dataesr/scientific-paragraphs-categorization huggingface dataset:
The AO model fits perfectly the T11 data and shows significant discrepancies for  T13 (0. 98,0. 73,0. 19) and B14 (0. 98,0. 76,0. 19). This means that the numerical model cannot predict the high densities of immature and adult albacore in the Bay of Biscay, where the highest catches of T13 and B14 are observed. Expectedly, the SPO model fit is much degraded for these fleets obtaining (0. 98,0. 82,0. 15) for T11 and the same score (0. 95,0. 49,0. 36) for T13 and B14. Hence, the use of Bay of Biscay fisheries in the AO optimization leads to a biased parameterization that could not pass the validation test on the independent dataset and different region. The SPO model predictions for spatial dynamics of Atlantic adult albacore are shown in Fig. 6. Similar predictions by the optimal AO model can be found for comparison in the SI (Fig.
Currently, SEAPODYM assumes a unique growth curve for the modelled stock, therefore we have used the same length-age and length-weight relationships in the Atlantic Ocean as in the SPO application. The validity of the SPO MLE model when applied to both North and South Atlantic stocks is a remarkable result. Additionally, we found that the SPO parameterization is more robust for simulating albacore dynamics in the AO than the parameterization derived by fitting to the AO fisheries data. The inter-comparison exercise conducted in this study detected a bias in the AO optimization, originating from using the large dataset for the European fisheries in the Bay of Biscay. It is highly likely that the 2 � resolution of the numerical model with weak representation of currents in the coastal zones is too coarse to simulate movements towards this coastal area.
Acknowledgments We are grateful to two anonymous reviewers and  Dr. Kevin Weng  for constructive comments and suggestions. We thank  Morgane Dessert  and  Thomas Gorgues  from  IRD ,  Beatriz Calmettes  from  CLS  for preparing model forcing data,  Peter Williams  and  Sylvain Caillot  from the  Oceanic Fisheries Programme of the Pacific Community (SPC)  for providing georeferenced fisheries data for the South Pacific albacore stock. The access to the fisheries data was facilitated by the  WCPFC  Project  62. This study was supported by the  European Union 's  Horizon 2020 Research and Innovation Programme  (project  AtlantOS , grant agreement No. 633211  and project  COMFORT , grant agreement No. 820989 ) and  GEF/FAO/ UNDP Pacific Islands Oceanic Fisheries Management Project II  ( GCP/ RAS/267/GFF ), implemented by the  Forum Fisheries Agency (FFA)  and  SPC.
We identified five master regulators specific to RA FLS, namely BATF, POU2AF1, STAT1, LEF1 and IRF4. TF activity of the identified master regulators was also estimated with the use of two additional, independent software. The identified TFs contribute to the regulation of inflammation, proliferation and apoptosis, as indicated by the comparison of their differentially expressed target genes with hallmark molecular signatures derived from the Molecular Signatures Database (MSigDB). Our results show that TFs influence could be used to identify putative master regulators of phenotypic traits and suggest novel, druggable targets for experimental validation. Rheumatoid arthritis (RA) is a chronic autoimmune disease that primarily affects the synovial joints, but can also cause systemic disorders of cardiovascular, pulmonary, and skeletal nature 1.
Results Putative co-regulatory network inference for the RA synovial tissue. We reconstructed a putative co-regulatory network using gene expression data from synovial tissue of RA patients using as control synovial tissue from OA patients, of three datasets: GSE55235, GSE55457 and GSE55584. We performed normalization and meta-analysis of the three combined datasets and obtained a list of 2865 differentially expressed genes (DEGs). Using the DEG list and the R/Bioconductor package CoRegNet  24  , we obtained a co-regulatory network. This network was then enriched with supporting regulatory evidence such as the TF binding site, ChIP-seq data, and protein interactions found in the CoRegNet embedded databases HIPPIE  27  , CHEA  28  , ENCODE  29  and STRING  30. The enriched network contained 126 TF and 1540 target genes linked together by 9716 TF-to-gene regulatory interactions.
