dataesr/scientific-paragraphs-categorization huggingface dataset:
Survey and data collection To test our hypotheses, we collected data from 1789 EE actors across the following cities and countries: Warsaw (103 obs. ) and Wroclaw (102 obs. ) in Poland; Lviv (96 obs. ) and Kyiv (120 obs. ) in Ukraine; Cluj (119 obs. ) and Bucharest (120 obs. ) in Romania; Plovdiv (100 obs. ) and Sofia (101 obs. ) in Bulgaria; Astana (104 obs. ) and Almaty (106 obs. ) in Kazakhstan; Batumi (62 obs. ) and Tbilisi (132 obs. ) in Georgia; Istanbul (89 obs. ) in Turkey; Klagenfurt (114 obs. ) in Austria; Zagreb (115 obs. ) and Osijek (105 obs. ) in Croatia; and Sarajevo (103 obs. ) in Bosnia and Herzegovina. The data was collected between December 2018 and February 2020. The sample was derived from the register of companies, the public authorities in each city, and from self-employed accountants, agents, and journalists.
17%, which is slightly higher than the other enterprise surveys of 19%  (Weber et al. , 2015). Parametric and non-parametric tests found no evidence of response bias with regards to geographical location, sector, field, or age of the respondents. The age structure of the dataset can be characterized as follows: 486 (27. 17%) observations were from stakeholders aged 29 or under, 614 (34. 32%) of observations were from stakeholders aged 30-39; 414 (23. 14%) of observations were from stakeholders aged  40-49; and 200 (11. 18%)  of observations were from stakeholders aged 50-59. The rest of the groups were over 60 years old. Almost 85% of respondents had a university degree or above. Our four major groups of stakeholders were entrepreneurs (33. 0% of sample), university professors (9. 2% of sample), policymakers (7.
Systematic literature search We extracted female Bateman gradients from a previous meta-analysis  [3]  and expanded this database by adding studies that have since been published. Specifically, we ran a systematic literature search using the ISI Web of Knowledge (ISI Web of Science Core Collection database; Clarivate Analytics) with the "topic" search terms defined as ("Bateman � " OR "opportunit � for selection" OR "opportunit � for sexual selection" OR "selection gradient � " OR ("mating success" AND "female � ")) on the 31st of March 2022. In this search, the timespan was defined as "2015today" because the literature search of the previous study had been carried out on the 25th of April 2015. In addition, we also screened all studies published after 2015 that cited Bateman's original paper.
Our sole inclusion criterion was that the study must report data allowing to assess the relationship between mating success and reproductive success for females. The search yielded 1,974 records of which 30 studies were considered eligible, providing a total of 39 additional estimates of female Bateman gradients. In addition, we included 4 estimates from an unpublished experimental study on the bean weevil Acanthoscelides obtectus (S. Fromonteil and colleagues, unpublished data) and 4 estimates obtained from a study on the red flour beetle Tribolium castaneum (L. Winkler and colleagues, unpublished data). Combining these estimates with the ones obtained from the previous meta-analysis added up to a final dataset of 84 studies reporting 120 female Bateman gradients from 77 species (Fig  3.
For those reasons, we tested the effect of the mating success method by contrasting estimates of Bateman gradients based on genetic (k = 77) versus copulatory mating success (k = 43). Studies using copulatory mating success relied on behavioural observations of the actual number of copulatory partners (k = 28) or the total number of copulations (k = 15). Second, we explored the impact of having unmated individuals included in the measurement of the Bateman gradient. Estimates including this zero-mating success category provide a combined estimate for the benefit of mating once and the benefit of having an additional mating partner (or copulation), whereas Bateman gradients excluding zero-mating success data capture only the latter. In the context of sexual selection, we are primarily interested in the benefit of having an additional mating partner (or copulation) rather than the benefit of mating itself, since the latter is essential for reproduction in outcrossing species.
For the majority of species (N = 66; 85. 7%), we estimated the proportion of multiply mated females using data provided in the primary studies  (Table D in S1 Text). For most of the remaining species, we extracted estimates of polyandry from secondary literature, except for 3 species for which we could only find verbal classifications of the mating system (see Table D in S1 Text for references). We then used these estimates to define the mating system as either low-polyandry or high-polyandry, depending on whether its value was lower or higher than 0. 5, respectively, because this value has been found to be the average level of polyandry in wild populations  [25]. In total, our dataset encompassed 16 high-polyandry and 61 low-polyandry species, for which we obtained 32 and 88 effect sizes, respectively.
