dataesr/scientific-paragraphs-categorization huggingface dataset:
g. Facebook and Twitter). Of the 194 people who voluntarily participated, only 5 met our criteria and were included in the final dataset. To increase our chances to find the target sample, we then opted for a paperand-pencil recruitment method and approached people on a university campus and surrounding areas. With this strategy, we recruited a total of 305 students, of whom 85 were smokers. Thus, the final sample consisted of 90 students-smokers, 4 including 67 women and 23 men. Most were French (90%). Mean age was 21. 20 (SD = 2. 86), ranging from 18 to 38 years old. Just over half (51. 1%) reported smoking from 1 to 5 cigarettes per day, and 45. 6% reported smoking between 6 and 10. After participants consented to take part in the study, we manipulated the salience of the student identity (vs.
DATA AVAILABILITY STATEMENT All data concerning this research are openly available at https://osf. io/79a5z/. ORCID Jérôme Blondé https://orcid. org/0000-0002-5486-4766 Matthew J. Easterbrook https://orcid. org/0000-0002-9353-5957 Peter R. Harris https://orcid. org/0000-0003-4599-4929 Fabien Girandola https://orcid. org/0000-0002-1253-1728 ENDNOTES 1 Although the identities in question in this paper may correspond to both personal and group identities, we assume that the identity processes that we study here operate at an individual level. 2 Note that an alternative identity can be salient in the mind of individuals either because they are able to bring it up spontaneously or because external triggers or cues activate it. 3 Note that our studies, with the current procedure, have received ethical approval from our IRB (Reference number: ER/PRH21/6), and that informed consent has been obtained from participants.
The OsHV-1 variant (GenBank accession number JN639858) identified in oysters during the 2010 outbreak presented typical mvar deletions in the C2-C6 region (open reading frame 4 [ORF 4]) but also shared two identical nucleotides with the reference strain (GenBank accession number AY509253) that differed from the variant mvar (GenBank accession number HQ842610)  (10). Little is known about the POMS infection process in New Zealand. A field study carried out in the summer of 2010 to 2011 recorded 14% spat mortality after 6 days of deployment on aquaculture farms, increasing to 50% after 9 days and 70% after 13 days -a relatively late dynamic of infection-with an increase in viral load and Vibrio detected in oysters after 4 days  (10). These field-based observations suggest that further knowledge of the dynamics of POMS infection is necessary to improve POMS risk management but also to support the New Zealand oyster aquaculture industry and to sustain the development of new mitigation strategies in New Zealand.
Specifically, four members of the Gammaproteobacteria class, consisting of Ketobacter, Pontibacterium, Profundimonas, and Nitrosococcaceae, and two members of the phylum Bacteroidetes (Algoriphagus and Maritimimonas) were identified. Network association analyses highlighted complex patterns of interrelationships between these bacterial taxa and corresponding phenotypes like viral load, viral gene expression, and survival rate, suggesting a possible bacterial consortium associated with host colonization as previously described in France  (47). All together, these data reveal some variations in the course of the disease compared to previous descriptions carried out in other countries. Despite a high viral concentration in the water, high mortality rates recorded in vulnerable families, and the presence of Mycoplasma, Amphritea, Pontibacterium, Ketobacter, and Maritimimonas associated with infected individuals, viral gene expression (based on ORF27, 38, and 87) occurred later than in other comparable studies, and oysters did not experience massive dysbiosis.
During a mortality episode of Pacific oyster juveniles in the summer of 2010 to 2011, Keeling et al. (10)  analyzed the C2-C6 region of the OSHV-1 virus isolated in the North Island of New Zealand. The authors reported that the isolated sequence shared similar variations with the reference OsHV-1 mVar. However, the New Zealand specimens were also carrying variations of two nucleotides common to the OsHV-1 reference strain (GenBank accession number AY509253) diverging from mVar  (10). Viruses generally exhibit high levels of genetic diversity, having the ability to produce diverse and genetically linked mutants. The level of de novo genetic diversity within viral populations likely influences viral pathogenicity, host determination, dissemination, and host immune evasion  (56). Prior to 2008 and the identification of OsHV-1 mVar in France  (7) , the following two genotypes of OsHV-1 were identified: the reference strain (57) and a second genotype OsHV-1 Var detected in Pecten maximus  (58).
