dataesr/scientific-paragraphs-categorization huggingface dataset:
Definition of multimorbidity Multimorbidity was defined by the presence of two or more pre-existing long-term physical or mental health conditions prior to the index pregnancy. We defined long-term conditions as conditions that have ongoing significant impact on patients, including conditions that are relapsing and remitting in nature. One of the wider research aims is to mitigate the effect of multimorbidity on adverse pregnancy outcomes. As pregnancy related conditions (e. g. , gestational diabetes and pregnancy induced hypertension) will be subsequently studied as maternal outcomes, they were not included in the definition of pre-existing multimorbidity. An exhaustive list of long-term health conditions was first identified from existing literature  [4, 9, 10] , in particular based on the work commissioned by Health Data Research UK on multimorbidity conceptualization  [10]  and health conditions that were leading indirect cause of death in the UK maternal mortality report (MBRRACE)  [4].
This list and phenome definitions were refined and harmonized through workshops with our research advisory group, consisting of patient and public representatives, clinicians from general practice, obstetrics, maternal medicine, psychiatry, public health, and data scientists. Seventy nine health conditions were selected on the following basis: (i) prevalence; (ii) potential to impact on pregnancy outcomes; (iii) considered important by women; and (iv) recorded in the study datasets. Diagnoses of these 79 long-term health conditions were determined from Read Codes version 2 (primary care datasets) and the International Classification of Disease 10th version (secondary care datasets)  [11]. The validity of diagnostic coding has previously been shown to be good in primary care records and generally health conditions under payment for performance schemes, such as Quality Outcomes Framework, are well coded  [12].
Data analysis The primary analysis was the prevalence of pre-existing multimorbidity in pregnant women. The denominator was the total number of index pregnancies identified in 2018, regardless of the pregnancy outcome. Additional analysis was performed for multimorbidity with at least one mental health conditions and active multimorbidity. Active multimorbidity limits common transient/episodic conditions (e. g. , mental health, dermatological and atopic conditions and headaches) to those that were active in the 12 months preceding index pregnancy (Additional file 3). Multivariable logistic regression was performed to examine the association of multimorbidity with maternal age (five-yearly categories), ethnicity, deprivation quintiles (patient level Index of Multiple Deprivation [IMD] for all three datasets), latest maternal pre-pregnancy body mass index (BMI) categories, latest pre-pregnancy smoking status, and gravidity (total number of pregnancies up to and including index pregnancy).
Obesity was considered a covariate (BMI categories) instead of a health condition. For CPRD, practice level IMD was available for all four nations, but patient level IMD was only available for England, therefore, the regression analysis was limited to England. We then described the prevalence of individual health conditions, and the prevalence of mutually exclusive multimorbidity combinations. Missing data were assigned to separate categories and included in the analyses. Sensitivity analysis was performed for CPRD (England), where missing ethnicity was imputed with data from linked hospital administrative data, and missing patient level IMD was imputed with practice level IMD. Study results were presented for each dataset separately. Data were not combined as there was a possibility of patient overlap between CPRD (Wales, Scotland) with both SAIL (Wales) and SMR (Scotland).
Study population Overall, there were 37,641 (CPRD), 27,782 (SAIL) and 6099 (SMR) pregnant women aged 15-49 years included in the analysis in 2018. Additional Fig. 1  presents the flow chart for the study population selection. The characteristics of the study cohort is presented in Table  1  and Additional Table  1. Most of the study participants were 20-34 years old, of White ethnicity, of normal weight or were overweight pre-pregnancy and were non-smoker pre-pregnancy. Linkage to area-based data for patient level IMD was available for 75% of the CPRD study cohort in England. There were more affluent women in the primary care dataset but vice versa for SMR.
Multimorbidity The prevalence of pre-existing multimorbidity in pregnant women was 44. 2% (95% confidence intervals [CI] 43. 7 to 44. 7%), 46. 2% (45. 6 to 46. 8%) in CPRD and SAIL respectively (primary care dataset) but was halved in SMR's secondary care and community prescription dataset, 19. 8% (18. 8 to 20. 8%). Over 70 % of pregnant women with multimorbidity had mental health condition/s: 31. 3% (30. 8 to 31. 8%), 33. 7% (33. 1 to 34. 2%) and 14. 6% (16. 0 to 17. 9%) of pregnant women had multimorbidity with at least one mental health conditions in CPRD, SAIL and SMR respectively. The prevalence of active multimorbidity was half that of the primary analysis in primary care datasets, 24. 2% (23. 8 to 24. 6%) and 23. 5% (23. 0 to 24. 0%) for CPRD and SAIL respectively, but remained similar for SMR, 17.
