dataesr/scientific-paragraphs-categorization huggingface dataset:
Quantitative changes between mature and ripe fruit proteome were assessed using a t-test after centering, normalizing and scaling the data to achieve a close to normal distribution (Methods section, Supplementary Fig. 1 ). Mature and ripe fruit displayed 52 and 22 proteins with a qualitative upregulation, and 88 and 86 proteins with a quantitative upregulation, respectively. Overall, 248 (14. 9%) of the proteins identi ed in this study displayed a differential abundance between mature and ripe fruit (Table  1 ). A functional enrichment analysis of the proteins with a differential abundance was performed using Gene Ontology (GO). Proteins more abundant in mature fruit were associated to glycogen and isocitrate metabolism, and protein localization (Fig. 3B ). No enrichment in any of the three GO sub-ontologies was found for proteins more abundant in ripe fruit.
Discussion Proteomic approaches, based on 1D electrophoresis followed by MS (Fig. 1 ), have been successfully used to better understand fruit ripening in Rosaceae family species such as apricot  (D'Ambrosio et al. , 2013)  and strawberry  (Bianco et al. , 2009). This approach has the great advantages of being among the most robust pipelines for proteomic analysis  (Fang et al. , 2010)  and being able to capture hydrophobic proteins. Conversely, 2D electrophoresis approaches lack the presence of hydrophobic proteins due to protein precipitation in the IPG matrix  (Molloy et al. , 1999). Considering that the physicochemical properties of P. persica primary transcripts' proteome were similar to those of the proteome analyzed in the present study (Supplementary Table  2 , Supplementary Fig. 2 ) and that low abundant proteins, such as TFs, could be detected in our dataset, the current analysis seems to truly re ect the fruit proteome, but with a bias in terms of primarily representing proteins with average physicochemical properties (Supplementary Fig.
2 ). When compared to proteins extracted from ripening P. armeniaca, using 1D SDS-PAGE gel followed by MS, 76% of the 245 proteins identi ed in mature/ripe apricot fruit were also identi ed in mature/ripe peach fruit  (D'Ambrosio et al. , 2013) , indicating that the proteins found in peach fruit mesocarp are characteristic of Prunus fruit. Further analysis using transcriptomic datasets reinforced this assertion (Fig. 2 ). In order to take full advantage of the proteins characterized in systems such as ripe fruit, it is important to follow a robust pipeline of MS analysis and protein quanti cation, to increase the con dence in the identi cations performed and to support differential accumulation analysis. MS analysis was performed using a stringent cutoff for both peptide and protein match.
Both sets point to a fruit mesocarp with active cellular compartment transport and protein folding. Mature and ripe fruit stages are the last in the fruit developmental curve, making it possible to compare protein-coding genes from our dataset with datasets that characterized ripening-related genes in P. persica  (GEO dataset GSE71561 and Lü et al. , 2018). Protein-coding genes expressed in mature/ripe fruit seemed to be time and tissue-speci c, given that close to 25% peak their expression levels in ripe fruit when compared to other developmental stages, and 52% peaked their expression in ripe fruit mesocarp, compared to other tissues (Fig. 2B  and 2C ). Fruit-speci c genes that displayed the strongest differentiation between peach and almond (P. dulcis) were also the most highly expressed, pointing to a functional specialization of protein-coding genes that are highly expressed in fruit  (Velasco et al.
8G121900) was shown to be able to degrade volatile acetate esters  (Cao et al. , 2019). In the present study, two CXE proteins were found to be more abundant in mature fruit than in ripe fruit, Prupe. 1G439300 and Prupe. 8G121500. The genes coding for these proteins were top-7 and top-8 in expression level in ripe fruit, among 19 putative CXEs in P. persica  (Cao et al. , 2019). Prupe. 1G439300 is the best homologue to the tomato SlCXE1  (Cao et al. , 2019)  and its protein-coding gene displayed a peak of expression in ripe fruit (GEO dataset GSE71561), being a good candidate for further characterization. Fruits can synthesize lactones that attract feeders for seed dispersal, mostly the γ-lactones decano-4lactone and/or dodecano-4-lactone  (Schöttler and Boland, 1996). These compounds, which primarily derive from oleic acids or derivatives, are metabolized by a series of enzymes, including epoxide hydrolases, to generate lactones such as undecano-4-lactone  (Schöttler and Boland, 1996).
