dataesr/scientific-paragraphs-categorization huggingface dataset:
in 2018 on multiple pregnancies, by Barba-Vasseur et al. in 2017 on preterm birth in single pregnancy, and by Brembilla et al. in 2019 on vulnerability markers in pregnant women  [33] [34] [35]. Outcomes. Birth weight and fetal growth restriction (FGR) were extracted from the Besançon computerized medical records (DIAMM software (version 7. 5, http:// www. micro6. fr/ mere-enfant. php) developed by the Association of Computerized Users in Pediatrics, Obstetrics and Gynecology  36  ) and from the Burgundy perinatal network records and paper medical records for Dijon. Births were classified as SGA if birth weight was < 10th centile for gestational age (in weeks) and sex. The threshold for the 10th centile of birth weight was estimated in a population of French newborns from single and multiple pregnancies by gestational age and sex  36.
In order to test for a classification effect, SGA was also defined according to another birth weight standard for gestational age and sex, estimated with data from the 2010 perinatal study  37. According to French, British, and Canadian recommendations, FGR was defined as a defect in fetal growth on two antenatal measurements taken two to three weeks apart  [38] [39] [40]. FGR was retained according to the ICD10 codes in medical records (O36. 5, P05. 0, P05. 1).
Major infant congenital abnormalities corresponded to any major congenital anomalies according to the European network of population-based registries for the epidemiologic surveillance of congenital anomalies  41. Infant congenital abnormalities considered for this study were determined before birth or at birth. The neighborhood socioeconomic level was estimated with a collective socioeconomic index calculated at the geographical scale of the French sub-municipal census block groups (IRIS) defined by the National Institute of Statistic and Economics Studies  42. Fifty IRIS in Besançon and 113 in the urban area of Dijon were included; population size ranged between 62 and 4811 inhabitants (mean = 2182 inhabitants). Variables related to family and household, immigration and mobility, employment and income, education and housing were extracted from the 2008 population census database.
From among these variables, 39 were selected because of their occurrence in the literature  [43] [44] [45]. The first component of a principal component analysis (PCA) was used to calculate a standardized socio-economic index following a reduction step. The socioeconomic index was calculated using the R package SesIndexCreatoR  43. A value of the socioeconomic index in the last decile was considered as low neighborhood socioeconomic level. Environmental residential exposures. The residential exposure assessments have been previously described  10, [33] [34] [35] [46] [47] [48] [49] [50]. The pa ticipants' addresses at the date of delivery were extracted from CPage software (ver-sion 2, https:// www. cpage. fr/) using the personal identification number and the date of delivery. This address identified the residential building.
Two pollutants related to road traffic were studied. NO 2 is a gaseous pollutant known to be the main indicator of road traffic  51. Particulate matters (PM 10 ) are also generated by road traffic and residential heating and were chosen because of their significant impact on human health and climate  52, 53. NO 2 and PM 10 exposure assessments were calculated at each mother's building considering a 50 m radius buffer centered on the building centroid. The NO 2 and PM 10 levels were calculated using a two-step emission and diffusion modeling. Air pollutant emissions were calculated from road traffic data using CIRCUL' AIR software (version 2. 51, www. atmo-grand est. eu), developed and used by all approved French Air Quality Monitoring Agencies (AASQA) (COPERT IV European standard methodology).
AASQA's pollution emission inventory was used to assess air pollutant emissions related to all activity sectors, especially heating, industries or agriculture. Air pollutant concentration was estimated 2 m above ground on a 25 m grid with reinforced gridding around the axes of emission, using the ADMS-Urban software (version 3. 1. 6, http:// www. cerc. co. uk/) for diffusion modeling. ESRI ArcGIS software (version 10. 1, https:// www. esrif rance. fr/) was used for spatial interpolation to increase the spatial resolution of the ADMS output. Air pollutant concentration expressed in micrograms per cubic meter (μg/m 3 ) was thus calculated at a 4m 2 (2 m × 2 m) raster. The validity of the 2 m result was estimated on the basis of data from four, two-weeklong measurement campaigns carried out during autumn and winter 2010 as well as spring and summer 2011.
