dataesr/scientific-paragraphs-categorization huggingface dataset:
New Approaches FishmiRNA integrates several new approaches that provide accurate, consistent, and exhaustive annotations of evolutionarily conserved miRNAs in ray-finned fishes, as well as the innovative ability to explore expression data sets across species and organs in a user-friendly interface. Two novelties of FishmiRNA for achieving miRNA annotation consistency among species are the full integration of the TGD in gene orthology assignments and a broad phylogenetic context. For that purpose, we built on our recent establishment of genome-wide synteny-verified miRNA gene orthologies among several fish species, including the spotted gar Lepisosteus oculatus whose lineage, the Holostei, diverged before the TGD. This work allowed us to infer the miRNA gene repertoire of the hypothetical Teleost-Holostei last common ancestor (TH-LCA)  (Desvignes et al.
2021 ). In addition, FishmiRNA annotations rely on small RNA sequencing expression data to first, identify miRNA gene loci and second, to detect the most abundantly expressed mature miRNAs, leading to data-supported annotation of both the 5p and 3p strands for the majority of genes (76% across the database). The novel annotation of four teleost species and of the bowfin, Amia calva, a second holostean outgroup to the teleosts  (Thompson et al. 2021) , further increased confidence in the inferred TH-LCA miRNA gene repertoire and in intermediate teleost ancestors. This ancestral reconstruction approach, a cornerstone of the FishmiRNA database, allows the retracing of gene evolution across lineages. Each miRNA gene annotated in FishmiRNA is thus linked to its orthologs among other teleosts and with the TH-LCA.
So far, FishmiRNA contains miRNA gene and mature miRNA annotations for 10 actinopterygian species, including eight teleost species (zebrafish, catfish, panga, medaka, molly, perch, stickleback, and icefish) and two Holostei species that diverged before the TGD (gar and bowfin) (table  1 ). These species were selected based on their broad phylogenetic distribution within rayfinned fishes, on their importance in evolutionary, aquacultural, and biomedical research, and on the availability of high-quality genome sequences. A unique approach of FishmiRNA unavailable in any other annotation database is to provide expression data for mature miRNAs. The consistent re-analysis of expression data for each species using the smallRNA-seq software Prost! (Desvignes et al. 2019 ) coupled with a graphical module enables users to compare the expression patterns of up to ten miRNAs from any species in the database, thus offering an innovative opportunity to incorporate in a study the evolutionary conservation of expression of any miRNA across species.
Finally, an important novel approach of FishmiRNA is the simplicity and efficient design of its user-friendly website. Contrary to other databases that incorporate hundreds of webpages, FishmiRNA database relies on a single webpage and two spreadsheets: an annotation table and an expression table, both of which can be interactively and quickly filtered, searched, and exported, in full or in part. This novel approach eases access to miRNA annotations and miRNA expression data and significantly increases the diversity of users reached by providing accessibility to miRNA data to anyone, with or without bioinformatic skills.
Graphic Information A summary of the annotation database is displayed at the top of the page in a phylogenetic context  (Rabosky et al. 2018) , including graphical representations of general statistics for each species (fig. 1 ). These immediate visual representations help users determine which species' annotation would be the most appropriate to apply to their experimental question or species of interest. For example, if FishmiRNA lacks a species, a user might select the phylogenetically most closely related species with the largest miRNA annotation. The phylogenetic coverage of FishmiRNA will grow as new species become annotated.
The FishmiRNA Annotation Database The FishmiRNA annotation database consists of a single table containing 38 columns and as many lines as there are miRNA genes in the database, currently 3,028 genes. Four columns present the taxonomy of the species and its reference genome assembly; six columns describe the miRNA gene with a name, potential previous names, a unique gene identifier, links to miRBase, Ensembl, and to other databases when available; six columns describe the miRNA hairpin with a name, its sequence, position, and strand in the genome assembly; two columns report gene orthology among teleosts and with the TH-LCA; three columns inform on gene clustering; one column summarizes 5p and 3p strand annotations, each detailed in eight columns providing the mature strand name, a unique mature identifier, a reference sequence, its position in the genome, and whether this mature miRNA can be produced by more than one miRNA gene.