Pathological features were categorized as "0" when absent and "1" when present and were converted to a percent presence of a given feature per family, keeping tank replication (n = 3). Bioinformatic analysis. Sequence data was demultiplexed using the MiSeq Reporter (version 2) and primers removed using Cutadapt (version 2. 6) (75) allowing for no indels and a minimum overlap of 17 base pairs (bp). Sequences were quality filtered (maxN = 0, maxEE = c(2), trunQ = 2), denoised, paired end merged (minOverlap = 10), and chimera filtered (method = consensus) using the "DADA2" R program (version 1. 14)  (76). Prior to quality filtering, forward and reverse reads were truncated at 226 and 220 bp on the 59 end, respectively, to remove the lower quality section. Amplicon sequence variants (ASVs) were taxonomically identified using the RDP Naïve Bayesian classifier algorithm (77) (implemented in DADA2 using the SILVA rRNA gene database (  78 )) (version 138; https://benjjneb.
github. io/dada2/training. html). Unassigned ASVs and those identified as nonbacterial were discarded. Additionally, potential contaminant reads were identified and removed with the MicroDecon R package (version 1. 0. 2) (79), and rare ASVs (ASVs for which the sum of the read did not exceed more than 2 reads in at least 3 samples) were discarded. Sequencing depth per sample was visualized with the "rarecurve" function of the "vegan" R package (version 2. 5. 7) (80), and samples with less than 10,000 reads were discarded to ensure that samples used in downstream analyses had sufficient sequencing depth to recover most of the diversity. Statistical analyses. Survival functions were computed according to Kaplan and Meier (1958) using RStudio 4. 1. 0 and the "Survival" R package (version 3. 2-13) (81).
The replication unit was the tank in which the 10 families were maintained. All mutual interactions among factors were tested, and Tukey's honestly significant difference test was used as a post hoc test. The normality of residuals and homogeneity of variances were graphically checked. Statistical analyses were performed in R studio, version 4. 1. 0 (R; https://www. R-project. org/). For viral gene expression, heatmaps were constructed using Multiple Experiment Viewer software (  [84]  http://mev. tm4. org/#/datasets/upload). Microbial taxonomic composition in seawater and in oysters was investigated and visualized at phylum and family levels using bar plots and the "ggplot2" R package (version 3. 3. 5) (85). Alpha diversity metrics such as ASV richness, Shannon, and Simpson indexes were computed with the "Phyloseq" R package (version 1.
34. 0) (86) and visualized with line plots using "ggplot2. " The effect of treatment, family type (vulnerable versus resistant versus highly resistant), collection date and their interactions on alpha diversity metrics of oyster microbiota was investigated with linear mixed-effects regressions (LMER) using the "lme4" R package (version 1. 1. 27) (87). Similarly, the effect of treatment and time after start of experiment and their interactions on alpha diversity metrics of seawater microbial communities were investigated with a LMER. The effect of treatment on the oyster microbial community composition and structure was visualized with a principal-component analysis (PCA) and tested with a permutational analysis of variance (PERMANOVA) using the "vegan" R package (version 2. 5. 7) (80) with the following parameters: adonis2 (Bray-Curtis distance matrix of read abundance table transformed to relative abundance ; infection Â family type 1 time Â family type, blocks = tank, permutations = 999, method = "bray," by = "terms").
Differences in core oyster microbiome between vulnerable and highly resistant families were investigated with heatmaps using the plot core function of the "microbiome" R package (version 1. 13. 8) (88) and using the "ComplexHeatmap" R package (version 2. 6. 2)  (89). Oyster bacterial genera associated with oyster mortality rate, ORF, and viral load were investigated with Pearson correlation based on centered-log ratio transformed read abundance to account for the compositional nature of the data and visualized with a heatmap using the "ggcorrplot" R package (version 0. 1. 3) (90). In addition, interactions between these bacteria were assessed with the CONet Cytoscape plugin (version 1. 1. 1) (91) using Pearson correlations and the Bonferroni multiple-test correction. Variables that could best predict mortality rate, including oyster bacteria ASVs transformed to centered-log ratio, viral load, and ORF concentration were identified using a Random Forest model trained [parameters, trControl = "repeatedcv," number = 5, repeats = 3, search = "grid"; tunegrid = expand.
grid (. try=c(1:8))] using the "caret" R package (version 6. 0. 88) (92) and visualized with bar plots using ggplot2. Ethics approval and consent to participate. The study was conducted according to the guidelines of the Declaration of Helsinki and approval of the Animal Ethics Committee was not applicable for the use of oysters. SUPPLEMENTAL FILE 2, XLSX file, 0. 03 MB. SUPPLEMENTAL FILE 3, XLSX file, 0. 02 MB. SUPPLEMENTAL FILE 4, XLSX file, 0. 02 MB.