Findings were similar in the sensitivity analysis of CPRD (England) using imputed data for missing ethnicity and IMD (Additional Table  4 ). In SAIL, the effect sizes of characteristics were generally similar to that in CPRD (England). In SMR, after adjusting for all characteristics, higher odds of multimorbidity were observed only in those age 20-24 and 25-29 years, had gravidity of 3+, BMI 30+, were smokers and ex-smokers and those from more deprived socioeconomic groups. The odds of multimorbidity were not higher in ethnic minority groups (Table  2 ).
Post hoc analysis Post hoc analysis was performed to explore whether the lack of association of multimorbidity with deprivation in our primary care datasets was, in part, due to the health conditions we used to define multimorbidity. Logistic regression was repeated in CPRD (England) with the list of 31 health conditions used to define multimorbidity in Barnet et al's seminal paper  [13] , the adjusted variables were added in a step-wise manner. After adjusting for maternal age, ethnicity and gravidity, increasing levels of deprivation were associated with higher odds of multimorbidity (most deprived quintile aOR 1. 30, 95% CI 1. 08 to 1. 57). This association was attenuated and was no longer significant when raised BMI and smoking status were added to the model (aOR 1. 05, 0. 87 to 1. 27, Additional Table  5 , Fig.
Main findings This study used contemporaneous, routinely collected datasets to study the epidemiology of multimorbidity (defined as having two or more long-term physical or mental health conditions) in pregnant women in the UK. Two in five pregnant women had pre-existing multimorbidity. One in five pregnant women had multimorbidity that were active in the year before pregnancy. Seven in ten pregnant women with multimorbidity had a history of mental health condition/s. In women with conditions that are known to be leading causes of maternal death  [4] , four in five had pre-existing multimorbidity. Pregnant women with multimorbidity were more likely to be older, multigravid, smoked or have raised BMI preconception.
Strengths and limitations This study utilized electronic health records which provided a rich source of data and is generalizable across different settings. It avoided misclassification bias associated with self-reported surveys. However, as with all research that use routine health records, it is subjected to residual confounding and can be limited by the quality and consistency of data entry by clinicians and administrators  [14]. We have attempted to improve the accuracy of health conditions ascertainment through the design of our phenome definitions (e. g. , using additional age limit and phenomes by prescriptions). The definition of multimorbidity used in this study was based on simple counting of conditions, without weightings. There is currently no single multimorbidity index that can measure multimorbidity in all settings definitively  [15].
Utility of the different datasets Compared with CPRD and SAIL primary care datasets, the prevalence of non-life-threatening health conditions such as allergic rhinoconjunctivitis, migraine, irritable bowel syndrome and, ultimately, multimorbidity was lower in the Scottish secondary care with linked community prescription dataset. This is likely to reflect that health conditions seen in primary care encompass the whole severity spectrum. Some common conditions, such as anxiety or  depression, may only present to primary care, some of which are managed conservatively without prescribed medications. In contrast, the Scottish secondary care and community prescription database would only capture the severe spectrum of a condition that requires hospital attendance or regular prescriptions and may under-estimate the prevalence of multimorbidity.
This confirms that primary care health records may be a more comprehensive data source to study pre-existing multimorbidity in pregnant women, and to identify the target at risk population for preconception intervention. Similar findings were observed in both CPRD, and SAIL add to the validity of these findings. Whilst CPRD offered the benefit of representing data from all four UK nations, SAIL offered a more complete coverage at a population level in Wales with good follow up throughout an individual's lifetime even when they change GP practices. Our study highlighted a shortfall in the recording of ethnicity and preconception body mass index, and to a.
