dataesr/scientific-paragraphs-categorization huggingface dataset:
Opportunistic infections were defined by search criteria pre-specified by UCB. [9] These criteria identified preferred terms from MedDRA to be considered when identifying opportunistic infections. Some preferred terms required evaluation on a case-by-case basis by the study physician. Patients could report adverse events spontaneously, following prompts from investigators at each trial visit, or through self-assessment materials including continuous measurement of mental health for depression and/or suicidality. An independent Data Monitoring Committee and Independent Cardiovascular, Gastrointestinal, Hepatic, and Neuropsychiatric Adjudication Committees regularly reviewed safety data. All potential IBD events were evaluated and classified by the independent Gastrointestinal Adjudication Committee.
A summary of the imputation methodologies used is provided in the main text. In instances where MI is used, the missing value was replaced by a set of plausible values, where each value was a Bayesian draw from the conditional distribution of the missing data given the observed data. Intermittent missing data was imputed using the Markov-Chain Monte Carlo method, followed by regression for monotone missing data. The planned multiple imputation procedures were based on an assumption of data missing at random. All statistical analyses were performed using Statistical Analysis System ® (SAS ® ) Version 9. 3 or higher. were included as fixed effects, and baseline values as covariates. ANCOVA: analysis of covariance; BKZ: bimekizumab; BASDAI: Bath Ankylosing Spondylitis Disease Activity Index; CI: confidence interval; CRP: C reactive protein; MI: multiple imputation; MRI: magnetic resonance imaging; nr-axSpA: non-radiographic axial spondyloarthritis; PBO: placebo; Q: question; r-axSpA: radiographic axial spondyloarthritis; Q4W: every 4 weeks; SE: standard error; TNFi: tumor necrosis factor inhibitor.
From Week 20 onwards, all patients were eligible for non-biologic rescue therapy at the investigator's discretion. All patients, investigator site personnel, and operational staff employed by the sponsor remained blinded to the initial treatment group assignment until completion of the maintenance period at Week 52. Study sponsor personnel who were not involved in conducting the trial remained blinded until the database lock for the Week 24 analysis. In response to the COVID-19 pandemic, a protocol amendment was made to include supportive analyses of an additional COVID-19 free data set, enabling the impact of the pandemic on the BE MOBILE trials to be evaluated. The COVID-19 free set consists of all patients in the randomized set who had no COVID-19 impact up to the primary efficacy endpoint at Week 16.
MeV infection upregulates type I interferon and leads to APOBEC3A expression To investigate the effects of viral stress on the expression of ISGs, we used the Schwarz liveattenuated vaccine strain of MeV, (GenBank accession no. AF266291. 1) [27]. To optimize the conditions of infection, THP-1 cells were infected with MeV at a multiplicities of infection (MOI) 0. 01, 0. 5, 0. 1, 1 and 3. The IFN-inducible THP-1 human acute monocytic leukemia cell line was chosen as it is permissive to MeV infection and facilitates viral replication  [18]. At 24 hours post-infection (hpi), total RNA was extracted and IFN-α and IFN-β expressions were quantitated by  RT.
Blood was collected 24 hours before infection, and mice were sacrificed 24 hours after infection, spleen and blood were collected and CD11b + cells were isolated. As visualized in Fig  2G , when compared to MeV-ΔV and NI, MeV generated a ~1. 5-fold increase in cytosolic mtDNA. MeV and MeV-ΔV replication levels were similar as analyzed by SYBR RT-qPCR on the MeV M gene, prior and post-infection (not shown). Altogether these data indicated that the source of cytosolic DNA is of mitochondrial origin, and MeV-V protein appears to be implicated in the mitochondrial network damages, allowing the release of mtDNA in the cytosol, evidenced in both cell culture and in vivo.
