dataesr/scientific-paragraphs-categorization huggingface dataset:
X-ray diffraction (XRD) XRD analyses were performed at the Scientific and Technical Resources Service of the Universitat Rovira i Virgili (URV) in Tarragona (Spain), using a Siemens D5000 diffractometer (Bragg-Brentano parafocusing geometry and vertical θ-θ goniometer) fitted with a curved graphite diffractedbeam monochromator, an incident-beam Soller slit, a 0. 06° receiving slit, and a scintillation counter as a detector. The angular 2θ diffraction range was between 5° and 70°. The data were collected with an angular step of 0. 03° at 6 s per step and sample rotation. Cuk α radiation was obtained from a copper X-ray tube operated at 40 kV and 30 mA. Identification of the minerals was achieved by comparison of the XRD diffractogram with the ICDD database (release 2007) using Diffrac plus Evaluation software  (Bruker-AXS 2009).
Magnetic properties A bulk sample (~ 500 mg) from each chert sample was extracted to analyze its magnetic properties. The analyses were carried out using a variable field translation balance (VFTB) and comprised the following sequence: (i) progressive isothermal remanent magnetization (IRM) acquisition curves, (ii) hysteresis loops (± 1 T), (iii) backfield coercivity curves, and (iv) thermomagnetic curves up to 700 °C in air. All these analyses were performed at the laboratory of paleomagnetism of the Universidad de Burgos (Spain). R y = ∑ i i (I i -I ci ) 2 W i = S i i V 2 i ∑ n j=1 S j j V 2 j ∑ i W i = 1 After correcting the hysteresis cycles for their dia/paramagnetic fraction, various rock-magnetic parameters were determined using the Rock Mag Analyzer software  (Leonhardt 2006). These included Ms (saturation magnetization), Mrs (remanent saturation magnetization), and Bc (coercive field).
The X-ray beam impinged perpendicularly upon the sample, and the tube and detector were placed with an excitation-detection geometry of 45° and a 2 cm sample-detector distance. The selected samples were left untreated and analyzed at several discrete locations, using relatively flat areas of each chert sample with no cortex or inclusions. Net areas of the K-fluorescence lines generated at each location were calculated by means of the PyMCA analysis software  (Solé et al. 2007). The R environment (R Core Team 2021) and the "ggplot2" package  (Wickham 2016)  were used to create boxplots of the p-EDXRF counts for major elements in the samples. A principal component analysis (PCA) of the p-EDXRF readings was then performed using the R packages "FactoMineR"  (Le et al. 2008)  and "factoextra"  (Kassambara & Mundt 2017).
Acknowledgements We want to thank  Debora Cano  and  Antonio de la Torre  from the  Servei de Recursos Científics i Tècnics of the Universitat Rovira i Virgili , who have helped us with the ICP-OES technology. Thanks to the  Archaeonat-Research Project UAM. Thanks also to  Rupert Glasgow  for the English revision of the manuscript and to the anonymous reviewers for their comments and suggestions that have improved the manuscript.
Statistical Analysis Among participants who reported a positive SARS-CoV-2 test over follow-up, we first compared sociodemographic factors and distribution of preinfection healthy lifestyle factors among those who responded to the PCC question vs those who did not. We then compared the prevalence of sociodemographic and lifestyle factors by the number of healthy lifestyle factors. Each variable was missing less than 5%. Indicator variables were used for any missing covariate information for categorical variables; no participants were missing data for continuous variables. We estimated the relative risks (RRs) and 95% CIs for the associations between the healthy lifestyle score and risk of PCC, adjusting for age, race and ethnicity, partners' education, census tract median household income, census tract percentage population with bachelor's degree or higher, health care worker status, and history of chronic diseases (fully adjusted model) using Poisson regression.
Results The mean (SD) age of 32 249 participants was 65. 9 (4. 5) years (range 55-75 years). Of those, 97. 2% were White, and 28. 7% were active health care workers. Participants missing PCC data (n = 233) (vs those nonmissing [n = 1981]) were more likely to be racial and ethnic minorities, have lower BMI, be health care workers, have higher socioeconomic status, sleep less, and be less likely to have type 2 diabetes (eTable 1 in the Supplement). We documented 1981 participants with a positive SARS-CoV-2 test over 19 months of follow-up. Among those participants, mean age was 64. 7 years (SD, 4. 6; range, 55-75); 97. 4% (n = 1929) were White; and 42. 8% (n = 848) were active health care workers. The median time from assessment of exposures (return of 2017 questionnaire) to SARS-CoV-2 infection was 35 months (IQR, 31 months-37 months).
