dataesr/scientific-paragraphs-categorization huggingface dataset:
Instead of inferring individual cell positions, we labelled cells prior to dissociation based on their position, measured their position, and reconstructed cell states and their spatial distribution over the limb bud as well as gene expression patterns. TATTOO-seq shares conceptual similarities to TOMO-seq  [71] , though with single-cell resolution and with the simplicity and rapidity of optic labelling over physical sectioning. Because our approach relies on in silico cell aggregates, instead of individual cells, it is suitable for relatively sparse datasets and works without imputation. Although the spatial resolution of this study is relatively coarse (at the order of 50 to 100 µm, depending on the quadrant considered), our approach could resolve finer spatial grids as it is only limited by the precision of the photoconversion and the capacity of flow cytometry to detect graded fluorescence levels.
Quality check and read mapping Reads were demultiplexed and count tables were built as described in  [36]  (scripts are available at http://compgenomics. weizmann. ac. il/tanay/). Cell-specific/well-specific labels and RMTs were extracted. Reads with low quality (Phred < 27) barcodes were discarded to prevent ambiguous or spurious assignments of reads to cells or unique molecules. R2 reads whose cell-specific or well-specific labels were unknown or mutated were discarded. R1 reads were mapped to the mouse genome (mm9) using Ensembl gene annotations (downloaded in July 2017) using Bowtie2  [74].
Metacell analysis We clustered the wild-type E11. 5 dataset using MetaCell  [37]. Briefly, an ideal metacell is a group of cells whose expression profiles are statistically equivalent to independent sampling from a single underlying transcriptional state. This is achieved by first creating a k-NN graph of the individual cells based on expression similarity and partitioning this graph into a disjoint sets of cells, metacells, which are both small (~100 cells each) and as close to the above ideal metacell as possible. Because plates were processed in two batches, we filtered out cells with fewer than 2000 or 2500 UMIs (lower quality transcriptomes) or more than 15000 UMIs (potential doublets). We excluded mitochondrial genes (annotated with the prefix "mt-") from the analysis. Variable genes were selected using the parameters Tvm = 0.
Identification of cell colors To infer a single cell color from the intensities of the PE (red, photoconverted) and FITC (green, nonphotoconverted) channels, we examined the ratio between the log of these intensities: 𝑠 = log ! 𝑃𝐸 log ! 𝐹𝐼𝑇𝐶 The distribution of this ratio exhibited the expected number of peaks (3 or 4) across all samples, though the exact location of the peaks and their separation varied. We therefore modeled the distribution of each individual limb separately as a mixture of skewed normal distributions whose parameters were estimated using an expectation-maximixation algorithm implementer in the R package mixsmsn  [75]  ("smsn. mix", family = "Skew. normal", nu = 3). Three components were used for the PD and AP patterns and four were used for the AER pattern. The color of each individual cell was determined using the posterior probabilities.
Then, the sbins containing cells belonging to M will be distributed according to: 𝑃 ',& (𝑆|𝑀) where θ is a parameter. Finally, the photoconversion model described in the previous section implies a distribution over the measured fluorescence given the sbin and the photoconversion patterns: 𝑃 & (𝐹 | 𝑆 = 𝑠) Given the above, we can calculate the expected fluorescence of cells belonging to M: As the distributions  ',& (|) are discrete, the parameter θ can be taken to be the complete definition of the distribution. To solve the minimization problem, we used a constrained interior-point algorithm, implemented in the scipy. optimize. minimize() function (from the scipy python package) with a method parameter of "trust-constr". To ensure convergence to a proper distribution function, the sum of the probabilities was constrained to 1: 7 𝑃 ',& (𝑆 = 𝑠 | 𝑀) ( = 1 And the value of the probability of each sbin was constrained to a segment which strictly contain the [0,1] segment: -0.
