dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGMENTS The research leading to these results of this article have received funding from the  European Union Seventh Framework Programme  ( FP72007-2013 )  ASSET  project under grant agreement number  FP7-HEALTH-2010-259348. The work was supported by  ITMO Cancer SysBio program  ( MOSAIC  project) and by  Institut National de la Santé et de la Recherche Médicale  ( U900  and  U830  budget). This work has received support under the program " Investissements d'Avenir " launched by the  French Government  and implemented by  ANR  with the references  ANR-11-BINF-0001  (Project  ABS4NGS ).
| Optical coherence tomography Spectral domain OCT examination was performed for both eyes of each participant by trained operators using Heidelberg-Spectralis ® SD-OCT (Heidelberg Engineering, Heidelberg, Germany, Heidelberg Eye Explorer Software version 1. 7. 1. 0). For peripapillary measurements, a 12° circle scan (3. 4 mm) around the optic disk was acquired using the built-in standard protocol ("RNFL-N"). For assessment of macular volume and individual retinal layers (thickness), a 30° × 25° OCT volume scan protocol (61 B-scans; ART = 13 frames) centered on the fovea was used. An independent rater evaluated the quality of the OCT scans according to the OSCAR-IB Consensus Criteria for Retinal OCT Quality Assessment  (Tewarie et al. , 2012). Scans with poor quality were excluded from subsequent analysis as well as OCT examinations with incomplete or incorrect scan protocol.
For case of patients with one excluded eye-scan, the other eye-scan was withdrawn from analysis as well to avoid asymmetry (when comparing both scans). An integrated automated segmentation algorithm (Spectralis Viewing Module version 5. 4. 7. 0; Heidelberg Engineering GmbH) was applied and afterward manually corrected for the presence of boundary detection errors by a single observer for all scans. The main OCT outcomes of interest were macular RNFL and GCIPL thickness within cylinders of 3 mm centered on the macula. However, to assure that these measurements are meaningful OCT measurements for the purpose of this study, we computed as well other OCT measurements (See supplemental material for details). This was to exclude that other OCT measurements might have a closer association with gray matter volume in our cohort.
| MRI All MRI data were acquired on a 3T MRI scanner (Skyra, Siemens Medical Systems, Erlangen, Germany). The MRI protocol included a magnetization prepared rapid acquisition gradient-echo (MPRAGE) T1-weighted sequence (TR/TE = 1900 ms/2. 46 ms; TI = 900 ms; 192 slices, slice thickness = 0. 9 mm, no gap; FOV = 240 mm; matrix = 256 × 256, flip-angle 9° for all scans) and T2 sequence (TR/ TE = 2800 ms/90 ms; 43 slices, slice thickness = 3. 0 mm, no gap, matrix = 256 × 256, FOV = 240 mm).
, 2006)  as well as by seven main functional systems defined by the Yeo 2011 atlas  (Yeo & Krienen, 2011). In addition, we used FreeSurfer software (Version 5. 2. 0) for cortical reconstruction and volumetric segmentation (http://surfer. nmr. mgh. harvard. edu/). Again, all brain masks and white /gray matter segmentation were inspected and manually corrected according to the recommendation from the FreeSurfer documentation. We extracted total gray and white matter volumes as well as volumes of cortical areas of the Destrieux parcellation  (Fischl, 2004). Corresponding regions of different atlases were defined by major overlap in MNI152 standard space that is, regions might correspond to more than one region in another atlas.
| Cohort A total of 54 RRMS patients (Table  1 ) with a complete brain MRI and retinal OCT data set were included in the analysis. Four untreated and three treated patients were excluded from analysis due to incomplete OCT data. Scans from seven patients showed poor quality in one or both eyes. All MRI data could be analyzed. Four patients had bilateral ON in the past. Single eye ON was documented in 11 patients for the right eye and in four patients for the left eye. The subset of patients with a neuropsychological assessment (n = 14) included four patients with a history of ON. In patients without a history of ON (n = 32), the correlation between VBM results from both left and right eyes was high (r =. 85, p <. 001). If all patients were included, the correlation remained moderate (r =. 56, p <.
