dataesr/scientific-paragraphs-categorization huggingface dataset:
The blots were subsequently incubated with the primary antibody (anti-HA; GenScript THE TM HA-tag Antibody, mAb, mouse, catalogue No: A01244-100; 0. 5 µg/mL) for one hour at room temperature or overnight at 4 °C. Primary antibodies were prepared in TBS-T with 200 µL Micro-O-Protect (80% ethanol, 7. 5% 5-bromo-5-nitro-1,3-dioxane, 7. 5% 2-methyl-4-isothiazolin-3-one hydrochloride) and stored at 4 °C. After incubation with the antibody solution, the membranes were washed as described above, and incubated with HRP-conjugated anti-mouse antibody (Sigma A9044; diluted freshly in TBS-T 1:10. 000). Afterwards, the membranes were washed again, and the signals were detected with the enhanced chemiluminescence kit (ECL® PLUS system, GE Healthcare). Images of uncropped blots are available in the Source Data file.
Acknowledgements We thank  Dr. Stephanie Ruf  and her team (in particular  Jennifer Bergander  and  Luisa Heinig ) for help with plant transformation and tissue culture, the  MPI-MP GreenTeam  for plant cultivation, the  MPI-MP  photographers for taking pictures of the plants, and  Prof. Dr. Dirk Walther  for help with codon optimization of the linker sequences. This research project received funding from the  Max Planck Society  and the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  ( ERC-ADG-2014 ; grant agreement No  669982 ) to R. B.
Pregnant women have a high risk of severe influenza, associated with obstetrical complications. The World Health Organization (WHO) has recommended influenza vaccination for all pregnant women since 2012. The vaccination coverage remains low worldwide, and in Europe, due to a lack of proposition from the health care providers, and a high refusal rate from the women. The primary aim of this study was to estimate the influenza vaccination coverage (IVC) in a population of pregnant women in France, and to analyse its evolution from 2009 to 2018. The secondary objective was to describe the vaccinated population and to find determinants associated with the vaccination. This retrospective cohort study is based on the EGB French health care database, a representative sample of the French population containing data from the health insurance system.
3 to 56. 1% in 2014-2015 and was not monitored by most of the member states, including France  24. A few studies all based on classical surveys with different methodologies estimated the IVC in France to be between 5. 4 and 26%  [27] [28] [29] [30]. In order to avoid the usual biases arising from classical surveys (selection bias and generalization of the findings), leading to a great variability in coverage estimates, and since the influenza vaccination is prescribed and fully reimbursed by the social security, we propose to use the French national healthcare insurance database to provide estimates of the IVC in pregnant women. In this study we sought to analyze the evolution of the annual IVC in pregnant women in France from 2009 to 2018, to evaluate the impact of the WHO 2012 recommendation  25  on vaccination behavior, ad to identify determinants of influenza vaccination uptake in pregnancy.
Methods Data source and design. A repeated annual cross-sectional study was performed by using data from the "Echantillon Généraliste des Bénéficiaires" (EGB), a representative sample of the French population, from January 1, 2009 to December 31, 2018. The EGB database contains the healthcare data of about 660,000 people, representing 1/97th of the French population. This sample of insured patients is randomized, anonymized and representative of the health care protected French population  31, 32. EGB contains sociodemographic information on the beneficiaries, as well as data on various health expenses reimbursed by the French healthcare insurance. That includes prescribed medications (identified according to their anatomical therapeutic chemical class, ATC), consultation with medical and para-medical professional, in private practice as well as public hospital, hospitalization and laboratory expenses.
We can also find data on chronic diseases such as ALD (long-term disease) codes, diagnosis and medical history coded during hospitalization using ICD-10 (international classification of diseases, tenth revision) and medical procedures referenced according to the CCAM (common classification of medical acts). The EGB database is a validated tool for pharmacoepidemiological studies in France and has already proven to be a reliable tool to assess vaccination coverage for other reimbursed vaccines  [33] [34] [35] [36].
