dataesr/scientific-paragraphs-categorization huggingface dataset:
Accession numbers of these queries are provided in the Supplementary Table  S2. These twelve proteins were first used as queries for BLASTP searches against the general sequence libraries Ensembl  [21]  and the National Center for Biotechnology Information (NCBI)  [22]  nr library. We also searched taxon-specific libraries: Archives for fungal sequences (Joint Genome Institute (JGI  [23] ) and FungiDB  [24] ; Diptera (Flybase  [25] , Hymenoptera (HymenopteraMine  [26] ), and aphids (Aphidbase  [27] ); nematodes (wormbase  [28]  and wormbase parasite [29]), human parasites (GeneDB  [30] ). MIF protein sequences were also searched in publicly available databases from genome sequencing consortia: Cyanophora paradoxa (Cyanophora genome project  [31] ), Cryptosporidium hominis (CryptoDB  [32] ), Globodera pallida (in the Sanger server at http://www.
sanger. ac. uk/cgi-bin/blast/ submitblast/g_pallida), Pristionchus pacificus (at http://www. pristionchus. org/blast/) and Meloidogyne species (at http://meloidogyne. inra. fr). All the BLASTP searches were performed using the scoring matrix BLOSUM62  [33] , no low complexity filtering, a cut-off E-value of 10 -10 and coverage >90% of the query length. The BLAST hits of the 12 queries against the libraries and databases were collected and redundancy at 100% identity between the hits was eliminated. To confirm the diversity and abundance of canonical (typical) MIFs in the species of interest listed in the Supplementary Dataset 1, we also performed TBLASTN searches using the same 12 queries (scoring matrix BLOSUM62, SEG low complexity filtering, cut-off E-value of 10 -10 , and coverage >90%) against expressed sequence tag (EST) libraries, whole genome shotgun contigs (WGS), or transcriptome shotgun assemblies (TSA) at the NCBI.
Statistical Analyses Statistical analyses were performed with the R package  [35] , version 3. 1. 3. The Multiple Correspondence Analysis (MCA  [36] ) was performed using the function 'dudi. acm' present in the package 'ade4  [37]. The potential links between the number of MIF sequences present in each species were analyzed as response variable of a generalized linear regression model (GLM  [38] ) using as predictors the taxonomy, the environment and the lifestyle (free-living or parasite). A quasi-Poisson distribution was used to account for under-dispersion in the data, and interactions were not included. Analysis of deviance was performed to check for the effect of the three predictors. In a second time, another identical GLM model was fitted for only four major phyla, with lifestyle as a predictor.
Multiple Sequence Alignments and Phylogenetic Reconstructions MIF protein alignments were performed using MUSCLE  [40]  with default parameters in Jalview version 2. 9  [41]  and examined in Jalview to detect and manually curate incomplete protein sequence predictions, sequencing errors and poorly aligned sequences. The alignments were automatically cleaned using trimAl  [42]  on gappyout mode with standard settings. Sequences containing less than 85% of amino acid overlaps with the rest of the alignment were removed. The 18S ribosomal RNA (rRNA) sequences were aligned using the specific SINA aligner  [43]  (SILVA Incremental Aligner) with default parameters. This tool is optimized for 18S sequences and is available in the SILVA databases  [44]  (https://www. arb-silva. de/). For phylogenetic reconstructions, we used the two complementary methods of maximum likelihood (ML) analysis  [33]  and Bayesian inference (BI) of phylogeny  [45].
For both methods, the best amino acid substitution models in the MIF phylogeny were estimated from the multiple alignments using ProtTest  [46]  version 3. The Wag model (without inclusion of a proportion of invariable sites or γ distribution of the rates of evolution) was systematically estimated to be the fittest for the MIF analyses. The ML analysis was performed using the PhyML  [47]  method implemented in the SeaView  [48]  (version 4) software with the following settings: amino acid frequencies (optimized), proposition of invariable sites and number of substitution rate categories estimated from the Datasets. BIONJ was chosen to create the starting tree and the best method between the NNI (Nearest Neighbor Interchanges) and SPR (Subtree Pruning and Regrafting) tree improvement methods were used to estimate the best topology, and the tree topology optimization option was chosen.
