dataesr/scientific-paragraphs-categorization huggingface dataset:
Subsequently, a detailed presence/absence analysis of the accessory genome was achieved through both the identification of positional homologous CDSs and carefully inspection of large insertions and deletions (indels) in whole-genome alignments constructed using the progressive algorithm of Mauve software version 2. 3. 1 (University of Technology Sydney, Sidney, Australia) (http://darlinglab. org/mauve/mauve. html). For confirmation purposes, reads were mapped using Bowtie2 version 2. 1. 0 (Johns Hopkins University, Baltimore, MD, USA) (http: //bowtie-bio. sourceforge. net/bowtie2/index. shtml) against all detected genomic regions of the accessory genome. Finally, the SNP density within the core-genome was also evaluated through the extraction of extended MAUVE core-alignments (encompassing both coding and non-coding regions) by keeping and concatenating regions where whole-genome sequences aligned over at least 500 bp.
Overall nucleotide distances in these extended core-genome alignments were then calculated using MEGA5 (http://www. megasoftware. net). Other in silico analyses performed over the draft genome sequences included: (i) MLST using the ST extraction tool available at the Bacterial Isolate Genome Sequence Database (BIGSdb) (Institut Pasteur, Paris, France) (http: //bigsdb. web. pasteur. fr/); (ii) prediction of putative antimicrobial resistance (AMR) genes using both CARD (https://card. mcmaster. ca/) and ResFinder (Center for Genomic Epidemiology, DTU, Kemitorvet, Denmark) (http://www. genomicepidemiology. org/); and (iii) prophage prediction using PHAST (University of Alberta, Edmonton, Canada) (http://phast. wishartlab. com/).
Nucleotide Sequence Accession Numbers Raw sequence reads of each K. pneumoniae isolate were deposited in Sequence Read Archive (SRA) under the accession numbers: SRR4046826 (for Kp703), SRR4046827 (for Kp45) and SRR4046828 (for Kp2948). Draft genome sequences were also submitted to DDBJ/ENA/GenBank, and are available under the accession numbers: MDUJ00000000 (for Kp703), MDUK00000000 (for Kp45) and MDUL00000000 (for Kp2948).
Biofilms with different ages were cross-sectioned and analysed by SEM in backscattered electron mode. Although for biofilms assembled on polystyrene this was a straightforward procedure, as previously described  [9] , for biofilms assembled on silicone a new procedure had to be established based on material sciences samples preparation protocols. This strategy allowed us to cross-section the biofilm and to determine the relative areas occupied by bacteria and EPS using ImageJ software (Figure  2A ). Representative micrographs of 4 h old biofilms of Kp703 (Figure  2B ), Kp45 (Figure  2C ) and Kp2948 (Figure  2D ) corresponding to the adherence phase are shown and at this early stage, the biofilms assembled by the three bacteria are different. The results obtained for the relative areas occupied by biomass (Figure  3A ) and EPS (Figure  3B ) within biofilms assembled on silicone are showed in Figure  3.
For easier comparison, the results previously published for polystyrene were also included although excluding the statistical analysis  [9]. No significant differences were found at 4 h, 12 h and 24 h between the bacteria relative areas within biofilms assembled on silicone by the three K. pneumoniae isolates (Figure  3A ). Nevertheless, the relative areas occupied by bacteria in 12 h old biofilms assembled on silicone by Kp45 were significantly higher than on polystyrene (p = 0. 042) (Figure  3A ). For both Kp703 and Kp2948 no statistically significant differences were found. polystyrene this was a straightforward procedure, as previously described  [9] , for biofilms assembled on silicone a new procedure had to be established based on material sciences samples preparation protocols. This strategy allowed us to cross-section the biofilm and to determine the relative areas occupied by bacteria and EPS using ImageJ software (Figure  2A ).
The phenotyping performed by the IMPC explores a range of vertebrate biology, and aims to collect data from seven males and seven females from each mutant line with data from strain-matched controls accumulated over time. Data are collected at 10 phenotyping centres, providing a unique opportunity to explore the role of sex on a phenotype within an experimental data set, and the role of sex on a treatment effect, where the treatment is a gene disruption event, analogous to a Mendelian genetic disease. Our findings show that regardless of research field or biological system, consideration of sex is important in the design and analysis of animal studies. All data are freely available at mousephenotype. org.
