dataesr/scientific-paragraphs-categorization huggingface dataset:
Each dataset was normalized and scaled, and a PCA was computed using the Seurat functions NormalizeData, ScaleData, and RunPCA. Training of the machine learning model was done either on the 6-month or the 2-month datasets using the scPred functions getFeatureSpace and trainModel with the model svmRadial (Fig. 6  and fig. S19, D  and E , respectively). We tested other models (e. g. , mda, knn, and random forest), but they gave either similar or worse results on the training set. Testing was done on the dataset that had not been used for training, measuring the percentage of cells for which a correct prediction was made by the trained model. Baseline values corresponding to the percentage of cells that would be correctly assigned just by chance were calculated as the proportions of cells present in the dataset for each category (for instance, 50 and 36% of cells in the 2-month dataset classified as "regenerated" or "epidermal," respectively).
Therefore, NP is the main structural component of the vRNPs. X-ray crystallographic analysis of NP structure has previously characterized an "intrinsically disordered region (IDR)-2" (72-DERRNKYLEEHPSAGKDPKKT-92), located in an RNA-binding groove that exhibits the highest conservation ratio among human, swine, and avian IAVs  (Kakisaka et al, 2016). In that regard, K87 is extremely conserved (>99. 9%) among 2400 (H5N1), 23400 (H3N2) and 18800 (H1N1) IAV strains (source: Influenza research database; https://www. fludb. org; selection criteria: (i) complete coding region sequences; (ii) no geographical or temporal restrictions; (iii) no host discrimination (iv) no elimination of duplicates; not illustrated). IAV is known to interact with the host cellular machinery, including the post-translational modifications (PTMs) components.
Metabolomic analysis of respiratory fluids using 1 H-nuclear magnetic resonance. Freeze-dried BAL samples were mixed with 210 µl of 0. 2 M potassium phosphate buffer in deuterium oxide (D 2 O) 99%, pH=7. 4. Samples were spiked with 8 µL of 3-trimethylsilylpropionic acid (0. 05 % wt in D 2 O) as an internal reference, and then samples were transferred to conventional 3 mm NMR tubes. 1 H-NMR spectra were obtained with an AVANCE III HD 600 Bruker spectrometer equipped with a TCI cryoprobe (Bruker). Standard water suppressed 1 H-NMR spectra were acquired at 298K using a "noesypr1d" pulse sequence with a relaxation delay of 20 s and 256 scans. Spectra were processed using Topspin software (Bruker). 1 H-NMR spectra were automatically reduced to ASCII files using the AMIX software package (Analysis of Mixture, version 3.
8, Bruker). Spectral intensities were scaled to the total spectral intensity, and the resulting data were analyzed by multivariate and univariate statistics. The NMR assignments were done using spectra online databases such as HMDB (http://www. hmdb. ca) and ChenomX NMR Suite (version8. 1 evaluation, ChenomX). The identification of succinate was carried out by supplementing a sample with exogenous succinate. Spectra analysis carried out with and without supplementation, allowed us to ensure signal specificity. Non-supervised multivariate principal component analyses (PCA) were performed with data generated by 1 H-NMR (BAL) using SIMCA v14 software (Umetrics). The scores were plotted in 2D vs the two first principal components to visualize possible clusters. Succinate quantification in BAL and tracheal aspirates was performed using Metaboanalyst software (www.
After washing, the slides were scanned using a G2565CA scanner system (Agilent Technologies), with a resolution of 3 µm and a 20-bit dynamic range. The resulting TIFF images were analyzed with Feature Extraction Software, using a GE1_107_Sep09 protocol. Identification of differentially expressed genes and functional investigations were done using GeneSpring software. Differentially expressed genes were identified using a moderated t test, and a P value cutoff of 5% was applied. A fold change cutoff of >2 was added to select genes expressed differentially between control and treated/infected conditions. For further analysis, data files were uploaded into the Ingenuity Pathways Analysis (IPA) software (Ingenuity Systems). The right-tailed Fisher's exact test was used to calculate a P value for the probability that each canonical pathway assigned to that data set is due to chance alone.