It is worth noting that the network consists of two distinct clusters connected by one TF, SOX5. Estimation of TFs influence in synovial fibroblasts using microarray gene expression. Next, we analyzed the dataset GSE107105 comprising seven RA FLS subpopulations and five OA FLS subpopulations, clustered according to their protein surface markers (CD34, THY1 and CDH11). For this dataset we calculated TF influence in two levels: (a) disease level, analyzing all RA FLS samples against all OA FLS used as a control, and (b) for each fibroblast subpopulation, comparing between similar subsets of RA and OA FLS, where possible. For the analysis, and the TF influence estimation we used the whole expression matrix. Using the reference network, we calculated TF activities, and we were able to identify four out of the five TF considered as master regulators in RA synovial tissue data (Tables 2).
However, populations E and F exhibit an entirely different profile that is highly similar to the ones obtained for OA FLS. POU2AF1 is absent from all subpopulations of RA FLS. Concerning OA FLS it is worth noting that the dominant TFs in all subpopulations are TFs of the HOX family and SIX1 and 3. Calculating TFs influence from scRNA-seq data of isolated fibroblasts. Finally, we analysed scRNA-seq data of isolated RA and OA FLS from two RA and two OA patients (GSE109449). First, we performed a dimensionality reduction using the tSNE algorithm  31  (Fig. 3A ). Then, we projected the patients' disease state (OA/RA) on the plot, and we observed the clustering between OA and RA patients. As seen in Fig. 3B , the patients are separated into three clusters, based on their disease state. RA is the dominant case in one of the three clusters and OA in a second, however all three clusters have both cases.
Subsequently, we calculated the TFs influence for the whole dataset using CoRegNet  24. The last step was to project on the tSNE clustering result, the TFs influence calculated using the whole dataset and not just DEGs. The aim was to see if at the single-cell level, the influence of the TFs identified as master regulators in RA synovium and RA FLS, would be considerably stronger in RA versus OA samples. Indeed, as shown in Figs. 3C-F , there is a concordance between the patients' disease state (OA/RA) and TFs influence. In essence, each of the identified TF has a strong influence on the gene expression of FLS from RA patients (coloured in green) while it has a weak influence on the gene expression of FLS from OA patients (coloured in purple). The five TFs previously identified as master regulators (STAT1, BATF, IRF4, LEF1 and POU2AF1) appear in the top 15 of the most influential TFs for RA FLS based on TF influence estimation using CoRegNet (Table  S4 ).
STAT1 and BATF are ranked as the second and the fifth most influential TFs, respectively. Comparison of the top 5 of the most influential TFs in different FLS subpopulations revealed GATA3 as omnipresent in both RA and OA samples. We also observed the presence of STAT1 and BATF in most RA samples. Lastly, we obtained two very different TF activity profiles for the subpopulation C of the OA FLS and the subpopulation E for the RA FLS (Table  4 ). We did not observe any apparent clustering according to the surface markers independently of the disease state (Fig. 3F ). Estimation of transcriptional activities of the master regulators identified by CoRegNet, using ISMARA and DoRothEA. In order to see if the TFs identified as master regulators in the RA synovium and FLS samples using the TF influence and the reference network obtained from CoRegNet 24 could be retrieved using an independent approach, we analyzed the datasets with two additional software (Table  5 ): (a) ISMARA  21  , which estimates the TFs activities by identifying the promoters, quantifying their genome-wide expression and then fitting a linear model, and (b) DoRothEA 20 a framework to estimate TF activities from gene expression data and consensus TF-target binding networks.
Microarray data of synovial tissue samples. Using ISMARA, BATF was identified as the top TF with the highest Z-score. IRF4, STAT1 and LEF1 were also retrieved in various rankings. POU2AF1 was not identified but we were able to retrieve the target of POU2AF1, POU2F1 (Table  S5 ). With DoRothEA, STAT1, BATF and IRF4 were found in the top 15 of the most active TFs in the RA synovium. POU2AF1 was not identified but its targets, POU2F1 and POU2F2 could be retrieved in the analysis in various rankings (Table  S6 ). Microarray data of FLS. Using ISMARA and DoRothEA, BATF, STAT1, IRF4 and LEF1 were retrieved in various rankings. Similarly to previous analyses, POU2AF1 was not identified, but its targets POU2F1 and POU2F2 were retrieved in the analysis in various rankings (Tables  S7, S8 ). scRNA-seq data of FLS.
