dataesr/scientific-paragraphs-categorization huggingface dataset:
Primary, secondary, and tertiary markers were extracted for each facial movement. 3D Procruste and asymmetry indices were developed. This allowed the identification of common thresholds of 10% for the asymmetry index and of 6 mm for the Procruste index, beyond which facial motions would be considered abnormally asymmetric. The normative database quantifies facial motions and allows assessment of the degree of clinical disorders by comparison. This protocol is currently being investigated in patients with chronic unilateral peripheral facial paresis.
This mirror technique does not fit our objective of assessing paretic facial motions, as the unaffected hemiface is influenced by the movements of the affected hemiface in both intended and unintended movements  [19, 39]. Hyperactive reactions due to synkinesia or opposite directions of facial movements of the flaccid hemiface affect symmetry  [40]. In this work, we propose to use a classical asymmetry index and a new Procruste index. To the best of our knowledge, this work is the first attempt to quantify 3D motion for the main facial muscle groups. It provides a normative database with which facial disorders can be compared. It is part of the preclinical phase of the government-funded VISAGE project (NCT04074018).
An experienced rater manually drew 25 dots on the face using a makeup pencil. A specific procedure was followed to identify the best location of each marker based on the maximum recruitment of the target muscle fibers and of the facial morphology. Table  2  displays the marker names and descriptions in relation to the photograph. Subjects were first trained to perform the 10 tasks in front of a mirror for visual feedback. They were then seated comfortably on a chair, and the instrumented helmet was set upon their head. The helmet was mounted with a rigid connection to the head. The room light intensity was adjusted to achieve clear views from all cameras, avoiding subject discomfort. 2. 2. VICON Cara Softwares. The Vicon Cara system comprises two softwares: Caralive records live captures with four backlighted cameras, and Carapost tracks all markers and provides their coordinates in a helmet-dependent orthonormal reference frame.
A configuration step consisted of detecting the markers (size, brightness threshold) and checking their tracking along the capture. The acquisition frequency was 50 Hz. A 2D anatomical reference plane was defined using 3 points: 2 external end points of the eyes and N0. The origin of the coordinate system was the centroid of the three points. The X-axis was the line between the 2 external canthi. The Y-axis was the line perpendicular to the X-axis that crossed the origin of the coordinate system in the reference plane. The Z-axis was the line perpendicular to the reference plane that crossed the origin of the coordinate system. All coordinates were converted to a text file format. 2. 3. Data Processing. Specific software was developed using MATLAB. The development of automation modules at different stages of motion processing is described in Figure  2.
As motion was recorded in mobile reference frames, the first step was to define a common reference frame from one evaluation to another for the same participant. A change in the initial frame proposed by the Carapost software was performed. The origin was placed on the N0 marker, which is the most central and easiest to position  [10, 13, 27]. The X-axis remained unchanged (line from N0 to the external canthus), and the Y-axis was positioned along the line N0-F0 to define the sagittal plane as (N0, y ! , z ! ) plane. The second step consisted of locating the maximal movement on the three contractions according to taskdependent clinical reference markers and optimal rest. In fact, it appeared that after a maximal contraction, markers rarely returned to their initial positions, making complete relaxation of the group of muscles difficult to achieve.
05% marker removed). Figure  3  represents the results for the zygomaticus task. It shows the displacement of the lower face markers in the chosen reference frame. Tables 4-6 display the database built for each task according to the part of the face involved (upper, middle, and lower part). Mean and SD are given for the 106 evaluations to take into account the intrasubject variation of motion from one evaluation to the other, averaging 7% for all the tasks. the paired markers. Central markers associated to the marker-pairs F, S, B, and M are, respectively, F2, S0, B0, M0, and N0 for all the others. Figure  5  reports the asymmetry indices computed from Equations (  3 ) to (4) at rest and maximal motion conditions for the markers for interest of each task (see Table  1 ). In Figure  6 , Procruste indices at rest and at maximal contraction are displayed for each task.
Discussion 4. 1. ROM Analysis. This work provides a database of 10 muscle displacements from rest to maximal voluntary contraction in terms of ROM and orientation. The ROMs of all pairs of markers for each task (see Tables  4 5 6 ) are consistent with clinical observations  [11]. The standard deviations are related to the diversity of motion performance between the participants. Figure  3  gives an example of how these data will be used in the clinic. It shows which markers are involved and at what level according to Equations (2) for the zygomaticus motion in a free of PF population. Central markers are involved in multiple ways. M0 and B0 are the most mobile for lower face tasks. For the middle face, procerus and canine tasks involve the displacement of the greatest number of markers. These tasks reflect global facial mobility.