Measurements were based on 863 passive samplers and the nine AASQA air pollution measurement stations for NO 2 (ATMO Franche-Comté and Atmosf ' Air Bourgogne). Validation statistics (r 2 ) range from 0. 64 to 0. 69. The validity of the PM 10 models were verified using the city's fixed air-quality monitoring network. Monthly maps of air pollutant concentration were established from January 2004 to December 2009 using hourly meteorological data and background pollution levels to account for the seasonal variations in air pollutant concentrations. Using the monthly maps, time-weighted average air pollutant exposure was assessed over the following defined pregnancy periods: first, second and third trimester, entire pregnancy and two months before delivery. Noise levels were calculated in accordance with the European Commission's Environmental Noise Directive 2002/49/CE using an environmental noise prediction model with MithraSIG software (version 3.
Because of the non-random distribution of missing data, a missing data class was used for categorical variables in multivariate analysis. Only two adjustment factors of the model had missing data: malnutrition and obesity (n = 219) and maternal smoking during pregnancy (n = 196), affecting only 2. 4% of pregnancies. Potential interactions between air pollution and noise indices were assessed in models where at least one exposure was significant (p-value < 0. 15). The interaction was tested using the p-value associated with the term from the regression analysis and comparing the Akaike Information Criterion (AIC) of models with and without the interaction term. Sensitivity analyses were conducted using four noise indices (L Aeq,day , L Aeq,evening , L Aeq,night , L den ), two source-specific noise indices (road and rail traffic related noise L Aeq,24 h ), and the other birth weight standard to define SGA as an outcome variable  37.
Maternal age at delivery and neighborhood socioeconomic level were also considered for adjustment in continuous form, or with a 2nd or 3rd order polynomial. Adjustment for year-season of conception was also considered in sensitivity analysis. Finally, a last analysis was conducted restricted to the subgroup of live births. Statistical analyses were performed with SAS software (version 9. 4, SAS Institute, Cary, NC: https:// www. sas. com/). Ethics. This study was approved by the French National Advisory Committee for the Treatment of Information in Health Research (CCTIRS) (registration number 15. 292, April 9th 2015) and by the French data protection authority (CNIL) (registration number DR-2015-736, December 24th 2015). All methods were carried out in accordance with the ethical standards of CNIL and the Declaration of Helsinki.
A coding effect cannot be ruled out because FGR implies a dynamic evaluation of fetal growth during pregnancy by obstetricians. However, this potential coding effect was reduced by the multicentric quality of this study. Specific attention was paid to the collection of data from medical records. The consultation of paper medical records resulted in the collection of potential confounding factors for the adjustment of the analyses. Data was missing for two adjustment factors of the model (malnutrition/obesity and maternal smoking during pregnancy) but involved only 2. 4% of pregnancies. Another limit was the absence of information about a potential move during pregnancy -we used the mother's address at delivery, which was recorded in the hospital information system upon admission, for geocoding.
Acknowledgements The authors acknowledge the local associations for air quality monitoring:  Atmo Franche-Comté  and  Atmosf 'air Bourgogne ;  O. Allemandet ,  A. Bourtembourg ,  A. Dussaucy ,  A. Eckmann ,  M. Puyraveau  and  M. Vasseur-Barba  from the  Centre Hospitalier Régional Universitaire (CHRU) of Besançon ;  J. Besson  and  S. Belhadj  from the  CHRU of Dijon ;  S. Rankin  for thorough proofreading of this article; and the  Maison des Sciences de l'Homme et de l'Environnement  ( MSHE USR3124 ) for assistance in this project. Computations were performed on the supercomputer facilities at the  Mésocentre de Calcul de Franche-Comté.
