dataesr/scientific-paragraphs-categorization huggingface dataset:
and in Section 2. To perform calibration, we evaluate the empirical JER of all threshold families of the learned template. Then, we select the largest b ∈ {1,. B} such that JER control holds on inference data for the threshold family t b. To avoid evaluating the JER of all threshold families, this search is done by dichotomy in practice. The resulting method is called "Notip" for Non-parametric true discovery proportion. As described above, Notip requires a training dataset in order to learn the template. An example of a template learned from a training data set is displayed in Figure 14. Note that learned templates do not have a parametric shape, but follow the shape of randomized p-value curves.
Variation of the number of detections for low sample sizes Because of the high cost of acquisition, many fMRI datasets comprise few subjects. This may lead to unstable behavior and limited statistical power. To study the impact of sample size on the inference procedure both at training and inference step, we perform two dual experiments. First, we compute the number of detections for the three possible methods as in Section 4. 2 , with the difference that the template is learned using n train = 10 subjects instead of n train = 113. Second, we use the standard template with 113 subjects but this time infer on 25 pairs of fMRI contrasts with any number of subjects n in f er , varying from n in f er = 8 to n in f er = 200.
This project was funded by a UDOPIA PhD grant from Université Paris-Saclay and also supported by the FastBig ANR project (ANR-17-CE23-0011), the KARAIB AI chair (ANR-20-CHIA-0025-01) and the SansSouci ANR project (ANR-16-CE40-0019). The authors thank Laurent Risser and Nicolas Enjalbert-Courrech for their precious help on writing and improving the sanssouci Python code, and Samuel Davenport for useful discussions about this work.
Cluster localization (z > 3), size, peak statistic and TDP lower bound at risk level α = 5% using the three possible templates (ARI, Calibrated Simes and Notip) on contrast pair 'look negative cue vs look negative rating'. Cluster subpeaks are also reported when relevant. This table can be generated using script https://github. com/alexblnn/Notip/blob/master/scripts/table_2. py.
4. 4. 4 Variation of the number of detections for low sample sizes Because of the high cost of acquisition, many fMRI datasets comprise few subjects. This may lead to unstable behavior and limited statistical power. To study the impact of sample size on the inference procedure both at training and inference step, we perform two dual experiments. First, we compute the number of detections for the three possible methods as in Section 4. 2 , with the difference that the template is learned using n train = 10 subjects instead of n train = 113. Second, we use the standard template with 113 subjects but this time infer on 25 pairs of fMRI contrasts with any number of subjects n in f er , varying from n in f er = 8 to n in f er = 200.
Dataset for Human Samples and Cell Lines RNASeq gene expression data from both TCGA (The Cancer Genome Atlas)  24, 25  and CCLE (Cancer Cell Line Encyclopedia)  26  were obtained from the web-based program UCSC Xena  27. Box-plots, Kaplan-Meier survival plots, scatter-plots and pvalues of these data were visualized and calculated using Graphpad Prism 7.
PCR Cells were grown under normal culture conditions followed by lysis with TRIzol Reagent (Invitrogen # 15596026), phase separation with chloroform, and RNA precipitation with isopropanol. RNA was then washed with 70% ethanol and redissolved with DEPC-treated water. First-strand cDNA synthesis from isolated mRNA was performed as outlined in the protocol for Superscript III Reverse Transcriptase (Invitrogen #18080051). For cDNA isolated from HEK-293T, MCF-10A, MCF-7 and MDA-MB-231 cells, polymerase chain reaction (PCR) was performed using primers spanning INSR exon 11 (Fwd: 5'-AAC-CAG-AGT-GAG-TAT-GAG-GAT-3'; Rev: 5'-CCG-TTC-CAG-AGC-GAA-GTG-CTT-3') to specifically detect INSR isoforms as previously described  29. PCR products were separated by gel electrophoresis and densitometry analysis of bands representing INSR-A (exon 11-, 600 bp) and INSR-B (exon 11+, 636 bp) allowing for relative INSR isoform expression levels to be calculated using Graphpad Prism 7.
Statistical modeling and analysis of tumour growth data Tumour growth data from NIC:INSR mice was filtered to ensure that all tumours considered had at least two measurements before the mouse was sacrificed. Visual examination of the data suggested an exponential growth pattern for the tumours. To directly test for this, both linear and log-linear mixed models were fit to the measured tumour volume data over time to estimate the average tumour growth rate for each mouse. The measured volume of each tumour was modelled as a function of the tumour age, including fixed effect terms for the interaction between the genotype and the age of the tumour, as well as a fixed effect intercept for each genotype. Random effects were included for the mouse to mouse variation in the slope of the model, and for the tumour to tumour variation in the intercept.