(Genbank accession number WP_011360252. 1, WP_015260126. 1, WP_006746032. 1, WP_017717428. 1 and WP_015181735. 1, respectively). As a reference taxonomic information, the species phylogenetic trees were reconstructed de novo on the 18S rRNA of species from selected phyla (e. g. , Alveolata or insects), using the Arabidopsis thaliana sequence as an outgroup. For the 18S rRNA, the best nucleotide substitution models were estimated for alignments using JModelTest  [50]  with default settings and the starting tree. The best method between the NNI (Nearest Neighbor Interchanges) and SPR (Subtree Pruning and Regrafting) tree improvement methods was used to create the starting tree. For illustrative purposes, MIF and species trees were colored in a similar way, based on broad phylogenetic taxa (plants, fungi, Alveolata, metazoan, etc.
Analyses on the basis of FEL, REL, SLAC, FUBAR, and MEME were conducted with the following settings: global dN/dS estimated with CI (likelihood profile based on 95% confidence interval), the significance level was set at 0. 1 and ambiguities were resolved. The substitution model selection was determined by the automatic substitution model selection tool included in the Datamonkey server. The trees required by FEL, REL, MEME, FUBAR, and Selecton software were reconstructed using the ML and BI methods. After verifying that the resulting trees were similar, we chose the ML trees for site selection analyses.
MIF Presence and Number Across Eukaryotic Kingdoms To link the abundance of MIF protein sequences with the taxonomy and ecology of species across eukaryotic kingdoms, we first investigated the presence of MIFs in species from protists, fungi, animals, and plants. In case of the former three taxonomic groups, we focused on phyla comprising both free-living species and parasites of plants and animals. In total, we analyzed 803 species for the presence and number of MIF sequences in relation to information on their taxonomy, their environment (fresh water, marine, or terrestrial) and their lifestyle (free-living heterotrophic or autotrophic species, plant parasite or animal parasite; see Materials and Methods). In addition, we considered the presence or absence of a sequenced genome and the number of available ESTs when no sequenced genome was available, thus gathering together data that provided a solid framework for our study (Supplementary Dataset 1).
Depending on the availability of a sequenced genome and on the quality of assembly and annotation, comprehensive identification of gene copy number was not always possible. We thus do not report here the number of MIF gene copies, but the number of distinct MIF protein sequences retrieved after extensive BLAST searches. The term 'MIF number' used throughout the manuscript therefore refers to the number of MIF proteins identified at the time of the study. Species possessing MIF protein sequences were detected in all main eukaryotic phyla with the exception of Rhizaria (Figure  1a ). However, the absence of MIF from the entire taxon is uncertain as only five species have a sequenced genome (Supplementary Dataset 1). To investigate a potential relation between the number of MIF sequences and taxonomic or ecological parameters, we removed from the analysis several basal eukaryotic taxonomic groups that were represented by only few species with available sequence data (i.
These are the Stramenopila, Alveolata, and, among metazoans, the nematodes and insects (Figure  1c ). Interestingly, while the global analysis, including the particular cases of fungi and Archaeplastida, suggested a reduced number of MIFs in plant-parasitic species (median value = 0, Supplementary Figure  S1c ), this phyla-specific study shows a higher number of MIF in plant parasites (Figure  1c ). In Stramenopila, insects, and nematodes, the number of MIFs detected in organisms having a plant-parasitic lifestyle has a median value of 3 and is significantly higher than in the animal parasite groups (P < 0. 01; Figure  1c ). Taken together, and on the basis of detection of MIF sequences in public databases, these findings suggest (i) that free-living autotrophic species have a median number of 3 MIFs, (ii) that, with the exception of fungi, plant parasites also have a median number of 3 MIFs, while (iii) both heterotrophic and animal-parasitic species have a lower and variable number of MIFs.
MIF Phylogenetic Reconstruction across Kingdoms MIF phylogenetic analyses were performed on a total of 720 high quality sequences presenting a full MIF domain and a minimum overlap of 85% with the rest of the aligned sequences. The details of specific sequences selected from the initial 803 species of plants, fungi, protists and animals for the phylogenetic analyses are provided in the Supplementary Dataset 1. Reconstructions were performed using the Bayesian Inference (BI) and the Maximum Likelihood (ML) methods. To avoid misinterpretation of poorly supported branches in the phylogenies, we used the Bayesian topology as a reference and reported the approximate likelihood-ratio test (aLRT) support values from the ML analysis together with the posterior probability (PP) values of the Bayesian analysis at corresponding branches.