Results from VBM analysis in pale red (p =. 04) to yellow (p <. 001) and significantly associated cortical regions (FreeSurfer parcellation) from GEE models. Displayed on MNI152 standard space the primary visual system are especially sensitive to these mechanisms  (He et al. , 2009). Our VBM findings could be confirmed with a second analyses method The FSL VBM toolbox uses voxelwise statistics, which might have been compromised by multiple testing even though the family-wise error rate is controlled and guarantees 95% confidence of no false positive findings  (Smith et al. , 2004). Moreover, the method is considered reliable and has previously been applied to investigate the association between OCT measurements and selected cortical regions in MS  (Gabilondo et al. , 2014). However, we aimed to add further evidence using the FreeSurfer software suite to investigate correlations between OCT and the key cortical regions identified by VBM.
We performed a meta-analysis of several public datasets of human and mouse mononuclear phagocyte subsets isolated from blood, spleen, skin or cutaneous lymph nodes, including by using a novel and user friendly software, BubbleGUM, which generates and integrates gene signatures for high throughput gene set enrichment analysis. This analysis demonstrates the equivalence between human and mouse skin XCR1 + DCs, and between mouse and human Langerhans cells.
Three research groups described CD141 +/high DCs in the human dermis using variable flow cytometry gating strategies which resulted in three distinct populations with different transcriptome profiles and functions  (Artyomov et al. , 2015; Chu et al. , 2012; Haniffa et al. , 2012). These contrasting findings have created confusion in the field on the identification of human dermal CD141 +/high DCs, their functions and their homology to mouse MP subsets  (Artyomov et al. , 2015; Chu et al. , 2012; Haniffa et al. , 2012). In this report, we demonstrate the utility of the novel BubbleGUM software  (Spinelli et al. , 2015) , with high throughput and automated Gene Set Enrichment Analysis (GSEA) capabilities, to integrate microarray transcriptome datasets by generating gene signatures for distinct MP populations from different tissues and species for cross-comparison and identification of homologies.
Microarray expression data All the microarray data used in the study were obtained from GEO database. Two datasets were used for mouse cell types and three datasets were used for human cell types (Fig. 1 ), to match as well as possible between tissues and species the diversity of the cell types studied. Within each species, we ensured having at least one cell type in common between the different datasets used, to enable crossnormalization and dataset effect correction. Moreover, we defined cell type-specific gene signatures in a consistent way, by comparing the cell type of interest to a set of reference cell populations that was equivalent across tissues and species. The mouse dataset "a" was generated by the Immgen consortium (GEO series identification number GSE15907). The mouse dataset "b" was generated by the Malissen laboratory (GSE49358, GSE65309 and GSE74276).
Human datasets "A" and "C" were generated by the Ginhoux laboratory from blood and skin MPs (GSE35457 and GSE60317, respectively). Dataset "B" was generated by the Klechevsky laboratory from skin MPs (GSE66355). All of these data have been analyzed in previous publications, GSE15907  (Elpek et al. , 2011; Gautier et al. , 2012; Heng et al. , 2008; Miller et al. , 2012) , GSE49358  (Tamoutounour et al. , 2013) , GSE65309  (Terhorst et al. , 2015) , GSE35457  (Haniffa et al. , 2012 ), GSE60317 (McGovern et al. , 2014) , GSE66355  (Artyomov et al. , 2015) , except for a subgroup of mouse dataset b (GSE74276). The list of microarray samples used, their GEO identification numbers and associated metadata are provided in supplementary file 1. The expression matrices for the different analyses performed in this study are available in GEO under the accession number GSE74316.
Generation of the microarray compendia and their analyses by PCA or hierarchical clustering The methodological procedure for the preprocessing of the different microarray datasets used in the study for meta-analysis is described in Fig. 1. The mouse Gene 1. 0 ST CEL files were processed through Bioconductor in the R statistical environment (version 3. 0. 2). Quality control of the array hybridization (NUSE plot) and normalization of the raw Affymetrix expression data with Robust Multi-chip Analysis  (Irizarry et al. , 2003)  were performed using the oligo package. PCA was performed using ade4 package to remove the dataset effect visible on the first principal component (Fig. S1A-B ). The two mouse datasets included in common epidermal LCs, blood classical monocytes (cMo), cutaneous lymph node (CLN) CD11b + migratory DCs (migDCs) and CLN plasmacytoid DCs (pDCs).
