dataesr/scientific-paragraphs-categorization huggingface dataset:
3, 4, 7, 9 fter decentralised (12 countries) or centralised (eight countries) antimicrobial susceptibility testing, all countries participating in Euro-GASP reported their antimicrobial susceptibility data through The European Surveillance System (European Centre for Disease Prevention and Control, Stockholm, Sweden). In addition to the antimicrobial susceptibility data, countries reported epidemiological data that were linked to the gonorrhoea cases through The European Surveillance System. 3, 4, 7 Epidemiological data collected with each isolate, where possible, included the date that the clinical specimen was obtained, the anatomical site that the specimen was isolated from, sex, age, sexual orientation, previous gonorrhoea diagnosis, other sexually transmitted infections diagnosed during the current gonorrhoea infection, place of residence, type of clinic that the patient visited, HIV status, and probable country of infection.
All gonococcal isolates were cultured as part of routine diagnostics, and isolates or data were submitted to the Euro-GASP surveillance study with no information that would enable patient identification; ethical approval was, therefore, not required. We tested viable isolates for which country representatives had permitted molecular typing with WGS. Antimicrobial susceptibility testing was done to determine the minimum inhibitory concentration (MIC) with Etests (bioMérieux; Marcy-l'Étoile, France) for cefixime and ceftriaxone, and an agar dilution breakpoint method or Etests for ciprofloxacin, azithro mycin, and spectinomycin. SIR (susceptible, intermediate, resistant) breakpoints from the European Committee on Antimicrobial Susceptibility Testing were applied.
Results Quality-checked WGS data linked to antimicrobial resistance profiles and epidemiological data were obtained for 1054 (87%) of 1218 isolates from 20 countries (no isolates available from Ireland). Sex of the patient was reported for 1045 (99%) of 1054 isolates and age of the patient was reported for 1023 (97%) isolates. Among the 1045 patients with reported sex, 884 (85%) were male and 161 (15%) were female. The median age was 31 years for men and 24 years for women, with a peak in the 25-30 age group for men (185 [21%] of 861 isolates with reported patient age and sex) and in the 20-29 age group in women (50 [31%] of 160 isolates with reported patient age and sex). Sexual orientation of the patient was reported for 499 (47%) of isolates, of which 294 (59%) were from a patient with a reported heterosexual orientation.
Despite the small number of discrepant isolates, this highlights the clinical importance of standardised and quality-assured phenotypic testing and WGS analysis in future Euro-GASP surveys. As previously described,  4, 7  the main limitations of Euro-GASP include the low number and possibly suboptimal representativeness of isolates from some countries, lower number of isolates from women, and low level of reporting of some key epidemiological variables, such as sexual orientation. However, the representativeness of Euro-GASP was evaluated in detail in 2017. This study  24  showed that the overall prevalence of antimicrobial resistance reported by Euro-GASP appropriately reflects the antimicrobial resistance situation in EU/EEA and weighting of the data did not provide very different overall antimicrobial resistance estimates.
6A ). As expected, a low frequency of CD103 + migDC subsets was observed in both transplanted pLNs and mLNs (Fig. 5b-c ), closely resembling the DC subset composition in endogenous pLNs (Supplementary Fig. 6B ). These data suggest that the composition of migDCs is mainly influenced by the skin microenvironment, independent of the origin of the transplanted LN. In contrast, resDCs maintained their mLN-specific subset composition (Supplementary Fig. 6B ) within transplanted mLNs (Fig. 5b  and d ), demonstrating that mLN SCs can modulate resDCs at the compartment level. To elucidate the modulation of resDCs and migDCs by mLN SCs at the molecular level, we isolated resDCs and migDCs from transplanted mLNs and pLNs eight to sixteen weeks after transplantation and performed transcriptome analysis by RNAseq L.
Interestingly, for both resDCs and migDCs a fraction of ~10% of the total location-dependent DEGs (log 2 FC = 0. 8, qvalue < 0. 05) was stably maintained in transplanted mLNs (Fig. 5e , Supplementary Fig. 6C ). migDCs retained the expression of characteristic marker genes, such as Ccr7, Arc, and Irf4  [35] [36] [37]  , as well as key biological pathways (Supplementary Fig. 6D  and E ). Interestingly, the skin DC marker genes Cd209, Cldn1, Mgl2, and Cd84 38-41 remained stably down-regulated in resDCs from transplanted mLNs (Fig. 5f ). Together, these data demonstrate that mLN SCs can modulate both migDCs and resDCs at the molecular level, thereby shaping their immunomodulatory properties. resDCs from transplanted mLNs retain Treg-inducing capacity. Finally, we wanted to assess the Treg-inducing capacity of resDCs and migDCs shaped by mLN SCs.
