dataesr/scientific-paragraphs-categorization huggingface dataset:
Phylogenetic analysis We successfully sequenced 19. 4% (7/36) of the positive tick pools. The seven sequences were obtained from ticks collected from five cows belonging to seven farms (Table  3 ). Three B2L sequences were obtained from two H. marginatum pools and from one R. bursa pool. The phylogenetic tree based on B2L gene sequences indicated that the three samples showed 99% and 100% nucleotide and amino acid identity, respectively. The three sequences showed 99% nucleotide identity and 100% amino acid identity with parapoxvirus strain 3/07 (Gen-Bank: KF478804) detected from cattle in Germany, with strain VR634 (GenBank: GQ329670) detected in humans in the United States and strain B074 (GenBank: KF478803) detected in humans in Germany. The seven ORF 032 gene sequences were obtained from four R.
bursa pools and three H. marginatum pools. The seven sequences showed 99%-100% nucleotide and amino acid identity with each other, 98% and 99. 8% nucleotide and amino acid identity, respectively, with strain 3/07 (GenBank: KF478816), and 95% and 99. 5% nucleotide and amino acid identity, respectively, with strain VR634 (GenBank: GQ329670). Overall, phylogenetic tree analysis based on amino acid sequences of B2L and ORF 032 genes (Figures  2  and 3 ) showed that the B2L and the ORF 032 gene of parapoxvirus detected in ticks collected from cattle in Corsica were similar to each other and grouped together with pseudocowpox virus.
However, a re-analysis of the PhyloFish RNA-seq database-providing gene expression data from 23 different ray-finned fish species  [61] confirmed the expression of a single LAMP2A transcript in all considered salmonids  [16]. This suggests that the loss of expression of the second LAMP2A transcript is of a common   B, A,  and C ) is represented by a green check mark or a red cross mark, respectively. For each species, the location of the gene is specified in the table. TGD, teleost-specific whole-genome duplication; SaGD, salmonid-specific whole-genome duplication. The absence of exon A in the second LAMP2 paralog of these two species, belonging to different super-orders, could be due to either independent losses or the specific loss of this exon in the common ancestor of teleost shortly after the TGD.
However, this second LAMP2 gene, which likely results from the TGD, appears to have been lost in all other teleost species investigated  [13]. Accordingly, in cave Mexican tetra (Astyanax mexicanus), zebrafish (Danio rerio), Northern pike (Esox lucius), Atlantic cod (Gadus morhua), and medaka (Oryzias latipes), a unique LAMP2 is found with all three alternative exons (Figure  5 ). Interestingly, genome analyses of various salmonid species have revealed the presence of two LAMP2 genes bearing the three alternative exons A, B, and C, which likely originated from the fourth round of WGD that occurred in the common ancestor of salmonids (SaGD) about 100 million years ago. However, a re-analysis of the PhyloFish RNA-seq database-providing gene expression data from 23 different ray-finned fish species  [61] -confirmed the expression of a single LAMP2A transcript in all considered salmonids  [16].
Using a motif analysis approach, we then identified candidate TF-binding motifs across OCRs induced upon modulating TCR, IL-2 and TCR + IL-2 signals respectively, and performed enrichment analysis for known motifs based on the HOMER/JASPAR database. We next ran a PCA analysis to examine which TFs may account for segregating the three sets of OCRs (Fig. 6e ). We discovered 21 TFs, among which 17 that were enriched in the OCRs induced by changes in TCR + IL-2 signals, while only two were enriched in OCRs induced by varying TCR or IL-2 signals only. Altogether, these results supported the idea that modulating the strength of both TCR and IL-2 signals together programmed memory CD8 + T cells with a greater set of OCRs and putative TFs binding sites, consistent with an improved functional fitness.
7a  and Supplementary Data 5). The greater chromatin accessibility in the promoters of most of these genes in OT-I memory cells primed with strong TCR and IL-2 signals (i. e. , WT, Lm-N4) was remarkable while these regions were largely closed in those that received weak TCR and IL-2 signals (i. e. , Il2ra mut/mut , Lm-T4). In T cells that received either weak TCR or IL-2 signals, chromatin accessibility was more nuanced (even if TCR signals had a stronger impact), suggesting that other mechanisms also contributed to accessibility. We next selected genes involved in the distinct biological processes highlighted above that included Tcf7, Tcf7l2, Dvl2, Mark2, Il2rg, Cdc16, Cdk17, Mapk9, Cacna1c, Cacng8. Tcf7, Tcf7l2, Dvl2 and Mark2 encoded proteins are involved in the activation the Wnt/β-Catenin pathway while IL-2Rγ transduces common γ-chain cytokine signals (i.
