dataesr/scientific-paragraphs-categorization huggingface dataset:
The clinical characteristics of the patients included in these validation datasets were not available. (A) Unsupervised analysis of obese patients with NAFL and NASH using a total of 150 genes: 9 genes were specific to NAFL patients and 141 genes were specific to NASH patients compared to the other groups of patients from GSE48452 and GSE61260 datasets. The principal component analysis identified 12 NAFL patients who were misclassified as NASH patients (referred to as NAFL-NASH patients; green dots; dark blue dots) and 25 NAFL patients who were not misclassified as NASH patients (referred to as NAFL-Steatosis patients; light blue dots). The dots represent each patient, 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 each axis.
Obese patients with NAFL, n = 37; obese patients with NASH, n = 41 according to the original publications. (B) The prediction analysis of microarrays identified 58 genes based on the threshold of 2. 6 calculated to achieve an optimal misclassification error rate for all groups of 0. 081 (upper panel) and for each group of 0. 0800 and 0. 083 (middle panel, dash lines). The table details the misclassified patients in each subgroup of patients analyzed. (C) Heatmap of obese patients with NAFL misclassified in two subgroups of patients based on the 58 genes identified in (B) and listed on the right. Identification of gene signatures and associated signaling pathways according to patient group in the learning and validation datasets. The new learning data matrix combining the four groups of human liver samples from the learning datasets (GSE48452 and GSE61260) was built based on the data from human liver biopsies using the same high-density oligonucleotide microarray version (see Methods)  33, 34.
The PCA of the new matrix showed a high variance between the two microarray results of both learning datasets (Supplementary Fig. S1A ). Then, the next step was to normalize both datasets to create the learning data matrix. A PAM was then applied to the new matrix using all control subjects from both datasets as "controls" to normalize the variables of the datasets (Fig. 1 ). We confirmed the homogeneity of the distribution of the patients from both datasets using all genes from the normalized new matrix by performing a PCA (Supplementary Fig. S1B ). Then, a PAM was performed on the standardized and corrected transcriptome arrays including the 182 samples divided into 4 groups. The PAM identified 1,549 genes with a threshold of 0. 78, corresponding to an overall low misclassification error rate of 31.
These results suggested that among the 1,549 genes identified, some genes could be only specific to a group and not to the other three groups, which could explain the good separation observed in the PCA (Figs. 1, 2B ). Finally, to confirm the signature composed of the 1,549 genes identified, two independent validation datasets (GSE63067 and GSE17470) were used (Fig. 1 ). A non-negative matrix factorization and a PCA showed that the 1,549 genes also allowed separating each group of patients included in the validation datasets (Supplementary Fig. S2 ). Thus, using a batch-adjust normalization approach associated with a gene prediction algorithm to combine two different Affymetrix ® microarray datasets, 1,549 genes were selected to distinguish healthy subjects, HO patients, obese patients with NAFL and obese patients with NASH.
This gene signature was also confirmed in two other independent GEO datasets. Interestingly, this specific set of genes allowed identifying a subgroup of 10 patients with NAFL among the 37 patients (27. 03%) who were classified as NASH patients. These results strongly suggested that these 10 patients with NAFL shared the same specific gene profile as NASH patients and that this specific gene profile could help to identify the process involved in the progression from steatosis to NASH. Identification of gene expression transitions associated with the inflammatory process, the apoptosis pathway and extracellular matrix remodeling. The PAM (Fig. 2A ) identified 186 genes that were only specific to control subjects, 12 genes that were only specific to HO patients, 9 genes that were only specific to obese patients with NAFL and 141 genes that were only specific to obese patients with NASH (Supplementary Dataset 1).
Then, "Enrichr" online software was used to identify the pathways associated with the genes identified (Supplementary Fig. 3 ). The 186 control-specific genes were involved in common signaling pathways such as translation driven by ribosomes, and functions such as estrogen signaling and circadian rhythm (Supplementary Fig. S3A ). Interestingly, HO patients also expressed genes (n = 12) involved in estrogen signaling, circadian rhythm (i. e. serotonin receptors), nuclear receptors and transcription factors, inflammatory process, and more importantly genes involved in the extracellular matrix remodeling (ECMR) (Supplementary Fig. S3B ). In both control subjects and HO patients, the identification of genes involved in the estrogen signaling pathway was in line with the over-represented female gender compared to NAFL and NASH patients (75 to 28 vs.
