dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments The work was supported by the  Scientific Exploitation of Operational Missions Program of the European Space Agency  through the project " Exploitation of S-1 for Surface Soil Moisture Retrieval at High Resolution " under Contract  4000118762/16/I-NB , and by the  Research Foundation Flanders  ( FWO-1224320N  and  FWO-1530019N ). Rolf Reichle  was supported by the  NASA SMAP mission. Wouter Dorigo  and  Martin Hirschi  were supported by the  ESA Climate Change Initiative Soil Moisture. Simon Dadson  acknowledge financial support from the  UK Natural Environment Research Council  ( NE/S017380/1 ). Yann Kerr  was supported by the  TOSCA program  and SMOS mission. This paper is also dedicated to honor the memory of  Alexander Loew  and and  Martyn Silgram.
Ubiquitous use of plasticizers has led to a widespread internal exposure of the European population. Until today, metabolites are detected in almost every urine sample analysed. This raised the urgent need for a toxicological interpretation of the internal exposure levels. The European Human Biomonitoring Initiative (HBM4EU) contributes substantially to the knowledge on the actual exposure of European citizens to chemicals prioritised within HBM4EU, on their potential impact on health and on the interpretation of these data to improve policy making. On that account, human biomonitoring guidance values (HBM-GVs) are derived for the general population and the occupationally exposed population agreed at HBM4EU consortium level. These values can be used to assess phthalate exposure levels measured in HBM studies in a health risk assessment context.
Introduction Human Biomonitoring (HBM) has increasingly been established globally as an instrument to inform policy and citizens about the exposure of the general public to anthropogenic chemicals. The European Human Biomonitoring Initiative (HBM4EU) is a joint effort of 30 European countries, and the European Environment Agency, co-funded by the European Commission under Horizon 2020 with the goal to improve chemical safety. Many countries in Europe and worldwide already run HBM programs to monitor exposure levels of environmental chemicals, some of them on a regular basis  (WHO, 2015). In Europe, these programs had previously worked independently of one another. As a result, comparability of the national HBM data is limited. HBM4EU has created a European network that improves knowledge for the European Union's environmental and chemical policy by harmonizing the planning and implementation of HBM studies, as well as sample and data analysis across national borders  (Ganzleben et al.
Methods The methodological approaches applied to derive HBM-GVs for the general and for the occupationally exposed population are outlined in detail in the strategy paper to derive HBM-GVs  (Apel et al. , 2020a). The strategy paper underwent a consultation process within the HBM4EU consortium to include the expertise of scientists from HBM4EU partner countries. Briefly, the strategy comprises that a literature research on recent toxicological and epidemiological data is conducted to find a robust exposure-health relationship to derive an HBM-GV. If present and reliable, human data is preferred over animal data. If it is not possible to establish a relationship between the internal concentration of the substance of concern or its metabolite(s) and a selected critical health effect in human data, a toxicity reference value (TRV) established by acknowledged authorities or committees can be used to derive an HBM-GV by means of toxicokinetic extrapolation.
General population A literature search of the toxicological and epidemiological database was performed to identify the most sensitive endpoint (i. e. critical effect) for DnBP. The epidemiological studies on DnBP did not allow for establishing a relationship between the internal biomarker concentration and the critical health effects. In line with the methodology for deriving HBM-GVs, the second option was explored: whether a TRV is available, adequate to derive an HBM-GV. In the Annex XV restriction report on four phthalates (ECHA, 2016), a derived no effect level (DNEL) of 0. 0067 mg/kg bw/d was derived based on the LOAEL of 2 mg/kg bw/d for developmental toxicity observed in the oral toxicity study in rats by  Lee et al. (2004). Critical effects were reduction of testicular spermatocyte development and mammary gland changes in the male adult offspring (ECHA, 2016;  Lee et al.
The HBM-GVs for the six substances presented in this paper form the basis for a harmonised health risk assessment of the European population. These values will be used to perform single-substance risk assessments by comparison with aligned HBM datasets from across Europe generated under the HBM4EU project. Furthermore, it is currently explored how HBM-GVs can be used to perform risk assessments for chemical mixtures (e. g. mixture of phthalates) to evaluate real-life exposure scenarios. In addition to the six HBM-GVs described in this paper, HBM-GVs have been derived for cadmium and bisphenol A  (Lamkarkach et al. , 2021; Ougier et al. , publ. submitted). It is anticipated that further HBM-GVs will be derived for bisphenol S, aprotic solvents (n-methyl-2-pyrrolidone, n-ethyl-2-pyrrolidone, dimethylformamide, dimethylacetamide), mercury and selected pyrethroids.
