dataesr/scientific-paragraphs-categorization huggingface dataset:
B. Experimental Protocol Our protocol consisted of two phases: a registration phase and a game phase. During the registration phase, we collected basic socio-demographic data about each player and scheduled players into a game session. During the game phase 2 weeks later, we brought players together into groups of 10, and did so for 8 sessions over 2 weekends-3 of the sessions were all female, 3 were all male, and 2 were mixed in gender. 11 Each session consisted of a series of games, including our voting game, and lasted approximately 2. 5 hours. 12 We introduced the experimental games to our participants as games designed to investigate "how people from Ile-de-France [Parisian region] make decisions about money. " 13 At the beginning of each 9. The recruiting team relied on ethnic profiling, inferring from dress and facial features whether participants were potential FFF.
The first asked voters to rank, in order of preference for group leader, the five candidates they had just met. The second asked voters to vote for a single candidate as leader. We measure Vote as the candidate ranked first by the voter. It is important to emphasize that voters systematically chose to vote for the candidate they ranked first: voters were thus expressing their true preference, not behaving strategically in this decision. We then use players' monetary allocations as leaders along with data from our simultaneous trust game-which we present in a later section-to illuminate whether the homophily we uncover in the voting game is a rational strategy to optimize the distributive payoff.
First, we focus on differences in family income, where we have missing data for N = 60 observations. We generate two variables for differences in family income, one where we assume that the difference is maximal when the voter does not vote for the candidate and minimal when the voter does vote for the candidate; and one where we assume the opposite. We then do the same for differences in education, where we have missing data for N = 40. We estimate Model (1) in Table  3  relying on these new variables, including them sequentially and then together. Our results persist. relying only on descriptive statistics, illustrates this pattern clearly by showing the distribution of leader rankings (where Rank 1 is the voter's first choice for leader) for dyads who share a religion (in light gray) and dyads who do not share a religion (in dark gray).
Sex differentiation in fish is a highly labile process easily reversed by the use of exogenous hormonal treatment and has led to environmental concerns since low doses of estrogenic molecules can adversely impact fish reproduction. The goal of this study was to identify pathways altered by treatment with ethynylestradiol (EE2) in developing fish and to find new target genes to be tested further for their possible role in male-to-female sex transdifferentiation. To this end, we have successfully adapted a previously developed bioinformatics workflow to a meta-analysis of two datasets studying sex reversal following exposure to EE2 in juvenile rainbow trout. The meta-analysis consisted of retrieving the intersection of the top gene lists generated for both datasets, performed at different levels of stringency.
Microarray approaches Modern genomic technologies, such as DNA microarray assays, are powerful tools to investigate initial responses to exogenous agents  [9] , and a considerable amount of microarray data has been and continues to be generated, for an increasing range of species  [10]. The rapid expansion of public genomic databases and the development of bioinformatics tools have considerably enhanced our ability to infer biological meaning from large gene lists. This technique has proven to be efficient for the study of predicted biological pathways and regulatory networks, with wide ranging applications from human clinical biomarker discovery to drug development  [11]. More recently, ecotoxicological studies aimed at determining the molecular mode of action (MOA) of environmental pollutants and identifying their biomarkers as indicators of exposure have been published  [11, 12].
Re-analysis and Meta-analysis In view of the considerations listed above, most microarray datasets remain underexploited. However, the fast evolution of (i) available data in public genomic databases, (ii) statistical analysis performances and (iii) data-mining bioinformatics tools has made it possible to gain new information through the re-analysis of previously published datasets. Moreover, the metaanalysis of several datasets is a way to yet further enhance analytical performance  [19] [20] [21]. In a previous work, we successfully adapted a microarray analysis workflow validated on human cancer data  [19, 22] [23] to a toxicogenomic study using rainbow trout as model organism  [24]. We measured gene expression in juvenile male fish exposed chronically to several doses of the potent xenoestrogen ethynylestradiol (EE2).
This allowed highlighting several pathways and ontologies related to EE2 exposure and to propose several potential specific and/or sensitive biomarkers of the intersex stage in juvenile rainbow trout. Here, we propose to apply this methodology to the re-analysis of a previously published dataset studying the effect of EE2 on a time-course experiment using juvenile male rainbow trout. We postulate that the meta-analysis of both datasets will allow highlighting the pathways and key genes potentially implicated in early male-to-female gonadal transdifferentiation.