As shown, the confusion matrix identified 12 obese patients with NAFL classified as control subjects (n = 3) or HO patients (n = 9), and 10 obese patients with NAFL classified as patients with NASH (Table  2 ). Also, the gene enrichment pathway analysis showed that some pathways were common to the different NAFLD subtypes (Supplementary Fig. S3 ). These results suggested that a subgroup of NAFL patients (i. e. the 10 patients who were misclassified) had already a gene signature similar to that of NASH patients. To test this assumption, the genes specific to patients with steatosis (n = 9) and NASH (n = 141) were combined to create a new matrix with 150 genes and two groups of patients from the learning datasets (37 + 42 = 79 patients in total, Fig. 1 ). An unsupervised cluster analysis of this new matrix was then performed and showed that among the 37 obese patients with NAFL, 12 patients were misclassified as obese patients with NASH (Figs.
3A, S4 ). Then, obese patients with NAFL were split into two groups: patients who were misclassified as obese patients with NASH (NAFL mixed with NASH or NAFL-NASH patients, n = 12) and the remaining NAFL patients (NAFL-Steatosis patients, n = 25). To define a specific gene signature in order to distinguish these two subgroups and to avoid any bias, a PAM was performed using all the genes of the microarrays (n = 19,085). The results showed that the best threshold was 2. 6 to achieve a minimum of overall misclassification error rate (0. 081) and this threshold allowed identifying 58 genes (Fig. 3B ; Supplementary Dataset 2), associated with good results for the misclassification rate as shown by the confusion matrix (Fig. 3B ). The unsupervised cluster analysis of the 58 selected genes showed that NAFL livers with a NASH signature (NAFL-NASH) and NAFL (NAFL-Steatosis) patients were clearly distinguishable (Fig.
3C ). Among these 58 genes, 54 were upregulated and 4 were downregulated (OAT, GNMT, AASS and CYP2C19) in the NAFL-NASH subgroup (Fig. 3C ; Supplementary Dataset 2). The 4 genes down-regulated in the NAFL-NASH subgroup were mainly involved in metabolic pathways such as amino acid metabolism which can lead to the synthesis of pyruvate and subsequently acetyl CoA, the precursors of linoleic acid and arachidonic acid synthesis (Supplementary Fig. S5A ). This finding was in agreement with what is expected in the fatty liver tissue, where the lipid metabolism is dysregulated  48, 49. The 54 genes distinguishing obese patients with steatosis who were misclassified as NASH patients (NAFL-NASH subgroup) were involved in ECMR, DNA regulation (DNA repair, replication, G1/S cell cycle), inflammatory processes and some were involved in insulin resistance and lipid metabolism (Supplementary Fig.
S5B ). Venn diagrams were used to compare the 58 genes that allowed distinguishing the NAFL-NASH and NAFL-steatosis subgroups to the 141 genes specific to obese patients with NASH. Only 14 genes were common to both gene sets (Fig. 4A ). The enrichment analysis of these 14 genes showed that they were involved in ECMR, inflammation and oncogenic pathways as expected (Supplementary Fig. S6A, B ). Among the 141 genes identified in obese patients with NASH, the top-ranked gene was FABP4 (+1. 082-fold in the NASH group, Supplementary Dataset 1). FABP4 was also the top-ranked gene among the 58 genes that allowed distinguishing NAFL-NASH patients from NAFL-Steatosis patients according to the PAM results (+1. 79-fold, Supplementary Dataset 2, Fig. 2C ). Furthermore, FABP4 was strongly associated with 4 genes involved in ECMR and oncogenic pathways (BRCA2, COL4A1, ITGA6 and MMP9, Fig.
Identification of a subgroup of NASH patients with a more aggressive gene profile according to MMP9 mRNA levels. From the learning datasets, 13 other genes were associated with high FABP4 expression levels in NAFL-NASH and NASH patients (Fig. 4A ). Among them, four were involved in ECMR: BRCA2, COL4A1, ITGA6 and MMP9. MMP9, a gene encoding for a matrix metalloproteinase, showed the highest scores (fold of +0. 8237, Fig. 4A  and +1. 420, Fig. 5A ). Also, the 14 genes common to NAFL-NASH and NASH patients were involved in PI3K/AKT/mTOR, inflammatory, ECMR and oncogenic pathways (Supplementary Fig. S6 ) suggesting a progression from NAFL to NASH. Thus, high MMP9 expression levels could be associated with the progression from steatosis to NASH and possibly with the progression from NASH to HCC or cirrhosis (Figs.
