dataesr/scientific-paragraphs-categorization huggingface dataset:
RNA extraction Culture medium was changed 24 h before harvesting the cells. Total RNA was isolated from confluent primary fibroblasts using an RNeasy Kit (Qiagen, Hilden, Germany) according to the manufacturer's protocol recommendations. RNA concentration and quality were determined on a NanoDrop Spectrophotometer (Thermo Scientific Inc. ) and integrity was verified with a Bioanalyzer 2100 (Agilent, Santa Clara, CA, U. S. A. ). The RNA integrity number in all cases was higher than 8Á5. To minimize technical variability, RNA extracts from four technical replicates of each sample were mixed. RNA-Seq data processing cDNA libraries were generated for each sample using the TruSeq RNA Sample Preparation Kit (Illumina, San Diego, CA, U. S. A. ) according to the recommended protocol. Ligation and library integrity were verified using an Agilent Bioanalyzer 2100.
Sample libraries ligated with unique adapter sequences were multiplexed six to a lane and were sequenced by Edinburgh Genomics (Edinburgh, U. K. ) using Illumina HiSeq HO v4 125 paired end sequencing. Quality control analysis on the resulting FASTQ files was performed using FastQC (Babraham Bioinformatics, Cambridge, U. K. ). Reads were adapter trimmed using cutadapt version 1Á3 (https://pypi. org/project/cutadapt/1Á3) with the parameters -q 30 -m 50 -a AGATCGGAAGAGC. Trimmed reads were aligned to Ensembl version 38Á81 of the Homo sapiens genome with TopHat2 version 2Á0Á13 (https://ccb. jhu. edu/software/tophat) using default parameters, except '-r -70 -mate-std-dev 75' to specify insert sizes. The BAM files were sorted by name using Picard-tools version 1Á115 SortSam (https://broadinstitute.
github. io/picard). Read counts were generated using HTSeq-count version 0Á6Á0 in unstranded mode (https://htseq. readthedocs. io) and Python version 2Á7Á3, with parameters -m union -i gene_id -t exon. Raw and processed data are stored at the National Center for Biotechnology Information's Gene Expression Omnibus, accession code https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE119501. RNA-Seq data were normalized using the Trimmed Mean of M-values. 17 An adjustment for possible batch effects was performed using the R package ComBat. 18 The transcripts that had an average expression per condition < 1 count per million and a coefficient of variation per condition > 100% were filtered. Differential expression analysis was performed with the Bioconductor package edgeR. 19 The conventional multiple-testing P-value correction procedure proposed by Benjamini and Hochberg was used to derive adjusted P-values.
Pathway activity analysis and protein-protein interaction The signalling circuit activity analysis method,  23  as implemented in the hiPathia R package (https://github. com/babelo mics/hipathia), was applied to all disease vs. healthy control comparisons. Under this modelling schema, signalling circuits are defined within Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways as the chain of proteins that connect a receptor protein to an effector protein that triggers specific cellular activities. The signal is propagated from the receptor protein along the proteins that compose the circuit by a recursive formula that takes into account the activity of both activator and inhibitor proteins. Finally, the list of the common dysregulated genes in all disease vs. control comparisons was uploaded to STRING,  24  a database that represents the known protein-protein interactions (PPIs).
Immunofluorescence analysis Skin cryosections from three healthy donors, two patients with RDEB and two patients with KS were immunostained against tenascin C (MAB2138) and periostin (sc-398631). Fluorescence quantification was measured using the ImageJ program (https://imagej. nih. gov/ij). Student's t-test was applied to compare the fluorescence intensity/area means of samples, using GraphPad Prism 5Á04 (GraphPad Software, La Jolla, CA, U. S. A. ).
Identification of common expression signatures linked to fibroblast activation and extracellular matrix deposition RNA-Seq counts were obtained for primary fibroblasts isolated from three healthy donors, nine patients with RDEB, three patients with KS and three patients with XPC. In total 22 970 transcripts, identified by their Ensembl IDs, were obtained for each sample after data quality assessment and normalization (Table  S2 ; see Supporting Information). The observed arrangement of the samples in the principal component analysis discarded any possible batch effect and organized the samples following the different disease groups. Notably, all genodermatosis samples are located in proximity within the plot, and are distinctly separated from controls (Fig. 1a ), pointing towards a similar transcription profile between the three genodermatoses.