We collected additional honey samples in August to examine the specialized metabolites in the honey with UHPLC-HRMS, using two purification methods to achieve wide coverage of compounds. The analyses were conducted at the taxonomic level of genus using relative read abundances of genera  32  and presence-absence data to ensure the robustness of the results and interpretation, as the DNA metabarcoding method can generate some biases in relative read abundances among taxa  33. Beebread and honey composition data were graphically compared using nonmetric multidimensional scaling (NMDS), and the multivariate homogeneity of group dispersions was tested using the PERMDISP procedure. Then, we tested the change in plant composition between the sample types using a permutational analysis of variance (PERMANOVA) with Hellinger distances.
As a complementary honey and beebread composition analysis, we identified indicator species that characterized each sample type using the IndVal procedure. The richness of genera within a hive in the two sample types was compared with linear mixed models and the number of shared genera with binomial generalized linear mixed model. To assess the proportion of available flowering plants used for nectar and pollen foraging, we compared flowering plants mapped in the surroundings to the ones detected in the samples using euler-diagrams. To find the factors affecting the foraging choices we again applied NMDS's and used redundancy analysis (RDA) to assess the influential variables, with Hellinger-transformed values. To find whether apiary area also has an effect on the composition of specialized metabolites in honey, we used multivariate data analysis.
The most abundant specialized metabolites discriminating the three apiary areas as well as the most abundant ones commonly occurring in all three apiary areas were annotated by comparing their mass spectrometry (MS) data with literature and databases. Taxa detected in honey and beebread. After bioinformatic filtering, there were 2,592,723 sequencing reads from the honey samples and 1,724,302 from the beebread samples. After filtering out samples with fewer than 5,000 reads (n = 5), we had 97 honey samples for analyses, with on average 26,656 (SD ± 10,877) reads per sample. Of these 39 were collected in June, 30 in July and 28 in August. For beebread we had 87 samples after the filtering with on average 19,769 (SD ± 7,249) reads per sample. 36, 28 and 23 of these were collected in June, July and August, respectively.
2B , Supplementary Table  S3 ). Overall, the number of genera was significantly higher in honey samples (mean 10. 16) than beebread samples (mean 7. 87) (Supplementary table  S4 ). To assess the proportion of plant genera shared between the honey and beebread communities in each hive, we analyzed paired samples of honey and beebread, collected from individual hives at the same time (June ). The proportion in hives ranged from 0. 10 to 0. 71 but was on average similar throughout the summer, ranging from 0. 35 to 0. 43 between time points (Fig. 2C ). There was no significant difference in the proportion of shared genera among the time points (GLMM, p = 0. 12, Supplementary Table  S5 ). Based on the indicative species analyses, to identify which plant genera are mostly selected either for nectar or pollen foraging, twelve genera are significantly associated with honey samples, compared to eight genera for beebread (Table  1 , for results based on presence-absence data see Supplementary Table  S6 ).
Composition of specialized metabolites in honey. The most abundant specialized metabolites occurring commonly across all apiary areas in the honey samples collected in August were annotated based on the MS data (Supplementary Text S1, Table  S10 ). Multiple isomeric monoterpene glycosides and tricoumaroyl spermidines, carboxylic and dicarboxylic acids, the plant hormone abscisic acid and the vitamin pantothenic acid were among the major metabolites found in all three apiary areas (Supplementary Table  S9 , Figs. S6  and S7 ). The impact of the apiary area on the specialized metabolite composition in honey was assessed by multivariate data analysis (Supplementary Text S1, Figs. S4, S5, S8 and S9), showing not only the occurrence but also the abundance of specific metabolites to differ between the apiary areas.
