dataesr/scientific-paragraphs-categorization huggingface dataset:
FST and nucleotide diversity were computed on non-sex-related contigs >500 kb in length, over sliding windows of 100 kb with 50 kb overlapping regions (windows of 100 kb and overlapping regions of 100 kb were tested and led to similar results). It is considered that a FST value greater than 0. 15 is significant in differentiating populations  (Frankham et al. 2010). Tables  Table 1. Assembly statistics for the genomes of O. alexandrae, O. priamus and T. oblongomaculatus. LR = long reads, SR = short reads, bp = base pairs. For BUSCO scores, S = single-copy genes, D = duplicated genes, F = fragmented genes, and M = missing genes out of 5,286 genes in odb10 lepidopteran database. Annotation was only done for O. alexandrae individuals.
Acknowledgments We thank two anonymous reviewers who provided very relevant and constructive comments to improve the study. This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (project  GAIA , agreement no. 851188 ). This project has been generously supported by the  Swallowtail & Birdwing Butterfly Trust , and we thank the Trust's Chair,  N. Mark Collins , for his continuous advice and encouragement. We thank  Henry S. Barlow  and  Ian Orrell  who helped with the organization of the field work as well as with the export and CITES permits. We thank  Conwell Nukara  for field work assistance in the Managalas Plateau. This study also benefited from lab facilities near Popondetta funded by the  Sime Darby Foundation.
Data Availability The birdwing genomes, mitogenomes and sequencing data in the present study, including Nanopore, Illumina, and RNA data are available from the Genome database and Sequence Read Archive (SRA) under the Bioproject accession number PRJNA938052, with the corresponding BioSamples accession numbers FC560: SAMN33424250; FC561: SAMN33424251; FC562: SAMN33424252; FC563: SAMN33424253; FC565: SAMN33424254; FC569: SAMN33424255.
Statistical analyses All data were Log transformed for statistical analyses. Two types of analyses were used: multivariate analyses to assess global visualization of the data and univariate analyses to focus on specific responses. These analyses are described in detail in the supplementary materials. All statistical tests were two-sided, nominal level of statistical significance was set to a = 0. 05 for effect size estimates, a = 0. 01 for normality tests and a = 0. 10 for interactions. All analyses were conducted using R statistical software v3. 5. 1. Citox software was used for the statistical analyses of body weight, rectal temperature, hematology, coagulation, blood biochemistry, C-reactive protein and urinalysis data. PathData software was used for the statistical analysis of organ weight data.
Acknowledgements Alix Raquin ,  Marion Aguirre ,  Elisabeth Marion ,  Jérémy Pontvianne ,  Antonin Asmus ,  Sandrine Painchaud ,  Julie Barrier ,  Stéphanie Husson  for experimental work at  Sanofi. Lucie Engelhart ,  Guillaume Dubost-Martin  and  Kevin Thibault Duprey  and the technicians for the animal care and in-vivo study phase follow-up at Sanofi Animal facilities. Clément Grandin study director at  Charles River Laboratories Evreux ,  Roger Legrand ,  Nathalie Bosquet  and their team at  Université Paris-Saclay, INSERM, CEA ,  UMR1184 ,  IDMIT Department, IBFJ, Fontenay aux Roses , for the in-vivo challenge study phase,  Stéphanie Blanc at Cynbiose  for the study 1B in-vivo phase. Stéphanie Pierson  for support in bioinformatics analysis. Nada Assi ,  Aymeric de Montfort ,  Thomas Billet  and  Joseline Ruiz  for statistical analysis.
Statistics Cronbach's alpha coefficient was used to determine the internal consistency of the assessment tool after each round. Cronbach's alpha value demonstrates how closely related a set of test items are as a group, and varies between 0 and 1, with 1 corresponding to 100% consistency. Consensus on round 4 (final round) was defined as Cronbach's alpha >. 80. Categorical variables were expressed as proportions (%). SPSS 25. 0 (IBM, Armonk, NY, USA) was used for the statistical analysis.
AvrStb9 encodes a secreted protein with a conserved protease domain shared among diverse ascomycete lineages Unlike other known fungal effectors, AvrStb9 encodes a large protein and shares sequence similarities with different fungal species. The re-annotated AvrStb9 gene model has five introns and encodes a 727 amino acid long protein with eight cysteine residues (Fig 3A  and 3B ). This protein has a predicted signal peptide sequence of 18 amino acids at its N-terminus and two serine-protease domains (C-terminal processing peptidase S41 family; positions  Fig 3B)  according to InterProScan (Panther entry PTHR-37049, e-value: 2. 7 e-64). HHpred searches in protein structure databases identified the secreted protease CPAF as the strongest hit (e-value of 1. 5e-27), which is involved in pathogenesis of the human pathogenic bacterium Table  1.