Proteins were subsequently transferred to nitrocellulose membranes, and probed with anti -NP (1/500), anti-NS1 (1/1000), anti-PA (1/1000), anti-PB2 (1/500), anti-M1 (1/1000), anti-M2 (1/1000), anti-CRM1 (1/500) or anti-β-actin (1/5000). Bound antibodies were revealed with an antirabbit IgG for NS1 and PB2 and anti-mouse IgG for other proteins (HRP linked) and ECL detection reagents. An automated imaging system (MF ChemiBis 3. 2, DNR BioImaging Systems) was used for detection, and the FUJI FILM MultiGauge software was subsequently used for analysis and quantification. RNA isolation and RT-qPCR. Cells in 6-well plates were lysed with 350 µL of "RA1" buffer (included in the Macherey-Nagel RNA extraction kit) and β-mercaptoethanol diluted 1/100. Total RNAs from cells or from purified A/Scotland/20/74 (H3N2) IAV were extracted using the NucleoSpin® RNA kit, including a step of genomic DNA digestion with DNase.
For nucleoprotein staining, cells were permeabilized with the Cytofix/Cytoperm TM Solution Kit or True Nuclear TM Transcription Factor Buffer Set, following manufacturer's instructions. Samples were further incubated for 1h at 4 °C with anti-NP-FITC antibody diluted 1/300e or isotype control. Flow cytometry data were acquired on a MACSQuant® Analyzer and analyses were performed using the VenturiOne software. Histopathology. Lungs were collected immediately after euthanasia and airways were washed and placed in 4% paraformaldehyde in PBS. Lung sections of approximately 4 µm thickness were cut and stained with hematoxylin-eosin. A study pathologist examined the tissue sections using light microscopy on a Leica Diaplan microscope in a blinded experimental protocol. All histopathological findings were graded in a semi-quantitative fashion on a scale of 0 to 4 (0: absent, 1: mild, 2: moderate, 3: severe, 4: extremely severe).
Generation of recombinant fluorescent IAV and time-lapse imaging. The 8 plasmid reverse genetics system  (Hoffmann et al, 2002)  was used to generate a recombinant virus expressing an eGFP reporter protein fused to the NS1 protein of the influenza A/WSN/33 (H1N1) virus. The NS1-eGFP-WSN virus was rescued using a previously described procedure  (Vidy et al, 2016)  and was propagated in MDCK cells. For long term time-lapse microscopy, A549 cells grown on cell culture in microwells were infected with NS1-eGFP-WSN at an MOI of 0. 5 pfu/cell. Pictures were taken every 10 min starting at 1 h post-infection and continuing until 24 h post-infection, on a Nikon BioStation microscope using BioStation IM software. The intensity of the GFP associated fluorescence per infected cell was quantified using Image J software.
We selected ions with charge state from 2 + to 6 + for screening. Normalized collision energy (NCE) was set at 30 and the dynamic exclusion was 45 s. Mass spectrometry proteomics raw data have been deposited with the ProteomeXchange Consortium via the PRIDE (Perez-Riverol et al, 2019) partner repository with the dataset identifier PXD018419 (username: reviewer53974@ebi. ac. uk, password: hTdy0lLL). For identification, the data were searched against the Homo Sapiens (UP000005640) and the influenza A virus (UP000162741) UniProt databases using Sequest-HT through proteome discoverer (version 2. 2 or 2. 4). Enzyme specificity was set to trypsin and a maximum of three-missed cleavages sites were allowed. Oxidized methionine, N-terminal acetylation, Succinyl-Lysine and carbamidomethyl cysteine were set as variable modifications.
The maximum mass deviation allowed was set to 10 ppm for monoisotopic precursor ions and 0. 02 Da for MS/MS peaks. The resulting files were further processed using myProMS  (Poullet et al, 2007). FDR calculation employed Percolator and was set to 1% at the peptide level for the whole study. To quantify the succinylated peptide that contained lysine K87, we extracted from the MS survey of the nano-LC-MS/MS raw files the ion chromatogram (XIC) signal using the retention time and m/z values of the well-characterized tryptic peptide ions using the Xcalibur softwares (manually). XIC areas were integrated in Xcalibur under the QualBrowser interface using the ICIS algorithm. Boxplots were constructed from two sets of three independent experiments (IAV and IAV + Succinate). Thus, three data points were used for each box.
FUNDING & ACKNOWLEDGMENTS This work was partially supported by the following grants:  Inserm ,  Université of Tours, Région Centre-Val de Loire FLU-MET # 2018-00124196 , VLM# RF2018052289  (to M. S. -T. ); FEDER Euro-FERI  (to M. S. -T. and C. P. ),  Studium research fellowship  (to P. S. H. and M. S. -T. ), SIRIC  Montpellier Cancer Grant INCa_Inserm_DGOS_12553  (to A. T. );  LabEx IBEID  Grant No. 10-LABX-0062  (to N. N. ); Ph. D. fellowship from  INRAe -Department  of animal health (to L. M. ). This work used the platforms of the Grenoble Instruct-ERIC center (ISBG;  UMS 3518 CNRS-CEA-UGA-EMBL ) within the  Grenoble Partnership for Structural Biology  (PSB), supported by  FRISBI  ( ANR-10-INBS-05-02 ) and GRAL, financed by the  University Grenoble Alpes -Ecoles Universitaires de Recherche CBH-EUR - GS  ( ANR-17-EURE-0003 ).
