dataesr/scientific-paragraphs-categorization huggingface dataset:
This native linker showed no contacts with neighbouring kringle domains in wild-type NK2 reported by Tolbert and colleagues (PDB code 3HN4)  (Tolbert et al, 2010)  and in NK2 in complex with heparin (PDB code 3SP8) and was therefore believed to not constrain the relative orientation of the K1 domains. Furthermore, a poly-histidine-tagged variant, designated K1K1H6, was produced to facilitate certain assays and a single kringle 1 domain polyhistidine-tagged variant (K1H6) as a monovalent control. As earlier studies had revealed a secondary low-affinity heparin-binding site in the kringle 1 domain affecting biological activity  (Lietha et al, 2001) , we designed two additional variants, K1K1S2 and K1K1S4. In these variants, we have introduced two or four reverse-charge mutations in each kringle (schematically shown in Fig 1A ) to test the implication of the basic residues naturally present at those positions and to evaluate their importance for the biological activity.
The collected datasets enabled the determination of the molecular structures at 1. 8 Å resolution and 1. 7 Å for K1K1 and K1K1H6, respectively. Both proteins crystalized in space group P 1 21 1 with K1K1 having two molecules per asymmetric unit and K1K1H6 having one. Structurally, K1K1 and K1K1H6 are nearly identical with a backbone root mean square deviation of 0. 8 or 1. 8 Å (Fig 3B ), depending on which of the two molecules in the asymmetric unit of K1K1 is used for alignment with K1K1H6. Both proteins show an elongated "stretched-out" conformation with the MET-binding sites exposed on opposite ends and the N-and C-terminus located at the centre  (Fig 3C). In K1K1H6, good electron density was also observed for the poly-histidine tag possibly because of stabilising interactions between the histidine residues and residues of the N-terminal kringle domain.
Using a low-resolution crystallographic dataset of NK1 in complex with MET567 (unpublished, briefly described in  Blaszczyk et al [2015] ), we could generate a model based on the alignment of K1K1 with the crystal structure of the NK1-MET567 complex. The theoretical scatter curve matched the experimental scatter curve remarkably well (χ 2 = 3. 6) with also in this model a slightly bent K1K1 conformation fitting best with the SAXS envelope. Moreover, the recently published cryo-EM structure of the NK1 dimer in complex with two SEMA domains  (Uchikawa et al, 2021)  perfectly accommodates our K1K1 SAXS envelope overlapping both NK1 kringle domains and supports the proposed model for receptor activation by K1K1  (Figs 4C and S2E). Overall, we have generated compelling structural support for a minimal MET receptor agonist that comprises two covalently linked MET-binding sites.
Further optimization was performed with different protein to crystallization solution ratios (1:2, 1:1, 2:1) in a 24-well plate using hanging drop. After 24 h, good crystals were obtained in a 2:1 protein-to-solution ratio condition. In addition to the cryo-protection intrinsic to the Morpheus screen, additional glycerol was added up to 25% of the final volume before crystals were collected using a CryoLoop (Hampton Research) and flash frozen and stored in liquid nitrogen. Crystallizations of K1K1 went through a similar optimization procedure and yielded good crystals in a condition of 100 mM Tris/ Bicine (pH 8. 5), 30 mM sodium nitrate, 30 mM sodium phosphate dibasic, 30 mM ammonium sulphate, 12. 5% wt/vol PEG 1000, 12. 5% wt/vol PEG 3350, 12. 5% vol/vol MPD. Data were collected at the ESRF in Grenoble, France, at beamline at BM14 for K1K1H6 and ID23-1 for K1K1 and was solved by molecular replacement using the NK1 kringle domain taken from 1BHT.
Crystal structure determination For both K1K1H6 and K1K1, the MTZ file generated by the ESRF EDNA framework Fast Processing System was used, downloaded through the ISPyB server. At the different session of measurement, the EDNA auto processing used AIMLESS (v0. 3. 3) and POINTLESS (v1. 9. 8) to create the MTZ file for K1K1, whereas for K1K1H6, the MTZ file was generated using AIMLESS (v0. 5. 2) and POINTLESS (v1. 9. 25). Molecular replacement was performed using Phaser (v2. 8. 3), the Phenix software package (v1. 19. 2)  (Liebschner et al, 2019) , using the kringle domain from 1BHT. pdb  (Ultsch et al, 1998)  followed by several rounds of refinement using Phenix refine  (Afonine et al, 2012)  and WinCoot  (Emsley et al, 2010) (v0. 9. 4. 1). The dataset of K1K1 and K1K1H6 had an upper resolution limit of 1.
