dataesr/scientific-paragraphs-categorization huggingface dataset:
Participants Preregistered plans. Based on the preregistration, we aimed to gather data among high school students, university students, and in a comprehensive sample that is close to representative among Hungarian adults. Data collection among high school students was challenging as political fake news content was impossible to use (for legal reasons deriving from the present, illiberal political context), the COVID-19 pandemic made it difficult to gather data and reduce attrition, and teachers and students were not sufficiently incentivized to provide reliable data (we could not pay them for legal reasons). We will write about the results in a separate manuscript. For the comprehensive sample aiming to draw 800-1200 respondents from the general population, we asked five polling companies and none of them could guarantee even a 40% attrition rate regardless of the incentive structure.
We therefore opted out of data collection with these companies. In the university sample our goal was to recruit at least 787 participants (with d=0. 2, alpha error prob=0. 05, power=0. 8 and number of predictors=1, based on Guess et al. 22  ) to be able to test our main hypothesis (H1). Participants of the study. Young adults participated from a Hungarian public university with various majors who took a credit course in which participation was voluntary. Among them 801 reached the randomized intervention or control materials (Mage=22. 02; SDage=4. 11; 73. 46% female; 95. 33% Caucasian, 34. 40% first-generation). We found this form of data gathering and sample the most suitable as polling companies could provide similar samples with much higher expected attrition. There was some attrition in the followup, as students did not provide an appropriate Student ID that prevented us from matching their follow-up responses to their intervention data.
We found that the species with the strongest PC1 loadings (Supplemental Table  S3 ) suggested a taxonomic gradient relating to oxygen tolerance, such as that previously described in calculus samples from the archaeological site of Middenbeemster in the Netherlands  15  , but there was no clear association with GC content. Low taxonomic assignment rates for the Pacific samples and the abundance of non-typical oral taxa that predominantly drive separation along PC1 suggest that taxonomic diversity in these samples may not be represented in the genomic databases we used for classification. We performed further taxonomic profiling with additional tools and databases, but were unable to resolve these issues, highlighting the difficulty of disentangling undescribed diversity from intrinsic biases in ancient calculus datasets, such as very short average DNA read lengths (≤ 70bp) (Supplemental figures S3, S4).
Comparison with global ancient calculus microbiome profiles As this is the first large ancient dental calculus dataset published from the Pacific, we next wanted to know whether the microbiome communities fall within the known variation of published dental calculus datasets from across the globe. We compared the species profiles of our Pacific calculus samples to those from Europe, Africa, and Asia using PCA (Figure  3A ). The Pacific samples and those from Japan  16  cluster at the same end of the plot, suggesting that this region may have a particular species profile characteristic. Sample clustering based on continent was significant by PERMANOVA (p < 0. 01, F = 2. 47, R2 = 0. 03114), while tests of beta-dispersion between the continents found differences only between the Pacific and Asia (Figure  3B , p < 0.
Within a cluster of samples, there is a tendency for samples with higher levels of heterozygosity to fall basal to other samples (e. g. , the cluster of samples from Taumako and Rurutu). It is likely that the Anaerolineaceae bacterium oral taxon 439 phylogeny is not that of a single taxon, but rather a collection of closely related species or strains; however, at present only one reference genome is available for oral bacteria in the family Anaerolineaceae. We additionally ran inStrain to test the ability to identify shared strains of Anaerolineaceae bacterium oral taxon 439 and T. forsythia within our samples. Following testing of inStrain with in silico-generated datasets to understand how ancient DNA damage patterns affect the strain assessments (Supplemental Figure  S8 ,9), we found identical strains in samples AMH001 and AMH004 (popANI > 99.
This is supported by studies of modern oral microbiomes, which indicate that perturbations in the community of dental plaque microbiomes are quickly rebalanced  28  and are generally stable across a variety of cultural practices  [29] [30] [31] [32]  , even when gut microbiomes from the same communities under comparison are substantially different. However, there is growing evidence that the oral species present in ancient dental calculus are not fully represented in current genomic databases, such as NCBI, used for taxonomic profiling. Because of this, community composition analyses may be missing taxa and underestimating diversity, and therefore signals of differences or change may be hidden. The Pacific calculus dataset appears to be particularly affected by taxonomic database bias, as it has a notably lower taxonomic assignment rate compared to European ancient dental calculus samples for which the distributions of read length and GC content overlap.
