dataesr/scientific-paragraphs-categorization huggingface dataset:
Molecular confirmation and subtyping All five positive T. gallinae DNAs were double confirmed by PCR amplification and sequencing of a large DNA fragment that combined the 18S ssrRNA gene and ITS region. Additional genotyping was successfully conducted using the hdg and the rpb1 genes. All five samples were identical for all four genetic targets and confirm a single clonal strain involved. The sequencing of these four genetic targets lead to three fragments of 1924 bp, 1033 bp and 1437 bp for the 18S ssrRNA + ITS region together, the hdg and the rpb1 genes, respectively and are deposited in GenBank under accession numbers MK172843-MK172857. The BLAST comparison showed 100% identity with other T. gallinae sequences deposited in GenBank for all four genetic targets (Supplementary File). The Boulonnais epidemic strain can be classified as genotype A/B or genotype IV based on the ITS region according to the nomenclatures of Gerhold et al.
Mainly based on data recorded by the bird   [78]  on a 371 bp fragment covering the ITS1-5. 8SrRNA-ITS2 region of our sequences MK172843 in red, 47 sequences of avian Trichomonas in black, six related sequences of Trichomonas infecting mammals in green and Tetratrichomonas gallinarum as the outgroup to root the tree. Accession numbers of the sequences are provided between vertical bars. The reliability of tree topologies, nodal robustness, and statistical branch support were assessed by non-parametric bootstrap analysis using 1000 replicates (only >50% shown). Avian Trichomonas genotype names according to the classification of Gerhold et al. [29]  and Grabsteinner et al. [34]  are given in light and dark blue, respectively. Recent avian Trichomonas genotypes reported by Marx et al. [52]  are given in purple.
Relying on the capacities of these different bodies together, it will become more feasible to monitor the situation for these avian diseases in a timely and evenly spaced manner across the country. Regarding the Nord-Pas-de-Calais, fortunately, it has an efficient SAGIR network, a running wildlife rescue centre and is among the most covered regions by bird ringers. It will therefore be possible to pursue monitoring of the local situation in the long term. Preliminary data show no recurrence of similar massive outbreaks during the following years. Additionally, initiatives toward the public such as specific survey (e. g. , the one conducted in Brittany) or dedicated online notification portal (e. g. , BTO -Garden Wildlife Health) could provide valuable data for avian disease surveillance and should be considered.
Results Strain identification and classification. Strain Marseille-P3254 was isolated from the ileal content of a 76-year-old woman who underwent upper and lower digestive tract endoscopy for esophagitis and colonic polyp. The patient gave an informed and written consent and the study was approved by the ethics committee of the Institut Hospitalo-Universitaire Mediterranee Infection under number 2016-010. As no microorganism grew from the negative control, we are confident that Merdibacter massiliensis was not an experiment contamination. Strain Marseille-P3254 could not be identified by our systematic MALDI-TOF MS screening as the score was 1. 763, suggesting that the corresponding species was not in the database (Figure  S1 ). Moreover, strain Marseille-P3254 exhibited a 90. 70% 16S rRNA sequence similarity with Eubacterium dolichum strain JCM 10413 T (GenBank accession no.
The genome is 2,468,496-bp long and its G + C content is 40. 1%. The type strain, Marseille-P3254 T , isolated from the ileum of a patient, was deposited in the CSUR and DSMZ collections under accession numbers CSUR P3254 and DSM 103534, respectively. The 16S rRNA and genome sequences are available in GenBank under accession numbers LT598590 and FTLC00000000, respectively.
Isolated colonies were identified using MALDI-TOF mass spectrometry (MS) protein analysis and a Microflex spectrometer (Bruker Daltonics, Bremen, Germany)  22. Spectra from strain Marseille-P3254 were imported into the MALDI BioTyper software (version 2. 0, Bruker) and analyzed by standard pattern matching (with default parameter settings) against 11805 bacterial spectra in the Bruker database (7854 spectra) enriched with spectra from bacteria isolated in our laboratory as part of the culturomics study (3951 spectra). Interpretation of the scores was performed as previously described  23. Moreover, the 16S rRNA gene was sequenced using the fD1-rP2 primer pair as previously described  24  , using a 3130-XL sequencer (Applied Biosciences, Saint Aubin, France). A phylogenetic tree was obtained using the Maximum Likelihood method and Kimura 2-parameter within the MEGA 7 software  25.
