dataesr/scientific-paragraphs-categorization huggingface dataset:
After 17 h at 65 °C, the hybridized slides were washed twice for 1 min at room temperature in GE Wash Buffer 1 and 1 min at 37 °C with GE Wash Buffer 2 (Agilent Technologies) and air-dried and scanned immediately. Microarray analysis. After hybridization, the scanned images were analysed using Feature Extraction software (v10. 7. 3. 1; Agilent Technologies). The data were normalized using intra-array median subtraction and log2 transformation. The raw data intensity files were read into R (www. r-project. org). The identification of differentially expressed genes from EC and CC F2 placentas was achieved using the Limma package  67. P values obtained by this analysis were adjusted for multiple testing using the Benjamini-Hochberg procedure. Probes with an adjusted P value < 0. 05 were considered significant.
Gene Set Enrichment Analysis (GSEA). The standard strategy to analyse transcriptomic data is based on a single-gene level that is very efficient when perturbations exert strong impacts on individual genes. This classical approach, however, becomes inefficient with subtle conditions because only a minor fraction of the information contained in whole-genome transcriptomic dataset is used, due to arbitrary thresholds used  24. Gene set enrichment analysis (GSEA) was established for use in metabolic studies in the 2000s  68. All detected unfiltered transcripts are mapped to their intended pathways (or other "sets of interest"). The subsequent analysis within specific pathways enables the study of the regulation of specific pathways that include moderately regulated (and non-regulated) genes usually lost due to experimental background noise.
Here, the GSEA approach  68  was used on transcriptomic data to systematically identify biological pathways enriched in genes associated with F1 foetal exposure and performed using the following parameters: 1,000 gene set permutations, weighed enrichment statistics, gene set size between 15 and 500 and signal-to-noise metrics. Regulated pathways were considered statistically significant when the false discovery rate (FDR) q-value was <0. 25. The GSEA-derived normalized enrichment score was used for the visualization of pathway regulation, using the KEGG (The Kyoto Encyclopedia of Genes and Genomes) and GO (Gene Ontology) databases. KEGG is a database resource for understanding high-level functions and utilities of the biological system, from molecular-level information, especially large-scale molecular datasets generated by high-throughput experimental technologies.
One of the main uses of GO is to perform enrichment analysis on gene sets. For example, given a set of genes that are up-regulated under certain conditions, an enrichment analysis will find which GO terms are over-represented (or under-represented) using annotations for that gene set. Thus C2:KEGG and several categories of the more detailed GO gene set database, i. e. C5:BP (Biological Processes), C5:CC (Cellular Component) and C5:MF (Molecular Function)  69  , were used to identify the top 15 pathways characterizing each group. Reverse transcription and real-time PCR. For validation of the microarray experiments, total RNA was extracted from pieces of the labyrinthine area from the same control (n = 8 CC) and exposed (n = 8 EC) placentas. The RNA was isolated on silica-based columns, using the RNeasy Plus Mini Kit with the effective elimination of genomic DNA, following the manufacturer's instructions (Qiagen, France).
A melting-curve analysis was then performed to verify the amplification of a single product. All primers were designed using NCBI Primer-BLAST and selected to generate amplicons with a length of 100-200 bp (Table  6 ). The primers were synthesized by Eurogentec. Standard curves were generated to calculate the efficiency of each set. Only primer sets with an efficiency of 1. 85 to 2. 1 were used for qPCR. The relative mRNA levels for each assay were computed from the Ct values obtained for the target gene. The data were pooled and analysed using RQ manager v1. 2 and dataAssist v1. 0 (Applied Biosystems). DataAssist ™ software is a simple, yet powerful data analysis tool for sample comparison when using the comparative cycle threshold (CT) method  70  for calculating the relative quantitation of gene expression.
Statistical analyses. All data (except placental transcriptomic data) are expressed as median[Q1;Q3], with the first (Q1) and third quartiles (Q3) corresponding to 25 and 75% of the scores, respectively. For all F1 maternal data, a linear model was used, with random effect of dams adjusted for treatment, birth date (indexed in 3 categories), integrating neonatal and peri-weaning data of these F1 females to account for their early nutritional status (litter size at birth and at weaning) using the linear mixed effects model (nlme package, R, Pinheiro, Bates, DebRoy, Sarkar and the R Development Core Team 2013. nlme. R package version 3. 1-111; http://www. r-project. org/). The β-value represents sample variability after adjustment. A negative β-value represents a reduction in the exposed group and a positive β-value represents an increase in the exposed group compared to controls.
