dataesr/scientific-paragraphs-categorization huggingface dataset:
2019). Van der Loos et al. (2013)  show that all SNPs together may explain up to 25% of differences in entrepreneurial behavior between individuals. Even if we are able to realize this prediction R 2 , the likelihood of misclassification of individual into occupational groups remains great. Hence, early speculations about the use of molecular genetic data for understanding and predicting entrepreneurship  (Shane 2010)  remain premature, at a minimum. Even though it may be useful to capture some of the (otherwise residual) variance in polygenic risk scores, the gene-based prediction of individual entrepreneurial behavior will remain of limited value 18 There is currently an important initiative to make a repository of polygenic risk scores for several datasets. However, the exact time window of this initiative is unknown  (Okbay et al.
2018). More (future) data sources can be found through portals such as the Database of Genotypes and Phenotypes (dbGaP,  Mailman et al. 2007 ) and the European Genome-phenome Archive (EGA,  Lappalainen et al. 2015). for individuals and entities such as governments and banks. 19 evertheless, capturing residual variance in polygenic scores may improve the understanding of the effects of environmental factors. In so-called gene-byenvironment ("GxE") studies  (Keller 2014 ; Thompson polygenic risk scores could be used to investigate how entrepreneurship results from the interplay between genetic endowments and environmental factors. For example, a recent study argues that cultural factors (as proxied by the taste for alcoholic drinks) may influence how genes shape different types of entrepreneurship  (Acs and Lappi 2019).
Funding information The  HRS (Health and Retirement Study ) is sponsored by the  National Institute on Aging  (grant number  NIA U01AG009740 ) and is conducted by the  University of Michigan. C. A. R. acknowledges funding from the  New Opportunities for Research Funding Agency Cooperation in Europe (NORFACE-DIAL  grant  462-16-100 ). A. R. T. is member of the Entrepreneurship and  Innovation Chair , which is part of  Labex Entrepreneurship (University of Montpellier, France ) and is funded by the  French government (Labex Entreprendre ,  ANR-10-Labex-11-01 ).
The sequences are available on the sequence read archive (SRA) database (Bioproject PRJNA540147). The FROGS pipeline was used to analyze the 512 amplicon sequences of bacteria and fungi  37. The raw reads were merged with a minimum overlap of 20 bp using FLASH  38  for bacteria and Vsearch for fungi  39  , filtered according to the following criteria: expected amplicon size of 470 bp for bacteria, minimal length of 400 bp for bacteria and 50 bp for fungi, and maximal length of 580 bp for both bacteria and fungi, respectively. No ambiguous nucleotides were allowed and both the primer sequences and the sequences without the two primers were removed with the Cutadapt tool  40. The sequences were then dereplicated and clustered using the swarm method  41  with an aggregation distance equal to 3 for clustering.
The chimera were removed using the Vsearch tool with the UCHIME de novo method  42  combined with a cross-sample validation. The bacterial and fungal OTUs present in at least 4 out of 256 samples and representing at least 0. 0005% of all sequences were retained and corresponded to OTUs with a minimum of 16 and 33 sequences for bacteria and fungi, respectively. For fungi, the ITSx tool was used to extract the highly variable ITS subregions from ITS sequences  43. The taxonomic affiliations were performed with BLAST+  44  using the 16S SILVA database (Silva 132) for bacteria and the UNITE fungal ITS database release version 7. 1 for fungi  45. At the end of the process a total of 2,395,758 sequences corresponding to 8,914 OTUs and 5,698,502 sequences corresponding to 1,360 OTUs were obtained for bacteria and fungi, respectively.
Data analysis. All the statistical analyses were performed using R (v3. 4. 3, Core Team, 2017). The number of sequence per sample was randomly subsampled to the lowest number, soils, BS samples and bacterial and fungal DNA and cDNA amplicons being normalized separately using the rrafey function from the vegan package version 2. 5-4  48  ; 6,399, 15,589, 4,846 and 3,727 for bacterial soil DNA, fungal soil DNA, bacterial BS DNA and fungal BS DNA, respectively, and 7,000, 6,000, 9,154 and 2,766 for bacterial cDNA, fungal soil cDNA, bacterial biostimulant DNA and fungal biostimulant DNA, respectively. Thirty-three samples out of the 512 total ones were below these thresholds and were removed for the further analyses. The rarefaction curves at each sampling date and before and after normalization was performed using the rarecurve function from the vegan package and are provided in Figures  S1  and S2.
