dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical analysis All values are given as means with standard deviation (SD). Statistics were performed using Statistica v. 13 software (StatSoft, Paris, France). Normality of population distributions was tested using the Shapiro-Wilk test. The appropriate tests were then used to detect significant differences: one-way ANOVA or Kruskal-Wallis tests followed by a post hoc test (HSD) or a Mann-Whitney test, respectively. The significance threshold was set at P < 0. 05. Correlations were calculated and a principal component analysis (PCA) were performed using R software and the FactoMineR package. Correlation matrix coefficients were obtained with a Pearson test using the R corrplot package.
TA B L E 2 Primer sequences used for Real-Time RT-PCR analysis Target gene Abbreviation Primer sequences (5′ to 3′) Database Accession number • C as previously described inPengam et al. 18S ribosomal RNA 18S rRNA 1 (F) CGGAGGTTCGAAGACGATCA GenBank AF308735. 1 (R) TCGCTAGTTGGCATCGTTTAT Adenosine monophosphate Ampkα1 (F) GCCACCATCAAAGAGATCCG GenBank XM_021590586 kinase α1 (R) TCAAACTTCTCACACACCTCC Adenosine monophosphate Ampkα2 (F) CGCTCAAGAGGGCTACCATT GenBank XM_021604240 kinase α2 (R) CAGACTCGGTGCTCTCAAAC Peroxisome proliferator-activated Pgc-1α 2 (F) CAACCACCTTGCCACTTCCT GenBank FJ710605. 1 receptor-γ coactivator-1α (R) GGTTCCAGAGATCTCCACAC Citrate synthase Cs 1 (F) AACCAACCTCACTCATCACCA GenBank XM_021566131 (R) GCAGCAGAAGCAGCCCATAA Superoxide dismutase 1 Sod1 3 (F) AGGCTGTTTGCGTGCTCAA GenBank AF469663.
1 (R) CCAATCAGCTTCACAGGACCAT Superoxide dismutase 2 Sod2 3 (F) CCACACCATCTTCTGGACAAAC Tigr TC123543 (R) GGAGCCAAAGTCACGGTTGA Glutathione peroxidase 1 Gpx1 3 (F) CCTGGGAAATGGCATCAAGT Tigr TC94794 (R) GGGATCATCCATTGGTCCATAT Uncoupling protein 2α Ucp2α (F) ACCAAACCGACGTTACCATGG GenBank NM_001124654. 1 (R) TTAAGAAGCGCGTCCTTGATG Uncoupling protein 2β Ucp2β (F) TATTGCCATGGTGACCAAGGA GenBank NM_001124571. 1 (R) CTACAGAGGAGTGGTGTAGTT NADH dehydrogenase 1 Nd1 (F) TTGTACCCGTTCTGTTAGCAG GenBank NP_008290 (R) TAGTTTTAGGCCGTCTGCGAT Cytochrome c oxidase 1 Cox1 (F) TCAACCAACCACAAAGACATTG GenBank NP_008292 (R) CACGTTATAGATTTGGTCATCC Cytochrome c oxidase 2 Cox2 2 (F) GAGGCAATAAAGGCTGTTTGGT GenBank NP_008293 (R) GCCGTTCCTTCTTTAGGTGTAA Cytochrome c oxidase 4 Cox4 4 (F) TACGTGGGGGACATGGTGTT Sigenae tcav0004c.
i. 22_3. 1. s. om. 8 (R) CCCAGGAGCCCTTCTCCTTC Adenosine triphosphate synthase 6 Atp synthase 6 (F) CTTCGACCAATTTATGAGCCC GenBank NP_008295 (R) TCGGTTGATGAACCACCCTTG Lactate dehydrogenase A Ldh-a (F) AGGTCATGGACCTGCAGCAT GenBank XM_021564046. 1 (R) GGCTGTACTTGACGATCTGG Lactate dehydrogenase B Ldh-b (F) GGTCCAGAGGAACGTCAACA GenBank XM_021577545. 1 (R) TGCCGATGACACGGTGTTTG The hybridisation temperature was 60 • C for all primers. Primers were designed by ourselves except those with a subscript number. (F): Forward, (R): Reverse. References: 1 Magnoni et al. (2013); 2 Eya et al. (2015); (2020). RNA concentrations were measured with a SimpliNano™ 3 Gunnarsson et al. (2009); 4.