Study population. All women who had data recorded on a delivery, vaginal birth, or caesarian section in the EGB database, during the period from January 1, 2009 to December 31, 2018 were included. The exclusion criteria were unwanted pregnancy and denial of pregnancy. We did not exclude pregnancies that resulted in stillbirth, or therapeutic abortion after 22 weeks of gestation. The corresponding ICD 10 diagnosis codes and CCAM medical procedures are listed in Supplementary Tables  1 2 3. Exposure. Influenza vaccine exposure of the pregnant women included was identified through the reimbursement for a seasonal influenza vaccine in the EGB database, up to nine months before the date of their delivery, using two codes indicating dispensation (ATC J07BB and PRS_NAT 3331). We assumed that a vaccine purchased was injected afterwards and we calculated the IVC based on the number of pregnant women who had a reimbursed influenza vaccine.
To evaluate the impact of the WHO 2012 recommendation  25  on vaccination in the first trimester, we formed 3 subgroups of women according to their term of pregnancy during the vaccination campaign that takes place from October to January. Women who gave birth from November to February were labeled T3 as they were in their third trimester during the vaccination campaign. Likewise, those who gave birth from March to May were labelled T2, and those who gave birth from June to August were labeled T1. Collected data. The collected data concerned were pre-existing medical condition, age, parity, CMUc (universal complementary healthcare insurance), and prescriber's specialty. We used the French influenza vaccination recommendation to select women with pre-existing conditions who would have been targeted by the vaccination campaign outside of their pregnancy.
Various conditions were included, and grouped in pulmonary, cardiac, neurological, renal, and hepatic disease, diabetes, and immunodeficiency (including HIV, stem cell and all organ transplantations, inflammatory and autoimmune diseases, hematological disease such as sickle cell disease and cancer). Data were extracted from the EGB database using the CCAM, ICD-10, or long-term chronic disease codes (Supplementary Tables  5 6 7 ). CMUc was used as a proxy for precarity since it is an insurance available only to patients with low income, to insure their access to healthcare free of charge. The women affiliated with the CMUc were considered socioeconomically deprived.
Ethics approval. There was no requirement for ethical approval for this study. The EGB database guarantees the confidentiality and anonymity of all data (agreement by the French Data Protection Authority, CNIL, June  14, 2005). The use of the EGB database for medical research and for this study in particular has been approved and authorized by the French data protection authority (Commission Nationale de l'Informatique et des Libertés, CNIL). This use is conditioned by a specific training with certification that the researchers must follow. Chouki Chenaf has obtained this certification and was allowed to access and analyze the EGB database for this study. Furthermore, there was no requirement for ethical approval for this study. The EGB database guarantees the confidentiality of all data and anonymity (agreement of French data protection authority on June 14, 2005).
Influenza vaccination coverage in pregnant women. From January 1, 2009 to December 31, 2018, 73,314 pregnant women were identified in the EGB database. Fifty-two denial of pregnancy and 1055 unwanted pregnancies were excluded from our cohort (Fig. 2 ). A total of 72,207 pregnant women were included in the study, whose characteristics appear in the Table  1. The overall influenza vaccination coverage (IVC) was only of 1. 21% in the 2009-2018 period (n = 875/72,207; 95% CI 1. 14-1. 30). The IVC slightly increased after the 2012 recommendation, from 0. 33% in the 2009-2012 period, to 1. 79% in the 2013-2018 period (p < 0. 001) (Fig. 3 ). Vaccinated women were older, less socially deprived, and had more pre-existing medical condition. There was no difference regarding the pregnancy outcome in the vaccinated and non-vaccinated population.
