dataesr/scientific-paragraphs-categorization huggingface dataset:
We also determined the intermediate precision based on the acquisition data of more than 4 weeks. The impact of the matrix on the precision and repeatability of t r measurements was also conducted. The results were obtained from the recovery of cyclic siloxanes added to CCl 4 extracts from the stratum corneum, epidermis, dermis, and receptor fluid at concentrations of 1, 5, and 25 µg/mL. We compared the results of precision and repeatability test between the samples containing CCl 4 (standard solutions) and extracts from the layers of the skin, and the impact of the matrix on the dispersion of the retention time of D4, D5, and D6. In addition, the variability of the retention time for the standard was conducted analogously for the internal standard (DNB) added at a concentration of 5 µg/mL to all solutions containing siloxanes (reference and recovery tests).
| Search strategy Articles published in English or Japanese between January 1, 1997 and November 20, 2018 were screened from PubMed, EMBASE, and ICHUSHI literature databases. Search strings were related to seasonal influenza in the Japanese population and included: "influenza"; "epidemiology" [and related terms]; "health economics" [and related terms]; "vaccine*"; "effectiveness"; "Japan"; "adult";as MeSH or Emtree terms; or in the title and abstract of articles (detailed in Supplementary Tables  1 2 3 ). The references of identified systematic reviews and meta-analyses were screened for further citations which were subject to the same selection process. Grey literature from local medical associations and government institutions in Japan were also assessed. These sources included: National Institute of Infectious Diseases, Ministry of Health, Labor, and Welfare (MHLW) Grant system, Japan Physicians Association (JPA), Online Receipt Computer Advantage (ORCA) surveillance, and MHLW Vaccine Committee.
| Meta-analysis methodology and statistical analysis Studies describing the relative risk (RR) of developing influenza in vaccine recipients vs non-recipients were meta-analyzed to estimate influenza vaccine effectiveness (VE) reported in the Japanese literature overall and stratified into predefined categories. Data were first organized into age-stratified RR estimates for each of three age groups: 1) <65 years old, 2) 65 years old and 3) mixed ages. Studies reporting data for multiple years were grouped as single estimates. Age-specific estimates were then categorized according to their study design (cohort vs case control); setting (nursing home/hospitalized vs community); and single or multiple seasons over which the studies were conducted. The dominant circulating influenza type/subtype was defined for each estimate as "H3", "H1", or "B" if >50% of viruses characterized by National Epidemiological Surveillance of Infectious Diseases (NESID) data over the seasons of study corresponded to these viruses; or "mixed" if there was no dominant virus.
15 As B strains dominated in only one season (2004/05), this category was combined with "mixed" for the meta-analysis. Adjusted, reported RR and their confidence intervals (CIs) were used for meta-analysis if provided. Otherwise, RRs and their CIs were calculated from reported data in the article. Four case control studies reported odds ratios (OR). RRs for these studies were estimated, taking the mean baseline risk from the unvaccinated groups of included cohort studies, an approach which resulted in RRs close to original ORs because the outcome is rare (<5%/year; data not shown). 19  Self-reported influenza was collected in surveys. *Presented influenza/ILI attack rates or estimated number of incidence exclusively for elderly population aged ≥50. † Reported VE was used in meta-analysis. If not explicitly reported, VE was calculated using study data.
Health economic burden and cost- effectiveness of preventive measures and peramivir and oseltamivir also performed similarly with respect to the time to defervescence (30. 9 ± 18. 7 hours vs 34. 7 ± 18. 6 hours) or survival rate [95. 7% (22/23) vs 100% (9/9), respectively]. 78 3. 4 | We identified only one health economic study which directly as- sessed influenza costs. Sruamsiri et al, used a nationwide database of Japanese patients with a diagnosis of influenza admitted for at least 2 days between April 2014 and March 2015, and applied a structural equation modeling approach to estimate length of stay and total hospitalization costs. 78 Influenza resulted in mean hospitalizations of 11. 3 days for pa- tients aged 16-64 years, rising to 16. 1 days in patients aged ≥65 years and mean costs of 609,196 JPY in younger adults; and 715,614 JPY in elderly patients (≥65 years).