For each of these control cell populations, the samples from the two datasets regrouped well together in the PCA or hierarchical clustering analyses after merging and correction of the dataset effect. Similar expression patterns were observed within each mouse dataset before and after data merging, quantile crossnormalization and dataset effect removal, when examining 52 control genes encoding key lineage-specific transcription factors, innate immune recognition receptors or signaling molecules (Fig. S1C-D ). This validated the adequacy of data preprocessing to prevent artefactual changes in the relative gene expression between cell populations or datasets. The Illumina Human WG-6 v3 and Illumina Human HT12 v4. 0 raw data files were processed through Bioconductor in the R statistical environment (version 3.
0. 2). Gene expression signals from GSE60317  (Haniffa et al. , 2012) and GSE35457 (McGovern et al. , 2014)  were merged using common probes. Quantile Normalization  (Bolstad et al. , 2004)  was applied on the merged expression arrays using the lumi package, prior to log 2 -transformation of expression values. Gene expression signals from GSE66355 were already background corrected and quantile normalized  (Artyomov et al. , 2015). Noise threshold was estimated at five based on the density of all gene expression signals. All values less than five were replaced by this noise threshold. Expression values were log 2 -transformed to enable comparison with the other two human microarray datasets. The three datasets were merged using common probes and quantile normalized. PCA was performed to remove the dataset effect visible on the first two principal components  (Figs.
S2, S3 and S4). CD14 + dermal MPs (CD14 + _DMPs) was a common population encompassed in all three datasets. In addition, both datasets B and C contained epidermal LCs. LC samples regrouped together in PCA or hierarchical clustering after the datasets were merged and corrected. This was also the case for CD14 + _DMPs from datasets B and C but the CD14 + _DMPs from dataset A was more distant. However, when examining individual gene contributions to PCA axes on the merged datasets before correction (Fig. S2B-D ), the vast majority of transcripts accounting for the differences between datasets according to PC1 and PC2 did not contribute significantly to differences between MP subsets according to PC3 and PC4. Many genes known as selectively expressed by, or affecting the biology of, MP subsets were strong contributors to PC4 but had only a weak contribution to PC1 (Fig.
S2D ). In contrast, the vast majority of the transcripts strongly contributing to PC1 were not known to affect the identity or biology of MP subsets, and were not differentially expressed between subsets of DCs or monocytes/macrophages within each dataset (Fig. S3 ). Careful scrutiny of the expression patterns of 68 genes encoding key lineage-specific transcription factors, innate immune recognition receptors or signaling molecules within each human dataset before and after data merging, quantile cross-normalization, and dataset effect correction revealed no obvious biases or artifacts from data pre-processing (Fig. S4 ). In particular, CD14 + _DMPs in all three human datasets expressed high levels of monocyte/macrophage genes and low levels of genes specific to other cell lineages, before and after data processing.
Hence, data preprocessing was adequate and did not cause artefactual changes in the relative gene expression profiling between cell populations or datasets which could confound the subsequent analyses and their interpretation. Human and Mouse datasets were then merged into a single matrix of orthologous genes identified using the Ensembl BioMart software with selection of "one-to-one" orthology relationships only (13,371 unique genes). Agglomerative hierarchical clustering (using Hmisc and cluster packages) and PCA (using ade4 R package) were performed on the merged human and mouse matrix. We used distinct distance metric/linkage parameters to generate several agglomerative hierarchical clustering trees, because this approach can reveal distinct similarity patterns in the data.
Generation of gene signatures and high throughput GSEA using BubbleGUM The BubbleGUM software  (Spinelli et al. , 2015)  was used to generate MP subset-specific transcriptomic signatures from specific datasets and to assess their enrichment across cell types from other datasets. BubbleGUM is an open-source software composed of two modules; i) GeneSign, which generates statistically significant gene signatures and ii) BubbleMap, which automatically assesses the enrichment of input gene signatures between all possible pairs of conditions from independent datasets, based on gene set enrichment analysis (GSEA) methodology  (Subramanian et al. , 2005 (Subramanian et al. , , 2007)) , and which generates an integrated graphical display. Using GeneSign, gene signatures of MP subsets were generated for each species (human and mouse), i.
Heatmaps Heatmaps were performed using Gene-E (http://www. broadinstitute. org/cancer/software/GENE-E/), using the final gene expression values for the human and mouse datasets after merging the various datasets into a single matrix, with removal of the first (or first two) principal component(s) of PCA which generated negative expression values. The expression values were not centered and reduced, in order to avoid misrepresenting the relative importance of individual genes by changing their variance. This allowed retention of genes with high variance in only one species but not the other. For the same rationale, global scale expression values were used for graphical output, rather than relative scales adjusted for each gene to its minimal and maximal expression values across the entire dataset.