Limitations. The limits of our study relate to the facial complexity. The uniqueness of each face requires a specific positioning of the markers by a senior expert. Another limit is the time consumption of our data analysis, which was estimated around one hour in total, including the 40-minute clinical time with the patient. Our tested population should also be extended both in terms of number and of diversity (including Hispanic, Black, and Caucasian populations) to include ethnical differences  [48]. 2 ). Data presented as mean ± standard deviation (%), n ' = 106. 1 ). Data presented as mean ± standard deviation (%), n ' = 106. spontaneous facial movements by measuring contraction velocities  [14, 19]  and nonvoluntary emotion-induced movements. This protocol is currently being investigated for PFP patients (clinical phase of the government-funded VISAGE project, n = 82) with the objective of quantifying the effect of two rehabilitation programs on the recovery of facial motion in chronic stages.
Conclusions An objective and reliable tool allowing to quantify facial motions usable in the clinical setting is actually lacking. Previous 3D capture motion devices were difficult to utilize for both experimental and data processing time-consumption reasons. We used a wearable instrumented helmet specially designed for tracking facial motions and proposed an experimental protocol to assess 10 facial muscle groups with associated asymmetry indices. To our knowledge, this work provides the most complete database of nonparetic facial motions from rest to maximal voluntary contraction. Measurements of local asymmetry of pairs of markers and overall asymmetry based on volume Procruste indices complete this facial mobility study. By comparing PFP recordings to this normative database, the proposed protocol will allow quantifying the severity of PFP disorders and assessing therapeutic options such as rehabilitation programs, botulinum toxin injections, or facial surgeries.
Finally, two clusters of pathways were downregulated in both DLBCL and FL compared to rLN, involved in antigen presentation to T cell via MHC class II (highlighted in green). Then, we used the PISCES R package  43  on our scRNAseq dataset to infer protein activity. As previously done with the differentially expressed genes, we filtered significantly dysregulated inferred proteins, either in Mo&Mac metacluster (Table  S3 ) or in each individual cluster (Table  S4 ). We observed a strong DLBCL Mo&Mac signature including macrophage/TAM markers (MAFB, PPARG) and IFN response (STAT1, STAT2, IRF1, IRF9) (Figures  S4B  and S4C ). The MDSC marker STAT3 was among the few inferred proteins upregulated in FL compared to rLN, as well as in DLBCL vs rLN. Overall, monocyte and macrophage transcriptome shows a significant deregulation in DLBCL, compared to both rLN and FL, whereas FL MNP niche shares features with both reactive and DLBCL LN.
e. ligands expressed on Mo&Mac and receptors on BEC) (Figure  S5B ). Regarding the Mo&Mac genes deregulated in FL, no specific interaction was found between BEC and Mo&Mac (data not shown). We confirmed the putative interaction between BEC and Mo&Mac metacluster in DLBCL via ANXA1 binding to FPR1 or FPR2 by using the NicheNet algorithm  46  (Figure  S5C ). On bulk RNASeq in Eµ-Myc vs control mice, 47 ANXA1 was overexpressed only by BEC and not by LEC, FRC, and DN, whereas FPR1 was not overexpressed on these cells and FPR2 was overexpressed on BEC and DN (Figure  S5D ). ANXA1, FPR1, and FPR2 were not differentially expressed by T and B cells in a scRNASeq dataset from human DLBCL and rLN samples  9  (Figure  S5E ), and ANXA1 was not differentially expressed by Mo&Mac in our own scRNASeq dataset (Figure  S5F ), therefore supporting ANXA1-FPR1/FPR2 interaction as a specific actor of BEC and Mo&Mac crosstalk in DLBCL.
An immunosuppressive phenotype of DLBCL TAM has already been proposed, characterized by an upregulation of PD-L1, identified as a prognostic marker of the disease. 22, 23, 50 Similarly, IDO1 expression and activity were also correlated with DLBCL patient outcome. 51, 52 Finally, LILRB1/2 have been identified as "don't eat me" signals after engagement by MHC class I and could thus contribute to impairment of apoptosis previously reported in DLBCL. 24, 53 Interestingly, LILRB2 and IDO1 were found coregulated with PD-L1 expression on CD68 pos TAM in DLBCL contributing to a poor prognosis signature. 54 erall, analysis of large datasets of bulk RNAseq recently underlines the pro-tumoral role of TAM, revealing the co-occurrence of a high amount of TAM, a proinflammatory gene expression profile, and an overexpression of PD-L1, IDO1, and immunosuppressive cytokines.