At the contrary, time vari- -and NH 4 + contents depended on straw addition, the NO 3 -increasing during soil incubation without but not with straw (Fig. 1 ). Immediate effect of biostimulant addition on soil microbial communities. The total and active bacterial communities detected in the BS were both only composed of Firmicutes (belonging mainly to Enterococcaceae, Carnobacteriaceae and Lactobacillaceae families); the dose of BS that was applied to the soil represented an input of 2. 91 × 10 5 16S copy per gram of dry soil of active Firmicutes (Fig. 2 , left part). The fungal communities in the BS were mainly composed of Ascomycota (data not shown), but the fungal abundance in the BS was too low to be detected by the qPCR reaction. The addition of the BS to the bare soil without straw (0 days), immediately induced a tenfold and twofold decrease in the bacterial 16S and the fungal ITS cDNA copy numbers, respectively (ANOVA, F = 11.
Acknowledgements This research was supported by the  "Association Nationale de la Recherche et de la Technologies" (ANRT)  through the convention No. 2016/0091  to  F. Binet  and  O. Klarzynski  and by the  University of Rennes 1  through its international mobility programm No. 12/2018  to E. hellequin. The authors thanks  Mikael Faucheux  from the  French national institute for agronomic research  ( INRA, Rennes ) for sampling the soil in the field before the experiment. Special thanks to  Sara Hallin  and  Jaanis Juhanson  from the  group research "Soil Microbiology" of the Department of Forest Mycology and Plant Pathology, Swedish University of Agricultural Sciences in Uppsala (SLU)  for the collaboration through a stay in SLU to conduct the quantification of bacteria and fungi in the soil samples.
Identification of SMAD7 as a RINF target gene candidate. To identify molecular mechanisms by which RINF might modulate TGFβ-responsiveness, we reanalyzed our previously published gene expression microarray datasets of K562 and UT7 5. 3 cells transduced with shRINF#4 or shControl  (7). A relatively discrete set of 193 gene candidates (Supplementary Table  S1 ) were downregulated by RINF knockdown in both cell lines. Strikingly, ingenuity pathway analyses (IPA) revealed that this gene list was enriched in genes belonging to the TGFβ signaling pathway (Supplementary.
RINF and SMAD7 mRNA expression are correlated in bone marrow from adult donors and patients with myelodysplastic syndrome. We asked if RINF and SMAD7 mRNA expression would correlate in primary human CD34+ cells from healthy donors and donors with myelodysplastic syndrome. To this end, we performed q-RT-PCR on CD34+ cells isolated from adult bone marrow donors (n=11) and found a highly significant correlation between RINF mRNA and SMAD7 mRNA (Pearson, rho=0. 684, p=0. 02) (Figure  6A ). We confirmed these data by analyzing an independent microarray dataset performed by Pellagatti et al. (46). As shown in Figure  6B , RINF and SMAD7 mRNA levels strongly correlated in CD34+ cells isolated from healthy donors (Pearson's rho=0. 784, p<0. 001, n=17). Interestingly, this correlation also reached statistical significance in CD34+ cells from MDS-5q patients (Pearson's rho=0.
603, p<0. 001, n=47) but not in other MDS (without del(5q) or 5q-). We then compared the intensity of this correlation with the other 5q genes (n=48 genes) from the CDR associated with high-risk, and the CXXC5 probe sets were the one correlating the best with SMAD7 probe set in both the healthy control group (rho=0. 819, and rho=0. 784) and MDS patients' cohort with del(5q) (Supplementary Figure  S6A ). Reinforcing the previously reported relevance of SMAD7 in MDS pathophysiology  (33, 34) , SMAD7 expression was not only extinguished in CD34+ MDS samples compared to normal samples (in the microarray dataset from Gerstung et al. (47) , Supplementary Figure  S6C ), but patients with the lowest SMAD7 expression have a significantly shorter overall survival (Supplementary Figure  S6D ). RINF-silencing alters genome-wide hydroxymethylation.
(46)  For each gene, a Pearson's correlation coefficient was calculated by comparing its expression to the one of SMAD7, and the list was ranked by descending order. Cell growth was also monitored, and the total number of RBC numerated at day 17 of EPO are presented on histograms (lower panel). cells were isolated from adult bone marrow (n=11). RINF and SMAD7 mRNA levels were measured by q-RT-PCR. In order to assess whether the expression of these two genes correlated in donors' samples, a correlation analysis was performed with the Pearson's (for non-parametric analysis) correlation coefficient method: rho (r= 0. 684), P(two-tailed)<0. 05 (B) RINF and SMAD7 gene expression data were extracted from a previously described microarray dataset performed by Pellagatti et al. (46). In this study, CD34+ cells were isolated from adult bone marrow (n=17).