Image analysis All PET/CT fusion images were obtained using the VivoQuant™ software (Invicro, USA). Each PET/CT image was visually interpreted and 3D regions of interest (3DROI) corresponding to the lungs were manually drawn for CT quantification and to determine their radioactivity content. Injected doses per animal were measured at the time of injection in MBq. Lung radioactivity content was expressed in MBq, converted to percentage of injected dose per gram of tissue (%ID/ g). PET scanner was indeed cross-calibrated to the dose calibrator and PET data were corrected for scatter and attenuation. All images were decay corrected for quantification. Additionally, other PET parameters were measured for [ 18 F]FDG and [ 18 F]FMISO (SUVmean, SUVmax), lung-tobackground ratio (LTBR). SUV values have been calculated using a general soft tissue value of 1 g/mL.
Fluorescence in vivo imaging Hypoxia fluorescence imaging was performed on NaCl-and BLM-receiving mice at D21. Mice under anaesthesia (1. 5% isoflurane) received intravenous injection (tail vein) of a carboxic anhydrase IX (CAIX) targeting tracer, Hypoxisense 680™ (2 nmol/100 μL), 24 h prior imaging. Hypoxisense 680™ (PerkinElmer, France) is a commercially available near-infrared (NIR) fluorescent agent which detects the surface expression of CAIX) protein, which increases in hypoxic tissues. Mice were then maintained under anaesthesia (1. 5% isoflurane) and placed on an imaging heated bed inside an optical imager IVIS® LUMINA III (Perkin Elmer, France). Fluorescence images were acquired with the optimal filter pair (λ ex : 660 nm, λ em : 710 nm). Images were processed through the Living Image® software (Perkin Elmer) for exclusion of background signals (auto-fluorescence, food).
Fibrosis progression and therapy efficacy have been measured by the variation of MLD between start and end of treatment (D9 and D23) in BLMreceiving mice treated or not with pirfenidone and nintedanib. This method therefore assumes that CT scan is an accurate tool to quantify lung fibrosis. In addition, as mentioned before, differences in CT scans between preclinical model and human IPF represent a limitation regarding the translation of our results in clinical setting. To further validate our approach, we demonstrated that early [ 18 F]FMISO lung uptake also correlated with end collagen content which is a validated readout in preclinical lung fibrosis studies. Our preclinical data are in line with recent clinical studies showing that intracellular hypoxia measured by [ 18 F]FMISO PET has prognostic impact in patients with breast and pancreas cancer  [32, 33].
Acknowledgements We thank the histology platform Cellimap for their invaluable and professional help. We would like to thank  Camille Drouet  and  Valérie Bordat  for their technical help. This work was performed within Pharm' image, a regional center of excellence in Pharmacoimaging. Support was also provided by the  French Government  through the  French National Research Agency (ANR)  under the program " Investissements d'Avenir"  ( ANR-10-EQPX-05-01/ IMAPPI Equipex ). Julie Tanguy  is funded by  "La Fondation du Souffle et le Fonds de Recherche en Santé Respiratoire ".
Phylogenetic tree reconstructions We inferred phylogenetic trees based on the GAPDH and ITS loci using maximum-likelihood (ML) analyses to identify the main lineages of C. geophilum strains. Our twolocus nuclear dataset contained alignment lengths of 597 and 949 bp for GAPDH and ITS, respectively, with a total of 1546 aligned bp for the 40 strains (including outgroups; Table  S2 ). The GAPDH locus had the most variable sites. The inferred GAPDH and ITS ML tree topologies revealed several incongruencies, but the concatenated partitions generated a fairly well-resolved phylogenetic tree, with a majority of branches with BS values >80; Figs 1B; Figs  S1  and S2 ). With minor exceptions, we found strong support for several monophyletic clades but those were not congruent with the geographical origin of the samples and host plants (Fig.
