dataesr/scientific-paragraphs-categorization huggingface dataset:
Data compilation, structure, and extraction We extracted costs data associated with IAS from the "InvaCost" database (Invacost 3. 0;  Diagne et al. 2020b). "InvaCost" is a comprehensive and harmonized compilation and description of monetary costs associated with biological invasions (9,823 entries, full data and details in https://doi. org/10. 6084/m9. figshare. 12668570). This database results from a systematic literature search made in three bibliographic repositories (i. e. Web of Science, Google Scholar and Google search engine), as well as specific searches directed towards pre-defined sources, experts and stakeholders (i. e. "Targeted Collection" through e. g. webpages of official organizations and institutions, national biodiversity managers, conservation practitioners, researchers specialized in biological invasions).
As a result, "InvaCost" also includes data published in languages other than English  (Angulo et al. 2021a ). All sources were screened for relevant cost information and collated to a standardized currency, i. e. 2017 equivalent US Dollars (US$), based on exchange rates provided by the World Bank (see  Diagne et al. 2020b  for details). Each entry collated in "InvaCost" contains a cost estimate depicted by a unique combination of cost descriptors (currently >60 columns in the database) including: (i) the bibliographic information of the documents reporting the costs; (ii) the information on the impacted area (e. g. location, spatial scale, environment -aquatic or terrestrial, and whether the location corresponded to a protected area and/or an island); (iii) the taxonomy of the IAS causing the cost, (iv) the temporal extent over which the cost occurred, or was predicted to occur; and (v) the typology of each reported cost (e.
g. type of cost -management actions or economic damages; impacted sector -activity, market or societal sector related to the cost; and the reliability of the cost value). Finally, a set of variables reported the raw and standardized cost values (see below), as well as the original currencies. From this data assembly we selected cost entries specific to the country of Ecuador (column "Official_country"), resulting in 153 entries (herein, "raw data"; Data are provided in the Suppl. material 1: S1a). Data for Ecuador comes from 19 references collected in the Web of Science (two references corresponding to six entries), Google Scholar (two references corresponding to 19 entries) and Google (two references corresponding to eleven entries). The remaining 14 references (117 "raw data" entries) were collected by the "Targeted Collection" specifically focused on Ecuador.
Data filtering Once all the data were annualized, we filtered the data using two important descriptors of the costs: the reliability of the cost estimate and the implementation of the cost (columns "Method_Reliability", and "Implementation" respectively of the database, Suppl. material 1: Table  S1b ). The reliability of the cost entries was categorized as 'High' if the approach used for cost estimation in the original source was reported, reproducible and traceable, and 'Low' if otherwise. The implementation of the cost entries was categorized as 'Observed' if the cost was actually incurred in the focal area, and 'Potential' if the cost was not empirically observed but only predicted to occur (see  Diagne et al. 2020b  for details on criteria used). Costs that were both observed and of highly reliable were considered "robust".
Thus, we obtained a first dataset that we called herein "robust data" containing 317 entries representing data for 26 genera and 36 species (Suppl. material 2: Table  S2 ). Excluded entries are classified as "non-robust" (147 entries, i. e. 'Low' reliability and/or 'Potential' implementation), this group of entries reports costs for one additional genus and species that is thus not included in the "robust data" (Suppl. material 2: Table  S3 ).
Analyses of the robust data using cost descriptors Invasion costs estimates were analyzed based on three descriptors: i. Taxonomy of the invasive species causing the cost at the Genus level of ("Genus" column in the database; Suppl. material 1: Table  S1 ). However, when multiple species were associated with the same costs, those entries were reclassified as "Mixed-taxa". ii. Socio-economic sectors impacted by the invasive species cost ("Impacted_sector" column in the database; Suppl. material 1: Table  S1 ) as the following: "Agriculture", "Authorities-stakeholders", "Forestry", "Health", "Environment" and "Mixed". The "Mixed" category was assigned when reported costs affected two or more economic sectors and it was not possible to assign individual values. iii. Type of cost reported ("Type of cost_merged" column in the database; Suppl.