23 We found 42 overlapping circuits in the three genodermatoses when comparing them against controls (Fig. S1a ; see Supporting Information). Effectors were associated with cell proliferation (AREG, MAPK8), transforming growth factor (TGF)-b signalling (PITX2) and ECM-cell interactions (PLAU, MAP3K4) (highlighted terms in Table  S5 ; see Supporting Information). A particular example of these circuits includes activation of BCL2an effector molecule with an antiapoptotic effectinitiated in multiple overexpressed nodes and propagated through the PI3K-Akt pathway (Fig. S1b ; see Supporting Information). To identify functional associations between the 186 differentially expressed genes, the STRING database  24  was used to generate PPI networks. The enrichment PPI P-value (0Á0013) indicates that the commonly altered genes do not represent a randomly scattered set of proteins, but a meaningfully connected set of genes in accordance with their biological functions.
This study was supported by grants from the  Spanish Ministry of Economy and Competitiveness  ( SAF2013-43475R ,  SAF2017-88908-R  and  SAF2017-86810-R ); from  Instituto de Salud Carlos III  and  CIBERER , cofunded with  European Regional Development Funds (ERDF)  ( PT13/ 0001/0007 ,  PI14/00931 ,  PI15/00716 ,  PI15/00956 ,  PT17/0009/0006  and  PI17/ 01747 ); and from the  European Union  ( HEALTH-F2-2011-261392  and  H2020-INFRADEV-1-2015-1/ELIXIR-EXCELERATEref. 676559 ). Additional funding from  Comunidad de Madrid  ( AvanCell-CM S2017/BMD-3692 );  Catalan Government  ( AGAUR 2014_SGR_603 ); ' Fundaci o La Marat o de TV3 ,  201331-30 ';  CERCA Programme/Generalitat de Catalunya ; and 'Fundaci on Cient ıfica de la Asociaci on Española Contra el C ancer', Spain.
Data We use GPS offsets from 46 stations and 4 InSAR frames. We use the co-seismic GPS offsets and errors provided by INGV (GPS data and data analysis center 2016). The co-seismic measurements were estimated based on 3 d of continuous GPS stations position before and after the main shock. We include GPS observations taken within 80 km in the north-south direction of the epicentre, the displacement at farther stations do not add more useful information to the inverse problem. Data errors are used to build a diagonal covariance matrix describing observational uncertainties. We use two ALOS-2 InSAR frames and two Sentinel frames. The ALOS-2 images have been acquired by the Japan Aerospace Exploration Agency and processed by  Huang et al. (2017b). The Sentinel-1 images have been acquired under the Copernicus program by the European Space Agency.
The Sentinel-1 interferograms have been processed using the TopsApp module of the InSAR Scientific Computing Environment (ISCE) software within the ARIA project (NASA/JPL-Caltech). The co-seismic interferograms are distributed in two ascending and two descending frames (more information can be found in Supporting Information Table  S1 ). For computational efficiency, we resample InSAR observations based on model resolution  (Lohman & Simons 2005). To build a data covariance matrix for the InSAR data, we mask the area of co-seismic displacement and estimate empirical covariograms as a function of distance between data points (Supporting Information Fig. S2 ). The InSAR covariance matrix is calculated from the best-fitting exponential function to empirical covariograms  (Jolivet et al. 2012) , and merged with the diagonal GPS covariance matrix to form the full data covariance matrix C d.
Bayesian sampling approach Instead of trying to infer the best solution of the inverse problem, we choose to sample the solution space and image its probable models. We solve our problem with a Bayesian sampling approach allowing us to infer the posterior uncertainty of inferred models. This approach relies on the AlTar package, a reformulation of the code CATMIP  (Minson et al. 2013). AlTar combines the Metropolis algorithm with a tempering process to realize an iterative sampling of the solution space of the source models. A large number of samples are tested in parallel at each transitional step. Additionally, an important resampling step is performed at the end of each tempering step. The probability of each sample to be selected depend on its capacity to fit the observations d obs within the uncertainties C χ.
Synthetic tests We first investigate the relative effect of uncertainties in the fault geometry and crustal properties using synthetic tests. A first target model consists of two 3-4 km large dip-slip patches of 150 cm amplitude, similar to the two main slip patches inferred from our initial models of Amatrice earthquake (Fig. 2 ). A second target model with inverted slip patches positions (i. e. deep southern slip patch and shallow northern slip patch) is also tested to evaluate the potential influence of the data distribution, which is one major difference with the simplified tests of RSS18 conducted on a semi-infinite fault. Assuming our preferred fault geometry (fault geometry A) and a 1-D layered elastic structure based on the CIA model  (Herrmann et al. 2011) , we compute the surface displacement due to our target model (see Fig.