1B ; Table  1 ). Overall, the clades that emerged from the sequenced European strains were depicted in the worldwide clades 5 and 6 described by  Obase et al. (2016). To improve resolution, we used genome-wide data and found highly congruent tree topologies compared to the GAPDH and ITS phylogeny. The phylogenomic tree was robust to the use of two near neutral datasets of either synonymous SNPs or randomly-selected genome-wide SNPs (Fig. 1C ). Both genome-based ML trees were wellresolved with most of branches being strongly supported (BS > 90; Fig. 1C ). This indicates that neutral divergence in the coding regions matches the phylogenetic relationships given by the divergence in non-coding or coding regions.
057, 1. 58, 1. 060, 1. 061 and 1. 064) clustered into the two major groups, reporting a new case of highly divergent lineages present in the same sampling site (Fig. 2A ). In addition, isolates from the Blizyn (POL) sampling site (1. 094, 1. 096 and 1. 097) also showed divergent lineages, but these were only represented in the clonal group with both mating types (Fig. 2A ). No fungal lineages exclusive to certain host tree species were observed in this dataset. The reference genome 1. 58 appeared at the edge of the recombinant genetic group. The first axes of the PCA captured 61. 9% and 9. 6% of the genetic variance (PC1 and PC2, respectively; Fig. 2B ). In total, the first three axes explained 76. 8% of the overall genotypic variation (Fig. S3 ). Consistent with the Neighbour-Net network, PC1 and PC2 separated three to four genetic groups with substantial differentiation between the Pfynwald strains.
Within-species genetic diversity and signatures of selection We assessed within-species genetic diversity across the 16 C. geophilum genomes and identified genomic regions with elevated diversity rates along the two longest scaffolds, i. e. , #1 and #2; Fig. 5A  and C , on which the density of coding sequences and TEs were negatively correlated (Fig. 5B  and D ). At the gene level, we estimated the nucleotide diversity per site (π) to be 0. 000-0. 020, 0. 000-0. 028 and 0. 000-0. 013 for the 'all  ', 'ref. cluster' and 'non-ref. cluster' datasets, respectively (Fig. 6A ). The genome-wide average π value was 3. 31 Â 10 À3 , 1. 31 Â 10 À3 and 1. 01 Â 10 À3 , respectively (Fig. 6A ). These results support that the ref. dataset (Fig. 6B ). This strongly skewed Tajima's D distribution when all strains are included towards positive values indicates strong population substructure (Fig.
To shed light on host specificity as a potential driver of genetic structuring within and among sites, detailed information on C. geophilum populations associated with roots of different host tree species is needed. Regarding the environment, adaptation to soil characteristics may prevent wide distribution, foster differentiation through ecological isolation and, ultimately, lead to independent and clonal lineages. Note that edaphic specialisation can be coupled with, or even supplanted by, the competition avoidance strategy of ECM species within soil communities  (Mujic et al. , 2016). Clearly, our dataset with 16 strains is limited and further population genomic studies, with more isolates per population, are required to be conclusive about the prevalence of sexual reproduction under varying ecological conditions throughout the species range.
Read mapping, variant calling and polymorphism filtering We downloaded sequencing data using fastq-dump, as implemented in the SRA-Toolkit v. 2. 9. 0 (http://ncbi. github. io/sra-tools/), and inspected read quality with FastQC v. 0. 11  (Andrews, 2017). We performed quality trimming of raw reads with Trimmomatic v. 0. 36  (Bolger et al. , 2014)  based on the following quality filters: ILLUMINACLIP: TruSeq3-PE. fa:2:30:10, SLIDINGWINDOW:5:10, LEAD-ING:10, TRAILING:10, and MINLEN:50. Trimmed reads were mapped on the reference genome 1. 58 using Bowtie v. 2. 3 with the -very-sensitive and -local settings  (Langmead et al. , 2009). The resulting BAM files were sorted by position and indexed with SAMtools v. 1. 6  (Li et al. , 2009). We applied the GATK v. 3. 7 and used the HaplotypeCaller to perform the SNP and indel discovery  (McKenna et al.
