Collect-2 accessions were first genotyped with 16 SSR markers using DNA from a plant obtained with a seed visually most representative of the seed stock of each accession. These data served to (i) ascertain the membership of accessions to one of the three genetic groups (O. sativa indica, O. sativa japonica, and O. glaberrima), (ii) detect accessions that could be considered as redundant (i. e. accessions bearing the same allele at each of the 16 SSR loci), (iii) assess the pattern of genetic diversity within each genetic group, for each collect. Based on such information and logistic issues, 600 accessions were selected for genotyping by sequencing (GBS). DNA libraries were prepared at the Regional Genotyping Technology Platform (http:// www. gptr-lr-genot ypage.
com), using the MATAB method for DNA extraction and the ApekI enzyme for DNA digestion. The libraries were single-end sequenced using Illumina HiSeq ™ 2000 (Illumina, Inc. ) at the Regional Genotyping Platform (http:// get. genot oul. fr/). The Fastq sequences were aligned to the rice reference genome, Os-Nipponbare-Reference-IRGSP-1. 0 with Bowtie2 (default parameters). Non-aligning sequences and sequences with multiple positions were discarded. Single nucleotide polymorphisms (SNP) were called using Tassel GBS pipeline v5. 2. 29  (Bradbury et al. 2007). First, the genotypic data for all accessions taken together were filtered for quality score (> 20) and bi-allelic status of SNPs. Second, the genotypic data of accessions from each genetic group were filtered separately for minor allele frequency (MAF > 1%), rate of missing data (< 20%), and heterozygosity (< 5%).
glaberrima. The relative importance of genetic diversity among subpopulations, among individuals within a subpopulation and within individuals, was assessed by analysis of molecular variance AMOVA with Arlequin software version 3. 5. 2. (Excoffier et al. 2010). The same software served to compute nucleotide diversity, expected heterozygosity per locus, the  Wright (1965)  F-statistics (F IS and F ST ) and the population parameter θ = 4N e µ , where N e is the effective population size and µ is the overall mutation rate at the haplotype level. The speed of decay of linkage disequilibrium (LD) in each subpopulation was estimated by computing r 2 between pairs of markers on a chromosome basis, using the "full matrix" option of Tassel 5. 2. 6  (Bradbury et al. 2007) , and then by averaging the results by classes of distance.
2019 ; http:// GeneO ntolo gy. org), that use RAP and MSU gene identity, respectively, as input. The chromosomic segments bearing loci under selection were also investigated for genes known to be involved in reproductive development processes, using available literature  (Wei et al. 2020 ; http:// www. model crop. org/). Likewise, chromosomic segments bearing loci under selection were investigated for enrichment in quantitative trait loci (QTL) involved in DTHD, using the Gramene QTL database (http:// www. grame ne. org/).
When all the 31 ILUS were considered, three additional known genes involved in biological processes related to heading were identified (Table  3 , Additional file 10: Table  S7 ). Investigation of the chromosomic segments bearing the 24 M2 type ILUS for the presence of QTLs involved in DTHD, counted 112 such QTL out of the 619 listed in the Gramene QTL database (http:// www. grame ne. org/) (Table  3 ; Additional file 10: Table  S7 ). This represented a density of one DTHD QTL every 0. 21 Mb, approximately 3. 0-fold higher than the expected average density of DTHD QTL, one every 0. 62 Mb. The 112 DTHD-QTL counted included a number of major QTL such as Hd1, Hd2, Hd5, Hd6, Hd8 and Hd11. When all the 31 ILUS were considered, the count of DTHD QTL reached 162, representing a 3. 3-fold enrichment.
Introduction Currently, there is growing interest in the discovery and the promotion of marine bioactive compounds. Most scientific research efforts have been concentrated on the mining of algal-derived components  [1, 2] , while the biochemical value of marine coastal plants, such as the Salicornia genus, has not been largely explored yet  [3] [4] [5]. Salicornia, also known as sea bean or samphire, is a halophytic plant growing on salt-saturated areas  [6] [7] [8] [9]  and classified into the Caryophyllales plant order, which regroups more than 30 species reported in the Plants of the World Online and eHaloph databases  [4]. Salicornia has been domesticated  [5, [10] [11] [12] [13] [14] [15] , and is traditionally used in the human diet for its nutritional benefits due to its high content of natural minerals, dietary fibers, polysaccharides, phytosterols, phenolic acids and flavonoids  [16] [17] [18].