The most abundant discriminant metabolites   were deduced from OPLS-DA models using the apiary areas as classifiers, and annotated (Table  S11 , S12). Their distribution among the three apiary areas was found to vary strongly (Supplementary Figs. S8  and S9 ). Using NMDS the specialized metabolites from both datasets seem to cluster in similar fashion, while the plant genera in honey samples from the same time points overlap more in apiary sites CD and EF (Fig. 5 ).
The apiary area consists of a mosaic of cultivated fields and managed forests, with differences in the ratios of these between the vicinity of each apiary, as determined by Corine land cover database (version 20b2, 2018, European 100 m raster database 62 ; Supplementary Fig. S3 ). We mapped the flowering plants and from the bee hives we collected beebread and honey samples at three time points during the summer 2021. The first sampling from the hives and flower counts were done in June (8. -13. 6. ), second in July (9. -14. 7. ), and third in August (10. -13. 8. 2021). For the sampling, which was only from the hives, we had the permission of the owners of the hives and thus it complied with all national rules and legislation in Finland, as no other permits are required. The flowering plants were assessed within 3 km distance of the hives, in sites selected by stratified (random) sampling with arbitrary allocation, in different habitat types indicated by Corine land cover database.
This was done to cut off lower quality ends before merging the paired ends for each gene region using VSEARCH  69  with a maximum of 80 differences allowed for overlap and a minimum assembly length of 150 bp. The merged reads were quality controlled by fastq_maxee, with maxee = 3. Primers were removed using cutadapt with a maximum of 0. 2 error rate for primers, and reads were kept with minimum length of 100 bp after primer removal. The merged and quality-controlled reads were only retained if they contained the expected primers at each end. The reads were then dereplicated into uniques and singletons were removed. The reads were denoised to zero-radius operational taxonomic units (ZOTU) using with unoise3 with USEARCH  70. A ZOTU table was built and the taxonomic assignation of ZOTUs was done by comparison against an ITS2 reference database from PLANTiTS 71 , accessed 21.
Statistical methods. We calculated the relative read abundances (RRA) of each plant genus per sample  32  , and RRA data was used for the analyses. We ran the analyses also using presence-absence (PA) data. Results from PA-analyses are in general agreement with the RRA-results and are available in the supplementary information. Statistical methods were implemented and figures generated in R version 4. 2. 2  74  , except for the specialized metabolite analyses (see below). P values of < 0. 05 were considered statistically significant. To identify the difference in the selection of floral resources by bees to produce honey or beebread, we first tested the multivariate homogeneity of the dispersion of groups between honey and beebread (PERMDISP, R function "betadisper" from the vegan package  75  , using the Hellinger-transformed data at genus-level).
Second, we quantify the difference in plant genus composition between beebread and honey using a permutational analysis of variance (PERMANOVA, with the "adonis2" function of the vegan package  75  ). As adonis2 does not allow random effects, the terms site, apiary, and hive were included in the model formula as fixed effects to account for pseudoreplication following the structure: To describe how honey and beebread samples differ and group into distinct clusters based on their composition of plant genera, we applied nonmetric multidimensional scaling (NMDS, with function "metaMDS" from package vegan  75  ). NMDS was further used to illustrate the temporal effects on the samples as well as comparison with metabolite datasets, for which pooled honey sample data from July and August were used.
NMDS analyses were performed with Hellinger transformed data. We analyzed the number of genera in each sample type (honey or beebread) with linear models, using time point as explanatory variable and hive as random variable, with function "lmer" from package lme4  76. Pairwise comparisons for time points were made using emmeans-package  77  with Tukey p-value adjustment. To analyze the proportion of shared and non-shared genera within the paired honey-beebread comparisons a binomial generalized linear model with logit-link function was used (using "glmer" function from package lme4 76 ), again using time point as explanatory variable and hive as random variable. Model assumptions were checked visually and with the R package DHARMa  78. To identify which plant genera are the most associated with the honey and beebread samples, we used the Indicator Species Analysis (IndVal, with function "multipatt" from the package indicspecies  79  , using 999 permutations).
To test the contribution of variables affecting floral choices, we used partial redundancy analysis (RDA) using Hellinger distances. The model follows the structure: As with the PERMANOVA model, the RDA controls the design variables experimentally to account for pseudo-replication. The model was also tested with the sequencing read depth, which appeared to decrease the observed conditional variation by approximately 5% (Supplementary Table  S8 ). Constrained ordination was tested by ANOVA-type permutation test in the vegan package. The variation associated with temporal, spatial and methodological variables were quantified by variation partitioning with the function "varpart" from the vegan package  75. Extraction of plant specialized metabolites. Specialized metabolites were purified from honey samples from 27 hives.