Phylogenetic tree reconstructions To infer the phylogenetic relationships of the 16 C. geophilum strains within the seven main clades described in  Obase et al. (2016) , we assembled a broader dataset (GAPDH and ITS, including the intron in the small subunit rDNA region) and reconstructed a twoloci phylogenetic tree based on maximum likelihood (ML) analysis. The additional strains (22 C. geophilum, one Pseudocenococcum floridanum and one G. stellatum) covered most of the phylogenetic diversity and intercontinental distribution of the species. This dataset includes important disjunctions in North America and potential cryptic taxa in European Clades 5 and 6  (Obase et al. , 2016) , which were investigated using a coalescentbased species delimitation approach  (Zhang et al. , 2013). For each locus, we aligned sequences using MAFFT v.
7 with default parameters  (Katoh et al. , 2019)  and excluded ambiguous regions using Gblocks v. 0. 91b with the less stringent settings  (Castresana, 2000). For each locus, we implemented the optimal substitution model, based on AICc scores calculated with the programme ModelTest-NG v. 0. 1. 7 (Darriba et al. , 2020; Table   S2 ). We ran ML tree searches with a randomised maximum parsimony starting tree and gamma-distributed site rates using RAxML-NG v. 1. 0. 0  (Kozlov et al. , 2019; Edler et al. , 2021). We performed 20 runs and set 1000 bootstrap replicates for each run, computed on the best-scoring ML tree with a bootstrap random-number seed. We fixed G. stellatum as the outgroup to root phylograms. At the genome-wide level, we generated two SNP datasets to infer independent phylogenetic tree reconstructions.
First, from the overall SNP data genotyped in all 17 strains (16 C. geophilum and one G. stellatum), we randomly selected a single SNP per one kilo base-pair (kbp) of the reference genome sequence using VCFtools v. 0. 1. 15 (Danecek et al. , 2011). Second, from the overall SNP dataset, we retained only synonymous variants identified in coding regions (see below SnpEff). A total of 16 391 and 68 145 SNPs were retained for the genomewide and synonymous SNP datasets, respectively. We used each SNP dataset for an independent ML tree reconstruction with RAxML-NG  (Kozlov et al. , 2019; Edler et al. , 2021). We ran the best ML tree by analysing 20 different starting trees and performed 1000 bootstrap replicates on the best-scoring ML tree with a bootstrap random-number seed. We implemented a general time reversible (GTR) substitution model and allowed for rate heterogeneity (GTR + G).
Recombination events and genetic structure To gain insights into recombination events and genetic structure, we generated an SNP dataset for intraspecific genetic analyses of C. geophilum using the following criteria: a single SNP randomly selected per one kbp of the reference genome and genotyped in a least 90% of the strains, i. e. , genotyped in at least 15 out of 16 strains. We used SplitsTree v. 4. 14. 6 to build up a Neighbour-Net network from all C. geophilum strains  (Huson and Bryant, 2006) , and uncorrected p distances calculated from the SNP matrix, which is the proportion of positions at which two sequences differ, to generate the network. The network was drawn based on equal angle splits. Tests of recombination within genetic groups of strains were carried out using the Φ test implemented in SplitsTree  (Bruen et al.
, 2006). The test identifies evidence of recombination based on phylogenetic compatibilities of neighbouring SNPs in the matrix. We ran the test with the default settings of a 100-character window size on the SNP matrix. Based on the Neighbour-Net network analysis, three genotype datasets were analysed to assess lineage-specific patterns; comprising all strains (referred as 'all'), a reference cluster which contained the reference genome 1. 58 (referred as 'ref. cluster' including 1. 005, 1. 057, 1. 061, 1. 064, 1. 076 and 1. 58), and a non-reference cluster which did not contain the reference genome  (referred as 'non-ref. cluster' including 1. 022, 1. 060, 1. 075, 1. 094, 1. 096, 1. 097, 1. 100, 1. 101, 1. 102 and 1. 104). We conducted Bayesian unsupervised genetic clustering using STRUCTURE v.