Phylogenetic affinities We reconstructed the phylogeny of all sampled species from published data in order to account for phylogenetic non-independence  (Fig D in S1 Text). Specifically, we extracted divergence times from the TimeTree database (http://www. timetree. org/; [60] ) and transformed the distance matrix into the NEWICK format using the unweighted pair group method with arithmetic mean (UPGMA) algorithm implemented in MEGA (https://www. megasoftware. net/; [61] ). In total, our analysis included 77 species with a broad distribution across the animal tree of life, with an overrepresentation of arthropods (N Species = 20), birds (N Species = 13), fishes (N Species = 15), and mammals (N Species = 8) (.
In order to complement our analysis of the mating system, we also ran a phylogenetic GLMM including estimates of the actual level of polyandry (i. e. , the proportion of multiply mated females) as a continuous predictor variable. All GLMMs were run with the MCMCglmm function of the MCMCglmm R package version 2. 29  [63] , using uninformative priors (V = 1, nu = 0. 002) and an effective sample size of 10,000 (number of iterations = 4,400,000, burn-in = 400,000, thinning interval = 400). All models were also run with alternative priors, which revealed qualitatively identical results. Moreover, we ran all models multiple times to verify convergence and checked for autocorrelation in the chains. For completeness, we also ran all GLMMs using the restricted maximum likelihood (REML) approach using the metafor R package version 2.
Moreover, we tested whether the year of publication influences effect sizes, which has been argued to be suggestive of other forms of biases  [54]. For example, the so-called bandwagon effect suggests that supportive results get easier published in a newly emerging field but over time scepticism about the theoretical foundations may arise and initially non-intuitive findings may find a more receptive audience. If true for the field of sexual selection, we may expect an increase of effect sizes for female Bateman gradients with the rising awareness in the community that sexual selection does not only operate in males. Some readers might wonder whether so-called sex-role reversed species are overrepresented in our dataset because species in which females are known to compete for males have repeatedly been studied to provide a proof of concept of Bateman's principles.
Moreover, the inclusion of human studies in our meta-analysis might be problematic for at least 2 reasons. First, mating success in human studies is often estimated in terms of number of pair bonds or marriages, which might be very different from the actual number of sexual partners. Second, the estimated level of polyandry in humans is the lowest among all species in our analysis (outlier analysis: χ 2 = 8. 367, P = 0. 004), which may bias the tested relationship between the female Bateman gradient and the level of polyandry. For those reasons, we ran an additional series of analyses excluding sex-role reversed species or estimates obtained from human studies. These analyses suggest that all results obtained from the analysis of the complete dataset remain robust after excluding sex-role reversed species (Tables E and F in S1 Text) or human studies (Tables G and H in S1 Text).
Results of intercept-only phylogenetically controlled GLMMs are shown for the entire dataset (global model) and subsets with respect to mating success method (copulatory versus genetic), mating success range (including versus excluding zero mating success category), study type (laboratory versus field studies), and mating system (low-polyandry versus high-polyandry species). Table shows number of effect sizes (k), number of species (N), effect size (r), and heterogeneity I 2 arising from phylogenetic affinities, between-study variation, and between-observation variation. Model estimates are shown as posterior modes with 95% HPD intervals in parentheses.
27 (0. 00, 0. 80) 0. 60 (0. 10, 0. 94) 0. 06 (0. 00, 0. 19) Low-polyandry species 16 0. 22 (-0. 02, 0. 45) 0. 065 0. 24 (0. 00, 0. 68) 0. 53 (0. 01, 0. 91) 0. 12 (0. 00, 0. 47) High-polyandry species 61 0. 41 (0. 15, 0. 66) 0. 004 0. 64 (0. 27, 0. 91) 0. 13 (0. 00, 0. 41) 0. 11 (0. 00, 0. 31) GLMM, general linear mixed-effects model; HPD, Highest Posterior Density. https://doi. org/10. 1371/journal. pbio. 3001916. t001 Fig 2. Forest plot. Graphs shows all sampled effect sizes (Pearson correlation coefficient of Bateman gradients) with 95% confidence limits in phylogenetic order. The code and data needed to generate this figure can be found at https:// salomefromonteil. github. io/META_SexSelFem/ and https://doi. org/10. 5281/zenodo. 7303598. https://doi. org/10. 1371/journal. pbio. 3001916.