Acknowledgements We thank  Dr. Fujimoto  and the  RIKEN Laboratory for Animal Resources and Genetic Engineering  for given us access to the KikGR-1 transgenic line (ref. CDB0201T-1 ), and  Dr. Alexander Aulehla  ( EMBL ) for sharing with us these mice. We acknowledge greatly the contribution of the members of the animal facilities at the  Institut Pasteur. We thank members of the  Spitz ,  Marlow , and  Tanay labs  for sharing reagents, ideas, and comments. We also thank  Dr. Chloé Moreau  for helping with the pilot photoconversion experiments. We particularly acknowledge the contribution of  Dr. Elodie Brient-Litzler  and  Dr. Jean-Christophe Olivo-Marin  ( Institut Pasteur, CiTech ) in the development of a single-cell initiative and technological platform at the  Institut Pasteur , which was essential to this project.
Methods Data were obtained from the observational Pediatric Inflammatory Bowel Disease Network for Safety, Efficacy, Treatment and Quality improvement of care inception cohort study. In this prospective international study, children aged 0-18 years with newly diagnosed IBD were enrolled and followed longitudinally. Details on this cohort and its data collection have been published elsewhere. 13 At fixed time points, data on caregiver's socioeconomic functioning (work and daily activities) and health-related quality of life (HRQOL) was collected via questionnaires. Patients included in the inception cohort between January 1, 2017, and July 1, 2021, were eligible for this study. Patients were excluded if no questionnaire was returned at either baseline or any followup visit. No minimum follow-up was required.
The WPAI questionnaire was completed at the time of diagnosis (baseline), then at 3 and 12 months after diagnosis. Questionnaires were completed during the study visit at the hospital or sent to patients' home addresses directly after the visit and returned. Questionnaires were completed by 1 or both parents, depending on the setting and caregivers' preference. This practice varied between visits for individual patients. Data from each completed WPAI questionnaire were analyzed separately, without averaging data if both caregivers completed the questionnaire. If only 1 caregiver completed the questionnaire, we conservatively assumed only 1 caregiver was affected by the child's disease. To investigate potential bias, a sensitivity analysis was performed to compare WPAI outcome measures between those visits where 1 caregiver completed the questionnaire and those where both caregivers completed the questionnaire.
Clinical Data Collection Baseline demographics and clinical data were collected routinely during hospital visits simultaneously with the distribution of questionnaires. Disease activity was scored at each visit, using the Pediatric Ulcerative Colitis Activity Index or the Weighted Pediatric Crohn's Disease Activity Index, and was categorized into 3 groups (remission, mild, moderate to severe) using validated cut-offs of these scores (Appendix 1). 17, 18 At baseline, disease location and extent, disease behavior, and the presence of perianal disease were classified according to the Paris classification (Appendix 1). 19 atistical Analyses Continuous data were presented as mean and SD/CI or median and IQR. Data obtained from the WPAI questionnaire were presented as means, irrespective of distribution, because means rather than medians are of fundamental interest in health economics evaluations.
These GEE models were also applied to calculate the adjusted estimates, by correcting for disease duration, IBD type, child's age at diagnosis, and disease activity (remission, mild, or moderate to severe). Possible interactions between IBD type and disease duration and between disease activity and disease duration were tested in these models. To investigate HRQOL, we included in these models caregivers' average HRQOL, as measured by the VAS. Data were analyzed using SPSS Statistics for Windows, version 25. 0 (IBM) and R statistics, version 4. 0. 3 (R Core Team). Employment details (eg, employment rate, weekly work time) at baseline were compared with employment details of the European Union (EU) in 2019, using a 1-sample t test. EU employment details were obtained from Eurostat, using the EU Labor Force Survey data.
It is a large population of children with IBD in whom caregivers' socioeconomic status was evaluated, with a relatively high response rate. The prospective nature and standardized data collection enabled us to assess the WPAI at identical follow-up moments for each patient, while accounting for detailed clinical data. Another important strength was the use of validated questionnaires, allowing us to compare our results with studies in other chronic diseases in children. One of the limitations of this study is that the WPAI questionnaire uses a recall period of 7 days, excluding the day of assessment, which is often the day of a hospital visit for regular follow-up or treatment. This practice could underestimate the impairments, but that would even further strengthen our findings of a big impact.