Cost descriptors Expenditures on "Management" constituted the large majority of the type of economic costs reported for Ecuador, with US$86. 06 million (99. 8%; n = 314 entries) involving control, eradication, monitoring and administrative management actions. The remaining 0. 2% of the costs are divided between economic costs due to "Damage" Only robust data is represented (i. e. both observed and highly reliable). Each point represents the cumulative cost for a given year whereas its size is proportional to the number of estimates for that particular year. Average annual costs are calculated in 5-year periods and are represented by dots and horizontal solid lines. Dashed lines connect the average annual costs for these 5-year periods. of US$0. 01 million (n = 1 entry) and "unspecified" costs amounting to US$0.
material 2: Tables  S3, S5. The entire costs reported in the database came from organisms in terrestrial environments. Here, we considered the Aedes mosquitoes terrestrial, since all the incurred costs are related to their adult life stage (i. e. control -for health and resources spent by health authorities due to dengue fever). Management actions on "mixed-taxa" of invasive species have fallen most heavily upon governmental organisms such as the Galapagos National Park Directorate and/ or other institutions such as the Charles Darwin Foundation, incurring expenditures of US$52. 44 million (n = 12 entries; Suppl. material 2: Tables  S2, S4 ).
Limitations and ways forward Quality control in databases is crucial for ensuring accurate assessments and conclusions, particularly in invasion science, where results are used to inform conservation managers, practitioners and environmental policy makers. We chose to use a highly conservative and robust dataset to draw our conclusions, and then delineated the pitfalls in our interpretation of the cost distribution. We are aware that our decisions to include or exclude some data might have consequences on our quantitative conclusions. For example, Aedes mosquitoes occupied third place in our list of the costliest species in Ecuador because we excluded its data from the most robust dataset; yet this species complex ranks much higher in economic costs assessments for other South American countries (such as Argentina,  Duboscq-Carra et al.
g. at least the main descriptors, such as spatial and temporal scale of the cost, the taxa involved, the type of costs and the economic sector impacted  (Diagne et al. 2020a, b). In fact, due to the lack of precise information about the dura-tion of the costs, six entries ("raw entries", Suppl. material 1: Table  S1a : L149-153) had to be excluded from the entire analysis. One of the excluded entries belonged to Culex mosquitoes reported for mainland Ecuador (i. e. an area with not many records), and was the only cost entry we have for the species (Suppl. material 1: Table  S1a ). The leading type of costs reported across the assembled dataset was expenditure in management. This is in contrast to results from the analysis of "InvaCost" data in other regions, where damage costs far outweighed management investments, for example, in Asia  (Liu et al.
2009) , that is biased taxonomically and/or geographically  (Pyšek et al. 2008)  but also on differential funding allocation  (Baker 2017 ). In addition, the complexity of evaluating some types of impacts (e. g. value of extinct or living species, ecosystem services, non-market items) is also probably part of the reason for the undervaluation of damage and losses  (Kallis et al. 2013; Meinard et al. 2016; Diagne et al. 2020a). We found robust costs for only 36 invasive species, whereas the Global Invasive species database reports 125 species known to be invasive in Ecuador (GISD,  Pagad et al. 2018). Therefore, more than 70% of the species reported as invasive in Ecuador do not have reported economic costs that are easily accessible. Even higher gaps between the number of species to be known as invasive and the number of species from which costs are reported, have been found in other countries in Central and South America, for example in Argentina and Mexico, where they report costs for only 10% of the known invasive species  (Duboscq-Carra et al.
2021; Rico-Sánchez et al. 2021 ) and other parts of the world, such as Germany  (Haubrook et al. 2021b)  or France  (Renault et al. 2021). In this study, we further noticed the bias on publication language. Half of the cost entries (51%, 78 out of 153 raw entries) were derived from the search in Spanish. Not only were there fewer publications about economic costs for Ecuador when compiling data with the most common search engines, but a large portion of the publications were obtained from directly contacting conservationists and managers ("Targeted Collection", Suppl. material 1: Table  S1a ). This strong bias was also found in other countries, such as Russia,  (Kirichenko et al. 2021) , Japan  (Watari et al. 2021) , France  (Renault et al. 2021)  or Spain  (Angulo et al. 2021b). For all these reasons, despite our dedicated efforts for assembling the most complete database  (Angulo et al.
lowing a workshop funded by the AXA Research Fund Chair of Invasion Biology and is part of the AlienScenario project funded by BiodivERsA and Belmont-Forum call 2018 on biodiversity scenarios. Thanks to Melissa Ballesteros for helping with the abstract translations. EA and LBM contracts come from the AXA Research Fund Chair of Invasion Biology of University Paris Saclay. CD is funded by the BiodivERsA-Belmont Forum Project "Alien Scenarios" (BMBF/PT DLR 01LC1807C). The authors also thank Virginia Duboscq-Carra for her contribution of compiling data for "InvaCost". We want to acknowledge all environmental managers, and researchers who kindly answered our request for information about the costs of invasive species. Last but not least, the authors thank Dr. Heinke Jäger for her thorough revision of the article which greatly improved it.