We examine trends in mortality inequality in six wealthy European countries using methods identical to the US analysis. Mortality inequality in these countries is of interest in its own right but also serves as a useful baseline for considering developments in the United States, contributing to a growing body of comparative literature on mortality differentials. Our main analysis focuses on six countries (England, France, Germany, the Netherlands, Norway, and Spain) for which consistent mortality data by geographic areas exist for the entire time period. All six are prosperous countries with well-developed health care and social welfare systems. The experience of these countries provides some insight into questions such as how low US mortality rates could fall given current medical standards; whether increasing gaps in life expectancy between the United States and Europe are driven only by lower-income areas or whether higherincome areas are also falling behind; and finally, whether mortality among Black Americans declined only relative to White Americans or whether it also declined relative to a European life expectancy benchmark.
Results Our main results for age-specific mortality rates are shown in Figs. 1 2 3 4 , representing the age groups 0 to 4, 5 to 19, 20 to 64, and 65 to 79. Each figure has three panels showing estimates for the years 1990, 2005, and 2018 and contains three heavy lines representing mortality rates for Black Americans, White Americans, and Europeans as defined in our study. Each marker on these figures represents a county group representing about 5% of a country's population. The lines drawn through the markers are simply least squares linear regression lines through the points. The data for each marker and the slopes of the regression lines are shown in SI Appendix, Tables S1-S4, along with P values for whether the slopes of the regression lines are equal to 0, whether the slopes for lines representing Black and White persons are equal, and whether the slopes of the White American and European lines are equal.
For this group, we are lacking   S1. the detailed data on death rates by single year of age to age-adjust the death rates, which is crucial to compare mortality across countries and over time. Fig. 5  summarizes the trends in age-specific mortality by showing life expectancy at birth for Black and White persons for each year from 1990 to 2018. We have also drawn a trend line using data from 1990 through 2012. This figure highlights the strong convergence between Black and White American mortality rates between 1990 and 2012. Over this period, White American life expectancy continued to improve, but Black American life expectancy improved faster. Fig. 5  shows that if mortality had continued to evolve at the same rate after 2012 as it did from 1990 to 2012, the gap in life expectancy between Black and White persons would have closed by 2036.
The gap between Black Americans and the six European countries included decreased by 19% between 1990 and 2012 but only by 8. 3% in the overall period from 1990 and 2018. Hence, the convergence in the US Black-White American mortality gap reflects real progress among Black Americans even relative to a non-US benchmark, but this progress has reversed after 2012. The diverging mortality experience between the United States and Europe is especially evident when analyzing a larger set of nine European countries, although some of these countries are missing data for 1990 (SI Appendix, Figs. S5-S9 ). Despite strong differences in social and economic starting points across these European countries, by 2018 their mortality gradients fall into a narrow band. Even countries like Portugal, which was much lower income than the European average in 1990, or the Czech Republic, which experienced the fall of the Soviet Union, were able to catch up to higher-income and more stable European countries in terms of mortality rates by 2018.
We refer to the county groups with the highest (lowest) fractions of their populations in poverty as the lowest-income (highest-income) counties. Our approach reassigns county groups in 1990, 2005, and 2018 to adjust for changes in county ranking and population size. Poverty rates are taken from the 1990 and 2000 Censuses and the 2014 to 2018 American Community Survey 5-y sample and interpolated for intermediate years. European Mortality. Data for nine European countries (Czech Republic, England, Finland, France, Germany, the Netherlands, Norway, Portugal, and Spain) come from the Institute for Fiscal Studies (IFS) project on Geographic Approaches to Inequalities in Mortality  (66)  and are treated similarly. Additional details on data sources are provided in SI Appendix. Figs. 1-4 include the six European countries that provide consistent mortality data from 1990 onward (England, France, Germany, the Netherlands, Norway, and Spain).