Whole exome sequencing Whole exome sequencing was performed by the VCGS Translational Genomics Unit using the Agilent SureSelect QXT (transposase-based fragmentation) Clinical Research Exome (CRE) technology with 100× coverage on an Illumina HiSeq2500 instrument. Library preparation, sequencing, and analysis were carried out as per the manufacturer's instructions. Variants were characterised applying the Cpipe (ver. 0. 9. 8) bioinformatic detection pipeline (http://cpipeline. org)  (Sadedin et al. , 2015). An internal database was used to filter technical and locally recurrent variants against participant data. Variant population frequency data was accessed from both GnomAD and ExAC. The predicted effect on gene function was calculated by the in-silico software programs SIFT (http://sift. bii.
a-star. edu. sg/), Polyphen2 (http://genetics. bwh. harvard. edu/pph2/), CADD raw scores  (Kircher et al. , 2014) , and MutationTaster2  (Schwarz, 2014). Gene-associated phenotypes were also assessed by searching human cases reported in OMIM (http://www. omim. org/), mouse models in the Mouse Genome Informatics database (http://www. informatics. jax. org/) and known roles in physiological processes.
For additional sequencing, denatured libraries were loaded onto an Illumina NovaSeq6000 and sequenced using one SP 100 cycle flow cell and one S1 100 cycle flow cell as follows: 28bp (Read1), 8bp (i7 index), 91bp (Read2). Read1 supplies the cell barcode and UMI, i7 the sample index, and Read2 the 3' sequence of the transcript. Data preprocessing was performed using cellranger 3. 0. 2 (10x Genomics) and a mouse GRCm38 reference. Final read depth was ~18,000 -26,000 reads per cell across all samples. Library preparation and NextSeq sequencing were performed at the Institute for Molecular Bioscience Sequencing Facility (University of Queensland). NovaSeq sequencing was performed at Microba, based at the Translational Research Institute in Brisbane.
Analysis of single-cell RNA sequencing Raw sequencing data in BCL format were processed into the single-cell feature counts using Cell Ranger software, version, from 10X Genomics. For the downstream analysis, we used Seurat package version 4. 0. 3 to load the six Cell Ranger count matrices into Seurat objects. Cells with fewer than 200 genes and more than 5% of the reads mapped to mitochondrial were filtered during the preprocessing step. To remove batch effects, each dataset was normalized with SCTranform  (Hafemeister 2019). We used the function SelectIntegrationFeatures() to choose the top 5000 variable genes across six datasets prior to running the FindIntegrationAnchors() function to identify integration anchors. These anchors were subsequently used to merge all six samples into one Seurat object using IntegrateData() function.
Differential expression analysis was performed using the Wilcoxon Rank Sum test to find the top differentially expressed genes of each cluster. Clusters annotation was based on differentially expressed genes as described in the results section. Stromal cells subclusters were determined by subsetting the respective clusters, rescaling the data and repeating unsupervised clustering on the subset of the larger Stromal Cells cluster. We obtained four subclusters using a higher resolution parameter when running the FindClusters() function. Markers for the clusters were found by performing differential expression analysis using Wilcoxon Rank Sum and the cluster annotation was then done based on the gene markers. Differential gene expression between conditions was performed with an "pseudo-bulk" expression approach  (Tung 2017).
Identification of single nucleotide variants by whole exome sequencing To provide a more complete genomic survey of our cohort, we performed WES analysis. We bioinformatically filtered the identified single nucleotide variants (SNV) against an internal database (Backhouse 2019). Following further screening for allele frequency (<0. 1%), type (nonsynonymous, splice site, frameshift), and CADD score, 26 variants were selected (Table  2 ). All variants were heterozygous and analysis of parental DNA, where available, showed their heritability. Some of the identified candidates have been linked to conditions associated with Müllerian anomalies. These included FRAS1 and FREM2, both associated with Fraser syndrome, a condition characterized by cryptophthalmos, genital malformations, syndactyly, and renal anomalies  (Saisawat 2012 ).
