dataesr/scientific-paragraphs-categorization huggingface dataset:
Thus, although obtaining a hamstervirulent phenotype should not be taken for granted, there is a potential for rapidly modifying other YF strains in the prospect of in vivo studies. Of note, other YFV strains (Jimenez p0 and p10) have been reported to be virulent in hamsters  36. Unfortunately, the only available sequence (Jimenez p0, GenBank AN: AY540480) is partial and does not include the region of interest in the E gene. Further analysis of Jimenez p0 and p10 sequences should prove valuable to determine if these strains share some of the determinants of YFV virulence in hamsters described in our study. Finally, the hamster-virulent strains described in this study are a tangible example of how a few mutations in the genome of flaviviruses can impact replicative fitness and virulence in an unusual host.
Winemaking and Samples All winemaking procedures were carried out in triplicate for both grape varieties. After manual destemming and crushing, musts (characteristics in Appendix A) were inoculated with yeasts (OKAY, 20 g•hL -1 ) and supplemented with DAP (di-ammonium phosphate, 150 mg•L -1 ). Fermentations were performed using 1 L "French Press" coffee plungers (small-lot fermentation adapted from the Australian Wine Research Institute (AWRI) method  [22]  at 22 • C over eight days. Cap management was carried out every day during AF by submerging pomace with the plunger. The density was monitored with a portable densimeter (DMA 35, Anton Paar, Les Ulis, France). At the end of AF, wine was pressed and the classical wine chemical parameters were determined (data not shown). Seven samples were taken during fermentation (Figure  5 ).
In order to obtain a global view of all bovine miRNAs expressed in different types of T. annulata-infected leukocytes and how they might contribute to parasite-induced leukocyte transformation and tumour dissemination we determined the miRNomes of infected macrophages (both virulent and attenuated) and infected B cells (TBL20 and TBL3) versus noninfected B cells (BL20 and BL3). Defining the miRNomes of different types of T. annulatatransformed leukocytes allowed us to observe that infection alters the expression of many known miRNAs. However, to identify miRNAs implicated in Theileria-transformed leukocyte dissemination, as opposed to immortalisation, we queried our datasets only for miRNAs whose expression is activated by infection, but dampened upon loss of transformed macrophage dissemination.
Identification of differentially expressed (DE) miRNAs To identify miRNAs whose expression is altered upon infection by T. annulata we determined the miRNomes of both infected versus non-infected B cells and virulent versus attenuated macrophages. The comparison between T. annulata-infected TBL20 B lymphocytes and their uninfected counterparts revealed potential involvement of many miRNAs in the transformation of the host cells, as reflected by the changes in their expression levels (Fig  1A ). We analyzed the differential expression (DE) of our miRNA sequencing data using DESeq2  [34]. 115 miRNAs are differentially expressed in TBL20 as compared to BL20 with a cutoff of adjusted p value < 0. 05. In order to increase the confidence level and limit our analysis to the most significantly DE miRNAs we used a second pipeline, baySeq  [35].
miRNA seq data analysis Raw miRNAseq reads are quality checked by fastqc  [54]. mirTools2. 0 [55]  pipeline is used for trimming ("Adaptor_trim. pl" script) and downstream analysis of known miRNAs. Sequencing reads are aligned to the bosTau7 genome using SOAP  [56]. Annotations are added from miR-Base 21  [57]  and Rfam  [58]  databases. The differential expression of each known miRNAs from their absolute read counts are analysed by DESeq2  [34]. Differential expression is tested based on the negative Binomial distribution and miRNAs with adjusted p-value < 0. 05 considered as statistically significant.
Acknowledgments We thank  Sarah Gallois-Montbrun  ( Institut Cochin, Paris ) for gift of the psiCHECK-2 vector. We thank  Brian Shiels  ( University of Glasgow ) for providing us the  BL20/TBL20  cells. We also thank members of the  Bioscience Core Laboratory (BCL) in KAUST  for producing the raw sequencing datasets and Academic Writing Services in KAUST for drawing the modified scheme of Bockelmann presented in Fig 7.