Results Sex as a biological variable within an experiment. We first assessed the contribution of sex using linear modelling to determine how often sex contributed to the variation in the phenotype in wildtype mice (control data) for an individual data set (a phenotypic test/trait at an individual phenotyping centre) (Supplementary Fig. 1a, b ). Phenotypes were classified as either continuous, such as creatine kinase levels, or categorical, such as vibrissae shape. Because body size is dimorphic between male and female mice, and many continuous traits correlate with body weight, we included weight as a covariate in our analysis for continuous traits. Using this approach, our analysis revealed that 9. 9% of data sets from categorical traits (54/545 data sets) were significantly influenced by sex at a 5% false discovery rate (FDR) (Fig.
While these studies are contained within facilities with high biosecurity, the microbiomes will differ from institute to institute. In fact, microbiomes will differ between individual litters depending on the maternal microbiome. This study comparing control data across many litters in effect accounts for this variation, which might go some way to explaining the consistency of the findings across sites. Sex as a modifier of a treatment effect. We next looked at the role of sex in influencing phenotypes in the context of gene ablation (Supplementary Fig. 1c-e ). Bespoke statistical analyses, distinct from those implemented on the IMPC portal, were used to assess sexual dimorphism and control the false positive rate. For this analysis, we used data collected from 2,186 mutant mouse lines, first assessing whether genotype significantly influenced phenotype, and if significant whether the effect was modified by sex.
Eye Figure  4  | Role of sex as a modifier of the genotype effect. The role of sex in explaining variation in phenotypes of knockout mice as assessed using data from the IMPC. (a) Classification for categorical data sets (n ¼ 1,220) with a genotype effect at 20% FDR. (b) Classification for continuous data sets (n ¼ 7,929) with a genotype effect at 5% FDR. The 'Cannot classify' effect arises when statistically an interaction is detected between sex and genotype but the model output is insufficient to specify where the interaction arises. The 'Genotype effect with no sex effect' effect is the classification when the null hypothesis of no genotype*sex interaction was not rejected; this may be because the effect is the same across sexes, or due to a lack of power to detect the differential effect across sexes.
Discussion Many authors have raised the need to address the role of sex in basic biological research and recommendations have been made, but with limited progress to date. Bespoke analysis of IMPC data, revealed that 9. 9% of qualitative and 56. 6% of quantitative data sets were SD in wildtype mice. Furthermore, as a mediator of a mutant phenotype, sex modifies the genotype effect in 13. 3% of qualitative data sets and up to 17. 7% of quantitative data sets. Our findings are consistent with a recently published study examining the role of sex in human genetic variation that found the effect was of modest magnitude but across a broad spectrum of traits  27. Further studies to understand the biological mechanism of the interactions reported herein are challenging because of the difficulty of designing experiments with sufficient sensitivity to consistently detect those interactions.
Methods Methodology consideration. Bespoke methods were developed to assess for prevalence of sexual dimorphism and are independent of the methodologies implemented on the IMPC portal. Ethical approval. Institutes that breed the mice and collect phenotyping data are guided by their own ethical review panels and licensing and accrediting bodies, reflecting the national legislation under which they operate. Details of their ethical review bodies and licences are provided in Supplementary Table  3. All efforts were made to minimize suffering by considerate housing and husbandry. All phenotyping procedures were examined for potential refinements that were disseminated throughout the consortium. Animal welfare was assessed routinely for all mice involved. Mouse generation. Targeted ES cell clones obtained from the European Conditional Mouse Mutagenesis Program (EUCOMM) and Knockout Mouse Project (KOMP) resource  29, 30  were injected into BALB/cAnN, C57BL/6J, CD1 or C57BL/6N blastocysts for chimera generation.