95 (0. 84 -1. 07) 3 1. 00 (0. 88 -1. 13) 4 0. 98 (0. 87 -1. 11) 5, most deprived 0. 96 (0. 85 -1. 09) Missing 0. 90 (0. 81 -1. 00) 0. 92 0. 93 0. 90 0. 88 0. 86 (0. 81 (0. 82 (0. 79 (0. 77 (0. 77 a South Asian for CPRD, Asian for SAIL and SMR Adjusted for maternal age, gravidity, ethnicity, BMI, smoking status, patient level IMD BMI body mass index, CI confidence intervals, CPRD Clinical Practice Research Datalink, IMD Index of Multiple Deprivation, OR odds ratio, Ref reference group, SAIL The Secure Anonymized Information Linkage databank, SMR Scottish Morbidity Records.
Example of health conditions that were more prevalent in ethnic minority groups from the National Community Child Health database (NCCHD), and this does not include pregnancies that resulted in early pregnancy loss; hence the gravidity data generated from this database is likely to be an under- estimation. Historical data from the SMR datasets were available from 2005 to 2019. This meant that if a pregnant woman had a history of a health condition prior to this time period, it may not be captured. This limitation is more likely to affect older women in the SMR pregnancy cohort and may partially account for the lack of associa- tion of maternal age with multimorbidity. Further limita- tions of each dataset are outlined in Additional file 5. ***Allergic rhinoconjunctivitis 15. 56 22. 65 17. 29 19.
For the evaluation of the cells infiltrating the tumor, the B16F10 tumor was excised, and the cell suspension was filtered through a 100 µM nylon filter (cell strainer, BD Biosciences). Single cell suspensions were prepared and washed using standard procedures. Cells were stained with fluorochromeconjugated antibodies before one-step fixation and RBC lysis. Cells were then analyzed by flow cytometry. Flow cytometry data acquisitions were performed using an LSRFortessa or a FACSCanto-I (BD Biosciences) apparatus and subsequent analyses were performed using FlowJo software (v10. 8. 1,Tree Star, Ashland, OR).
Statistics All data are presented as mean ± standard error of the mean (SEM). Statistical comparisons between experimental groups were performed using one-way ANOVA and Tukey's multiple comparisons post hoc tests. Two-Way ANOVA with Dunnett's multiple comparisons post hoc tests was used for statistical analysis of tumor growth curves. Statistical analysis of the survival was performed using Log-Rank tests. All statistical analyses were performed with the GraphPad Prism software (V8, GraphPad, San Diego, Ca, USA). For all, the threshold for statistical significance was set at p<0. 05 (*p<0. 05, **p<0. 01, ***p<0. 001 and **** p<0. 0001).
Funding The present work was funded by a grant No  ANR-18-CE92-0046-01  from  Agence National de la Recherche (ANR)  to SA, and by grants No  310/13 ,  SFB1328-A13 ,  SFB1328-Z02  from the  Deutsche Forschungsgemeinschaft (DFG)  to FK-N, BR, and TM.
Participating cohorts PACE consists of over 40 international birth and child cohorts with a goal of performing coordinated Epigenome Wide Association Studies (EWAS) followed by meta-analysis to understand relationships between methylation and both exposures and child health outcomes  [36]. Twenty-one independent cohorts contributed data to this study. We included 8,438 newborns from 17 cohorts in the analysis of newborn blood DNA methylation and sex (ALSPAC, CHAMACOS, CHS, EARLI, EXPOsOMICS, GECKO, Gen3G, Generation R, GOYA, INMA, IOW F2, MoBa1, MoBa2, MoBa3, NEST, PREDO, Viva). For the child methylation analysis, we included 4,268 children from eight cohorts (ALSPAC, BAMSE, CHAMACOS, CHOP, Generation R, HELIX, IOW F1, Viva). Detailed methods on the individual cohorts participating in the cord blood and child analyses are provided in the supplementary methods.
Cohort Specific Statistical Analyses Each cohort performed independent EWAS according to a common analysis plan. Each cohort used recorded child sex with females as the reference group. Each study used batch covariates most appropriate for their cohort (e. g. principal components or plate number) or a method such as ComBat  [39]. Cell composition was estimated using estimateCellCounts in the minfi R package  [38]. For cord blood analyses, the 'CordBlood' reference data set  [40]  was used to estimate proportions of seven cell types (CD8+ T-cells, CD4+ T-cells, NK cells, B-cells, monocytes, granulocytes, and nucleated red blood cells), while older child models used the 'Blood' reference data set  [41]  which estimates proportions of six cell types (CD8+ T-cells, CD4+ T-cells, NK cells, B-cells, monocytes, and granulocytes).