Two samples were prepared with approximately 25 mg of bacterial biomass per tube harvested from several culture plates. Briefly, fatty acid methyl esters were separated using an Elite 5-MS column and monitored by mass spectrometry (Clarus 500 -SQ 8S, Perkin Elmer, Courtaboeuf, France)  26. GC/MS analyses were carried out as previously described  27, 28. Spectral database search was performed using MS Search 2. 0 operated with the Standard Reference Database 1A (NIST, Gaithersburg, USA) and the FAME mass spectral database (Wiley, Chichester, UK). DNA extraction and genome sequencing. Genomic DNA (gDNA) of strain Marseille-P3254 was extracted in two steps: a mechanical treatment was first performed by acid-washed glass beads (G4649-500g Sigma) using a FastPrep BIO 101 instrument (Qbiogene, Strasbourg, France) at maximum speed (6.
Total information of 2. 8 Gbp was obtained with 274 K/mm 2 cluster density with a cluster passing quality control filters of 97. 6% (5,537,000 passing filter paired reads). Within this run, the index representation for strain Marseille-P3254 was determined to 9. 48%. The 524,686 paired reads were assembled using the SPAdes version 3. 10. 1 software  29. The option "careful" was used in order to reduce the number of mismatches and short indels. Default parameters were applied for K values, i. e. , k-mer values of 127, 99, 77, 55, 33, and 21. SSPACE  30  and GapFiller  31  were used to combine contigs with default parameters. Finally, manual finishing was performed by using similarity searches based on BLAST searches, and synteny blocks were detected by progressive alignment using the Mauve software  32.
Genome annotation and genome comparison. The genome was annotated as previously described  23. In addition, we compared the genome from strain Marseille-P3254 to those of the closely related species Eubacterium dolichum strain JCM 10413 T (accession number ABAW00000000), Faecalitalea cylindroides strain JCM 10261 T (AWVI00000000), Dielma fastidiosa strain DSM 26099 T (CAEN00000000), Holdemanella biformis strain DSM 3989 T (ABYT00000000), Streptococcus pleomorphus strain DSM 20574 T (ATUT00000000), Holdemania massiliensis strain DSM 26143 T (CALK00000000), Clostridium innocuum strain DSM 1286 T (AGYV00000000) and Anaerorhabdus furcosa strain ATCC 25662 T (FUWY00000000). For this, we used the Genome-to Genome Distance Calculator (GGDC) web server available at http://ggdc. dsmz. de to estimate the overall similarity among compared genomes and to replace the wet-lab DNA-DNA hybridization (DDH) by a digital DDH (dDDH)  12, 13.
The average nucleotide identity at the genomic level was also estimated using the orthoANI  14  and AGIOS 4,33 software. Antibiotic resistance genes (ARG) were searched using the ARG-ANNOT database and Bio-Edit interface  34. Assembled sequences were searched against the ARG-ANNOT database under moderately stringent conditions (e-value of 10 -5 ) for the in silico ARG prediction. These putative ARGs were further confirmed through a BLAST search against non-redundant (nr) database in GenBank. The presence of pathogenesis-related proteins was investigated using PathogenFinder 1. 1  35. Predicted protein sequences of strain Marseille-P3254 were used as queries to search the NCBI GenBank non-redundant protein sequence database. These results were formatted to generate a network of protein sequences using the Cytoscape tool  36.
Virus The isolate CHIKV 0621 was used in this study. This viral strain was isolated from a traveller from India and possesses an amino acid change (A226 V) in the envelope glycoprotein E1  [13] [14] [15]  [GenBank accession number DQ443544]. The virus had been passaged three times in C6/36 cells prior to use in the experiments. Virus titration was performed by focus-forming assay (FFA) as previously described  [16]. The titre of the frozen virus stock was estimated as 10 9 focus-forming units per mL (FFU/mL). All infectious experiments were conducted in a BSL-3 insectary (Institut Pasteur, Paris).