A similar linear model was used for F2 foetal data with a random effect of dams adjusted for treatment, litter size, foetal position in the horn (indexed in 3 categories -A, B or C -to indicate the position of the foetus in the horn, with A: at the top of the horn near the ovary, B: in the middle of the horn or C: at the bottom of the horn towards the vagina) and foetal sex as an explicative factor. For fatty acid profiles, linear mixed models identical to those used for maternal and foetal data were used to analyse each fatty acid for each profile. Principal Component Analyses (PCA) were performed using FactoMineR (R package  71  dedicated to multivariate data analysis  69  ) to analyse the biological relevance of the significant differences observed using the linear model.
Figure1. Expression of FBXO32, IL23A, and TAGAP in placentas of F2 of control (CC) and exposed (EC) conceptuses at 28dpc, whose mothers were or not in utero exposed to diesel engine exhausts, respectively. Expressions were analyzed by microarray (left column) and RT-qPCR (right column). Microarray data were first log2-transformed and then normalized by an intra-array median subtraction. Relative expression of genes obtained by RT-qPCR was normalized by the expression of two housekeeping genes, RPL18 and EIF4E2. Boxplots are representative of the median value and quartiles. Significant differences between conditions were figured out by * (for P value < 0. 05).
Acknowledgements The authors thank the  UCEA (Unité Commune d'Expérimentation Animale)  for housing the rabbits and participating in the experiments. The authors thank  John Boere  and  Paul Fokkens  of the  Netherlands National Institute for Public Health and the Environment  for valuable technical assistance during the diesel engine exhaust exposure. The authors would also thank  "ProfileXpert ", service de ≪Génomique  & Microgénomique≫ ,  Université Lyon 1 ,  SFR santé LYON-EST ,  UCBL-INSERM US 7-CNRS UMS 3453 , 8 av. Rockefeller 69373  Lyon Cedex 08 , for validation by RT-qPCR. The authors wish to thank  Marine Guinot  ( BDR ) for technical support in the preparation of liver histological slides and APEX from ONIRIS, Nantes for advice and assistance with the liver histological analyses.
Moreover the authors thank all the Master students involved in this project ( Pauline Hoarau ,  Jérôme Delmotte , and  Julie Dechartres ) and those who participated in this project from near and far, from the  BDR  Unit or elsewhere. The authors are members of  COST Actions FA0702 GEMINI  " Maternal interaction with gametes and embryo ",  FA1201 EPICONCEPT "Epigenetics and periconceptial environment"  and  BM1308  " Sharing advances on large animal models (SALAAM)". This study was supported by   ANR  grant  ANR-13-CESA-0011-EPAPP (P. Chavatte-Palmer ) and by  ERC  consolidator grant  N°311765 - E-DOHaD  (PI,  R. Slama ).
Neutron Reflectometry: In a neutron reflectivity experiment the ratio between the intensity of the incident and reflected neutron beam is measured as a function of the momentum transfer, Q z in the direction perpendicular to the interface. [55] The reflectivity as a function of Q z is related to the scattering length density profile, ρ(z), along the direction perpendicular to the interface that can be defined as ( ) z bn j j j ∑ ρ = (1) where b j and n j indicate the scattering length and the number of the nuclei j. The analysis of reflectometry data involves the description of the interface in terms of a series of parallel layers each characterized by an average scattering length density, thickness, and roughness. [58]  The calculated profiles are then compared to the measured ones in a fitting procedure whose quality is assessed by using χ 2.
Acknowledgements F. L. and L. J. contributed equally to this work. The authors thank the  Institut Laue Langevin  for the allocation of beam time (doi: 105291/ ILL-DATA. 8-02-574and  doi: 105291/ILL-DATA. 9-13-411 ) and the  Partnership for Soft Condensed Matter  for the use of the laboratories. Small 2019, 1805046 1805046 (15 of 15) © 2019 The Authors. Published by  WILEY-VCH Verlag GmbH & Co. KGaA , Weinheim The authors thank the  European Research Council , the  Academy of Finland  and the  Magnus Ehrnrooth foundation  for the financial support, and the  CSC -IT Center for Science (Espoo, Finland)  for computational resources. H. M. -S. acknowledges support from the  European Regional Development Fund OP RDE  (project  ChemBioDrug  no. CZ. 02. 1. 01/0. 0/0. 0/16_019/0000729 ). The authors thank  Dr.
