dataesr/scientific-paragraphs-categorization huggingface dataset:
Funding The research leading to these results has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme. Project PURPOSE , grant agreement  758056. J. C. Nieto-Fuentes  acknowledges support from the  CONEX-Plus  programme funded by  Universidad Carlos III de Madrid  and the  European Union 's  Horizon 2020 research and innovation programme , under the  Marie Skłodowska-Curie  grant agreement  801538. J. A. Rodríguez-Martínez  acknowledges the financial support provided by the  Spanish Ministry of Science and Innovation  under the  programme Proyectos I+D Excelencia 2017. Project  APPLIED ,  DPI2017-88608-P.
To broaden the climatic scope of our study we included two species from the Mediterranean coastal habitat and two from temperate European grasslands, both from regions where temperatures have been increasing and in which herbivory plays a key role. We grew ancestor and descendant lines in a common environment and subjected the plants to a full-factorial combination of drought and simulated herbivory. We further used double digest restriction-site associated DNA (ddRAD) genotyping data to compare F ST and Q ST values between ancestors and descendants to understand the adaptive significance of observed phenotypic changes. Specifically, we addressed the following questions: (1) Do ancestral and contemporary populations differ in their phenotypes? (2) Does this differentiation result from selective or random processes? and (3) Do ancestral and contemporary populations differ in their responses to drought and simulated herbivory, and if yes, are there synergies or trade-offs between these two types of responses?.
Study species and seed origins We investigated four plant species, Matthiola tricuspidata (L. ) R. Br. (Brassicaceae) and Plantago crassifolia Forssk. (Plantaginaceae) from the French Mediterranean coast and Clinopodium vulgare L. (Lamiaceae) and Leontodon hispidus L. (Asteraceae) from temperate Belgian grasslands (Table  1 ). The two Mediterranean species are halophytic herbaceous species originating from sandy beaches; M. tricuspidata is an insect-pollinated annual and P. crassifolia a wind-pollinated perennial. The two temperate species are both insect-pollinated perennial, mesophilic forbs and typically found on dry calcareous soils. Although L. hispidus occurs mainly in dry grasslands and C. vulgare prefers thermophile woodland margins, both study populations had originated from calcareous grasslands prone to drought.
Measurements At 2 wk after seedling establishment, and before the first treatments were applied, we estimated initial plant size as a covariate through vertical top-down photographs of all pots in a standardised photo box using a high-resolution digital camera. The amounts of green pixels per picture, calculated with a custom script in Python, were used as estimates of plant size. Throughout the experiment, we recorded the flowering of plants as the days when the first open flowers (M. tricuspidata, C. vulgare, L. hispidus) or anthers (P. crassifolia) were visible. To assess resource investment into aboveground biomass at the time of flowering we measured plant height for M. tricuspidata and C. vulgare, the length of the longest leaf for P. crassifolia, and the rosette diameter for L. hispidus.
Q ST was estimated as Q ST = V POP /(2 9 V A + V POP ) = V POP /(8 9 V FAM + V POP ) , where V POP is the phenotypic variance between the two temporal origins and V A the genetic variance within temporal   (Wright, 1951). We calculated V POP by first running a linear model with early size and the four treatment combinations, then extracting the residuals and using these in a linear mixedeffects model including temporal origin and its treatment interactions (V POP = V temporal origin +V temporal origin 9 treatments ) as genetic factors and seed families (V FAM ) as random factors. We resampled data 999 times from the original dataset to estimate a mean value and bootstrapped standard error for the Q ST of every measured trait. We estimated neutral genetic differentiation (F ST ) between the two temporal origins based on 2257-5785 biallelic SNP markers per species (W.
Durka et al. , unpublished) , using the function stamppFst from the R package STAMPP  (Pembleton et al. , 2013). A more detailed description of the SNP genotyping can be found in Methods S1. The raw data have been deposited in the European Nucleotide Archive under the accession no. PRJEB47887 (https://www. ebi. ac. uk/ena/browser/view/PRJEB47887).
Acknowledgements This research was financially supported by a  PhD scholarship of the Deutsche Bundesstiftung Umwelt (DBU)  and a travel fund of the  Reinhold-und-Maria-Teufel-Stiftung  to RR. We thank authorities in  France and Belgium  for issuing the permits for seed sampling,  Madalin Parepa  ( University of T€ ubingen ) for discussion on data analyses,  Stephan Schreiber  and  Matthias Bernt  for their help with sequencing and for supporting the bioinformatic analyses,  Jun Hee Jung  for providing the Python script for analysing early plant size, and Andreas D€ orling,  Lisa Henres ,  Johanna Igel ,  Christiane Karasch-Wittmann ,  Johannes Kasper ,  Erik Lemke ,  Anne Schloesinger ,  Eva Schloter  and  Selina Schwarzbach  for supporting the work in the glasshouse. We thank two anonymous reviewers for their constructive remarks.