They can be found both in fruit skin as well as mesocarp  (Zhang et al. , 2010). According to our data, lactone metabolism would also be differentially regulated in mature and ripe fruit, due to preferential accumulation of epoxide hydrolase EPH2 (Prupe. 7G162300, Pirona et al. , 2013)  in mature fruit. On the other hand, the SRK2C kinase (Prupe. 6G192200), which has been correlated to lactones using QTL analysis  (Sánchez et al. , 2014) , is exclusively found in ripe fruit (Table  1 ). The target of SRK2 is unknown, and given its high expression (GEO dataset GSE71561), differential accumulation during the ripening process (Table  1 ), and correlation with lactone biosynthesis, it becomes an interesting candidate for further characterization. Fruit ripening has been characterized as a senescing process, with cell membrane deterioration being the hallmark.
Fatty acid desaturase 1 (FAD1, Prupe. 7G076500), a ω-6 Oleate desaturase  (Sánchez et al. , 2013) , displayed increased abundance in ripe fruit (Fig. 4B , panel II), a pattern similar to the one displayed by its transcript in the P. persica Yulu variety  (Zhang et al. , 2010) , in the GSE71561 dataset, and by its closest orthologue in Fragaria vesca  (Sánchez-Sevilla et al. , 2014). FAD1 gene expression was also sensitive to the ethylene signal transduction pathway inhibitor 1-methylcyclopropene (1-MCP), a compound known to negatively affect peach fruit aroma  (Cai et al. , 2019). This increase could imply that FAD1 could be involved in linoleic and linolenic acids biosynthesis in ripe fruit, in a similar way to its best homologue in Arabidopsis thaliana (AtFAD2,  Zhang et al. , 2010) , or could be involved in γ-decalactone biosynthesis, as proposed for the F.
Solute Transport Aquaporins are transmembrane proteins that enable water and small neutral solute translocation across cellular membranes. Among the different types of aquaporins, those located in the vacuolar membranes are called tonoplast intrinsic proteins (TIPs). TIPs are classi ed into ve groups (TIP1-5), and are able to transport hydrogen peroxide and glycerol, in addition to water  (Regon et al. , 2014). The P. persica aquaporin TIP1-1 (Prupe. 7G125900) was found to be more abundant in ripe than in mature fruit mesocarp (Table  1 ). A close homologous (93% identity) protein-coding gene from sweet cherry (P. avium) was among the three most expressed aquaporins in the fruit, being mainly expressed in mesocarp throughout fruit development  (Chen et al. , 2019). The P. persica PpTIP1-1 transcriptional pattern was very similar (GEO dataset GSE71561), with the gene being expressed at a regular level throughout mesocarp development.
The top ve ions in each survey scan were then subjected to data-dependent zoom scans followed by low energy collision induced dissociation (CID) and the resulting MS/MS spectra were converted to peak lists in BioWorks Browser v3. 2 (www. thermo. com) using the default LTQ instrument parameters. Peak lists were searched against a custom database containing protein sequences from Prunus persica whole genome assembly, v1. 0 and common laboratory contaminants (downloaded from NCBI, www. ncbi. nlm. nih. gov) using the Mascot searching algorithm, v2. 3 (www. matrixscience. com). Mascot parameters for all databases were as follows: allow up to two missed tryptic sites; xed modi cation of carbamidomethyl cysteine; variable modi cation of oxidation of methionine; peptide tolerance of +/-200ppm; MS/MS tolerance of 0.
Data Processing The Mascot output was analyzed using Scaffold v4. 8. 2 (www. proteomesoftware. com) to probabilistically validate protein identi cations. Assignments were validated in Scaffold according to the following criteria: Protein Threshold -1. 0% FDR; Minimum number of peptides -1; Peptide threshold -0. 1% FDR. To be able to accurately determine changes in protein levels, average total ion chromatograms (Average TIC) was retrieved from Scaffold for each protein assessed in each of the six samples under evaluation  (Asara et al. , 2008). Average TIC for each protein was estimated using only those peptides identi ed with high con dence (Figure  1D ).
Data pre-treatment To circumvent the issue of missing data, average values were calculated for those proteins that had only two biological replicates per biological condition (mature or ripe) and were used as third replicate. All the following analyses were thus performed with proteins which had at least two data points in O1 and O2, being the other proteins discarded. Protein data was scaled by using pareto scaling (van den  Berg et al. , 2006) , normalized using Quantile Normalized procedure and centered at zero by subtracting the mean value, using InfernoRDN  (version 1. 1. 5970. 31895, Taverner et al. , 2012). This allowed the data to be transformed to achieve a close to normal distribution, required for many statistical analyses of variance (Supplementary Figure  1 ). Q-Q and Box-plot graphs were also performed using InfernoRDN.