3 µm to measure the 3D distance between three FISH signals, whereas double Pc/Ph immunolabelling coupled with single FISH (Fig. 5g , h and Supplementary Fig. 11g, h ) were acquired with a pixel size of 94 nm and a z-step of 0. 3 µm. RNA FISH experiments were quantified using ImageJ software. First, a Gaussian filter with a radius of 1 was applied to reduce noise. Z-projection was then calculated using maximum intensity projection. Second, areas corresponding to the PSs of interest were manually drawn. We then measured their surface area and counted the number of FISH spots using the option "find maxima". Distances between DNA FISH signals were measured using Volocity software (Perkin Elmer, Coventry). A fine (3 × 3 × 3 pixels) filter was applied to reduce noise, and the three FISH signals were segmented using the threshold option "get objects".
To quantify the enrichment of Pc/Ph in nuclear foci (Supplementary Fig. 11 ), we used Image-J software to perform a Gaussian filter (radius = 1) and a 3 µmthick maximum intensity projection. Then, we computed local maxima and average nuclear intensity in 25 nuclei located in the head. Similarly, we used Image-J software to quantify Pc/Ph enrichment at Scr and abdA loci (Fig. 5g, h ). We performed a Gaussian filter (radius = 1) and used the FISH channel to compute local maximum corresponding to FISH spots. Then, we measured the pixel intensity in the channel corresponding to Ph or Pc immunolabelling at the local maxima previously identified. We divided the later intensity with the average nuclear intensity to calculate Ph or Pc enrichments. Finally, we used Mann-Whitney U-test to compare distributions obtained in two different conditions.
Acknowledgements We acknowledge the  Drosophila  and imaging MRI facilities, member of the  National Infrastructure France-BioImaging  supported by the  French National Research Agency  ( ANR-10-INBS-04 , "Investments for the future"). We thank  Satish Sati  and  Frédéric Bantignies  for critical reading of the manuscript. G. C. Research in the laboratory of G. C. was supported by grants from the  European Research Council  ( ERC-2008-AdG no. 232947 ), the  CNRS , the  FP7 European Network of Excellence EpiGeneSys , the  European Union 's  Horizon 2020 research and innovation program  under grant agreement No. 676556  (MuG), the  Agence Nationale de la Recherche  (N. ANR-15-CE12-006-01 ), the  Fondation pour la Recherche Médicale  (Grant no. DEI20151234396 ), the  French National Cancer Institute (INCa , Project no.
Genome sequencing and phylogeny In order to obtain genomic data representative of the diversity of Xanthomonas strains responsible for CBB, we produced whole genome sequences for 17 strains from the four genetic lineages of X. citri pv. fuscans and X. phaseoli pv. phaseoli that affect beans. In addition, we sequenced two strains of X. citri pv. mangiferaeindicae, one strain of X. citri pv. anacardii, three strains of X. oryzae pv. oryzicola, and used 51 other publically available Xanthomonas genomes for a total of 72 whole genome sequences (Table  1 ). Stenotrophomonas maltophilia strain R551-3 and Xylella fastidiosa strains 9a5c and Temecula1 were used as outgroups for further analyses  [19] [20] [21]. Annotation revealed from 3209 to 5405 coding sequences (CDS) per Xanthomonas genome (Additional file 1).
Another observation was that the chromosome contained regions with various 24-mers shared by the NF1 lineage and any of the fuscans, NF2 or NF3 lineages (in green, blue or black in Fig. 3 , respectively). This suggests that the regions shared by the NF1 and the other lineages diverged since the split between the NF2, NF3 and fuscans lineages. By contrast to what was observed for the chromosome, all specific 24-mers found in plasmid a were simultaneously shared  not found in corresponding analysis na not applicable due to the absence of corresponding CDS in outgroup a identifiers correponding to the numbers shown in Fig. 3  b accession numbers for strain CFBP6456 c genes with 100% identity over 95% of their length according to Aritua et al. [17]  by the four genetic lineages of strains responsible for CBB (in purple in Fig.
This can be compared to our knowledge on the origin of the lineages and their genetic diversity. The causal agent of CBB was first isolated and identified by Smith in 1897  [30]  as a yellow pigmented strain, later shown as belonging to the NF1 lineage. Burkholder later isolated the first fuscous strains from beans grown in Switzerland in 1924  [31]. However there are no data to document a putative pre-existence of one, the other, or both types of strains prior to their first identifications. The genetic diversity of the yellow and fuscous strains was revealed by various methods. Amplified or restriction fragment length polymorphism analyses  [16, [32] [33] [34] , amplified polymorphic DNA fragments  [32, 35] , pulse field gel electrophoresis  [33] , and multilocus sequence analysis  [23]  all revealed that both types of strains are more or less equivalent in terms of genetic diversity.