Statistical analysis Repeated measures one-way ANOVA followed by Tukey's multiple comparison post-tests were used to identify potential differences in steroid concentrations between all the reproductive stages. The Shapiro-Wilks normality test was used to determine Gaussian distribution. Data are presented as mean and SEM. Pearson's correlation was used to analyze associations between plasma and saliva steroid concentrations in mare. Statistical analyses were performed using GraphPad Prism version 6 (GraphPad Software, La Jolla California, USA). A significant difference was detected when P < 0. 05.
5  [125]. When a negative surface meets another negative surface, repulsive forces are produced. Thus, in theory, MFGs and STEC should repel each other. However, it is important to note that the bacterial surface charge should be measured in an appropriate buffer that mimics the properties of raw milk, such as milk ultrafiltration permeate. There is a lack of recent experimental data with appropriate physicochemical conditions to assess the involvement of these forces in the association of STEC with MFGs.
Statistical Analysis For pharmacological in vitro assays, the data were analyzed from four (DPPH radical analysis) and six (for O. - 2 , H 2 O 2 , and HO. ) independent assays using ANOVA in the case of data normality and variance homogeneity both checked using the Graphpad Prism software. In the other cases, the Kruskal-Wallis test was used at the 5% level (p = 0. 05) using the Graphpad Prism 8. 0. 1 software. Results were presented as a bar chart with means ± SD.
DPPH Scavenging Activity As displayed in Figure  2 , trolox (positive control) exhibited a remarkable ability to inhibit the DPPH radical. A concentration range of trolox was analyzed in order to calculate the IC 50 values and validate the test (data not shown). The concentration of 3. 91 mg. L -1 of trolox showed a statistically significant inhibition of the DPPH radical compared to the negative control. Indeed, the percentage of inhibition increased from 0 ± 1. 19 (MeOH negative control) to 29. 80 ± 2. 45% for the trolox concentration of 3. 91 mg. L -1 and the percentage of inhibition reached 60. 04 ± 3. 63% (Dunn's multiple comparison test, p < 0. 0001) for the trolox concentration of 7. 81 mg. L -1. IC 50 could be calculated for trolox and the concentration range tested (6. 29 mg. L -1 ).
FUNDING This study was supported by the project  BioSMART -Biobased smart packaging for enhanced preservation of food quality  -Grant agreement No. 745762 , funded by the  Biobased Industries Joint Undertaking (BBI-JU)  under the European Union's  Horizon 2020 Research and Innovation Programme. This study was also supported by the  University of Lille  through the  ALIBIOTECH program  funding administered by the  Hautsde-France Region.
Acknowledgements The authors would like to thank the following for their review of the survey questionnaire:  Lorna Clark ,  Target Malaria ,  Imperial College London ;  Richard Shaw ,  Centre for Agriculture and Bioscience International (CABI) ,  United Kingdom ;  Walther Enkerlin ,  Joint Food  and  Agriculture Organization of the United Nations (FAO) /  International Atomic Energy Agency (IAEA) Programme on Nuclear Techniques in Food and Agriculture ;  Junko Shimura ,  United Nations Convention on Biological Diversity ;  François Diaz ,  World Organisation for Animal Health (WOAH) ;  Christine Leon Rolez , WOAH;  Daniel Donachie , WOAH;  Glen Gifford , WOAH;  Antonino Caminiti ,  European Commission ;  Kenneth Loh ,  Australian Ministry of Agriculture ;  Rob Atkinson ,  Australian Ministry of Agriculture ;  Dominique Coutinot  (independent consultant);  Pierre Ehret ,  French Ministry of Agriculture ;  Xavier Langlet ,  French Ministry of Agriculture ;  Virginie Montagne ,  Bioline ;  Denise Bastron ,  French Agricultural Research Centre for International Development (CIRAD) ;  Marie-Laure Setier Rio ,  Entente Interdépartementale de Démoustication (EID) / Interdepartmental Agreement for Mosquito Control; and  Héloïse Lemoine ,  Institut de Recherche pour le Développement (IRD) / French National Research Institute for Sustainable Development.