2. 3. 4  (Pritchard et al. , 2000). We used an admixture model with correlated frequencies and no prior regarding the population of origin  (Falush et al. , 2003). The K parameter was tested for values ranging from 1 to 5, with 10 repetitions for each tested K value. We used 50 000 iterations as a burn-in period and 100 000 iterations per run for the Monte Carlo Markov Chain (MCMC) replicates. We visually inspected the convergence of parameters and diagnosed the optimal K value based on the stabilisation of -Ln(K) values. We performed a principal component analysis (PCA) to explore genotypic variability based on the SNP matrix using the R package SNPRelate v. 1. 20. 1  (Zheng et al. , 2012).
05) given the heterogeneity in genotypes and small sample size. CNV regions were further filtered based on the normalised read depth. Deletions and duplications were retained if the normalised read depth was <0. 4 and >1. 8, respectively. CNV regions called for the reference genome 1. 58 were aligned against the reference genome for validation, which showed possible issues in read alignment and/or the local assembly quality. Hence, gene deletions and duplications among C. geophilum strains were only retained if the reference strain showed a normalised read depth between 0. 7 and 1. 3 for the same region. A gene was considered affected by a duplication or deletion if more than 50% of the coding sequence length was overlapping with a CNV region using the R package ggplot2 v. 3. 3. 0 (Wickham, 2011).
Within-species genetic diversity and signatures of selection To improve our understanding of genomic signatures of selection, we analysed three species-specific estimates of gene diversity and their deviations from genome-wide average; nucleotide diversity per site (π), Tajima's D, and pN/pS ratios. We assessed these population genetic statistics for individual genes using the R package PopGenome v. 2. 6. 1  (Pfeifer et al. , 2014)  and included all SNPs falling within coding sequence boundaries that were genotyped in at least 15 out of 16 C. geophilum strains. At the genome-wide level, a negative Tajima's D is indicative of a population size expansion (e. g. , after a bottleneck), whereas a positive Tajima's D is supporting a decrease in population size. At the genespecific level, negative Tajima's D values are indicating of recent positive selection, while positive values are imputable to balancing selection  (Hohenlohe et al.
, 2010). For π estimates, a decrease in gene-specific values in comparison to the average genome-wide level suggests a signature of positive selection  (Hohenlohe et al. , 2010). Regarding pN/pS, a ratio greater than one implies positive selection, whereas a value less than one assumes purifying selection  (Tanaka and Nei, 1989). The three genotype datasets (see above) were analysed to evaluate the robustness of the results. To further examine the genes putatively under selection, we identified top-candidate genes based on the 20 lowest Tajima's D values in each of the two datasets, the ref. and nonref. cluster. Functional annotation of the top-candidate genes was carried as outlined in  Peter et al. (2016)  and according to the JGI annotations (mycocosm. jgi. doe. gov), including predicted gene ontology (GO), EuKaryotic Orthologous Groups (KOG), protein family clarification using the InterPro database  (Blum et al.
Chromosomal synteny analysis of the MAT1 locus We analysed chromosomal synteny of the MAT1 locus containing the MAT1-1 and MAT1-2 idiomorphs based on our de novo genome assemblies of individual C. geophilum isolates. We used SPAdes v. 3. 11. 1 to assemble genomic scaffolds based on the paired-end Illumina sequencing data described above  (Bankevich et al. , 2012). The SPAdes pipeline includes the BayesHammer read error correction module and builds contigs in a stepwise procedure based on increasing kmer lengths. We defined a kmer range of 21, 33, 45, 67 and 89 and enabled the careful option to reduce mismatches and indel errors in the assembly. Assembled scaffolds were error-corrected based on read realignments. We used the tool QUAST v. 4. 6. 0 to retrieve assembly statistics for each strain  (Gurevich et al.