Cells overexpressing a STrEP-mCherry protein were used as control to evaluate the amount of RNA-nonspecific binding. At 24 hpi with-MeV or MeV-ΔV, RIG-I-specific RNA ligands were purified from total cell lysates by affinity chromatography. To identify the nature of bound RNA molecules to RIG-I, Next Generation Sequencing (NGS) analysis was performed using the Illumina technology and was normalized to non-specific RNA binding to STrEP-tagged-Cherry protein. We observed that there is ~6. 5 times more mtRNA than viral RNA binding RIG-I, demonstrating that one of the main sources of RNA binding to RIG-I, eventually cascading into IFN/A3A productions, is of mitochondrial origin (Fig  6A ). To highlight the binding specificity of mtRNAs to RIG-I vs m-Cherry during MeV or MeV-ΔV infection, the raw sequence counts obtained for each position were processed using the DESeq2 R package and aligned to the mitochondrial genome.
Analyses of MT-COI mutations reveal an APOBEC3A signature To analyze the mtDNA mutational spectrum, THP-1 infected cells at MOI 0. 1, 0. 5, 1 and 3 were subjected to digitonin fractionation to obtain purified mitochondria and cytosolic To prove that editing occurs outside of the mitochondria, A3A-A3H-V5 tagged constructs were transfected and mitochondrial localization was analyzed by confocal microscopy using TOM20 as a mitochondrial marker. As observed in S7 Fig, no colocalization of any A3 enzyme with the mitochondrial network was observed. Altogether, these data demonstrated that upon mtDNA release in the cytosol, the IFN pathway was induced, leading to A3A production. In turn, A3A edited mtDNA in its specific 5 0 TpCpG signature to shut down the cytosolic DNA danger signal.
Anti-ALAS1 (1/1000) and the cellular fractionation validation was performed on whole cell lysates as well as mitochondrial and cytosolic partitions. 18 h post-MeV infection, cell lysates were assayed for proteins of the IFN pathway using antibodies from the RIG-I pathway antibody kit (Cell Signaling) according to manufacturer recommendations. Anti-MeV-V rabbit polyclonal antibody was kindly provided by Dr. Kaoru Takeuchi  [47]. DNase I was assayed using an anti-DNase I antibody (1/500; Thermo Scientific). After imaging, proteins were quantified by normalization to β-actin or MEK1/2 using ImageJ software.
Deep sequencing was performed with Illumina cBot and GAIIX technology. Sequenced reads were cleaned of library adapter and base pairs occurring at 5 0 and 3 0 ends with a Phred quality score <20 were trimmed off by Alientrimmer  [51]  (https://gitlab. pasteur. fr/GIPhy/AlienTrimmer). Reads with a minimum length of 35 were subject for the next analysis. For the SNP analysis, duplicated reads were removed with Picard-tools v2. 23. 3 (https:// broadinstitute. github. io/picard/; Broad Institute). Single Nucleotide Variants (SNP) detection was performed with bcftools mpileup V1. 13  [49]  to extract and detect their occurrence. This workflow of analysis was resumed in a nextflow workflow  [52]  available at https://gitlab. pasteur. fr/aghozlan/measles.
Based on the model, we suggest that neutrophils could serve as a promising target against psoriasis, either alone or in combination with other components of the IL-17/PGE2 axis components. While the model recapitulates the known events of the disease, it will be interesting to challenge predictions of the model with new experimental data, however, this is beyond the scope of this work. As in every modeling effort, the model's ability to represent specific events is limited by the available knowledge about the mechanisms underlying such events. Additionally, technologies such as single-cell RNA sequencing could aid with defining and fine-tuning specific mechanisms and parameters of the model, such as the cell population sizes in normal and treated conditions. However, only a few datasets are currently available for psoriasis.
Model construction A Prior Knowledge Network (PKN) encompassing the main inter-and intra-cellular signaling events taking place in the development of psoriasis was manually curated from scientific publications about the disease and its progression. The PKN consists of biological entities (nodes) with a reported role in the disease and their regulatory interactions (edges). Each node represents a cell type in various physiological states (called Cell nodes), a ligand or its respective receptors, a transcription factor, or other intermediate signaling proteins (collectively called Signaling nodes). The interactions between nodes were collected from the available literature and from databases with causal molecular interactions, such as SIGNOR 3. 0 (Perfetto et al. , 2016). Over decades of research on the disease, multiple innate and adaptive immune cells have been described for their role in psoriasis.