GWAS analyses For Genome-Wide Association Analysis (GWAS), we used the linear mixed model implemented in the R package 'GAPIT'  [54]. To account for population structure and reduce spurious associations, a kinship matrix using the VanRaden method  [55]  and the first three principal components computed from SNP data were included a covariate in the model. We used a Bonferroni threshold of p < 2. 67e-7 (α = 0. 05) to declare significant SNPs associated with phenotypic variations. Linkage Disequilibrium (LD) measures (r 2 ) for the significant SNPs were obtained with PLINK v. 1. 9 using the-r2 command and LD blocks were visualized using the R package 'LDheatmap'. SNPs with an r 2 � 0. 5 were considered in strong LD.
Genetic mapping of Stb9 A genetic linkage map was available for the Beaver/Soissons DH population and contains 312 polymorphic loci: 121 SSR, 49 AFLP, 135 DArT markers and 7 major genes  [46]. The map covers 1,526 cM with an average density of one marker per 12. 6 cM. QTL mapping was conducted using RAUDPCs and RAUDPCn phenotypes obtained from the isolate IPO09593 (AvrStb9) with R/qtl software v. 1. 48-1 [56]. Linkage analysis was performed for each trait with an initial Simple Interval Mapping (SIM), followed by a Composite Interval Mapping (CIM). For SIM, 1000 genome-wide permutations were used to calculate the significant logarithm of odds (LOD) threshold. Only QTLs that showed p-values less than 0. 05 were considered significant. Association mapping in the BW panel was done following the exact same procedure as in the fungal population (see GWAS analyses).
The 220 BW varieties have been previously genotyped on an Affymetrix Axiom 410K single nucleotide polymorphism (SNP) array  [57]. Procedure of allele calling, filtering and quality check of genotype data are described in Balfourier et al. [58]. For GWAS analysis, we excluded markers with less than 5% minor allele frequency and more than 10% missing data and kept a set of 186,961 SNPs that passed quality control criteria and could be anchored on the IWGSC annotation v2. 1  [59]. We used filtered SNP data and phenotypes (RAUDPCs and RAUDPCn) from the isolates IPO323 (avrStb9) and IPO09593 (AvrStb9) to perform marker-trait associations.
Gene annotation and expression analysis Predicted genes in the candidate region were retrieved from the Z. tritici reference genome annotation  [60]. We used RNA-seq data  [23]  mapping onto the reference genome IPO323 to manually curate Zt_1_693 (AvrStb9) gene model. Canonical and non-canonical splicing sites were confirmed by de-novo transcriptome assembly obtained with DRAP  [61]. The final gene model differed from those from previous annotations due to errors in predicting correctly introns and includes two missing exons. Candidate genes were screened for the presence of a secretion signal using SignalP 3. 0 server  [62]. Blastp was used to search for AvrStb9 orthologous and paralogous proteins against the reference strain IPO323 and closely-related Zymoseptoria species. Protein domains were searched using Interproscan and structure homology prediction was performed in the HHpred server; <https://toolkit.
tuebingen. mpg. de/tools/ hhpred>. For gene expression analysis, RNA-seq data samples from in vitro and in planta (1, 4, 9, 14 and 21 dpi) conditions were obtained from Rudd et al. [23] , and gene expression quantified as fragments per kilobase of exon per million fragments mapped (FPKM) using Cuffdiff v. 2. 2. 1  [63]. AvrStb9 DNA sequences are deposited in NCBI Genbank (accession numbers OP376566 and OP376567).
Prediction and comparison of protein structures The virulent avrStb9 and avirulent AvrStb9 (from isolates IPO323 and IPO09593, respectively) mature protein structures (i. e. without the signal peptide) were predicted with the ColabFold v1. 5. 2: AlphaFold2 using MMseqs2 with default parameters  [64, 65]. Structural similarity searches against the full Protein Data Bank (PDB)  [66]  were performed using the DALI webserver available at <http://ekhidna2. biocenter. helsinki. fi/dali/> [67]. Sequences and structures of the CPAF homodimer were extracted from the PDB with accession numbers 3DOR-A and 3DOR-B  [25]. To further investigate similarities between protein tertiary structures, the RCSB PDB website <https://www. rcsb. org/alignment> [68]  was used to align AvrStb9 against avrStb9 and AvrStb9 against CPAF (chains A and B) using the TM-align algorithm  [69].