Statistical analysis The time-dependent effect of the mosquito species on mosquito midgut infection, systemic infection and virus transmission was analysed by Firth's penalized likelihood logistic regression by considering each phenotype as a binary response variable. Penalized logistic regression, implemented through the logistf R package  [21]  was used to solve problem of separation that can occur in logistic regression when (i) the outcome has high prevalence, (ii) when all observations have the same event status for a combination of predictors or (iii) when a continuous covariate predict the outcome too perfectly. A full-factorial generalized linear model that included the time post virus exposure and the mosquito species was fitted to the data with a binomial error structure and a logit link function.
Statistical significance of the effects was assessed by an analysis of deviance. Virus titres in mosquito's bodies, heads and saliva were compared between species by a Mann-Whitney-Wilcoxon rank sum test stratified on the time post-exposure as implemented in the wilcox_test function from the coin R package  [22]. The effect of time post exposure on each quantitative phenotype for each species was assessed using a Kruskal-Wallis rank sum test. Time post exposure was converted into ordered factors to implement both tests. The intra-host dynamic of systemic infection was assessed by a global likelihood function for each mosquito species as described in Fontaine et al. [23]. Probabilities of systemic infection at each time point post virus exposure were estimated with a 3-parameter logistic model.
Amplicons sequencing reactions were performed by using Big Dye Terminator v1. 1 cycle sequencing kit (Applied Biosystems, Foster City, California, United States) and purified by ethanol precipitation. Cycle sequencing was performed on ABI3730XL sequence analyser (Applied Biosystems) using both the forward and reverse primers to create a consensus sequence and increase haplotype reliability. COI sequences from the two specimens were submitted to the identification tool of BOLD (Barcoding of Life)  [27]. A set of 36 COI sequences from Ae. geniculatus and 2 COI sequences from Ae. albopictus were obtained from GenBank. An Aedes echinus COI sequence (GenBank accession number: MK070853) was kindly provided by Dr. Andreas Krüger from the Bernhard-Nocht-Institute for Tropical Medicine (BNITM), Hamburg, Germany.
COI sequences from our two specimens (GenBank accession numbers: MK796909, MK796910) were aligned to the sequences retrieved from GenBank using ClustalW v. 2. 0. 12  [28]. All COI sequences excepting both from Ae. albopictus were then imported in nexus format to the PopArt program  [29]  to create a TCS haplotype network using a 437 bp COI section covered by all 39 sequences from Ae. geniculatus and Ae. echinus mosquitoes. Representative COI sequences (i. e. unique haplotypes over the full COI sequences) were analysed with RAxML v. 8. 2. 10  [30]  to generate the best-scoring maximum-likelihood (ML) tree of 10 runs with 100 thorough bootstrap replicates to infer the reliability of the branches. Topology was rooted to an Ae. albopictus (MG198601) COI sequence. The GTR nucleotide substitution model was chosen from a list of substitution models implemented in RAxML based on corrected Akaike's Information Criterion (AICc) value using the PartitionFinder v2 software  [31]  with the linked branch length option.
Results The COI sequence from one specimen from the Albanian population of Ae. geniculatus that has been exposed to CHIKV in this study has been sequenced. This COI sequence matched a Dahliana geniculata (an alternative name for Ae. geniculatus based on Reinert et al. [33] ) sequence with 100% similarity and 100% probability of species assignment. This bold entry has an "early-release" status at the time of accession with no information excepting a rough location in Central Europe (most probably Germany). This sequence clusterized with two other COI sequences from Aedes specimens collected in Germany deposited in GenBank as Ae. geniculatus  [34]  (Figure  1 ). While being very closed from the Ae. geniculatus genetic cluster, this cluster can be distinguished from the Ae. echinus specimen on both the haplotype network (Figure  1(A) ) and phylogenetic analyses with a high branch support (Figure  1(B) ).
Interestingly, a COI sequence from a specimen collected in Austria and identified as Ae. geniculatus (GenBank accession number: KM280584) clustered apart from all other sequences. The COI sequence of our Ae. geniculatus specimen from Albania, matched an Ae. geniculatus sequence with 100% similarity and 100% probability of species assignment. This specimen clustered with Ae. geniculatus mosquitoes collected all over Europe. A total of 134 Ae. geniculatus and 124 Ae. albopictus engorged females were analysed, considering the mosquito mortality during the experiment. Indeed, 16 and 17 females died prior to sampling, respectively for Ae. geniculatus and Ae. albopictus. High midgut infection prevalences (>85%) were obtained for both species from the first time point after virus exposure. With 100% midgut infection among the exposed mosquitoes, Ae.