Acknowledgments This work was supported by a  French Government  grant managed by the  French National Research Agency (ANR)  under the  program ''Investissementsd'Avenir " with reference  ANR-11-LABX-0021-01-LipSTIC Labex. We thank the  LipSTIC Labex , the  Regional Council of Bourgogne Franche-Comté  and the  FEDER  for supporting this work. TA was supported by  La Ligue contre le Cancer. CH and FV were supported by the  French ''Investissements d'Avenir" program , project  ISITE-BFC  (Contract  ANR-15-IDEX-03 ).
Data analysis Data were analyzed with the Cytognos Infinicyt v2. 0 software. NK cells were gated according to the CD45+/CD56+/CD3-/CD19-pattern of expression that excluded T (CD45 + CD3 + CD19-CD56-) and B lymphocytes (CD45 + CD19 + CD3-CD56-). NK subpopulations were identified based on the expression patterns combination of the CD56, CD94, CD16, and CD57 markers. The immature subpopulation was defined as CD56 bright CD94 hi CD16 -CD57-, the mature subpopulation as CD56 dim CD94 med CD16 + CD57-, and the hypermature subpopulation as CD56 dim CD94lowCD16 + CD57+. For analyzing the flow cytometry data in a reproducible and objective manner we took advantage of the Principal Component Analysis (PCA), an algorithm implemented to reduce data variance and interpret multidimensional information  (40).
Statistical analysis We performed the statistical analysis using Graph Pad Prism 8. 0. 1™ (Graph Pad Software, San Diego, CA, United States) and SPSS v20™ (IBM SPSS Software, Chicago, IL, United States). Figures and tables were created in Graph Pad Prism 8. 0. 1™. Bar graphs and scatter dot plots show means with standard errors of means (SEM). We analyzed the differences in means of normally distributed variables using the unpaired t-test, and of non-Gaussian distributed data using the Mann-Whitney non-parametric test. Distribution of each KIR gene in the AML and healthy control groups was determined by the use of contingency tables and Chi squared test was calculated to determine the significance of the differences between various allelic groups. One-sample t-tests were used for analyzing the individual ratios of various NK cell populations and Pearson's and Spearman's correlation coefficients were implemented to assess positive associations between the measured variables.
BACKGROUND: Type 2 cytokines IL-4/IL-5/IL-13 play an important role in pathogenesis of type 2 conditions, including asthma. Dupilumab, a human monoclonal antibody, blocks the shared receptor component for IL-4/IL-13, inhibiting signaling. In phase 2b (P2B) (NCT01854047) and phase 3 VENTURE (NCT02528214), dupilumab reduced annualized severe exacerbation rates (AER), improved forced expiratory volume in 1 second (FEV 1 ), and was generally well tolerated in patients with uncontrolled, moderate-to-severe, or oral corticosteroid (OCS)e dependent severe asthma. OBJECTIVE: The post hoc assessment of dupilumab efficacy versus placebo in P2B and VENTURE in patients stratified by allergic status. METHODS: Allergic asthma was defined as total serum IgE ‡30 IU/mL and ‡1 perennial aeroallergenespecific IgE ‡0.
9, 10  Dupilumab, a fully human monoclonal antibody, blocks the shared receptor component for IL-4 and IL-13, thus inhibiting their signaling. 11, 12 It is indicated for use in patients with type 2 inflammatory diseases, including atopic dermatitis, asthma (regardless of allergic status), and chronic rhinosinusitis with nasal polyps. 13, 14 In the phase 2b doseranging study (P2B; NCT01854047), add-on dupilumab 200/ 300 mg every 2 weeks (q2w) versus placebo reduced annualized rates of severe exacerbations and improved lung function, and was generally well tolerated, in patients with uncontrolled, moderate-to-severe asthma. 15 In addition, in the phase 3 LIB-ERTY ASTHMA VENTURE study (NCT02528214), add-on dupilumab 300 mg therapy significantly reduced oral corticosteroid (OCS) use while simultaneously reducing the rate of severe asthma exacerbations and improving lung function in patients with OCS-dependent severe asthma.
