dataesr/scientific-paragraphs-categorization huggingface dataset:
45 mm pore size, Whatman, Maidstone, UK) using a microarray robot (Sprint, Arrayjet, Roslin, UK). The printed arrays were incubated with several monoclonal Antibodies (mAbs) and Carbohydrate Binding Modules (CBMs) listed in Table  S1. The arrays were scanned at 2400 dots/inch with a CanoScan 880F, Soborg, Denmark). The software Array-Pro Analyzer 6. 3 was used to quantify probe signals (Media Cybernetics, Rockville, Maryland, USA), as described by  Gao, Fangel, Willats, Vivier, and Moore (2021). The raw data were normalized and converted into a heatmap for visualization. The relative abundances of different polymers epitopes are displayed on a scale of 0-100. The values are means from three biological repeats and four dilutions.
, 2021). Moreover, the extraction yields of FRV were not significantly different among FRESH, SUN, and CTR skins (57-60%). Indeed, although flavan-3-ols and low-molecular tannins can access more sites, their interactions with skin cell walls are All data are expressed as average value ± standard deviation (n = 3). Sign: ***, **, * and ns indicate significance at p < 0. 001, 0. 01, 0. 05 and not significant differences, respectively, among treatments (FRESH, SUN and CTR) and between the two different withering modalities (SUN, CTR) according to ANOVA( a ) and T-tests ( b ). Different Latin letters among the same raw indicate significant differences ( a ) according to Tukey-b test (p < 0. 05) for ANOVA. TPI: total phenolic index; TAn: total anthocyanins; CT: condensed tannins by methyl cellulose assay; FRV: flavanols reactive to vanillin; Dp-3-G: delphinidin-3-glucoside; Cy-3-G: cyanidin-3-Glucoside; Pt-3-G: petunidin-3-glucoside; Pn-3-G: peonidin-3-glucoside; Mv-3-G: malvidin-3-glucoside; -G: simple glucosides; Acetyl-G: acetylated glucosides; Cinnamoyl-G: cinnamoylated glucosided; EC: epicatechin; C: catechin; # : extraction yields (%) are calculated using the data expressed as mg/kg berries.
Descriptive and statistical analyses In total, 32 answers to our questionnaire were analyzed. Sociodemographic variables were quantitative and qualitative. The other variables extracted from the questionnaire were derived from 20 closeended and seven open-ended questions. For descriptive analysis of the variables, graphic quantifications were conducted in Excel 2016 using dynamic tables. XLSTAT 2021. 4. 1 (Addinsoft, 2022) was used for a series of statistical tests. The normality of the distribution of men's and women's age pyramids per country was assessed through a Shapiro-Wilk test (alpha = 0. 05), and was rejected in all cases except for female vendors from Benin (data not shown). Differences in distribution were compared using a non-parametric Kolmogorov-Smirnov test (alpha = 0. 05). Linear regression analysis between age and seniority was conducted to assess the correlation between the two variables.
, 2021b). Gendered structures could be important to consider in the bushmeat trade dynamics as, together with ethnic group selection, they may lead to differentiated responses to a same constraint. Whether gender influenced perceptions on the measures against the COVID-19 pandemic was not directly tested in our study but could be further investigated using our database and future surveys. One striking pattern was the significant, positive correlation between age and seniority among the bushmeat vendors. To our knowledge, this is the first time that a study demonstrates on a large scale (West and central Africa) that being a bushmeat trade vendor means engaging into a stable career. Such a pattern may be at least partly explained by the overall low level of education observed among vendors, low education -coupled with filiation and/or ethnicity-fostering the will to build longterm careers in the bushmeat trade  (Randolph et al.
Bushmeat vendors mostly received health-related information from television channels and social networks, and relied on the fixist assumption that if a species has never been a disease-vector, then it will never become one. In the case of awareness campaigns targeting the link between emerging infectious diseases and the consumption and trade of bushmeat, media will have to be better monitored in order to increase true knowledge (vs. fake information) on zoonotic risks and transmissions, notably by including the reasoning behind microbial evolution and host shift. Our study revealed a complex pattern of dynamics and perceptions across bushmeat markets and vendors from different West and central African countries. We conclude that understanding the dynamics of the bushmeat trade will require a multidisciplinary approach where socioeconomic, political, health and conservation aspects will be scrutinized together as part of long-term surveys where observation data are collected.