Haplotypes in common with the maximum-likelihood phylogenetic tree (B) are represented with numbers. B -Phylogenetic relationships between unique COI haplotypes from Ae. geniculatus, Ae. echinus and Ae. albopictus mosquitoes. Inferences were calculated with a maximum-likelihood method implemented in RAxML v. 8. 2. 10  [30]. Topology was rooted to the Ae. albopictus (MG198601) COI sequence. Bootstrap support values are indicated on each node in red. COI sequences from single Ae. geniculatus specimens from Tirana, Albania (mosquito population that was used in this study) and Paris, France are represented in red and blue, respectively. GenBank accession numbers are displayed for each sequence. 5. 43e-13) and the mosquito species (analysis of deviance, p-value = 4. 10e-15) but the time post exposure effect was not significantly different across the mosquito species according to logistic regression (analysis of deviance, interaction term, p-value = 0.
geniculatus species and from the Ae. echinus specimen, as represented by a Spanish sequence collected in 2011  [34]. Another specimen collected in Austria (KM280584) and submitted in GenBank as Ae. geniculatus (Figure  1 ) appears to be genetically divergent from all other specimens. Three mosquito species from the subgenus "Finlaya" (Theobald, 1903) can be considered as the closest relative species of Ae. geniculatus, namely Ae. echinus, and Aedes gilcolladoi  [34]. Our morphological determination of the Ae. geniculatus species was done as follow. At the adult stage, the female of Ae. geniculatus is morphologically indistinguishable from Ae. gilcolladoi specimens. While being morphologically very close to Ae. echinus mosquitoes, Ae. geniculatus adults females specimens can be distinguished from the former species by the absence of a pale scaly spot on the metameron  [12].
In addition, it is possible to distinguish Ae. geniculatus from both other species at the larval stage by the ramification of most of the silks present on the abdominal segments IV to VII. Indeed, Ae. echinus and Ae. gilcolladoi have rigid silks generally with 7 or more branches while Ae. geniculatus specimens have rigid silks with less than 6 branches  [12]. These determination criteria were verified for adults and larvae from the mosquito population used in our study. As discussed by Krüger and colleagues, it is possible that the specimen identified as Ae. echninus in Spain (COI GenBank accession number MK070853) belongs to the Ae. gilcolladoi species. We can also make the assumption that the Austrian specimen identified as Ae. geniculatus (COI GenBank accession number KM280584) belongs to the Ae.
albopictus in Europe but population densities of Ae. geniculatus generally didn't reached those of Ae. albopictus in peri-urban areas. More data are needed to fully characterize the level of contact with human hosts. As an anecdote, we captured Ae. geniculatus on Jim Morrison grave, one of the most visited graves by tourists of all nationalities in Pere Lachaise cemetery, in the centre of Paris, where millions of people come every year, strengthening the threat for this species in local virus transmission. Its flight dispersion is not considered superior than Ae. albopictus. Consequently, this species would not carry viruses outside of the area treated in the frame of the vector control intervention. Arbovirus vertical transmission is a rare event, and its occurrence hazard is correlated with vector density and the surveillance of virus.
Acknowledgments We thank  Pr. Despres Philippe  for providing technical assistance and  Dr Andreas Krüger  for giving us a early access to the A. echinus COI sequence. The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication. The contents of this publication are the sole responsibility of the authors and do not necessarily reflect the views of the  European Commission. A. M. , L. D. and J. P. carried out the experiment. A. F. , J. P. and A. M. analysed the data. J. P. , G. L. , A. F. and A. M. wrote the manuscript with support from P. R. A. M. and P. R. supervise the project. A. M. , P. R. and A. F. conceived and planned the experiments. E. V. , S. B. , L. D. , A. M. and J-C. G. contributed to field sampling, mosquito species identification and sample preparation.
The pitfalls of the most commonly used heterosis indices The non-univocal relationship between h P and the most commonly used heterosis indices has two consequences. (i) Comparing index values for a given trait in different crosses and/or environments and/or developmental stages leads to erroneous conclusions whenever these factors have an effect on the scale of the trait and/or on the difference between the parental values (i. e. on z m or z b ). Possible differences in actual heterosis levels between these conditions cannot be detected. (ii) This problem is even more pronounced when studying different traits because each trait has its own scale of variation, making H MP and H BP (and to a greater extent H mp and H bp ) useless for comparing the real levels of heterosis of these traits. These pitfalls can easily be illustrated from our maize dataset.