An outstanding challenge to reconstructing past microbial genomes from metagenomes is distinguishing multiple closely-related species and strains within a metagenome. Attempts to study migration patterns through the microbiome, therefore, come with a degree of inherent uncertainty when attributing microbial DNA to particular species and strains. Due to issues such as contaminated ("dirty") reference genomes  35  , which include sequences not derived from the species of interest, or incomplete databases  36  , it is possible that sequencing reads from multiple species are inappropriately aligned to a reference genome  20, 35  , creating noise in the data analysis. This issue appears to have particularly affected our reconstruction of Anaerolinaeceae bacterium oral taxon 439, for which we observed high rates of SNP heterozygosity.
Our results indicate the high potential of dental calculus to be well-preserved in geographic and climatic conditions that are otherwise unfavorable to DNA preservation, opening the possibility to explore archaeogenetics data in formerly poorly-accessible locations. Although we did not observe the microbial community composition of the calculus microbiome structuring by island or time period, the low taxonomic assignment rate suggests that there is additional taxonomic diversity in the Pacific calculus samples beyond that currently represented in databases, highlighting the need for studies of dental plaque biodiversity in broad, global contexts. Individual species reconstructions have the potential to reveal evolutionary patterns that mirror the migration patterns of their human hosts, but further work disentangling closely-related species and strains within ancient dental calculus metagenomes, as well as revealing currently unknown species diversity, is needed to allow accurate identification of individual species and to perform reliable phylogenetic reconstructions.
General data processing Data analyses were conducted in R v. 4. 1. 0  39  , unless otherwise stated. General packages used were tidyverse v. 1. 3. 1  40  , readxl v. 1. 3. 1  41  , ggpubr v. 0. 4. 0 42 and janitor v. 2. 1. 0  43. The color palette for the study is from the R package microshades v. 0. 0. 0. 9000  44. Regression models were drawn to data with a generalized linear model with geom_smooth in ggplot2 as part of tidyverse. Preprocessing DNA sequencing data was preprocessed using the nf-core/eager v. 2. 3. 3 pipeline  45. Default options were used unless otherwise stated. Taxonomic profiling was done with MALT v. 0. 4. 1  46, 47  with a custom database  10. Comparative datasets of published microbiome studies were also processed using the same procedures. One comparative dataset was used to assess preservation with the program SourceTracker  13  , and consisted of 10 non-industrialized gut samples  48, 49  , 10 industrialized gut samples  50, 51  , 10 skin samples  52  , 10 subgingival and 10 supragingival plaque samples  50  , 10 archaeological bone samples 10 , 10 modern dental calculus samples 10 and 10 archaeological sediment samples  53.
In addition, 10 archaeological bone samples from Taumako and 10 from Viti Levu were included as local environmental controls; this data had been produced during genetic screening of human remains for human population genetic studies at MPI-EVA laboratories in Jena, Germany. Because bones are free of microbes during life, microbes detected in these samples provide a good proxy for local post-mortem colonization  54. A second comparative dataset was used to compare calculus species profiles across the globe, and consisted of ancient calculus samples from 6 previously published studies. These included samples from Japan  16  , Europe and Africa 10 , Europe  15, 18, 24  , and Europe, the Caribbean, North America, and Mongolia 11 (Supplemental Table  S2 ). Preservation Preservation was assessed using SourceTracker v.
1. 01  13  , PCA, and the R package cuperdec  10, 55. Putative environmental and laboratory contaminants in the dental calculus samples were identified using the R package decontam v. 1. 6. 0  56  , with the prevalence method. To evaluate whether preservation was related to environmental conditions, a dataset consisting of annual average temperature and annual total rainfall was compiled for each island in this study  57. Missing data for some islands was obtained from alternative sources  58, 59. Preservation of microbial DNA in calculus and of human DNA in bone samples was compared in the Pacific and in Europe using previously published datasets  1, 5, 15, 24, 60, 61. For details see Supplemental Methods.