Single cell transcriptomics of the differentiating uterus Although Hnf1b was ablated specifically in the MD epithelium, uterine hypoplasia extended to the stromal and muscular compartments of the uterus. To better understand the specific molecular and cellular changes in each cell type, we performed single cell RNA-Seq analysis at P0, the time when immature MD cells start differentiating into specialized uterine cells. Single cell transcriptomic analysis identified nine distinct cell populations in both control and Hnf1b mutants (Figure  7A  and Suppl Figure  2 ). We assigned identities to these clusters based on expression enrichment of specific markers and the Mouse Cell Atlas single cell RNA-Seq database  (Han 2018). The cluster C1 represented the most abundant cell population expressing stromal markers such as Des, Htra3, Cnrip1, and Csrp1.
Single cell transcriptomic profile reveals dysregulation of key pathways during reproductive tract differentiation. To gain further insight into the molecular processes that were disrupted by Hnf1b ablation, we analyzed the gene expression profiles of the three main clusters of epithelial, stromal and proliferating cells. Consistent with our histological findings, both Gene Ontology and Ingenuity Pathway Analysis (IPA) showed that the most affected processes involved differentiation, proliferation, and cell development (Table  3 , Suppl Table  2 ). In addition, IPA revealed that organismal development, survival, embryonic development, and organ morphology were the most enriched functional annotations for the differentially expressed genes in our datasets (Table  4 ). The role of Hnf1b in the MD epithelial cells including what genes are downstream of Hnf1b are unknown.
We then interrogated the Ingenuity Knowledge Database for all factors that have been reported as downstream of and having a direct relationship with Hnf1b. We overlaid our dataset of differentially expressed genes to identify factors that could be targets of HNF1B in the MD epithelium (Figure  7E ). This analysis revealed the patterning gene Mafb, which is directly activated by HNF1B in response to retinoic acid  (Sturgeon 2011) , and the estrogen pathway genes Igfbp3 and Pcbd1, the latter of which is a binding partner of HNF1B  (Ferrè 2014 ). In addition, other genes included Mmp10, Eps8, and Plau, which are all involved in cell proliferation, migration, and differentiation. Pathway explorer analysis in IPA showed that these factors were also involved in cell polarization, which is a critical process for epithelial function (Suppl Figure  6 ).
Study Design and Patients SERENA is an ongoing, longitudinal, non-interventional study with prospective and retrospective collection of primary data on the retention, safety, and effectiveness of secukinumab in patients with moderate-to-severe chronic plaque-type PsO, active PsA, or active AS. This study is being conducted at 438 sites with PsA and AS patients being enrolled across Europe [Austria, Belgium, Bulgaria, Croatia, Czech Republic, Estonia, France, Germany, Greece, Hungary, Ireland, Italy, Netherlands, United Kingdom], Israel and Russia. The design, eligibility criteria, methodology, and statistical analysis of this study have been published previously  [28]. Patients aged C 18 years with moderate-tosevere plaque PsO, PsA, or AS who had received secukinumab for at least 16 weeks before study registration were included in the study.
Patients with medical or psychological conditions preventing them from participation in the study, or patients participating in other interventional clinical trial or in any other Novartis-sponsored non-interventional study (NIS) generating data on secukinumab treatment were excluded. Realworld data are collected prospectively in visits every 6 months after enrolment. The study will end either when all enrolled patients complete at least 2 years of follow-up or when the 5-years visit has been documented, whichever occurs later. This interim analysis included both target set population and safety set population (Supplementary Fig. 1 ). The safety set consisted of patients who received at least one dose of secukinumab treatment after signing the informed consent and the target set included patients fulfilling all of the inclusion criteria and none of the exclusion criteria.
Statistical Analyses Descriptive statistics were used to analyze the data presented as observed. Effectiveness data were assessed in all patients of the target set while safety data were analyzed in all patients who received at least one dose of secukinumab after providing written informed consent. Quantitative data are presented by N, mean, standard deviation, and selected quantiles: minimum (0%), lower quartile (25%), median (50%), upper quartile (75%) and maximum (100%). Qualitative variables are presented as absolute and relative frequency distributions. Two-sided 95% confidence intervals (CI) were provided for all binary response rates. Retention rate was defined as the percentage of patients who have not discontinued secukinumab treatment, while a treatment break was defined as interruption of secukinumab therapy for at least 3 months after last injection.