Molecular and histological characterization of PDXs and PPCs. To assess whether our culture conditions exert a clonal selection pressure  16  , we performed an in-depth molecular analysis of all matched PDX tumors and PPCs. First, we compared global DNA copy number alterations in matched PDX tumors and PPCs (passage<11) using array-CGH (aCGH). Overall, we detected a high level of concordance between PDXs and PPCs (Fig. 3a  and Supplementary Fig. 5 ). Nevertheless, in accordance with previous studies, we also noticed some focal differences in DNA copy numbers between PDX and corresponding PPCs which were more pronounced in DMEM-derived clones (Fig. 3a ). Next, we analyzed the mutational landscape in PDXs and corresponding PPCs by exome sequencing. If available, we also compared data from PDX and PPC with patient germline data.
To test whether histological RMS features are preserved in our models, we generated s. c. xenografts with passage 4-6 PPC cells (cellderived xenografts; CDX) and compared their histological characteristics with the PDX and original patient tumors, if available. Tumor sections were assessed for cell and tissue morphology by haematoxylin and eosin (H&E) staining and for presence of cells with skeletal muscle differentiation by immunohistochemical detection of DES-MIN and MYOGENIN. Impressively, both PDX and CDX show characteristic RMS architecture and a degree of MYOGENIN and DESMIN positivity, which is in line with published data showing that number of MYOGENIN positive cells discriminates ARMS from ERMS (Supplementary Fig. 7A, B ). Altogether, these findings showed that PPCs are epigenetically and genetically stable and faithfully recapitulate tumor histology when transplanted in vivo.
Correlation analysis further underscored the high degree of overlap among different AKTi and MEKi, suggesting on-target activities of these compounds (Supplementary Fig. 9C ). The table depicts all confirmed somatic SNVs in cases with available matched germline sequencing data. For samples without available germline data, comparison to dbSNP and 1000 g databases was used to identify potential non-synonymous SNVs in the same genes. Focal amplifications refer to genes with more than 10 gene copies. c Principal component analysis comparing DNA methylation profiles of RMS PDXs, PPCs and cell lines. DNA methylation data for 15 RMS PDX and PPC pairs, 9 additional PDXs and 10 cell lines were analyzed using the top 5000 most varied DNA methylation probes across all samples. Samples were indicated as color coded dots.
Taken together, heterogeneity at Fig. 6  In vitro and in vivo validation of afuresertib and trametinib for RMS treatment. a Comparative analysis of afuresertib (AKTi, blue) and BI-847325 (MEKi, red) effects on cell viability. Data were computed from the drug screen experiments. Gray and white areas indicate the range below and above 50 percent inhibition of cell viability, respectively, which were chosen as range of high and low drug response (Mean; n = 2 biological replicates). b BLISS synergy scores from the combination treatment of indicated cells treated with a combination matrix of afuresertib and trametinib (Mean with range; n = 2 biological replicates). Scores were determined with the synergyfinder webtool. c Cell viability of indicated PPCs treated for 72 h with increasing concentrations of the MEK inhibitor trametinib.
All primary antibodies were diluted 1:1000 in milk. For secondary antibodies, HRP-linked anti-mouse IgG (Cell Signaling, #7076) and HRP-linked antirabbit IgG (Cell Signaling, #7074) were used at 1:5000 dilution in milk. DNA copy number analysis. Genomic DNA was extracted from PDX pieces or cultured cells using the DNAse® Blood&Tissue Kit (Qiagen, #69506) following the manufacturer's instructions. The aCGH assay was performed using the CytoScan™ HD Array Kit according to the manufacturer's protocol (Affymetrix, Thermo Fisher Scientific, MA, USA). The raw data of each single sample was analyzed with the Chromosome Analysis Suite (ChAS) software (Version 3. 1. 1. 27, Affymetrix). Exome sequencing and analysis. DNA was isolated from PDX and PPCs using the Qiagen DNeasy Kit. For exome sequencing, exome enrichment was performed using the Agilent SureSelectXT Human All Exon V6 kit.
Paired-end sequencing was performed using the Illumina system. The sequences were demultiplexed using bcl2fasta in the illumina CASAVA pipeline to get fastq files. For processing of the data, the whole exome sequencing pipeline web tool was used  43. This included the bwa tool (0. 7. 10) for alignment of the sequences with the human genome, Picard (1. 119) for duplicate removal and GATK (2. 8-1) with the GATK algorithm for calling of SNVs and insertion/deletions. Variants were further filtered using the following criteria: Coverage ≥10, ambiguous mappings per variant ≤5, Phred-scaled consensus quality ≥50, variant confidence quality ≥1. 5, strand bias Fisher exact test ≤60, not present in dbSNP, not present in 1000 g (minor allele frequency <0. 01). Genewise target coverage data was used to identify amplified genes, using a threshold of 10 fold  43.