7, 56 We identified an upregulation of HLA-DR on the BEC clusters in FL and DLBCL, as well as a significant enrichment of BEC-CD36 and BEC-S100A9, which probably reflects endothelial activation. Notably, CD36 has been directly involved in the neoangiogenesis process. 58, 59 Exploring the mechanism underlying the BEC/TAM crosstalk, we combined our MNP scRNASeq data with published murine BEC scRNASeq data, and predicted a specific paracrine interaction between ANXA1-expressing BEC and FPR1/FPR2-expressing Mo&Mac in DLBCL, with a possible additional autocrine ANXA1-FPR2 signalling in BEC. Of note, ANXA1, FPR1, and FPR2 were not differentially expressed in FL samples compared to rLN in published datasets of stromal, endothelial, T, and B cells. 7, 9 ANXA1 is an anti-inflammatory calcium-regulated protein, whose only know receptors are the FPR G-protein coupled PRR formyl-peptide receptors.
ANXA1, FPR1, and FPR2 expression were shown to correlate to patient prognosis and expression of immunosuppressive genes in DLBCL samples. Neoangiogenesis and infiltration of immunosuppressive TAM subsets have been previously associated to poor prognosis in DLBCL. Our data suggest that both processes could be linked, through an ANXA1-FPR1/2 interaction involving BEC and Mo&Mac, leading to an infiltration of immunosuppressive TAM. Targeting TAM recruitment, survival, and function has become a major therapeutic goal in lymphoma and a specific focus on ANXA1-FPR1/2-mediated interactions may be of clinical interest in DLBCL.
75 Additionally, we used murine BEC data for in silico identification of BEC-Mo&Mac interactions. Finally, other co-regulations and crosstalk mechanisms could be at play between Mo&Mac and stromal or endothelial cells in FL and DLBCL, which we might have missed in this study. (F) Kaplan-Meier survival curves and log-rank test comparing patients with high proportion of FPR1 and ANXA1 (top) and high proportion of FPR2 and ANXA1 (bottom) to those with low proportion (GSE117556). 48 Selected genes statistically enriched in groups of patients are indicated. See also Figures  S5  and S6. Further information and requests for resources and reagents should be directed to and will be fulfilled by the Lead Contact, Mikael Roussel (mikael. roussel@chu-rennes. fr) LN 60 NA NA X X X LN 53 NA NA X X X LN 19 NA NA X X X FL LN 85 1-2 - X X X LN 45 3a (relapse) R-CHOP, Len X X LN 75 1-2 - X X LN 65 1-2 (relapse) R-CHOP X X LN 65 1-2 (relapse) R-CHOP X LN 79 1-2 - X X LN 79 3a - X LN 67 1-2 (relapse) R-CHOP X LN 58 1 - X X DLBCL LN 74 non-GC - X X LN 69 non-GC - X X LN 52 GC - X X LN 74 ABC - X X X LN 75 non-GC - X X LN 53 GC - X X *.
Mass Cytometry Preprocessing After acquisition, intrafile signal drift was normalized and. fcs files were obtained using CyTOF software. To diminish batch effects, all files were normalized on EQ Beads (Fluidigm Sciences) using the premessa R package. Files were then uploaded to the Cytobank cloud-based platform (Beckman Coulter). Data were first arcsinh-transformed using a cofactor of 5. For all files, live single cells were selected by applying a gate on DNA1 vs. DNA2 followed by a gate on DNA1 vs. Cisplatin, then beads were removed by applying a gate on the beads channel (Ce140Di) vs. DNA. We performed the FlowSOM algorithm on the previous UMAP results, using all events and all channels except CD3, CD19, and CD45, and the following parameters: clustering method = hierarchical consensus, iterations = 10, number of clusters = 100, number of metaclusters = 12 (for the stromal/endothelial population) or 13 (for myeloid cells).