The Classic Slip Inversion Python library created by  Romain Jolivet  (Jolivet et al. 2015) was used to build inputs for the Bayesian algorithm and to perform the optimization of the inverse problem. Figures were generated with the Matplotlib and  Seaborn Python  libraries. This study was partly supported by the  French National Research Agency (ANR)   EPOST  project  ANR-14-CE03-0002. TR is supported by a fellowship from the  French Ministry of Research and Higher Education. MS was supported by  U. S. National Science Foundation  grant  1447107. A simple version of the code used to calculate the uncertainties in assumed fault dip, strike and position can be freely downloaded at github. com/thearagon/epistemicuncertainties.
Although human endogenous retroviruses (HERVs) expression is a growing subject of interest, no study focused before on specific endogenous retroviruses loci activation in severely injured patients. Yet, HERV reactivation is observed in immunity compromised settings like some cancers and auto-immune diseases. Our objective was to assess the transcriptional modulation of HERVs in burn, trauma and septic shock patients. We analyzed HERV transcriptome with microarray data from whole blood samples of a burn cohort (n = 30), a trauma cohort (n = 105) and 2 septic shock cohorts (n = 28, n = 51), and healthy volunteers (HV, n = 60). We described expression of the 337 probesets targeting HERV from U133 plus 2. 0 microarray in each dataset and then we compared HERVs transcriptional modulation of patients compared to healthy volunteers.
Moreover, in these contexts, viral reactivation is often observed, especially for Herpes Viruses  (18, 19). This reactivation is associated with an immunosuppressive state  (20). We therefore hypothesize that HERV, like latent viruses, may reactivate and be transcribed in vivo after inflammatory injuries. Given that several groups showed that some probes Abbreviations: HERV, Human endogenous retrovirus; LTR, Long Terminal Repeats; PAMP, pathogen-associated molecular pattern; LPS, Lipopolysaccharide; PMA, phorbol-12-myristate-13-acetate; HV, healthy volunteers; ICU, Intensive Care Unit; TBSA, Total Burn Surface Area; ABSI, Abbreviated Burn Severity Index; ISS, Injury Severity Score; SAPSII, Simplified Acute Physiology Score II; MFI, Medians of Fluorescence Intensity. of commercial whole genome microarray do target HERV loci  (21, 22)  (such as Affymetrix U133 plus 2), we retrospectively explored microarray datasets obtained in our lab to study the HERV transcriptome modulation in various contexts of injuries in vivo.
Traumas cohort 105 patients with severe trauma were admitted at HCL. Briefly, patients were included when they were under mechanic ventilation, with an Injury Severity Score (ISS) over 25 and were at least 18 years old. Inclusion / exclusion criteria and ethical considerations of the cohort have been previously published elsewhere  (24). The main clinical variables are summarized on Table  S1. Samples were collected at day 1 (D1) or day 2 (D2) after trauma. Data from 22 healthy volunteers were also used to make comparisons with patients (identical with septic shock cohort 2).
Flow Cytometry Validation Cohort Burns Whole blood samples (EDTA tubes) from 13 burn patients sampled at D1 and D7 and admitted in Edouard Herriot hospital at Lyon, France were recruited as part of the EARLYBURN study (NCT02940171). Patients were aged from 21 to 84 (mean = 53), 12 men. The mean TBSA was 33% (from 20 to 52%). All samples from these patients were used for CD300LF protein analysis, and 7 of these 13 patients were used for CD55 protein analysis.
Septic shocks Whole blood samples (EDTA tubes) from 22 septic shock patients sampled at D1/D2, D3/D4/D5, and D6/D7/D8 after shock and admitted in Edouard Herriot hospital at Lyon, France were recruited as part of IMMUNOSEPSIS study (NCT02803346). Patients were aged from 23 to 81 (mean = 68), 16 men. Eleven samples were used for CD300LF protein analysis and 11 other samples for CD55 protein analysis.
Healthy volunteers Whole blood samples (EDTA tubes) were purchased from the Etablissement Français du Sang (n = 18). Donors were aged from 21 to 63 (mean = 50), 12 men and 6 women. They were age-matched with burn and septic shock cohorts. According to the standardized procedure for blood donation, written informed consent was obtained from healthy volunteers (HVs) and personal data for blood donors were anonymized at time of blood donation and before blood transfer to a research lab.