Genome-wide DNA-methylation analysis. Genomic DNA from eight PDX/PPC pairs was treated with bisulfite (EZ-96 DNA Methylation Kit [Zymo Research])  44  , and then DNA methylation was analyzed on the Infinium MethylationEPIC (EPIC) BeadChip (Illumina). In addition, DNA methylation data from 12 RMS PDXs and 10 conventional RMS cell lines previously generated on the Infinium HumanMethylation450 (HM450) BeadChip were included in this analysis. Raw IDAT files from both sources were processed and normalized using the noob method in the minfi package  45, 46. Only probes that overlapped between EPIC and HM450 were studied in this analysis. Probes with detection P-value >0. 01 in at least one sample, probes located on X and Y chromosome, non-CpG probes, probes containing a SNP at the single-base extension or CpG site, probes with genetic variants overlapping the body of the probes and probes identified as crosshybridizing were discarded  47.
Then, the slides were washed in a 0. 4x saline sodium citrate (SSC) / 0. 3% (vol/vol) IGEPAL solution (Sigma-Aldrich) for 2 min at 72 °C, followed by 1 min in a 2× SSC / 0. 1 % (vol/vol) IGEPAL solution at room temperature. The slides were air dried, and the nuclei were counterstained with Vectashield Mounting Medium containing DAPI (REACTOLAB S. A. , Servion, Switzerland). Microscopic images were acquired using the Axio Imager. Z2 microscope (Carl Zeiss AG, Feldbach, Switzerland) and 50 nuclei were analyzed using the Isis software (MetaSystems Hard & Software GmbH, Altlussheim, Germany). Unsupervised hierarchical clustering. Unsupervised two-way hierarchical clustering was done with the software D-chip. Statistics. Data analysis was performed using GraphPad Prism (version 8). Statistic tests and number of biological replicates (N) per each experiment are outlined in figure legends.
Acknowledgements We acknowledge the expert technical help for animal experiments by  Stephanie Kasper. We would especially like to thank  M. Dyer  and  E. Steward  for providing orthotopic PDX models from  St Jude Children's Hospital through Childhood Solid Tumor Network. The work was supported by grants from the  Swiss National Science Foundation  ( 3100-156923  and  3100-175558 ), the  Clinical Research Priority Program (CCRP)  " Precision Heamatology/Oncology " and the  Childhood Cancer Research Foundation Switzerland  to BS. Concerning samples originating from PARIS, the PDX development was supported by the  Société Française de Lutte contre les Cancers et les Leucémies de l'Enfant et l'Adolescent (Fondation Enfants et Santé) ,  la Ligue Nationale Contre le Cancer , the  Fondation AREMIG , and the  Association Thibault BRIET, la Ligue Nationale Contre le Cancer  and by the following grants:  ERA-NET TRANSCAN JTC 2014  ( TRAN201501238 ),  TRANSCAN JTC 2017 (TRANS201801292 ) and  H2020-lMI2-JTl-201 5-07  ( 116064 -ITCC P4 ).
Data availability The exome seq, array CGH and DNA methylation data has been deposited in the database of genotypes and phenotypes (dbGaP) under the accession code phs002051. v1. p1. All numerical data underlying Figs. 1 2 3 4 5 6 and Supplementary Figs. 1-11 are provided as a source data file. All the other data supporting the findings of this study are available within the article and its supplementary information files and from the corresponding author upon reasonable request. A reporting summary for this article is available as a supplementary information file. PPCs can be obtained from the corresponding author through an MTA. All data associated with this study are present in the paper or the Supplementary Materials. Source data are provided with this paper.