1% HCOOH (solvent A) and acetonitrile + 0. 1% HCOOH (solvent B). The column temperature was 40 °C, the flow rate was 0. 45 ml/min, and the gradient was as follows: 0-15 min, 5-25% B in A; The mass spectrometer was run in the ESI negative mode for separation method 1 and in the positive mode for separation method 2. The MS parameters were as follows: Probe heater temperature was 350 °C, capillary temperature was 330 °C, sheath gas flow was 50 arbitrary units, auxiliary gas flow was 10 arbitrary units, capillary voltage was 3 kV in the negative mode and 3. 5 kV in the positive mode, and S-lens RF level was 60. Scan range was m/z 100-1,500, and resolution was 70,000 (FWHM) for full MS and 17,500 (FWHM) for data dependent MS 2 scans. During the first 1. 0 min (separation method 1) and 0. 9 min (separation method 2) of elution, the eluent bypassed the mass spectrometer, and no data were recorded in order to avoid contamination of the MS with high levels of carbohydrates that were expected to be still present in the samples despite the purification measures.
As blank samples, the solvents used for preparation of the samples were injected, and as QC samples, pooled samples were prepared from both sample types (purification method 1 and 2) by mixing 10 µl of replicate 1 of each sample. Blank samples were injected at the beginning, in the middle and at the end of each sequence, and the QC samples were injected at intervals of 10 runs. Data processing and evaluation for specialized metabolites. Raw analytical data were processed with Compound Discoverer 3. 2 using the following parameters: Retention time window for spectra selection was 1-32 min for dataset 1 (acquired in the ESI negative mode with separation method 1) and 0. 9-26 min for dataset 2 (acquired in the ESI positive mode with separation method 2). Retention time alignment was performed with adaptive curve model (maximum RT shift 2 min, maximum mass tolerance 10 ppm).
For detecting and grouping unknown compounds, S/N threshold was 3, minimum intensity threshold was 5,000,000 for dataset 1 and 10,000,000 for dataset 2, and RT tolerance was 1 min. S/N threshold for gap filling was 20. The output, a data matrix consisting of retention time and intensity of every feature in every sample, was exported to MS Excel for further treatment. In both datasets, first, features derived from the analytical background were removed, and the peak areas of the ISTD in all samples were graphically compared in order to inspect the dataset for samples that had not been injected properly. On this basis, sample 23_2 was excluded from dataset 1, and sample 5_2 was excluded from dataset 2. Then, all remaining peak areas were normalized to the peak area of the ISTD in the respective run.
In order to remove unreliable features, means and relative standard deviation were calculated for all features detected in the pooled quality control samples. Features with a relative standard deviation above 33% were considered unreliable and removed from the datasets. These pretreated datasets were subjected to multivariate data analysis (MVDA) using SIMCA 17 (Sartorius). Prior to MVDA, data were log-transformed and pareto-scaled. For unsupervised MVDA, principal component analysis (HCA) and hierarchical clustering analysis (HCA) were used. For supervised MDVA, OPLS-DA models with three classes that correspond to the three apiary areas were constructed. For tracking metabolites occurring at high level across all apiary areas, average peak areas per apiary area were calculated for each metabolite in the two datasets.
Acknowledgements We would like to thank  Jason Rissanen ,  Tristan Ubaldi  and  Jaakko Kuurne  for the help with fieldwork and  Marjo Kilpinen  and  Eija Hakala  for assisting in the laboratory work. We also thank the two beekeepers for their expertise and maintaining the hives during the study. The study was funded by a grant to HW from  Kone foundation , a grant to ML from  Emil Aaltonen Foundation  ( 220131 ) and a grant to DF from the  Finnish Cultural Foundation  ( 00180246 ). NAWI Graz is thanked for supporting  Central Lab Environmental, Plant & Microbial Metabolomics. Open access funded by  Helsinki University Library.