61 and 1. 39, respectively, a resolution cut-off was set to 1. 8 for K1K1 and 1. 7 for K1K1H6. The data collection and refinement parameters and statistics are given in Table  S1. The data collection and refinement statistics are given in Table  S1. The molecular graphics presented in this manuscript were generated with PyMOL (v2. 4. 1) and Chimera (v1. 15)  (Pettersen et al, 2004).
Small-angle X-ray scattering (SAXS) Solution scattering data were collected at ESRF BM29 using a sec-1 frame rate on Pilatus 1 M detector located at a fixed distance of 2. 87 m from the sample, allowing a global q range of 0. 03-4. 5 nm with a wavelength of 0. 01 nm. SEC-SAXS experiments were carried out using the Nexera High Pressure Liquid/Chromatography (HPLC; Shimadzu) system connected online to SAXS sample capillary. For these experiments, 35 μl of each sample at the concentrations indicated in the Table  S2  were injected into a Superdex 200 PC 3. 2/300 Increase column (GE Healthcare), pre-equilibrated with 25 mM Tris (pH 7. 4), 150 mM NaCl. Frames corresponding to protein peaks were identified, blank subtracted, and averaged using CHROMIXS2. Radii of gyration (Rg), molar mass estimates, and distance distribution functions P(r) were computed using PRIMUS in the ATSAS package (v3)  (Petoukhov et al, 2012).
The plate was incubated at 23°C for 30 min in a dark box. Finally, Ni-NTA coated donor beads (6760619C; 10 μl, 50 μg/ml; PerkinElmer) were added, and the plate was further incubated at 23°C for 60 min in a dark box. The emitted signal intensity was measured using standard Alpha settings on an EnSpire Multimode Plate Reader (Perki-nElmer). Measurements are expressed as technical duplicates (mean ± SD, n = 2). The data corresponding to the 3 nM MET condition were subjected to a non-linear regression analysis using four-parameter sigmoidal equation using SigmaPlot software (v13 and v14. 5).
SKOV-3 cell migration assays Agonist-induced cell migration was measured by seeding 50,000 SKOV-3 cells resuspended in serum-free RPMI supplemented with 0. 1% BSA (BioFroxx) in each of the upper wells of a 96-well Boyden chamber (AC96, Neuro Probe). HGF/SF, K1K1, K1K1S2, and K1K1S4 was loaded in the lower compartment, and cells were left to migrate in a humidified 37°C incubator with 5% CO 2 for 6 h. Afterwards, nonmigrated cells were removed, and the cells migrated over the collagen-coated (100 μg/ml Purecol; Nutacon) 8-μm-pore-sized membrane (Neuro Probe) were fixed in 4% paraformaldehyde in PBS and stained using HCS CellMask Green stain (Thermo Fisher Scientific). Fluorescent intensity was imaged using an Azure C600 (Azure Biosystems), and migration was quantified by image analysis using the AzureSpot Pro analysis software after backgroundadjustment and charted in GraphPad Prism.
Survival assay In a clear bottom 96-well plate (#353072, Flacon, Corning), 12,500 MDCK cells were seeded and incubated for 24 h in DMEM supplemented with 10% FBS. The culture media was next added with increasing concentrations of the HGF/SF, K1K1, K1K1H6, and NK1 agonists together with the apoptosis inducer anisomycin (1. 4 μM) for 15 h. The cells were then washed with PBS to eliminate dead cells and then incubated for 1 h in 200 μl of HBSS (#14025092, Gibco, Thermo Fisher Scientific) containing 100 μM of Resazurin (#B21187; Sigma-Aldrich). Fluorescence was then measured with Envision multimode reader (Ex: 560 nm, Em: 590 nm) with maximal signal gain set (90%) on control cells wells and on top/top reading configuration. The data were normalized to percentage of control cells signal and subjected to a non-linear regression analysis using four-parameter sigmoidal equation with minimum = 0 and maximum = 100 using SigmaPlot software (v13 and v14.
Acknowledgements We thank  SATT-Nord (France)  for financing the proof-of-concept experiments and  Thierry Chassat  from the PLETHA animal facility in the  Lille Pasteur Institute  for helpful advises and kind availability. Work in the laboratory in Pavia was made possible through funding from the  Italian Ministry for Universities and Research (Departments of Excellence initiative)  and from the  Leukemia Research Fund (AIL Trentino). We would like to acknowledge  Dr Maristella Magi  ( University of Pavia ) for help at the  ESRF beamline ,  Dr Claudia Scotti  ( University of Pavia ), and  Dr Dimitri Y Chirgadze  ( University of Cambridge, UK ) for help with structural analysis of  K1K1  and  K1K1H6  and  Maria Cristina Barbieri  for technical support and  Dr Raymond Pierce  for proofreading.