As in per tumour analysis, the log-linear model was found to be a better fit than the linear model. Significance testing was done as above, comparing the explanatory power of models fit using the genotype information to those fitting a single slope for mice from all genotypes. All models were estimated using the afex (1. 0-1) (CRAN. Rproject. org/package=afex) and lme4 (1. 1-27. 1)  33 packages, as implemented in the R Statistical Computing Environment (4. 1. 2) (R-project. org/). Plots were generated using the ggprism (1. 0. 3) (CRAN. R-project. org/package=ggprism) and ggplot2 (3. 3. 5) (ggplot2. tidyverse. org) packages. INSR, IGF1R, p-AKT, p-ERK and Ki67 stains were quantified using QuPath bioimage analysis software (Bankhead et al. , 2017). Briefly, images of all slides of a given stain were loaded into a single project file where stain vector and background estimates for each antibody were used to improve stain separation using colour deconvolution.
Ras/ERK signaling pathways in mammary epithelial cells. To expand on the previous immunohistochemical analyses of INSR expression in BC, we analyzed gene expression of INSR (by RNASeq) from The Cancer Genome Pan-Cancer Atlas (TCGA PANCAN) dataset  24, 25. Levels of INSR mRNA expression in BC were increased relative to more than half of other obesity-linked cancers (Figure  1A ), and to three-quarters of all cancer types (Supplementary Figure  1A ). INSR mRNA expression varied amongst both histological (Figure  1B ) and molecular (Figure  1C ) BC subtypes, but not TMN stage (Supplementary Figures  2F-I ) and was highest in invasive lobular carcinomas and the luminal A molecular subtype, corroborated by the observation of higher INSR expression in tumours positive for either estrogen receptor (ER; Supplementary Figure  2C ) or progesterone receptor (PR; Supplementary Figure  2D ).
04) in primary breast tumours, as measured by RNASeq from the TCGA Breast dataset in UCSC Xena Functional Genomics Explorer. (E) qRT-PCR analysis showing relative expression of INSR-A and INSR-B isoforms in HEK-293T, MCF-10A, MCF-7 and MDA-MB-231 cell lines. (F) Western blot analysis of INSR expression in HEK-293T, MCF-7 and MDA-MB-231 and MCF-10A cell lines. MDA-MB-231 cells were transduced with an shRNA targeting INSR to demonstrate specificity. Blots were probed with antibodies against INSR and GAPDH. (G) Western blot analysis of MCF-10A cells grown in supplemented DMEM/HAM (Basal) or starved overnight in the absence of insulin and FBS (Starved), and then either stimulated with 100 nM insulin for 5 minutes (Starved+Ins) or pre-treated with 1 µM OSI-906 for 2 hours before stimulation with 100 nM insulin for 5 minutes (Starved+OSI+Ins).
Acknowledgements We would like to thank the staff in the  Animal Research Center at the University Health Network (UHN)  for help with animal husbandry and  UHN 's  Applied Molecular Profiling Laboratory  for assistance with immunohistochemistry. This work was supported by the  Canadian Institutes of Health   Research Project Grant  ( 162296 ) and the  Canadian Cancer Society Grant  ( i2I16-2  grant # 704786 ) to V. S. L. P. is a recipient of The  Princess Margaret Hospital Foundation Graduate Fellowships in Cancer Research. Y. P was a recipient of the  Canadian BC Foundation postdoctoral fellowship  and  R. D. of the  Banting Postdoctoral Fellowship  from the  Government of Canada.
Covariates Maternal age, ethnicity, education level, parity, weight and height were ascertained using questionnaires. Data on weight and height were used to calculate BMI (kg/length 2 ) and was ascertained from the Swedish National Birth Register. Serum cotinine levels with the following cut-offs, below 0. 2 ng/mL, 0. 2-15 ng/mL or higher than ng/mL, were used to categorize participants as non-smoker, passive smoker or active smoker, respectively.
, 2017) , the product interaction term of TPOAb status was added to all TSH and FT4 analyses to investigate any effect modification by TPOAb status. We utilized multiple imputation by chained equations to impute missing data of covariates, pooling 25 imputed datasets for analyses  (Buuren et al. , 2011). All statistical analyses were performed using R statistical software version 3. 6. 3 (packages mice; rms and corrplot; https://www. r-project. org/).