Data availability The sequence datasets generated during the current study are available in the Sequence Read Archive repository, in the BioProject PRJNA889252 https:// datav iew. ncbi. nlm. nih. gov/ object/ PRJNA 889252? revie wer= gfl7m n7q3m t60s7 0onkm bf0mfh for the review, once accepted, in https:// www. ncbi. nlm. nih. gov/ sra/ PRJNA 889252.
| Electronic search query We conducted a systematic literature search of NCBI PubMed OR (Food Hypersensitivities) OR (Hypersensitivities, Food) OR (Hypersensitivity, Food) OR (Allergy, Food) OR (Food Allergy) OR (Allergies, Food) OR (Food Allergies)). The detailed electronic query is available in the Appendix S1 and the protocol was registered in PROSPERO with the ID CRD42022325240. We used the EndNote X9. 3. 3 software for reference management. The present systematic review follows the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) reporting guidelines. 19.
| Functional annotation and pathway analyses We performed gene annotation and enrichment analyses on the genes that exhibited differential methylation associated with the FA phenotype. Our approach aimed to decipher common pathways from the studies that were included in the systematic review. From each study, the list of differentially methylated genes was extracted and tabulated for data analysis. In the functional annotation and enrichment analysis, we did not include the genetic loci that did not reach epigenome-wide significance and those retrieved from association studies addressing outcomes other than the FA phenotype. We used the Database for Annotation, Visualization and Integrated Discovery version 6. 8 (DAVID) for functional annotation. 20 We considered for the biological interpretation the following outputs: gene ontology (GO) biological process (GOTERM_BP, biological process), GO molecular function (GOTERM_MF, molecular function) terms that were associated with both a p-value <.
05 within the cluster and an enrichment score above to 1. 3. Then, ChIP-X Enrichment Analysis 3 (ChEA3) software  21  was used to perform the enrichment analysis of transcription factor (TF) that ranks TFs associated with the gene set from ENCODE ChIP-Seq library. We considered the most significant TFs using the Fisher exact test p-value threshold of <. 05. The Ingenuity Pathway Analysis (IPA) software (QIAGEN) was used to identify the biological pathways using the same list of differentially methylated genes. We uploaded the gene list in the IPA software and applied the IPA core analysis. We considered the most significant canonical pathways using the Benjamini-Hochberg multiple testing correction p-value threshold of <. 05. We reported the proportion of differentially methylation genes highlighted in the analysis for every significant IPA canonical pathway, considering the total number of genes annotated for the same IPA canonical pathway as a reference.
| Functional annotation analysis [33] [34] 36, 37  Four hundred sixty-four differentially methylated genes (complete list in Table  S3 ) were identified. Among them, 11 genes with epivariations were found in at least two studies (Table  4 , Figure  2 ). Gene-functional clustering analysis using the DAVID database showed no significant association after FDR for multiple testing, but two significant (unadjusted p-value <. 05) enriched clusters of genes. Functional annotations of genes from cluster 1 are involved in "GO:0019882~antigen processing and presentation, GO:0050870~positive regulation of T cell activation, GO:0002504~antigen processing and presentation of peptide or polysaccharide antigen via MHC class II, GO:0032395~MHC class II receptor activity, GO:0002503~peptide antigen assembly with MHC class II protein complex, GO:0002381~immunoglobulin production involved in immunoglobulin mediated immune response," and genes from cluster 2 are involved in "GO:0006468 protein phosphorylation, GO:0004672 protein kinase activity, GO:0004674 protein serine/threonine kinase activity" (Table  S4 , Figure  3 ).
| DISCUSS ION Our systematic review highlighted only 16 eligible articles, including seven studies that assessed the association between FA and targeted epivariations and nine publications that assessed the association between FA using EWAS. Most studies with a candidate gene approach were focused on the shift of naive T cell differentiation from Th1 toward Th2  [22] [23] [24]  and the alteration of regulatory T cell function. 23, 25, 26 They suggest a role of DNA methylation in CMA and the rebalancing toward the Th1 state and tolerance acquisition. 38, 39 The decrease in methylation level of FOXP3-a gene associated with tolerogenic responses-has a significant effect on TA B L E 5 List of significant transcription factors (TFs) enrichied in gene set from ENCODE ChIP-seq library using ChIP-X Enrichment Analysis 3 software.