Between-group differences in the slope of change in eGFR were analysed using a random intercept random slope model using on-treatment data. The slope, the joint frailty, and the MMRM models included the same covariates as the Cox model. All analyses were performed using SAS version 9. 4 (SAS Institute, Cary, NC, USA). All P-values reported are two-sided, and P < 0. 05 was considered as statistically significant in all cases. No adjustments for multiple testing were made from the exploratory nature of the study.
Acknowledgement We are grateful to Armin Schweitzer for technical and editorial help as well as artwork. Conflict of interest: M. B. is supported by the Deutsche Forschungsgemeinschaft (German Research Foundation; TTR 219, project number 322 900 939) and reports personal fees from Abbott, Amgen, Astra Zeneca, Bayer, Boehringer Ingelheim, Cytokinetics, Edwards, Medtronic, Novartis, Recor, Servier, and Vifor during the conduct of the study. S. D. A reports grants and personal fees from Vifor Int. and Abbott Vascular, and personal fees from Astra-Zeneca, Bayer, Brahms, Boehringer Ingelheim, Cardiac Dimensions, Novartis, Occlutech, Servier, and Vifor Int. Personal fees from Boehringer Ingelheim during the conduct of the study. J. B. reports consulting fees from Boehringer Ingelheim, Cardior, CVRx, Foundry, G3 Pharma, Imbria, Impulse Dynamics, Innolife, Janssen, LivaNova, Luitpold, Medtronic, Merck, Novartis, NovoNordisk, Relypsa, Roche, Sanofi, Sequana Medical, V-Wave Ltd.
Methods This was an online cross-sectional study conducted between 11 May 2020 and 8 June 2020, and the data were collected 3:4 via a global online survey study (Jisc: https ://ww w. jis c. ac. uk/on line-surve ys). The research was internally funded by the University of Oxford. The study obtained approval by The Central University Research Ethics Committee, University of Oxford (reference number: R69636/RE001) and was advertised widely on social media by endometriosis support groups around the world and by endometriosis and women's health researchers and clinicians. The survey was designed in collaboration with the support group Endometriosis UK and consisted of both validated outcome measures (see section titled 'Domains assessed') and relevant multiple-choice questions, some of which had an extra option for a free text response if the answer was not already provided.
Statistical analysis Data were extracted and processed using the IBM SPSS Statistics software, Version 27. A mean score for all respondents was calculated for each NRS pain scale. The scoring for the PROMIS and NIH Toolbox scales was done using the HealthMeasures tool which produces a t-score value per respondent per scale. A mean of t-scores was then calculated for each scale. Descriptive statistics were calculated for all the variables, including mean, s. d. ands. e. and median, where appropriate. Frequencies and percentages were calculated for the effect of the pandemic on endometriosis symptoms and mental health. Non-parametric tests (chi-square and Spearman's bivariate correlations) were used for analyses that included ordinal data (change in endometriosis pain, TF and mental health), while parametric statistical testing (ANOVAs and Pearson's bivariate correlations) was used for the continuous variables (PROMIS scales and PCS).
A mediation analysis was employed to explore whether the relationship between the reported change in mental health as a predictor and changes in pain or TF as outcomes was facilitated by the following hypothesised mediators: pain catastrophising, perceived stress or emotional support. As a sensitivity analysis, the mediation analyses were repeated on the population of respondents who reported no alteration of their endometriosis medication treatment due to the pandemic. The mediation analysis was run using PROCESS on IBM SPSS Statistics software, Version 27  (Hayes 2013).
Acknowledgements We would like to thank the following groups for advertising the study and publishing the link to the online survey on their websites and social media resources: Endometriosis. org;Endometriosis Association of Ireland; Endometriosis Research Center; Q  Endo ;  EndoActive Australia and New Zealand ;  Endometriosis Australia ; The Endometriosis Network; Endometriosis NZ; Trinidad and Tobago Endometriosis Association; EndoFrance; EndoHome; ENDOmind;  Endometriose-Vereinigung Deutschland ,  Associação Portuguesa de Apoio à Mulheres com Endometriose, Endometriosis and me, Associação Brasileira de Endometriose e Ginecologia Minimamente Invasiva; EndoMadrid and EndoEuskadi.
marcescens is becoming more widespread as a problematic opportunistic pathogen, we aimed to assemble and analyse a balanced genomic dataset that reflected the entire Serratia genus. In this work, we supplement existing publicly available, published Serratia genome sequences by sequencing over 200 Serratia isolates that are non-clinical in origin, and mainly belonging to species other than S. marcescens, from the historic Grimont collection. This allows us to bring together previous molecular and biochemical knowledge from this important genus and place it in a contemporary genomics framework. Our findings not only explain why previous definitions of the genus Serratia were robust, or not, they also highlight important differences in diversity, plasticity and niche adaptation of the species within it.