Figs. 1-4 include a mean Europe mortality rate, representing the population-weighted average of mortality rates across these countries in each ventile. SI Appendix, Figs. S5-S9 show analogous figures using all nine of the European countries in the IFS study. See SI Appendix for further information about these countries. Mortality rates across all countries and years are age-adjusted using the 2015 US population, based on 5-y age groups. The following describes the respective area definitions, ranking measures, and available data years for each of the European countries included in Figs. 1 2 3 4 : England, local authorities ranked by a deprivation index, for 1992 to 2017; France, départements ranked by poverty rate, for 1990 to 2018; Germany, districts ranked by per-capita income in 1990, 2005, and 2016 (1990 excludes East Germany because of the exceptionally high mortality in East Germany around the time of reunification); the Netherlands, municipalities ranked by poverty rate, 1995, 2005, and 2016; Norway, municipalities ranked by poverty rate, 1990 to 2018; and Spain, municipalities ranked by median income, 1990 to 2016.
Further details on area definitions and the poverty or deprivation variables used for ranking areas can be found in ref. 51. Life Expectancy Data. We construct US Black and White American life expectancy at birth based on 1-y mortality rates for the years 1990 to 2018. The Human Mortality Database (67) provides life expectancy estimates for the European countries in our study, while life expectancy estimates for England are provided by the United Kingdom Office for National Statistics. Decompositions. We offer breakdowns of the contributions of location, age, and cause of death to Black and White American life expectancy and to the closing of the racial mortality gap. Each breakdown is based on asking how life expectancy would have changed if all other factors besides the one being considered had remained constant at their 1990 levels.
Our hypothetical life expectancy measure ignores interaction effects because they cannot be assigned to a specific age. Hence, our results should be interpreted as the partial effect of a given factor, which is sometimes referred to as the exclusive life expectancy impact of an age-specific effect  (68). For further details, see SI Appendix. Data Availability. All datasets used in this study are publicly available online or can be obtained from the National Vital Statistics offices. See SI Appendix, section C, for additional details and download links.
ACKNOWLEDGMENTS. We thank  J. B. , S. K. , and  K. G. S. for organizing the IFS working group on geographical approaches to measuring inequality in mortality, with financial support from the  Economic and Social Research Council Centre  for the Microeconomic Analysis of Public Policy at  IFS  (Grant  ES/T014334/1 ). C. C. received support from the  Science and Technology Foundation (Fundação para a Ciência e a Tecnologia [FCT]) , the  European Social Fund , and the  Centro Operational Programme  (Grant  SFRH/BD/132218/2017 ). P. S. received support from the  Centre of Studies in Geography and Spatial Planning  (Grant  UIDB/04084/2020 ), through an  FCT fund.
When using the 'think aloud' technique, an interviewer asks participants to verbalize their thoughts as they are formulating a response, with the interviewer intervening very little otherwise. In 'probing' , the interviewer has a more active role, asking targeted questions and follow-up probes to understand a participant's interpretation of the question and reason for their selected response  [13]. The 'think aloud' technique has proven challenging to implement across certain cultural contexts and so verbal probing is more suited to CCCIs  [17] , though participants spontaneously sharing their thought processes is never discouraged. The first round of cognitive interviews will involve 24-32 individuals in most country sites (with a maximum of 50 in any site) [See 'Data collection method/s' section for details].
• Whether answer options are complete and used appropriately. • Whether participants feel that they (and others) could give an honest answer. • Whether participants perceived the questions to be phrased in a sensitive manner. All cognitive interviews will be conducted in private and audio-recorded, with the participant's consent. Interviews will be conducted in the presence of only the data collector, where possible. In previous cognitive interview studies, participants have been given a gift voucher or other modest 'token of appreciation' as a thank you for their time  [25]. Each site will offer something similar, not intended to exceed the relative equivalent of USD 20, or an amount deemed appropriate for this type of research for local research teams and ethics review boards/committees.
Storing digital data: Digital files (audio, data analysis, interview notes, completed survey instrument) will be stored securely on a password-protected computer and on password-protected cloud storage such as Dropbox. Access to files on cloud storage will only be granted to select research staff who will be participating in the data analysis. The original interview audio recordings will be destroyed after 2 years, while additional study materials will be destroyed after 1 additional year. Each site will be responsible for maintaining the content of each interview: the audio file of interview and any written notes, and a record of the completed survey instrument. Within-site analyses, as described above, will result in a completed matrix file which is shared to WHO/HRP. WHO/HRP will pool these files, as described above, generating one master matrix that contains data from all sites in a given wave.