5A  and S6 ). According to epidemiological studies, at least one third of NASH patients would progress to cirrhosis and HCC  1, [3] [4] [5] 41  , that is why we investigated MPP9 mRNA expression level in the four group of patients from the learning datasets (GSE48452 and GSE61260). As expected, MMP9 mRNA expression levels allowed distinguishing NASH patients from control subjects, and HO patients and NAFL patients (p-value <0. 05, ANOVA, Fig. 5B ). Furthermore, the boxplot analysis of the 4 groups of patients showed that at least 90% of control subjects, HO patients and NAFL patients expressed low MMP9 mRNA expression levels, when a threshold corresponding to the third quartile of control subjects was selected. According to this threshold, this analysis showed that MMP9 mRNA expression levels allowed distinguishing two subgroups of NASH patients, one with high MMP9 level (23 patients) and another with low MMP9 level (19 patients, Fig.
5B ). Identification of a subgroup of NASH patients with a more aggressive gene profile according to MMP9 mRNA levels. Then, we focused on the two subgroups of NASH patients distinguished by their MMP9 mRNA expression level (Fig. 5B ). The analysis of the clinical and biological outcomes of the subgroups of NASH patients from the learning datasets showed that the subgroup with high MMP9 mRNA level had higher hepatic fat content, inflammation, BMI, NAS, leptin and adiponectin levels and this subgroup mainly included female patients (Fig. 5C ). Then, the PAM of both subgroups and the whole transcriptome (n = 19,085 genes) identified 330 predictive genes (229 were upregulated and 101 were downregulated) for NASH patients with high MMP9 level (Fig. 5D , Supplementary Dataset 3). Using GO-Elite software associated with the Pathway Commons database to analyze the matrix of 330 genes, we showed that the 101 genes down-regulated in NASH patients with high MMP9 level were related to pathways involved in G2/M DNA damage and cell cycle checkpoints, whereas the 229 genes upregulated in these patients were involved in inflammatory processes including the T-cell receptor signaling (TCR) pathway, MAPK, JNK, p38 activation and leading to CD4+ and CD8+ T cell activation and interleukin (IL)2, IL6, and IL12 secretion, the nuclear factor activated T-cells (NAFT) calcium-calcineurin pathway and hypoxia pathways and genes related to the AP1 complex (e.
g. FRA1 and FRA2 family members). These results showed that, in the one hand, cell signaling pathways, and especially the G2 DNA damage checkpoint, were inhibited, allowing the resumption of cell cycling and entry into mitosis  50. On the other hand, inflammatory processes were activated with T-cell and interleukin activation (Fig. 5E , Supplementary Dataset 3). Also, the gene set enrichment analysis using the KEGG database in NASH patients with high MMP9 expression levels significantly identified ECMR pathways (NES = +2. 14, p-value <0. 0001; Fig. 5F , Supplementary Dataset 4) as shown by the unsupervised cluster analysis and represented by the associated heatmap. These ECMR pathways were associated with a significantly increased expression of genes involved in the CXR/CCL chemokine pathway (Fig.
Data are presented as enrichment plots using Kyoto encyclopedia of genes and genomes (KEGG) and REACTOME databases in the left panels and as heatmaps with details of genes in the right panels. NAFL patients and two subgroups of NASH patients could be distinguished based on their median MMP9 mRNA expression levels (9. 97-fold and 16. 25-fold mRNA expression level, respectively). High FABP4 and MMP9 mRNA expression levels are associated with a poor prognosis in NAFLD or NASH patients. The analysis of genes identified in NAFL-Steatosis patients and NAFL-NASH patients (n = 58 genes, see Fig. 3C ) and in NASH patients (n = 141 genes) with high MMP9 level showed that 4 genes, FABP4, MMP9, HELLS and TREM2, were shared between these three groups of patients (Fig. 4A ). HELLS and TREM2 are involved in global immune responses.