Histones are phylogenetically entrenched in several bacterial clades We conducted a systematic homology search for histone fold proteins in bacteria. Across a phylogenetically balanced database of 18,343 bacterial genomes, we found 416 proteins that contain a histone fold domain (Table  S1 ). 1. 86% of genomes encode at least one histone fold domain, compared to 92. 8% of genomes that encode HU. In agreement with prior work (Alva and Lupas 2018), we identified two major histone size classes, containing either a singlet or a doublet histone fold. Both classes are typically devoid of other recognized domains (Fig. 1a , Table  S1 ). Like their archaeal homologs, most bacterial singlets also lack the long, disordered N-terminal tails that are characteristic of eukaryotic histones  (Henneman et al.
Bacterial histones have essential roles outside the Bdellovibrionota Is B. bacteriovorus unique among bacteria in using histones to organize chromatin? We examined publicly available gene expression data and generated additional transcriptomic and proteomic data. At both the transcript and protein level, we find high expression of histone-fold proteins in several distantly related bacteria (Fig. 6a ). We also observe notable phylogenetic persistence in clades where gene expression profiles are not available, including in the Planctomycetota and Elusimicrobia (Figs. S7-10). We became particularly curious about Leptospira interrogans, the causative agent of leptospirosis, whose histone (Uniprot ID: A0A2H1XGH2 gene ID: LA_2458) is exceptionally abundant (ranked 4 of 1502 quantified proteins; Figs.
Homology survey of histone fold proteins PFAM HMM models of known histone fold domains (Histone (PF00125), CBFD_NFYB_HMF (PF00808) and DUF1931 (PF09123)) were searched against a phylogenetically diverse database of 18,343 bacterial genomes (Table  S3 ) using hmmsearch (v 3. 1b2) with the -cut_ga option to ensure reproducibility. In addition, a list of eight prokaryotic histone fold seed sequences were obtained from (Alva and Lupas 2018) and used to direct homology search with Jackhmmer (v 3. 1b2). Jackhmmer results with p-values <1e-3 were kept. Hits were combined and proteins longer than 200 amino acids discarded. All proteomes in the database were obtained from NCBI (accessed on 20 May 2020). When GenBank proteome files were not available, proteomes were predicted using Prodigal (v2. 6. 3), with default settings.
Proteomics data processing Data were processed using the MaxQuant software platform (v1. 6. 10. 43)  (Tyanova et al. 2016) , with database searches carried out by the in-built Andromeda search engine against the GenBank proteome of each organism. A reverse decoy database approach was used at a 1% false discovery rate (FDR) for peptide spectrum matches. Search parameters were as follows: maximum missed cleavages set to 2, fixed modification of cysteine carbamidomethylation and variable modifications of methionine oxidation, protein N-terminal acetylation, asparagine deamidation and cyclisation of glutamine to pyro-glutamate. Label-free quantification was enabled with an LFQ minimum ratio count of 1. The 'match between runs' function was used with match and alignment time limits of 0. 7 and 20 minutes respectively.
Cells were imaged on a Nikon Ti-E epifluorescence microscope equipped with a Apo x100 Ph3 oil objective lens (NA: 1. 45) and images were acquired on an Andor Neo sCMOS camera using Nikon NIS software. The following filters were used for fluorescence images: mCherry (excitation: 555 nm, emission: 620/60 nm), DAPI (Hoechst 33342): (excitation: 395 nm, emission: 435-485 nm). DNA was stained by the addition of Hoechst 33342 at a final concentration of 5 µg/ml. Images were analysed with FIJI software with the MicrobeJ plugin  (Ducret et al. 2016). mCitrine expressing cells were imaged on a Leica DMRB with phase contrast and DIC for transmitted light illumination. To image mCitrine during predation, cells were fixed (1% paraformaldehyde, 5 min, quenched using 150 mM glycine) and DNA stained by DAPI in 1x PSB (5min of staining removal by centrifugation).
Apo protein crystallization Recombinant Bd0055 in storage buffer (1 M NaCl, 1 mM EDTA, 50 mM HEPES pH 7. 5, 10% glycerol) was crystallized through hanging drop diffusion in a mother liquor (3. 2 M (NH 4 ) 2 SO 4 , 100 mM BICINE pH 9) by gently mixing 1 µl of protein to 1 µl of mother liquor on a coverslip and sealing it onto a pre-greased well of a 24 well crystal tray containing 1 ml of mother liquor. Crystals formed within 24 hrs and matured within 3 days. Crystals were looped and frozen in liquid nitrogen. X-ray diffraction data were collected on a Rigaku XtaLAB MM003 and indexed in DIALS. Molecular replacement was done using Phenix and structures were refined in Coot. Short, idealized alpha-helices were used as search models, most of the remaining structure was built automatically using AutoBuild.