MBE For rendering purposes, only nine columns are displayed by default, but users can select their own column preferences or display the entire table (fig. 2 ). Users can also filter the database by species, by FishmiRNA Gene ID, hairpin and mature miRNA names, and by orthology relationships among teleosts and with the TH-LCA. In addition, users can search the database by genomic location or by a Blast search of sequences on hairpins or mature miRNA sequences (fig. 2 ). The entirety or filtered parts of the annotation database can be immediately exported as a spreadsheet or a GFF file. Hairpin and mature miRNA sequences can also be exported in a click in FASTA format for ready use with smallRNA-seq analysis software or other purposes (fig. 2 ). FishmiRNA, the Fish MicroRNA Annotation and Expression Database.
The FishmiRNA Expression Database The FishmiRNA expression database consists of a single table containing 23 columns and as many lines as mature miRNAs detected in the smallRNA-seq libraries analyzed. Three columns provide information on the mature miRNA name, its unique mature identifier, and the sequence of the most highly expressed isomiR; and ten columns that contain the raw counts and ten columns that contain normalized read counts (reads per million, RPM) for each mature miRNA in a selection of major organs (brain, gills, heart ventricle, skeletal muscle, intestine, liver, ovary, testis, hematopoietic kidney, and spleen). Not all species have expression data for all of the selected organs; the entry "No_Data" signals these cases. For rendering purposes, only the mature miRNA name, its sequence, and normalized read counts are displayed by default.
Similar to the annotation table, users can configure The entire annotation database, or subsets of it, can be exported in various convenient formats. The magnifier icon opens the corresponding gene identification card that contains all the FishmiRNA information related to this gene. Selecting genes using tick boxes on the left allows the filtering of the FishmiRNA expression database for mature products of selected genes. Desvignes et al. doi:10. 1093/molbev/msac004 MBE columns shown in the expression table, filter by species, mature miRNA name, unique identifier, or explore expression data by Blast. The entire expression database or filtered parts of it can also be immediately exported (fig. 3 ). One of the most useful features of the FishmiRNA expression database is its graphical module.
The expression profile of each mature miRNA can be visualized by a click on the blue histogram button associated with its name (fig. 3 ). This link opens a modal box displaying expression data in a histogram with the organ of highest expression in red and the mean expression across organs marked by a horizontal red line. Furthermore, by selecting mature miRNAs using tick boxes next to the mature miRNA name, users can instantly plot on the same graph the expression profiles of up to any ten FIG. 3. Exploring the FishmiRNA Expression database. Screenshot of the Expression section of the database. Like the Annotation section, users can choose to add or remove columns in the display and can filter and search the database by species, mature miRNA name, and unique identifier. The entire expression database, or subsets of it, can also be exported in convenient table formats.
The histogram icon displays the corresponding mature miRNA expression pattern in a variety of organs. Users can also select up to any 10 mature miRNAs using tick boxes on the left to display their expression patterns on the same graph. FishmiRNA, the Fish MicroRNA Annotation and Expression Database. doi:10. 1093/molbev/msac004 MBE mature miRNAs in the database. All graphs generated can also be exported in various image formats using the dropdown menu located at the top-right corner of the graph (fig. 3 ).
Quick Download Links and Origin of Analyzed Data To facilitate the dissemination, re-use, and transparency of data provided in FishmiRNA, the download section of the FishmiRNA database provides quick links for downloading, per species or for all species, annotation files in FASTA format, and raw output files from the smallRNA-seq software Prost! used for the annotation and expression analyses  (Desvignes et al. 2019) (fig. 4). Furthermore, links to the publication of each original annotation are provided along with links to the NCBI BioProject of the expression data analyzed and the article that published these original data (fig. 4 ).