0299, m/z 271. 0248 and m/z 285. 0405, respectively). The signal intensity of most of these fragment ions tended to decrease drastically by the increase in the fragmentation collision energy, and completely disappeared in the HCD80 spectra. These fragment ions were usually generated through decarboxylation and other mechanisms releasing, for instance, CH 4 , CH 2 O, C 2 H 2 O and C 3 H 6 O 2 losses. For glycosylated flavonoids, they came from the [(M-H)-glycoside] moiety, as it was observed in their HCD30, 40 and CID30 spectral data (Figure  S1 ). In the present panel of flavonoid standards, the neutral loss of C 6 H 10 O 5 , corresponding to the glucose moiety (162. 0523 Da), attested to the presence of O-glycosylated structures  [39]. Sugar loss, corresponding to rutinose (C 12 H 20 O 9 ), was also monitored in the fragmentation pathway of hesperidin.
The second Ion population was obtained in higher fragmentation conditions. A set of seven flavonoid-specific fragment ions were identified in a mass range lower than m/z 170, especially starting from HCD60. Globally, they appeared in HCD60 conditions and reached their highest abundance at the HCD80 level. The fragment ions m/z 83. 01385, m/z 107. 0139, m/z 125. 0244 and m/z 151. 0037 were found in all mass spectral data of the flavonoid standards. The fragment ions m/z 123. 0452 and m/z 133. 0295 were also selected due to their high abundance in HCD60 and HCD80 MS 2 scans in spectral datasets of nine flavonoid standards. The appearance of m/z 169. 0142 and m/z 285. 0405 ions was noticed in HCD60 and HCD80 MS 2 scans of three flavonoid precursors and matched the exact mass of gallic acid, kaempferol and luteolin.
It is worth mentioning that fragmentation data acquired at HCD60 were useful for the differentiation of flavonoid isomers by their individual infusion. Indeed, different fragmentation tendencies were observed for the isomeric couple kaempferol/luteolin and apigenin/genistein. In HCD60 conditions, it was noticed that kaempferol showed an array of abundant fragment ions below m/z 150, whereas luteolin displayed a predominant fragment ion, which was the m/z 133. 0291 (Figure  2a ). Similar fragmentation behavior was observed for the apigenin, which displayed the high intense fragment ion m/z 117. 0316, whereas genistein was recognized by its intense fragment ion m/z 133. 0291 (Figure  2b ). The stepped fragmentation mode was tested to compile both fragment ion sets detected in HCD40 and HCD80 spectra and cover all the flavonoid standards differing in subclass, methylation and glycosylation substitution.
On the contrary, retention times of aglycone flavonoids corresponded to the XIC MS 2 trace of fragment ions lower than m/z 199. It is noteworthy to mention that such an untargeted method based on the fragment ion search strategy permits a large inventory of class-specific compounds in spite of their variable intensities and the complexity of the plant extract matrix. The developed untargeted mass spectrometry method and data processing approach were thus proposed to treat speedily metabolomic big data without requiring either preliminary time-consuming extraction procedures or fractionation treatments to simplify the sample matrix. Data-processing performed on Compound Discoverer TM software to mine candidate flavonoids in the model sample allowed for the detection of 1068 compounds. Application of filtering criteria from the data processing workflow permitted the selection of 27 final exact masses.
Because of their wide structural diversity and their subsequent fragmentation pathways, a threshold of a minimum of three class-specific fragment ions observed in their HCD60 ± 20 MS 2 OT scans was used to annotate putative flavonoids. All the candidate flavonoids were detected with a mass error lower than 5 ppm and an area peak max greater than or equal to 1 × 10 4. The use of Search Mass Lists node permitted the identification of 20 compounds based on their exact mass and their match with online and local databases. Notably, eight of them showed multiple matches with flavonoid isomers (Figure  4 ). In the datasets, 12 flavonoids and 6 phenolic acids were found in grape seed extract. Most of them were flavan-3-ols, flavonols and phenolic acid derivatives detected in a glycosylated form, such as catechin-hexoside.