Annotation and phylogenomic analyses Structural and functional annotation of whole genome assemblies was performed using the automated pipeline Eugene-PP (version 1. 2)  [63] , using SWISS-PROT as protein database and training protein database (http:// www. uniprot. org/). Additional functional annotation of all predicted CDS was performed with InterProScan (version 4)  [64]. A presence/absence matrix of ortholog groups was constructed using OrthoMCL (version 2. 0) on amino acid sequences from all predicted CDS at an inflation index of 1. 5  [65]. This matrix was then used for defining core and pan genomes. Phylogenetic trees were constructed using CVTree (version 4. 2)  [66]  using the aminoacid sequences of all predicted CDS from the 75 genomes used in this study. CDS gains and losses were analysed using the Most Parcimonious Reconstruction function from the APE package (version 3.
2)  [67]  to search for the most parsimonious succession of events explaining the repertoire of ortholog groups at each node of the phylogenetic tree. Searching for genes monophyletic for X. citri pv. fuscans and X. phaseoli pv. phaseoli strains A phylogenetic approach was used to search for genes for which strains from X. citri pv. fuscans and X. phaseoli pv. phaseoli form a monophyletic group. For this we selected 3202 CDS using an R script to search the orthology matrix for genes that were present in all X. citri pv. fuscans and X. phaseoli pv. phaseoli strains and in at least one strain from Rep-PCR group 9. 2 or 9. 4  [11]  plus one another strain from Rep-PCR group 9. 5 or 9. 6, in order to avoid getting trees were X. citri pv. fuscans and X. phaseoli pv. phaseoli appear monophyletic due to a lack of correspondig genes in the strains inbetween.
CDS were aligned using MAFFT (version 7) with L-INS-I strategy  [68]. Neighbour-joining trees were constructed using APE (version 3. 2) under the Kimura 80 model  [67]. CDS monophyletic for all X. citri pv. fuscans and X. phaseoli pv. phaseoli strains, or alternatively for the NF1 and another lineage (i. e. NF2, NF3, or fuscans), were retrieved using the APE package (version 3. 2)  [67]. Searching for genes containing k-mers specific for X. citri pv. fuscans and X. phaseoli pv. phaseoli strains A k-mer-based approach was used to search for genes containing short specific sequences present in all strains from X. citri pv. fuscans and X. phaseoli pv. phaseoli but absent in other strains. For this, we used SkIf (version 1. 0)  [25]  with a k-mer size of 24 (or 24-mer), and using X. citri pv. fuscans strain 4834-R genome as reference.
24 -mers Recombinant HGT Atypical GC% Nonsynonymous sites Aritua et al. [17]  Overview of the 115 genes putatively involved in bean specificity (Continued)Identifier a Accession number bPredicted function Gene Gained Monophyletic 24-mers Recombinant HGT Atypical GC% Nonsynonymous sites Aritua et al. [17]  Overview of the 115 genes putatively involved in bean specificity (Continued)Identifier a Accession number bPredicted function Gene Gained Monophyletic 24-mers Recombinant HGT Atypical GC% Nonsynonymous sites Aritua et al. [17]  Overview of the 115 genes putatively involved in bean specificity (Continued)Identifier a Accession number bPredicted function Gene Gained Monophyletic 24-mers Recombinant HGT Atypical GC% Nonsynonymous sites Aritua et al. [17].
Acknowledgements The authors thank  Sébastien Carrère  for his help on genome annotations and  Laurent D. Noël  for sharing the genome sequence of strain CFBP6865. The authors also thank the  French Network on Xanthomonads (FNX)  (https:// www. reseau-xantho. org/)for recurrent scientific exchanges on Xanthomonas. Authors benefited from interactions promoted by  COST Action FA 1208  (https://www. cost-sustain. org). We thank the  CIRM-CFBP (Beaucouzé, France)  for strain preservation and supply.