Insect MIFs Insects show a diversity of MIF number, ranging from none to four. No MIF sequence were identified in the current databases for several clades such as Hymenoptera, Diptera, and Phthiraptera (Figure  5a ), suggesting multiple independent losses of MIF genes in the common ancestors of each of these orders. The phylogenetic reconstruction of MIFs from insects clearly differs from the species tree and does not support a monophyletic relationship between insect MIF sequences and the MIF or D-DT variant from chordates (Figure  5b , Supplementary Figure  S5b ). The Coleoptera, Blattodea, and Orthoptera genera possess a single MIF sequence, while Lepidoptera species possess two variants belonging to two distantly related monophyletic groups (Indicated as L1 and L2 in the Figure  5b ). As previously shown, aphids possess multiple MIF sequences  [18] , and three of the aphid MIFs are closely related among each other (A1, A2, and A3 in the Figure  5b ), while a fourth one (A4, Figure  5b ) is strikingly different  [18] , supporting the suggestion that after the first duplication event expected to occur prior to metazoan diversification (between 757 and 1.
Secondly, MIF number may also be underestimated in some taxa because the sequence databases (genomes, transcriptomes, etc. ) are incomplete and present varying degrees of depth, quality, and curation. For example, the apparent absence of MIF in Rhizaria is likely due to the lack of sequence data (only 5 genomes sequenced). In contrast, the low number of typical MIF sequences identified in fungi cannot be related to a poor database quality as the fungal kingdom is one of the most extensively studied in terms of high-quality genomes and transcriptomes. Similarly, important sequencing efforts have been made on plants and on parasitic species (whether animal or plant parasites) belonging to various taxa, because of their medical, veterinary or agricultural importance. It is therefore likely that underestimation of MIF number occurred mostly in free-living heterotrophic taxa such as echinoderms, annelids or myriapods that are not the focus of the present study.
Funding: This research was funded by the  ANR-DFG  collaborative project ' X-Kingdom-MIF -Cross-kingdom analysis of macrophage migration inhibitory factor (MIF)  functions' to C. C. and H. K. ( ANR-16-CE92-0014-01 ),  J. B. ( DFG BE 1977/10-1 ),  K. H. K. ( DFG Ko 1208/26-1 ) and  R. P. ( DFG PA 861/15-1 ), which is co-funded by the  French Agence Nationale de la Recherche (ANR)  and the  German Deutsche Forschungsgemeinschaft (DFG). The work was also supported by the  French National Research Agency  through the  'Investments for the Future' LABEX SIGNALIFE: program  reference #  ANR-11-LABX-0028-0. J. B. also was partly supported by  DFG  projects  SFB1123/A3  and SyNergy  EXC1010.
nonalcoholic fatty liver (nAfLD) may progress to nonalcoholic steatohepatitis (nASH) and ultimately to cirrhosis and hepatocellular carcinoma (Hcc). prognostic markers for these conditions are poorly defined. The aim of this study was to identify predictive gene markers for the transition from NAFL to nASH and then to poorer conditions. Gene expression omnibus datasets associated with a prediction analysis algorithm were used to create a matrix composed of control subject (n = 52), healthy obese (n = 51), obese with NAFL (n = 42) and NASH patients (n = 37) and 19,085 genes in order to identify specific genes predictive of the transition from steatosis to NASH and from NASH to cirrhosis and HCC and thus patients at high risk of complications. A validation cohort was used to validate these results.
Despite many years of research to identify non-invasive predictive markers for NASH using sophisticated algorithm approaches, none of the known markers is reliable enough to remove the need to perform liver biopsy due to the large spectrum of disease manifestations ranging from simple steatosis to HCC  8, 21, 24, 25, 27, 28, 30. Because gene expression is highly specific and very sensitive to environmental changes, exploring comprehensive gene expression levels in liver biopsies may help to identify markers of progression from steatosis to NASH. Thus, the aim of this study was to identify predictive gene markers for the transition from NAFLD to NASH and then to poorer outcomes using hepatic gene expression microarray datasets.