Single cell RNASeq Preprocessing After sequencing, libraries were mapped to the human GRCh38 genome and demultiplexed using CellRanger (10X Genomics, v. 4. 0. 0). Data from each library were then further processed using the R package Seurat (v. 4. 0. 2) on R v. 4. 0. 0 with Rstudio v1. 4. 1717. Genes expressed in three or fewer cells were excluded from downstream analysis. For each library, cells expressing a number of genes above or below 2 median absolute deviations (MAD) or a percentage of mitochondrial transcripts above 2 MAD were filtered out. Cell surface protein and HTO counts were normalized using the centered log ratio transformation (CLR) method from the NormalizeData function. HTO-tagged samples were demultiplexed using the HTODemux function with a positive quantile threshold set to 0.
95. Gene expression counts were normalized using the NormalizeData function with the default scale factor of 10,000. Single cell RNASeq integration, dimension reduction, and clustering We used the standard anchor-based workflow for dataset integration in Seurat to adjust for technical and biological sources of variation between samples. All samples were then merged together and integrated data were scaled and centered using the ScaleData function. We then performed a principal component analysis (PCA) based on the top 2000 variable features followed by a graph-based clustering using the FindNeighbors and the FindCluster functions, with a resolution of 0. 4. Results were visualized using a nonlinear dimensional reduction UMAP and cell clusters were annotated based on the expression of canonical markers.
Then, we focused on MNP clusters. All cells included in the MNP clusters previously identified (i. e. moMac, Mac-C1Q, SIGLEC6-cDC, CLEC9A-cDC1, LAMP3-cDC, cDC2, MNDA-cDC2) were extracted, and the same integration, scaling, clustering (resolution of 0. 65), annotation, and visualization workflow was applied. Differentially expressed genes analysis Differentially expressed (DE) genes analysis was performed using the FindMarker Seurat function, with a minimal fold-change of 1. 5, a minimum of 30% of gene-expressing cells in at least one of the sets of cells compared, and the default Wilcoxon rank sum test. Adjusted p-values were obtained with a Bonferroni correction using all genes in the dataset. To address inter-samples heterogeneity, we performed multiple DE genes analyses while iteratively removing one of the samples from the analyses, and kept only "robust" DE genes that were found statistically DE in all sub-analyses.
We performed DE gebes analyses to compare sample types (i. e. rTonsil, rLN, FL, DLBCL) for global monocyte/macrophages population as a whole (Mo&Mac metacluster) (Table  S1 ). and then for all individual MNP clusters (i. e. CD14-moMac, S100-moMac, CD16-moMac, IL1b-moMac, Mac-C1Q) and DC clusters (i. e. MNDA-cDC2, CD1E-cDC2, CREM-cDC2, LAMP3-cDC, SIGLEC6-cDC, CLEC9A-cDC1) (Table  S2 ). Volcano plots were created with the R package EnhancedVolcano. Pathway enrichment analysis and visualization of DE gene was performed following Reimand et al. recommendations  42  , using g:Profiler and EnrichmentMap application on Cytoscape. DE gene lists were defined as described above and ranked by log2FC. Pathway enrichment analysis with g:Profiler was performed on ordered gene lists on Reactome and GO:Biological processes databases while removing not manually reviewed annotations, with a q-value <0.
01, a size of query/term intersection ≥3, and a size of functional category between 5 and 350. Results were exported and subsequently uploaded in Cytoscape for visualization with EnrichmentMap, with a FDR q-value cutoff set to 1 and selection of the "Filter genes by expression" option. The prefuse force directed layout algorithm weighted using the gene set similarity coefficient was chosen for visualization. Auto annotation followed by manual arrangement of network nodes and updating of theme labels was then conducted, and nodes were colored based on their gene list of origin. Genes co-expressions were assessed using the FeatureScatter tool from the Seurat package and Pearson correlations.
Protein activity analysis The R package PISCES is a regulatory-network-based methodology for the analysis of single cell gene expression profiles that allows protein activity inference. 85 Data were normalized using SCTransform and CorDist functions, then cluster-specific metacells were generated with a minimum of 200 cells included. These metacells were used as inputs for gene networks generation with the R package corto, 86 using as centroids DoRotheA regulons (confidence level A to E)  87  and a p-value <1. 10 -10. After creation of a PISCES object, CPM normalization, and generation of a gene expression signature, the protein activity signature was obtained with VIPER. As protein activity includes scaled negative values, we applied a linear correction to the inferred protein data and set the minimum protein activity value to zero before DE analysis.