Acknowledgments Fieldwork in Côte d'Ivoire, Benin and Cameroon, and the co-authors PG, SGB, CD, ADM, and AA, were supported by  ANRS-MIE  ( AFRICoV -ANRS COV6 ). KJG and SGB were also supported by the  Ministère de l'Enseignement Supérieur et de la Recherche Scientifique (MESRS) of Côte d'Ivoire , as part of the " Trace-Brousse" research project (Franco-Ivorian Cooperation  within the framework of the  Second Contract of Debt Reduction-Development Contracts -C2Ds ) and  National Research Foundation (South Africa ) grant UID: 133615/Nr :  COV19200617532886. KJG received a PhD scholarship from " Appui à la Modernisation et à la Réforme des Universités et Grandes Ecoles de Côte d'Ivoire " ( AMRUGE-CI_N • 2 ). PG and AA received funding from  BUSH-RISK  ( FCT IC&DT 02/SAICT/2017-n • 032130 ). AA was supported by the  Strategic Funding   UIDB/04423/2020  and  UIDP/04423/2020.
Transcriptome Analysis Sequence reads were converted into FASTQ files. Reads under 300 bp or with a quality score under 9 were discarded. The remaining reads were aligned on the human GRCh38. p13 and C. trachomatis D/UW-3/CX strain transcriptome of reference (GeneBank assembly accession numbers GCA_000001405 and GCA_000008725 respectively) using minimap2  22, 23  version 2. 24. To quantify transcripts, the resulting alignments were given to Salmon version 1. 8. 0 24. To explore differentially expressed genes, replicates count data were used on DESeq2 version 1. 32. 0  25  in one single DEseq model. Gene set enrichment analysis with both upregulated and downregulated genes Log2FC>1. 5 or Log2FC<1. 5, respectively was performed using Enrichr, a web server enrichment analysis tool  26, 27  , and BioPlanet 2019 database for cellular and signaling pathway analysis  28.
Acknowledgments The authors would like to thank L2I team ( Julie Morin ,  Laetitia Bossevot ) in the IDMIT department for their help to perform the multiplex assay for cytokine detection, as well as members of the Life&Soft company ( Ségolène Diry ,  Léo d'Agata ,  Cassandra Gaspar  and  Eric Ginoux ) for the transcriptomic experiments. The authors would also like to thank  Dr Frank Follmann  ( SSI, Danemark ) for kindly providing the CT strain used for the in vitro infection of A2EN cells,  Drs Luc de Chaisemartin  ( UMR 996, Hôpital Bichat, France ) and  Julien Lemaître  ( IDMIT department, CEA, France ) for providing advice concerning the development of the neutrophil functional assays and  Dr Agathe Subtil  ( Institut Pasteur, Cellular Biology of Microbial infection unit ) for helpful discussion on CT infection and inhibition.
Data collection Pest data were retrieved from the Epiphyt database an epidemiological surveillance database held by the French Ministry of Agriculture. Apple scab and codling moths were monitored from 2015 to 2019 according to standardised protocols based on observations of pests or damage to different plant organs from one to several times each year (Table  S1 ). Data are available at the plot level. The observation plots are not precisely located but their municipality is known. For each of the two main pests i. e. apple scab and the codling moth data are available from two observation protocols each corresponding to a different period of the pest's life cycle. Plots were distributed all over France except for the plots monitored for adult codling moths which were located only in western France (Fig.
Apple scab records The rst two variables that we retrieved from the database are the presence/absence of shoot damage in the plot and the date of rst occurrence of shoot damage expressed as the number of days from the 1st of January. The data came from a single protocol based on the visual evaluation (presence or absence of damage) of 500 shoots per orchard at each observation date from April to June. On average 113 ± 34 (standard deviation, SD) plots were monitored each year in a total of 169 municipalities. The date of rst occurrence was only recorded when damage was present which was the case for 40 ± 15 (SD) plots each year on average (Table  1 ). These two variables are associated with early pest abundance. The third retrieved variable was the presence/absence of fruit damage in the plot in June.
Weather data To describe the weather context of each plot each year we used the interpolated meteorological dataset of Météo-France (Safran). This dataset contains estimated weather information for France based on 8 km x 8 km grid cells. Many weather variables are available among which we retrieved daily relative humidity rainfall and temperature (average and minimum values). For each year from 2015 to 2019 to associate the pest data with weather variables the centroid of the municipality in which each Epiphyt plot was located was associated with the corresponding grid cell. Weather variables used in statistical models seeking to explain pest occurrence were selected based on their expected effect on the pest life cycle. For both pests the following variables were thus selected: proportion of days with rainfall average relative humidity and average temperature.