HBM-GVs are a valuable tool for the evaluation of exposures in a health-risk assessment. They constitute a common basis for assessment and they are easy to use as they can directly be compared to HBM data. The HBM4EU project was the first initiative proposing HBM-GVs agreed upon in a broader European consensus. In addition, the LoC assigned to each derived HBM-GV highlight data gaps and thus can help to foster research activities for future HBM-related international initiatives.
Site description and experimental design The experimental site is part of the Agro-ecosystems, Biogeochemical Cycles and Biodiversity long-term experimental stations on environmental research (SOERE ACBB), managed by the French National Institute of Agricultural Research and Environment (INRAE), located in Lusignan (46 • 25 ′ 12. 91 ′′ N; 0 • 07 ′ 29. 35 ′′ E), France. The climate is oceanic, with a mean annual precipitation of 796 mm and mean annual temperature of 11. 4 • C, a monthly minimum of -1. 9 • C and a monthly maximum of 30. 6 • C (data from  Agroclim, 2006 Agroclim, -2011)). In 2011, soil cores (0-30 cm depth) were collected at 16 locations to characterise bulk density, pH and carbon (C) and nitrogen (N) content for the site. Soil cores were sent to the analytical laboratory of INRAE (Arras, France) for pH, C and N measurements following the NF ISO 10390, 10,694 and 13,878 respectively.
Bulk density was measured with calibrated cylinders. The soil is a Plinthic Cambisol  (Moni et al. , 2010)  with a silt loam texture, the 0-30 cm soil layer is characterized by a pH-H 2 0 of 6. 67, C content of 10. 67 mg g -1 , N content of 1. 27 mg g -1 and bulk density of 1. 48 g cm -3 (Supplementary Table  S1 , data from Arras, France, Soil Analysis Laboratory). Experimental treatments were established in 2005 and management of the crops and grasslands was ensured according to agricultural practices to achieve yields and forage production close to the potential determined in the region by soil and climate. Before the start of the experiment, the site was homogenously cultivated as a succession of winter triticale  (2004) (2005) , winter barley  (2003) (2004)  and rapeseed, winter wheat or winter barley  (2000) (2001) (2002).
Briefly, each earthworm-sampling event consisted of applying three 10 L applications of water with an increasing concentration of formaldehyde (0. 25%, 0. 25% and 0. 40%) on one square meter. To recover earthworms unable to reach the surface, a block of soil (25 × 25 × 20 cm), corresponding to an area of 1/16 square meter, was hand-sorted inside the sample square (for more details, see  Pérès et al. , 2010). In the laboratory, earthworms were counted, individually weighed (±10 mg), assigned to a stage of development (juvenile, sub-adult and adult), identified to species level using  Bouché's (1972)  key and assigned to an ecological category: epigeic, epi-anecic (Lumbricus sp. ), strict-anecic (Aporrectodea sp. ) or endogeic  (Bouché, 1977; Jégou et al. , 1998; Hoeffner et al. , 2019). Juvenile earthworms that could not be determined at the species level (29% of the dataset) were allocated to species according to the proportion of adult and sub-adult identified within a square meter and within the same functional group.
Acknowledgements This work was supported by the  EU   SoilMan  project (grant number  01LC1620 ) funded through the  2015-2016 BiodivERsA COFUND call for research proposals , with national funders the  Federal Ministry of Education and Research (BMBF) , the  French National Research Agency (ANR) , the  Swedish Research Council for Environment, Agricultural Sciences and Spatial Planning (FORMAS) , the  Spanish Ministry of Economy and Competitiveness (MINECO) , the  Executive Agency for Higher Education, Research, Development and Innovation Funding (UEFISCDI) , the  Estonian Research Council (ETAG). We would like to thank the  National Research Infrastructure "Agro-écosystèmes, Cycles Biogéochimique et Biodiversité " (ACBB) http://www. soere-acbb for providing access to an excellent field experiment and the "the  AnaEE France  ( ANR-11-INBS-0001 )", " AllEnvi " and " CNRS-INSU " which support it.