Gene ontology and pathways analysis Gene ontology analysis was performed using Phytomine (https://phytozome. jgi. doe. gov/phytomine/begin. do). The Benjamini-Hochberg test, with a "max p-value" set to 0. 05, was used for Multiple Testing Corrections. Redundant GO terms were removed with the webtool REVIGO (Supek et al. , 2011), using the following parameters: 1. Allowed similarity: small (0. 5); 2. GO categories associated to: p-values; 3. GO term sizes database: Arabidopsis thaliana; 4. Semantic similarity measure to use: SimRel. Next, CirGO  (Kuznetsova et al. , 2019)  was used to plot a concise version of the GO analysis. Pathway diagrams were built by rst running the program DeepEC  (Ryu et al. , 2019)  to annotate the enzymes included in the 26,873 protein-coding genes from the P. persica genome version 2.
1. Next, this annotation was used to generate the input le for the program Pathway Tools  (Karp et al. , 2016) , which was used to infer the pathways and reactions from P. persica (Supplementary File 1). Principal Component Analysis (PCA) PCA analysis was run on normalized and centered data using the Explore/PCA tool from InfernoRDN  (Taverner et al. , 2012). Transcriptional analysis of the Gene Expression Omnibus dataset GSE71561 Data from 13 peach samples, which included six samples from fruit seeds (days 41, 54, 69, 83, 111 and 125 after full bloom -DAFB), the same six samples from fruit mesocarp and one sample from owers from the mid-season Fantasia variety, was downloaded from the GSE71561 dataset. Thus, samples covered until the commercial ripening stage. The microarray data was log2 transformed following the protocol available from the online tool GEO2R  (Barrett et al.
This task was performed using the Limma R package  (Ritchie et al. , 2015) , following the protocol available from the online tool GEO2R  (Barrett et al. , 2013 ) and using the "decideTests" function. To improve the statistical con dence of the analysis, the probes that targeted the same gene had to display the same "decideTests" results in order to be considered for further analysis. This approach reduced the original dataset size from 29,045 genes to 18,074 genes (62. 2%). These 18,074 genes included information from 1,136 protein-coding genes (68. 3%) present in our proteomics dataset.
Alternatively, calibration curves (delivered calibration curves I and II as well as internal laboratory-specific calibration curves) were combined (building the sum of the normalized Ct values) aggregating the number of dose estimates. Finally, different software tools such as Statistica 9. 0 software (StatSoft), GraphPad Prism, version 6. 04 (GraphPad Software, La Jolla, CA, USA), R 3. 6. 2 or Sigma Plot 14 (Jandel Scientific, Erkrath, Germany) were employed to fit the calibration curves and to perform dose estimates. Gene expression analysis using DNA microarrays was performed on the Agilent platform as described elsewhere  11. The mRNA (400 ng of total RNA per sample) was transcribed into cDNA with an oligo-dT primer, followed by transcription into cRNA labeled with cyanine 3-CTP (Quick-Amp Labeling Kit, One-color, Agilent).
cRNA purification was performed with the RNeasy Mini Kit (Qiagen) and dye incorporation and cRNA yields were measured with the NanoDrop-1000 spectrophotometer. Labeled cRNA samples were applied on the DNA microarray slides (44 k whole human genome, G4112F, Agilent). For hybridization, DNA microarrays were placed into a hybridization oven (44 K Agilent) at 65 °C for 17 h. After hybridization, DNA microarrays were washed and slides were immediately scanned with the Microarray Scanner (G2505 B, Agilent) as recommended by Agilent. The pre-processing procedure and subsequent statistical analysis were applied separately using Agilent  Feature Extraction Software Version 9. 5. 1 (Agilent processed signal value) and Agilent GeneSpring GX software. By initial data filtering control features and non-uniform outliers were excluded, as well as signals that were not significantly above the background intensity in 25% of all samples.
2 Gy versus true dose of 2. 38 Gy) and 2A (reported average dose of 0. 15 Gy versus true dose of 1. 56 Gy, Table  3 ). True doses in the 0. 2 Gy range resulted in estimates within the same order of magnitude. Thus, for sample 1B, the average reported dose was 0. 15 Gy versus the true dose of 0. 18 Gy, and for sample 3A, the average reported dose was 0. 07 Gy versus the true dose of 0. 25 Gy (Table  3 ). Inter-laboratory comparison revealed consistent dose estimates over all contributions, and the reported dose estimates differed in 75% of the data by ≤ 0. 14 Gy (Fig. 4 ). Differential gene expression changes in the blinded samples were calculated from each team's FDXR and DDB2 measurements relative to the 0 Gy samples of calibration curve I. This produced values of 0. 20-0. 84 (FDXR) and 0.