Landscape context The landscape was characterised at the municipality level. These administrative areas covered 2776 ± 3242 ha on average (mean ± SD). Maps were created by combining; (i) the French parcel identi cation system (RPG a geographic database for registering farmers' parcels under the Common Agricultural Policy) which provided information on the type of crop or grassland (temporary or permanent) in each parcel from 2015 to 2019; and (ii) BD TOPO® (v2 2017 IGN, Institut géographique national) which provided information on woodland orchards and vineyards. Land cover information from these two landscape databases was combined with a priority on the RPG which is lled in every year (R package alm,  Allart et al. 2021 ). Then the proportion of area covered with orchards woodland and grassland as well as orchard fragmentation (i.
2017). Considering data at the national level made it possible to explore a large gradient of weather conditions and landscapes. However, because this dataset was not originally compiled for scienti c purposes there were a few limitations. First we had to strongly simplify the dataset (e. g. modelling the probability of occurrence rather than abundance of pests) to account for differences in data monitoring along years or regions. This likely reduced the ability of our analysis to detect landscape effects but increased its robustness. Second landscape characteristics may covary with weather conditions. To address this possible bias, we included weather variables in the models. These variables as expected impacted pests particularly their date of rst occurrence but none was found to strongly covary with landscape variables (Figure  S1  and low model variance in ation factors) indicating that we were able to disentangle weather and landscape effects.
Last we could have overlooked some regional features that impacted pest populations (e. g. different farm structures or apple cultivars). However residual variations in our models were homogeneous among the French regions suggesting that this was not the case except for the date of rst scab occurrence for which we found no landscape effect (Figure  S4 ). Despite these limitations we acknowledge that such large datasets initially built for advising farmers also contain precious information that nicely complete more classic information provided by empirical studies usually performed in landscape ecology (e. g. several elds surveyed along landscape gradients). Analysing such datasets makes it possible to explore the variability of landscape-scale effects in multiple climatic contexts and appears to be a step forwards in the direction of building predictive models to anticipate pest outbreaks.
Acknowledgments: the authors would like to thank the  French Ministry of Agriculture Direction générale de l'alimentation Bureau Santé du végétal  for providing the epiphyt data and  l'Agence  bio for the data related to organic agriculture. This research and LE's PhD was funded by the  French Ministry of Agriculture  ( ARPHY ECOPHYTO  project) and the  INRAE Metabio  metaprogramme.
Genetic diversity and phylogenetic analyses Partial and complete genome sequences of MSV obtained during this study were compared to the 885 sequences described in  (Kraberger et al. 2017). Multiple sequence alignments were performed using MUSCLE  (Edgar 2004 ) implemented in SEAVIEW v4. 7  (Gouy et al. 2010). Sequence pairwise identities of a selection of MSV sequences were calculated with SDT v1. 2  (Muhire et al. 2014). Maximum likelihood phylogenetic trees were reconstructed with SEAVIEW using the best-fitted nucleotide substitution models (Tamura 3-parameter+G and GTR+G+I for the partial and the complete genome datasets, respectively) determined with MEGAX  (Kumar et al. 2018 ) and 100 bootstrap replications. Phylogenetic trees were drawn using FigTree v1. 3. 1 (http://tree. bio. ed. ac.
uk/software/figtree/). The genetic diversity of partial genome dataset was estimated using the best-fitted nucleotide substitution model, with standard errors of each measure based on 100 replicate bootstraps, as implemented in MEGAX. Genetic differentiation of MSV populations according fields, sites, years and rice production systems were estimated by analysis of molecular variance (AMOVA) obtained by performing 1000 permutations as implemented in Arlequin v5. 3. 1. 2  (Excoffier et al. 2005). Recombination signals in MSV from Burkina Faso were identified using the seven algorithms implemented in RDP v4. 97  (Martin et al. 2015). Recombination events detected by at least 5 methods and with P-values below 10 -5 were considered.
e. 99. 6% and 100. 0% genetic identity, respectively). The 19 sequences obtained during this study were compared to MSV complete genome dataset gathering 885 sequences from all the MSV strains, including 8 MSV-G sequences from West Africa (Nigeria and Mali) and Gran Canaria on wild grasses and 695 MSV-A sequences from all over Africa with 5 MSV-A collected in 2008 in Burkina Faso on maize  (Kraberger et al. 2017). Analyses to detect recombination events on this sequence dataset demonstrated that none of the 19 complete genome sequences obtained during this study were recombinant. Phylogenetic analyses showed that the MSV-G isolates identified during this study were closely genetically related (more than 98. 9% of genetic identity; Figure  2 ). As previously demonstrated with partial genome analyses, no genetic differentiation was observed between MSV collected in rice or other Poaceae (FST(Rice/Poaceae) = 0.