16S rDNA Sequencing and Bioinformatic Analysis The V3-V4 hypervariable region of the bacterial 16S rRNA gene was amplified using a two-step PCR and sequenced on MiSeq (Illumina, 2 × 250 bp) as previously described  (Spor et al. , 2020). A Jupyter Notebook developed in-house was used to analyze the sequence data. Briefly, sequences were assembled using PEAR  (Zhang et al. , 2014)  with default settings. Further quality checks were conducted using the QIIME pipeline  (Caporaso et al. , 2010 ) and short sequences were removed (<400 bp). Reference-based and de novo chimera detection together with clustering in OTUs were performed using VSEARCH  (Rognes et al. , 2016)  and Greengenes' representative set of 16S rRNA sequences as the reference database. The identity thresholds were set at 94%. Representative sequences for each OTU were aligned using Infernal  (Nawrocki and Eddy, 2013)  and a 16S rRNA phylogenetic tree was constructed using FastTree  (Price et al.
, 2010). Taxonomy was assigned using RDP Classifier  (Wang et al. , 2007)  and the latest released Greengenes database  (v. 05/2013; McDonald et al. , 2012). α-Diversity metrics (PD whole tree, species richness, and equitability index) were determined from rarefied OTU tables of 25,700, 24,500, 28,300, and 23,100 sequences per sample for soils A, B, C, and D, respectively. Sequences were deposited in the SRA at NCBI under the accession number SUB8712901.
Statistical Analyses Statistical analyses were carried out using RStudio statistical software (version 1. 2. 5033). For each soil, we built an ad hoc analysis of variance (ANOVA) model to determine the effects of treatments on gene abundances and on the alpha diversity indices of bacterial communities. Normality and homogeneity of the residual distribution were inspected and log10-transformations were performed when necessary. Differences in response variable related to the treatment (antibiotic treatment, manure amendment, and strain inoculation) were detected using the following model: Y ijkl = µ + A i + T j + I k + A : T ij + A : I ik +T : I jk + A : T : I ijk + ε ijkl , where Y ijkl is the response variable, A is the fixed effect of manure amendment (i = 1,2), T is the fixed effect of sulfamethazine treatment (j = 1,2), I is the fixed effect of inoculation by Microbacterium sp.
Computational Methods All DFT and G 0 W 0 calculations have been performed with the Vienna Ab initio Simulation Package (VASP)  [52] [53] [54] [55] [56]  and in the case of phonon calculations we additionally used phonopy to compute phonon dispersions  [57]. For the interpolation of the G 0 W 0 bandstructure we have used the wannier90 package  [58]. All calculations have made use of the PBE pseudopotential set generated with VASP  [59]. The structural relaxations have been performed on a 24x4x6 k-mesh with an energy cutoff of 460 meV. The phonon dispersion calculation has been performed using the vdw-optB88 functional and a 4x2x1 supercell with a 4x4x3 k-mesh. The post-processing has been performed with the phonopy package  [57]. The calculation of the phonon spectra at Γ which are compared to the Raman data has been performed on a k48x4x6 mesh using a single unit cell.
5 • geometry using the PBE functional. Taking the ratio of these two and multiplying the 300 meV experimental gap, we obtain a correction of the STS gap to 181 meV. Frozen phonon bandstructures after DFT calculation using the PBE functional. We displayed the result for the four Raman active phonons presented in section 5 of the main text. They show similar behavior as the results after G0W0 calculation. In the G0W0 calculation, however, the group velocity of the electrons is increased. that the energy cutoff for the response function and the number of unoccupied states are not independent convergence parameters and thus, we use the standard method and converge them simultaneously  [61]. The bandstructures have been obtained via post-processing using the Wannier90 package  [58]. For the presented convergence studies we have employed the Density Functional Theory solution using the PBE functional as starting point.