Funding The research leading to these results has received grants from the  Genoscope  ( 3X 154/AP2006-2007 ,  XANTHOMICS 18/AP2009-2010 ) and the  French National Research Agency  ( XANTHOMIX ANR-2010-GENM-013-02 ). LSG was funded by a postdoctoral grant from the  XANTHOMIX ANR  project, and another postdoctoral grant from Angers-Loire Metropole, France. MR was funded by a PhD grant from  Angers-Loire Metropole, France. This work was supported by  France Génomique National  infrastructure, funded as part of " Investissement d'avenir" program  managed by  Agence Nationale pour la Recherche  (contrat  ANR-10-INBS-0009 ).
Materials and methods As institutionalization concerns the links between actors inside a network, we have identified these actors using different data sources. For the research actors, using the bibliographical database Scopus, we extracted papers containing keywords on agroecology and then we extracted a sub-corpus of European organizations (e. g. , research labs, institutes, experiment stations) whose name contains these keywords in European languages1 using the Scopus field AFFILORG. While there are already many bibliographical studies on agroecology that analyze the textual content of bibliographical records  (Brym et al. 2016 , Wezel et al. 2009 ), our approach is original in its focus on organizations that put agroecology in their name, which we interpret as a deliberate act of institutionalization.
This approach also allows us to reveal organizations that have institutionalized agroecology, only to abandon the use some time later, shining the light on historical dynamics. After identifying the actors (i. e. , organizations), we analyze their collaborations on joint publications using the CorTexT Manager software2. As there is no equivalent source for training actors, we built a database through Google queries of websites to identify the definitions of agroecology publicly posted and the syllabi used (including the references). The category 'training actors' is made up of a variety of organizations whose actions are more or less dedicated to education and training about theoretical or practical aspects of agroecology (including organic agriculture, permaculture). Additional information found on their webpages enables the identification of their collaborators.
This difference in the availability and type of data for the two groups of actors explains, in part, our decision to present their results separately. As institutionalization is also about discourse production, actors' textual productions reflect institutionalization patterns that express their « reflexive distance » from their actions  (Musson et al. 2016). In methodological terms, we use co-word analysis  (Callon et al. 1983) , also implemented in the CorText Manager software  (Chavalarias et al. 2013). Co-word analysis, that is the extraction and visualization of word co-occurrence, is a way to identify the main themes, and semantics, inside the actors' discourses that we gathered in the first step (title and abstract Scopus records and training webpages). CorTexT manager automates the identification of statistically specific words associated with their authors (using a chi 2 test).
A first interpretation could come from a bias introduced through Scopus, the main database for our research results. Indeed, Scopus only lists publications of international scientific journals, usually published in English, French or Spanish, and many Italian social science journals, for example, are not included in Scopus. However, the representation of certain countries in these scientific journals is less or even absent (especially for Eastern European countries). A second interpretation of this phenomenon stems from a bias in methodology. Since we considered only those involved in research using the terms "agroecology" or "agro-ecology" in first the publication abstracts and keywords, and then in the official names of their research organizations, it is possible that some research institutes or units do not formally identify with agroecology.
On the other hand, from 2011 onwards, the number of publications continues to grow while the number of players stabilizes. An important feature of this data is that the research actors do not appear to exist until 1971. Before 1980, Scopus detects only two actors: Nikola Pushkarov Institute of Soil Science and Agro-ecology in Bulgaria and Augustenberg Agricultural Technology Centre in Germany. However, a brief history of the Nikola Pushkarov Institute indicates that the first Bulgarian Agroecology Department was founded at the Bulgarian National Agricultural Centre in 1911  (Teoharov 2016 ), now named "Institute of Soil Science, Agrotechnologies and Plant Protection "Nikola Pushkarov". The temporal limit therefore seems to be a bias of the Scopus tool, possibly linked to the non-digitalization of older publications and/or the use of languages of writing not included in the tool.
Some also offer specializations in more general Masters programs on sustainable agri-food systems. Montpellier SupAgro also offers an online course (MOOC), which is gradually spreading in English and Spanish. The Many actors in practical training are not represented in this study. Indeed, data collection for this study was not easy due to a lack of resources or centralized databases that can be consulted online and because of increasing competition between actors (i. e. , fewer publicly available resources shared outside of existing networks). This lack of information should not, however, obscure the increasing presence of these actors in the agroecological training landscape in Europe. Let us remember that the training actors work at a different speed relative to academic organizations. While academic training is easy to spot, with large European universities attracting large numbers of students and rapidly adapting to social and technological trends; actors in practical training attract smaller physical audiences (although their online videos have greater reach) by responding to more pragmatic needs for practical know-how, while at the same time engaging in a more social and political discourse.