Discussion Main results. Our primary endpoint was to analyze the evolution of the IVC in pregnant women in France from 2009 to 2018 using the French national healthcare database. We showed that the IVC increased slightly since the 2012 recommendation but remained extremely low (only up to 4. 1% in 2018), thus keeping pregnant women and their fetuses at risk of complications from influenza infections. In addition, the coverage for women who started their pregnancy during the vaccination campaign remained lower than those of women in their 2nd and 3rd trimester, thus questioning the impact of the recommendation, that encouraged the vaccination of women at all term of pregnancy. The IVC was higher for older women, and women with pre-existing condition at risk for severe influenza, and lower in socially deprived women as often described in other studies  30, [37] [38] [39].
Strengths and limitations of the study. The main strengths of the study are the size of the study population and the reproducibility of the calculation of the IVC through the EGB database. Our administrative method is reliable to assess a vaccination coverage in a limited time, on a large representative sample, with less bias than a declarative study. Our IVC results on patients over 65 years old are similar to the French official data, validating the use of the database to evaluate the IVC. Its limitations are those of the database, that can only record reimbursed vaccines, leaving out over the counter purchase and workplace vaccination. Influenza vaccination is recommended for patients over 65 years old, with chronic disease, obese patients, and pregnant women and their vaccine is supposed to be prescribed and fully reimbursed.
In addition, anyone can buy it over the counter, for a reasonable price. We assumed that a pregnant woman would turn to her physician before making any health decision during her pregnancy and would have the prescription for the vaccine. However, women who purchased the vaccine over the counter on their own initiative or got the injection in their workplace would have been falsely classified as "unvaccinated" in our study. Even though our data cannot be fully exhaustive due to this easy access to the vaccine outside of the traditional health care pathway, our results are useful for analysis and comparison purposes. We identified some limitations in our secondary endpoints: Pre-existing medical condition were assessed through the existence of a long-term disease data claim, or hospital discharge quotation of an act or a pathology, as well a medication prescribed related to the pathology.
We may have overlooked woman that had a condition that put them at risk of a severe influenza, but who requires no specific treatment and had no record of it in their files. Parity was estimated with the data available in EGB and is indicative. The data collected goes back to 2005, and any pregnancy occurring before that date will not have been taken into account, as well as any prior pregnancy that may have taken place abroad. We chose to define socioeconomic deprivation by the CMUc affiliation, a specific health insurance with full coverage for low-income patients. However, socioeconomic deprivation is a wider concept that includes criteria such as insecure employment, lack of familial support, and reduced access to culture, sport or vacation  56. The EPICES score (Evaluation of precariousness and Inequalities in Health Examination Centers) is more reliable to describe socioeconomic deprivation  57, 58.
Perspectives for research and healthcare. A study evaluating the knowledge, attitudes, and practices of healthcare professionals regarding influenza vaccination in France could help to improve the IVC, taking into consideration the global reluctance towards vaccination in France. The IVC could be improved by targeted information campaigns towards all healthcare professionals involved with pregnancy care, and towards women in general. A national information campaign was issued on French television to promote influenza vaccination during the winter of 2020-2021, and pregnant women were listed as a priority. The impact of such campaigns could be monitored, by issuing IVC data yearly. Our evaluation of IVC in pregnant women with the EGB database may lead the way to other vaccination evaluations, such as pertussis vaccination coverage.
Variables coded directly from the relevant publications: All data from the literature search on publications reporting the effect of dominance rank on reproductive success were entered prior to the first submission of the preregistration. S and DL performed the data extraction. We initially coded eight papers independently, for which we both extracted the same values and classified the approaches in the same way. S and DL also independently went through the studies included in  Majolo et al. (2012)  and agreed on which to include and which not. After this, S and DL independently identified and coded articles, with occasional cross-checks and discussions of any border line cases. We extracted the relevant information to calculate the effect sizes and their associated variance. In addition, we coded a set of variables to characterize the methodological approach.