Materials and Methods Small RNA sequencing data presented in FishmiRNA are all publicly available in NCBI (table  1 ). Organ sampling and library preparation protocols as well as sequencing platforms may differ between species and therefore expression patterns may not be fully comparable. All species were, however, reanalyzed the same way and for each species, Illumina sequencing libraries were simultaneously analyzed using Prost! (Desvignes et al. 2019) , selecting for read length from 17 to 25 nucleotides with a minimum of five identical reads. Publicly available genome assemblies were used for each species (table 1). Gene and mature miRNA annotations were performed as described previously  (Desvignes et al. 2019) , using orthology and ohnology relationships established across species  (Desvignes et al.
Acknowledgments The authors thank all our past and present fish miRNA collaborators for interactions that revealed the need for FishmiRNA. This work was supported by the  National Institutes of Health  (Grant Nos. NIH R24 OD011199 ,  NIH   5R01 OD011116 , and  NIH   R01 GM085318  to J. H. P. ), the  National Science Foundation Office of Polar Program (NSF   OPP-1543383  to J. H. P. and T. D. ), and the  Agence Nationale de la Recherche  ( ANR-18-CE20-0004  to J. B. ). This work benefited from access to the  University of Oregon  high performance computer, Talapas. MGX acknowledges financial support from France G enomique National infrastructure, funded as part of " Investissement d'Avenir" program  managed by  Agence National pour la Recherche  ( ANR-10-INBS-09 ).
Molecular analyses: genotyping by sequencing (GBS) GBS was used to identify thousands of SNPs on the sampled individuals, following the protocol developed by  Elshire et al. (2011). GBS libraries construction, sequence data generation and SNP identification were performed at the University of Minnesota (USA), which transmitted a VCF file. Briefly, the genomic DNA of T. fortunei samples was digested using restriction enzyme BamHI. Single-end 100 bp reads were produced, then demultiplexed fastq files were generated using Illumina bcl2fastq software, adapter sequences were trimmed from the 5` and 3` ends of reads using Cutadapt. The Stacks version 2. 0  (Catchen et al. 2013)  denovo_map. pl workflow was used to generate a raw VCF file, first to generate the loci catalogue from a random selection of 20 samples, then default parameters were used to produce the raw VCF file for all samples.
Genetic diversity The genetic diversity (He) across all populations and for each population (for both microsatellites and SNP markers), and for each microsatellite locus (locusby-locus analyses) was analysed with the Hierfstat package  (Goudet 2005)  in R (R Core Team 2021). He was calculated for both type of markers, together with their standard deviations. For microsatellites, 95% and 99% confidence intervals were calculated when normal distribution and homoscedasticity of He values were confirmed at the 5% level using the Shapiro-Wilk and Bartlett tests, respectively. Differences in genetic diversity among loci and among populations were additionally tested using the Wilcoxon-Mann-Whitney signed rank test and the Kruskal-Wallis rank test, respectively, using a false discovery rate correction  (Benjamini and Yekutieli 2001).
Inbreeding and structure indices The Arlequin software  (Excoffier and Lisher 2010)  was used to compute the inbreeding coefficients (Fis) over all populations and per population (microsatellites and SNP markers) as well as per locus for microsatellite data. Analyses of molecular variances (AMOVA) were used to test for any significant deficit or excess of heterozygotes. The Arlequin software was also used to compute the Fst indices overall populations and per populations pair (Suppl. Tables  4, 5 , 6, for microsatellites and SNP markers). The Fis and Fst values were considered as significantly different from 0 using a resampling procedure with 10′000 permutations. In addition, the mean Fst per population with their standard deviation and the 95% confidence intervals were calculated (when normal distribution and homoscedasticity of Fst values were confirmed among populations using Shapiro-Wilk and Bartlett tests, respectively, at the 5% level).
Dispersion dynamics (microsatellites) Regarding microsatellite data, the effective number of alleles (Nae) was calculated according to  Maruyama (1970). The allelic richness per population and per locus was computed with the Fstat software version 2. 9. 4  (Goudet 1995)  with a minimum number of three individuals corresponding to the size of the Isola Madre population. Differences in allelic richness among populations were tested using a Kruskal-Wallis rank test. The number of rare alleles was calculated as the number of alleles with frequencies lower than 5% within populations. Linkage disequilibrium among loci was tested with the Arlequin software  (Excoffier and Lisher 2010 ) and the significance level assessed using a resampling procedure of 10′000 permutations. Corrections for multiple tests were applied, using a false discovery rate correction  (Benjamini and Yekutieli 2001).