Construction of disruption plasmids For AvrStb9 disruption, the hygromycin resistance marker (hph) was cloned between the 1,000 bp up-and downstream flanking regions of the ORF, respectively. The following DNA fragments were generated and cloned into pCAMBIA0380 plasmids (GenBank: AF234290. 1). The up-and downstream flanks were obtained by PCR on gDNA of IPO323 with primer pairs 1&2 and 5&6 (S7 Table) respectively using Q5 Hot Start High fidelity DNA polymerase (NEB, Evry, France). The hph was amplified from pCAMBIA0380 with the primers 3 and 4 (S7 Table ). pCAMBIA0380 was digested by EcoRI and XhoI and used for the cloning of three PCR fragments with Gibson Assembly cloning kit (NEB, Evry, France) according to the supplier's indications. The ligation product was introduced into NEB 5α competent Escherichia coli cells (NEB, Evry, France), transformants selected on kanamycin (50 μg.
Population genetics and selection analyses In addition to the fungal population used for genetic analyses, we extended our sequence diversity and selection analyses to include a global Z. tritici population originating from four distinct geographic locations, including the middle East (n = 30, Israel), Australia (n = 27, Wagga Wagga), Europe (n = 33, Switzerland) and North America (n = 56, Oregon, USA). Procedures of de novo genome assemblies and sequence extraction were previously described  [78]. We used DnaSP v6  [79]  to calculate summary statistics of the population genetic parameters associated with AvrStb9. Sliding window analyses of nucleotide diversity (π) and Tajima's D were conducted in a window length of 200 bp and a step size of 20 bp. To test the selective pressure acting on AvrStb9, we estimated the rates of non-synonymous (dN) and synonymous (dS) substitutions using CodeML from the PAML package  [80].
A dN/dS (ω) ratio of 1 indicated neutrality, whereas ω < 1 or > 1 suggested purifying or diversifying selection, respectively. However, the presence of intragenic recombination could bias the ω rate testing. To account for these issues, we performed the analysis with a non-recombining phylogenetic tree generated from the RDP4 software using RaxML with 100 bootstraps  [81].
Acknowledgments We thank  David Gouache ,  Henriette Goyeau ,  Gert H. J. Kema ,  Marc-Henri Lebrun , Frederic Suffert and  Anne-Sophie Walker  for their contribution in establishing the fungal population;  Johann Confais  for his help in the virulence assays;  Robert King  and  Jason Rudd  for providing IPO323 RNA-seq data;  Florence Cambon , the VegePole and GENTYANE platforms for the development of the Courtot NILs;  Marc-Henri Lebrun  for critical reading of the manuscript and valuable feedback; and  Sophie Bouchet  and  Fanny Hartmann  for assistance with the initial GWAS analyses.
Funding: T. C. M. received support from the  French Fund  to support Plant Breeding (FSOV 2008 H). J. A. and T. C. M. received support from the  French National Research Agency (program BIOADAPT , grant no. ANR-12-ADAP-0009-04 ), including the salary of J. C. S. F. and T. C. M received support from  BASF France SAS  (V. I. S. A. project), including the salary of R. A. INRAE BIOGER benefits from the support of  Saclay Plant Sciences-SPS  ( ANR-17-EUR-0007 ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
In Sects. 3 and 4 we state the selection criteria for AC objects and their selection function. Section 5 describes the properties of the AC sample on the latest XXL dataset, including redshift estimates and multi-wavelength methods of confirmation. In Sect. 6, we estimate the missed fraction within XXL and its consequences for the final cosmological analysis of XXL and other X-ray surveys. We summarise our results in Sect. 7. Throughout the paper, unless otherwise stated, we assume a WMAP9 cosmology with Ω M = 0. 28, Ω Λ = 0. 72, and H 0 = 70 km s -1 Mpc -1.
Injections into simulated XMM observations We performed realistic Monte Carlo image simulations of XMM-like observations (hereafter pointings) to assess the detection threshold of clusters with central point source contamination using the InstSimulation software  (Valtchanov et al. 2001). Soft-band XMM pointings were produced from scratch using a combined exposure time of 10 ks (see Fig. 1 ). Two background components -the non-resolved vignetted AGN photon background and the unvignetted, uniform particle backgroundwere added according to  Read & Ponman (2003). These simulations faithfully reproduce the characteristics of the three EPIC detectors and have been used to characterise the cluster selection function of the XXL and X-CLASS surveys (see  Pacaud et al. 2006; Koulouridis et al. 2021; Garrel et al.