RNA-Sequencing and qPCR validation Total RNAs from twelve F0 male brains (6 MIX, 6 Control), four F2 male brains (2 Control, 2 MIX -due to limited material available) and eight F2 female brains (4 Control, 4 MIX) were extracted using the RNeasy Plus Universal Mini Kit (Qiagen, France) following manufacturer procedure. Quality and quantity were evaluated by electrophoretic migration and spectrometry. 1 μg total RNA per sample was sequenced by the platform MGX. Library was prepared using a TrueSeq adapter kit. Validation of the library was performed on Fragment Analyzer (Standard Sensitivity NGS kit) and using qPCR (ROCHE Light Cycler 480, France). Total RNAs were sequenced on Illumina HiSeq 2500 generating 50-nucleotide single-end reads. The data discussed in this publication have been deposited in NCBI's Gene Expression Omnibus  (Edgar et al.
, 2002)  and are accessible through the GEO Series accession number GSE146175 (https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi? acc=GSE146175). Biological validation of RNA-Seq results was performed using quantitative Polymerase Chain Reaction (qPCR) on individuals that were produced independently from the ones used for RNA-Seq analyses (Section S3).
An adjusted p-value for multiple comparisons (padj) < 0. 05 was considered as a statistically significant difference and a p-value < 0. 05 was used to show trends. The results were displayed in a heatmap generated using displayr (www. displayr. com). RNA-Sequencing analysis: Quality of the fastq files was checked using the FastQC software (v0. 11. 8)  (Andrews, 2010). TrueSeq adapters were removed using Cutadapt (v2. 1)  (Martin, 2011). Clean reads were aligned to the last version of the zebrafish genome (Ensembl GRCz11, annotation GRCz11. 97) with STAR (v2. 7. 0)  (Dobin et al. , 2013). The number of reads mapping to each gene were counted with HTSeq (v0. 11. 2)  (Anders et al. , 2015). Differential expression analysis between MIX and Controls of the respective sex and generation was performed using R (v3.
6. 1) and RStudio (v1. 2. 1335), package DESeq2 (v1. 24. 0)  (Love et al. , 2014). Genes showing a padj < 0. 05 were considered as significantly differentially expressed genes. Identification of enriched biological pathways was performed against Gene Ontology (GO) and Reactome databases. Overall, the method followed the guidelines presented in  Reimand et al. (2019). Gene Set Enrichment Analysis (GSEA, v4. 0. 3) was used against GO terms related to biological processes, molecular and cellular functions, and Reactome pathways, retrieved from g:profiler server (release 2019-07-31 and 2019-10-02 respectively). The ranking metric used in GSEA analyses was the Walt statistic given by DESeq2 (Esteve-Codina, 2018). Only gene sets containing 15-200 genes were selected to limit the results to the most informative pathways.
Acknowledgements The authors wish to thank  Bettie Cormier  and  Ajay Pradhan  for assistance with sampling and data analysis, respectively, as well as  Jorke Kamstra  and anonymous reviewers for helpful comments on the manuscript. This research was funded by the  French National Research Agency , project  Fish'N'POPs  ( ANR-13-CESA-020 ). CB further acknowledges financial support from  France Génomique National  infrastructure, funded as part of " Investissement d'avenir" program  managed by the  French National Research Agency  (contract  ANR-10-INBS-09 ). Appendix A. Supplementary data.
github. io/mimulus_voyage. Population structure in the native range is less clear from the worldwide PCA, although the North Pacific clade and particularly the Aleutian Islands populations are well differentiated along the first principal component (Fig. 3 ). Worldwide groupings by K-means cluster analysis (Fig. 4  and Supplementary Fig. 1 ) partition North American samples into three groups, New Zealand into two groups, and the single populations from the Faroe Islands and Germany in one group each, largely consistent with the results above. Non-native UK populations form two groups, one mixed with European and Eastern North American samples, and another with New Zealand samples. Native, non-Alaskan populations are distributed in five groups. Aleutian populations form a separate group not shared with other geographic regions.