Plates were loaded into a 37 °C incubator attached to custom Tecan Freedom EVO 75 robotic platform programmed to conduct automated robotic time series that collected supernatant for cytokine analysis and cell pellets for surface/intracellular marker analysis. For cytokine quantifications, co-culture supernatants were thawed at room temperature for 1 h and cytokines stained with mouse Th1/Th2/Th17 cytokine kit (BD) according to the manufacturer's protocol. For proliferation and activation, cell pellets were incubated with fluorescently tagged antibodies (see Supplementary Data 5) at room temperature for 30 min. All samples were next acquired on a BD Fortessa FACS HTS. Preparation of cell suspensions and staining for flow cytometry analysis. Spleens or lymph nodes (inguinal and cervical) were dissociated on a nylon mesh.
Newly generated tetramers (1/400-1/ 500 dilution) were used to stain cells for 1 h at 4 °C. For transcription factor (TF) intracellular staining, cells were fixed and stained according to the eBioscience Foxp3 Transcription Factor Staining Buffer Set protocol. Data acquisition was done using BD LSR II, FACSAria III or Cytek Aurora flow cytometer. All flow cytometry data were analyzed using FlowJo v9 or v10 software (TreeStar). T cell sorting. CD8 + T cells were negatively selected from spleen using anti-CD4, anti-CD11b, anti-MHC II, anti-TER119, anti-B220 and anti-CD19 mAbs (Supplementary Data 5), which all were added and incubated at 5 μg/mL for 30 min at 4 °C. Cells were then washed and incubated with anti-rat Ab magnetic beads at 1 bead/target cell for 40 min at 4 °C (Dynabeads sheep anti-rat IgG, Invitrogen).
Pelleted cells were stored in 700 μl of TRIzol (Life Technologies) at -80C until RNA extraction. Total RNA was extracted using the RNAeasy Micro kit with RNase-Free DNase Set (Qiagen) according to the manufacturer protocol. The quality score and quantity of purified RNA was assessed with a Bioanalyzer RNA 6000 Pico Chip (Agilent). Total RNA was then converted to cDNA, amplified and hybridized to Affymetrix Mouse Transcriptome Array 1. 0 Pico. Raw CEL files were preprocessed and normalized using Affymetrix Expression Console (version 1. 4. 1. 46) and resulting data were analyzed with the Affymetrix Transcriptome Analysis Console (version 3. 1. 0. 5). We calculated fold-differences between experimental groups and tested significance using one-way ANOVA (unpaired). Significantly up and downregulated genes were defined with at least a 1.
The quality score and quantity of purified cDNA library was assessed with Qubit dsDNA HS Assay kit (Life technologies) and Bioanalyzer High Sensitivity DNA assay (Agilent). The library samples were performed on Illumina HiSeq (2X150bp and dual index configuration) by Genewiz Inc. Reads were aligned to the Mouse reference mm10 using STAR aligner (v2. 4. 2a)  53. Quantification of genes annotated in Gencode vM5 were performed using featureCounts (v1. 4. 3) and quantification of transcripts using Kallisto (v0. 46. 1). QC was collected with Picard (v1. 83) and RSeQC 54 (http:// broadinstitute. github. io/picard/; v2. 6. 5). Normalization of feature counts was done using the DESeq2 package (v1. 10. 1). Differentially expressed genes were identify using negative binomial distribution as implemented in DESeq 2 (R package).
Genomic Alignment of sequencing reads; for all sequenced data, paired-end reads were trimmed for adaptors and removal of lowquality reads using Trim_galore (v0. 3. 7). Trimmed reads were mapped to the Mus Musculus genome (mm10 assembly) using Picard (v1. 92). Reads were then filtered to exclude mitochondrial DNA or duplicates using samtools (v0. 1. 19). For peak calling, all positive-strand reads were shifted 4 bp downstream to center the reads on the transposon binding event. We used MACS2 (v2. 1. 0) at a p-value of 0. 01. Irreproducible discovery rate (IDR) calculations using scripts provided by the ENCODE project (https://www. encodeproject. org/software/idr/; v2. 0. 2 and v2. 0. 3) were performed on all pairs of replicates, keeping only reproducible peaks showing an IDR value of 0. 05 or less.