Microarray Analysis Microarray data are available on the Gene Expression Omnibus (GEO) website for Burn [GEO:GSE77791], SS1 [GEO:GSE57065], and SS2 [GEO:GSE95233] cohorts. The preprocessing methods were comparable in all datasets. Microarray normalization and statistical analysis were performed using R/Bioconductor (R v3. 2. 3). Quality assessment was performed through simpleaffy (v2. 46. 0)  (27). After removing outlier samples the raw data were normalized, adjusted for background noise and summarized using the GCRMA (Guanine Cytosine Robust Multi-Array) algorithm with default parameters  (28). COMBAT algorithm (29) was used to remove batch effect on Burn and Trauma cohorts. The 337 probesets from the U133 Plus2. 0 microarray targeting HERVs have been identified and selected as described elsewhere  (21, 22).
qPCR were performed on a LightCycler instrument using Light Cycler 480 Probes Master for the genes and reference genes and on SYBR Green I master for HERVs. Final volume of 20 µL contains 0. 5 µM of primers. For genes, an initial denaturation step of 10 min at 95 • C followed by 45 cycles, 10 s at 95 • C, 29 s annealing at 60 • C, and 1 s extension at 72 • C, Taqman) was performed. For HERVs, an initial denaturation step of 5 min at 95 • C followed by 45 cycles of a PCR protocol (10 s at 95 • C, 15 s at 55 • C and 15 s at 72 • C, SYBR Green program), melting curve protocol was performed. The Second Derivative Maximum Method was used with the LightCycler software (Release 1. 5. 1) to automatically determine the crossing point for individual samples. Standard curves were generated by using serial dilutions of cDNA standards prepared from purified PCR amplicons obtained with the corresponding primers (Table  S2 ).
ETHICS APPROVAL AND CONSENT TO PARTICIPATE EDTA blood tubes were obtained from EFS (Etablissement Français du Sang) and used immediately. In accordance with EFS standardized procedures for blood donation, written noobjection was obtained from healthy volunteers to use the blood for the research and personal data for blood donors were anonymized before blood transfer to our research lab. Protocols of the discovery and validation cohorts were approved by local ethics committees. Non-opposition to inclusion in the protocols was systematically recorded from patients or next of kin.
As previously published  (21, 22) , we extracted data from 337 probesets targeting HERVs loci from the whole genome U133 plus 2. 0 microarray datasets. Among them, a majority had low expression levels, within background levels (Supplemental Figure  1 ). Based on hierarchical clustering analysis, 64 probesets (19%) were expressed (i. e. , above background) for burns, 60 probesets (18%) for traumas, 164 for septic shock 1 (49%) and 63 for septic shocks 2 (19%). The 25% most variant probesets (n = 84) across samples in each dataset revealed that several probesets were even highly expressed (Figure  1 ). In each dataset, the hierarchical clustering highlighted a clear difference between patients and HV, suggesting a modulation of HERV expression following injury. Interestingly, over these top 25% most variant probesets selected in each dataset (resulting of 127 distinct probesets), 44 (35%) were similarly modulated in the four datasets, and 102 (80%) in at least 2 datasets (Supplemental Figure  2 ).
In order to analyze the HERV transcriptome modulation associated with injury, we performed a supervised analysis comparing HERV expression in injured patients at D1 (admission) and HV, in each dataset separately. The comparison (accounting for multiple testing correction with absolute fold change higher than 1 and corrected p-value lower than 0. 01) between burn patients and HV resulted in 19 differentially expressed HERVs (Supplemental Figure  3A ). The comparison between trauma patients and HV resulted in 27 differentially expressed HERVs (Supplemental Figure  3B ). The comparison between septic shock patients and HV resulted in 19 and 46 differentially expressed HERVs for cohorts 1 and 2 respectively (Supplemental Figures  3C, D ). Altogether, 56 distinct probesets targeting HERVs were differentially expressed among all 4 datasets, clearly discriminating HV from patients at ICU admission (Supplemental Figure  4  and Table  S3 ).
Taking into account the global profile for each probeset, 16 (28. 6%) had higher expression in patients compared to HV and 40 (71. 4%) were down-modulated in patients. Interestingly, 5 probesets were differentially expressed in all 4 datasets and 16 in at least 3 of them (Figure  2A ). All 5 commonly modulated probesets had consistent expression profile across the 4 datasets. Four were over-expressed in patients compared to healthy volunteers (Figure  2B ). The 5th probeset, down-modulated in all datasets, maps at multiple locations in the genome and was not considered in further analyses. Among the 4 remaining modulated probesets, 1 HERV from ERV24B_Prim-int family (236982_at), is within 2kb from the PTTG1IP gene and 3 are within a gene. A HERV from LTR33 family (230354_at) is within an intron of SLC8A1 gene.