Funding This project has been supported by the Exchange in Endocrinology Expertise (3E) program of the European Union of Medical Specialists (UEMS), Section and Board of Endocrinology and the ATHENA project, funded under the European Union's Horizon 2020 Programme for research, technological development and demonstration, grant agreement no. 825161. The SELMA study was funded by grants from the Swedish Research Council (Formas). The sample analyses were supported by Region Skåne and the Medical Faculty at Lund University, Sweden.
| Mutational analysis MasterPure Complete DNA extraction kit (Epicentre, code MC85200) and QuickExtract™ FFPE DNA extraction solution (Lucigen) were used to purify DNA from fresh frozen and FFPE tissues, respectively. Mutational analysis was performed using a next generation sequencing (NGS) approach for the following 17 genes: KRAS, BRAF, EGFR, TP53, NOTCH1, PIK3CA, CDKN2A, TERT, NRAS, HRAS, PTEN, FBXW7, EP300, CASP8, AIP, GNAS and USP8, known to be involved in the development of pituitary tumours (see Table  S1 ). 13 Target enrichment of hot spot regions and generation of primer libraries was performed following previously reported protocols and methods. 14 FASTQ files, obtained after loading libraries onto MiSEQ (Illumina), were filtered (PHRED quality score >Q30; length >100 bp) and processed in a Galaxy Project environment,  15  using hg38 human reference genome with Bowtie2, GATK local realignment, HaplotypeCaller and Picard MarkDuplicates.
Mutations were visualised using BAM files loaded onto the Integrative Genomic Viewer. Only mutations with a variant allele frequency (VAF) threshold >20% and a coverage depth of 50x in both strands were reported. The pathogenic score was calculated on Polyphen-2 web tool (http://genetics. bwh. harvard. edu/pph2/), the in silico predictor of functional effects of gene variants, and verified on COSMIC database. 16 Each NGSs experiment was designed to allocate ≥1k reads/region, to obtain a depth of coverage ≥1000x. An exploratory analysis was performed on 25 fresh frozen samples and repeated on FFPE samples of the same tumours, confirming the same mutation profile. Therefore, 86 FFPE (81 PAs/ PitNETs and 6 carcinomas) samples were considered in the subsequent analyses.
| Statistical analysis Mutational and DNA methylation differences among non-aggressive and aggressive PAs/PitNETs and carcinomas were determined by Fisher's exact test. For all tests, significance was defined as p <. 05. Kruskal-Wallis test was used to define the informative CpGs for each group of genes to which they belong. Statistical analysis was performed using IBM SPSS version 23. 0 and Stata 12. carcinomas) were non-functioning (Table  1 ). Clinico-radiological and IHC data of 33 patients with PAs/PitNETs had been previously reported. 11 an age at first surgery was 52. 1 ± 16. 1 years in patients with PAs/PitNETs, and 54. 5 ± 7. 3 years for those with carcinomas. Followup was 49. 8 ± 31. 7 months for PAs/PitNETs and 46. 3 ± 21. 5 months for carcinomas (Table  1 ).
ACKNOWLEDGEMENTS The study was funded by the research grant 'Aggressive and recurrent Pituitary Tumours: study of risk factors and involved genes (RECurrence in Aggressive PItuitary Tumours: RECAPIT; P. I. :  Prof. Diego Mazzatenta )', winner of the Tender for Translational Medical Research 2019, by the private non-profit organisation ' Fondazione Cassa di Risparmio di Bologna', Bologna, Italy ; and by the ' Ricerca Corrente ' grant (P. I. :  Prof. Caterina Tonon ) by The  Italian Ministry of Health  to the  IRCCS Istituto delle Scienze Neurologiche di Bologna.
R248V; VAF 28%; pathogenic (0. 93); PRL GNAS 0 1 0 1 p. D858N; VAF 26%; pathogenic (0. 99); SG GH USP8 2 0 0 2 p. P690R; VAF 35% pathogenic (0. 92); DG ACTH p. S718P; VAF 38% pathogenic (0. 99); DG ACTH AIP 1 4 1 4 p. V301M$; VAF 25%; benign (0. 01); SG ACTH p. Q228K°; VAF p. R188W; VAF 51% pathogenic (0. 98); FSH/LH p. R307Q; VAF 72%; pathogenic (0. 99); FSH/LH p. E61K §; VAF 100%; pathogenic (0. 99); FSH/LH 100%; benign, germline (0. 03); PRL p. S53N; VAF 38%; pathogenic (0. 99); PRL CDKN2A 0 0 1 1 p. D74A°; VAF 30%; pathogenic (0. 99); PRL Note: Genes were considered mutated for variant allele frequency (VAF) > 20%. §, $,°= mutations were detected in the same pituitary tumour. Pathogenic scores were calculated by Poly-Phen-2 web tool (http://genetics. bwh. harvard. edu/pph2/) and verified on COSMIC database.