Correlation between symptoms and MSV accumulation in systemic leaves was validated by PCR and Sanger sequencing (data not shown). Unfortunately, most likely due to the versatility of the agroinoculation process on rice, only few cases of successful infections have been observed, and not all agroinoculation modalities (i. e. infectious clone vs. plant species) succeed during the same experiment. Nevertheless, by clumping the results of the two experiments, the MSV agroinoculation led to successful infection in 46 out of 770 plants (i. e. 6. 0%;   Supp6 ). Twenty-eight days after agroinoculation, we measured the height and the number of leaves of MSV-infected and non-infected plants. We noticed that the earlier the emergence of symptoms, the greater was the impact on plant growth, leading to high between-plant heterogeneity in the dataset.
ACKNOWLEDGEMENT This work was performed thanks to the facilities of the "International joint Laboratory LMI PathoBios: Observatory of plant pathogens in West Africa: biodiversity and biosafety" (www. pathobios. com; twitter. com/PathoBios). We are very grateful to Sylvain Zougrana, Yacouba Kone, Edouard Kabore, Moustapha Koala, Manaka Douanio, Amadou Diallo, Fabrice Nikiema, Bouda Zakaria, Ouedraogo Alassane, Fatoumata Gnacko, Daouda Hema, Drissa Tou, Traoré Momouni, Dabire Roméo, Martial Kabore and Noël Ouattara for their contributions to the fieldwork in Burkina Faso. We thank the rice farmers from Badala, Bama, Senzon, Banzon, Tengrela and Karfiguela for their kind collaboration. We also thank Michel Peterschmitt, Cica Urbino and Martine Granier for the valuable discussions and for kindly providing us the infectious clone pBC-KS::MSV-A(R2), and Jamel Aribi for plant care in IRD greenhouses.
Funding:  French National Research Agency «Investissements d'avenir» program  ( ANR-10-LABX-001-01 ),  Agropolis Fondation  ( ANR-16-IDEX-006 ),  French National Research Agency   "young researchers" program  ( ANR-20-CE35-0008-01 ),  CGIAR   Research Program   on Rice Agri-food Systems (RICE) ,  Cooperation  and cultural action department of the  French Embassy (SCAC) in Burkina Faso.
We hypothesize that relict stands have lower genetic variation within versus higher genetic variation among them, assuming that the artificial introduction of genetic variants increases genetic variation within and reduces genetic differentiation among populations. Second, we compare intraspecific genetic structure at chloroplast and nuclear marker loci of relict and managed stands (old and young). We raise the hypothesis that seed transfer might have increased genetic diversity within and reduced genetic structure among managed populations, especially the young ones. Third, we utilize all datasets (nuclear and chloroplast DNA markers) to distinguish recent contacts from sympatric occurrences over several generations in mixed oak stands. Here, we expect low genetic introgression and degree of haplotype share in the case of recent contact and vice versa in the case of sympatric occurrences over several generations.
Study populations, species and sampling design To address our research questions, we performed analyses based on genotypic data of 5067 individuals of Quercus petraea, Q. robur or Q. pubescens from 154 sites in Central Europe. 2489 individuals from oak stands, henceforth called AQUAREL population set, were sampled between the years 2019 and 2021 in the framework of the AQUAREL project (Adaptation of Quercus on Relict Sites;  Neophytou et al. , 2020). The remaining individuals had been sampled either in the framework of the Interreg-IV project QREG (Regeneration of oaks in the Upper Rhine lowlands) between the years 2009 and 2012 (2482 trees from 88 stands) or were part of the first author's doctoral thesis (Q_OLD population set, 96 individuals from two stands;  Neophytou et al. , 2010). The 88 populations of the QREG project were further subdivided into i) 76 populations genotyped at 21 nuclear (nSSR) and 10 chloroplast (cpSSR) microsatellite loci, hereafter called QREG_R population set (R from the term reference, used in the QREG project for these stands) and ii) populations genotyped at 6 nuclear (nSSR) and 10 chloroplast (cpSSR) microsatellite loci, which are referred to as QREG_M (M from the term monitoring, as these were stands used for monitoring of natural regeneration).