The resulting chimeras were mated to C57BL/6N mice, and the progeny were screened to confirm germline transmission. Following the recovery of germline-transmitting progeny, for the majority of lines, heterozygotes were intercrossed to generate homozygous mutants  10. A few knockout lines were generated on other genetic backgrounds, as detailed in the data output and presented on the IMPC portal. For these lines, control data from the equivalent genetic background was collected. All lines are available from http://www. mousephenotype. org/. Genotyping and allele quality control. The targeted alleles were validated by a combination of short-range PCR, qPCR and non-radioactive Southern blot, as described previously  31, 32. Housing and husbandry. Housing and husbandry data was captured for each institute as described in Karp et al.
33  and is available on the IMPC portal (http://www. mousephenotype. org/about-impc/arrive-guidelines). Phenotyping data collection. We have used data collected from high-throughput phenotyping, which is based on a pipeline concept where a mouse is characterized by a series of standardized and validated tests underpinned by standard operating procedures (SOPs). The phenotyping tests chosen cover a variety of disease-related and biological systems, including the metabolic, cardiovascular, bone, neurological and behavioural, sensory and haematological systems and clinical chemistry. The IMPRESS database (https://www. mousephenotype. org/impress), defines all screens, the purpose of the screen, the experimental design, detailed procedural information, the data that is to be collected, age of the mice, significant metadata parameters, and data quality control (QC).
Experimental design. At each institute, phenotyping data from both sexes is collected at regular intervals on age-matched wildtype mice of equivalent genetic backgrounds. Cohorts of at least seven homozygote mice of each sex per pipeline were generated. If no homozygotes were obtained from 28 or more offspring of heterozygote intercrosses, the line was classified as non-viable. Similarly, if o13% of the pups resulting from intercrossing were homozygous, the line was classified as being subviable. In such circumstances, heterozygote mice were analysed in the phenotyping pipelines. The random allocation of mice to experimental group (wildtype versus knockout) was driven by Mendelian inheritance. The individual mouse was considered the experimental unit within the studies. Further detailed experimental design information (for example, exact definition of a control animal) for each phenotyping institute, or the blinding strategy implemented is captured with a standardized ontology as detailed in Karp et al.
Data can only be QC failed from the data set if clear technical reasons can be found for a measurement being an outlier. Reasons are provided and this is tracked within the database. QC is an ongoing process; therefore, changes in data composition can occur between different data set versions if an institute later identifies an issue with the data. Analysis within this manuscript used IMPC data set version 4. 2, published 8th December 2015. Wildtype data sets. Wildtype data sets were assembled for a trait by selecting wildtype mice that were collected at the same institute, on the same genetic background, the same pipeline and with the same metadata parameters (for example, instrument). The subsequent statistical pipeline required that data was available for both sexes and there were more than 100 data points per sex.
The nearest body weight measure was associated with data provided it was within þ / À 4 days of the collection of the trait of interest. Wildtype-knockout data sets. Wildtype-knockout data sets were assembled by selecting data from wildtype mice to associate to the data from the knockout mice that were collected at the same institute, from the same genetic background, the same pipeline, and with the same metadata parameters (for example, instrument). The nearest body weight measure was associated with the data provided it was within þ / À 4 days of the collection of the trait of interest. A data set was only assembled for a knockout line and trait if data was available on both sexes, there were greater than five readings for each sex for the knockout mice, and body weight data was available.
The analysis methods used were developed specifically to answer the biological question of the prevalence of SD and therefore are distinct from the statistical output presented on the IMPC portal. For each statistical analysis a flow diagram summarizing the analysis pipelines is available in Supplementary Fig. 1. For continuous variables, regression analysis is necessary to assess the effect after accounting for sources of variation such as batch. As such, the estimated effect observed in the regression model cannot always be seen when visualizing raw graphs (Supplementary Fig. 1f ). Sex as a biological variable for categorical wildtype data. For categorical traits, the data were recoded to 0 to represent 'as expected' phenotypes or 1 to represent 'not as expected' phenotypes. A Bias Reduction Logistic Regression  34   (a) Looking across IMPC knockout lines identified as having 10% or more traits with a significant genotype effect, the proportion of traits classed as SD varied.