DNA binding (FP) 50 µM protein was titrated using an Opentrons OT-2 liquid handling robot into 10 nM Alexa488 end-labeled 147 bp DNA by serial dilution down to a protein concentration less than the DNA probe in reaction buffer (10 mM NaCl, 1 mM EDTA, 50 mM HEPES pH 6). After incubation at RT for > 15 min (overnight incubation did not change overall results), Fluorescence polarization data were collected using a BMG Labtech CLARIOstar. Data were analyzed in Prism and fit to an [Inhibitor] vs. response -four parameter nonlinear regression curve.
DNA binding (FRET) FRET experiments were conducted simultaneously with the FP experiments, as the 147 bp DNA used was labeled on the opposite end of the Alexa488 with an Alexa647 fluorophore. Data were collected on a BMG Labtech CLARIOstar through excitation of 488 nm light while recording the emission at 647 nm. Data were analyzed in Prism and fit to an  [Inhibitor]  vs. response -four parameter nonlinear regression curve.
X-ray diffraction data were collected at the ALS synchrotron (12,397. 9 eV, 225 mm detector, ∆Φ = 0. 25) and indexed in DIALS. Molecular replacement was done using Phenix and structures were refined in Coot. The apo Bd0055 structure was used as a search model along with 2 ssDNA fragments (2 and 3 bp). This construct was conjugated into B. bacteriovorus HD100 as described previously  (Lambert et al. 2003)  and resulting exconjugants screened for kanamycin sensitivity and screened by PCR to test for either gene deletion or revertant to wild-type. Screening primers were: Bd0055KO_S_F 5' atctggagcttcacttcccg 3'and Bd0055KO_S_R 5' ggtgatgatccgggctctaa 3'.
Dataset availability All datasets are publicly available. Proteomics data have been deposited to PRIDE (PXD039405) and RNA-seq data to GEO under accession GSE220534. All genomes used were publicly available with no usage restriction (Table  S3 ). Structure data have been deposited at the Protein Data Bank (PDB 8FVX for apo Bd0055 and 8FW7 for DNA bound Bd0055).
ACKNOWLEDGMENTS We thank the  LMS Proteomics Facility  for generating mass spectrometry data, the  LMS Genomics Facility  for RNA sequencing, and  Annette Erbse  at  CU Boulder  for help with crystallography. PR, JT, CL, and RES are funded by a  Wellcome Trust   Investigator Award in Science  ( 209437/Z/17/Z ). Work by AH, KMS, and TW is funded by a  UKRI MRC   core grant  to TW ( MC-A658-5TY40 ). KL and SL are funded by the  Howard Hughes Medical Institute.
3a , b and Supplementary Fig. 2a, b ). Due to the resolution of this technique, spots overlaying the FAE were included in the SED group for the analysis. We then used the Seurat package to find markers that differed between the SED and the rest of the lymphoid tissue and expressed it as a heathmap (Fig. 3c )  28. Among the most differentially expressed genes were CCL20 and CCL23 that are associated with the FAE in mouse models, and ITGAX that is expressed by DC in the SED, which confirmed that our approach was successful  29. Providing further validation, other lymphoid markers included BCL6 and TCL1A identifying the GC and naïve B cells were instead preferentially expressed in the follicle area  30 31. Interestingly, the most differentially expressed gene between the SED and the rest of the follicle was DNASE1L3 , with the complement C1Q, C3 and C1R also enriched in the SED (Fig.
DNASE1L3 expression is not associated with apoptotic debris in humans, though it is in mice The enrichment of DNASE1L3 in the SED and its absence from the GC was surprising, because the most commonly described role for DNASE1L3 is the removal of apoptotic debris, including those generated by B cell selection in the GC response  42 41 44 23. To ask whether the SED is an area enriched in apoptotic debris in humans, we incorporated the TUNEL assay into IMC Panel 1 (Supplementary Table  2 ). To overcome the incompatibility of the RNAscope protocol and the TUNEL protocol we applied IMC Panel 1 to sections serial to those that identified DNASE1L3 distribution by RNAscope with IMC Panel 2 (Supplementary Table  2 ). Data from ROI containing GALT from colon (n=6), ileum (n=5) and appendix (n=5) were acquired.