To support our hypothesis of relict character, we inspected various data including forest habitat and forest site mapping, forest management plans and vegetation documentation in case of nature reserves. • "Managed old", originating from former coppice or coppice-withstandards forests converted into high forests. They were established before the Second World War. Growing on productive sites, they are managed for timber production. At the time of sampling, these populations were at understory reinitiation or old-growth phase. • "Managed young", established after the Second World War. They mainly originate from artificial plantation, whereas the information  about the seed origin is generally lacking. They are currently managed for timber production. In Table  1 , the number of populations per category and population set are summarized.
Nuclear microsatellite genotyping All individuals of the AQUAREL population set were genotyped at 22 nuclear microsatellite (nSSR) loci. The following loci were amplified by means of PCR: (a) EST-SSRs (derived from expressed sequence tags; ESTs): PIE020, PIE102, PIE152, PIE215, PIE223, PIE227, PIE242, PIE243, PIE267 and PIE271  (Durand et al. , 2010) ; (b) gSSRs (genomic, presumably neutral SSRs): MSQ13 from  Dow et al. (1995) ; QpZAG15, QpZAG104 and QpZAG110 from  Steinkellner et al. (1997) , as well as QrZAG5b, QrZAG7, QrZAG20, QrZAG65, QrZAG87, QrZAG11, QrZAG96 and QrZAG112 from  Kampfer et al. (1998). For the QREG_R population set, existing genotypic data of 21 nSSR loci were used  (Neophytou, 2014a; Neophytou, 2014b). Eleven commonly genotyped loci could be standardized between AQUAREL and QREG_R and were, thus, included in the joint AQUAREL-QREG_R nSSR dataset.
Taxonomic identification by means of Bayesian cluster analysis Within each of the AQUAREL, QREG_R, QREG_M and Q_OLD nSSR datasets (Table  1 ), a Bayesian clustering analysis was run to assign individuals to each species group and to identify potential introgression. For this purpose, we applied the STRUCTURE method  (Falush et al. , 2007; Pritchard et al. , 2000). For the published nSSR dataset of the QREG_R population set, we used the STRUCTURE results from  Neophytou (2014a). For all other datasets (nSSRs for AQUAREL, QREG_M, Q_OLD), we carried out new STRUCTURE runs. For this purpose, we used the application STRAUTO v1. 0  (Chhatre and Emerson, 2017)  in conjunction with GNU parallel  (Tange, 2011) , which allowed automation and parallelization of STRUCTURE runs on a 40-core standalone computer, with following options: 50,000 burn-in, 100,000 MCMC iterations, admixture model and correlated allele frequencies.
We set consecutive values of K assumed clusters from 1 to 10, performing 20 independent runs for each K-value. We further processed the data in the online platform CLUMPAK  (Kopelman et al. , 2015) , which implements the CLUMPP method  (Jakobsson and Rosenberg, 2007)  in order to identify different cluster modes (in case of multimodality) and averaged the membership proportions of individuals to clusters across runs. To address the question of the most meaningful number of K inferred clusters, we employed i) P(X|K) method of  Pritchard et al. (2000) , ii) the ΔΚ-method of  Evanno et al. (2005) , iii) the parsimony index method of Wang (2019) by using the software KFinder  (Wang, 2019). To assist species identification, we used reference samples of known taxonomic origin (details in Supplementary Material, Section SM3).
Haplotypic diversity Haplotypes and phylogenetic relationships among them were resolved based on Neophytou and Michiels (2013) (details in Supplementary Material, Section SM4). In order to compare the haplotypic diversity within and across stands, we first grouped individuals and populations according to their species identity. Then, we calculated the haplotypic diversity within populations (h S ) and the genetic differentiation for unordered alleles (G ST ) for all relict, managed old and young stands, as well as separately within each species and in mixed populations with the software PermutCpSSR 2. 0  (Burban et al. , 1999; Pons and Petit, 1996). For further regional and species-specific comparisons, we repeated the same analyses for the populations situated in the Upper Rhine Valley (Table  S1 ).