Methods Learning datasets and statistical analysis. GSE48452 and GSE61260 microarray datasets were selected and downloaded from the genome expression omnibus (GEO) database on NCBI website (http://www. ncbi. nlm. nih. gov/geo/) and were used as learning datasets. Briefly, gene expression levels were measured in human liver biopsies using the same high-density oligonucleotide microarray ([HuGene-1_1-st] Affymetrix ® Human Gene 1. 1 ST Array [transcript (gene) version]) as previously published. Each array included more than 750,000 unique 25-mer oligonucleotide probes interrogating more than 28,000 genes  33, 34. GSE48452 and GSE61260 respectively included a total of 73 and 109 human liver samples grouped into control samples (n = 14 and 38), healthy obese (HO) samples (n = 27 and 24), obese and NAFL samples (n = 14 and 23) and obese and NASH samples (n = 18 and 24).
Thus, a total of 52 control samples, 51 HO samples, 37 obese and NAFL samples and 42 obese and NASH samples could be analyzed when both GSE datasets were combined  33, 34. The characteristics of the patients who provided these samples are summarized in Table  1. Data were analyzed using R 3. 5. 2 software  35  following the study flowchart summarized in Fig. 1. First, each matrix of data was normalized with robust multi-array average (RMA). Briefly, RMA is an algorithm used to create an expression matrix from Affymetrix ® data. The raw intensity values are corrected for background, log2 Inflammation 0 [0-0] 0 [0-0] 0 [0-0] 2 [1-2] NAS 0 [0-0] 0 [0-0] 1. 5 [1-2] 5 [4-6] Fibrosis 0 [0-1] 0 [0-0] 0 [0-1] 1 [0-1] Leptin level (pg/ml serum) transformed and then quantile normalized. Then, a linear model is fit to the normalized data to obtain a measure of the gene expression for each probe pair on each array and then combined with structured query language (SQL) request  36.
Then, both batches were normalized using prediction analysis for microarrays (PAM) with "batchadjust" implemented in "PAMR" for R package at the "pamr. train" function  37, 38. Then the threshold was calculated to determine the minimum number of genes allowing distinguishing the four sample groups based on the calculation of the minimum misclassification error for each group leading to a table of true versus predicted values (pamr. confusion), from a nearest shrunken centroid fit (pamr. adaptthresh)  38. Thereafter, the genes of interest were sorted based on their best score (pamr. listgenes) and the gene(s) with the best score which survived the thresholding from the nearest shrunken centroid classifier were plotted (pamr. geneplot). The gene lists generated by the different PAM were compared using the "Venn" function in "gplots" for R package, indicating the number of overlapping transcripts in each sample group  39.
Validation datasets. Two independent human liver biopsy datasets were selected and downloaded (GSE63067 and GSE17470; http://www. ncbi. nlm. nih. gov/geo/) to validate the genes of interest identified using Figure  1. Study flowchart for the identification of 58 genes allowing predicting the subgroup of NAFL patients likely to progress to NASH. The first prediction analysis of microarrays (PAM) performed on the learning datasets (GSE48452 and GSE61260, blue) identified 1,549 genes allowing differentiating the four groups of patients (Controls, heathy obese patients, obese patients with NAFL and obese patients with NASH). The 1,549 genes were then validated using validation datasets (GSE63067 and GSE17470, yellow). Data were then analyzed (green). Nine genes were only specific to obese patients with NAFL and 141 genes were only specific to obese patients with NASH.
The flowchart results are shown in Fig. 2  to Fig. 6  and in Supplementary Fig. S1  to Supplementary Fig. S5. the learning datasets (GSE48452 and GSE61260) after the PAM. Here, gene expression levels were measured using HG-U133_Plus_2. 0 array (na22 platform, Affymetrix ® ) and CodeLink Human Whole Genome Bioarray (GE Healthcare/Amersham Biosciences), respectively  40, 41. The GSE63067 dataset included 7 control samples, 2 NAFL samples and 9 NASH samples  41. The GSE17470 dataset included 4 control samples and 7 NASH samples  40. The clinical characteristics of the patients who provided these samples were not available. To validate the genes selected in both validation datasets, two independent unsupervised approaches were used, the non-negative matrix factorization (NMF) and the principal component analysis (PCA).
The "NMF" (R-package) was used here as an unsupervised clustering method for samples using gene expression microarray data from the validation datasets  42. PCAs were performed using "FactoMineR" for R-package. Each sample and the ellipses centered onto the mean representing the 95% confidence interval (CI) were plotted on the PCA. The probabilities associated with the F-test of the variance analysis along the dimension axes (α = 0. 05) were calculated  43. Figure  2. Identification of 1,549 genes differentiating the four groups of patients from the learning datasets (GSE48452 and GSE61260). (A) The transcriptome of the four groups of patients from GSE48452 and GSE61260 datasets was analyzed using a prediction analysis of microarrays (PAM) algorithm and 1,549 genes were identified with a threshold of 0.