2009; Cooper, Hedges, and Valentine 2009). Using the method of meta-regression, with a database including all available field experimental studies of racial and ethnic discrimination in hiring, we model the relative rate of discrimination against minority groups as a function of the country, the minority group, and other study characteristics. Our procedure follows three basic stages: first, to identify all existing field experiments of hiring discrimination; second, to develop a coding rubric and to code studies to produce a database of their results; and third, performing the statistical meta-analysis to draw conclusions from the combined results. We discuss each of these steps in turn (some of our discussion in this section reprises parts of  Quillian et al. 2017).
We used three methods to identify relevant field experiments: searches in bibliographic databases, citation searches, and an e-mail request to corresponding authors of field experiments of racial-ethnic discrimination in labor markets and other experts on field experiments and discrimination. Our first search method was bibliographic search. Our search covered the following bibliographic databases and working paper repositories: Thomson's Web of Science (Social Science Citation Index), ProQuest Sociological Abstracts, ProQuest Dissertations and Theses, LexisNexis, Google Scholar, and National Bureau of Economic Research Working Papers. We searched for some combination of "field experiment," "audit study," or "correspondence study" and sometimes included the term "discrimination," with some variation depending on the search functions of the database.
To improve our coverage of non-English publications, we also searched two French-language indexes, Cairn. info and Persée, and two international sources, IZA Discussion Papers (a German working paper archive) and International Labour Organization International Migration Papers. Finally, we conducted a search with Italian, Spanish, Portuguese, and Dutch translations of the search terms and other terms frequently used in these languages to describe field experiments in hiring discrimination in Google Scholar. The search was first performed in March 2014 and repeated in August and September 2014 and in November 2015. Searches in Italian, Spanish, Portuguese, and Dutch were conducted in November 2015 and February 2016. Our second search method was citation search. Working from the initial set of studies located through bibliographic search, we examined the bibliographies of all review articles and eligible audit studies to find additional field experiments of hiring discrimination.
Sample and Selection of Countries Our selection of the nine countries we focus on was driven by data considerations. We found that standard errors were too large to be informative for country estimates based on only one or two studies. There are nine countries in our sample with three or more field experiments: Belgium, Canada, France, Germany, Great Britain, the Netherlands, Norway, Sweden, and the United States. Our final sample in these nine countries includes a total of 97 distinct studies, including 159 estimates of discrimination against distinct minority groups. Many studies include more than one minority group (e. g. , African American and Hispanic or Pakistani and Chinese people), which is why there are more effects than studies. Table  1  shows the breakdown of effects and studies across the nine countries.
Coding We coded key characteristics of the studies into a database for our analysis. Coding was based on a coding rubric, which listed the characteristics and included coding instructions. To develop the rubric, we initially read several studies, and based on this, we developed an initial coding rubric of factors we thought might influence measured rates of discrimination. It was subsequently refined as coding progressed. To ensure reliability, almost all studies in our analysis were coded independently by two raters. Studies were coded by fluent readers in English, French, German, and Dutch. One study report in Swedish and one in Norwegian were coded by a single rater. We then reconciled the results of the two codings, performing further investigations to find the correct answer on coding decisions in cases of disagreement.
An important point to note about our analysis is that our estimates examine only hiring, not other aspects of the employment relationship. To the extent that processes of statistical discrimination or stereotyping are important contributors to hiring discrimination, the uncertainty inherent in hiring may make race and ethnicity a more important basis of discrimination at the point of hire than for other aspects of the employment relationship, such as wage setting and termination, for which employers have more complete information. Likewise, the jobs included in field experiments of discrimination are typically those listed in public sources such as job databanks and newspapers advertisements. Our results do not well capture the situation of jobs advertised primarily through word of mouth or through less mainstream job sources such as ethnic newspapers.