Dataset 1 [24] In this experiment, all-male rainbow trout (Oncorhynchus mykiss) fry (INRA experimental fishfarm, Monts d'Arrée-PEIMA, Sizun, France) were exposed from the onset of first feeding [Day 0 = D0 at 60 days post-fertilization (dpf)] to 136 dpf to 5 nominal concentrations of 17αethynylestradiol (purity 98%, Sigma-Aldrich, Germany): 0 (solvent control), 0. 01 μg/L, 0. 1 μg/L, 1 μg/L and 10 μg/L, with 3 tanks per condition. The actual EE2 concentrations were measured in each tank at 6 time points using the Quantitative Ethynylestradiol Enzyme Immunoassay (EIA) Kit (Marloie, Belgium) according to the manufacturer's instructions. Mean concentrations of EE2 ± SD were 0. 08 ± 0. 06 μg/l; 1. 62 ± 1. 74 μg/l and 9. 88 ± 5. 06 μg/l. The 0. 01 μg/l EE2 concentration was under the detection limit (set at 0.
02 μg/l). At the end of the exposure, the gonads were collected from all fish, immediately frozen in liquid nitrogen and stored at -80°C until RNA extraction. The gonads from ten fish were pooled to reach enough material for further analyses. Gene expression measurement was performed on 6 replicates per condition (2 pools of gonads per tank) on a one-color 8x60K oligonucleotide array (Agilent Technologies) designed by the INRA-LPGP microarray platform (Beaulieu Campus, Rennes, France). Expression profiles obtained from the DNA microarray dataset were validated with the measurement of several genes by real-time reverse transcription-polymerase chain reaction (RT-PCR) as described previously  [25].
Dataset 2 [26] In a previous experiment (Guiguen et al. , unpublished data), transcript expression was recorded over a time-course analysis on gonads sampled following feminization of genetically all-males population (M-control) with estrogens (M-EE2) or masculinization of genetically allfemales population (F-control) with either androgen (F-11ß) or an anti-aromatase (F-ATD). The present analysis is limited to estrogen-treated fish. Feminizing treatment of all-male populations (PEIMA, Sizun, France) with 17α-ethynylestradiol (EE2, 20 mg/kg of food, Sigma,.
Intersection II 2 DEGs from the four contrasts in dataset 1: CT1; CT2; CT3; CT4 DEGs from contrasts including EE2-treated fish versus male control fish: MT-MC D7 to D111. Intersection III 3 CT1; CT2. These two contrasts were previously correlated with gonad intersex feature 4. MT-MC D7; MT-MC D16; MT-MC D30. These contrasts cover early developmental stages corresponding to differentiating gonads, with first morphological differences appearing between male and female gonads.
Microarray analysis workflow The initial microarray analysis workflow  [24]  was adapted to handle another type of microarray platform (dataset 2) and to fit with the meta-analysis scheme and work hypothesis. Briefly, this workflow includes (i) the analysis of pre-treated data by (ii) ANOVA analysis followed by Scheffe's post hoc pairwise comparisons. Thereafter, (iii) biologically relevant sets of contrasts and gene list intersections between the two datasets were selected at different levels of stringency. The remaining gene lists, supposed to be enriched in true positive DEGs, were (iv) submitted to over-representation analysis methods (ORA). (v) A set of most relevant DEG was selected using volcanoplots, membership of enriched pathways and/or GOterms and kinetic profiles in both analyses.
Statistical analysis Data analyses were performed using the R statistical software version 2. 15. 3 available on the R-Project website (http://cran. r-project. org) and a set of packages available in the Bioconductor repository (http://www. bioconductor. org). This methodology used for the first dataset  [24]  was also applied to re-analyse the dataset 2. Therefore, brief descriptions and specificities related to this dataset analysis are provided below. See supporting information for detailed scripts. 2. 4. 1. Pre-processing and normalisation procedures. The dataset was configured in numeric matrix. The expression values were first submitted to a quantile-quantile normalisation using the normalize. quantile function from the preprocessCore package (S1 Dataset). 2. 4. 2. ANOVA. A two-way ANOVA was performed, with the time-course (D0 to D 111) and sex as criteria, using the lmFit function of package Limma, the only statistical method able to perform ANOVA with more than one factor level (here the 7 developmental stages tested) on microarray data.