Data Availability Structural dataset for K1K1 and K1K1H6 are available from the PDB data bank with id code 7OCL and 7OCM, respectively. The SAXS data are available from the SASBDB database with id codes SASDLM9, SASDLN9, and SASDLP9. Complete source datasets (. zip and. pdf files) have been uploaded together with the supplementary materials for each figure.
Study populations Two study groups were included: (1) patients with a clinical diagnosis of AK and a single lesion eligible for laser surgery, enrolled at the National Institute for Health, Migration and Poverty (NIHMP), Rome, Italy  [22] ;  (2)  patients with a histologically-confirmed diagnosis of AK performed at the San Gallicano Dermatological Institute (ISG), Rome, Italy. The socio-demographic data and lesion site were recorded. Informed consent was obtained from all the subjects enrolled at NIHMP. The patients enrolled at ISG provided a general consent for research use of surplus tissue at the moment of biopsy collection. The study was approved by the Ethics Committee of the NIHMP (2014) and the San Gallicano Dermatological Institute (CE943/17; RS/1090/18).
Statistical analysis Descriptive statistics were used to summarize the characteristics of the study populations. The overall and typespecific prevalence of PyVs in HS versus AK scrapings (unpaired groups) were compared using Chi-square tests. The heatmap showing the results for matched HS and AK pairs was generated with the ClusVis program in order to show each patient status for each PyV (negative/ positive) simultaneously in HS and corresponding AK scraping. When the paired HS-AK scrapings were both positive for a specific PyV, scraping pairs were also compared in terms of MFI value, a semi-quantitative measure of the viral load. To investigate possible determinants of positivity for any PyV, MCPyV, HPyV6, and JCPyV, univariate analyses were performed taking into account the following variables: age and sex for HS samples; age, sex and lesion site (sun-exposed: scalp, forehead, cheek, nose, auricle, lips; unexposed sites: arms, chest and torso) for AK samples.
Marine biofouling communities, including biofilms, are composed of many eukaryotes with high taxonomic and functional diversities. However, molecular characterization of eukaryotic diversity of marine biofouling has been barely developed due to the only recent interest in research areas such as marine renewable energies, antifouling technologies, or plastic pollution. The aim of this study was to compare the diversity and taxonomic composition of biofouling through different metabarcoding approaches used to detect the widest range of taxa from samples collected in several contrasted marine environments (French Atlantic and Mediterranean coasts). Thus, we assessed four DNA extraction methods and six primers pairs targeting the 18S rDNA gene (including the V1-V2, V4TAR, V4UNI, V7 and V9 regions) and the COI gene, the latter with two databases (BOLD and MIDORI).
In addition the influence of primers selection was analyzed at three sites to compare geographic variations in eukaryotic diversity. Although none of the extraction methods greatly altered the community diversity or composition. we have observed that eukaryotic biofouling community diversity and structure varied depending on primers pairs, reference databases and sites. 18S rDNA regions allowed the detection of more taxa at the species level, including microeukaryotes, while the COI recovered more ASVs, but with a large proportion that remained taxonomically unassigned probably because BOLD and MIDORI specifically targeted metazoans. Interestingly, the spatial pattern obtained with both COI and 18S rDNA markers were similar showing that spatial selection occurred throughout a wide diversity of eukaryotic taxa.
Several studies have described the formation and composition of prokaryotic biofilms  (Marszalek et al. , 1979; Dang and Lovell, 2016; Pollet et al. , 2018; Caruso, 2020; Catao et al. , 2021)  while the characterization of marine eukaryotic biofouling communities, especially using molecular approaches, has attracted less attention from the scientific community. In addition, most molecular studies of eukaryotic biofouling have generally focused on the diversity of dominant groups, the detection of specific taxa such as non-indigenous marine species (NIS)  (Ammon et al. , 2018b; Rey et al. , 2020)  or the studies based on marine navigation  (Schultz et al. , 2011) , including different substrates and ecosystems. In addition to the characterization of microeukaryotes, the acquisition of large metabarcoding datasets to supplement with morphological inventories, which are time-consuming and rely on extensive taxonomic expertise, would be helpful.