Meta-analysis All cohorts submitted the results of their cohort level EWAS to the Children's Environmental Health Laboratory (N. Holland-PI) at the University of California, Berkeley, USA. We then performed a fixed effects meta-analysis weighted by the inverse of the varianceusing the software METAL  [43] -for the main model, which adjusted for seven cell-type proportions and batch. Shadow meta-analyses were conducted independently by L. Küpers at the University of Bristol, UK to verify results. All further analyses were conducted in R version 3. 5. 2 [44]. Since all but one study utilized the 450K BeadChip array (child blood samples from the IOWF1 cohort use the EPIC BeadChip) only probes present on the 450K BeadChip array were included in the analysis. We excluded SNP control probes (n = 65).
The majority of cohorts included probes mapping to the X and Y chromosomes; however, some cohorts were only able to provide results for autosomal probes leaving a total sample size of N = 8,438 for autosomes, and N = 5,213 for subjects with data for sex chromosomes. Filtering of previously identified crossreactive probes  [45]  was performed during processing of meta-analysis results. For autosomal probes, this left a total of 390,810 CpG sites measured for association with sex at birth in the meta-analysis. We adjusted for multiple hypothesis testing using the stringent Bonferroni method, and considered CpG sites with Bonferroni adjusted p-values < 0. 05 significant (e. g. 1. 3 x 10 -7 for 390,810 tests for autosomes in newborns and 1. 1x10 -6 for 46,979 tests for lookup level correction in children).
Enrichment Analyses Before enrichment analysis was performed, CpG sites were annotated to nearby genes using the IlluminaHumanMethylation450kanno. ilmn12. hg19 package. We defined I 2 >50% as reflective of a high level of between-study heterogeneity  [46]  and restricted enrichment analysis to those CpGs with I 2 ≤ 50%, which included 17,243 CpGs (in 8,059 genes) for the cord blood analysis and 10,436 CpGs (in 5,572 genes) for the older child analysis. Using these genes, we performed enrichment analyses at two different levels: pathways and diseases. Detection of KEGG pathway database  [47]  over-representation against a universal Homo Sapien background was assessed by hypergeometric tests  [48]  using the gometh() function in the package missMethyl  [49]. This function, which was designed specifically for gene set enrichment analysis of methylation data, minimizes bias due to the uneven distribution of probes in the Illumina 450K and EPIC BeadChip arrays.
Newborns Results from 17 independent cohorts from the Pregnancy And Childhood Epigenetics (PACE) Consortium were included in the newborn meta-analysis (N=8,438). Newborn cohort sizes ranged from 53 to 1,319 participants. There was an even distribution of males (51%) and females (49%). Two cohorts, NEST and EARLI, performed separate models for European and non-European participants, resulting in two additional datasets (N=19 datasets total). The majority of datasets were made up of participants of European ancestry (N=15 datasets, N=7,576 participants). Other datasets included Hispanic, Mexican-American, African-American, and mixed ethnicities. A summary of the participating newborn cohorts and datasets is included in Table  1. The results of the individual cohort level newborn models are summarized in Supplemental Table  1.
Children For the analysis in older children, data from nine independent cohorts were meta-analyzed (N = 4,268) (Table  2 ). Child cohorts ranged from 234 to 1,053 participants with an average of 497 participants per cohort and had a similar distribution of males (53%) and females (47%). Similar to the newborn participants, the majority of child datasets contained participants of European ancestry (N=4,034 from 7 cohorts), with other contributions from a Mexican-American cohort. Lambdas for individual autosomal analyses ranged from 1. 05 to 5. 59 (Supplemental Table  3 ). Among older children, most of the X chromosome sites (9,313) were significant. For the autosomes, there were 40,219 Bonferroni significant sites associated with child sex (Supplemental Table  4 ). Like newborns, the majority of significant autosomal CpG sites had lower methylation levels in males than in females, which can be seen on the plot in Figure  1b.