Its predictive power has been well ranged in our previous benchmark of method performances  [27]. 2. 4. 3. Post-hoc comparisons. A post-hoc evaluation was performed using Scheffe's method with the makeContrasts() and eBayes() functions provided in the Limma package (88 contrasts comparing all experimental conditions). The results were summarized using decideTests() and summary() (number of significant genes positively or negatively regulated for each evaluated contrast). The Benjamini-Hochberg procedure was used to adjust the p-values (correction for multiple testing)  [28]. Considering the very large number of genes per contrast group, we later focused the analysis of dataset 1 on four groups that were the most relevant with regards to the biological context, comparing reference samples with samples treated with EE2 at different concentrations.
The contrast labels were defined as control vs. For the 2 nd dataset, a differential gene expression analysis was performed using the two-way ANOVA, considering the developmental stages (D0 to D111) and sex (male and female) as criteria. Eighty-eight contrasts were performed comparing all the experimental conditions. We postulated that gene expression comparisons make sense between fish at the same developmental stage. To this end, we defined 19 physiologically relevant contrasts as follows: (i) we selected the 7 male-EE2 (= male test = MT) versus male-control (MC) contrasts at the same sampling time (MT-MC D7, MT-MC D16, MT-MC D30, MT-MC D63, MT-MC D91, MT-MC D111); (ii) the 7 male-EE2 versus female-control (FC) contrasts at the same sampling times (MT-FC D7, MT-FC D16, MT-FC D30, MT-FC D63, MT-FC D91, MT-FC D111), and (iii) the 8 male-control (MC) versus female-control (FC) contrasts at the same sampling times (MC-FC D0, MC-FC D7, MC-FC D16, MC-FC D30, MC-FC D63, MC-FC D91, MC-FC D111).
For each contrast, R software topTable() function was used to extract top-ranked genes and relevant statistics (logFC, Average Expression, t statistic, p-value, adjusted p-value, B statistic). 2. 4. 4. Meta-analysis. In theory, meta-analysis of microarray data consists of analyzing several related datasets at once  [22]. The statistical analysis is then made on merged datasets. In our analysis, this scheme was impossible as the platforms used were heterogeneous. Here, the meta-analysis consists of retrieving DEGs common to both analyses. The use of intersections between top gene lists has proven to be effective in filtering relevant genes  [19, 22, 24]. Three intersections (Intersection I, II and III) were defined for further analyses (Table  1 ). Intersection I has been proposed to retrieve genes common between the two DEGs lists from the two datasets.
We postulated that this represents mechanistic signatures of EE2 treatment on rainbow trout male juveniles. This group therefore contained the intersection between all DEGs in the four contrasts in dataset 1 (CT1 to CT4) and all DEGs in the twelve contrasts including EE2-treated fish in dataset 2 (MT-MC D7 to D111, and MT-FC D7 to D111). This group contained 1,535 DEGs and was further used in the pathway overrepresentation analysis (see result section 3. 2. 1. ). To further focus on genes differentially expressed in male treated fish, a smaller group of DEGs (Intersection II) was obtained at the intersection between all DEGs in the four contrasts in dataset 1 (CT1 to CT4) and all DEGs in the six contrasts including EE2-treated fish versus male control fish in dataset 2 (MTMC D7 to D111). This group contained 438 DEGs and was further used in the ontology overrepresentation analysis (see result section 3.
2. 2. ). To further investigate the trans-differentiation process, we aimed to focus on genes potentially involved in the early response to EE2 treatment (Intersection III). The statistical and bioinformatics approaches applied here were aimed at retrieving true positives among contaminated large gene lists. For this, we focused on the DEGs related to the lower doses used in dataset 1 (corresponding to contrasts CT1 and CT2). We have reported previously that these conditions are correlated with gonad intersex feature development in rainbow trout male juveniles exposed to EE2  [24]. In dataset 2, the interesting contrasts were those at the early developmental stages, 7, 16 and 30 days after treatment. In developing rainbow trout, this period corresponds to differentiating gonads, with the first morphological differences appearing between male and female gonads.