Taxonomic profiling Taxonomic profiles were generated with three tools, MALT, Kraken2, and MetaPhlAn3. The MALT table was generated as described above as part of the nf-core/eager run, and was used for all community composition analyses. Additional taxonomic profiles were generated with Kraken2 and MetaPhlAn3 to assess whether altering parameters or using non-standard databases increased the number of taxonomic assignments in samples (Supplemental Figure  S3 , Supplemental Figure  S4 ). For details see Supplemental Methods.
Community composition Principal component analysis (PCA) was conducted on the decontaminated species table of only the Pacific data, and again on a decontaminated species table of the Pacific data plus the global comparative data, using the R package MixOmics (Supplemental Table  S3 ). Drivers of variation in the community composition were tested with a PERMANOVA from the R-package vegan v. 2. 5. 7  62  , with euclidean distances and 999 permutations. Homogeneity of multivariate dispersions was tested with the function betadisper from the R package vegan. Canonical correlation was performed using the R package variancePartition  63. To determine if particular species were associated with environmental conditions on the islands, geographic location, sample age, or library characteristics, we used the R package specificity  64  and the decontaminated species table.
Functional analysis Functional analysis was performed using HUMAnN3 66. All reads < 50bp were removed from the fastq files prior to running HUMAnN3, because these are generally too short to be classified after translation. The bowtie2 mapping parameters were adjusted to account for aDNA damage patterns (-D 20 -R 3 -N 1 -L 20 -i S,1,0. 50). The standard gene family output table with UniRef90 gene clusters was grouped to KEGG Orthology, and analysis was performed on the KEGG orthologs. Poorly preserved samples as well as those with < 750 orthologs were removed, and orthologs present at < 0. 005% abundance were filtered out prior to analysis. A PCA was performed using the same steps as with taxonomy. PERMANOVA was performed with the adonis function in the R package vegan  67.
9), using MultiVCFanalyzer v. 0. 0. 87  68  (Supplemental Tables  S4, S5 ). Only samples with at least 1000 SNPs and a mean genomewide coverage of at least 2X (for T. forsythia) or 5X (Anaerolineaceae bacterium oral taxon 439) were included. The coverage requirement was increased for Anaerolineaceae bacterium oral taxon 439, because its percentage of heterozygous SNPs was higher. Neighbor-joining trees were built using distance matrices generated with the TN93+G4 substitution model, as this was determined by testing with the DECIPHER R package  69, 70  to be the best model for each dataset. The trees were rooted using the midpoint, determined with the package R phangorn  71. The phylogenetic trees were constructed and visualized using R packages ape v. 5. 5 72 , ade4 v. 1. 7. 17  73  , adegenet v.
Strain sharing Strain sharing across samples was assessed with inStrain  76. A test dataset was generated in silico to test for the effects of ancient DNA damage on popANI calculations. For details see Supplemental Methods. The coverage overlap and popANI between all samples was visualized and compared in R (Supplemental Figures  S8  and S9 ). Strain sharing across Pacific calculus samples was assessed with inStrain for 6 species that were highly abundant across the Pacific calculus dataset: Actinomyces dentalis (GCF_000429225. 1), Anaerolineaceae bacterium oral taxon 439 (CP017039. 1), Desulfobulbus oralis (CP021255. 1), Eubacterium minutum (CP016202. 1), Olsenella oral taxon 807 (CP012069. 2), and Tannerella forsythia (NC_016610. 1). We focused only on Anaerolineaceae bacterium oral taxon 439 and Tannerella forsythia, for which we generated whole genome SNP-based phylogenies.
The script polymut. py from cmseq  19  was used to assess whether samples contained multiple strains of the species Tannerella forsythia and Anaerolineaceae bacterium oral taxon 439 based on the ratio of non-synonymous vs. synonymous sites (dN/dS). We performed a test to determine the expected dN/dS of mapping reads against an incorrect reference genome by using three species of Fusoboacterium, which were formerly considered subspecies of F. nucleatum: F. nucleatum, F. polymorphum, and F. vincentii. We calculated the ANI of each genome compared to the other two (Supplemental Figure  S12A ) and used polymut. py to calculate the dN/dS for short read datasets of each genome mapped to the three reference genomes (Supplemental Figure  S12B-G ), and took the average across all genomes, which was 1.