Data analysis Sequence data were processed in QIIME2  (Bolyen et al. , 2019). Forward reads were trimmed at 280 bp and reverse reads at 200 bp. The trimmed reads were quality filtered, denoised, chimera-checked and clustered into amplicon sequence variants (ASVs) using dada2  (Callahan et al. , 2016). Taxonomic classification was performed using the SILVA database v132  (Quast et al. , 2013). ASVs identified as archaea, chloroplasts and mitochondria as well as singletons were removed. All ASVs present in the NTC were also removed, except for three ASVs corresponding to Carsonella and 'Ca. P. mali' (Table  S2 ). For alpha-and betadiversity analyses, each sample was rarefied to an even sampling depth of 9001 reads. Bacterial community alpha diversity was determined using the R package 'vegan'.
Bacterial species richness was calculated using the species richness estimator Chao1, bacterial diversity was determined using the Shannon and Simpson diversity indices, and bacterial community evenness was determined using Pielou's evenness index. Differences in alphadiversity indices between species were tested using the Kruskal-Wallis Rank Sum Test followed by pair-wise Dunn tests. Betadiversity was analysed using principal coordinates analysis (PCoA) based on Bray-Curtis and Jaccard distances in the R package 'phyloseq'  (McMurdie & Holmes, 2013). Shared or species-specific genera were visualized using the R package 'UpsetR'. Differences in the microbial community between vector and nonvector species were analysed using (i) Random Forest Analysis implemented in the Qiime1. 9 script supervise-d_learning.
py and (ii) the Indval function performing Dufrene-Legendre indicator species analysis from the R package 'indicspecies'. Both algorithms allow the identification of indicator taxa with enough discriminative power to distinguish bacterial communities. Only taxa with a p value < 0. 01 and present in at least 50% of individuals were retained as potential indicator taxa. Differences in microbiome composition depending on phytoplasma infection were tested using Adonis and Anosim analyses implemented in Qiime. Importantly, 'Ca. P. mali' reads were removed from the dataset when testing the impact of phytoplasma infection on microbiome composition. Potential differences due to the sampling year and sampling site were tested using Anosim, Adonis implemented in Qiime and the Envfit function from the R package 'vegan'.
These factors might have influenced microbiome composition to some extent. To address the potential influence of environmental differences between different sites and sampling years, we performed a PCoA based on Bray-Curtis distances on the rarefied ASV table and analysed whether microbiome composition was influenced by (a) psyllid species, (b) sampling year and (c) locality. This was done using Anosim, Adonis and Envfit tests on these three factors. Although both sampling year (Adonis: R 2 = 0. 1, p = 0. 0001, Anosim: R = 0. 29, p = 0. 0001, EnvFit: R 2 = 0. 36, p = 0. 001) and locality (Adonis: R 2 = 0. 47, p = 0. 0001, Anosim: R = À0. 01, p = 0. 53, EnvFit: R 2 = 0. 39, p = 0. 014) were significantly correlated with microbiome composition, we found that the factor 'psyllid species' had the best fit with the data (Adonis: R 2 = 0.
Impact of phytoplasma infection on microbiome composition We next investigated whether infection with 'Ca. P. mali' could be correlated with changes in microbiome composition in the two AP-vector species C. melanoneura and C. picta. To this end, all 'Ca. P. mali' reads were removed from the dataset, to eliminate the presence of phytoplasma as confounding factor. Adonis and Anosim analyses did not detect any significant differences between the microbiomes of infected versus uninfected individuals in either host species (C. melanoneura: Adonis: R 2 = 0. 04, p = 0. 74; Anosim: R = À0. 04, p = 0. 62/C. picta: Adonis: R 2 = 0. 10, p = 0. 21; Anosim: R = 0. 08, p = 0. 13). Indeed, for C. melanoneura, all but one of the infected individuals clustered together with the microbiomes of uninfected individuals, while the microbiomes of uninfected C.
Bozen-Bolzano and the Austrian Science Fund (to Hannes Schuler) and by the Autonomous Province of Trento and the Associazione Produttori Ortofrutticoli Trentini (project SCOPAZZI to Gianfranco Anfora, Omar Rota-Stabelli and Tobias Weil). The authors thank Dana Barthel and Cecilia Mittelberger for providing samples and Jennifer Morrow and Liliya Štarhov a Serbina as well as three anonymous reviewers for providing helpful comments and suggestions. Data were processed using the Vienna Scientific Cluster, Austria. Finally, the authors thank the Department of Innovation, Research and University of the Autonomous Province of Bozen/Bolzano for covering the open access publication costs.