The use of Search Mass Lists node permitted the identification of 20 compounds based on their exact mass and their match with online and local databases. Notably, eight of them showed multiple matches with flavonoid isomers (Figure  4 ). In the datasets, 12 flavonoids and 6 phenolic acids were found in grape seed extract. Most of them were flavan-3-ols, flavonols and phenolic acid derivatives detected in a glycosylated form, such as catechin-hexoside. Moreover, three exact masses matched with type-B procyanidins. Notably, the nontargeted method permitted the detection of seven unreported flavonoids with their unidentified isomers in the model sample by the detection of characteristic fragment ions in their HCD60 ± 20 spectra. These observations thus confirmed the efficiency of the nontargeted ddMS 2 /MS 3 method to tag candidate Mass spectral data acquired with the model grape seed sample allowed for confirmation of the efficiency of the untargeted method to mine and annotate flavonoids in complex plant matrices by the use of 15 characteristic fragment ions.
Method Application 2. 2. 1. Method Application for the Nonscreening of Flavonoids in Salicornia europaea Extracts The developed nontargeted Top3 ddMS 2 /MS 3 method was carried out to mine flavonoids in Salicornia extracts prepared either with 30 mM Tris-HCl pH 7 or 10 mM NH 4 Ac. Data processing performed with a nontargeted workflow designed using Compound Discoverer TM software allowed for the detection, in total, of 25 candidate flavonoids after applying filtering criteria (background is false; -5 ≤ mass error ≤ 5 ppm; area peak max ≥ 1 × 10 4 ; class-specific fragment ions ≥ 4). An additional filtering option was used to select only candidate flavonoids showing CID30 MS 2 /MS 3 scans triggered after the detection of characteristic fragment ions (MS depth less than or equal to 3). All the detected compounds were reported with a mass error lower than 5 ppm, as shown in Table  2.
In the dataset, the use of the node Search Mass Lists permitted the fast identification of candidate flavonoids: five glycosylated flavonoids were detected and matched with flavonoids reported in online and local databases. The identification of quercetin-3-β-D-O-glucoside, quercetin-3-(6"-malonyl)-glucoside, isorhamnetin-hexoside, luteolin and its di-glycosylated form confirmed their specificity to the Salicornia genus  [21, 26, 30, 31]. These observations are in accordance with the literature, which reports quercetin-glycoside derivatives as typical flavonoid structures in Salicornia  [45]. Flavonoid glycosides were easily elucidated by the detection of their [(M-H)-glycoside] moiety and their related neutral loss, revealing the basic structure of the glycoside group, e. g. , hexose or pentose.
Likewise, the structural annotation of quercetin-3-(6"-malonyl)-glucoside can be easily identified by its characteristic malonyl-hexose loss  [39, 42, 45]. Nonetheless, the fragmentation data did not permit the precise definition of the position of the glycoside group on the flavonoid skeleton core and their exact nature, as was reported for isorhamnetin-hexoside and luteolin-di-hexoside. However, the elucidation of the glucose and its position in the structure of quercetin-3-β-D-O-glucoside was confirmed, with its similar retention time and fragmentation data to the available standard. Note that 4 -OH-5,7,2 -trimethoxyflavanone 4 -rhamnoside and luteolin-O-sulfate are reported here for the first time in the Salicornia europaea sample. Moreover, three flavonoids displayed multiple matches: more than 30 structural propositions were given from local databases, e.
g. , Arita Lab 6549 Flavonoid Structure Database, and corresponded to mono-and polymethoxylated flavonoids classified mostly in flavones, isoflavones and flavonols subgroups. Interestingly, 14 candidate flavonoids were not reported in the literature because no match was observed with local databases. Nevertheless, the observation of the m/z 301. 0354 fragment ion in mass spectral data of m/z 505. 0983 assumed its structural affiliation with a quercetin-acetyl glucoside, which may be quercetin-3-O-(6"acetyl glucoside). Indeed, Alves et al. recently demonstrated that the fragmentation of quercetin-3-(6"-malonyl)glucoside leads to the decarboxylation of its malonyl substitution and the appearance of the product ion matching unambiguously with the structure of m/z 505. 0983, displaying an acetyl glucose group  [45].
Most of the flavonoids identified in Salicornia europaea extracts were quercetin, luteolin and isorhamnetin derivatives  [21, 26, 30, 31]. For the first time, four sulfated flavonoid derivatives were detected In Salicornia europaea. If this method is relevant to cover all the candidate flavonoids in Salicornia extract, it is limited in terms of distinction of isomeric forms. Although fragmentation data based on Retro-Diels-Alder mechanisms and retrocyclization could provide information in HCD60 ± 20 MS 2 scans on the number of flavonoid substituents and their possible location on the A, B and C rings, they did not permit the positioning of them on a specific carbon of their skeleton ring. Moreover, the abundance of isomeric forms renders the manual structural elucidation of candidate flavonoids fastidious and inaccurate.