Acknowledgements We are very thankful for the technical support by  Oliver Wittmann  and  Sven Doucha-Senf. Financial support for the  Health Protection Agency  was provided by the  National Institute for Health Research Centre for Research in Public Health Protection, UK. This work was supported by the  Institute of Nuclear Chemistry and Technology  statutory grant, Poland, and by the  Center for High-Throughput Minimally-Invasive Radiation Biodosimetry, National Institute of Allergy and Infectious Diseases  Grant Number  U19 AI067773 ,  USA.
As for the vegetative stage, the increase in total leaf GS activity at 15DAS was comparable in the eight transgenic hybrid lines (Fig. 1b  and see Supplementary Data 8). The leaf GS isoenzyme protein content of the CH, the NS, and the eight GS overexpressors was examined using protein gel blot analysis. At the vegetative stage, in the leaves of the CH, two polypeptides were detected. The most abundant 40 kDa polypeptide corresponded to the cytosolic form of GS (GS1), whereas the 44 kDa polypeptide, which was barely detectable, corresponded to the plastidic form of GS (GS2). Compared to the CH and the NS, a three-to four-fold increase in the amount of GS1 protein was observed in the eight GS overexpressors, (Fig. 1a ). When the leaf GS proteins were analyzed at 15DAS, a two-fold increase in GS1 protein content was determined in the transgenic hybrids.
Data 8), also indicating that such an increase was similar in the mesophyll cells and BSCs. The impact of GS overexpression on plant phenotype and kernel production was determined using the eight independent transgenic hybrids grown in the field in 2011 (location Finch, MA, USA). At plant maturity, no significant differences were seen in the dry matter accumulation of the vegetative parts of the shoots. In contrast, a significant increase in kernel yield, expressed as mass per plant (g/plant) was observed, which when compared to the CH and the NS was on average 7% higher (P = 0. 04), (Fig. 1c , see Supplementary Data 2 and 8 for the statistical analysis). Although the increase in leaf GS activity was similar in the eight transgenic hybrids, this increase was not necessarily correlated with the increase in kernel yield.
3b  (see Supplementary Data 9). It can be seen that kernel production was variable from one year to another, as well as from one location to another in the same year. When a significant increase in kernel yield was observed in the Gln1-3 overexpressors, it occurred whether kernel yield was low (e. g. , around 67 Ql. ha -1 in Alleman in 2010) or high (e. g. , around 90 Ql. ha -1 in Ashkum in 2013). In some locations such as Findlay in 2013, the average increase in kernel yield was not significant. This was due to the fact that only 2 (H14 and H32) out of the six independent transgenic hybrids tested had a higher kernel weight per plant compared to the control hybrids (Supplementary Data 4, sheet Yield per Event Location Year, Gray highlight). The impact of Gln1-3 overexpression on kernel yield was further assessed using multi-environmental analysis.
This selectable marker cassette was flanked by 2 DS elements (5′ Ac and 3′Ac  34  ). Two copies of a synthetic Gln1-3 full length cDNA  15  (accession numbers D14577. 1 and X65928) including their 3′ non-coding regions, one flanked by the cassava vein mosaic virus promoter (CsVMV promoter  35  ) fused to the rice actin1 first intron  36  to enhance the activity of CsVMV promoter and the 3′Nos terminator and the other one flanked by the promoter of the maize Rubisco small subunit (RbcS)  37  and the 3′Nos terminator. The nucleotide sequence of Gln1-3 was 98% similar at the amino acid level to that of Gln1-4 the other gene encoding GS1  13. The resulting recombinant plasmid used for transformation was pBIOS1459 (Supplementary Fig. 1 ). Transgenic plants were then cultivated in a glasshouse (18-24 °C) from April to September 2008 and either selfed or pollinated with the WT line A188 to produce seeds.