78, corresponding to an overall misclassification error rate of 0. 315 (upper panel) and the misclassification error rate for each group of patients is detailed (lower panel). (B) Principal component analysis of the four groups of patients differentiated using the 1,549 genes identified. The dots represent each patients from each GSE, the lines are the ellipses centered onto the mean (colored squares) representing the 95% confidence interval, and the p-value is the probability associated with the F-test of the analysis of variance along the axes of the first and second dimensions (α = 0. 05). Gene set enrichment analysis. The lists of genes identified at each step of the analysis of learning datasets were used as data entries for computing enrichment with existing lists created from prior knowledge organized into gene-set libraries.
We used Enrichr, a freely available integrative web-based and mobile software application (http://amp. pharm. mssm. edu/Enrichr/) that includes 17 gene-set libraries, an alternative approach to rank enriched terms, and various interactive visualization approaches to display enrichment results  44, 45. Among the 17 libraries, we searched WikiPathways 2016 and/or KEGG 2016 (Kyoto Encyclopedia Gene and Genome) libraries. The algorithm calculated the p-value, Z-score and combined score for each signaling pathway identified. Using methods based on the Z-score and combined score has been shown to be the best approach to recover a higher number of correct terms  45. Thus, only the signaling pathways with a significant p-value (p-value <0. 05) were ranked based on their combined score and represented as a bar graph and network.
Also, to pin-point certain genes associated with a signaling pathway, data were represented as clustergrams for better clarity. To identify specific molecular signatures, we also used MSigDB database software v5. 2 developed by the Broad Institute (http://software. broadinstitute. org/gsea/msigdb/)  46. KEGG and Reactome databases were used to identify the genes and signaling pathways significantly related to the NASH group from the learning datasets. Data were represented as enriched set plots.
A one-way ANOVA was used to analyze the distribution between the patient groups using "beeswarm" for R package and was represented by boxplots. Patient clinical data were analyzed to assess the predictive FABP4 and MMP9 mRNA expression levels. NAFL and NASH patients were divided into two subgroups: patients with low FABP4 and MMP9 mRNA levels (NAFL_ FABP4_MMP9_L and NASH_FABP4_MMP9_L) and patients with high FABP4 and/or MMP9 mRNA levels (NAFL_FABP4_MMP9_H and NASH_FABP4_MMP9_H). The clinical events, defined as a worsening of the liver disease, were recorded during the follow-up for each patient. In the Data were analyzed using a Fisher's exact test and represented as circular layouts ("circos plot") using "circlize" for R package. ethics approval and consent to participate. The institutional review board of the hospital (Paul Brousse hospital-Centre des Resources Biologiques Paris-SUD, Bio Banking Number: 0033-00089) approved the study and written informed consent was obtained from all patients.
Results clinical characteristics of samples from the learning dataset matrix. For further biostatistical analyzes (Fig. 1 ), we combined both GSEA (GSE48452 and GSE61260) that have been published previously  33, 34  to create a new learning dataset matrix. The clinical data from this learning dataset were based on the clinical data available in the GEO dataset website and included 52 control subjects, 51 HO patients, 37 obese patients with NAFL and 42 obese patients with NASH classified according to their NAFLD-activity score as previously described  20, 21. The clinical characteristics of the different groups of patients are summarized in Table  1. Briefly, control subjects were older than patients who underwent bariatric surgery in the other groups. Women were significantly more represented in the four groups (p-value <0.
0001, chi-squared test). Importantly, the number of women was higher in control subjects and HO patients than in obese patients with NAFL and NASH (75/28 versus 47/32, p-value = 0. 0581, chi-squared test), but there was no significant difference in gender distribution between obese patients with NAFL and NASH (21/16 versus 26/16; p-value = 0. 641, chi-squared test). As expected, the BMI was significantly higher in the three groups of obese patients compared to control subjects, with significantly higher serum leptin levels and lower serum adiponectin levels. However, no difference in age, BMI, serum leptin and adiponectin levels was observed between the three groups of obese patients. To confirm the statistical results from the new learning dataset matrix, we used validation datasets (GSE63067 and GSE17470).