For peak annotation and analysis, peak assignment was done using ChipSeeker (v1. 24. 0)  56. Promoter regions were defined as peaks that overlapped a region that was + /-3 kb from the transcriptional start site (TSS). Intragenic (intronic and exonic) peaks were defined as any peak that overlapped with annotated intronic and exonic peaks, respectively, based on the annotation database. Intergenic peaks were defined as any non-promoter or non-intragenic peaks and were assigned to the gene of the nearest TSS based on the distance from the start of the peak. Conserved and unique open chromatin regions (OCRs) were defined using the bedTools intersect function (v2. 29. 1) at different level of comparison as represented in Fig. 4b. First level of comparison allowed us to identify unique (non-overlapping) peaks between WT N4 and WT T4 (A); MUT N4 and MUT T4 (B); WT N4 and MUT N4 (X); WT T4 and MUT T4 (Y).
We assessed variability among the ATAC-seq datasets by performing Pearson correlation across samples only considering overlapping peaks across conditions. Over-representation of BP GO terms was calculated using PANTHER and visualized in scatterplots using ggplot2 in R. Open Access This article is licensed under a Creative Commons Attribution 4. 0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in a credit line to the material.
Acknowledgements The authors thank the Einstein FACS and genomic core facilities, and  John Wherry  ( U Penn ) for sharing his ATAC-seq protocol. This work was funded by the  National Institute of Health Grants (NIH)   AI103338 ,  Hirschl Caulier Award  to G. L. L. C. received fellowships from  ARC ,  Fondation Bettencourt-Schuller  and the  American Association of Immunology (AAI). S. S. C. and E. G. were respectively supported by  NIH  training grant  T32 AI070117  and  NIH  MSTP training grant  T32 GM7288. Core resources for FACS were supported by the  Einstein Cancer Center  (NCI cancer center support grant  2P30CA013330 ).
Bioinformatic Analysis The predicted amino acid sequences of the alternative oxidase homologues of N. fowleri and N. lovaniensis were obtained from Genbank (NCBI) using the following accession numbers; GCA_008403515. 1 (N. fowleri), GCA_003324165. 1 (N. lovaniensis). The amino acid sequence of the AOX from N. gruberi was obtained from uniprot (D2V4B2). The Phyre2 web portal for protein modelling, prediction and analysis was used in intensive mode  (Kelley et al. , 2015)  to derive the structure for NgAOX. Structures were then downloaded and analyzed using PyMol. For sequence alignment Clustal Omega  (Sievers et al. , 2011)  was used, output was downloaded as fasta file and analyzed using Jalview 2. 11. 0  (Waterhouse et al. , 2009). Sequence identifiers for AOX alignments were: Trypanosoma brucei brucei (Q26710), Cryptosporidium parvum (Q6W3R4), yeast; Candida albicans (A0A1D8PEM4), plant; Arabidopsis thaliana (Q39219), and, fungi; Neurospora crassa (Q01355).
Phylogenetic analysis Three local databanks of proteomes, representative of all diversity from eukaryotes, bacteria and proteobacteria were assembled from the National Center for Biotechnology Information (NCBI): 193 proteomes from Eukaryotes (one per genus); 1017 proteomes from Bacteria (3 per family) and 1082 proteomes from Proteobacteria (1 per genus). Homology searches were performed using HMMSEARCH, from the HMMER-3. 1b2 package  (Johnson, Eddy and Portugaly, 2010) , with the option --cut_ga to screen all the proteomes in the three databanks for the presence of AOX pfam domain (PF01786. 17). All the hits were then manually curated in order to discard false positives. The remaining hits were aligned using  MAFFT-v7. 407 (Katoh and Standley, 2013)  with the linsi option and trimmed with BMGE-1.