cluster' dataset showed a non-normal distribution of Tajima's D with an excess of negative values, which is indicative of a population expansion. The non-ref. cluster showed a distribution largely centred around 0 under neutrality and constant population size. A slight deviation towards positive values suggests a historic decrease in population size or some population substructure. The distribution of the pN/pS ratio assessed from all dataset suggests purifying selection (Fig. 6C). Then, we characterised the top-candidate genes putatively under selection by identifying those with the lowest Tajima's D estimates in either the 'ref. cluster' or 'non-ref. cluster' (see Experimental procedures). Based on functional annotation, we found that those top-candidate genes differed to some extent between the ref.
Acknowledgements We thank  Stephanie Pfister  and  Barbara Meier  for help in the laboratory; the staff at the  Joint Genome Initiative  for sequencing. We thank  Claire Veneault-Fourrey  and  Joey Spatafora  for helpful comments and discussions. This project was supported within the framework of the  ARBRE/WSL  project ' Blacksecret ' by grants from the  WSL  and the  French National Agency of Research (ANR) , and as part of the ' Investissement d'Avenir' programme  ( ANR-11_LABX-0002-01 ) of  Labex ARBRE  ( CFP15 ; to AK and MP) and the  EC-supported Network of Excellence Evoltree  ( GOCE-016322  to MP). This work was also funded by the  U. S. Department of Energy Joint Genome Institute , a  DOE Office of Science User Facility , and supported by the  Office of Science of the U. S. Department of Energy  under Contract No.
The resulting eigenvalue problem is solved by varying the non-dimensional wavevector k * along the irreducible path [G À X], with G ≡ (0, 0) and X(≡ p/A, 0), with A the lattice parameter using the commercial software Cosmol 5. 5. As a second step, we assume free boundary conditions applied to the top surface of the unit cell and of infinite halfspace at the bottom surface of the unit cell, which better reflects the condition of surface seismic wave propagation. This is modeled by adding a 8l-long soil-like domain plus a 2l-long PML region (where l is the wavelength) below the unit cell, as shown in Figure  2A , where for the sake of synthesis, only the hierarchical unit cell model is reported. Dispersion diagrams are calculated by applying the same aforementioned procedure. Figure  2B  reports a schematic representation of the finite element models used to perform dynamic transient calculations to evaluate and compare the attenuation performances of the ordinary and hierarchical unit cells.
Additionally, STAT1 and STAT2 comprise a C terminal transactivation (TAD) domain: the STAT1 transactivation domain (IPR022752) binds selectively to the Transcription Adaptor putative Zinc finger (TAZ)2 domain of C CREB-binding protein (CBP)/p300, while the STAT2 transactivation domain (IPR022756) binds to the TAZ1 domain of this protein  (Wojciak et al. , 2009; Bhattacharya et al. , 1996; Zhang et al. , 1996)  (Fig. 1A ). This domain confers to STAT1 and STAT2 an additional capacity to regulate gene expression since CBP and P300 are histone acetyltransferases that control acetylation of histones in nucleosomes, thus regulating chromatin remodelling and gene transcription. The vertebrate stat repertoire emerged from an ancestral sequence present in the common ancestor of protostomes and deuterostomes with all STAT typical domains  (Wang and Levy, 2012) , through WGD, tandem duplication and dispersion  (Copeland et al.
Classification and nomenclature of stat genes in tetraploid species based on the example of salmonids The survey of the stat gene cluster in salmonid fish highlighted a nomenclature issue for stat genes in polyploid species. The current annotation of such complex duplicated genomes is often misleading because of assembly errors. Some annotations inherited the nomenclature used at the time of the first and often single gene discovery by homology cloning and lack consistency with annotation in other fish species. Regarding salmonid stat genes, the rainbow trout stat1a1 and stat1a2 were annotated stat1-1 and stat1-2 with no reference to the stat1a group defined previously in non-salmonid teleost such as zebrafish. The stat1a3 was left annotated as "uncharacterized protein" whereas phylogeny and blast against the mammalian protein database allocated it to the stat1 group.