Day 12 corresponds to the occurrence of oocyte meiosis and D27 to the beginning of ovarian lamellar structure development in females. Gametogenesis starts from 60 dpf  [29]. The third group was composed of the union intersection between contrasts CT1 and CT2 in dataset 1 and MT-MC D7, MT-MC D16 and MT-MC D30 in dataset 2. The union intersection included DEGs common to at least one condition in dataset 1 and one condition in dataset 2 (for example DEG in CT1 and MT-MC D7). This retrieved DEGs that could be false negatives in other contrasts considered. To further filter these DEGs, (i) we confronted this list to the pathways and ontologies retrieved in previous steps. Thereafter, (ii) we used visualization techniques. First (a), we submitted them to a Volcanoplot analysis, then (b) we looked at their kinetic profiles in both analyses.
To be able to use DAVID tool (see below), we used ensembl gene ID for Danio rerio (ENSDARG), the only fish species with pathways available on Kegg  [30]. For EASE, as no entry exists for ensembl gene ID, Gene Symbol identifiers were used, because the most accurate. We have updated the latest version of the AGENAE program (January 2013) (Analysis of Breeding Animals' Genome; http://www. agenae. fr/programme_agenae/) for these two annotations, using UniProt (http://www. uniprot. org) or e!Ensembl (http://www. ensembl. org) databases. 2. 4. 6. Visualizations. Several bioinformatics tools were used to gain insights of the dataset, at different levels of organization (gene expression, pathways, ontologies) depending on the analysis stringency of the group of DEG tested. The "Intersection I" group of DEG (see results) was submitted to the web tool DAVID (Database for annotation, Visualization and Integrated Discovery; http://david.
abcc. ncifcrf. gov/home. jsp) [31, 32] , to retrieve pathway maps from KEGG following the method applied by Pierre et al, 2010  [19] , and with the genes on the 60K rainbow trout Agilent array set up as the 'background population'. The "Intersection II" group was submitted to the EASE software (version 2. 0)(Expression Analysis Systematic Explorer) available on the DAVID website  [33]. EASE is a desktop version of DAVID allowing more freedom toward the identifiers entering the program, which suits our heterogeneous Swissprot annotation (several species orthologs). Gene symbol was selected in the Input Genes setting. Then we choose the "Find over represented gene categories" function. In the "Population file" window we enter the entire array annotation without redundancy. Then we set the "Run Basic Analysis" function.
Finally, Volcanoplots were made to visualize the DEG of interests using the plot function in the R software. A volcanoplot is a graph sorting the results by p-value (Yaxis),-log fold change (X-axis) and over/under-expression. In such graphs, the most interesting genes are usually located in the upper left and right corner of the plot, depicting genes with low p-values and high fold changes. Flowchart of the workflow of the microarray meta-analysis methodology and associated results. This flowchart summarizes the bioinformatics workflow used for the meta-analysis of two datasets studying sex reversal following exposure to EE2 in juvenile rainbow trout. Both datasets (dataset 1 and dataset 2, pre-treated data) were subjected to ANOVA analyses followed by Scheffe's post hoc pairwise comparisons.
Thereafter, biologically relevant sets of contrasts (coloured and in bold in the flowchart) were selected (differentially expressed genes (DEGs) between control and EE2-treated fish in dataset 1; DEGs between control and EE2-treated fish at the same developmental stage in dataset 2). The meta-analysis consisted of retrieving the intersection of the top DEGs lists generated for both datasets, performed at different levels of stringency. This allowed creating different groups of DEGs (Intersections I, II and III) for ORA analyses downstream, given that pathway or Ontologies research have different constraint concerning the group of genes to enter in the program. It is worth noting that each group of DEGs has different biological meaning described further in the text in the results section. Intersection I (low stringency) contained the intersection between all DEGs in the four contrasts in dataset 1 (CT1 to CT4) and all DEGs in the twelve contrasts including EE2-treated fish in dataset 2 (MT-MC D7 to D111, and MT-FC D7 to D111).