Identification of Flavonoids in Salicornia europaea Extracts Based on In Silico Predictive Combination Flavonoid-Specific Chemical Substitutions in Known Parent Molecules Flavonoids for which the structural identity was not assigned or resulted in multiple matches were identified by a data processing method assisted with a list of predictive combinations of flavonoid-specific chemical substitutions. For this purpose, the unknown candidate flavonoids were characterized based on the structures of flavonoids reported in previous studies on the Salicornia genus and Arita databases. The calculation of their mass shifts and the prediction of the most likely structural prediction are reported on Table  3. The calculation of the FISh score was given to strengthen confidence in their identification.
8 × 10 7 ± 5. 8 × 10 6 in NH 4 Ac extracts, respectively. Nonetheless, an individual study of area peaks acquired for every flavonoid showed that 12 of them were extracted more efficiently in NH 4 Ac under sonication, whereas 5 others were preferentially extracted in Tris-HCl, as is shown in Figure  6. Indeed, their single annotation in HCl or NH  4  Ac extracts assumed that their area peaks was in one of the extraction conditions below the threshold fixed at 1 × 10 4 , according to the result-filtering criteria applied with Compound Discoverer TM software. Note that m/z 505. 0989, m/z 519. 1144 and m/z 387. 1085, presuming to be quercetin-3-O-(6"acetyl glucoside), isorhamnetin-7-O-(6"acetyl glucoside) and 7-acetyl-5-hydroxy-3 ,4 ,5 trimethoxyflavanone, respectively, displayed the highest signal intensities, especially in the NH 4 Ac extract.
Instrumentation Analysis of flavonoids was carried out using an Ultimate 3000 RSLC system (Ther-moFisher Scientific, Bremen, Germany) coupled with an Orbitrap Fusion Lumos Tribrid mass spectrometer (ThermoFisher Scientific, Waltham, MA, USA) operated in negative mode. Data processing for the structurally intelligent annotation of both known and unreported structures was carried out using Compound Discoverer 3. 2 TM (ThermoFisher Scientific, Waltham, MA, USA). In parallel, Mass Frontier 7. 0 TM (HighChem, Bratislava, Slovakia) was used for the structural prediction of novel flavonoids and their in silico fragmentation.
Electrospray ionization conditions were sheath gas 50 (arb), auxiliary gas 10 (arb), sweep gas 1 (arb), spray voltage 2500 V in negative mode and rf lens 50%. Ion transfer tube and vaporizer temperatures were fixed at 350 °C. For the top3 ddMS 2 /MS 3 analysis, scan events are detailed on Figure  8. For every plant extract obtained in 30 mM Tris-HCl or 10 mM NH 4 Ac buffers, raw data obtained in Top3 ddMS 2 /MS 3 OT acquisition were processed on Compound Discoverer 3. 2 TM software by the use of an untargeted workflow (Figure  9 ). The flagging of candidate flavonoids was based on a computational labeling of characteristic fragment ions determined in a prior-targeted fragmentation of the flavonoid standards. All the compounds showing fragmentation patterns specific to the flavonoid compound class were listed in a final table of results with a mass tolerance lower than 5 ppm and a minimum peak intensity at 1 × 10 4.
For this purpose, the untargeted workflow was based on the selection of spectra in raw data and alignment of retention times between samples, with a maximum shift of 0. scans were performed to confirm the detection of flavonoids and displayed the following settings: scan rate 33,333 Da/s, peak width 0. 5 FWHM, isolation width 2 Da. CID30 ddMS 3  IT scans were applied on the highest m/z values of fragment ions detected in HCD60 ± 20 by the use of mass trigger settings. For every plant extract obtained in 30 mM Tris-HCl or 10 mM NH4Ac buffers, raw data obtained in Top3 ddMS 2 /MS 3 OT acquisition were processed on Compound Discoverer 3. 2 TM software by the use of an untargeted workflow (Figure  9 ).
Untargeted Screening of Flavonoids Based on Fragment Ion Search (FISh) Strategy Structural prediction of unknown flavonoids and isomeric forms was performed with a data processing workflow combining an in-house-created exhaustive list of chemical functions and substitutions previously reported in flavonoids, called transformations, with structures in the local in-house database of 85 flavonoids, serving as parent molecules for the transformations. The software virtually creates a list of formulas resulting from all possible combinations of transformations with a parent molecule, and then attempts to find matching signals for these formulas. The reporting of an identified compound is then conditioned by the presence of fragments matching the original fragments of the parent molecule in the associated MS2 spectrum, as well as fragments showing a mass shift matching the mass of the transformation (or the sum of them if several transformations are involved).