Microarray and data analysis Analysis of global gene expression profiles. After validation of RNA quality with Bioanalyzer 2100 (using Agilent RNA6000 nano chip kit), 400 ng of total RNA was reverse transcribed following the Genechip WT plus Reagent kit (Affymetrix). Briefly, the resulting double strand cDNA was used for in vitro transcription with T7 RNA pol. After purification, 15 µg of cRNA was used for reverse transcription with random primers. The cDNA obtained was then purified and fragmented. After control of fragmentation using Bioanalyzer 2100, cDNA was end labeled with biotin using Terminal Transferase (using the WT terminal labeling kit of Affymetrix). cDNA was then hybridized to GeneChip® Human Transcriptome Analysis 2. 0 (Affymetrix) at 45°C for 17 hours. After overnight hybridization, chips were washed on the fluidic station FS450 following specific protocols (Affymetrix) and scanned using the GCS3000 7G.
The image was then analyzed with Expression Console software (Affymetrix) to obtain raw data (cel files) and metrics for Quality Controls. Minimum information about a microarray experiment (MIAME)-compliant documentation of the microarray experiments have been deposited in Gene Expression Omnibus under the accession number GSE140770. The Affymetrix HTA2 dataset analysis was performed by GenoSplice technology (www. genosplice. com). Data were normalized using quantile normalization. Background corrections were made with antigenomic probes and probes were selected according to their %GC, cross-hybridization status and potential overlap with repeat region as previously described  (2, 3). Only probes targeting exons and exon-exon junctions annotated from FAST DB® transcripts (release fastdb_2014_1) were selected  (4, 5).
Acknowledgements This work was supported by  INSERM, Paris-Descartes University, Ligue Nationale Contre le Cancer (LNCC) ,  Cochin Institute , and the  Laboratory of Excellence GR-EX. AA was supported by  LNCC ,  Société Française d'Hématologie, Fondation pour la Recherche Médicale , and  Boehringer Society  (travel grant). GM was supported by fellowship grant from the  Ministère de l'Enseignement supérieur et de la Recherche  and  Société Française d'Hématologie. E. S-B is supported by the  National Center for Scientific Research (CNRS)  and her team was supported by  LNCC  and  Fondation de France. We are also grateful for the support from the  French-Norwegian exchange program  (Aurora to F. P. ). Authors thanks  Pr. Jérôme Larghero  and  Thomas Domet  from  Centre de Ressources Biologiques / Banque de Sang de Cordon de l'AP-HP  ( Saint-Louis Hospital ) for providing cord blood units,  Anne Dubart-Kupperschmitt  for the pTRIPDU3/GFP vector (40),  Johan Lillehaug ,  Øystein Bruserud ,  Camille Humbert ,  Pierre de la Grange (Genosplice) ,  Eric Nguyen ,  Sabrina Bondu ,  Alexandre Artus ,  Amandine Houvert ,  Nadège Bercovici ,  Emmanuel Donnadieu ,  Franck Letourneur ,  Brigitte Izac ,  Sebastien Jacques , and  Alain Trautmann  for technical assistance and scientific discussions, as well as Life Science Editors for reading and editing the manuscript.
RINF silencing results in diminished RBC production without impacting cell viability. We next investigated consequences of RINF knockdown on erythropoiesis and cell viability (see experimental design in Figure This temporal expression pattern mirrors RINF expression data extracted from three independent transcriptome datasets (microarray or RNA-seq) performed by Merryweather-Clarke et al. (43), An et al. (44), and Keller et al. (45) with adults or cord-blood CD34+ cells (Supplementary Figure S2). Taken together, our data shows that RINF expression peaks in erythroid progenitors and proerythroblasts during cytokine-induced erythropoiesis.
We defined accumulation as the difference in behavioral imitation rates between 2 successive demonstrations d t-1 and d t-2 preceding a private choice c t. A Learner paying attention only to the last demonstration should display a positive differential. Similarly, propagation was measured as the difference in behavioral imitation rates between 2 successive private trials c t+1 and c t+2 following a demonstration d t. A Learner that uses demonstrations only to bias exploration should display a positive differential. We analyzed these metrics in both empirical and simulated data (Fig  4 ). Models were simulated using the set of the best fitting parameters. In addition to the VS and MB models, we included 2 variants of the DB model (see Methods). The first variant, DB1, corresponds to the standard implementation of DB that has been widely considered in the literature, which implements a "pure" DB process  [6, 17].