3A ). In the hierarchical clustering analyses where mouse and human LCs did not regroup together, they were still associated with subsets of cDCs and not with monocytes or monocyte-derived cells (Fig. 3B ). High throughput GSEA performed using our BubbleGUM software  (Spinelli et al. , 2015)  showed that, when compared to each of the populations of monocytes or monocyte-derived cells, mouse and human LCs were enriched for the genes more highly expressed in DCs compared to monocytes/macrophages (DC_vs_Mo/Mac gene signatures) (Fig. 4 , ❶, ❺, blue boxes). Conversely, the reciprocal Mo/Mac_vs_DC gene signatures were enriched in each of the populations of mouse or human monocytes or monocyte-derived cells analyzed when they were compared to LCs (Fig. 4 , ❶, ❺, red boxes). Expression profiles of genes contributing to these GSEA patterns are depicted as heatmaps of genes with higher expression in cDCs and LCs (Fig.
The CD141 + DCs isolated by Chu et al. expressed the monocyte marker CD14 and corresponded to CD14 + dermal monocyte-derived macrophages  (McGovern et al. , 2014). The identity of the CD141 + dermal cells studied by Artyomov et al. was less obvious, although they were CD14 -and CD1a low and stained with an anti-XCR1 antibody. Interestingly, the three isolation strategies resulted in dermal CD141 +/high DCs with contrasting transcriptome profiles. In order to further assess the identity and lineage relationships of the two reported dermal CD141 high/+ DCs, we reanalyzed the human DC, monocyte and macrophage microarray compendia associated with these studies  (Artyomov et al. , 2015; Haniffa et al. , 2012; McGovern et al. , 2014). Of note, all three datasets included CD14 + _DMPs, and LCs were shared between the Artyomov and McGovern datasets, allowing to control dataset compatibility as discussed in Section 2.
The SK_CD14 + _DMPs shared similar transcriptomic characteristics even though differences linked to datasets of origin existed. In PCA and hierarchical clustering, human epidermal LCs (SK_LCs) regrouped together irrespective of their dataset of origin (Figs. 2  and 3 ). In contrast, strikingly, the two sets of skin CD141 +/high _DDCs from  Haniffa et al. , and Artyomov et al. , clustered  in very different areas by PCA (Fig. 2A-B ) and hierarchical clustering (Fig. 3A-B ). By PCA, SK_CD141 high _DDCs_A from the pioneering publication which reported their discovery  (Haniffa et al. , 2012)  were close to SK_CD1c + _DDCs and distant from SK_CD14 + _DMPs, while the converse was observed for the SK_CD141 + _DDCs_B described in the publication which reported lack of close homology of these cells with human BD_CD141 high _DCs and mouse XCR1 + DCs  (Artyomov et al.
For each known pair of homologous mouse and human MP subset, a very strong, specific and significant enrichment of the corresponding gene signatures from one species was also observed in the other species, providing excellent positive and negative controls for the BubbleGUM analysis. These included the enrichment of the following gene signatures: human BD_CD141 high _DC in mouse CLN_XCR1 + _LT_DC (Fig. 4 , ❷), human SK_CD14 + _DMP and SK_Mac in mouse SK_Mac (Fig. 4 ,    4. Analysis of the homologies between human and mouse MP subsets by high throughput GSEA using BubbleGUM. Gene signatures specific to each subset of MPs, or their subgroups, were generated using GeneSign separately for the mouse and human compendia. These signatures obtained in one species were then assessed for enrichment in all possible pairwise comparisons between MP subsets of the other species using the BubbleMap module of BubbleGUM.
Data are represented as Bubbles, bigger and darker for stronger and more significant enrichment, in a color matching that of the condition in which the GeneSet was enriched (blue for the population indicated in blue characters on the annotation on the left of each figure, red for the populations to which the comparison is performed). The strength of the enrichment is quantified by the NES which represents the number and differential expression intensity of the genes enriched. The significance of the enrichment is measured by the false discovery rate (FDR) value (q) representing the likelihood that the enrichment of the GeneSet was a false-positive finding (e. g. , if q = 0. 25, a similar enrichment is found in 25% of the random GeneSets used as controls). This q-value was further corrected for multiple testing, leading to a higher stringency of the significance threshold used.