Additional parameters such as Fill Gaps or Mark Background were used to indicate missing peaks, correct alignment errors and discriminate peaks from blanks. Finally, the node Search Mass List was applied in order to annotate compounds using local databases, i. e. , an in-house-created database containing 85 flavonoids, and the software included Arita Lab 6549 Flavonoid Structure Database. In parallel, the Search mzCloud and Search ChemSpider nodes were used to interrogate online molecular and spectral databases. Filtering criteria were applied to select candidate flavonoids detected in plant extracts with the following settings: background is false; -5 ≤ mass error ≤ 5 ppm; area peak max ≥ 1 × 10 4 ; class-specific fragment ions ≥ 2; MS 2 equal to data-dependent for preferred ion; MS depth less than or equal to 3.
Acknowledgments: The authors wish to thank  Christophe Dunand  for daily exchanges,  David Roujol  and  Josiane Chourré  for technical support, and the  GenoToul Imaging platform of FR Agrobiosciences Interactions and Biodiversity  for easy access to the Nanozoomer (https://www. fraib. fr/fraib_eng/ Technological-platforms/Cell-Imaging, accessed on 29 June 2023).
Funding: The authors are thankful to the Centre National de la Recherche Scientifique (CNRS) and to the Paul Sabatier-Toulouse 3 University for granting their work. H. K. has been funded by the TULIP LabEx project (ANR-10-LABX-41; ANR-11-IDEX-0002-02). Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable.
Phenotypic statistical analysis Means and standard deviations of the values obtained for TPC, AAC, and AA (AA_DPPH and AA_FRAP) were calculated. The Shapiro-Wilk test was used to check the normality of the data. One-way analysis of variance (ANOVA) and Tukey's test (p<0. 05) were conducted using the shapiro. test, aov, and TukeyHSD functions of R-stats package  (R Stats, 2022)  of R software  (R, 2022)  to 1) compare the means between and within populations for each variable and 2) compare the variations of TPC, AA, and AAC contents according to skin and flesh tuber colors (Supplementary Table  S1 ). The tuber phenotype color traits were previously obtained  (Berdugo-Cely et al. , 2017)  using the descriptors of the CIP (Centro Internacional de la Papa) to characterize native potatoes  (Goḿez, 2014).
The following statistical analyses using the quantitative traits were independently run in diploid and tetraploid genotypes. Using the R-stats package  (R Stats, 2022)  a Pearson correlation (p<0. 05) was used to check the relationship among variables. A Principal Component Analysis (PCA) was performed, and the two first components of the PCA were used to generate a multivariate clustering analysis using the Ward method and the Euclidean distance. These multivariate analyses were implemented in the R-factoextra package  (Kassambara and Mundt, 2020).
Population structure, genetic diversity, and linkage disequilibrium analysis The genetic structure analyses based on the Bayesian clustering simulation in STRUCTURE software  (Pritchard et al. , 2000)  were implemented on 1) the mixed population composed of diploid and tetraploid accessions and conducted separately on 2) diploid accessions, and 3) tetraploid accessions to assess the effect of various ploidy levels on the reconstruction of the population stratification. These analyses were simulated from one to ten subpopulations with five replicates for each dataset, assuming an admixture model and correlated allele frequencies with a burn-in of 100000 and 50000 Markov Chain Monte Carlo (MCMC) iterations after the burn-in. The number of subpopulations among the samples was determined using the Evanno method  (Evanno et al.
, 2005)  in STRUCTURE Harvester  (Earl and vonHoldt, 2012). The PCA was conducted in the adegenet R-package (Jombart and Ahmed, 2011) using the identified genetic structure, which was confirmed by an analysis of molecular variance (AMOVA´s) and Phi values with 1000 permutations in the poppr R-package  (Kamvar et al. , 2014). The expected heterozygosity (He) was calculated to analyze the genetic diversity in each population using the adegenet package. We determined the linkage disequilibrium (LD) of the diploid, tetraploid, and mixed potato populations following the methodology reported by  Vos et al. (2017). For the SNPs with a known physical position in the reference genome, all possible pairwise correlation coefficients (r 2 ) were computed. The LD mean and decay per population were calculated using this information.