Diversity and structure plots Principal components analyses (PCA, Suppl. Fig. 1 ) were performed with the Hierfstat package  (Goudet 2005)  in R (R Core Team 2021) for microsatellites and the SmartPCA software  (Patterson et al. 2006)  for SNPs. To represent Reynold's distances  (Reynolds et al. 1983 ) on graphs, non-metric multidimensional scaling analyses with a minimal stress value (MDS, Suppl. Fig. 2 ) were performed in R (R Core Team 2021) with the Hierfstat, Mass, Vegan and Reshape2 packages and using the Reynold's matrices computed in Arlequin  (Excoffier and Lisher 2010). The distinction between the two lakes was analysed to search for a possible genetic structure on a larger scale. Graphic representations and ellipsoids of 95% confidence intervals of the centroid for each lake region (PCA plots, Fig.
3 ) were performed in R using the Ggplot2 package  (Wickham 2016 ). In addition to ellipsoids of 95% confidence intervals, analyses of variance (AMOVA) considering the two lake regions (Lake Maggiore and Lake Lugano) were performed. Two indices (Fsc, representing the proportion of variation due to differences among populations within a group, and Fct, representing the proportion of variation due to differences between groups of populations) were used to test whether the grouping criterion is significant or not at the 5% level (resampling with 10′000 permutations). A Bayesian approach was used to infer K, the optimal number of groups in the datasets  (Pritchard et al. 2000). This method uses multi-locus genotypes and allows finding the best number of populations in a dataset, assuming that each inferred population follows panmixia.
For microsatellite data, the Structure software version 2. 3. 4  (Pritchard et al. 2000)  was used to compute the admixture rates per individual according to the K clusters, and the Structure Harvester software  (Earl and Vonholdt 2012)  was used to determine the more likely K using the Evanno's DeltaK method  (Evanno et al. 2005). Each run, from K = 2 to K = 10, was repeated 20 times and performed with 100′000 burn-in iterations and 100′000 subsequent Monte Carlo Markov chains. For SNP data, the FastStructure and ChooseK softwares (Raj et al. 2014) were used. For both markers, the results for the 21 analysed populations (Suppl. Fig. 3 ) were then mapped with individuals coloured according to their assignment to the K panmictic populations (Fig. 4 , displayed in the ArcGis and Photoshop software).
Data generation Over the 200 sampled individuals, one individual was excluded due to a low number of reads and was not considered in the following analyses. Over the 199 remaining samples, a total of 73′878 markers were identified and 30′862 markers (that represent 19′095 loci) were selected after filtering. For the analyses with Arlequin software version 3. 5. 2. 2 (Excoffier and Lisher 2010), the allowed level of missing data was reduced to 5%, which led to a subsample of 13′931 markers out of 30′862.
Acknowledgements For the laboratory work and for the genetic analyses, we would like to thank all those that have helped us, especially  Régine Niba ,  Luisa Carvalho  and  Mathieu Perret  (CJBG) as well as  Karl Perron  ( University of Geneva ) who kindly provided us the pUC19 digested with smaI. For the visit on the Isola Madre, we would like to thank  Gianfranco Giustina  and  Simone Colongo. For their precise advice on the species and information, we would like to thank  Gian-Reto Walther  (FOEN),  Boris Pezzatti  and  Marco Conedera  (WSL),  Vincent Fehr  ( University of Aarhus ) and  Phaedra Sys. For the financial support, we would also like to thank the  Augustin Lombard  from the  SPHN of Geneva  for fieldwork and microsatellite development and the  Academic Society of Geneva  for GBS analyses.