Statistical analysis Data were analyzed for subgroups of patients with allergic asthma versus those not meeting criteria for allergic asthma for each study separately in the intent-to-treat (ITT) population for efficacy parameters and in the exposed population for biomarkers. In P2B, data were combined across dupilumab q2w doses. Annualized severe exacerbation rates over the 24-week study periods were analyzed using a negative binomial regression model; total number of events onset between first dose date and last dose date þ 14 days for P2B and between first dose date and week 24 or last contact date (whichever came earlier) for VENTURE were the response variables, with treatment group, pooled countries/regions, number of severe exacerbation events before the study, and baseline eosinophil strata (plus baseline optimized OCS dose for VEN-TURE) as covariates, and log-transformed standardized treatment duration as an offset variable, for both studies.
Change from baseline in pre-BD FEV 1 and FEV 1 /FVC ratios, ACQ-5 score, and AQLQ global score were assessed in each subgroup using mixed-effect models with repeated measures. Treatment group, pooled countries/regions, baseline eosinophil strata, visits, baseline optimized OCS dose (for VENTURE), treatment-by-visit interaction, baseline values, and baseline-by-visit interaction were covariates; age, gender, and height were added as covariates for pre-BD FEV 1 and FEV 1 /FVC ratio in VENTURE. For VENTURE, absolute and percentage reductions of the OCS dose at week 24 were analyzed in each subgroup using an analysis of covariance (ANCOVA) model; treatment group, baseline optimized OCS dose, pooled countries/regions, and baseline eosinophil strata were covariates. For patients who discontinued or had missing data on oral glucocorticoid dose at week 24, missing data were handled with the use of a pattern-mixture model by multiple imputations.
For VENTURE, the proportion of patients with a !50% reduction in the OCS dose-achieving the reduction of the OCS dose to <5 mg/d, achieving the maximum possible reduction of the OCS dose per protocol, and no longer requiring OCS at week 24 (for patients with the baseline OCS dose 30 mg/d)-were compared in each subgroup using a logistic regression model; treatment group, optimized OCS dose at baseline, pooled countries/ regions, and baseline eosinophil level subgroup were covariates. Missing data were imputed the same way as for the percentage reduction of the OCS dose. Change from baseline in type 2 biomarkers and specific IgE were assessed in the exposed population, and separately in each subgroup, using rank ANCOVA models due to non-normal distribution of the end points; baseline biomarker level, pooled countries/regions, baseline eosinophil strata, and baseline optimized OCS dose for VENTURE were covariates.
Similar analyses were performed in patients with evidence of allergic asthma for percent change from baseline in IgE-specific allergens (only patients with positive specific allergens at baseline were included in the analyses). Across all analyses, significance was attributed as P <. 05. As all predictive analyses were post hoc, all P values were not adjusted for multiple testing and should be considered nominal. Analyses were performed using SAS version 9. 4 (SAS Institute, Inc, Cary, NC).
0 (Biomatters Ltd. , Auckland, New Zealand)  [23]  and compared with those available in the GenBank database by the Basic Local Alignment Search Tool (BLAST; http://blast. ncbi. nlm. nih. gov/Blast. cgi) for species identification. Nested PCRs were performed for Giardia spp. and Cryptosporidium spp. as follows. For Giardia, a nested PCR amplifying partial triosephosphate isomerase (tpi) gene (532 bp) was used, to also detect all known assemblages  [24, 25]. Another nPCR was used to detect Cryptosporidium spp. , targeting a fragment of the 18S rRNA gene  [26].