ACKNOWLEDGMENTS We are extremely grateful to  Jim Dollimore  and  Will McKay  at  Biomarine Ltd  for provision of adult broodstock and to  Hannah Mae  and the rest of the hatchery team at the  Cawthron Aquaculture Park  for the technical support to produce the biological material. We also thank  Karthiga Kumanan ,  Bridget Finnie ,  Shenae Muirhead , and  Joanna Copedo  for their invaluable help during the challenge and sampling. Thank you to the molecular team ( Branwen Trochel ,  Ulla von Ammon ) for technical assistance and to  Isabelle Arzul  and  Steve Webb  for histological advice. We thank the team at the  Sydney School of Veterinary Science, Infectious Disease Laboratory, Farm Animal Health, Sydney University, Australia  for providing us the OsHV-1 B-region plasmid and, more particularly,  Paul Hick  and  Alison Tweedie  for their precious advice.
Funding was provided by the  New Zealand Government's Ministry of Business, Innovation and Employment  through the  Cawthron Shellfish Aquaculture program  (contract no. CAWX1801 ) and the  Royal Society of New Zealand  through the Catalyst Leaders:  International Leader Fellowship Fund  (contract no. ILF-CAW-1801 ). L. D. , J. V. , P. B. , and Z. H. , conception and design; L. D. , J. V. , J. -F. B. , J. B. , P. B. , A. R. , and Z. H. , data collection and performance lab trials; L. D. , J. B. , and X. P. , preparation of samples for sequencing; A. R. , histopathological assessments; O. L. , bioinformatic analyses; L. D. , J. V. , A. R. , and O. L. , writing. All authors, revision/editing. J. V. and Z. H, funding. All authors read and approved the final manuscript. We declare no competing or financial interests.
Acknowledgements The authors thank the guest editors of this Special Issue of the EURO Journal on Computational Optimization (EJCO),  Miguel Anjos ,  Tamás Terlaky  and  Tibor Illés , for their kind and honorable invitation to write this article about some precious moments in the history of the EUROPT working group. We thank them for all their guidance and support, and we include into our thanks the Editor in Chief of EJCO,  Immanuel Bomze , and his editorial team. With all these scholars we spent unforgettable moments and served at the side of the many members of EUROPT. We are deeply thankful to all of them. Last but not least we extend our sincere gratitude to the  Association of European Operational Research Societies (EURO)  which always supported EUROPT and each of us at every moment, and made these fruitful and beautiful years of service, dedication and friendship possible.
Structure determination and refinement Diffraction data for PLAAT4 NTD were collected at ESRF beamline ID30A-1  (Bowler et al. , 2015; Svensson et al. , 2015). Automated data reduction performed using the autoPROC pipeline  (Vonrhein et al. , 2011)  yielded a 94 % complete dataset extending to 1. 73 Å resolution (Table  1 ). Phasing was performed by molecular replacement in Phaser  (McCoy et al. , 2007)  using the trimmed, refined model of PLAAT3 as a search model (7ZOM, with non-conserved residues truncated at C β positions). Manual model building in Coot  (Emsley and Cowtan, 2004; Zander et al. , 2016)  was alternated with refinement using Refmac  (Murshudov et al. , 2011)  to complete the model and adjust it to the correct sequence. The PDB-REDO webserver  (Joosten et al. , 2014)  was used to determine optimal weights for refinement in Refmac.
Model coordinates and structure factors are deposited at the RCSB Protein Data Bank under accession number 7ZOT. To assess the conformational flexibility afforded the protein in the crystal lattice we also performed ensemble refinement using phenix. ensemble_refinement (Burnley et al. , 2012)  with the default settings, using the MX-PRESTO software stack to access HPC compute resources (https://www. nsc. liu. se/support/presto/MX-PReST O/index. html).