We used the epn_ratio to separate the 'peakiness' of the two classes, selecting a threshold of epn ratio ≥ 0. 2. After applying this cut, the majority of pure clusters have lower epn_ratio values compared to the AC class (Fig. 3 , bottom panel). We also imposed a cut on the core radius, epn_ext ≥ 5 , similarly to the C1 and C2 criteria. We selected a slightly higher value of 5 rather than 3 for the pure clusters to compensate for the fact that the AC sources are, by defintion, more peaked. The final criteria for the AC selection is summarised in Table  2. We emphasise that given the use of X-ray image simulations, no light cone information is provided, and therefore the density of points in Figs. 2 and 3 is not physically relatable to the real ratio of C1 and AC clusters. Nevertheless, we estimated the misclassification rate of C1 to AC clusters for the simulated dataset.
Data processing and sample selection We implemented the AC criteria within the latest version (hereafter V4. 3) of the XXL pipeline to undertake a systematic search for AC clusters within the survey footprint. Details of the most recent XXL pipeline are given in XXL Paper XLVI; below we summarise the salient aspects. First, event lists were created from raw observation data files (ODFs) using the SAS software  (Gabriel et al. 2004 ) tasks emchain and epchain, filtered for solar soft photon flares. The cleaned event lists were then used to produce images of 2. 5 per pixel to correctly sample the XMM PSF ( 6 on-axis) using evselect. Three images were produced -one for each EPIC detector (MOS1, MOS2, and PN) -for three energy bands: [0. 3-0. 5], [0. 5-2. 0], and [2. 0-10. 0] keV. In what follows we predominantly focus on [0.
5-2. 0] keV images as this is most relevant for cluster detection and characterisation. Departing from the earlier use of approximately 700 single XMM pointings spread over 50. 9 deg 2 of the extragalactic sky, the most up-to-date version features images that are mosaicked into 68 × 68 'tiles' (the term 'mosaic' is reserved for images consisting of more than one EPIC detector). One tile was created per EPIC instrument, pixelised at 2. 5 using the SAS tasks attcalc and evselect. The tiling layout is designed such that there is a 4 overlap between tiles, with approximately 20-25 pointings per tile. The three individual tile images were coadded into a single mosaic prior to running the XXL source detection pipeline (described in Sect. 2. 2). We detect 27 AC candidates in the northern field, 23 of which are 'pure' AC objects, three are C2-AC, and one is C1-AC.
Redshift confirmation Out of the 33 candidates, 11 have spectroscopic redshift confirmations published in XXL Paper XX. For six additional objects, we derived spectroscopic redshifts from public (e. g. SDSS, GAMA, AAT) or XXL private data stored in the CEntre de donéeS Astrophysiques de Marseille 2  (hereafter CESAM). Two objects have spectroscopic confirmation from the New Technology Telescope (NTT) operated by the European Southern Observatory (ESO). In total, 19 objects in the full catalogue are spectroscopically confirmed. For the objects that possess no spectroscopic confirmation, we report a photometric redshift estimate within 120 arcsec of the X-ray cluster centre where available. Photometric redshifts of the clusters were measured using the Wavelet Z Photometric (WaZP) cluster finder, which uses wavelet-based density maps of galaxies selected in photometric redshift space, removing any assumptions on the cluster galaxy population  (Aguena et al.
2021). Details on the method used to compute the individual galaxy redshifts is detailed in  Gschwend et al. (2018). Where referenced, we refer only to the WaZP-based cluster photometric redshifts. All sources with a confirmed WaZP redshift estimate have a S /N 3. 0, above which the occurrence of false detections is considered to be negligible. We prioritised the use of a wavelet-based cluster finder rather than one based on the red sequence to confirm the AC sources for the main reason that we are searching for clusters with central AGN contamination, which may appear more 'blue'  (Klesman & Sarajedini 2014) , posing issues for colour-based cluster finders. In the absence of WaZP estimates, we used photometric information from the XMM-BCS survey  (Šuhada et al. 2012)  or those which were publicly available via the NASA/IPAC Extragalactic Database (NED).