When culturing resDCs from mLNs and pLNs in the presence of the Bmpantagonist Noggin, mLN resDC-mediated Treg induction was significantly reduced (Fig. 6f ), whereas exogenous supplementation with Bmp2 resulted in an increased frequency of de novo  f Scatterplots show frequencies of Foxp3 + Tregs among CD4 + T cells after co-culture with resDCs obtained from indicated locations with or without supplementation of exogenous Noggin (100 ng/ml). Data pooled from two to three independent experiments (n = 3-7). migDC, migratory dendritic cell (Lin -CD11c + MHCII high ); resDC resident dendritic cell (Lin -CD11c high MHCII + ); Lin CD3 + CD45R + Ly6G + F4/80 high , Endo endogenous, Tx transplanted induced Foxp3 + Tregs in the presence of mLN resDCs (Supplementary Fig. 7B ). Together, these results suggest that mLN SCs can directly modulate resDCs and instruct them with Treg-inducing properties, thereby substantially contributing to the maintenance of intestinal tolerance.
It is important to note that the microbiota-dependent imprinting of tolerogenic properties within mLN SCs is not merely restricted to the neonatal phase as colonization of adult GF mice also resulted in stable Treg-inducing properties within mLNs, implying that mLN SCs retain functional flexibility until encountering microbiota-derived signals. These findings contrast with other microbiota-dependent tolerogenic mechanisms known to operate solely during the neonatal phase but not in adults  1, 3, 4. Surprisingly, microbiota-derived signals scarcely impacted the transcriptomes of FSCs. In accordance with previous comparative microarray analyses  23, 24  , a large number of DEGs were identified between FSCs from pLNs and mLNs. Together, these data suggest that anatomic localization mainly determines the transcriptomes of FSCs rather than colonization status.
2% BSA. Cells were washed, resuspended in PBS containing 0. 2% BSA and measured at LSRII or LSR Fortessa flow cytometers with Diva software (BD Biosciences). Data were analyzed using FlowJo software (Tree Star). LN transplantations. For transplantations into the popliteal fossa, BALB/c-recipient or C57BL/6-recipient mice were anesthetized with ketamine (WDT) and xylazine (CP Pharma), the skin of the popliteal fossa of the right hind leg opened, and the endogenous pLNs and surrounding fat tissue removed. pLNs or mLNs dissected from donor mice were placed into the popliteal fossa, and the cut sewn with absorbable suture (Catgut). Before subjecting to further experimental procedure, recipients were housed for a minimum of eight weeks to ensure restoration of the lymphatic and blood vessel system.
The initial PCR was performed for 20 cycles with the following primers: 27 F: 5′-AGAGTTTGATCMTGGCTCAG-3′; 338 R: 5′-TGCTGCCTCCCGTAGGAGT-3′ Barcodes, indexes and Illumina sequence adaptors were appended via two additional PCR reactions. Firstly, a barcode was added in 15-cycle PCR reaction using the following primers: F2: 5′-ACACTCTTTCCCTACACGACGCTCTTCCGATCTTTATGCCAAGAGTT TGATCMTGGCTCAG-3′; R2: 5′-AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGACG CTCTTCCGATCT-3′ (Exemplary barcode indicated in italic). In a second step, an index was added in a 10-cycle PCR reaction using the following primers: F3: 5′-AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGACG CTCTTCCGATCT-3′; R3: 5′-CAAGCAGAAGACGGCATACGAGATCGT GATGTGACTGGAGTTC-3′ (Exemplary index indicated in italic). For the bioinformatics processing, paired-end raw sequences were assembled  60  , subsequently aligned (gotoh algorithm with reference database SILVA) and preclustered (diff = 2) using MOTHUR.