Determination of oligomeric state in solution To determine the oligomeric state of PLAAT4 NTD in solution, protein samples were subjected to Size Exclusion Chromatography coupled Small Angle Xray Scattering (SEC-SAXS) and Multi-Angle Light Scattering (SEC-MALS) at beamline EMBL-P12 (PETRA III, DESY, Hamburg, Germany). 16 µl protein samples at a concentration of 10 mg ml -1 were loaded onto an S75 Increase 5/150 analytical SEC column (Cytiva) in 20 mM HEPES, pH 7. 4, 150 mM NaCl, 2 mM TCEP, 1 % v/v glycerol with a flow rate of 0. 3 ml min -1 using the BECQUEREL beam line control software. The SAXS intensities were measured as 2400 × 1 s individual X-ray exposures using a Pilatus 6M 2D-area detector from the continuously flowing column eluent. The 2D-to-1D data reduction, i. e. , radial averaging of the data to produce 1D I(s) vs s profiles, were performed using the SASFLOW pipeline incorporating RADAVER from the ATSAS 3.
0 suite of software tools  (Manalastas-Cantos et al. , 2021). Data frames were processed using CHROMIXS  (Panjkovich and Svergun, 2018)  and Guinier analysis and P(r) distributions were fitted using PRIMUS  (Konarev et al. , 2003)  (Supplementary Table  1 ). The crystal structure, both dimer and monomer, and NMR-models were fitted to experimental data using CRYSOL  (Svergun et al. , 1995)  and SREFLEX  (Panjkovich and Svergun, 2015)  was used for flexible refinement. A sEC-MALS experiment was performed by in-line measurements using a Wyatt Technologies Mini-Dawn TREOS multi angle light scattering detector coupled to an OptiLab T-Rex refractometer (RI). A 16 µl sample was injected onto a Superdex 75 Increase 5/150 column (Cytiva) equilibrated in 20 mM HEPES, pH 7. 4, 150 mM NaCl, 2 mM TCEP, 1 % v/v glycerol, with a flow rate of 0.
, 2011)  using 10 % of the initial 98,518 spots for indexing, allowed the integration of diffraction intensities belonging to a single lattice, and yielded a 94 % complete 1. 73 Å dataset with good statistics (Table  1 ). Further structure elucidation proved straightforward, with phasing by Molecular replacement using the core domain of PLAAT3 (7ZOM) as a search model. PLAAT4 NTD crystallized in space group C2 with two molecules in the asymmetric unit. Compared to the crystal structures of the closely related PLAAT3, where the central loop (L1) encompassing residues 40-57 is disordered, we observed clear electron density which allowed us to model L1 in both chains, through several iterative cycles of manual model building and refinement (Fig. 2a ). Both molecules in the asymmetric unit agree well overall with each other (r.
Acknowledgements We are grateful to  Cy Jeffries  and  Zuzanna Pietras  for help with SAXS data collection and processing. We are grateful to the Joint EMBL-ESRF structural biology group for access and support to the  ESRF MX  beamlines. This work benefited from access to the  HTX Lab at EMBL Grenoble  supported by funding from the  European Union 's  Horizon 2020 Programme  under the projects iNEXT (grant  653706 ) and iNEXT  Discovery (grant   871037 ); research at the NKI was supported by an institutional grant of the  Dutch Cancer Society and of the Dutch Ministry of Health, Welfare and Sport ; EvC acknowledged funding by the  Knut and Alice Wallenberg foundation.
Soil characteristics can also affect biodiversity within both soil and above-ground assemblages. Soils are a fundamental determinant of plant communities  [19] , with soil biota being linked to them directly through symbiosis and herbivory, and indirectly via decomposition and nutrient cycling  [20] , but global patterns of soil fauna biomass may not follow plant biomass  [21]. We analyse biodiversity data  [22, 23]  from 19,651 above-ground and 7155 soil assemblages (comprising vertebrates, invertebrates, plants, and fungi) (Table  1 ) in different land uses worldwide (Fig. 1  and Table  2 ), alongside global datasets of soil characteristics  [24]. Because soil assemblage data are often less taxonomically precise than data from above-ground assemblages, the response variable we model is the summed abundance of all taxa sampled.
This measure is very much less sensitive to change than more information-rich measures that incorporate species identity  [25] , but it has the advantage that significant differences between models cannot be artefacts of differences in taxonomic precision. The Soil Biodiversity Observation Network (SoilBON) have proposed population abundance as an Essential Biodiversity Variable  [7]. To accommodate heterogeneity due to the wide range of sampling methods and macroecological gradients in the dataset we used mixed-effects models to test three main hypotheses (expanded on in Additional file 1: Table  S1 ): (1) Because some land uses imply very different levels of perturbation to soil versus above-ground microenvironments, and because soil organisms are less mobile and more sensitive to microclimate change  [26] , we expect differences in how assemblages from these two settings respond to land use.