55, Table  1 ). Finally, when comparing all best one-to four-wave introduction models, with all possible fivewave introduction models, the most likely introduction history identified consisted of a first introduction from ALE followed by four subsequent waves from the AKBC, NORTH, SOUTH and COAST (E4 model; p = 0. 55, Table  1 ). Full demographic parameters (e. g. , estimated population sizes and introduction times per genetic group; E4 model) are presented in Supplementary Table  1. Classification of the datasets simulated under the best one-to five-wave introduction scenario showed that 41. 6% of the simulated datasets under E4 scenario were correctly classified, and a 50. 6% probability that a simulation classified as E4 truly originated from this model (Table  2 ). Thus, the combination of the type and number of molecular markers and model prior specifications we used here contain enough information to confidently differentiate scenarios with different number of introductions (e.
Only perennial plants are documented in the invasive range. We sampled populations of M. guttatus in the native range of western North America and the main areas of introduction in eastern North America, Europe and New Zealand for a total of 521 individuals from 158 populations (Fig. 1  and Table  3 ). In the native range, the samples included 70 previously genotyped populations  34  , spanning Arizona to British Columbia, plus an additional population from Vancouver Island. To fill the gap of previous studies, and to specifically address the hypothesis of an Alaskan origin of introduced UK populations, we collected samples from 32 populations in Alaska, including 14 populations from the Aleutian Islands (Attu, Unalaska, Akutan and Unimak) (Supplementary Data 1). Voucher specimens of the newly sampled populations are deposited in the University of Alaska herbarium (ALA).
In the introduced range, we sampled four populations in eastern North America, one from the Faroe Islands, one from Germany, six from New Zealand, and 43 from UK populations from Cornwall to the Shetland Islands. As an outgroup we included three diploid individuals from a population of M. glabratus from Michigan, USA. We also The comparisons are made at the final selection step between the most likely one-to four-wave introduction models and all possible five-wave introduction models. The table shows how many of the 10,000 simulated datasets generated under a given scenario (A2 to E5, rows) were classified into each demographic scenario (A2 to E5 columns). The number of incorrect classifications is then used to compute the overall classification error. The last column shows the percentage of simulated models classified as E4 (which was the most likely scenario for the observed genetic dataset).
Bold numbers indicate correct classification, and underlined numbers indicate >10% incorrect classification. (B) Probability of a given number of origins given that the E4 model is selected. sampled three tetraploid UK M. guttatus, 19 individuals of M. luteus from both native and introduced ranges (with which M. guttatus hybridises in the introduced range to produce a sterile but widespread triploid, M. x robertsii), three M. × robertsii, and three M. peregrinus (the allohexaploid species derived by whole genome duplication from M. x robertsii 57 ; (Supplementary Data 1). In total, we had samples from 103 populations of M. guttatus from the native range, and 55 populations from the introduced range (Table  3 ). Full sample details are provided in Supplementary Data 1. Genotyping. To obtain DNA for genotyping, we germinated field-collected seeds from all new populations in a controlled environment facility at the University of Stirling.
We extracted genomic DNA from fresh leaves or flower buds using the DNeasy Plant Kit (Qiagen, Germantown, MD), with samples standardised to 100 ng DNA for library preparation. We used genotyping by sequencing (GBS) to generate genome-wide polymorphism data  58. For GBS library preparation, we used the same protocol as Twyford and Friedman  34  , using the enzyme PstI, and pooling samples in a 95-plex (plus one blank water control) for 100 bp single-end sequencing on the Illumina HiSeq 4000 at the University of Oregon. We analysed raw sequence reads using the Tassel5-GBSv2Pipeline  59  , using the M. guttatus v2 genome  60  as a reference. Alignment with the reference genome was done with BWA with default parameters in Tassel5-GBSv2Pipeline. To obtain variant sites we used a minimum allele frequency of mnMAF = 0.
Nucleotide diversity per site was calculated separately for each of 155 populations, both native and invasive, using the option --site-pi in VCFtools. An overall pairwise nucleotide diversity across the (π GENOME ) for each population was then obtained by averaging across all sites. For population genetic analyses in M. guttatus, we filtered the SNP data (44,552 loci from 521 M. guttatus individuals) using VCFtools version 0. 1. 15  61 and kept only biallelic loci that were genotyped in at least 75% of all individuals, which reduced the number of genotyped SNPs to 1820 loci. The parameters we used in this filtering step were: max-missing = 0. 75, mac = 3, min-alleles = 1, max-alleles = 2. We then removed individuals with <50% genotyped loci, reducing the number of individuals from 521 to 474.