Phylogenetic analysis of alternative oxidase proteins We carried out an exhaustive search for AOX homologs in updated eukaryotic and bacterial databanks. As bacterial hits were mainly from proteobacteria, we also searched for AOX homologs in a proteobacteria databank with more diversity within this phylum. We identified 323 AOX homologs: 265 from 193 eukaryotic taxa, 56 from 1,082 proteobacterial taxa, and 2 from 896 bacterial taxa (other than proteobacteria) (Suppl table  S1 ). Regarding the eukaryotic distribution, in addition to previously described groups Alveolata, Euglenozoa, Metazoa, Choanoflagellates, Stramenopiles, Fungi, Rhodophya, Heterolobosea and Viridiplantae  (Pennisi et al. , 2016) , we identified AOX homologs in some eukaryotes that were not reported: Apusuzoa, Amoebozoa, Filasterea, Haptophyceae and Rhizaria.
). Real time respirometry reveals AOX confers cyanide resistance In order to assess whether N. gruberi respires from either the classical respiratory chain or through AOX, real-time respirometry data was collected. Cells were cultured for three days and washed with M7 media without the addition of glucose. Afterwards, 2 ml of cell resuspension was seeded into the respirometer chambers at a density of 10 5 cells per ml. After establishing the routine respiration, a series of drugs were added sequentially to each chamber and oxygen flux was monitored (Figure  5 ). Initially, 1 mM of complex IV inhibitor potassium cyanide was injected into the chambers, revealing an increase in oxygen flux and decrease in oxygen concentration, which would suggest that respiration via alternative oxidase pathway is active, as the presence of cyanide has no effect on N.
Real-world evidence (RWE) has traditionally been used by regulatory or payer authorities to inform disease burden, background risk, or conduct post-launch pharmacovigilance, but in recent years RWE has been increasingly used to inform regulatory decision-making. However, RWE data sources remain fragmented, and datasets are disparate and often collected inconsistently. To this end, we have constructed an RWEgeneration platform, Immunolab, to facilitate data-driven insights, hypothesis generation and research in immunological diseases driven by type 2 inflammation. Immunolab leverages a large, anonymized patient cohort from the Optum electronic health record and claims dataset containing over 17 million patient lives. Immunolab is an interactive platform that hosts three analytical modules: the Patient Journey Mapper, to describe the drug treatment patterns over time in patient cohorts; the Switch Modeler, to model treatment switching patterns and identify its drivers; and the Head-to-Head Simulator, to model the comparative effectiveness of treatments based on relevant clinical outcomes.
Pharmaceutical companies use RWE as part of integrated evidence-generation plans, identifying evidence needs across the product life cycle, to advance understanding of treatment patterns, patient subpopulations, comparative effectiveness, and marketplace adoption, and to support value-based reimbursement scenarios. Research and development teams use RWE to supplement clinical findings, and even to inform patient recruitment in RCTs. However, pharmaceutical research has struggled to fully embrace RWE, and disparate datasets, siloed data environments, and the absence of easily accessible analytic tools have slowed the adoption of RWE. To this end, we have constructed a RWE-generation platform to accelerate evidence generation. This tool aims to demonstrate the value of a "platform strategy"-combining richly curated clinical data, patient cohorts, a variety of analytic techniques, and intuitive dashboards-to help accelerate hypothesis generation using real world data.
Methods Immunolab: an insight-generation platform for the evolving RWE landscape Immunolab is designed to address research questions related to drug development, as well as pre-and post-launch evidence generation needs. This is particularly significant for type 2 inflammatory diseases such as asthma and chronic rhinosinusitis with nasal polyps, which have high prevalence, high rates of comorbidity, and diverse clinical management. Researching type 2 inflammatory diseases requires a "broader" view of data and need of analytic tools to better understand patient treatment journeys, comorbidities, and methods to help predict outcomes. A schematic figure with the different components of the Immunolab platform (hypothesis, data source, treatments, advanced evidence generation engine and results) and how they are connected to each other has been provided in Figure  1.
Infrastructure Immunolab was built on a RWE environment, a secure cloud-based system based on a high-performance computing spark cluster, machine learning (ML) libraries, and data visualization tools. The system was designed to be flexible, allowing easy integration of new tools and capabilities as required by the evolving demands of RWE, while maintaining strong data protection and access integrity. Optum ® de-identified electronic health record (EHR) data source The Immunolab platform utilizes patient cohorts, which are regularly updated, from the Optum de-identified EHR dataset  (2007) (2008) (2009) (2010) (2011) (2012) (2013) (2014) (2015) (2016) (2017) (2018) (2019). Optum data are de-identified in line with the Health Insurance Portability and Accountability Act statistical de-identification rules and managed according to relevant data use requirements  (12, 13).