Pathogens Target gene Primers Sequence (5 0 -3 application within MEGA7 software  [37]. The Akaike Information Criterion (AIC) option in MEGA7 was used to establish the best nucleotide substitution model adapted to each sequence alignment. Tamura-Nei model with a invariant sites (I)  [38]  was used to generate the gltA and the 16S rRNA trees. A maximum likelihood (ML) phylogenetic inference was used with 2000 bootstrap replicates to generate the phylogenetic tree in MEGA7. Homologous sequences of Rickettsia were used as outgroup to root the trees, including the gltA sequences from Rickettsia belli and Rickettsia canadensis (AB297809), and the 16S sequence of Rickettsia parkeri (NR118776).
Lastly, Cryptosporidium spp. 18S rRNA gene nPCR revealed positive samples for Cryptosporidium sp. apodemus genotype I (MH912997; 92% of nucleotide identity) in one Moroccan worm lizard. Also, the nPCR yielded positive results for bacteria such as Pseudomonas aeruginosa (CP050335; 99. 5% of nucleotide identity) in one Egyptian cobra, and Morganella morganii (CP032295; CP064828; 98. 5% of nucleotide identity) in one puff adder and one Montpellier snake. Additionally, fungi (Diutina catenulate-MK394156; 99. 8% of nucleotide identity) were detected in an Egyptian cobra. Representative sequences herein generated were deposited in GenBank (accession numbers OQ633057 to OQ633061 for gltA; OQ630499 to OQ630503 for 16S rRNA; OQ632771 to OQ632773 for 18S rRNA; OQ672452, OQ695490 and OQ695491 for cox1 and 18S rRNA).
Discussion A wide diversity of microorganisms and parasites were detected from reptiles that are brought to the souks of Jemaa-El-Fna square in Marrakech. Importantly, while some of the identified parasites are specific of reptiles and are non-pathogenic (i. e. , Hepatozoon), many of the bacteria, fungi and parasites detected have a zoonotic potential. Data demonstrated the presence of vector-borne (e. g. , Anaplasma, Rickettsia, Leishmania), as well as orally transmitted zoonotic pathogens (i. e. , Cryptosporidium, Pseudomonas, Morganella and Diutina) associated to reptiles thus representing a risk of infection to vendors and charmers of the souks of Marrakech. Overall, the 118 reptile specimens examined fairly represent the typical Moroccan herpetofauna associated to the practices of traditional medicine, magic, and snake charming  [5].
Phylogenetic analysis Obtained sequences were first analyzed using SnapGene Viewer. Forward and reverse reads were then aligned and merged in MEGA 7. 0 software to obtain complete sequences. These sequences were aligned to reference sequences reported in GenBank using ClustalW algorithm of MEGA 7. 0 software. Sequences were afterwards trimmed and phylogenetic tree was constructed by the Maximum Likelihood method (using Kimura 2-parameter model) implemented in MEGA 7. 0. Bootstrap values are based on 1000 repetitions. The tree topology was also confirmed with the Neighbor-Joining (implemented in MEGA 7. 0). The new Belgian sequences reported in this study are available in GeneBank under accession numbers OP413851-OP413899. Genetic divergence was computed with MEGA 7. 0 using the p-distance model and applying default settings.
Rickettsia spp A qPCR targeting the gltA gene was used to test for the presence of Rickettsia spp. in a total of 715 ticks. This included 318 ticks collected from 3 sites in the province of Luxembourg, given its high prevalence of Rickettsia in cattle sera (Table  1 ), and 397 ticks collected from 3 sites in the province of East Flanders (Fig. S1 ). Rickettsia DNA was detected in 7. 1% (CI 95% :11. 2-16) of the tested ticks (Table  4 ). DNA samples of 51 Rickettsia-positive Ixodes ticks were sequenced and a total of 49 sequences of gltA gene was successfully obtained and analyzed. The phylogenetic tree (Fig. 4 ) shows that 48 out of 49 Belgian sequences were identified as R. helvetica. These sequences exhibited at the nucleotide level 99. 6-100% identity to the reference sequence (GenBank: U59723) and were 99.