Generalizing the results To test the robustness of our results in another experimental setting and a larger cohort, we analyzed an additional dataset (N = 302) from a recently published study  [14]. This experiment, hereafter noted as Exp 3, differs from ours in several aspects (see Methods). First of all, the outcome contingencies were not static, but followed a random walk (Fig 5A ), and Demonstrator choices were actual choices recorded from 2 previous participants playing the exact same reward contingencies. One of the participants used as Demonstrator displayed relatively poor skill (0. 60 ± 0. 05), while the other relatively good skill (0. 84 ± 0. 03). The same Demonstrator choices were not necessarily displayed across participants. As a result, the observed Demonstrator's correct choice rate ranged from 0.
At the end of each session, the experimenter came in both cabins to set the subsequent one, and participants were instructed to begin each session at the same time. The debriefing of the experiment was done with each pair of participants, and all participants were debriefed about the cover story after the experiment. The third dataset presented in this paper comes from a previously published study  [14]. The main differences between our design and theirs are summarized in Table  2  and include reward contingencies changing on a trial-by-trial basis (random walk); the Demonstrator choices consisted in recorded choices of previous participants playing exactly the same contingencies with no social component (therefore in this experiment no deception was involved); the ratio between observational and private trials was 1:2 (and not necessarily the same demonstrations were presented to the participants); some participants (roughly 50%) received information about the IQ (raven matrices) of the Demonstrator.
Model fitting procedure Model comparison and parameter optimization. In a first phase, we compared the different implementations of each model. The best implementation was then included in the final model space for comparison (cf. S2 Fig). Each model was fitted separately to the behavioral data of our 3 experiments. First, the free parameters of each model were optimized as to maximize the likelihood of the experimental data given the model. To do so, we minimize the negative log-likelihood using the fmincon function in Matlab, while constraining the free parameters within predefined ranges (0<α<1,0<β<+1). The negative log-likelihood NLL is defined as NLL ¼ À logðPðDatajParameters; ModelÞÞ: Then, from the negative log-likelihood we derive the Akaike Information Criterion (AIC)  [53]  defined as AIC ¼ À NLL À p; where p is the number of free parameters.
� � � p<0. 001, Wilcoxon test. Underlying data can be found in https://github. com/hrl-team/ mfree_imitation/. AIC, Akaike Information Criterion; DB, decision biasing; IQ, intelligence quotient; MB, modelbased imitation; NS, not significant; RW, Rescorla-Wagner; SD, Skilled Demonstrator; UD, Unskilled Demonstrator; RW, Rescorla-Wagner; VS, value shaping. https://doi. org/10. 1371/journal. pbio. 3001028. g005.
Funding: SP was supported by an  ATIP-Avenir grant  ( R16069JS ), the  Programme Emergence(s) de la Ville de Paris , the  Fondation Fyssen  and the  Fondation Schlumberger pour l'Education et la Recherche (FSER)  and the  FrontCog grant  ( ANR-17-EURE-0017 ) BB was supported by the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (819040 -acronym: rid-O). BB was supported by the  NOMIS foundation and the Humboldt Foundation. AN's salary was funded by the  ATIP-Avenir. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Library preparation and sequencing Illumina sequencing libraries were generated for each amplicon library using NEXTflex DNA sequencing kits with NEXTflex DNA barcode with eight-base indices (Bioo Scientific). Each library was subject to a 300 bp paired-end sequencing run on an Illumina MiSeq 2500 platform (run 1) at the University of Exeter Sequencing Service. All samples were pooled on the same 'lane', so that any trend identified between sample sets was not the product of between-lane sequencing efficiency/dynamics. All the cores with Life-Guard™ added at the surface (LG-ship) (4, 6, 13 and 14) were sequenced for a second time using one lane of Illumina MiSeq (run 2) (Fig. 1c ). Sequences are available at the NCBI SRA, accession numbers: SAMN11051173-204 (Bioproject PRJNA521526).