The Statistical Code In order to have a theoretical feedback, the experimental data have been compared with simulations performed with the statistical code GEMINI ++  [19] , which describes the decay of the excited compound nucleus. The simulated events were filtered through a software replica of the experimental setup and, then, selected in the same way of the experimental events  [15]. In Fig. 2 , the simulated angular distributions of protons and -particles are shown. The experimental trend and dependence on vcm are reproduced in the angular region from 29. 5° to 150. 5°, while at very forward angles (8. 8°-17. 4°) significant deviations from the experimental angular distribution are observed. Quantitatively, the divergences between the simulated and experimental angular distributions are shown in Fig.
Molecular Formula Assignment from Pseudomonas sp. COW3 Crude Extract The crude CLP extract from Pseudomonas sp. COW3 was re-solubilized in and submitted to a HRMS analysis using a SYNAPT-G2-Si mass spectrometer operating in sensitivity-positive modes. The HRMS-spectra are reported in the Figure  1   The molecular formula of produced compounds can be deduced from the m/z (z = 1) using an approach that combines regular Kendrick mass defect calculations with knowledge stored in the NORINE database. Briefly, the m/z of a compound is represented as coordinates in the RKMD/NKM 2D-plot and the corresponding molecular formula can be deduced  [37]. The exact m/z measurements were submitted to Kendrick Formula Predictor for the computer-assisted assignment of the molecular formulae of each compound, C.
S1. Each module contains an adenylation (A), condensation (C) and thiolation (T) domains, and two TE domains for peptide release and cyclization. Scale bar represents 2. 5 kb. In NRPSs, adenylation (A) domains recruit and select amino acids for peptide biosynthesis  [14]. Subsequent in silico analysis of the A domain substrate using antiSMASH v5. 0 allowed prediction of the amino acid composition of the peptide moiety. A phylogenetic analysis was carried out with the A domains retrieved from COW3, COW65, the bananamide A-C producer BW11P2, the MDN-0066 producer LMG 27940 and putative bananamide-producers, including Pseudomonas sp. MS586, MS82, DR 5-09, BS3668, Z003-0. 4C (8344-21) and R45 (see Table  A5 ). Our results showed that similar amino acids as those of COW3 may be recruited by COW65, while BS3668 and Z003-0.
MS586 (80-84%), Pseudomonas sp. MS82 (80-85%) and Pseudomonas sp. R45 (80-85%) (Supplementary Table  S1 ). Consequently, a cladogram inferred from the concatenation of regulatory, export and NRPS gene protein sequences clearly delineated the novel bananamide D-G, bananamide A-C and MDN-0066 into these three distinct members of the bananamide group including the putative CLP-producing strains (Figure  5 ). The NPRS biosynthetic gene cluster and flanking regions for Pseudomonas spp. COW3 and COW65 have been deposited on Genbank with accession number MN480426.
In this study, we present the first use of KMD in the discovery of novel bananamide variants. KMD identified the two major bananamides produced by Pseudomonas sp. COW3 (D and E). KMD approach for molecular formula assignment to CLPs is therefore an effective and reliable method for the determination of the molecular formulae of CLPs using the experimentally determined high resolution mass (HRM). The HRM is often compared to masses of CLPs on NORINE database, thereby enabling the discovery and identification of novel CLPs. Consequently, bioinformatics analyses revealed the eight amino acid composition of CLP produced by COW3 as follows: FA-D-Leu-D-Asp-D-Thr-D-Leu-D-Leu-D-Ser-L-Leu-L-Ile, which is consistent with the "colinearity rule" of nonribosomal peptides and chemical analysis. We discovered that bananamide D-G are novel bananamide derivatives, which differ from MDN-0066 (P.
Crude CLP Analysis of COW3 via the Kendrick Mass Defect Method for Molecular Formula Assignment Crude CLP extract from Pseudomonas sp. COW3 was re-solubilized in methanol and directly injected at 15 µL/min in a SYNAPT-G2-Si mass spectrometer (Waters, Manchester, UK), operating in positive and sensitivity modes. The sample was electrosprayed at an applied voltage of 3 kV, using a desolvation gas (N 2 ) flow of 500 L/h, a nebuliser gas flow of 6. 5 bar and desolvation temperatures of 150 and 300 • C, respectively. The full MS survey scans were acquired at a 20,000 (FWHM) resolving power, over the mass range of 200-2000 m/z. Peaks were analyzed using Mass Lynx software (ver. 4. 1; Waters). Mass spectra were externally calibrated in positive mode using a solution of 5 mM sodium formate (Sigma-Aldrich, Saint-Quentin-Fallavier, France) in 50/50 (v/v) acetonitrile (ACN)/H 2 O.