Acknowledgements We thank our colleagues  Dr. Mélisande Blein-Nicolas ,  Dr. Michel Zivy ,  Dr. Judith Legrand  and  Pr. Christine Dillmann  for their helpful comments on the manuscript, and  Dr. Hélène Citerne  for English corrections. We are grateful to the key persons at the  INRA Station of Saint-Martin-de-Hinx , of  Euralis and of MASseeds  for the 2014 and 2015 field experiments. These experiments were funded by the  French Agence Nationale de la Recherche  ( Amaizing  project  ANR-10-BTBR-01 ).
Statistical analysis Differences between samples (A0, A130, A130S, A130F) were studied by analysis of variance (one-way ANOVA). Significance was set at p < 0. 05. Tukey's post-hoc leastsignificant-differences method was used to describe means with 95% confidence intervals. The statistical analyses were performed using Statistica software, version 12 (Tulsa, OK, USA).
Peak-calling The next step of miRkwood is to locate expression signals into the set of reads that have not been eliminated in the previous step. For that, we have developed a method that is both scalable and takes advantage of the secondary structure of the precursors. First, we identify areas in the genome that have been enriched in aligned reads using a classical k-mean clustering method. Then, for each such peak we look for RNA duplexes by testing whether the sequence can bind to a sequence located in the vicinity (up to 350 nt upstream or downstream of the peak) by establishing C-G, A-U and G-U interactions. This condition ensures that the peak can be part of a potential stem-loop structure, and reduces the number of peaks to consider in the next step. This search is achieved using a custom program (see Additional file 1, Section 2).
Secondary structure of the hairpin precursor After peak detection, miRkwood aims at determining which sequences can fold into a stable stem-loop structure. This step is critical because precursor stem-loops are very heterogeneous in plants. The main idea is to use the RNALfold software of the RNA Vienna package  [18]  to identify locally stable secondary structures without any prior knowledge. This computation is time consuming, which is why we have made sure that only a limited number of peaks are kept from the previous step. We then select stable secondary structures that are compatible with a miRNA stem-loop in terms of length and number of base pairings. In Section 3 of Additional file 1, we provide an advanced discussion on the parameters used and the accuracy of the approach.
Lastly, miRkwood provides two additional pieces of information for each prediction. First, it checks if the miRNA (when it exists) could possibly originate from a duplication. For that, it reports whether the sequence is present elsewhere in the genome, and whether this alternative location corresponds to another miRNA precursor found by miRkwood. Second, it checks whether the miRNA is conserved through evolution by comparison with a database of known miRNAs (see Additional file 1, Section 5). Alignments are performed with the piccolo alignment tool, which is particularly effective for processing short sequences, such as miRNAs  [22].
The other GFF file compiles all miRNAs and precursors of miRNAs available in miRBase V21. User's reads should be previously mapped on one of those 12 assemblies, and the coordinates of the resulting alignments stored in a custom BED file. A tool to automatically convert SAM/BAM file to the BED file is provided with miRkwood. The database for the alignments to known miRNAs is miRBase V21 (file mature. fa), but can be updated as new versions of miRBase are released. The stand-alone version is freely distributed under the GNU Affero General Public Licence (v3. 0). The code is available on GitHub: https://github. com/miRkwood-RNA/ miRkwood. The user can provide any genome and any annotation file of his/her choice. To make the installation and the usage easier, we offer a Galaxy wrapper (available on the Test Toolshed) and a Docker image.
Datasets To evaluate the performance of miRkwood and benchmark against ShortStack  [10] , a dataset composed of 6. 2 million Illumina raw reads and obtained from inflorescences from A. thaliana previously published by Wang et al. (2011)  [23]  was downloaded from the SRA-NCBI database (accession number SRX058635). AGO1 and AGO4 associated small RNAs (AGO1-IP and AGO4-IP) data obtained from the same biological sample were downloaded from the GEO database (accession numbers GSM707682 and GSM707690 for AGO1-IP and GSM707686 for AGO4-IP). They were composed of 969, 586, 884,610 and 2,171,046 non-redundant reads, respectively.