Acknowledgements We are grateful to  E. Baldini ,  I. Mazin  and  Yann Gallais  for enlightening discussions throughout the course of this work. This work is supported by the  European Research Council  ( ERC-2015-AdG-694097 ),  Grupos Consolidados  ( IT1249-19 ) and the  Flatiron Institute , a division of the  Simons Foundation. We acknowledge funding by the  Deutsche Forschungsgemeinschaft (DFG)  under  Germany's Excellence Strategy -Cluster of Excellence Advanced Imaging of Matter (AIM)   EXC 2056 -390715994  and by the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ) -SFB-925 -project  170620586. Support by the  Max Planck Institute -New York City Center for Non-Equilibrium Quantum Phenomena  is acknowledged. S. L. acknowledges support from the  Alexander von Humboldt foundation.
Data Set and Data Preparation The athlete data (name, surname, year of birth, sex, nationality) were obtained directly from the website of the Berlin Marathon 1. We were able to download the entire dataset for each available year in JSON format and then convert it to an Excel file using a custom Python script. The weather data on race day was downloaded from the website of "Deutscher Wetterdienst" 2  with temperature (maximum, minimum, average in degrees Celsius), sunshine (duration in hours), precipitation (mm), cloud cover (duration in hours), and atmospheric pressure (mbar) and filtered for the respective race dates. We chose the data from the weather station Berlin Dahlem because of its proximity to the Berlin Marathon route. and 2019 (with the exception of 1978 and 1980 for which no data was available), including the runner's finish time in the format HH:MM:SS, along with their sex and age, and the year of the marathon.
Since this study focused on the influence of weather conditions on finish times by performance tiers, the age information was not used. The second file was a register of the weather conditions during each of the marathons between 1974 and 2019, including temperature values (average, maximum, and minimum), and average atmospheric pressure and precipitation (along with other variables such as sunshine and cloud cover hours that are not used due to their high correlation with the temperatures). These files were visually inspected in an Excel spreadsheet first, where minor changes were made (renaming of header columns and removing of unused columns) and then uploaded into a Google Colab notebook, where Python was used to conduct the statistical processing and to create the results tables and charts.
DNA extraction and sequencing DNA extraction of the enterobacterial strains was performed using the MagAttract HMW DNA Kit (Qiagen) according to the manufacturer's recommendations. To prepare paired-end libraries we used the Nextera XT DNA Library Preparation kit (Illumina). Libraries were sequenced on the Illumina MiSeq sequencing platform using v2 sequencing chemistry (500 cycles) or on the Illumina NextSeq500 system using v2. 5 chemistry (300 cycles). Accession numbers of in-house sequences submitted to the NCBI GenBank database are included in (Table  S1 ).
Genome selection and phylogenetic analysis All genome sequences not generated in this study were obtained from publicly available prokaryotic genomes (NCBI GenBank). The quality of in-house sequenced genomes was checked with FastQC v0. 11. 5 (https:// github. com/ chgibb/ FastQC0. 11. 5/ blob/ master/ fastqc), and low-quality reads were trimmed using Sickle v1. 33 (https:// github. com/ najoshi/ sickle). The processed reads were de novo assembled with SPAdes v3. 13. 1  [40]  and annotated with prokka v1. 12  [41]. The genomes were screened for the presence of >45 kb of the complete pks genomic island using standalone BLAST +v2. 8. 1  [42]  and antiSMASH v5. 0. 0  [43]. The pks island found in the genome of the Escherichia coli strain M1/5 (accession no. CP053296) was used as a reference sequence.
Phylo-grouping of pks-positive strains The Escherichia coli and Klebsiella strains that harboured the colibactin gene cluster were allocated to their corresponding sequence types using mlst v2. 16. 1 (https:// github. com/ tseemann/ mlst), which detects sequence types using the PubMLST typing schemes. The Escherichia strains were further classified into their phylogenetic lineages using the standalone tool, EzClermont v0. 4. 5 (https:// github. com/ nickp60/ EzClermont). The phylogeny of all the clb-positive strains was reconstructed using the whole genome sequencebased approach published by Adeolu and co-workers  [48]. Analysis of the diversity of the colibactin and yersiniabactin gene clusters involved virulence gene multi-locus sequence typing (MLST) for both polyketide determinants as previously described  [38].