Molecular Formula Assignment with Kendrick Mass Defect The Kendrick mass (KM) related to the CH 2 pattern is calculated from the molecular formula of NORINE-referenced compounds and from the experimentally measured masses. A dealiasing, which corresponds to a mathematical shift that leads to the mass to fit the spectral width without aliasing, was performed by shifting the KM value by -0. 28, equation according to  [38] : KM = (IUPAC protonated monoisotopic mass f rom NORINE or experimentally measured m z ×( CH 2 nominal mass CH 2 exact mass )) -0. 28 (1) Subsequently, the dealiased KM value is rounded to the nearest whole number and defines the nominal Kendrick mass (NKM), equation: Nominal Kendrick mass (NKM) = dKM rounded to the nearest whole number The NKM is then subtracted from Kendrick mass to obtain the regular Kendrick mass defect (RKMD), equation: Regular Kendrick mass de f ect (RKMD) = KNM -dKM (3) RKMD and the NKM values were calculated for each known and curated molecular formulae extracted from the NORINE database.
Analytical and Preparative Liquid Chromatography (LC) from Crude Extract Using crude CLP extract from Pseudomonas sp. COW3, analytical LC-MS data were collected on a 1100 Series HPLC (Agilent Technologies, Santa Clara, CA, United States) equipped with an ESI ionisation source, coupled to a type SL MS detector. The HPLC was equipped with an analytical Kinetex C18 reversed-phase column (150 × 4. 6 mm, 5 µm particle size; Phenomenex, Torrance, CA, USA). An elution gradient of H 2 O/CH 3 CN (25:75 to 0:100, v/v) over 20 min was applied at a flow rate of 1 mL. min -1. The crude CLP extract was dissolved in methanol and the purification conditions of each CLP were optimized by using an Agilent Technologies 1100 Series HPLC device equipped with a Luna C-18 analytical reversed phase HPLC column (250 × 4.
Genome Sequencing and Assembly For genome sequencing, Pseudomonas sp. COW3 (N3 producer) and COW65 (a putative N3 producer,  [9] ), were grown in Luria Bertani (LB) broth for 24 h at 28 • C with continuous shaking at 150 rpm. Genomic DNA was extracted using the Wizard Genomic DNA Purification Kit (Promega Corporation, Madison, WI, USA) according to manufacturer's instructions. Single-end or paired-end sequence reads were generated using the Illumina HiSeq2500 or MiSeq system at the BASECLEAR B. V. (Leiden, The Netherlands). FASTQ read sequence files were generated using bcl2fastq2 version 2. 18. Initial quality assessment was based on data passing the Illumina Chastity filtering. Subsequently, reads containing PhiX control signal were removed using an in-house filtering protocol. In addition, reads containing (partial) adapters were clipped (up to a minimum read length of 50 bp).
Genome Annotation, Genome Mining and Bioinformatics Analyses Genome sequences were automatically annotated using the RAST annotation pipeline  [65, 66]. Furthermore, genomes of previously sequenced strains (Supplementary Table  S1 ) were re-annotated using the RAST annotation pipeline and also submitted to antiSMASH v5. 0  [67]. Genome mining was conducted on the annotated genomes, and comparison of NRPS proteins with other protein sequences in GenBank database was done by BLAST search (https://blast. ncbi. nlm. nih. gov/Blast. cgi). The adenylation (A) and condensation (C) domains sequences of the non-ribosomal peptide synthase (NRPS) genes were extracted. Sequence alignment was carried out using MUSCLE  [68]  in the software package Geneious Prime, 2019, and the cladograms were inferred by Neighbour Joining.
The thioesterase (TE) domain sequences of the non-ribosomal peptide synthase (NRPS) gene were extracted. Sequence alignment was carried out using MUSCLE  [68] , and the phylogenetic tree was constructed by maximum likelihood with 1000 bootstrap replication, in the software package MEGA6  [69]. The antiSMASH v5. 0 enabled the prediction of the amino acid composition of the peptide moiety. Moreover, proteins were translated from the nrps and flanking genes of bananamide D-G-and other CLP-producing strains (Supplementary Table  S1 ). The protein sequences were concatenated, aligned and used to construct a phylogenetic tree in Geneious Prime, 2019 (Biomatters, Auckland, New Zealand).