Obtained phylotypes were filtered for an average abundance of ≥ 0. 001% of all samples. Annotation was performed as previously described  59. Non-metric multidimensional scaling analysis (NMDS) is based on Bray-Curtis similarity on the phylotype level using PRIMER. Shannon index was calculated with R's package phyloseq 61. SC isolation. For SC isolation, skin-draining pLNs (inguinal and axillary or popliteal) or mLNs (small intestinal and colon/caecum-draining) were resected and digested in RPMI 1640 medium (Gibco) containing 0. 2 mg/ml collagenase P (Roche), 0. 15 U/ml dispase (Roche) and 0. 2 mg/ml DNase I (Roche) as described previously  23. After digestion, cells were kept at 4 °C in PBS containing 0. 2% BSA and 5 mM EDTA (Roth). CD45 -cells were enriched by autoMACS separation after magnetic labeling of CD45 + cells using anti-CD45-APC (30-F11, eBioscience) followed by anti-APC microbeads (Miltenyi Biotec) or anti-CD45 Nanobeads (Biolegend).
Following purification, the mRNA was subjected to library preparation using Script Seq v2 Library preparation kit (Epicenter). Sequencing was carried out on Illumina HiSeq2500 using 50 bp single reads. Sequenced libraries were assessed for read quality using the FastQC tool and clipping performed using default settings of fastq-mcf. The Gene Expression Omnibus (GEO) accession number for the RNAseq data reported in this paper is GSE116633. Library preparation low-input RNA-seq. Total RNA was extracted from FACSsorted FSCs and resDCs using the RNeasy Plus Micro Kit (Qiagen). cDNA was synthesized and amplified using template switching technology of the SMART-Seq v4 Ultra Low Input RNA Kit (Clontech Laboratories), followed by purification using the Agencourt AMPure XP Kit (Beckman Coulter). Library preparation was performed with Nextera XT DNA Library Prep Kit (Illumina).
The Agilent Technologies 2100 Bioanalyzer was used to control quality and integrity of nucleic acids after each step. Deep sequencing was carried out on Illumina HiSeq2500 using 50 bp single reads. Sequenced libraries were assessed for read quality using the FastQC tool. The GEO accession number for the RNA-seq L data reported in this paper is GSE116633. Library preparation scRNA-seq. Single cells were sorted by FACS ARIA III sorter (BD) and collected in PBS containing 0. 04% w/v BSA at a densitiy of 400 cells/μl. Chromium™ Controller was used for partitioning single cells into nanoliter-scale Gel Bead-In-EMulsions (GEMs) and Single Cell 3' reagent kit v2 for reverse transcription, cDNA amplification and library construction (10xGenomics). The detailed protocol was provided by 10xGenomics.
SimpliAmp Thermal Cycler was used for amplification and incubation steps (Applied Biosystems). Libraries were quantified by Qubit TM 3. 0 Fluometer (ThermoFisher) and quality checked using 2100 Bioanalyzer with High Sensitivity DNA kit (Agilent). Sequencing was performed in paired-end mode (2 × 75 cycles) using NextSeq 500 sequencer (Illumina) to attain approximately 80,000 reads per single cell. The GEO accession number for the scRNA-seq data reported in this paper is GSE116633. RNA-seq/RNA-seq L analysis. Libraries were aligned versus the mouse reference genome assembly GRCm38 using the splice junction mapper Tophat2 v1. 2. 0 with default parameterization  64. Reads aligned to annotated genes were quantified with the htseq-count program  65  and determined read counts served as input to DESeq2 66 for pairwise detection and quantification of differential gene expression.
RPKM (reads per kilobase of exon length per million mapped reads) values were computed for each library from the raw read counts. For scatterplots and heatmaps only genes with an annotated official Gene Symbol were included. Gene ontology analyses were performed using the R package TopGo 67. The R packages pheatmap and ggplot2 were used to generate heatmaps or scatterplots, respectively. scRNA-seq analysis. Data were demultiplexed using Cell Ranger software (version 2. 0. 0) based on 8 bp 10X sample indexes and paired-end FASTQ files generated. The cell barcodes and transcript UMIs were processed as previously described  68. Subsequently, read 2, which contains the transcript insert sequence, was aligned to mouse UCSC mm10 reference genome using STAR aligner with default parameters  68. The alignment results were used to quantify the expression level of mouse genes and generation of gene-barcode matrix, which were further processed with Seurat (version 2.