Concordance between historical biochemical phenotyping and metabolomic predictions The current taxonomic structure of the Serratia genus, summarised by Grimont and Grimont, 2005  40  , is based on 41 phenotypic and biochemical tests used to differentiate between different Serratia species or species-complexes. Many of the Serratia isolates originally used to define the genus taxonomy were sequenced here (Fig. 1 ; Supplementary Data 1), presenting a unique opportunity to reconcile this historical biochemical metadata with genomic predictions. First, we calculated the genus pan-genome using a population structure-aware approach  41. The pan-genome comprised 47,743 discrete gene groups (Fig. 2a ), of which 2252 were present in at least 99 percent of all genomes in the dataset (which would be defined as a traditional core genome), however, only 1655 of these were present in at least 95 percent of genomes within each FastBaps lineage, and therefore are core to all lineages (Fig.
Contribution of plasmids to gene content and flow varies across the Serratia genus To understand the potential contribution of plasmids to the plasticity seen in this genus, we searched for plasmid contigs in our genus-wide dataset. This uncovered 409 putative plasmids in 228 genomes and 9 species, 301 (73%) of them present in S. marcescens (Fig. 6 ; Supplementary Data 2). The collection of identified plasmids displays a wide range of sizes (~1-310 kb) and GC content (~30-66%), indicating diversity. However, the distribution of these traits varied amongst Serratia species (Supplementary Figs. 13-15). For example, plasmids identified in S. marcescens and liquefaciens show a markedly broader range of size and GC content compared with those detected in S. entomophila and quinivorans. Seventy out of a total of 113 predicted plasmid replicons were found within S.
In other words, the data suggest that both the loss and re-acquisition of these elements are of benefit to S. marcescens at various times. Alternatively, it is also possible that the pattern observed may reflect multiple events of gene loss alongside these prophage integrations. It is also noteworthy that the environment from which strains were isolated across our assembled dataset tends to match the environments and niches with which each biotype was historically associated  21. Of particular interest is the observation that the predominantly hospital-associated biotypes of S. marcescens that were defined in the 1980s (A5, A8, TCT) sit within L9 and L12 defined in the current study. These lineages are mainly comprised of recentlysequenced genomes from hospital settings, including a large collection of clinically-derived S.
Sequence data quality control Read sets obtained from all samples were compared to the MiniKraken database by Kraken v1. 1. 1  57  , and then corrected using Bracken v1. 0. 0  58  which assigns reads to a specific reference sequence, species or genus. If reads were not able to be assigned to a taxonomic class, they were classed as 'unclassified'. Any read sets that belonged to genera other than Serratia were discarded from any further analysis, along with any assemblies obtained from those read sets. Any read sets with more than an estimated five percent of heterozygous SNPs across the whole genome were removed from further analysis, in addition to any assemblies obtained from those read sets. Heterozygous SNPs were calculated using a software pipeline from the pathogen informatics team at the Wellcome Sanger Institute.
Specifically, read sets from each Serratia sample were aligned to the genome of S. marcescens Db11. Reads were aligned using bwa v0. 7. 17  59  , and parsed using samtools v0. 1. 19  60 and bcftools v0. 1. 19  60. Reads were considered as heterozygous if there were at least two variants at the same base, both supported by a number of reads that was fewer than 90 percent of the total reads mapped to that site. Read coverage to each strand was considered independently. The minimum total coverage required was 4×, and the minimum total coverage for each strand was 2×. Calculated heterozygous SNP coverage was then predicted by scaling the number of observed heterozygous SNPs against the proportion of the reference that was covered by read mapping. Eight genome sequences from the Pasteur collection dataset and one from the UK hospitals set were removed due to the above criteria.