Untransformed control hybrids corresponding to the cross between A188 and RBO, were used either as the female or male parental lines. In addition, maize hybrids (H14, H20, H22, H32, and H39) were produced, using two other testers (AAX3 and AAX7). The list of the hybrids tested over different years for either their biochemical characterization or for their agronomic performance is presented in Supplementary Data 1a. The number of independent transgenic hybrid lines used for the different field trials was varied from one year to the other depending on seed availability. The level of N fertilization was 175 kg/ha supplied before sowing and N provided by the soil was estimated at 60 kg/ha. Both phosphorus (P 205 ) and potassium (K 20 ) were also applied at 100 kg/ha. Water deficit experiments were conducted in order to attain a decrease in kernel yield of approximately 20-30%.
, Logan, UT, USA). Kernel yield (KY), thousand kernel weight (TKW), and kernel moisture (KM) were measured using the on-board equipment of the plot combine harvester. Kernel weight was then normalized to moisture at 15% using the following formula: Normalized kernel weight = measured KW × 100-measured moisture (expressed in %)/85 (100normalized soil moisture at 15%). In all the field experiments kernel yield was expressed in quintal per hectare (Ql. ha -1 ). For all biochemical analyses performed at the vegetative stage (V) and at 15DAS leaf and kernel samples were harvested in the year 2011 in Finch (MA, USA) using a pool of the two BULKS-NS (NS) and the wild type hybrid (CH) as controls, and the transgenic lines H12, H14, H18, H20, H22, H23, H27, and H32 (Supplementary Data 1a). As 25% of the plants were removed from the different plots or used for leaf sampling, this 2011 field trial which was partly destructive was not included in the evaluation of yield over the five years of experimentation.
45  ). The original population called BALANCE was used to extract 375 DHIL at the 3rd generation of mixing (G8). The crossing scheme used to create the MAGIC panel is illustrated in Supplementary Fig. 5. The 375 DHILs along with the 16 founder lines and the tester were genotyped with the Axiom maize 600 K array  46. The parental origin of each allele in the DHILs was inferred using the R/qtl package  47  and each DHIL was represented as a mosaic of the founder genomes. Knowing the parental mosaics of each DH lines, the SNP detected from the sequencing of the founders were projected on each DHIL (allowing in silico genotyping). The MAGIC panel allowed to detect more than 12 million single nucleotide polymorphisms (SNPs) following next generation sequencing (NGS) data of the 16 founders and of the tester line.
Fixed factor (construct). A test of the differences in terms of the least square means was made for the fixed effects at the construct level between the control hybrids (CH and NS) and the transgenic hybrids. The association between the SNPs, and kernel yield measured in different years and locations, was performed using a mixed linear model (MLM) implemented through ASReml statistical package that fits linear mixed models using residual maximum likehood (REML)  53  , (VSN International Ltd) on R environment  54. The model used was the K model  55. The association study was conducted using the leave one chromosome out (LOCO) method in which the kinship is calculated from all the SNPs but those located on the chromosome where the tested marker is located  56, 57. For the field trials and phenotyping of the MAGIC panel, the experiments were carried out following an alpha-lattice design with two replicates for each trial.
Acknowledgements We thank  C. F. for preparing the constructs; C. J. , A. B. , A. G. , and C. S. for maize transformation; S. L. and E. B. for taking care of the transgenic plants in the greenhouse; I. M. , A. L. , and  A. S. Canoy  for molecular analyses; I. D. for the field trial applications in the USA and B. K. , D. E. , C. A. , and  K. A. for nurseries management and field trials with the transgenic plants. We also thank  Alain Murigneux  and  Morgan Renault  for their contribution to the creation of the Maize MAGIC Population,  Marie-Hélène Tixier  and  Jeremy Lopez  for the management of the field trials for the association genetics study,  Magalie Leveugle ,  Jean-Philippe Pichon , and  Hervé Duborjal  for their involvement in the genotyping by sequencing project;  Johan Robin ,  Nicolas Sounac  and  Frédéric Sapet  for their involvement in in silico genotyping.
Data Analysis Arthropod assemblages in native and exotic forests were characterized by their diversity and composition using alpha diversity, beta diversity, and sites' contribution to beta diversity. Statistical analyses were run using the R analysis language  [45]. Alpha diversity was expressed using Hill numbers corresponding to Chao's species richness (q = 0), exponential Shannon diversity (q = 1), and Simpson diversity (q = 2) with an interpolation-extrapolation method  [46]. Alpha diversity values were calculated at the type of forest level and at the site level. Alpha diversity estimates were calculated using the function "iNEXT" in the R package iNEXT version 2. 0. 20. Alpha diversity estimates at the site level were compared using a Kruskal-Wallis test. We computed β-diversity on presence-absence data using Jaccard's dissimilarity coefficient.