Acknowledgements The data reported in this study appear in part in the PhD theses of  A. Wallenstein  and  H. Wami. We thank  M. K. Mammel  ( United States Food and Drug Administration ) for providing Escherichia coli strain N1. We thank  K. Tegelkamp  and  O. Mantel  ( Münster ) for excellent technical support. We gratefully acknowledge  CPU  time on the high performance cluster  PALMA@WWU Münster.
2. 5042. Genes with a P value adjusted with the Benjamini-Hochberg procedure <0. 05 and a fold change ≤ 0. 6 or ≥ 0. 6 were qualified as differentially expressed genes (DEGs). Normalized counts from DESeq2 analysis were transformed with rLog (regularized log transformation) function from DESeq2 library. rLog-transformed counts were used for variance analysis such as principal components analysis or hierarchical clustering (data not shown). MA plots were constructed from DESeq2 analysis: the mean of normalized counts across all samples for each gene (base mean) was plotted versus the base 2 of logarithm of (mean normalized count at day 16 minus mean normalized count at day 8) (Log2 (fold change)). A clustered heat map was finally constructed with transformed counts of DEGs with pheatmap function from pheatmap library.
Cluster trees were computed with the Euclidean distance correlation method. Functional clustering of GO biological process terms enriched for genes either upregulated or downregulated at day 16 was performed with online Metascape software  (13). Online STRING software V11. 0 was then used to analyze proteinprotein interactions for DEGs into functional clusters. An MCL clustering with an inflation parameter of 3 was performed. Statistics. All statistical comparative analyses were performed with the software Prism (GraphPad) using a nonparametric Mann-Whitney test, except for differential 3′DGE RNA expression analysis which was done using R (as described below). A P value < 0. 05 from the Mann-Whitney test was considered as a significant difference.
As expected, ApoB protein was well expressed in lysates from intestinal biopsies (Fig. 4B ). More importantly, despite the absence of specific stimulation in our experimental conditions, we were able to detect the presence of ApoB in the basolateral media (Fig. 4B ), suggesting that the protein secretory pathways were preserved in our biopsies. Ex vivo PCSK9 secretion was also tested using intestinal biopsies (proximal, medial, distal intestine, or colon) from WT or Ldlr-deficient mice. Although we readily confirmed PCSK9 protein expression in intestinal explant homogenates, we were unable to detect PCSK9 secretion in both basolateral and apical media from Ussing chambers. To rule out the possibility that the ELISA detection method lacked sensitivity, we also performed acetone precipitation and WB analysis from basolateral media and confirmed the absence of secreted PCSK9 proteins (data not shown).
DISCUSSION The aim of this study was to determine whether intestinal cells can secrete PCSK9. This issue was raised by apparently contradictory results from two independent studies regarding the ability of the enterocyte to secrete PCSK9  (6, 11). While in vivo data from PCSK9 LivKO mice indicated that the liver is the sole organ analysis for GENES UP and for GENES DOWN is shown. D: Protein-protein interactions for the downregulated genes belonging to the cluster "Cholesterol biosynthetic process" are shown. Line thickness indicates the strength of data support. Four clusters with connected nodes are distinguished according to color of bubbles. E: Log2(fold change) for each downregulated gene of the cluster "Cholesterol biosynthetic process" is indicated. F, SREBF2, HMGCR, and LDLR expression relative to that of cyclophilin was analyzed by RT-qPCR in samples from nine independent experiments.
Funding and additional information This work was initialy supported by the  Fondation Leducq  ( Transatlantic Network of Excellence Program : "The function and regulation of PCSK9: a novel modulator of LDLR activity", to B. C. ). This work was further funded by  Fondazione Cariplo , the  "R égion des Pays de La Loire", and the Fondation "G énavie". This work was also supported by the  French national research projects Int2PCSK9  ( Deciphering the Intestinal role Intracellular   PCSK9-ANR-16-CE14-0025-01 ) and  CHOPIN (CHOlesterol Personalized Innovation - ANR-16-RHUS-0007 ) funded by the  Agence Nationale de la Recherche  and coordinated by the  CHU of Nantes.