Analysis of public datasets We used 2 published stromal/endothelial murine lymphoma datasets for the analysis of interactions between our MNP scRNASeq dataset and BEC. 45, 47 For the CellphoneDB analysis, we collected scRNAseq data generated from Eµ-Myc lymphoma mice LN BEC. 45  Using the homologene R package, we generated a conversion table of the mouse gene names into human gene names, then filtered each mouse name to match a unique human gene and created a Seurat object by matching each count to each converted mouse gene name. For the Nichenet analysis, we used bulk RNAseq data generated from the same mouse model,  47  after a similar conversion of mouse gene names into human gene names using the homologene R package. and published BEC populations from lymphoma mice. 45 Ligand-receptor interaction scores were computed and tested using CellPhoneDB statistical analysis method with the default parameters and a p-value threshold set at 0.
05. We then focused on statistically relevant predicted interactions involving up-or down-regulated genes in DLBCL vs rLN moMac, either at the RNA level or inferred with PISCES. We used the Nichenet R package to identify top ligand-receptor and ligand-targets interactions between DLBCL-moMac and lymphoma-BEC. For this approach, we created a data matrix using the Seurat function AverageExpression on moMac from each sample to mimic bulk RNASeq data. We defined a DLBCL-moMac signature as the genes up-or down-regulated in DLBCL compared to rLN either at the RNA level or inferred with PISCES. For BEC, we used the published dataset GSE126033,  47  after converting murine genes into human genes. Expressed genes were defined in sender and receiver cells as an average log2 expression above 0. 2 for moMac and 7 for BEC.
The DLBCL-moMac signature was used as the gene set of interest in the receiver population (i. e. DLBCL-moMac) to predict how ligands can impact target genes and rank receptors according to their activity. Then the best upstream ligands expressed by sender cells (i. e. BEC) were filtered. Segmentation and data analysis for imaging mass cytometry MCD files were converted to ome. tiff format through MCDViewer (Standard BioTools). We normalized all ROI (99th percentile normalization) and performed a scaling between 0 to 100 using FIJI software. 82 Blood vessels were constituted of BEC defined as CD31 pos PDPN neg annotations using the QuPath Software's pixel classification. 83 Areas of 25µm around the blood vessels structure were arbitrary defined using the Expand annotations function in QuPath.
Interactome analysisInteractome analyses were performed with CellPhoneDB (v2. 0. 0) 44  on Python v3. 7 and Nichenet (v1. 0. 0) 46 on R v. 4. 0. 0 with Rstudio v1. 4. 1717. CellPhoneDB is a manual curated repository of ligands, receptors and their interactions, integrated with a statistical framework for inferring cellcell communication networks from single cell transcriptome data. Only receptors and ligands expressed in more than 10% of the cells in any given population were considered. We performed pairwise comparisons between all moMac clusters (Mo&Mac metacluster and all individual MNP clusters [i. e. CD14-moMac, S100-moMac, CD16-moMac, IL1b-moMac, Mac-C1Q]) from our dataset.
Acknowledgments: Authors wish to thank all donors and also  Catherine Blanc  and  Aurélien Corneau , from the  CyPS core facility at Sorbonne University (Paris)  for access to the Helios mass cytometer;  Yael Glasson  from the  IRCM (Montpellier)  for access to the Hyperion; the  H2P2 (Rennes)  for slide preparation; the  Biosit CytomeTri (Rennes)  for cell sorting; the  CRB platform (Rennes)  for samples management;  Steve Genebrier  for CyTOF experiments;  Maxime Leplat  for additional graphic illustrations;  Mathieu Laurichesse  ( Lorient ) for samples preparation. Funding: Juliette Ferrant is recipient of a fellowship from the  FHU CAMIn (Federation Hospitalo-Universitaire Cancer Microenvironnement et Innovation). Simon Léonard  was supported by a specific grant from the  LabEx IGO program  (n°  ANR-11-LABX-0016 ) funded by the  «Investment into the Future» French Government program , managed by the  National Research Agency (ANR).
This work was supported by grants from the  COmite de la REcherche Clinique et Translationnelle  ( CHU of Rennes ), from the  Ligue contre le Cancer  ( Comité 35  et  85 ), from the  Institut Carnot-CALYM (Calym-Janssen   2019 ), from the  Agence Nationale de la Recherche  ( ANR-17-CE15-0015 StroMAC ), from the  Fondation ARC pour la Recherche sur le Cancer  ( PGA1 RF20190208534 ), and from  la Ligue Nationale Contre le Cancer (Equipe labellisée).