Quality filtering, selection of operational taxonomic units (OTUs) and taxonomic assignment Raw data from the Illumina libraries were assigned to individual samples by their barcodes. Merged (overlapped) paired-end reads were created using PEAR  [26]  and any unmerged reads were excluded. Cutadapt was then used to remove primer sequences and for quality control  [27]  using default parameters, de-replication was performed using VSEARCH  [28]. Processed amplicons were clustered into OTUs using SWARM v2  [29]. Taxonomic assignment was accomplished by taking the most numerous sequence from each OTU as a search seed for STAMPA (https://github. com/ frederic-mahe/stampa) against PR2 V4 SSU rRNA database (version gb 203)  [30]. Chimeric sequences were identified and removed using UCHIME  [31]  implemented in VSEARCH.
Diversity analysis and community composition Alpha-(within sample) and beta-(among samples) diversity analyses were conducted on both sample types from run 1 (LG-in situ and LG-ship). Any OTUs assigned as Metazoa (19% of the reads) were excluded. The Shannon, Chao1 and PD calculation methodology include a default rarefaction approach and so were calculated using the QIIME v1 scripts (alpha_rarefaction. py) resulting in 18,564 reads per sample. An analysis of variance (ANOVA) was conducted to test for group differences using R  [32]. Species (OTUs) abundance distribution and species (OTUs) accumulation curves, based on the number of reads, were compared between treatment groups using R (ggplot2 library  [33] ). For this approach we had to rarefy the dataset using a different approach (QIIME v1 scripts-single_rarefaction.
py). For individual sample comparison the data were rarefied to 19,454 reads, for samples pooled by core the data were rarefied to 473,271 reads. For the beta-diversity, Bray Curtis distance matrices from a relative abundance and a presence/absence (Sorenson's index) OTU table were performed using R (Vegan library). Unweighted and weighted UniFrac  [34]  distance matrices were calculated using QIIME v1 scripts. The phylogenetic tree was calculated using FastTree2 using the 'make_phylogeny. py' QIIME v1 script using the sequences aligned with MAFFT v7  [35]. The diversity profiles were represented as a non-metric multi-dimensional scaling (NMDS) ordination using the MASS package  [36]  in R  [32]. Clusters based on Bray Curtis and UniFrac distances were generated using hierarchical clustering (UPGMA) using the hCLUST function in R.
As mentioned above, the LG-ship samples were subjected to additional sequencing (run 2) to further explore the diversity profiles. Runs '1' and '2', were pooled by sediment core and normalised (473,589 reads-as the defined minimal threshold across all replicates with run 1 and 2 combined). Stacked bar charts were generated with the reshape2 and ggvis libraries in R to identify taxonomic composition of these samples. Group specific phylogenetic analysis SSU phylogenetic trees were calculated for the OTUs identified from runs '1' and '2' combined. We limited our phylogenetic analysis to OTUs that were composed of ≥20 reads and so these results are limited to OTU groups with increased relative representation. The resulting trees encompass a large amount of sequence diversity, yet are derived from a short alignment of the ~400 bp V4 region of SSU rRNA gene, resulting in a highly skewed data matrix with relatively few informative alignment positions but very many sequences.
Phylogenetic diversity of osmotrophic-lysotrophicsaprotrophic eukaryotes Next, we aimed to investigate the diversity of OTUs putatively branching with OLS taxa. These classifications were based on STAMPA analysis against a PR2 rRNA database  [30] , followed up with phylogenetic analysis. The eukaryotic groups of interest include: fungi, non-metazoan holozoa such as Corallochytrium  [47]  and the osmotrophic stramenopiles, e. g. pseudofungi  [48] , thraustochytriales and labyrinthulales  [49, 50]. In addition, we also investigated the MAST groups, including MAST-6, which have been shown to be present in marine sediments  [37, 51]  but for which the trophic function cannot currently be inferred based on phylogenetic position.
Ribosomally active stramenopile lineages OTUs with ≥20 reads classified as MAST or as pseudofungal were incorporated into a stramenopile phylogeny  [37]  (Fig. 4a ). This analysis significantly expanded the known diversity of many of these groups. Specifically, MAST-6 (Figs. 4a  and S2 ) was previously only represented by 7 GenBank SSU sequences and 17 'OTU 97 pyrotags'  [37] , yet the OTUs detected here encompassed 46 OTUs with three (OTU-478, -450 and -856) showing high relative abundance (>1000 reads). We also detected significant additions, in terms of V4 tags, for a number of MAST Fig. 2  Descriptive characteristics of the diversity profiles recovered from amplicon sequencing with and without in situ RNA preservation. a-d Non-metric multi-dimensional scaling (NMDS) ordination plots comparing push cores/treatments and sample replicates.