This group contained 1,535 DEGs (1,171 DEGs having a functional annotation with Danio rerio Ensembl! identifiers-ENSARG) and was further used in the pathway overrepresentation analysis. A smaller group of DEGs (Intersection II) was obtained by retrieving the intersection between all DEGs in the four contrasts in dataset 1 (CT1 to CT4) and all DEGs in the six contrasts including EE2-treated fish versus male control fish in dataset 2 (MTMC D7 to D111). This group contained 438 DEGs (245 DEGs having a functional annotation with Swissprot identifiers) and was further used in the ontology overrepresentation analysis. The third group (Intersection III-highest stringency-103 DEGs) was composed of the union intersection between contrasts CT1 and CT2 in dataset 1 and MT-MC D7, MT-MC D16 and MT-MC D30 in dataset 2.
Term Count EASE-score   all the contrasts considered. Second, the kinetic profiles of remaining genes were visualized in both datasets. Volcanoplots are presented in Fig 3. These graphs show the comparison between the five contrasts considered for the 103 DEGs of the Intersection III group. DEGs were selected from these graphs as follows (i) Location in the right or left corners of the graphs (ii) Membership of at least two contrasts between dataset 1 and dataset 2, and (iii) Consistency of fold change type (positive or negative) between both involved contrasts. In total, 60 DEGs were selected by this approach. Among this list, 23 DEGs belonged to enriched Pathways/GO Terms. For the sake of clarity, only cyp11b is represented on graphs, which was interestingly found at a very similar position in all the contrasts considered (i.
e. left corner of the graph). The next step was to look at the kinetics profiles of the 60 DEGs selected by the Volcanoplot analysis from the two whole datasets considered (CT1 to CT4 in dataset 1 and MTMC D7 to D111 in dataset 2). The kinetics profiles for the 20 genes in dataset 1 and dataset 2 are given in Fig 4A  and 4B  and Fig 5A  -5T , respectively. Selection was based on deleting genes whose maximum expression was under 1. 5 in overexpressed genes or over -1. 5 in underexpressed genes. This retrieved 42 DEGs in dataset 1 and 22 DEGs in dataset 2. Through the identification of DEGs common to these two lists, we found 20 DEGs of particular interest (Table  4 ). Among these genes, 7 belonged to significantly enriched Pathways/GO Terms. It is worth noting that, among the 20 DEGs selected, there were several exceptions.
TEKT1 and Kindiss have opposing fold changes, as they are overexpressed in dataset 1 and underexpressed in dataset 2. Moreover, Table  3. List of significantly enriched GO terms. This table lists the significant GO terms (P-Value/EASE score < 0. 01) retrieved by the EASE software for the Intersection II group in this analysis (245 well annotated DEGs recognized by Gene Symbol identifiers in EASE among 438 DEGs IDs). This group represents the DEGs following a steroid modulation of immature gonads in rainbow trout by EE2, under several types of treatment. The EASE-Score column represents the P-Value calculated by DAVID.
GO ID Term EASE score (P-Value)  Aox1 in dataset 1 and Crsp2 in dataset 2 have a maximum log2 (Fold Change) expression over -1. 5. However these genes were selected in Volcanoplots and displayed interesting expression patterns, with a peak at low concentrations in dataset 1 and/or at early time points in dataset 2. They were then kept as interesting genes. GO In order to focus on relevant genes potentially related to early transdifferentiation processes, the final step was to compare the final list of 20 DEGs to differential gene expression existing between female and male control fish. For this purpose, we compared the list of 20 DEGs with all the DEGs in the contrasts in dataset 2 comparing DEGs between control females versus control males in the developmental stages considered (MC-FC D7, D16 and D30, Table  of.
Differentially expressed genes and pathways Both dataset analyses generated a high number of differentially expressed genes (DEG) following EE2 treatment, with nearly half of the array content displaying differential expression patterns in dataset 1 (12,742 non-redundant genes), and one third in dataset 2 (2,974 nonredundant genes). Since EE2 treatment is able to induce complete sex reversal of fish gonads, it is not surprising that this impact was detected at the transcriptional level. A similar magnitude of gene expression disruption was also observed in female fish masculinized by androgen 11βhydroxyandrostenedione  [6]. This highlights the major role of steroid hormones in triggering the gonad transcriptome, and their potency. Interestingly, 1,535 DEGs were found to be common to both analyses.