This hypothesis is confirmed to the extent that the two actors who have published the most articles (the Agroecology Department of Aarhus and the Agroecology UMR of Dijon) tend to involve several members of their teams in the same scientific contribution. The significant size and pooling of expertise within their research teams is thus an explanatory reason: the Agroecology Department of Aarhus has 270 employees and 6 major areas of study; and the Agroecology UMR of Dijon employs 250 permanent staff for 4 major areas of study. Finally, the collaborations uncovered in the database are not limited to a European research area or to links between actors who identify themselves with agroecology. This suggests that while we do see a form of institutionalization with the formation of a core research organization focused mainly on agroecology, this is still very limited within the European research area and these core teams of researchers are often collaborating outside of institutionalized settings to inform their knowledge production.
The semantic analysis of the publications included in the database of research actors and containing the terms "agroecology" or "agro-ecology" in their titles or abstract highlights these specific notions. However, the sub-corpus constituted by these publications comprises only 76 publications (1. 3%) and 10 actors (18%). This thus demonstrates that institutional affiliation with agroecology covers a broad thematic spectrum and is not contained only to articles that focus specifically on a stabilized concept of 'agroecology'. Nevertheless, this spectrum remains below the programmatic ambition expressed in reference texts  (Dalgaard et al. , 2003; Francis et al. , 2003). In the realm of research discourse, as revealed by this analysis, agroecology remains a scientific question of interactions among agroecological sub-systems.
Remerciements Je tiens à remercier les relecteurs. tricesde la revue, ainsi que  Claire Lamine et Florian Charvolin qui dirigent ma thèse et les collègues de mes deux laboratoires de rattachement qui m'ont accompagnée dans l'amélioration de la version initiale de ce texte. Cette recherche a été réalisée dans le cadre du projet  Européen Rethink  ( Programme Ruragri Eranet ), avec la contribution financière du CASDAR (Compte.
No differences in filament formation were microscopically observed between conidia alone incubated with or without the inhibitors. Nonetheless, when comparing filament formation in the presence of cells, we confirmed that filament formation was decreased in the presence of epithelial cells and interestingly, we observed that this decrease was inhibited by the presence of PI3-kinase inhibitors (Fig. 4A ) and not by p38 MAPK and ERK1/2 inhibitors (data not shown). These observations were quantified and confirmed using the galactomannan amount measured as a % of the control (fungus growth without cells and without kinase inhibitors). We showed that p38 MAPK, ERK1/2, and PI3-kinase inhibitors (LY294002 and wortmannin) did not exert a significant direct inhibitory effect on galactomannan release (103.
We determined the percentage of extracellular germinated conidia and intracellular germinated conidia, relative to the total number of conidia, in the absence or presence of LY294002. Statistics. Data are presented as the mean value ± standard error of mean (SEM). Statistic tests were performed using Prism 6. 00 software (GraphPad Software, La Jolla, CA). Differences between two groups were tested using the unpaired t-test. ANOVA was performed to compare quantitative variables across groups. To correct for multiple testing, we used the Bonferroni's method (comparisons of selected groups); p < 0. 05 was considered statistically significant. Correlation between microscopic score and galactomannan measurement was obtained by using the Pearson's test.
Acknowledgments The authors acknowledge the financial support of the  Future and Emerging Technologies (FET) Program  within the  Seventh Framework Program  ( FP7 ) for Research of the  European Commission , under the  FET-Proactive TOPDRIM  Grant No. FP7-ICT-318121. S. O. thanks  Stefano Lepri  for useful discussions and suggestions and she acknowledges the  Deutsche Forschungsgemeinschaft via  Project  A1  in the framework of SFB 910.
ACKNOWLEDGMENTS We would like to acknowledge  Ariena van Bruggen  and  Alexander Byth Carstens  for contributing to conceiving the original idea for this manuscript. We would also like to thank  Julia Harding ,  Patsy McGaughy ,  Jennifer Van Court ,  Andrew Walker ,  Rodrigo Almeida ,  Tracy Clevel  and  Monica Cooper , and  Julie McNamara  for helpful discussions.