16S amplicon-barcoding data production Quality control of DNA, PCR amplification, library construction and MiSeq Illumina sequencing were performed by Macrogen (Seoul, South Korea) using 337F (16S_337F, 5'-GACTCC TACGGGAGGCWGCAG-3') and 805R (16S_805R, 5'-GACTACCAGGGTATCTAATC-3') primers to amplify the V3-V4 region of the 16S rDNA gene  [25]. The sequencing data (fastq) for this study are accessible in the ENA (European Nucleotide Archive, https://www. ebi. ac. uk/ ena) database under the PRJEB55863 (ERP140807) bioproject.
Then forward and reverse corrected reads were merged with a minimum 20 bp overlap, and the removeBimeraDenovo function from DADA2 was used to remove chimeric sequences. Eighty-two percent of reads passed chimeric filtering. The numbers of reads filtered, merged and non-chimeric are indicated in S2 Table. A mean of 58. 6% of reads passed all filters (denoising, merging, non-chimeric), with a minimum of 15,347 and a maximum of 31,134 reads in filtered libraries, yielding a total of 2,712 ASV. ASV were then assigned at the taxonomic level using the DADA2 AssignTaxonomy function, with the Silva 16S reference database (silva_nr_v132_train_set)  [28]. We subsequently filtered out plasts (especially mitochondria from root samples) to keep only ASVs assigned to the Bacteria or Archaea kingdoms. A last filtering was done to remove ASV with <10 read occurrence across all libraries.
A dataset of 1,647 ASV was used for subsequent diversity analyses. A Neighbour-joining phylogenetic tree of the 1,647 ASV was constructed using MEGA11  [29]  by first aligning ASV sequences with MUSCLE  [30]  and then building a Neighbour joining-tree based on a distance matrix corrected with the Kimura 2P method. Metadata and ASV tables and the phylogenetic tree were uploaded to the NAMCO server for downstream microbiota diversity analyses (https://exbio. wzw. tum. de/namco/,  [31]. NAMCO is a microbiome explorer server based on a set of R packages, including Phyloseq for diversity analyses  [32]  and PICRUSt2 for functional predictions  [18]. Alpha-diversity analyses (observed richness, Shannon and Simpson diversity, statistical test with pairwise post-hoc Dunn test) were performed with Phyloseq and tidyverse, ggpubr, rstatix, multcompView R packages and plotted with ggplot2.
Beta-diversity (NMDS, PERMANOVA) was performed with Phyloseq and Vegan. PICRUSt2 functional predictions were performed to infer metabolic capacities from our 16S amplicon ASV. Functions were predicted in three classes: enzyme classification (EC), KEGG orthology (KO) and molecular pathways (PW). Data were normalised with relative abundance, and a Kruskal-Wallis test was performed across conditions (medium used for CDA and CIA) with the ALDEx2 package  [33]. Circular phylogenetic tree annotations and mapping were obtained with iTOL  [34]. Additional R scripts for the DADA2 pipeline, Phyloseq, and the production of figures are freely available on GitHub (https://github. com/lmoulin34/Article_ Moussa_culturingbias). For the OTU clustering approach, the FROGs pipeline (  [26] ; http://frogs. toulouse.
Quality filtering and diversity indices of 16S amplicon libraries (CIA versus CDA) We first assessed the quantity and quality of reads produced for each amplicon library originating from direct rice root or rhizosphere genomic DNA extraction (CIA) or from DNA extracted from cultures (CDA) of the same samples grown on bacterial culture media. A range of 24,000 to 44,000 reads (mean 36,120) was obtained for all 16S amplicon libraries (S2 Table ). Rarefaction curves (Fig 2A ) showed sampled diversity saturation for each library, with a clear difference between the CIA reads (much higher in alpha diversity) compared to CDA. After DADA2 pipeline processing, we obtained 2,712 amplicon sequence variants (ASV) that were assigned at the taxonomic level using the Silva database. One library (S36) was removed from the analysis (from CIA) as it showed only 3 ASV.
Then we performed differential analyses on the mean relative abundance of bacterial genera in each condition, using a Kruskal-Wallis test (α = 0. 05). Table  2  shows the 50 most abundant genera in the CIA and their mean relative abundance in each media dataset (whole dataset is available in S4 Table ). Among the 20 most frequent bacterial genera in the CIA, eleven were detected in the CDA. These were Devosia (8. 25% of all genera), obtained on TSA10, TSA50 or NFB media; followed by Pseudoxanthomonas (3. 62%), which was found in all media conditions except RF, then Stenotrophomonas (3. 36%), Bacillus (2. 29%), Pseudomonas (1. 42%) and Allo/Neo/Para/Rhizobium (1. 3%) found in all media; and finally Sphingopyxis (2. 1%) detected in TSA50; Streptomyces (1. 48%) in NGN and Pseudolabrys (1. 47%) in NFb.