Data Accessibility The sequencing data for this study is deposited in the European Nucleotide Archive under accession PRJEB61887. The scripts used for analysis and figure generation are available on github at https://github. com/ivelsko/pacific_calculus. Data processing Within the nf-core/eager pipeline, poly-G stretches were removed from the raw data, as they are a common by-product of the two-color chemistry sequencing strategy used by Illumina's NextSeq. Human DNA was removed from the dataset by mapping to the human reference genome GRCh38, and only unmapped reads were retained for downstream microbiome analyses. To produce an OTU table of the microbes present in the samples, the dataset was aligned using MALT v. 0. 4. 1  11, 12  to a custom database containing all bacterial and archaeal assemblies (scaffold/chromosome/complete levels, up to 10 randomly selected genomes per species) from RefSeq and the human HG19 reference genome  13  via the nf-core/eager pipeline.
In addition, the dataset was aligned to the NCBI nt database (as of October 2017), to screen for eukaryotic DNA. MEGAN v. 6. 17. 0 14 was used to export OTU tables from the resulting MALTproduced rma6 files, using summarized read counts at both the genus and species level. Preservation Preservation was investigated and visualized using SourceTracker v. 1. 01  15. A species-level OTU table was used as input, and the reference metagenomic datasets described in the main text were used as sources. During the SourceTracker analysis, rarefaction was performed to 10,000 reads, with a training data rarefaction of 5,000 reads. For principal component analysis (PCA), species-level read counts of all dental calculus samples and sources (including an additional 9 modern dental calculus samples from the same study as listed in the main text) were compared.
The R-package cuperdec  13, 16  was used to identify well-preserved samples (adaptive burn-in method, cut-off 50%), and 73 (out of 103) samples were carried on to further analyses based on this analysis. To remove potential contaminant species, we used the R package decontam. The samples were separated into groups based on the processing lab, and blanks and archaeological bones were used as proxies for contamination sources (cut-off 0. 25 for all groups, for both blanks and bones). Contaminants from all labs were combined into a single list of all contaminants, and all contaminants were removed from all samples. The comparative calculus datasets were likewise assessed with decontam and all contaminants were combined into a single list, along with those from the Pacific calculus dataset here, and all contaminants were removed from all datasets for the comparative analyses.
Annual average evapotranspiration was compiled from 1717. The proportion of taxa estimated to originate from the oral microbiome (i. e. , dental plaque and dental calculus) by SourceTracker was used as a proxy for preservation of the archaeological dental calculus samples. The effects of environmental variables and/or sample age on preservation were investigated using beta regression with a complementary log-log link function to account for observed heteroscedasticity. Using ANOVA, it was found that the model was not significantly improved by adding the random effects of laboratory and/or island (p»1). Step AIC (using both directions) was used for model selection. To reliably estimate parameters for the model, statistically influential data points were removed, and the model-fitting process was repeated until a stable dataset was reached.
Model fit was measured using the R 2 value as suggested by Ferrari and Cribari-Neto  18  for beta regression models. Preservation of calculus was determined by the percentage of the sample that was determined to be from an oral source (calculus, supragingival plaque, and subgingival plaque) with SourceTracker. Preservation of human DNA was determined by the percentage of endogenous human DNA in shotgun sequenced data, and was taken from published values. Human DNA preservation in the Pacific was taken from Posth, et al. 2018 (Supplemental Table  S3 )  19  and Liu, et al. 2022 (Supplemental Table  S2 )  20  , human preservation in England was from Schiffels, et al. 2016 (Supplemental Table  S1 )  21  and Patterson, et al. 2022 (Supplemental Table  S1 )  22  , and calculus preservation in Europe was taken from SourceTracker values of data from  23, 24.