Weights were calculated by dividing the number of soil sites by the number of above-ground sites within each biome. In addition to the models required to test the three hypotheses, we also constructed a set of simpler models with single or additive terms to fully characterise which terms in the full model contributed the most explanatory power. We also undertook two sensitivity analyses. Because the biome-weighting is not commonplace (despite the ubiquity of geographic biases in biodiversity databases  [30] ), our first sensitivity analysis repeated the modelling without it. The second sensitivity analysis addresses the point that soil and above-ground assemblage data sets obviously have very different compositions in terms of which major taxonomic groups are well represented. While different impacts of land use on soil and above-ground assemblages would still be important even if they simply reflected such taxonomic differences, we also ran the same models using only the data for invertebrate taxa.
Further work with models incorporating functional traits robust to coarse taxonomic resolution would be valuable. Better documentation of the taxonomic and functional diversity of soil fauna would also help overcome some of these limitations, so we echo calls for better soil biodiversity information systems  [6, 44]. Additionally, except for fungi, micro-organisms are unrepresented in both soil and above-ground datasets-assemblage data from metabarcoding and metagenomic approaches  [7, 45]  will enable the use of more information-rich biodiversity measures. Above-ground and soil taxa may be active and sampled at very different spatial scales, and soil property data with a spatial resolution of 250 m used here may not accurately reflect that experienced by the biota. Site-specific soil property data were available for some studies used in this analysis but were too insufficient or inconsistent to incorporate.
Better standardisation of soil biodiversity surveys with a minimum level of environmental measurements collected would be a valuable contribution to the field  [9, 46]  as would explicit tests of spatial and temporal heterogeneity  [47]. Likewise, models that consider other drivers, such as climate change, alongside land use will also improve understanding. Correlative models such as ours are sufficient for developing indicators and models for monitoring and combating biodiversity loss but there is also a need for an improved understanding of the mechanisms linking land use, soil properties, and biodiversity responses  [8, 48]. Future analysis of this dataset using structural equation models (SEMs) could be used towards this, to disentangle the direct and indirect effects of soil properties and land use on communities.
Biodiversity data In the absence of a well-developed catalogue of global soil biodiversity  [44] , we initially searched within the PRE-DICTS database  [22, 23]  for soil assemblage data, defining soil assemblages as those sampled within the soil; at the soil surface, or in the leaf litter. The database is a global compilation of studies that have each compared non-cultivated species assemblages at multiple sites facing different landuse and related pressures  [22]. To the 59 studies (from 38 source publications with 1356 sites and 1570 taxa) of soil assemblage data previously in the PREDICTS database, we added 46 further studies (from 25 sources with 2726 sites and 3857 taxa (Tables  1  and 2  ). Above-ground assemblage data came from the other 509 studies (from 422 source publications, with 20,634 sites and 22,721 taxa) in the PRE-DICTS database at that time (October 2016).
Explanatory variables Using the information in the original papers, each site was classified into one of six categories of land use-primary vegetation, secondary vegetation, plantation forest, cropland, pasture or urban-and either low, medium, or high use intensity (see Additional file 1 and  [22]  for full definitions). Most combinations of land use and use intensity (henceforth, LUI) had large enough sample sizes in both the above-ground and soil subsets, but even after targeted literature searching to augment the database's holdings of urban data, there were insufficient sites for robust comparison of above-and below-ground in urban land use sites. The above-ground subset comprised primarily arthropods, plants, and vertebrates whereas the soil biodiversity subset was mostly arthropods (Table  1 ).
Nine soil properties widely reported to influence soil biodiversity (Additional file 1: Table  S1 ) were obtained from the SoilGrids250m database  [24]  using ESRI Arc-GIS 10. 3  [51]. Values were not available for 49 sites, which were therefore removed from the analysis. We averaged the values from depths 0, 5, 15 and 30 cm as no biodiversity data sources sampled at depths greater than 30 cm. The soil properties were expected to be collinear so, before model construction began, generalised variance inflation factors (GVIFs) were calculated  [52]. Among the soil texture properties, the percentage of clay had the lowest GVIF so was chosen in preference to percentages of silt or sand. Successively dropping the variable with the highest GVIF until all remaining GVIFs were low enough to suggest collinearity was not a major issue (all GVIF < 1.