Cells were then centrifuged and stained with 500 μl of 1μM intercalating Ir (Fluidigm) in perm buffer (0. 3% saponin [Fluidigm] in C-SM) and 100 μL intercalatin Ir (Fluidigm) for 30 minutes at room temperature. Finally, cells were washed twice with PBS and twice with Milli-Q water before resuspended at 0. 5 x 106 cells/mL in Milli-Q water with EQ beads (DVS Sciences). Data was then immediately acquired using a Fluidigm Helios mass cytometer, and exported in. fcs format. The mild collagenase digestion of gut biopsies reduced the expression of TACI and CCR6 by mass cytometry; we excluded it from all further analysis.
Analysis of mass cytometry data FCS files loaded into Cytobank (https://mrc. cytobank. org/) underwent quality control and were filtered to live CD19+ cells only as described previously  15 13. Equal numbers of events were extracted from each sample (n=9214) and viSNE dimensionality reduction was run on the combined events using markers CD45RB, IgD, CD20, IgA, CD138, CD21, CD38, CD10, CD27, CD24, IgG and IgM. SPADE was used for clustering based on viSNE co-ordinates, and the resulting nodes were manually placed into B cell subset bubbles based on their expression of key markers (Fig. 1b ). Marker expression, subset classification and viSNE coordinates were then exported and loaded into R for visualisation and quantification.
Markers were visualised back on the images using custom R scripts. The full set of spatial transcriptomic data was then analysed using the BayesSpace package in R, with raw counts log-normalised via the spatialPreprocess function, using 15 principal components and 2000 highly variable genes  73. For spatial clustering, the number of clusters was determined by inspection of tuning plots created using the qTune function, with 7 clusters for sample STA and 8 clusters for sample STB. The spatialCluster function was then used to spatially cluster the data for each sample individually, with spots being assigned to clusters based on similarity of their transcriptomes along with a Markov Chain Monte Carlo algorithm using a spatial prior to encourage neighbouring spots to cluster together. For each sample, the cluster deemed to best represent lymphoid tissue was selected, and the Seurat objects were filtered to spots in these clusters.
Data from both samples were merged, and a principal component analysis was run. Harmony was used to correct any batch effects between the samples  71. Further sub-clustering of spots was done using this harmonised reduction, resulting in a set of sub-clusters present in both images. Marker genes for each sub-cluster were found using FindMarkers, and sub-clusters were relabelled based on their expression of cell lineage markers. A list of SLE associated genes related genes was collated, and correlations between the expression of each of these genes with the lymphoid spots were calculated using the RcmdrMisc package 74 using Spearman correlation.
Slides were washed twice in PBS-T for 5min and incubated for 1h with anti-rabbit AlexaFluor555 (Invitrogen, #A32794) or anti-rabbit AlexaFluor647 (Invitrogen, #A32795TR) diluted 1:500. Slides were then washed and coverslipped as described above. As blocking reagent and antibody diluent a solution containing 5% BSA in PBS-T was used. Slides were imaged using the Eclipse Ti-A Inverted confocal microscope using 20x, 40x or 60X objectives. For the quantification, images from the different transcripts analysed were separated in single RGB channel and imported in CellProfiler  75. Images were then made binary using the "Threshold" module and masked. The proportion of pixels masked was then divided by total pixels of the transcript of interest.
IMC-RNAscope combined protocol Slides were processed as above for RNAscope protocol using DNASE1L3 (ACDbio, #576121-C1) and ITGAX (ACDbio, #419151-C2) and Eubacteria 16S (#464461-C3) probes. C1 oligos were developed with TSA-Digoxygenin (Akoya Biosciences, NEL748001KT), C2 oligos with TSA-FITC, (Akoya Biosciences, #NEL741001KT) and C3 oligos with TSA-Biotin (Akoya Biosciences, NEL749A001KT) diluted 1:100 in TSA buffer. After the RNAscope protocol, slides were washed, blocked, and stained using Panel 2 and 3 (Supplementary Table  2 ) as described in the IMC paragraph above. Panels 2 and 3 incorporated anti-Digoxygenin (RD System, #611621), anti-FITC (Standard BioTools, #3174006B) and anti-Biotin (Biolegend, #409002) metal-tagged antibodies to detect the RNAscope staining output. For quantification of co-expression of DNASE1L3 and Lysozyme or DNASE1L3 and NOX2 expressing cells, DNASE1L3+ cells were segmented using the FindPrimaryObjects module in CellProfiler using the DNASE1L3 channel as input  75.