Taxonomic profiling Kraken2 was run with default parameters, using two databases (described in  25  ): a custom RefSeq database and the same custom RefSeq database with the addition of MAGs from Pasolli, et al. (2019). This allowed us to test whether including additional diversity in the database resulted in a substantial increase in the number of reads assigned to taxonomy. However, we found that it did not, and that this profiler is particularly affected by the read length. MetaPhlAn3 was run with two different sets of parameters: default, and custom settings optimized for assignment of ancient DNA (-D 20 -R 3 -N 1 -L 20 -i S,1,0. 50 and minimum read length of 35 bp). The use of ancient-optimized parameters substantially increased the number of species identified in each sample (Supplemental Figure  S4A ) and we found that the number of species identified by MetaPhlAn3 with ancient-optimized parameters and by MALT were correlated (Supplemental Figure  S4B ).
The optimal number of sample clusters within the Pacific calculus dataset was determined using the Gap statistic with clusGap from the R package cluster  26  with 500 bootstrap replicates, on both the same MALT species table used for compositional analysis, as well as on a MALT genus table that was cleaned and filtered following the same steps as the species table. The optimal number of clusters was found to be one for both the species and genus tables (Supplemental Figure  S2 ), so no further cluster analysis was performed.
Strain sharing Simulated metagenome testing. Strain sharing across samples was assessed with inStrain  27. Two 10M bp paired-end simulated read datasets were generated to test the effects of aDNA damage patterns and clipping, using the genomes of the 10 species in the ZymoBiomics kit that was used by Olm, et al. for testing, in the proportions described in the kit. The genomes were processed with gargammel 28 in two batches, to create datasets with two different damage profiles based on the damage of the samples sequenced here. Two samples were selected for references, HCLVMBCX2-3505-07-00-01_S7, which is non-UDG treated, and EFE002. B0101, which is UDG-half treated. Both samples were mapped against the Anaerolineaceae bacterium oral taxon 439 genome (GCA_001717545. 1) with bwa (-n 0. 02 -l 1024)  29  , and the bam files were used as input to mapDamage  30  to generate damage profiles and read length distributions that were used as input to gargammel.
The simulated datasets were processed with the nfcore/eager pipeline the same way as samples, and adapter-trimmed un-collapsed paired reads were pulled out of the eager output with a custom script. The paired end reads were mapped against all 10 reference genomes in the ZymoBiomics kit, combined into a single fasta file with bwa (-n 0. 02 -l 1024). The effect of read end masking to remove aDNA damage was tested with a custom python script  31  , with mapped reads from UDG-treated reads masked 1 base on either end, and non-UDG-treated reads masked 9, 11, 13, and 15 bases on either end, based on the C-T transversion ratio along read lengths determined by mapDamage. InStrain profile was run on the unmasked and masked mapped read bam files with insert sizes 12, 24, 36, and 48, and inStrain compare was run on all output of the profile step.
Based on the output of these simulated data tests, a mask length of 11 and insert size of 12 were selected for processing real samples, as these returned a maximum popANI with minimal loss of coverage. Pacific calculus samples. Strain sharing across samples was assessed with inStrain for 6 species that were highly abundant across the Pacific calculus dataset. Paired-end reads were mapped against reference genomes for Actinomyces dentalis (GCF_000429225. 1), Anaerolineaceae bacterium oral taxon 439 (CP017039. 1), Desulfobulbus oralis (CP021255. 1), Eubacterium minutum (CP016202. 1), Olsenella oral taxon 807 (CP012069. 2), and Tannerella forsythia (NC_016610. 1), combined into a single fasta file. Only Anaerolineaceae bacterium oral taxon 439 and Tannerella forsythia had sufficient genome breadth and depth of coverage across enough samples for reliable strain comparison with inStrain.
Mapped reads in each bam file were masked according to their UDG treatment, with UDG-half-treated samples masked 1 base at both ends, and non-UDG-treated samples masked 11 bases at both ends. inStrain profile was run on the masked bam files with an insert size of 12, and the default value of 5X minimum coverage was required for samples to be included in inStrain analysis. inStrain compare was run on all output files of inStrain profile. We focused only on Anaerolineaceae bacterium oral taxon 439 and Tannerella forsythia, for which we generated whole genome SNPbased phylogenies. The script polymut from cmseq  32  was used to assess whether samples contained multiple strains of the species Tannerella forsythia and Anaerolineaceae bacterium oral taxon 439 based on the ratio of non-synonymous vs.