Acknowledgments We acknowledge the interesting help and advice from  Dr. Laura Lucchese  from  Instituto Zooprofilattico Sperimentale delle Venezie, Legnaro, PD, Italy  for the Rickettsia spp. serodiagnosis. We thank also  Prof. Dr. Cornelia Silaghi  from the  Friedrich-Loeffler-Institut, Germany  and  Dr. Ana Santos  from  Centro de Estudos de Vectores e Doenças Infecciosas ,  Instituto Nacional de Saúde Dr Ricardo Jorge, Lisboa, Portugal  for positive controls for the serodiagnosis of A. phagocytophilum in cattle and diagnosis of A. phagocytophilum by qPCR, respectively. We acknowledge also  Nick De Regge  and  Sophie Vanwambeke  for the interesting scientific discussions. We are additionally grateful to  Martine Marin ,  Tiziano Fancello ,  Ana Soarez ,  Marwa Rabhi ,  Françoise Maréchal  and  Sophie Vroomen  for their technical assistance, and for  Charlotte Sohier  and  Claudia Van Den Eynde  for helping with tick collection.
To extract the immune-disparities, we analysed the well-established tumour immune-landscapes in The Cancer Genome Atlas (TCGA) datasets, to delineate the most T cell-depleted tumours  (Thorsson et al. , 2018). Previously, an immunogenomics analysis had identified six pan-cancer immune-landscape classes (C1-to-C6; for class-labels, see X-axis of the heatmap in Fig. 1A )  (Thorsson et al. , 2018). We uniformly analysed them, across 3546 patients spanning 13 cancer-types, for major immune cell fractions or their ratios, and T cell-receptor (TCR) richness (a metric for TCR poly-clonality) (Fig. 1A )  (Naulaerts et al. , 2021; Thorsson et al. , 2018). As compared to C1-C3/C6, C4/C5-tumour immune-landscapes showed the most striking depletion of CD4 + /CD8 + T cell-fractions, TCR richness, and even DCs abundance (Fig.
1A ). Contrastingly, C4/C5-tumours had high ratios of macrophages-to-CD8 + T cell fractions and M2 (anti-inflammatory) to M1 (pro-inflammatory) macrophage-polarizations (Fig. 1A ). Consequently, in an integrated multi-cancer dataset of 704 patients (spanning 5 cancer-types) whose tumours were transcriptome-profiled before ICBs-treatment  (Eddy et al. , 2020) , overall survival of patients with C4/C5-tumours was significantly shorter than those with immunogenic C2/C3/C6-tumours (Fig. 1B ). This highlighted the suitability of TCGA C4/C5-tumours as transcriptomic representatives of ICB-resistant non-immunogenic tumours, to which we will refer to as T cell-depleted tumours. Next, we looked at the transcriptomic inverse correlation between expression of ligand-receptor, cellreceptors, or cell-ligand, pairs in TCGA C4/C5-tumours e.
Murine TC1-tumours phenocopy key immune-disparities of human T cell-depleted tumours To formulate a preclinical DCvax-IT, it was necessary to delineate a pre-clinical syngeneic tumour model mimicking the major immune-disparities of human T cell-depleted tumours. Therefore, we conducted a reanalysis of an existing dataset composed of tumour transcriptomes of 11 commonly used (subcutaneous) murine tumour models in immuno-oncology  (Mosely et al. , 2017)  (Fig. 2A ). This comparative expression analyses involved genetic signatures (Supplementary Table  1 ) for prolymphocytic IFNγ/effector signalling, macrophages, type I IFN/ISG-response, or DCs  (Mosely et al. , 2017; Sprooten et al. , 2021). This revealed that the triple-oncogene driven c-H-Ras + HPV16-E6 + HPV16-E7 + TC1-tumours  (Smahel et al.