As a matter of fact, the very small size of some eukaryotes and/or their insufficient morphological discernible features appear to considerably limit their detection and identification, leading to the underestimation of their diversity  (Carugati et al. , 2015). Thus, molecular approaches appear as a promising alternative for standardized and accurate species identification compared to the use of traditional morphological surveys  (Aylagas et al. , 2016; Obst et al. , 2020). However, several methodological parameters (e. g. , DNA extraction, use of multiple primer sets and more or less specific databases, bioinformatics processing, and sequencing depth requirements) vary considerably among studies and may affect the conclusions made from environmental samples  (Giebner et al. , 2020; van der Loos and Nijland, 2021; Zaiko et al.
, 2022). Efforts are thus needed to improve the reliability of DNA metabarcoding monitoring in general but especially for biofouling communities. The analysis of eukaryotes based on molecular approaches represents a specific challenge because eukaryotes possess resilient cellular structures which can give poor DNA yield during extraction  (Hermans et al. , 2018). In addition, the choice of marker genes for environmental DNA studies remains a critical step and closely depends on the taxa of interest as well as the taxonomic levels obtained. Currently, two genes, or markers, have been frequently targeted in marine eukaryote DNA studies, those coding nuclear ribosomal DNA (rDNA) and the mitochondrial cytochrome oxidase c subunit I (COI). The former is a more conserved marker that detects more diverse taxa whereas COI is a more variable marker that provides information at a higher taxonomic resolution (down to the species level) but databases are only adapted for metazoans  (Tang et al.
, 2018b; Atienza et al. , 2020; Azevedo et al. , 2020; Rey et al. , 2020). However, the use of molecular approaches for the study of eukaryotes is biased by the proportion of reads which is not correlated with the initial proportions of taxa in the sample. These differences can be influenced by both PCR-bias and gene copy number that varies considerably across taxa and sometimes within taxa  (Elbrecht and Leese, 2015; Jeunen et al. , 2019; Lamb et al. , 2019). Furthermore, another aspect that may bias the results of metabarcode-based biodiversity assessments of biofouling is the taxonomic assignment of ASVs. Regardless of the primers used, sequence identification is highly dependent on the completeness of the chosen reference database and often tailored to certain taxonomic groups. Consequently, identification is mainly limited to specimens belonging to wellknown taxa  (Pompanon and Samadi, 2015).
Bioinformatic processing The initial quality control check of reads in 'fastq' files was performed using FastQC (Andrews, 2010), forward and reverse primers were trimmed from the paired-end-reads using 'Cutadapt' (v. 3. 1)  (Martin, 2011). Sequences were quality-filtered, denoised and merged into ASVs (Amplicon sequence variants) using the pipeline DADA2  (Callahan et al. , 2016) within R (v. 4. 1. 0). Briefly, sequences were filtered based on visual impaction of the error rates according to the quality score ≥ 30 and after chimeras were detected and removed with the 'removeBimeraDenovo' script in DADA2. Each representative of 18S rDNA ASV sequences were affiliated using Blast+ (v. 2. 10) and the naïve Bayesian classifier method  (Wang et al. , 2007)  against the PR2 (Protist Ribosomal) database (v 4.
13. 0) (Guillou et al. , 2013). The BOLD (Barcode Of Life Data system) (v. 2019) (Ratnasingham and  Hebert, 2007)  and MIDORI MARINE (GenBank) (v. 2018) databases, implanted on the Galaxy platform  (Escudiéet al. , 2018)  were used for the COI sequences. Based on the initial assignment, we organized our taxonomic profile to keep the taxonomy of ASVs with more than 95% identity and 75% sequence coverage. Below these thresholds, if an ASV showed a bootstrap confidence score <75% at each level, it was binned into unknown. After processing, ASVs with abundance lower than 0. 005% were removed  (Bokulich et al. , 2013). Moreover, sequences assigned to organisms unlikely to live in marine environments (i. e. , contamination from human DNA and terrestrial species) or unclassified at kingdom level were removed from the dataset.
Analyses were then only focused on the sequences that were annotated as marine eukaryotes referring in the World Register for Marine Species database (WoRMS Editorial Board 2021). The raw sequence data generated in this study have been deposited in the NCBI Sequence Read Archive under the accession number BioProject PRJNA777059. It is indeed well known that 18S is not suitable for species assignment but instead allows for broad taxonomic coverage, while COI is appropriate for distinguishing species (due to its high evolutionary rate) but prone to strong amplification biases and known to overlook some taxonomic groups  (Leray and Knowlton, 2016; Duarte et al. , 2021). For these reasons, we worked at the genus level for the 18S rDNA data and at the species level for the COI data. Additional details on bioinformatic procedure were given in supplementary materials.