Statistical Analysis Statistical analyses were performed using the Statistica AXAZ software package (StatSoftCR, Czech Republic). All experiments were performed in eight replicates. S. aureus biofilm formation was represented as the average of absorbance values measured at λ = 550 nm (A 550 ) ± standard deviation (SD) per well on 96-well Nunc polystyrene microtiter plates; bioluminescence after 24 h of growth for the bioluminescence-based reporter E. coli pSB1142 exposed to P. aeruginosa supernatant producing long-chain AHLs (C10-C14) was expressed as normalized bioluminescence treated and/or untreated with cBM MSC CM. The average values were recorded as relative light units to bacterial growth (RLU/A 550 ) ± SD. A one way ANOVA and Tukey's post hoc test were used to detect significant differences between the positive control and individual experimental groups, and a statistical significance was accepted at p < 0.
For therapeutic use, naturally occurring AMPs may be a more practical and cost-effective substitute to synthetic ones for traditional antibiotic therapy. To the best of our knowledge, only a few articles have, to date, studied canine MSC immune modulation  [41] [42] [43] , and no canine-derived MSC-related AMPs have been studied. Harman et al. [44]  described the presence of four distinct AMPs produced by equine MSCs, namely cystatin C, elafin, lipocalin 2 and cathelicidin. With the exception of elafin, first described in the abovementioned research article, others have been documented as being produced by MSCs from other sources and species (predominantly human and murine). Similarly, canine MSCs could produce diverse antimicrobial molecules. To date, more than 3244 antimicrobial peptides from six kingdoms (364 bacteriocins/peptide antibiotics from bacteria, 5 from archaea, 8 from protists, 21 from fungi, 360 from plants and 2406 from animals, including some synthetic peptides) have been registered in the Antimicrobial Peptide Database (APD, http://aps.
Euroimmune AG, Lübeck, Germany) following the manufacturer's instructions. They were also tested for IgG to DENV and CHIKV at the National Reference Centre for Arboviruses in Marseille, France (IL-G) with an in-house ELISA for with peroxidase labelled anti-monkey IgG (KPL, Gaithersburg, MD, USA) as a secondary antibody. The remaining 590 sera were also screened for antibodies to DENV and CHIKV with the commercial ELISA kits described above and additionally for ZIKV IgG antibodies (Monkey Zika Virus IgG (ZV-IgG) ELISA Kit, MyBio-Source, Inc. , San Diego, CA, USA). Sera with positive or equivocal ELISA results were repeated and then tested by Plaque Reduction Neutralization Test (PRNT)  [29] [30] [31]  on Vero cells (ATCC #CCL-81) with a cutoff value of 90% (PRNT 90 ). Neutralization curves were generated using GraphPad Prism software, and the resulting data were analyzed by nonlinear regression to estimate the dilution of serum required to inhibit 90% of infection.
Blood-meal analysis Engorged blood-fed female mosquitoes (n = 122) were retained individually and their abdomens aseptically separated from the head and thorax by sharp dissection. Their DNA was extracted using a DNEasy Blood Mini Kit (Qiagen, Hilden, Germany) and used in a qPCR with primers designed to anneal to the hydroxymethylbilane synthase (HMBS) gene as described by Wei et al. [35]. DNA extracted from the whole blood of five AGMs were used as positive controls and their sequences (ELIM BIOPHARM, Hayward, CA, USA) aligned with Clustal Omega  [36]  to obtain a 222 nucleotide sequence for the HMBS gene of Chlorocebus aethiops sabeus (Additional file 2: Text S2) (BankIt2363830 AGM_seq MT742560). Amplicons obtained from the blood-fed mosquitoes were also sequenced and raw sequence data was compared with the AGM HMBS (BankIt2363830 AGM_seq MT742560) and human HMBS sequences NG_008093 on GenBank using Clustal Omega.
However, no single prediction model is recommended by guidelines to predict pre-eclampsia. Risk stratification continues to be based on the presence or absence of individual clinical markers, and not by multivariable risk prediction models. Any recommendation to use a prediction model in clinical practice must be underpinned by robust evidence on the reproducibility of the models, their predictive performance across various settings, and their clinical utility. An individual participant data (IPD) meta-analysis that combines multiple datasets has great potential to externally validate existing models  [7] [8] [9] [10]. In addition to increasing the sample size beyond what is feasibly achievable in a single study, access to IPD from multiple studies offers the unique opportunity to evaluate the generalisability of the predictive performance of existing models across a range of clinical settings.