The analyses assume that batch is a source of variation that adds noise in an independent normally distributed fashion. When weight is included, the analysis assumes that there is a linear relationship between weight and the variation of interest. Analysis was restricted to data sets with more than 100 data points per sex, and thus would be a data set comprising multiple batches and therefore would be robust to the analysis  15. Using the output from the two pipelines assessing the role of sex as a source of variation, the reproducibility of the role of sex across institutes was assessed for a variable that had been measured at three or more institutes. As data sets have differing size, and sensitivity varied across institutes, discordant results were classed as those where the effect of sex was in opposing direction.
Sex as a modifier of genotype effect-in categorical data. For each trait of interest, the data were recoded to either 0 to represent 'as expected' phenotypes or 1 to represent 'not as expected' phenotypes. The statistical pipeline, comparing the abnormality rates in the knockout mice against the baseline population was optimized to maximize sensitivity whilst maintain control of the type-one errors. In summary, a two stage process was used where first the genotype role was assessed and, if statistically significant, then the genotype effect by sex was assessed. To reduce the multiple testing burden, potential filters were used to allow analysis of only data sets that have the potential for statistical significance to be queried. To assess potential for a genotype effect, the Mantel-Haenszel alpha star (the minimal attainable P value for a data set) was calculated.
If a data set had potential (Po0. 05), then the role of genotype was assessed using a one-sided Cochran-Mantel-Haenszel mid P value to compare the proportion of abnormalities events difference between the knockout and wildtype groups, stratified by sex. After multiple testing adjustments, using the Hochberg method to control the FDR to 5%, data sets were selected for stage 2 testing of an interaction. The interaction was assessed by comparing abnormality rates between the sexes of the knockout data only using a Bias Reduction Logistic Regression with a likelihood ratio test that compared a test model (YBsex) with a null model (YB1). Prior to the assessment, the potential was assessed using a LR_KO alpha star P value, defined as the most extreme P value possible arising as a function of the number of abnormal calls and number of readings within a data set was used as a filter to select data sets for statistical testing for stage 2 (Po0.
The resulting P values were adjusted for multiple testing using the Hochberg method to control the FDR to 5%. Y $ Genotype þ Sex þ GenotypeÃSex þ Weight þ 1 j Batch ð Þ ; ð3Þ Y $ Sex þ Weight þ 1 j Batch ð Þ ; ð4Þ Y $ Genotype þ Sex þ Weight þ 1 j Batch ð Þ ; ð5Þ Y $ Sex þ Genotype : Sex þ Weight þ 1 j Batch ð Þ : ð6Þ A final model was fitted (equation 6) to estimate the genotype effect by sex which was used to classify the genotype effect. The estimated genotype effect for each sex, and associated standard error, was standardized by dividing the values by the average of the average wildtype male and female mice to allow comparison across traits. Data sets were also given a workflow classification depending on how the knockout data were collected. Multi-batch data sets were defined as those with four or more distinct batches consisting of three or more batches within one sex and two or more for the other sex.
One-batch data sets were defined as those with knockout mice collected in one batch. All other workflows were classed as low-batch. When a genotype effect was detected for a data set, the effect was classified as described in Supplementary Fig. 1d. For example, if a data set was significant at stage 1 but not for stage 2, as there was evidence of a genotype effect but the genotype*sex interaction was not significant, the effect would be classified as 'genotype effect with no sex effect'. The calls were reviewed individually by biologists to validate the calls made by the computational pipeline. Where questions were raised on a computational call, if a statistical issue could be identified (for example, a continuous variable was bound and thus was not appropriate for a mixed model methodology) then all data sets for that variable were removed.
16. Wildtype data was taken from five procedures (clinical chemistry, dual-energy X-ray absorptiometry (DEXA), immunophenotyping, haematology and open field) giving 60 traits. The simulated wildtype-knockout data sets were then examined statistically to assess the type-one error rate control at stage 1 and stage 2. The control of type-one errors for stage 2 was also assessed under the null for stage 2 in the presence of a genotype effect that affected both sexes equally. Simulated data was constructed based on the signal characteristics (mean, variance and sex effect) of five clinical chemistry traits to give 14 male and 14 female data points in 300 batches. Batch variation was simulated under the assumption it was normally distributed with mean zero and defined variance that was 25% of the estimated s.