They consist of removing plants within a particular distance of locations identified as containing infection. The rationale is to remove (or treat) locations that are likely to be infected but not yet showing symptoms  [11, [33] [34] [35]. Here, we used recent georeferenced inspection and eradication data supplied by extensive surveillance at the landscape scale (representing an area of 28 hectares) to fit a mechanistic and spatio-temporal model of FD spread from one season to the next. The model builds on the work of  [36]  regarding the spread of Huanglongbing through citrus groves. It provides insights on the dispersal kernel that underlies FD spread. For coherent integration of information from different observations gathered at plant and field scales, we work within a Bayesian framework.
The index is computed by using the ape package  [47]  available in the statistical software R. Similarly, we define the Ripley's L function to characterise clustering/dispersion of point patterns at multiple distances as L(r) = K(r)/π (8) where K(r) is the Ripley K function defined as K(r) = A n(n -1) i,j 1 dij ≤r. A represents the area of the window, n is the number of data points, and the sum is taken over all ordered pairs of points i and j in the window. d ij is the distance between the two points, and 1 dij is the indicator that equals 1 if the distance is less than or equal to r. This spatial index is computed without requiring any aggregation of the points and therefore gives a more complete description than statistics that utilise aggregation. The index is computed by using the spatstat package available in the statistical software R  [48].
Indeed, e. g. with weak Allee effects, subtle interactions between tail fatness (rapidly versus regularly varying) and per capita growth rate of the epidemic near zero determine whether the spread is accelerating  [63, 64]. However, our observations are mainly collected in three fields within a bounding box of 232 m * 202 m (i. e. over 4. 7 ha). It is well known that data confined to relatively small spatial scales can blur the precise estimates of the form of dispersal at large distances, and in particular the shape of the kernel's tail  [65]. The selected dispersal at long distances depends on both the kernel considered and the distances over which the dataset is collected (  [66] ). The inference framework used considered a non-spatialized primary infection rate term, (t), modelling the introduction of FD in the 3 targeted fields from a set of remote fields.
Acknowledgments The authors wish to thank the people who sampled or provided data:  Sophie Bentejac ,  Morgane Le Goff  and  Charlotte Labit  ( GDON des Bordeaux );  Dominique Vergnes  ( FREDON Aquitaine ); and  Gontran Casella  ( Syndicat des Bordeaux et Bordeaux Supérieur ). We also thank  Sophie Bentejac  and  Charlotte Labit  for their critical and careful reading of the manuscript. This study received financial support under research contracts  Co-Act2  and  RISCA  ( Plan National Dépérissement du Vignoble).
A bibliometric pipeline identifies 249 highly cited publications addressing plant pathogen effectors To identify the most cited publications that addressed plant pathogen effectors over the last two decades, we implemented a previously described bibliometric pipeline  (Petre et al. , 2022) , which mostly uses the Web of Science database (https://www. webofscience. com) and a reference management software (here Mendeley). We selected the top three most cited research articles or reviews published each year between 2000 and 2020, which we identified via three successive searches using the key terms 'plant pathogen effectors', 'plant pathogen avirulent effectors', and 'plant pathogen virulent effectors' (Figure  1A ; Dataset 1; see Supplementary Methods, Supplementary Figure  S2  for details).
Interestingly, the annual citation rates of research articles and reviews are comparable (24 vs. 28 on average; Figure  1D ). In addition, the current impact factors of the journals in which HIPEs were published do not correlate with HIPEs annual citation rate (Figure  1E ). Thus, publication types and journal impact factors poorly explain the variation of citation rates between HIPEs. As a note, only 19 out of 249 HIPEs overlap with the collection of highly-cited papers in plant immunity (HIPPYs) described in our previous bibliometric analysis  (Petre et al. , 2022 ; Dataset 1, column B). To summarize, HIPEs represent an original collection of influential studies that pertains to plant pathogen effectors, and that is suitable for further analyses.
A small influential community publishes the majority of the HIPEs To identify the research community that publishes the HIPEs, we extracted from HIPEs metadata the main countries and institutions to which HIPEs corresponding authors are affiliated, as well as the journals that published them. Overall, 19 countries, 87 institutions (comprising 154 affiliated corresponding authors), and 67 journals published the HIPEs (Dataset 1). Only a handful of scientific actors contributed most of the collection (Table  1.