Acknowledgements We are grateful to the dozens of sexual health researchers from around the world who have provided important input into the development of the survey instrument, which this protocol seeks to test. Special thanks to the participants of the 2020 hackathon, organized by  WHO/HRP ,  LSHTM , and hosted by  APHRC in Nairobi: Noor A Ahmad ,  Juliana Anderson ,  Michele Andrasik ,  Nathalie Bajos ,  Soazig Clifton ,  Jennifer T Erausquin ,  Amanda Gabster ,  Amanda Gesselman ,  Evelyn Gitau ,  Lianne Gonsalves ,  Chimaraoke Izugbara , Osmo.
Ethics procedures As the study only involved professionals and referred to an educational improvement initiative with expert health care professionals as participants, it did not raise any of the ethical issues flagged by the European Commission in the Horizon 2020 Programme Guidance "How to complete your ethics self-assessment"  [12]. Therefore, ethics approval by an ethics review board/committee was deemed not to be necessary. This study fully complies with ethical principles, relevant EU, and international legislation  [13]  including the Charter of Fundamental Rights of the European Union and the European Convention on Human Rights  [14]. Data were stored in a database on a password-protected computer as an encrypted file. Due to the European General Data Protection Regulation (GDPR), this database is not openly available.
Study samples Participants with a bipolar disorder diagnosis, available genetic data and AAO information were selected from independent data-sets, including those previously submitted to the Psychiatric Genomics Consortium (PGC) Bipolar Disorder Working Group 13 and the International Consortium on Lithium Genetics (ConLiGen). 21 hese consortia aggregate genetic data from many cohorts worldwide. Our analyses comprised 34 cohorts with 12 977 patients with bipolar disorder who have European ancestry from Europe, North America and Australia. For a description of sample ascertainment, see the Supplementary Material. The authors assert that all procedures contributing to this work comply with the ethical standards of the relevant national and institutional committees on human experimentation and with the Helsinki Declaration of 1975, as revised in 2008.
Quality control and imputation of genotype data The cohorts were genotyped according to local protocols. Individual genotype data of all discovery-stage cohorts were processed with the PGC Rapid Imputation and Computational Pipeline for GWAS (RICOPILI) with the default parameters for standardised quality control, imputation and analysis. Before imputation, filters for the removal of variants included non-autosomal chromosomes, missingness ≥0. 02, and a Hardy-Weinberg equilibrium test P < 1 × 10 -10. GWAS, genome-wide association study; AAO, age at onset; MAD, median absolute deviation, PAO, polarity at onset; PAO-M, mania/hypomania before depression; PAO-D, depression before mania/hypomania. a. We calculated the median absolute deviation using 1. 4826 as constant. b. We defined three categories of polarity at onset: PAO-M, mania/hypomania before depression; PAO-D, depression before mania/hypomania; and PAO-X, mixed.
Acknowledgments The authors thank  Jacquie Klesing , Board-certified Editor in the  Life Sciences (ELS) , for editing assistance with the manuscript. BOMA-Australia sample: we thank  Gin Mahli ,  Colleen Loo , and  Micheal Breaskpear  for their contribution to clinical assessments of a subset of patients and also  Andrew Frankland  for his work in collating clinical record data. WTCCC sample: this study makes use of data generated by the  Wellcome Trust Case-Control Consortium. A full list of the investigators who contributed to the generation of the data is available from www. wtccc. org. uk. French sample: we thank the psychiatrists and psychologists who participated in the clinical assessment of patients in France ( C. Henry ,  S. Gard ,  J. P. Kahn ,  L. Zanouy ,  R. F. Cohen  and  O. Wajsbrot-Elgrabli ) and thank the patients for their participation.