Publicly available genome sequences Previously published, publicly-available assembled genome sequences were downloaded from the NCBI GenBank database (https://ftp. ncbi. nlm. nih. gov/genomes/genbank/) as of 19/03/2019. Genomes were downloaded if the species was attributed to any of the following: Serratia sp. , odorifera, rubidaea, plymuthica, liquefaciens, grimesii, oryzae, proteamaculans, quinivorans, nematodiphila, ficaria, entomophila or marcescens. Assemblies smaller than 4. 5 Mbp or larger than 6. 5 Mbp were removed from the analysis, along with any assemblies comprised of more than 250 contigs. Quast v4. 6. 0  61  was used to extract statistics for genomes and genomic assemblies, specifically whole genome GC content, number of contigs and assembly size. Initial phylogenetic trees with additional non-Serratia reference sequences (Yersinia enterocolitica, Rahnella aquatilis and Dickeya solani) were computed, and genomes detemined by visual inspection as being non-Serratia or close to non-Serratia members of Enterobacteriacaeae were removed from any subsequent analysis.
Genome assembly and annotation The assembly method used for genome assembly and annotation for each genome is detailed in Supplementary Data 1. For samples sequenced using short-read only data, genomes were assembled in two different ways depending on their origin. Isolates in the Institut Pasteur collection were assembled through assembly pipelines at the Wellcome Sanger Institute. For each sample, sequence reads were used to create multiple assemblies using VelvetOptimiser v2. 2. 5 (https:// github. com/tseemann/VelvetOptimiser) and Velvet v1. 2  62. An assembly improvement step was applied to the assembly with the best N50 and contigs were scaffolded using SSPACE v2. 0  63  and sequence gaps filled using GapFiller v1. 11  64. For isolates from UK hospitals that were only sequenced by short-read technology, these short reads were assembled using SPAdes v3.
Pan-genome analysis Pan-genomes were calculated from 664 Serratia sequences using Panaroo v1. 2. 3  70  , with Prokka-annotated genomes as input. For initial protein clustering within Panaroo, a protein sequence identity similarity threshold was set at 95 percent (0. 95) and the length difference cutoff was set at 95 percent (0. 95). The subsequent clustering of these groups into protein families was performed using a threshold of 70 percent identity (0. 7). The '-clean-mode' flag was set to 'moderate'. A core-gene alignment was created using the '-a' flag, specifying mafft as the aligner using the '-aligner' flag, with core genes specified by being present in at least 95 percent of genomes (631/664). Pan-genome gene accumulation curves were generated using the specaccum function from the R package Vegan v2.
5. 7  71  , with 100 random permutations. Population structure-aware classification of genes across the genus was performed upon the gene presence/absence matrix created by Panaroo through the use of the twilight analysis package  41. Groups were defined by the lineages set by the third level of FastBaps clustering (see below), and singleton lineages were included in the analysis ('-min_size 1'). The core and rare thresholds were set at 0. 95 and 0. 15, respectively. Preliminary core-gene alignments using the pan-genome software Roary v3. 12. 0  72  , including all downloaded genomes from the NCBI GenBank database, duplicate genomes from the Pasteur collection and non-Serratia Enterobacteriacaeae members, were computed for initial tree-drawing to remove contaminants and assess whether duplicate strains (from data supplied in strain name information, for example, labels on agar stabs from strains in the Pasteur collection) were found in the same position in the tree.
Genomic assemblies were then clustered base on this cutoff value, using the script fastANI_to_clusters. py which uses the networkx package (https://networkx. github. io/), and visualised using Cytoscape v3. 7. 1  78. The phylogeny was partitioned into lineages defined through hierarchical bayesian clustering using FastBaps v1. 0. 4  79. FastBaps was used to cluster the phylogeny over four levels, with the third level selected for lineage designation. The SNP sites-filtered core-gene alignment was used as input to FastBaps, alongside the rooted phylogenetic tree to provide a guide for the hierarchical partitioning.