CONCLUSIONS The present analysis reports for the first time, retention rates and effectiveness of secukinumab for at least 2 years after initiation and treatment interruption data. Secukinumab demonstrated high retention rates and sustained effectiveness for over at least 2 years after inclusion in the study in PsA and AS patients in the real-world setting. Treatment interruptions may occur in clinical practice due to a variety of safety-related, personal, and circumstantial reasons. No impact on the effectiveness of secukinumab over the intermediate treatment breaks was observed. Safety data collected prospectively for up to 2 years confirm the favorable safety profile of secukinumab and do not reveal any new signals. Research grants, consultancy fees and/or speaker fees from AbbVie, Celgene, Lilly, Pfizer, Gilead, MSD, Novartis and UCB; Andreas Bounas: None to disclose; Nicola Gullick: Research support, consultancy fees and/or speakers fees from AbbVie, Celgene, Eli Lilly, Izana, Janssen, Novartis, UCB; Eric Lespessailles: Received speaker and consultant fees from Amgen, Expanscience, Lilly and MSD, and research grants from AbbVie, Amgen, Lilly, MSD and UCB; Jan Brandt-Juergens: Consultancy fees and speaker honoraria from AbbVie, Pfizer, Roche, Sanofi-Aventis, Novartis, Lilly, MSD, UCB, BMS, Janssen, and Medac; Rasho Rashkov: Consultancy fees and speaker honoraria from AbbVie, Amgen, Pfizer, Novartis, MSD, UCB, Roche and Janssen; Barbara Schulz: Employee of Novartis; Effie Pournara: Employee of Novartis with Novartis stock; Piotr Jagiello: Employee of Novartis.
ACKNOWLEDGEMENTS This study was designed by the scientific steering committee and Novartis personnel. The authors thank the patients who participated in this study; the study investigators;  John Gallagher ,  Novartis Pharmaceuticals UK Limited, London, UK , for medical guidance and valuable review. Funding. This study and the journal's Rapid Service Fee were funded by  Novartis Pharma AG, Basel, Switzerland. Medical Writing, Editorial, and  Other Assistance. Medical writing support, under the guidance of the authors, was provided by  Dhanya Mukundan ,  Novartis Healthcare Private Limited, Hyderabad, India. Medical writing support was funded by  Novartis. Authorship. All named authors meet the International Committee of Medical Journal Editors (ICJME) criteria for authorship for this article, take responsibility for the integrity of the work as a whole, and have given their approval for this version to be published.
However, since these concentrations led to a strong biofilm reduction in vitro (data not shown), they were lowered to 2 and 16 µg/mL for tobramycin and colistin, respectively, in order to detect a potential synergistic effect. Results showed a significant increase in the antibiofilm activity of tobramycin against EA-extract-pretreated biofilm (Figure  5 ). In contrast, no synergistic activity was recorded with colistin. This can be explained by the difference in the mechanisms involved in biofilmassociated tolerance to aminoglycoside antibiotics (tobramycin) and antimicrobial peptide (colistin) and/or by a possible denaturing effect of EA extract on colistin. For aminoglycoside antibiotics, which act at the intracellular level by targeting bacterial protein synthesis, various studies have highlighted the major role played by negatively charged EPS matrix in limiting the diffusion of such polycationic compounds through the biofilm, thus blocking their effects.
0 mL of SDW. Just before proceeding to the microscopic observations, 6. 0 mL of SDW, together with 1. 0 µL of Syto9, were added. Microscopic observations were made with Zeiss-Axiotech microscope using a 20 X/0. 50 (Zeiss, EC Plan-Neofluar) objective and equipped with an HXP 120 C light source. Images were acquired with a digital camera (Zeiss AxioCam ICm 1) and the set of photos was processed with ZEN software.
The injector and detector temperatures were 250 and 220 • C, respectively. Hydrogen was used as carrier gas at a flow rate of 1. 0 mL/min. The injection volume of the prepared extract solution (2. 5 mg/mL) was 5. 0 µL. The total running time of the GC-MS system was 36 min. Finally, molecules were identified using Xcalibur software.
Statistical Analysis All values were expressed as mean ± SD for three independent experiments. The student t-test was used to calculate the significance of the differences between the mean effects of the extract and those for the associated untreated control in the CFU counts method after checking equality of variances with Levene's test (p-value < 0. 05). Statistically significant values were defined as a p-value (* <0. 05, ** <0. 01 or *** <0. 001). SPSS 22. 0 software (SPSS, IBM Corporation, Armonk, NY, USA) was used in the statistical analysis.