Involvement of the master regulators in primary RA FLS phenotypic outcomes. To study the implication of the five identified master regulators to the major functional phenotypic outcomes of RA FLS, namely apoptosis, inflammation, proliferation and migration/invasion, we retrieved hallmark molecular signatures from MSig database  26. We compared the signatures with the differentially expressed genes in the scRNAseq data set (GSE109449). We kept only the genes having a p-value lower than 0. 05 and a log fold change (logFC) higher than 1. 5 in absolute value (Tables  S10-S13 ). Next, we searched to see which of the genes identified as contributing to hallmark signatures were under the control of the five TFs characterized as master regulators (Fig. 4 ). We identified 12 DEGs that belong to the pro-apoptotic signature, 13 DEGs that belong to the proliferation signature and 18 DEGs that belong to the inflammatory signature.
Transcriptomic data of synovial tissue were used to infer a global, reference network of tissue-specific transcriptional activity. As a control, we used synovial tissue from OA. OA is a degenerative, non-autoimmune and non-inflammatory disease of the joints that also causes cartilage damage and bone erosion  35. Inflammatory mediators observed in OA joints are thought to be the downstream effectors of the pathogenesis of the disease. Even though it's called non-inflammatory arthritis, OA can still result in some inflammation of the joints. The difference is that this inflammation probably results from wear and tear, as a secondary effect of the disease and not as a characteristic feature. Moreover, OA is a degenerative disease, while RA involves autoimmunity. While it is hard to distinguish between a characteristic feature and a secondary effect, the fact that inflammation in RA stems from the activated autoimmune pathways, allows us to hypothesize that there should exist different mechanisms that drive the onset, progression and pathogenesis of the two diseases.
For the analysis, we used CoRegNet  24  , a tool for network inference based on TF influence estimation. The tool CoRegNet has successfully been used to identify driver regulators for bladder cancer  36  , master regulators in fatty liver disease  37  and hepatocellular carcinoma  38  and sex-specific gene regulation in drosophila  39  , among other studies. Two additional software was used for calculating TF activities of the samples, for seeing if we could retrieve the TFs already identified with CoRegNet, independently of the reference network. Two opposite TF influence profiles were obtained for the OA patients and the RA patients using the global network inferred from the synovial tissue gene expression. This result corresponds to the fact that we used only the differentially expressed genes between RA and OA patients to perform the network inference so that only autoimmune changes specific to RA are revealed.
To verify that these TFs also play a role in RA FLS and they were not the result of the contribution of other cells present in the synovial tissue samples, the second step of our study involved the analysis of transcriptomic data from RA and OA FLS subpopulations. These datasets are related to Mizoguchi et al. study  49  where seven fibroblast subsets with distinct surface protein (CD34, THY1 and CDH11) phenotypes were isolated. Using the reference network obtained from the synovial tissue as a basis, we calculated the TF influence of the FLS transcriptome. We performed this step twice, one for comparing TF profiles in RA FLS versus OA FLS, and subsequently for comparing TF activities between subpopulations of RA and OA FLS sharing the same surface markers. The first part of the analysis revealed that four out of the five identified TFs as master regulators for the RA synovium were also characteristic for the RA FLS samples.
POU2AF1 was not retrieved in the microarray dataset for FLS. Instead, the TF NFKB1 appeared in the top 5. Analysis comparing RA and OA FLS according to their surface markers revealed two RA FLS subpopulations, E and F respectively, with a significantly different TF activity profile in comparison to the other RA FLS subpopulations. The analysis of the scRNA seq data revealed STAT1, BATF, IRF4, LEF1 and POU2AF1 in the top 15 of the most influential TFs for RA FLS, with STAT1 and BATF ranking as the second and the fifth most influential TF, respectively. Regarding the subpopulation-specific TF activity profiles, STAT1 and BATF were dominant in RA samples. The transcription profiles of the subpopulation C of the OA FLS and the subpopulation E of the RA FLS were very different in comparison to the other subpopulations.