Comparison of newborns and older children There was considerable overlap between significant sites in newborns and children. Of the 46,979 CpG significant in newborns, 68% (31,850) met look-up level significance (p<1. 1 x 10e -6   for 46,979 tests) in the smaller dataset of older children (Supplemental Table  5 ) demonstrating that many of these relationships with sex persisted over time. Of these overlapping sites, 99. 6%  (31, 727)  show methylation differences in the same direction. Additionally, there were  10, 229  CpGs that were Bonferroni-significant (genome-wide significance, p<1. 1 x 10 -7 ) only in the older children, despite the smaller size of this dataset (Supplemental Table  6 ).
Further, our analyses were restricted to cord and peripheral blood. Sex-specific methylation in other tissues, such as placenta, may also have implications for disease across the life course. We report novel findings of autosomal methylation differences between males and females using robust statistical models with a large sample size that was well-powered to assess small effect sizes. We used a cord blood reference dataset, which includes nucleated red blood cells to estimate and adjust for cell-type heterogeneity in newborns  [72]. All cohorts ensured correct classification of sex prior to analyses using sex chromosome methylation data as a quality control measure. We also included analyses of methylation at two distinct time-points (newborns and older children) suggesting that methylation differences by sex at many of these CpG sites were relatively stable throughout childhood.
The Covid-19 pandemic and associated lockdowns triggered worldwide changes in the daily routines of human experience. The Blursday database provides repeated measures of subjective time and related processes from hundreds of participants (over 9 countries) tested on 14 questionnaires and 15 behavioral tasks during the Covid-19 pandemic. The easy-to-process database and all data collection tools are made fully accessible to researchers interested in studying the effects of social isolation on temporal information processing, time perspective, decision-making, sleep, metacognition, attention, memory, self-perception, and mindfulness. Blursday also includes vital quantitative statistics such as sleep patterns, personality traits, psychological well-being, and lockdown indices. Herein, we exemplify the use-value of the database with quantitative insights on the effects of lockdown (stringency, mobility) and subjective confinement on time perception (duration, passage of time, temporal distances).
g. , WHO; https://www. covidminds. org; Yamada et al. , 2021), but how the pandemic affected our sense of time, although a prominent topic of pandemic-related anecdotal reports, has not been examined in a comprehensive and systematic manner. The Blursday project tackles this challenge and provides an unprecedentedly rich and comprehensive dataset for characterizing temporalities of hundreds of participants collected in 9 different countries (4 continents) during the peak of the lockdown periods. The uniqueness of the Blursday database is its large number of widely utilized behavioral tasks (performance measures) combined with questionnaires (self-reports), demographics and subjective confinement trackers (state measures). The database includes participants tracked longitudinally in and out of lockdown (from 2020 on) together with control groups of participants tested for the first time outside of Covid-19 lockdowns (from 2021 on).
In a longitudinal Brazilian study  25  , participants initially perceived an expansion of time which steadily decreased over the course of the following weeks. These surveys confirmed self-reported temporal distortions during lockdown but do not converge on their underlying explanatory causes. Several experimental limitations include the absence of controls, which prevents assigning a causal role of lockdown to temporal distortions, and the possibility that cross-cultural factors and differences in the stringency of the lockdown measures played a crucial role in the different trends observed across countries. All studies used questionnaires and ratings, and none included psychometric tasks. The Blursday database includes a large and diverse battery of questionnaires, tasks and tests across cultures, a longitudinal assessment of these factors within individuals and, importantly, control participants that are naive to all tasks, tested outside of lockdowns, and whose number can be incremented over time.
We provide a comprehensive dataset that captures subjective time and timing behaviors of a large number of participants on nearly all aspects of temporal information processing (interval perception and production, spontaneous tapping, synchronization, implicit timing) together with measures of working memory, decision-making, self-perception, metacognition, sleep patterns, personality traits, and well-being. The Blursday database provides an extensive benchmark for future studies that would incorporate new control data although the current database already contains some control data (see Methods; Supplementary Information Figure  1 ). We make all tools available online for researchers wishing to test participants post-lockdown using the same approach or parts of it. We fully describe the database and importantly, provide examples illustrating its potential use based on a few fundamental analyses providing quantitative insights on time perception during this historical period.