1 ) using a ROC curve analysis. The individual expression levels of FABP4 and MMP9 did not allow clearly distinguishing the three groups of patients, in particular NAFL patients from NASH patients (Supplementary Fig. S7 ). However, when both markers were combined, the unsupervised PCA and ROC curves showed a distinction between Control subjects, NAFL patients and NASH patients (Fig. 6A, B ). The boxplots analyzes of FABP4 and MMP9 expression levels from the GSE datasets or the RT-Q-PCR analyzes showed that some NAFL and NASH patients had high or low FABP4 and MMP9 mRNA expression levels (Figs. 4B, 5B, H ) and a misclassification of patients from the Validation cohort was observed in each group of NAFL and NASH patients as shown by the CART (characteristic and regression tree) analysis (Supplementary Fig.
Discussion Using publicly available gene expression data from liver biopsies from NAFLD patients, we identified gene markers for the progression from NAFL to NASH and from NASH to cirrhosis/HCC. For a few years, the use of previously published gene expression omnibus datasets has been an approach to identify more reliable diagnostic and/or prognostic markers in cancer  [54] [55] [56]. Therefore, our approach avoided conducting a new clinical trial, and it allowed significantly increasing the number of patients per group while having well-defined groups of patients. Indeed, our cohort is one of the largest cohorts of well-defined NAFLD patients investigated so far. Furthermore, we confirmed our data in a smaller cohort of patients, which could be considered a more ethical approach. Using an unbiased machine-learning approach of prediction analysis of microarray data, we combined two independent GEO datasets from two independent cohorts of patients, GSE48452 and GSE61260  33, 34  , to create the largest matrix of NAFLD patients associated with gene expression microarray data.
Then, a PAMR batch-adjust algorithm was used for both GSE to avoid introducing multiplicative and systematic biases at each step of the microarray experiments and between two or more independent experiments that were performed on the same microarray platform, which led to new biological findings with increased statistical power  57. This way, we identified 1,549 genes allowing differentiating the four groups of patients and we confirmed these data in two independent human GEO (GSE63067 and GSE17470)  40, 41. Interestingly, the data showed that 27. 03% of NAFL patients (10 out of 37 patients) were re-classified in the group of HO patients. As expected, this result confirmed what was already known: the histological scoring system is limited to classify and predict the outcome of NAFL patients.
conclusion In conclusion, using publicly available GEO datasets and an original machine-learning analysis, we identified gene signatures that could help to determine the outcome of patients with steatosis likely to progress to NASH, and the outcome of patients with NASH likely to progress to cirrhosis and/or HCC. Liver biopsies cannot be avoided, however, the use of predictive markers could strongly reduce the number of biopsies during patient follow-up and enable a better management of the patients. Thus, we identified a predictive gene signature in human liver that could be used in patient clinical follow-up, as well as in clinical trials focused on the development of drugs to treat NASH patients, including two main genes, FABP4 and MMP9, the proteins of which could be quantified in the serum and used as non-invasive prognostic markers for NAFL and NASH progression.
33,34cteristics of patients from the learning dataset (GSE48452 and GSE61260). Data are expressed as a mean ± SEM or a median [interquartile]. Data were collected from previously published GSE48542 and GSE61260 datasets33,34. **p-value <0. 01 and ***p-value <0. 001 versus Controls (unpaired t-test). Gender distribution across the 4 groups: p-value <0. 0001 (Chi-squared test). F: female; M: male; NAFL, nonalcoholic fatty liver; NAS: NAFLD activity score; NASH, nonalcoholic steatohepatitis.
Acknowledgements We would like to thanks " La Fondation pour la Recherche Médicale " and " DHU Hepatinov " for supporting this work and thanks  Dr Preeti Ramadoss  for her reading, correction and critical input for this manuscript. This work was supported by core funding of  Inserm  and  Université Paris-Sud. FC was recipient of a grant support from " La Fondation pour la Recherche Médicale " (SPF20111223621) and of the award from " DHU Hepatinov ".
Data availability GSE48452, GSE61260, GSE63067 and GSE17470 have been selected from genome expression omnibus (GEO) database repository on NCBI website (http://www. ncbi. nlm. nih. gov/geo/) and already published  33, 34, 40, 41. All data generated or analyzed during this study are included in this published article and its Supplementary Information Files including the Excel tables from the biostatistical analysis.