Indication of AGN presence We performed two diagnostic checks to indicate the presence of an AGN within the AC objects. We did this for all 33 sourcesclusters and individual galaxies -as we aim to quantify to what extent the peaked X-ray profile is indicative of an AGN, irrespective of the object morphology. We began by searching for any publicly available optical spectra for QSOs at or near the object position, using SDSS, NED, and CESAM databases. If no QSO spectrum was available, we searched for the presence of emission lines in the BCG spectrum as an indicator of ionised gas, which may suggest the presence of AGN activity. Four cluster candidates were followed up with the MISTRAL spectrograph 3  to search for emission lines that could confirm AGN presence (see details in Appendix A). The red markers denote AC objects that have the ten largest hardness ratios (an independent X-ray indicator of AGN presence) described in Sect.
5. 5. All objects are coloured according to the level of point-source contamination in the soft X-ray band, which is defined in Sect. 5. 6. Secondly, we used observations from the publicly available All-Sky Wide-field Infrared Survey Explorer (WISE) data release in four photometric bands, centred at 3. 4, 4. 6, 12, and 22 µm and referred to as W1, W2, W3, and W4, respectively. We probed the infrared power law of AGNs by measuring the flux f of the AC sources in adjacent bands, namely f W1 , f W2 , and f W3. Hot dust emission from the torus heated by AGN activity is expected to result in high flux ratios, hence allowing us to confirm AGN presence. We searched for all mid-infrared counterparts within an angular radius of 60 from the AC position, resulting in 33 mid-IR matches (the full AC catalogue) all within <11 of the object centre.
In other words, 5% of genuine clusters are excluded from the cosmological dataset due to contamination from a central AGN. Strikingly, after removing the point-source contribution from the AC sources, clusters can be recovered in the 0. 8 ≤ z ≤ 1 range, suggesting that AC and cool-core clusters may help to explain the deficit of detected X-ray clusters above z > 0. 6 (Fig. 9 ). Such a deficit has been reported in X-ray cluster samples based on the predicted number density of clusters using the Planck CMB cosmological model Planck Collaboration XXIX (2014). This deficit has been observed within both the north- XXL AC sample XXL AC clusters recovered as C1 XXL AC galaxies C1 threshold Fig. 8. AC clusters recovered as C1 by detection pipeline after the pointsource contribution from the central region of the cluster is removed.
Since the X-ray luminosity depends on the gas density squared, it is expected that X-ray clusters at high redshift are more likely to be detected if they have more peaked profiles, hence the AC classification is a critical tool to recover clusters that are otherwise missed by the C1 classification alone. Finally, we note that all the C1/C2-AC objects, after removing the central point-like emission, are no longer classed as C1 objects. This is not unexpected since we are removing the majority of the flux from objects that are already classed as C1/C2 by the pipeline. While these objects do not impact the cosmological dataset, since they are known by definition in the C1 selection function, the number of C1/C2-AC may inform as to the coolcore fraction of the C1 sample. Interestingly, these objects also displayed no clear signature from an AGN from the criteria outlined in Sect.
Summary and conclusions The AC sample is the first pipeline-derived catalogue of clusters with measurable AGN contamination within the XXL survey. In particular, the characterisation of the 25 AC clusters, using X-ray and multi-wavelength diagnostics, forms a valuable dataset to better understand the evolution of AGN with clusters and their impact on cosmology. We used extensive XMMlike image simulations to define a parameter space to capture AC clusters by modelling the point-like and extended X-ray emission simultaneously. We then applied this criteria within the XXL pipeline to generate a sample of clusters impacted by AGN presence or with cool core signatures. Our work revealed that AGN contamination in clusters is present well into the intermediate redshift range (0. 5 < z < 1), consistently with other studies (e.
g. Logan et al. 2018; Maughan & Reiprich 2019). We found that removing the point-source flux contribution from these objects allows for the recovery of genuine clusters in parts of the mass-redshift plane currently excluded by the canonical cluster selection function, implying that clusters are 'missed' by current X-ray detection methods. We estimated the impact of these missed clusters to be of the order 5% in the most recent XXL dataset. Finally, we quantified the impact on the Ω m -σ 8 parameter space as a result of improperly accounting for these missed objects within the selection function. Consequences are not drastic for small XXL-like areas, but likely to be more substantial for other X-ray surveys. Our future work will involve finding additional, complementary methods to determine AGN presence within clusters, exploiting both the spectral and image properties of these objects; for example, machine learning methods to denoise and increase the spatial resolution of XMM-Newton images  (Sweere et al.