Lastly, we did not observe any apparent clustering according to surface markers. Transcription factor activity of the master regulators was also estimated with two additional software, ISMARA and DoRothEA. Unlike these tools, CoRegNet identifies cooperative regulators of genes. It considers, rather than the expression of regulators, their influence to detect co-regulators sharing a certain number of common targets. The results are not directly comparable since the three software use different algorithms to assess TFs activity, however most of the TFs identified with CoRegNet as having a strong influence in RA FLS samples were also retrieved from the ISMARA and DoRothEA analysis, confirming in an independent manner that these TFs do play an active role in RA FLS. Analysis of the DEGs in the scRNA-seq data revealed genes implicated in hallmark molecular signatures related to major functional phenotypic outcomes of RA FLS, namely apoptosis, inflammation, proliferation and migration/invasion.
Further analysis using the co-regulatory network obtained with CoRegNet highlighted genes under the control of the master regulators that are involved in apoptosis (suppressing pro-apoptotic markers), proliferation (suppressing proliferation markers) and inflammation (activating inflammatory markers). Very few characteristic migration markers were differentially expressed and none under the control of the master regulators identified. In conclusion, this study contributes to a better understanding of RA FLS regulation by identifying diseaseand cell-specific master regulators, some highlighted for the first time as playing a pivotal role in fibroblasts. In an effort to limit misleading results and misinterpretations, statistical tests were performed-where feasible-to ensure homogeneity of the datasets used for sex and age (see Methods section and supplementary Tables  14 15 16 ).
It should be noted, though, that besides age and sex, medical treatments and disease duration may potentially affect the TF levels in the patients. However, there are technical difficulties in accessing this type of data (often missing from original studies, not published along the datasets in public repositories, not available for all selected datasets, supplementary data not accessible) that do not allow for further adjustments using appropriate statistical tests. To ensure biological relevance of our results we have used, besides our main methodology, two independent software for cross validation, and finally we have cross checked our findings with experimental results in peer-reviewed RA studies. Experimental validation of the TFs identified as putative master regulators using in vitro settings and additional analyses are required to understand how different TF activity profiles coordinate gene expression in RA synovium and RA FLS subsets, determining disease and cell-specific phenotypic traits, cellular destiny and disease severity.
Methods Data description and analysis for the inference of the co-regulatory reference network. For the inference of the co-regulatory reference network, we used three genome-wide transcriptomic datasets from the Affymetrix HG-U133 A/B array comprising a total of 79 samples of synovial tissue, including 20 Controls (C), 26 OA and 33 RA patients. The first dataset (Berlin dataset, GSE55235) includes 30 samples: 10 RA, 10 OA and 10 C. The second dataset (Jena dataset, GSE55457) includes 33 samples: 10 C, 13 RA and 10 OA. The third dataset (Leipzig, GSE55584) includes 16 samples: 6 OA and 10 RA. We used R version 3. 5. 3 to perform the exploratory data analysis and the differential expression analysis, with an eight-core processor, with 32Go of RAM running on Windows 10 (64 bits) and the Affy package version 1.
62  50. We normalised the genes expression using the mas5 function from the Affy package. Then we deleted the weakly expressed genes with the function mas5calls which performs the Wilcoxon signed rank-based gene expression presence/absence detection algorithm. Next, we performed differential expression analysis to identify genes that are differentially expressed between RA and OA patients using as a significance threshold an adjusted p-value (FDR) equal to 0,05. As the number of controls was lower than the number of OA samples, we decided to use OA samples as reference. OA remains a reasonable control for RA since OA is a non-inflammatory, non-autoimmune disease. We annotated the lists of DEGs using the file provided by Affymetrix. The annotation allowed us to retrieve the GenBank accession number and Gene Symbol for each probe.