For each targeted region, we made multiple RT-qPCR designs. We identified several distinct patterns of expression comparing HERVs and nearby genes (Tables  2):  (i) for PTTG1IP and MIR3945HG regions, we observed no or low signal from the HERV loci (data not shown), (ii) for SLC8A1 (Figure  3 ) and NFE4 (Figure  4 ) regions, we observed a high signal from HERVs elements, but no or lower signal on the genes, (iii) for CD55 (Figure  5 ) and CD300LF (Figure  6 ) regions, we observed a middle or high signal from both HERV loci and genes. To better interpret the results, we extracted from Ensembl the genome annotation and showed in genomic context, the microarray and the RT-qPCR results of SLC8A1 (Figure  3 ), NFE4 (Figure  4 ), CD55 (Figure  5 ) and CD300LF (Figure  6 ) regions. SLC8A1 has 11 known transcripts.
The design targeting the gene showed also up-modulation of CD55 and a very high absolute normalized expression in patients compared to HV (Figure  5C ). [Of note 1555950_a_at probeset, targeting most of CD55 transcripts, was also up-modulated in patients, and with a high expression level (data not shown)]. We also confirmed by flow cytometry on monocytes and neutrophils that CD55 expression was higher in patients than in HV, confirming an up-modulation at the protein level in patients (Figure  5D ). The MLT1D HERV element, targeted by the 1553043_a_at probeset is located in 3 ′ UTR of CD300LF-201, 202, 203, 204, and 207 protein-coding transcripts (Figure  6A ). We made several RT-qPCR designs, targeting either the HERV locus only (PCR1) or both HERV and 3'UTR of CD300LF (PCR2, Figure  6B ).
DISCUSSION We took advantage of previous microarray analyses on four cohorts of severely injured patients to assess the modulation of HERV transcriptome in acute inflammation. We showed that several loci were expressed and modulated after acute injury. Surprisingly, a large majority among the modulated HERVs were down-modulated in patients compared to HV, whereas a global and massive gene up-modulation has been observed after severe injuries  (17). Five HERVs were modulated in patients compared to HV in all four datasets and 16 HERVs in at least 3 datasets, suggesting a similar inflammatory triggered modulation in all models. We validated expression profiles by RT-qPCR on 6 regions, allowing us to explore more precisely the modulation pattern of the HERVs and the neighbor genes. Interestingly, all these 6 HERVs have detected signals in RNAseq experiments from lymphoid cells and whole blood datasets [Ensembl Rnaseq tracks  (33) ].
ACKNOWLEDGMENTS Authors would like to gratefully thank  Maria-Paola Pisano ,  Marie-Angélique Cazalis ,  Boris Meunier ,  Julie Mouillaux  and  Estelle Peronnet  for their kind advices. They also thank all clinical research assistant for the collection of blood samples, especially  Hélène Vallin  and  Valérie Cerro. Finally they gratefully thank  Anne Portier  and  Marie-Angélique Cazalis  for all experiments made on samples.
DATA AVAILABILITY STATEMENT Microarray expression data has been deposited on NCBI Gene Expression Omnibus and are accessible through GEO accession numbers GEO:GSE77791, GEO:GSE57065 and GEO:GSE95233. Data from microarray experiment for trauma cohort are available at Hospices Civils de Lyon-bioMérieux-UCBL1 "Pathophysiology of Injury Induced Immunosuppression, " Groupement Hospitalier Edouard Herriot, France.
Gene conversion We tested for the signatures of recombination and gene conversion in the rbcS gene copies using the Recombination Detection Program v4. 56 software (RDP4;  Martin et al. , 2015). We used Chimaera, 3seq, GENECONV, MaxChi, and SiScan with their default parameters. The nucleotide alignment created for the phylogenetic reconstruction was used as an input for the gene conversion analyses.
Selection Positive selection analysis in rbcS was performed using the mixed effects model of evolution (MEME) implemented in HyPhy v2. 2. 6  (Pond et al. , 2005). We used the MG94 codons substitution base model  (Muse and Gaut, 1994)  and we corrected for multiple testing using a false discovery rate (FDR;  Benjamini and Hochberg, 1995)  with a threshold of 0. 1. We selected the MEME model because it is more suitable than the branchsite mode  (Zhang et al. , 2005)  for estimating site-specific probabilities  (Lu and Guindon, 2014). Positions under positive selection were plotted on the known protein structure of Spinacia oleracea (Chains B, C, E and H of 1RCX of the Protein Data Bank;  Taylor and Andersson, 1997)  using the software PyMol v1. 3 (The PyMOL Molecular Graphics System; Schrödinger, LLC).