Lamont TCCON XCO 2 The Total Carbon Column Observing Network (TCCON; Wunch et al. (  2010 )) provides measurements of XCO 2 from the earth's surface at a global network of sites. Two TCCON sites within our North American domain were operating in 2010. The Park Falls site has significant drop-outs, especially in summer, so we choose the Lamont TCCON site (36. 6 N, 97. 49 W) for comparison  (Wennberg et al. , 2017). We average GOSAT soundings in a box of 6 latitude and 12 longitude centered at the Lamont site and match them to the TCCON data averaged for the hour of the GOSAT overpass of this regional box. We also computed hourly-averaged simulated XCO 2 from the models for these GOSAT soundings to compare to both GOSAT and TCCON XCO 2. During our period of comparison, summer of 2010, this region covers a large gradient of surface CO 2 fluxes.
Despite the presence of outliers in the early summer , daily average GOSAT and TCCON XCO 2 soundings agree within 1-2 ppm, with discrepancies likely due to sampling and representation errors. The mean residuals or ACOS GOSAT, CMS-Flux, and WRF XCO 2 relative to TCCON for this period are 0. 088 ± 1. 856 ppm, 0. 868 ± 1. 042 ppm, and 0. 965 ± 0. 981 ppm, respectively (Figure  7b ). The residuals from the model simulations are generally, but not exclusively, positive and are more similar to each other than to either of the observations. This implies that model transport differences are small compared to other model-data differences at this time and location. The synoptic-scale variability in the summer atmospheric CO 2 mole fractions is well represented during short-term events (e. g. , near DOY 218).
Horizontal Mean Wind We selected a group of rawinsonde sites (Table  4 ; Figure  8 ) including west coast, east coast, Gulf Coast, and mid-continent North American sites for comparison of model and observed wind speed and wind direction at mandatory rawinsonde reporting levels. Our intent is to identify any regional distinctions in biases or variability that might help to explain the CO 2 model-data differences. Each of these sites had more than 11 months of data for 00 UTC soundings in 2010 at the mandatory reporting levels of 925 hPa, 850 hPa, 500 hPa and 250 hPa which we used in our analysis. Continental mountain and high plains sites were not included because they lack 925 hPa data, and cannot easily be compared with mandatory levels at lower elevation sites. Model locations were assigned as the nearest grid cell with similar surface height.
Conclusions We have established a framework for effectively nesting a mesoscale model within a global model achieving approximate mass conservation of the trace gas CO 2. The CMS-Flux and WRF simulated column-averaged GOSAT XCO 2 samples are comparable within a few parts per million with some spatial differences, across all seasons and all geographic locations in the WRF North American domain. The models used here have very different horizontal and vertical resolutions and computational grids. Scaling the surface fluxes appropriately could be done with a readily-available mass-conserving regridding utility rather than using the custom-computed scaling factors used in this experiment. The code for the provision of the boundary conditions from the global model to the mesoscale model is available from https://github.
ParameciumDB (https://paramecium. i2bc. parissaclay. fr) is a community model organism database for the genome and genetics of the ciliate Paramecium. ParameciumDB development relies on the GMOD (www. gmod. org) toolkit. The ParameciumDB web site has been publicly available since 2006 when the P. tetraurelia somatic genome sequence was released, revealing that a series of whole genome duplications punctuated the evolutionary history of the species. The genome is linked to available genetic data and stocks. ParameciumDB has undergone major changes in its content and website since the last update published in 2011. Genomes from multiple Paramecium species, especially from the P. aurelia complex, are now included in Parame-ciumDB. A new modern web interface accompanies this transition to a database for the whole Paramecium genus.
INTRODUCTION ParameciumDB was launched in 2006 as a community model organism database, to accompany publication of the Paramecium tetraurelia somatic genome sequence that revealed a series of whole genome duplications had occurred in the lineage  (1). Built using the Generic Model Organism Database toolkit (GMOD, http://gmod. org/), Parame-ciumDB linked the genome to genetic data  (2). Since then, an increasing number of genomic datasets and new tools such as BioMart (3) have been integrated  (4). The primary mission of ParameciumDB was to support Paramecium research for the community attracted to this unicellular model characterized by good classic and re-verse genetic approaches and large cell size (∼120 M). These properties facilitate study of the complex biological processes, conserved in multicellular organisms, found in these ciliates.