To address the problems of several probes corresponding to the same gene and of several genes having several symbols (aliases) we averaged the expression of each gene by calculating the mean of all the gene's probes. After that, for each gene, we kept all the possible aliases. The differential expressed gene list contained 2865 gene symbols, and it was used for the inference of the co-regulatory network. Inference of the co-regulatory reference network. CoRegNet is a Bioconductor R-package. Based on a transcriptomic dataset, it can be used to reconstruct a co-regulatory network and integrate regulation evidence. The package uses the hLICORN algorithm  51  , which identifies cooperative regulators of genes. One key breakthrough in the exploration of networks was to consider, rather than the expression of regulators, their influence by evaluating the expression of target genes to detect master regulators.
We used the CoRegNet package  24  version 1. 20. 0 to infer the network. We gave it as input the annotated expression matrix with the gene symbol as row names and the samples (OA/RA) as columns. Then, we run in parallel on 48 cores, the function hLICORN to obtain the co-regulatory network. Regulatory and cooperative evidence were used for gene regulatory network and co-regulatory network enrichment. Calculating TF activities with ISMARA and DoRothEA. To validate the results and see if the TFs identified as master regulators in the reference network with CoRegNet could be retrieved with a different approach, we employed in our analysis two additional software which calculates TFs activity: ISMARA  21  and DoRothEA  20. ISMARA is an online tool that models genome-wide expression or ChIP-seq data, in terms of computationally predicted regulatory sites for transcription factors.
The input required for running ISMARA is either expression data (microarray CEL files) or ChIP-seq data to identify the key transcription factors driving the observed expression. Results include, for each regulatory motif, inferred activities across the input samples, predicted genome-wide targets, enriched pathways and functional classes of genes, and direct interactions between regulators. This tool uses a linear model and performs a Bayesian inference of the motif activities to obtain both bestfit activities and error-bars on the activities. The second tool, DoRothEA, is an R package containing a list of transcription factors and their transcriptional targets. The target genes are organized into regulons coming from different types of evidence like literature curated resources and interactions inferred directly from gene expression.
Based on the expression of each regulon, the tool estimates the activity of its corresponding TF. TFs influence generation using fibroblasts gene expression microarray dataset. We used microarray data of freshly isolated synovial fibroblast subsets in patients with RA and OA to refine the previously obtained results. The dataset GSE107105 includes 34 samples from three OA and three RA donors. First, we generated TF influence profiles for RA and OA patients and then, for each fibroblast subpopulation. Next, we used the co-regulatory network inferred at the previous step and the influence of the co-regulators to detect possible variations. TFs influence generation using scRNA-seq dataset. We used scRNA-seq data we got from the GEO database (GSE109449) collected from freshly isolated synovial fibroblasts of 2 RA patients and 2 OA patients.
The fibroblasts were gated by protein surface markers: CD34, THY1 and CDH11 (126 samples for A, 41 samples for B, four samples for C, 65 samples for D, 13 samples for E, 92 samples for F and four samples for G subpopulation, for correspondence, see Tables  3  and 4 ). The first step of the analysis was to preprocess the data. We used Single Cell Experiment R package  52. It is specifically designed for storing data from sc experiments. Then we filtered low abundance genes because most of them are non-informative and are not representative of the biological variance of the data. We only kept genes having an average expression level higher than one. We also filtered genes that are in a small number of cells by keeping only the ones expressed in at least five cells. A complementary way to filter genes is to keep only proteincoding genes.
scRNASeq profiles not only protein-coding genes but also long non-coding RNA and pseudogenes. These genes are often much noisier than protein-coding and might be interesting to remove them. To do that, we annotated the expression matrix using Biomart R package and kept the genes having "protein-coding" as biotype. After that, we performed quality control using isOutlier function from Scater R package to filter lowquality cells and low-quality features. The following step of our analysis was to normalize the data using a size factor approach. Once the quality control finished, we identified the highly variable genes (HVG). To do that, we calculated the variance and the mean expression for each gene. Then, we performed a dimensionality reduction using the tSNE algorithm  31. In addition to the tSNE clustering, we used hierarchical clustering and SC3 clustering, an unsupervised clustering method for scRNA-seq data  53.