The dataset contains 444 effect sizes from 187 studies on 86 mammalian species. Z-transformed effect size: we converted all effect sizes to Z-transformed correlation coefficients (Zr). In cases where articles reported a pairwise correlation coefficient, we directly use this value. In cases where authors had used alternative statistical approaches (e. g. t-test comparison between two groups of individuals), the test statistics were converted to the statistic 'r' using formulas provided by  Lakens (2013) ,  Lajeunesse et al. (2013) , and  Wilson (2019). In cases where authors reported individual-level data reflecting dominance rank and reproductive success (for example in the form of a table that listed for groups of dominants and subordinates their mean and deviation of reproductive success or for every individual their rank and reproductive success), we calculated correlation coefficients directly from a 2-by-2 frequency table (when comparing classes of high-to low-ranking individuals) or from linear regressions (when individuals had continuous ranks).
Species identity: we recorded the common name and the latin species name as listed by the authors. We referred to the Mammal Diversity Database  (Burgin et al. (2018) ) to resolve instances where species attributions had been changed since the publication of the original study. Study site: we recorded the name of the study site as listed by the authors in the method section. The focus of this variable is to determine whether multiple observations are from the same species from the same study population, and we accordingly assigned different names for the study site label in case two or more different species had been studied at the same site. Measure of reproductive success: we recorded which aspect of reproduction dominance rank was associated with. We classified reproductive traits into six classes: -age at first reproduction (includes age at first birth, age at first conception, age at first menstrual cycle); -infant survival (includes rates of mortality of offspring prior to their independence; proportion of pregnancies carried to birth); -survival (includes rates of mortality of females per year, age at death); -infant production (includes litter size, offspring weight, litter mass, number of offspring per year, probability of birth in a given year, number of surviving infants per year); -interbirth interval (includes time between live births, number of cycles to conception, number of litters per year); -lifetime reproductive success (includes total number of offspring born or surviving to independence for females who had been observed from first reproduction to death).
Variables extracted from the broader literature for each species/populations: The following data were added prior to the analyses. For most of these, we extracted information from the relevant papers or publications reporting on the same population. For some of these, we used previously published species' averages, because records from each population for each specific period during which the effect of dominance rank on reproductive success were measured were not available for a large enough sample. We list sources we used to obtain these data. Litter size: the number of offspring per birth; data available for each population, we used the average as reported by the authors (based on the data in  Jones et al. (2009) ). Interbirth interval: the time in months between consecutive births; data available for a limited set of populations, we used the average as reported by the authors.
Phylogeny We generated a single consensus phylogeny for the mammalian species in our sample from the most recent complete mammalian time-calibrated phylogeny  (Upham, Esselstyn, and Jetz (2019) ). We downloaded a credible set of 1000 trees of mammalian phylogenetic history from vertlife. org/phylosubsets/ (July 2020) and used TreeAnnotator (version 1. 8. 2 in BEAST:  Drummond et al. (2012) ) to generate a maximum clade credibility (MCC) tree (median node heights and a burn in of 250 trees). We trimmed the tree to match the species in our sample (in one instance using a close relative, Canis lupus instead of Canis familiaris ) and converted branch lengths using functions of the package ape  (Paradis and Schliep (2019) ).
Analyses We performed all analyses in the statistical software R (version 4. 0. 3; R Core Team (2020)). We built separate models for each prediction. For some predictor variables,we could not find data to match to all observed effect sizes, and excluded these cases with missing data from the respective analyses. We report the sample size for each analysis. To assess the robustness of the findings and whether modeling decisions might have an influence on our results, we used a frequentist and a Bayesian approach to build the statistical models. For the frequentist approach, we fit meta-analytic multilevel mixed-effects models with moderators via linear models using the function"rma. mv" in the package metafor (Viechtbauer (2010)), taking into account the sampling variance as measurement error and including models that account for the potential correlations among effect sizes due to shared phylogenetic history among species  (Nakagawa and Santos (2012) ).