Reference genome polishing Reads obtained from nanopore were filtered with Filtlong v0. 2. 1 (https://github. com/rrwick/Filtlong) using --min_mean_q 80 and --min_length 200. Cleaned reads were then aligned to the most recent version of the F. vesca genome v4. 0. a1  33  , with the annotation of F. vesca genome v4. 0. a2 downloaded from the Genome Database for Rosaceae (GDR) (https://www. rosaceae. org/species/fragaria_vesca/genome_v4. 0. a2), using minimap2 v2. 21  34  with parameters -aLx map-ont --MD -Y. The generated BAM file was then sorted and indexed with samtools v1. 11. We used mosdepth v0. 3. 1 to verify that coverage on chromosomic scaffolds was over 50 X. Sniffles v1. 0. 12a with parameters: -s 10 -r 1000 -q 20 --genotype -l 30 - Transposable elements annotation was carried out using the EDTA transposable element annotation pipeline v.
Processing and alignment of bisulfite-converted reads Sequencing data was analyzed by data collection software from read alignment to DNA methylation analysis: Epidiverse/wgbs pipeline  31. The pipeline included quality control using FastQC v. 0. 11. 9 (http://www. bioinformatics. babraham. ac. uk/projects/fastqc/) and Cutadapt v. 3. 5 (https://github. com/marcelm/cutadapt/). Genome mapping was performed using erne-bs5 v. S1  for more details). To calculate global methylation ratios, output files from wgbs pipeline were pre-filtered for a minimum coverage of 5 reads using awk command and only the common cytosine positions were kept among all the samples using bedtools 2. 28. 0. The data were tested for statistical significance with an unpaired Student's t-test. p < 0. 05 was selected as the point of minimal statistical significance in all the analyses.
Identification of differentially methylated regions (DMRs) First, bedGraph files from wgbs pipeline were pre-filtered for a minimum coverage of 5 reads using awk command. These output files were then used as input for the EpiDiverse/dmr bioinformatics analysis pipeline for non-model plant species to define DMRs 32 with default parameters (minimum coverage threshold 5; maximum q-value 0. 05; minimum differential methylation level 10%; 10 as minimum number of Cs; Minimum distance (bp) between Cs that are not to be considered as part of the same DMR is 146 bp). The pipeline uses metilene v. 0. 2. 6. 1 (https://www. bioinf. uni-leipzig. de/Software/metilene/) for pairwise comparison between groups and R-packages ggplot2 v. 3. 3. 5 and gplots v.
Differential gene expression analysis RNA-seq analyses were performed as previously described in  3. Briefly FastQC v. 0. 11. 9 and trimmomatic v. 0. 39 packages were used for quality control and trimming. Salmon v1. 4. 0 was used to map sequence reads to the Fragaria vesca reference transcript dataset. DESeq2 package was use for the quantitative differential gene transcription analysis at the European Galaxy platform with default parameters (Fig. S2 ).
Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of differentially methylated and expressed genes All methylated and differentially expressed genes were annotated based on GO and KEGG annotation downloaded from the Genome Database for Rosaceae (GDR) (https://www. rosaceae. org/species/fragaria_vesca/genome_v4. 0. a2). To better understand the potential function of the differentially methylated and expressed genes, GO functional classification of these genes was performed by AgriGOv1. 2  38. Genes and promoters were classified by genes contained hypo and hypermethylated DMRs. Similarly, all DEGs were extracted with p-adj< 0. 05. The GO slim library was used as reference GO reference type. Fisher's exact test p-values were calculated for over-representation of the differential methylated genes in all GO categories and Hochberg (FDR) as multi-test adjust method.
default parameters to generate the BAM files. Methylation calling and methylation bias correction was performed with Methyldackel v. 0. 6. //github. com/dpryan79/MethylDackel) with only uniquely-mapping reads. The pipeline used Nextflow v20. 07. 1 to run multitask in parallel. Because plant chloroplast DNA are not methylated 37 , reads originating from those sequenced were used to evaluate the bisulfite conversion rate. The pipeline is available at https://github. com/EpiDiverse/wgbs. An average of 82,771,701 reads (~ 50X coverage) were produced per sample, of which 81% mapped properly to the F. vesca genome. The average non-bisulfite conversion rate among the samples was 0. See Table.