The Immunolab platform interface Immunolab platform is a user-friendly web-based interface that employs maps, drop-down menus, and intuitive graphs to perform and display analyses. Immunolab allows the user to easily select pre-specified "analytical modules" with a primary purposes of hypothesis generation. Cohorts to be assessed by the respective analytical module can be generated within the Optum EHR dataset by applying automated eligibility criteria based on diagnosis and treatment codes.
An ML algorithm, Light Gradient Boosting Machine (LightGBM  (14) , is trained on this data table and learns to discriminate whether a regimen change will occur for a patient based on the available attributes e. g. , demographic profiles, clinical characteristics of disease, and patient clinical phenotype). For patients experiencing a treatment change, the attributes are calculated based on the period leading up to the change. Attributes for patients who do not experience a regimen change are based on the most recent data available. Relevant drivers of the treatment changes are then derived based on the SHapley Additive exPlanation (SHAP) approach, a unified analytical approach to evaluating the output of different ML models (Figure  4 ). The SHAP value of any single covariate represents its relative effect on the model's prediction.
By assessing the full range of covariates in this way, we can see which covariates are, on average, the key drivers of the model's patient-level prediction. Aggregating all model explanations from all patients in a population provides accurate population-level model explanations. Diseases within the Immunolab real-world data cohort. COPD, chronic obstructive pulmonary disease; EHR, electronic health record; RW, real-world.
Observed values for the endpoints are calculated over the entire treatment exposure, and covariates are calculated for each treatment exposure based on the period before start of exposure. Both approaches attempt to correct for confounders by including these covariates in the outcome model, for Gestimation, or the propensity model, for IPTW. The outcome model and propensity model are estimated using LightGBM, allowing the models to consider a large set of pre-treatment covariates. Switch Modeler-Key drivers of patients with AD switching between treatments. The SM uses SHAP to determine drivers of the observed changes in prescribing patterns. The value of each binary variable data point is indicated by color (blue: low/no; red: high/yes). The effects of the covariate on risk prediction are shown in log-odds scale on the horizontal axis; covariates are listed in descending order by relative importance in terms of driving prescribing patterns in the subpopulation of interest.
Future direction With the increasing availability of digital and digitized healthcare data, and broadening demand for RWE across pharma, standardized methodologies, and platform tools like Immunolab are complementary to the current one-question-ata-time approach to generating evidence. Unlike RCT methodologies, which have for a long time been under the auspices of practice quality guidelines (such as the International Conference on Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use Good Clinical Practice guidelines) and clinical trial registries (ClinicalTrials. gov and EudraCT. eu), rigorous methods are required to optimize RWE in the regulatory context, and international standardization of using RWE in healthcare decision-making has been attempted only relatively recently  (18).
In recent years, the US Food and Drug Administration has developed innovative programs (including the Sentinel Initiative) to accelerate the use of RWE to support assessments of safety and to facilitate label extensions. To do so, a full life-cycle RWE platform is needed that would allow exploring these opportunities by generating hypotheses and testing assumptions. Platform such as Immunolab seek to address this issue by using ML protocols to optimize modeling of patient histories, standardizing analytical methodologies, and constructing consistent outputs based on these analyses. Future expansions integrating new analytic modules and additional data sources into platforms such as Immunolab can put the means of rapid analytic exploration into the hands of researchers, making both data and analytics available to them.
Acknowledgments We thank  M. Thierry Meylheuc  and  Ms. Christine Longin  from the imaging facility at the microscopy and imaging platform MIMA2 ( MIMA2 ,  INRAE , 2018. Microscopy and Imaging Facility for Microbes, Animals and Foods, https://doi. org/10. 15454/1. 5572348210007727E12,Jouy-en-Josas, France) for precious help in SEM and TEM ; the members of the  IERP (INRAE)  and PHEA (CRSA) animal facilities, and of the @bridge histology plateform ( Université Paris-Saclay ,  INRAE ,  AgroParisTech , GABI) ; and  Catherine Brenner  for the use of Seahorse XF device (IGR). Thanks to BioRender for the graphical abstract. Funding was provided by the  ANR-17-CE15-0019-01  grant and a  Marie Skłodowska-Curie Actions  fellowship.