d. Body weight data was generated by random sampling from the average signal for a wildtype female mouse. Resampling studies mimicking a random workflow were run as described in ref. 16 to build wildtype-knockout data sets (iterations 2,000). Signal was added to the knockout mice as a proportion of standard deviation (0, 0. 5, 1, 1. 5, 2) to represent a main effect genotype effect. The resulting data set were then examined statistically to assess the type-one error rate at stage 2. The SD hit rate comparison across screens excluded screens with o35 hits. Enrichment analysis. A list of genes (GSID ¼ GS248996) and their SD hit rate (per cent of measures showing a statistically significant sex*genotype interaction) were entered into the GeneWeaver database. Genes with a 4% or greater hit rate (GSIDS ¼ GS248973) were stored in a gene set.
METHODS All participants were recruited from the outpatient clinic of the department of ophthalmology at the University Hospital in Bern, Switzerland. The study protocol was approved by the local ethics committee and is in accordance with the Declaration of Helsinki and the International Ethical Guidelines for Biomedical Research involving Human Subjects (Council for International Organizations of Medical Sciences [CIOMS]). The study is registered at ClinicalTrials. gov (NCT01981148) with the title ''Measurement of Retinal Autofluorescence with a Fluorescence Lifetime Imaging Ophthalmoscope. '' Informed written consent was obtained from all participants before study entry.
Stages of AMD were classified as follows: early AMD with small to medium drusen, intermediate stage with large (>125 lm) more confluent drusen and/or pigmentary abnormalities, and late AMD with geographic atrophy or exudative AMD with intraretinal fluid as sign of retinal neovascularisation. 11 The existing drusen were divided into soft drusen and reticular pseudodrusen according to their appearance in OCT and the corresponding infrared image. 4 Data were further analyzed considering the lens status (phakic or pseudophakic).
Fluorescence Lifetime Imaging Ophthalmoscope and Image Analysis Fluorescence lifetime data were acquired using a fluorescence lifetime imaging ophthalmoscope based on a Heidelberg retina angiograph (HRA) Spectralis system (Heidelberg Engineering). The FLIO system and its technical background are described in previous publications including detailed laser safety calculations. 13, 14 etinal autofluorescence was excited using a 473-nm pulsed laser raster-scanning the central fundus (308) with a repetition rate of 80 MHz. The laser exposure is well below the limits set by the International Electrotechnical Commission. 27 mitted photons were detected by highly sensitive hybrid photon-counting detectors (HPM-100-40; Becker&Hickl, Berlin, Germany) and registered by time-correlated single-photon counting (TCSPC) modules (SPC-150; Becker&Hickl).
Photons were separately detected in two channels according to their wavelength: in a short spectral channel (wavelengths 498 to 560 nm, SSC) and in a long spectral channel (560 to 720 nm, LSC). A minimum of 1000 photons per pixel was recorded within the macular center in both channels. In parallel, eye movements were tracked by an inbuilt infrared reflectance camera to ensure correct allocation of detected photons within the 256 3 256-pixel frame. Recorded fluorescence lifetime data were analyzed using SPCImage 5. 4 software (Becker&Hickl). A binning factor of one was applied, averaging the photons of an individual pixel point with the directly adjacent pixels to increase the numbers of photons per pixel. A fluorescence decay histogram was generated for every single data point in both spectral channels using a bi-exponential decay function.
The resulting individual lifetime components within the SSC and the LSC were a short (T1) and a long (T2) decay time with their corresponding relative amplitudes (¼relative intensity) a1 and a2, whereby T1 << T2 and a1 >> a2. Of these four components T1, T2, a1, and a2, the mean fluorescence lifetime tau mean (Tm) can be calculated. It represents the amplitude weighted mean fluorescence decay time per pixel and wavelength channel. T m ¼ a 1 Ã T 1 þ a 2 Ã T 2 a 1 þ a 2 ð1Þ FLIO data was averaged and analyzed using the custom made FLIO reader (ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland). A standard ETDRS grid (Circle diameters central area [C] 1 mm, inner ring [IR] 3 mm, outer ring [OR] 6 mm) was used for analysis of individual retinal areas. 13 For analysis of specific regions of interest (ROI), a small circle with 0.