3. 1)  69. The separately acquired mLN and pLN datasets were merged. All cells with less than 250 or more than 3,500 detected genes per cell were filtered out. Moreover, cells with more than 4. 5% read mapping to mitochondrial genes were considered as dead cells and removed. The remaining cellular transcriptomes were normalized, log transformed and only cell clusters (non-endothelial SC) with expression for Pecam < 1 were used for the further analysis. Nonendothelial SC transcriptomes were regressed against the variables, number of UMIs and percent of mitochondrial reads. From the two independent experiments 2786 and 4483 non-endothelial SCs were obtained for mLNs and pLNs, respectively. Variable genes were identified with default settings. Based on the PCEl-bowPlot, 22 and 18 PCAs were used for the clustering of mLNs and pLNs, respectively.
Cumulative Z-scores were calculated based on the scaled expression per gene per cluster across the defined gene signatures. Statistical analysis. Group sizes were estimated according to a presumed standard deviation (SD) and an expected type I error of <0. 05. The sample size was adjusted, if required, based on initial results. For all figures, each data point represents a single mouse if not stated otherwise. In Treg induction assays, samples were excluded from the statistical analysis if <500 adoptively transferred OvaTCR + CD4 + cells could be acquired. For comparison of unmatched groups, two-tailed Mann-Whitney was applied. If comparing more than three groups one-way ANOVA followed by Bonferroni's post-test was used. All data are presented as mean or mean ± SD, and p-values <0. 05 deemed significant (*p < 0.
Acknowledgements We thank  Lothar Groebe  for cell sorting,  Robert Geffers  for RNA-seq analysis,  Daniel Hebenstreit  for advice on RNA-seq data analysis,  Fabio Pisano  for support during infection experiments,  Rainer Glauben  for advice on DSS colitis experiments,  Himpriya Chopra  for technical fluorescence microscopy support,  Vincent Gardeux  for advice on scRNA-seq analysis,  Jana Niemz  for critical reading of the manuscript and  Maria Ebel  and  Silke Kahl  for technical assistance. This work was supported by the  Hannover Biomedical Research School (HBRS) , the  Center for Infection Biology (ZIB)  from  Hannover Medical School , the  German Research Foundation  ( SPP1656 ,  Ho2236/9-2 ,  PE 2840/1-1 ) and the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie  grant agreement No  656319.
Search and information sources We searched the MEDLINE database using the PubMed interface using a combination of search terms for the concepts (i) 'antibiotics', (ii) 'quality' and 'quantity of use' and (iii) 'variation' (for the detailed search strategy see The cut-offs were chosen for pragmatic reasons, since we felt that otherwise the number of eligible studies would be too vast without offering much information about variation due to the small number of entities.
Study selection and data collection process All the steps of this systematic review were carried out using the Distiller SR V R software (Evidence Partners, Ottawa, Canada). Duplicates were removed before title and abstract screening using the algorithm provided by Distiller SR V R. One reviewer (V. Z. ) screened all titles and abstracts. A second reviewer (B. H. ) independently screened a random subset of 700 abstracts (13%). For 3/700 (0. 4%) references screened by both reviewers there was disagreement regarding inclusion/exclusion. All three references were later excluded at full-text screening level. All full-text assessments were performed by one reviewer (V. Z. ) and, in case of uncertainty, discussed with another investigator (B. H. ) until a consensus was reached. Data extraction was performed by one reviewer (V.
Z. ) using a standardized data extraction form; any uncertainty about extracted data was discussed with another investigator (B. H. ). Data regarding authors, setting (inpatients or outpatients), country/region, last year of data collection, study design, level of variation (providers, units, hospitals etc. ), number and characteristics of participants, data source, category of the numerator and denominator of the measure, full description of the measure, mean or median (as available in the text) and measure of variability (maximum-minimum range, IQR or SD as presented in the text) were collected (for definitions see Table  S2 ). We extracted data for overall antibiotic use and if the study also reported variation for specific antibiotic classes we extracted data only for b-lactams, quinolones and macrolides (except if the study described variation of only specific antibiotic classes but no overall antibiotic use) since we felt that extracting data for all individual classes would go beyond the scope of this review.