Statistical analyses Statistical analyses and plots were performed within RStudio  (R version 4. 0. 4, packages 'vegan', 'ggplot', 'stats', 'phyloseq'). Samples were rarefied to the minimum sample read depth for each primer. All further analyses were assessed on a richness index because the correlation between true abundance/biomass of a species and DNA signal strength obtained by metabarcoding is undetermined  (Jeunen et al. , 2019). Alpha-diversity was assessed using ASV richness and Chao1 index. For the analysis comparing alpha diversity between the six primers and the two databases, the samples were also standardized to the same number of reads per sample besides per primer. Data were tested for normality using the Shapiro-Wilks test and were statistically tested with an analysis of variance (ANOVA) followed by Tukey Honest Significant Difference (HSD) test and the homogeneity of dispersion was assessed using the 'betadisper' function.
DNA extraction methods effects on eukaryotic diversity and taxonomic composition Amplicons obtained with the four extraction methods and the three surface types (n = 36 samples) were sequenced returning 3,489,945 raw sequence reads. After refining, a total number of 1,267,175 reads was distributed across all the samples. Sufficient sequencing coverage was obtained, except for one replicate (PVC extracted with PMS), which was removed from the dataset as it did not meet the rarefaction threshold of 36,205 reads (Figure  S2 ). Sequences represented 323 non singleton and assigned ASVs. Regarding alpha-diversity, there was no difference in richness among the surface types (PVC, HDPE, and PA) (ANOVA, p = 0. 12547) while there was a statistical difference across the extraction method used (p < 0.
Diversity and community composition depending on genetic markers and databases To determine the composition and diversity depending on primer pairs, we focused on the sequences corresponding to the 54 samples immersed at Toulon, representing three types of surfaces in three replicates, and which were all amplified by six primers pairs. Rarefaction curves indicated that the coverage of sequencing was sufficient for all primers (Figure  S6 ). Only one PVC sample with V7 primer amplification had not reached the normalization threshold (36,389 sequences). High percentages of taxonomy assignment were reached for ASVs obtained with the 18S rDNA primers until the genus level (74 to 85% corresponding to 55 to 94% of reads), whereas the taxonomy assignment at the species level fall between 52 to 65% (corresponding to 38% to 71% of reads) (Table  1 ).
94 (p < 0. 001). However, COI allowed a similar community structure with respect to the database choice (Figure  2B ). NMDS analysis, based on Jaccard's index, revealed a separation between 18S rDNA regions and COI (Figures  S7A, S8 ) with a significant beta dispersion among COI affiliated with both databases (betadisp, p = 0. 01). We did not detect any significant interaction between surface types, contrary to primers and databases (PERMANOVA, p< 0. 001). Although NMDS plots showed distinct clustering across regions of 18S rDNA, high and significant correlations were found between distance matrices suggesting a similar structure between each primer (Mantel's > 0. 7, p < 0. 01) (Figure  S7B ). Comparing the taxonomic composition and structure between markers, only 8 and 14 classes were detected for COI using BOLD and MIDORI databases, respectively, compared to 47 ± 4 for 18S rDNA.
Clear differences were observed in the 33 most abundant genera (relative abundance threshold ≥ 0. 5%) recovered by each primer. Harpacticus and Schlerochilus (Arthropoda), Watersipora (Bryozoa), Cephalothrix (Nermertea) and Obelia (Cnidaria) were dominant genera with all 18S rDNA primers (Figure  3A ). On the contrary, both Jassa species (J. slatteryi and J. marmorata, Arthropoda) were the only species dominant with the two COI databases (Figure  3B ). Overall, very few species were dominant following the COI, regardless databases. Indeed, only 10 species showed abundances greater than 0. 1% and many species remained unknown/ unidentified, such as many species of Nematodes, Mollusks, Cnidaria, and Arthropods. A greater number of genera were abundant in the COI samples assigned with MIDORI and which showed dominant genera such as Obelia (Hydrozoa) and some genera in Bryozoa.
Spatial variations of marine eukaryotic community structure The effect of primer choice on the characterization of marine biofouling community was then assessed based on samples from contrasted marine systems. DNA extracts, obtained with PB method, from samples scraped on PVC panels deployed at Toulon, Banyuls and Saint-Nazaire were also amplified with the six primer pairs (i. e. , COI and the five hypervariable regions of 18S rDNA) (n = 35 samples). Then, we compared results at a larger scale across the three sites. First, the richness and relative abundances of the major eukaryotic groups from the three localities are presented for the five regions of 18S rDNA and COI affiliated with the two databases in the Figures  5, 6. Chao1 index was generally higher with 18S rDNA primers than with COI.