Acknowledgements The  European Training Network "EpiDiverse " received funding from the  EU   Horizon 2020 program  under  Marie Skłodowska-Curie  grant agreement No  764965 ; The  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program ;  No 725701 "BUNGEE"  to  E. B. Funding  for open access charge: Agroscope institutional funding. This study was supported by  INRAE ,  Angers-Nantes , France and  Agroscope, Nyon-Switzerland. We would like to thank all the members of the  EpiDiverse consortium  (www. epidiverse. eu)and the  Crop Genome Dynamics research group  for invaluable support,  Katharina Jandrasits  for preparing WGBS libraries and  Dr. Marta Robertson  for the careful reading of the manuscript.
DATA AVAILABILITY The datasets generated and/or analyzed in this study are available in the Zenodo repository (DOI: 10. 5281/zenodo. 6141713). The sequencing data from this study have been submitted to European Nucleotide Archive (ENA, www. ebi. ac. uk/ena/, accessed on ERP135585) under the project PRJEB50996. The Bisulfite-sequencing raw read fastq accessions under ERR8684931-ERR8684954. The raw reads of the RNA-sequencing under the accessions: ERS11547432-ERS11547437 and ERS12247535-ERS12247537.
2. 1. 1 with 1 O 10 ( 211110 default parameters to generate the BAM files. Methylation calling and methylation bias correction was performed with Methyldackel v. 0. 6. //github. com/dpryan79/MethylDackel) with only uniquely-mapping reads. The pipeline used Nextflow v20. 07. 1 to run multitask in parallel. Because plant chloroplast DNA are not methylated 37 , reads originating from those sequenced were used to evaluate the bisulfite conversion rate. The pipeline is available at https://github. com/EpiDiverse/wgbs. An average of 82,771,701 reads (~ 50X coverage) were produced per sample, of which 81% mapped properly to the F. vesca genome. The average non-bisulfite conversion rate among the samples was 0. See Table.
pityocampae and O. kuvanae were determined using one-day-old P. ricini eggs. For this analysis, O. pityocampae and O. kuvanae females (8 and 7 females, respectively) that emerged from stored eggs were chosen randomly. Thirty eggs were supplied on a daily basis to each newly emerged female until the females died, and the parasitoids that emerged from the parasitized eggs were counted every day. The emergence rate, the development time and adult longevity were analysed using a general linear model (GLM). Percentage data were normalized using an arcsine transformation  (Zar, 1999). The means were compared with Duncan's test at a significance level of α = 0. 05  (McKenzie and Goldman, 2005; Minitab Release 14, SAS Institute, 2003).
In planta quantification of the B. cinerea Actin gene (Bc Actin), realized by real time PCR, showed a significant reduction in the level of the Bc Actin transcript in bacterized plants compared to the control ones at 24 and 72 hpi (Figure  6A ), indicating a protective effect conferred by PsJN. In parallel, the strain PsJN-gfp was still detected by PCR 72 h after B. cinerea challenge (data not shown). We observed also that PsJN showed high metabolic activity on glucose, fructose and malic-, tartaric-, citric-and succinic acids that are major sugars and organic acids of grapevine (Figure  7 ), which could give it a competitive advantage on inflorescences. Four days after the infection with B. cinerea, the development of the pathogen was delayed in bacterized inflorescences compared to those treated only with PBS (Figure  6B ).
Usually, the target to aplify is chosen based on the taxonomic information it may provide. Commonly, genes that encode for ribosomal RNA molecules are targeted. In this way, potentially all members of the community are represented (amplified) and after sequencing they can be identified (by comparison with available databases). After data analysis, the output is taxonomic information; the composition of the microbial community can be obtained. Amplicon sequencing has been extensively used to study the microbial ecology of foods, particularly the evolution of microbial populations during production of fermented foods but also to explore the microbial spoilage phenomena. 23 y metagenomics or shot gun sequencing it is possible to obtain information regarding the genomic content of the different members of the microbial community.
The SAFFI approach The SAFFI project aims to improve infant food microbial safety providing new knowledge to the food industry and competent authorities regarding the prevalence and behavior of pathogens. Omics approaches is the fil rouge in the effort to reach this aim. Two distinct but complementary objectives are being sought. The first objective is to perform a detailed survey of the microbiota of raw materials, intermediates, final products and importantly of the environment under real production conditions. For this purpose, an intensive sampling campaign has been implemented, covering different seasons throughout the year and focusing on collecting samples that can be correlated (in time and space). Also, relevant metadata are being collected (particularly focusing on physicochemical parameters of the samples).