We analysed two datasets, one for all sampled Mimulus taxa, and one for M. guttatus, with both datasets, including M. glabratus as an outgroup. Each analysis used the full GBS sequences with invariant sites (123,141 loci including both variant and invariant sites), filtered to include 8798 sites with <50% missing data. We calculated population allele frequencies using the counts file library (cflib) python scripts that accompany  64. Model-fitting was performed with ModelFinder  65. IQ-TREE analyses subsequently used the best-fitting model (TVM + F + G4) allowing for excess polymorphism (+P) and with five chromosome sets per population (+N5). Tree searches were performed with settings recommended for short sequences, including a small perturbation strength (-pers 0. 2) and large number of stop iterations (-nstop 500).
We further used fastStructure version 1. 0 71 to infer population structure across M. guttatus populations using a Bayesian framework. For this analysis, we randomly subsampled the data to include a maximum of three individuals per population (408 individuals in total) from both native and introduced ranges, and analysed values of K from 2 to 8. Introduction history reconstruction by ABC. Our preliminary analyses indicated that introduced M. guttatus had a complex origin with multiple introductions in different non-native regions. In order to gain a more detailed understanding of the demographic history of non-native populations, we focused on the introduction of M. guttatus to the UK, which has been best studied both historically and genetically  30, 31. Therefore, we implemented an approximate Bayesian computation (ABC) approach to determine the most likely M.
guttatus introduction history in the UK. For this analysis, we used the pruned data set consisting of 1498 SNPs but included only individuals from the native range or the UK (399 individuals). Individuals from the native range were grouped into one of five groups ("genetic group"). Four of these groups (AKBC, NORTH, SOUTH and COAST) have been previously identified in analyses using native populations only  34, 37. The fifth group (Aleutian Islands, ALE) is identified in the present study (see Results section). To assign native individuals to each of these genetic groups, we used the group membership obtained in the K-means cluster analysis, which produced genetic groups that closely correspond to the native clades previously identified in Twyford and Friedman  34  and Twyford et al. 37  (see "Results" section).
A full version of other assumptions and simulation parameters is given in Supplementary Notes 1. For each demographic model, we simulated 10,000 genetic datasets consisting of 1435 independent SNP genotypes for 798 haploid individuals distributed following the sample size of all six populations in the real data set using Fastsimcoal2 version 2. 6. 0. 3 72 called by ABCtoolbox version 1  73. We passed a custom bash script to ABCtoolbox to add missing genotypes to the simulated data set at an identical rate to the observed level in the real data. Then, we used ABCtoolbox to call the arlsumstat programme  74  to compute summary statistics from the simulated genotypes that were combined into diploid genotypes by randomly pairing haploid genotypes assuming Hardy-Weinberg equilibrium  75. We computed all available statistics within and between populations for biallelic loci (67 summary statistics).
Moreover, a second set of four models, which assume that the most likely origin in the previous round constitutes the second introduction, while the first introduction originated from one of the four other native populations (Table  1 ). We compared the most likely single introduction model and the eight two-waves introduction models. We then considered more complex models, comparing nine three-waves introduction models and the most likely single and two-waves introduction models (Table  1 ). We subsequently compared models assuming four-waves and five-waves of introduction while still including more simple models in the comparisons (Table  1 ). Demographic models were compared using a random forest approach implemented in the R package abcrf  76. We built a classification random forest model using 1000 trees and a training data set consisting of the summary statistics computed for the 10,000 simulated genetic datasets for each model.
We estimated the classification error rate for each model using an "out-of-bag" procedure to quantify the power of the genetic data given the models and prior distribution specifications to differentiate the different demographic models. Then, we used the summary statistics computed based on the observed genotypic data to predict the demographic model that best fit the data using a regression forest with 1000 trees. We report the number of "votes" for each demographic scenario and the approximation of the posterior probability of the most likely model. We used the overall most likely scenario to simulate 100,000 genetic datasets using parameters and prior distributions described above to estimate demographic model parameters. We built a regression random forest model implemented in abcrf based on the summary statistics using 1000 trees.