International Prediction of Pregnancy Complications (IPPIC) Network We undertook a systematic review of reviews by searching Medline, Embase, and the Cochrane Library including DARE (Database of Abstracts of Reviews of Effects) databases, from database inception to March 2017, to identify relevant systematic reviews on clinical characteristics, biochemical, and ultrasound markers for predicting pre-eclampsia  [12]. We then identified research groups that had undertaken studies reported in the systematic reviews and invited the authors of relevant studies and cohorts with data on prediction of pre-eclampsia to share their IPD  [13]  and join the IPPIC (International Prediction of Pregnancy Complications) Collaborative Network. We also searched major databases and data repositories, and directly contacted researchers to identify relevant studies, or datasets that may have been missed, including unpublished research and birth cohorts.
Inclusion criteria for IPPIC validation cohorts We externally validated the models in IPPIC IPD cohorts that contained participants from the UK (IPPIC-UK subset) to determine their performance within the context of the UK healthcare system and to reduce the heterogeneity in the outcome definitions  [14, 15]. We included UK participant whole datasets and UK participant subsets of international datasets (where country was recorded). If a dataset contained IPD from multiple studies, we checked the identity of each study to avoid duplication. We excluded cohorts if one or more of the predictors (i. e. those variables included in the model's equation) were not measured or if there was no variation in the values of model predictors across individuals (i. e. every individual had the same predicted probability due to strict eligibility criteria in the studies).
IPD collection and harmonisation We obtained data from cohorts in prospective and retrospective observational studies (including cohorts nested within randomised trials, birth cohorts, and registrybased cohorts). Collaborators sent their pseudoanonymised IPD in the most convenient format for them, and we then formatted, harmonised, and cleaned the data. Full details on the eligibility criteria, selection of the studies and datasets, and data preparation have previously been reported in our published protocol  [13].
Quality assessment of the datasets Two independent reviewers assessed the quality of each IPD cohort using a modified version of the PROBAST (Prediction study Risk of Bias Assessment) tool  [16]. The tool assesses the quality of the cohort datasets and individual studies, and we used three of the four domains: participant selection, predictors, and outcomes. The fourth domain 'analysis' was not relevant for assessing the quality of the collected data, as we performed the prediction model analyses ourselves since we had access to the IPD. We classified the risk of bias to be low, high, or unclear for each of the relevant domains. Each domain included signalling questions that are rated as 'yes', 'probably yes', 'probably no', 'no', or 'no information'. Any signalling question that was rated as 'probably no' or 'no' was considered to have potential for bias and was classed as high risk of bias in that domain.
Missing data We could validate the predictive performance of a model only when the values of all its predictors were available for participants in at least one IPD dataset, i. e. in datasets where none of the predictors was systematically missing (unavailable for all participants). In such datasets, when data were missing for predictors and outcomes in some participants ('partially missing data'), we used a 3-stage approach. First, where possible, we filled in the actual value that was missing using knowledge of the study's eligibility criteria or by using other available data in the same dataset. For example, replacing nulliparous = 1 for all individuals in a dataset if only nulliparous women were eligible for inclusion. Secondly, after preliminary comparison of other datasets with the information, we used second trimester information in place of missing first trimester information.
For example, early second trimester values of body mass index (BMI) or mean arterial pressure (MAP) were used if the first trimester values were missing. Where required, we reclassified into categories. Women of either Afro-Caribbean or African-American origin were classified as Black, and those of Indian or Pakistani origin as Asian. Thirdly, for any remaining missing values, we imputed all partially missing predictor and outcome values using multiple imputation by chained equations (MICE)  [17, 18]. After preliminary checks comparing baseline characteristics in individuals with and without missing values for each variable, data were assumed to be missing at random (i. e. missingness conditional on other observed variables). We conducted the imputations in each IPD dataset separately. This approach acknowledges the clustering of individuals within a dataset and retains potential heterogeneity across datasets.
We generated 100 imputed datasets for each IPD dataset with any missing predictor or outcome values. In the multiple imputation models, continuous variables with missing values were imputed using linear regression (or predictive mean matching if skewed), binary variables were imputed using logistic regression, and categorical variables were imputed using multinomial logistic regression. Complete predictors were also included in the imputation models as auxiliary variables. To retain congeniality between the imputation models and predictive models  [19] , the scale used to impute the continuous predictors was chosen to match the prediction models. For example, pregnancy-associated plasma protein A (PAPP-A) was modelled on the log scale in many models and was therefore imputed as log(-PAPP-A).