JAC vi19 American studies since these were the most represented regions. Data regarding measures for antibiotic use in paediatric patients are presented as a separate category. Measures were extracted from each study and a measure's category was attributed to each of them. The complete list of measure categories is reported in Table  S3. Given our broad search strategy, we identified a wide range of different antibiotic use measures, often applying to specific medical conditions (e. g. otitis media, urinary tract infections or community-acquired pneumonia) or settings (e. g. ICUs, neonatal ICUs, emergency departments). In addition, most studies presented only aggregated data, making it challenging to summarize variation in a standardized way. In the absence of better alternatives, we decided, whenever possible, to report the extent of variation in a given antibiotic use measure for each study by calculating the ratio between the reported maximum and minimum value of the measure (MMR).
Inpatient setting (81 studies) High-income countries Overall, 75 studies (75/143, 52. 4%) met inclusion criteria for this setting: 47 from Europe, 17 from the USA and Canada, 3 from Australia, 1 from Israel, 1 from Japan and 6 from more than one WHO region (see Table  S2  for the definition of WHO regions). Thirty-eight unique measures were extracted for this setting with sometimes different studies reporting identical measures. Overall, 169 'non-unique' measures reporting data about variation (also called 'variation data' in this review) were represented for this setting. The most frequent measures concerned 'quantity' of antibiotic use, with 91 variation data extracted from 58 studies. The three most frequent unique measures (out of the 10 belonging to this category) were DDDs (reported 34 times for overall use or for specific antibiotic classes and medical conditions and with different denominators), followed by 'percentage of treated patients' (reported 31 times in different settings and for different health conditions) and DOT (reported 13 times both for overall and specific antibiotic classes use and for specific medical indications and with different denominators).
Low-to middle-income countries We found three different measures described for this setting (Table  4 ), two belonging to the category 'quantity' (DDDs and percentage of treated patients) and one belonging to the category 'compliance with guidelines' (percentage of appropriate/inappropriate prescription). Six studies could be included for this setting (three of which were from the Western Pacific WHO region); one was an international study that included countries from more than one WHO region and the others were from four different countries. No study included data collected after 2011. Three studies addressed the paediatric population. Variation was mainly described among hospitals (four studies, 66. 7%) with an MMR of 2. 1 (IQR 2-2. 2) for the measure 'percentage of patients treated with antibiotics' (three studies) and an MMR of 5.
Europe We included 38 studies (38/56, 67. 8%) for this setting, including 15 ESAC studies. No study reported data collected after 2011. More than half were international studies (20/38, 52. 6%); the rest were single-country studies from nine different countries. Italy was the country with the highest number of studies (6/38, 15. 8%) followed by France (4/38, 10. 5%). Most of the studies reported variation among countries (15/38, 39. 5%) or geographical areas within the same country (e. g. regions, provinces, districts) (7/38, 18. 4%). Ten studies (26. 3%) reported variation among providers and six among outpatient clinics. Overall, most studies (36/38, 94. 7%) included measures for the category 'quantity' of antibiotic use, with the three most represented measures being: (i) DDDs (reported in 21 studies); (ii) percentage of treated patients (reported in 10 studies); and (iii) percentage of antibiotic prescriptions (reported in 9 studies).
27 trengths of this review include a broad search strategy and the inclusion of studies from a variety of different settings. There are, however, also several limitations to our work. We only searched the MEDLINE database over a 10 year period and we did not explore other sources of information, such as relevant surveillance websites or single-country data. Another limiting factor is that we did not perform a quality assessment of included studies and we did not explore the relationship between extent of variation in measures and outcomes of antibiotic use, e. g. resistance, costs or rates of C. difficile infections. Although we included a large variety of measures described in the literature, we did not address possible benchmarks for the measures and we did not try to explore reasons for the observed variation as most studies did not provide information regarding this issue.
Acknowledgements We acknowledge the input of Elodie von Dach in providing help with the literature search and data collection and all the members of the DRIVE-AB steering committee for critically reviewing the manuscript. Preliminary results of this study have been presented as a poster presentation ( P1294 ) at the 26th European Congress of Clinical Microbiology and Infectious Diseases (ECCMID), 9-12 April 2016, Amsterdam, The Netherlands.
Funding The research leading to these results has received support from the Innovative Medicines Initiative Joint Undertaking under grant agreement no. 115618 (Driving re-investment in R&D and responsible antibiotic use -DRIVE-AB -www. drive-ab. eu), resources of which are composed of financial contribution from the European Union's Seventh Framework Programme (FP7/2007-2013) and European Federation of Pharmaceutical Industries and Associations (EFPIA) companies' in-kind contribution.