Prediction of enriched functions in CIA compared to culture-based approach We performed a functional prediction analysis using PICRUSt2 to infer metabolic capacities from our 16S amplicon ASV. In order to assess the predictive ability of the PICRUSt2 algorithm on our dataset, we focused on the specific enzyme nitrogenase (EC. 1. 18. 6. 1) prediction in CDA libraries that included medium with (TSA, RF) or without nitrogen (NGN, NFb) (Fig  5A ). As expected, we observed nitrogenase enrichment (p = 0. 00492) in nitrogen-free NFb and NGN media, with NGN medium exhibiting much higher enrichment than NFb. The non-selective medium (TSA) and the plant-based medium (RF) did not enrich bacterial taxa with the nitrogenase function  (Fig 5A). We also aimed to predict which functional pathways were specific to CIA compared to CDA in order to help design conditions to capture the yet unculturable diversity.
1% frequency cut-off. Applying the same cut-off on our dataset would indicate the detection of 121 genera in the CIA, with 36 (29%) of them present in the CDA. From all media used in the CDA, we could recover a total of 142 bacterial genera, with each medium capturing 15 to 23 specific genera (Fig  4C ). The only exception was the plant-based rice flour medium, which in our study captured low bacterial diversity compared to the other media, probably due to its low composition complexity. Plant-based media have been suggested to be a good alternative to popular bacterial chemical media for increasing the cultivability of plant-associated microbes  [16] , but the use of homogenised roots, leaves or exudates has been recommended to complement minimal or more complex media. The recovery of specific ASV from the CDA that were not detected in the CIA was an unexpected finding in our study.
It would be better to assess rice microbiota diversity by substantially increasing the sequencing depth in order to get a better image of the overall diversity. The frequency differences between ASV exceeding 10e 3 (at the genus level, S4 Table ) that we found in our study means that they would not be detected in the CIA, while they would be in the culture-based approach. This was a crucial finding since several studies have underlined the role of rare species (also called satellite taxa) in plant-microbe interactions and more broadly in key ecosystem functions  [13, 39]. Increasing the representativeness of taxonomic diversity in databases should also be the focus of further scientific research since many ASV cannot be affiliated to taxonomic ranks due to missing descriptions of these taxa in taxonomic databases.
We also tried to predict functions and metabolic pathways that would be enriched when using different types of media, and we also conducted statistical tests to highlight functions that were missing from our culturable approach. Metagenome-guided isolation and cultivation of microbes has been developed in recent years, but these approaches are based on metagenomic sequences and the reconstruction of genomes and metabolic pathways  [19]. The massive sequencing effort focused on a highly diverse range of bacteria from different environments has led to the development of genome database and prediction tools that may be used with simple amplicon taxonomic markers  [18]. We applied a prediction tool to our dataset to investigate the ecology and functional capacities of our detected bacteria. We found that many taxa with anaerobic metabolisms such as methanogenesis (methane production), methanotrophy (methane degradation) and methylotrophy (one-carbon reduction), or with photosynthetic capacities, were missing from CDA (Fig  5 ) compared to the CIA.
Acknowledgments This study was carried out using the facilities of the  PathoBios International Joint Laboratory (LMI)-Observatory of Plant Pests and Diseases in West Africa-Biodiversity and Biosafety  (www. pathobios. com; twitter. com/PathoBios). We are very grateful to  Issouf Sanga ,  Laurentine Kabre ,  Sylvie S. Guissou ,  Alexandre P. Ouedraogo  and  Manaka Douanio  for their contributions to this research. We also thank  Gilles Bena ,  Pierre Czernic ,  Ludivine Guigard  and  Nicolas Busset  for constructive discussions throughout this project.
A rs-fMRI dataset obtained from 43 twin pairs aged between 10 and 30 years was used to extract FC features at multiple spatial scales, from each network link to the whole-brain network. Applicability of twin models were then tested and, when possible, decomposed the feature variance into genetic and environmental components. Based on limited evidence to date, we hypothesized that the majority of FC metrics at different network scales would be under stronger genetic control compared to shared environmental control. In view of the spatially heterogeneous individual FC variability [ 25 ], we expected non-uniformly distributed genetic-environmental influences on local FC patterns. Furthermore, as heritability estimates may vary according to the connection densities of the network [ 21 ],we expected greater genetic effects in relevant long-range functional pathways compared to small-network short-scale pathways.