For the Bayesian approach, we estimated relationships as implemented in the package rethinking using the function "ulam" (McElreath (2020)) to fit with Markov chain Monte Carlo estimation in stan  (Stan Development Team (2020) ). We fit multilevel models that include the sampling variance as measurement error  (Kurz (2019) ) and the shared phylogenetic history as a covariance matrix. Weakly regularizing priors were used for all parameters. We drew 8000 samples from four chains, checking that for each the Gelman-Rubin convergence diagnostic 'R-hat' values are less than 1. 01 indicating that the Markov chains have converged towards the final estimates. Visual inspection of trace plots and rank histograms were performed to ensure that they indicated no evidence of divergent transitions or biased posterior exploration.
We provide all code (archived version:  Lukas et al. 2021a)  showing the setup of the various models and the plots, the input files containing the data (archived version:  Lukas et al. 2021b ) in a github repository: https://github. com/dieterlukas/FemaleDominanceReproduction_MetaAnalysis. In addition, the github repository also contains a simulated dataset with the same structure as the actual data, which we used to assess the fit of our models in the preregistration.
Preregistration We preregistered hypotheses, methods, and analysis plans: https://dieterlukas. github. io/Preregistration_MetaAnalysis_RankSuccess. html The literature search was completed before the first submission of the preregistration. All variables that were coded directly from the source publications (Z transformed effect size, variance, sample size, species identity, aspect of reproductive success, classification of rank, duration of study, population type, and social group size) were also entered prior to the first submission. In July 2019, S worked with a preliminary subset of the data (143 effect sizes), and investigated publication bias, the overall mean and variance in effect sizes, and whether effect sizes differed according to which reproductive output was measured. We added the data on the following explanatory variables and started analyses in July 2020 after the preregistration passed pre-study peer review at Peer Community In Ecology: Paquet (2020).
Sampling bias The funnel plot of the complete dataset showed a strong asymmetry, indicating that our sample is biased towards including many studies with low precision and high positive effect sizes. To better illustrate this sample bias, we used a different way to plot the data  (Nakagawa, Lagisz, O'Dea, et al. (2021) ) that was suggested after we had written our preregistration. We also added further analyses, based on functions in the packages 'metafor' (following  Nakagawa, Lagisz, Jennions, et al. (2021) ) and 'rethinking'  (following McElreath (2020) ), to determine the potential causes of the bias in our sample and the influence on what effects should be expected in new samples.
1). We applied tests for 'publication bias' that expect a standard distribution of p-values  (Preston, Ashby, and Smyth (2004) ) to our data, which suggest that effect sizes with a p-value smaller than 0. 05 are about four times more likely to be reported than effect sizes with a p-value larger than 0. 50. Studies with smaller sample sizes have a higher risk to report inflated effect sizes due to a higher likelihood of Type I and Type II errors. In our dataset, the average effect sizes at smaller sample sizes are more extreme than those at larger sample sizes (effect sizes range from -0. 89 to +1. 33 for studies with a sample size of 20 or smaller, while for studies with sample sizes larger than 20 they range from -0. 37 to +1. 24). However, it is not just that the spread of values is larger for studies with smaller sample sizes, but the positive bias in effect sizes we observe decreases with the sample size of studies (metafor estimate 95% confidence interval lower -0.
Result 1. 3: Similarity of effect sizes from the same study and from the same species To the base model, we added random effects to account for non-independence due to effect sizes originating from within the same study, from studies performed on the same population and on the same species. The estimate of the overall effect size did not change in this model accounting for non-independence (metafor estimate of overall effect size when accounting for non-independence lower +0. 22 to upper +0. 31, rethinking estimate lower +0. 26 to upper +0. 35) from the overall effect estimated in the base model. Effect sizes from the same species and the same study, but not from the same population, tend to be similar to each other. The absence of a population effect could be because the 'study' and 'population' effects are likely to be confounded, as there are very few observations of the same population but from different studies in our dataset.