6 ). This confirmed our prediction that only SK_CD141 high _DDCs_A but not SK_CD141 + _-DDCs_B were the tissue equivalent of blood CD141 high _DCs (Fig. 6A , ❸, green box). SK_CD141 high _DDCs_A and SK_CD141 + _DDCs_B were enriched with gene signatures from distinct blood MP populations (orange box, Fig. 6A ) providing further support of their distinct identities. The human BD_CD141 high _DC gene signature was significantly enriched in the human SK_CD141 high _DDC_A when compared to each of the other human skin MP subsets examined, including SK_LCs and SK_CD141 + _DDC_B (Fig. 6A ; ❸, green box), as previously reported using the CMAP method  (Haniffa et al. , 2012). In contrast, none of the human blood MP signature was consistently enriched in SK_CD141 + _DDC_B when compared to all other human skin MP subsets (Fig.
Discussion Recent reports characterized three different cell populations identified as dermal 'CD141 + DCs' with overlapping phenotypes but each with unique transcriptome profiles, functions, and lineage relationships to other tissue DCs in humans and mice  (Artyomov et al. , 2015; Chu et al. , 2012; Haniffa et al. , 2012). This discrepency in the literature has caused confusion in the field regarding how best to identify these cells and define their precise functions. In this study, we aimed to clarify these conflicting reports and to define murine and human skin MP subsets, their intra-species tissue equivalents and inter-species homologs, using comparative genomics. By exploiting public datasets for MP subsets from blood, spleen, skin or cutaneous lymph node of humans and mice, we rigorously identified DC subsets, monocytes and macrophages in these tissues and aligned them across species.
The signatures obtained in one tissue were assessed for enrichment in all possible pairwise comparisons between MP subsets from the other tissue using BubbleMap. Data are represented as in    et al. , 2015; Sere et al. , 2012). In particular, only prenatally-derived LCs are relatively resistant to irradiation as a consequence of high constitutive expression of the DNA-repair machinery molecule CDKN1A  (Price et al. , 2015). It is also possible that murine LCs are functionally adapted to fur-bearing mice skin compared to human LCs. Alternatively or in addition, differences in the experimental protocols used for the isolation of MP subsets from mouse versus human skin may also confound comparison of LCs between these 2 species, including differential contamination by other cell types. These differences and heterogeneity may be difficult to evaluate using bulk-population analysis but could be explored with single-cell transcriptome profiling.
Contrary to what was recently reported  (Artyomov et al. , 2015) , we showed that the human skin DCs equivalent to human blood CD141 high XCR1 + DCs are bona fide CD141 high XCR1 + DDCs and not LCs, and we showed that human and mouse LCs are homologous. Why did our interpretation of the analysis of the gene expression profiling data from these authors differ from their own? The discrepancies between our report and Artyomov et al. are primarily methodological in nature. Firstly, the cells isolated as human skin CD141 + DDCs in Artyomov et al. were not bona fide CD141 high XCR1 + DDCs but rather cells of the monocytic lineage, as we showed through re-analysis of their transcriptome profile. Secondly, the interpretation by Artyomov et al. of their gene expression profiling data was further confounded by a suboptimal match for cell type composition between the different MP datasets used, namely between the human blood versus skin datasets, and between the mouse versus human skin datasets.
Regarding the nature of the CD141 + DDCs from the human "B" dataset, rigorous identification of bona fide human CD141 high XCR1 + dermal DCs is challenging. It requires the use of careful marker combinations for flow cytometry sorting as well as performing a posteriori analyses to ensure of the identity of the cell type isolated  (Vu Manh et al. , 2015a , 2015b ). Unfortunately at present, there isn't a single surface marker that will reliably identify bona fide human CD141 high XCR1 + dermal DCs. CD141 is not unique to these cells but promiscuously expressed by blood and skin CD1c + DCs  (Haniffa et al. , 2012) , a fraction of skin CD14 + cells  (Haniffa et al. , 2012)  and MoDCs  (Balan et al. , 2014). Although XCR1 uniquely identifies these cells at mRNA level in the human, to the best of our knowledge there isn't yet a reliable commercially available antibody against human XCR1.