dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2022). Importantly, platelets are anucleate cells that lose their RNA and have a short life span in the blood circulation  (Angénieux et al. , 2016; Mills, Green and Ingolia, 2017). Therefore, variations in their RNA content is likely to be the result of an effect on their progenitors (megakaryocytes)  (Davizon-Castillo et al. , 2020). However, the underlying mechanism is unclear. Additionally, a recent clinical trial (NCT02453139) revealed platelet cloaking in patients with metastatic prostate cancer, and a connection between CTC and white cell numbers  (Brady et al. , 2020) , highlighting the clinical significance of platelet-CTC interactions. Long-term administration of aspirin, which reduces platelet aggregation and activation, inhibits the COX-1/TXA2 pathway in platelets and consequently, decreases platelet aggregation on tumor cells, endothelial activation, tumor cell adhesion to the endothelium, and recruitment of metastasispromoting monocytes/macrophages and ultimately the formation of a pre-metastatic niche  (Lucotti et al.
Correlation analysis In total 462,675 samples had TL estimates from both TelSeq and qPCR methods and pairwise Pearson correlation was assessed using the R `cor` function. To assess the contribution and degree of collinearity between Telseq and qPCR methods we fit the following model linear model using inverse rank normal transformed age, TelSeq and qPCR (adjusted T/S ratio -UKB field 22191) age ~ TLTelSeq + TLqPCR + sex We then used the R package olsrr (v0. 5. 3) to compute variance inflation factors (VIF) for each of the predictors, finding a mean VIF of 1. 125 indicating no evidence of collinearity. Overall removing TLTelSeq or TLqPCR from the model reduced R 2 by 0. 10 and 0. 14 respectively.
WGS TL measurement confounder analysis and adjustment. We fit a linear model to NFE ancestry samples, using TelSeq, qPCR TL metrics as dependent variables selecting the available (and non-colinear) WGS metrics 'total read count', 'uniformity of coverage', calling pipeline (deCODE or WTSI), as well as age and sex as a biological control. We used the inverse rank normal transform to scale all variables to facilitate comparison. We found that that all three WGS metrics were significantly associated with TelSeq TL measurements (Supplementary Table  3 ) and so to adjust for this we refit the linear model, excluding age and sex and taking the residuals as the adjusted TelSeq TL for downstream analyses.
PCA TL score Across all 461,461 individuals with both TL measurements, we used the R built-in function 'prcomp' to combine the adjusted TelSeq and adjusted T/S Ratio qPCR(UKB Field 22191) inverse normal transformed TL estimates. Each PCA consisted of two orthogonal principal axes whose sample scores were considered separate TL measurements or 'TL scores' with PC1 and PC2 explaining 70% and 30% of the variance respectively. To assess performance for single and combined TL metrics we randomly sampled 10,000 participants from the full dataset. We used this training set to fit a simple linear model of a given TL metric with age (i. e. age ~ TLmetric). Then using the held-out participants we used the model to predict age and assessed prediction performance as the root mean squared error (RMSE) of the age predictions.
Single causal variant fine mapping For variant fine-mapping under the single causal variant we selected autosomal variants from NFE GWAS and divided these into approximately independent LD blocks using regions defined in  (Berisa and Pickrell 2016). We then used the single variant finemapping  (Wakefield 2007; Wellcome Trust Case Control Consortium et al. 2012)  approach as implemented in https://github. com/ollyburren/rCOGS to assign 95% credible sets.
ExWAS We carried out a virtual exome-wide association analysis (ExWAS) of TL using WGS genotypes stratified by NFE (n=439,491), SAS (n=9,349), AFR (n=8,162), EAS (n=2,362), ASJ (n=1,201), and AMR (675) ancestral groups. Briefly we selected unrelated individuals within each ancestry strata with TL and WGS data using the same method as described in `Sample QC`. We took forward variants that passed the variant QC as described in Wang et al. which had a MAC>5. We used a linear model of the form TLPC1~ genotype + age + sex + age2 + PeddyPC1:4 + SequenceSite to assess the association of genotype with TL using the R 'PEACOK' package  (Wang et al. 2021). Here genotype was coded as either a genotypic (AA=0, AB=1, BB=2), dominant (AA=0, AB=1, BB=1) or recessive model (AA=0, AB=0, BB=1) where A and B are the reference and alternate alleles.
Collapsing Analysis To assess the contribution of very rare variants we carried out a collapsing burden analysis stratified by ancestral groups as per ExWAS analysis, using the method described in Wang et al. Briefly, we aggregated qualifying variants based within the unit of a gene for each ancestral grouping and use these counts in a linear regression using the R 'PEACOK' package using the same covariates as for the ExWAS. We defined 10 qualifying variant tests (ST8) that includes a synonymous model as an empirical control. We used the empirical modelling of the null distribution from Wang et al. to define a genome-wide significant threshold of p<1e-8. In total we assessed 18,930 genes across all 10 models. For NFE analyses we report best QV model-gene pair for which P ≤ 1x10 - 8 for PC1 and P >1 x 10 -8 for PC2.
This article proposes an explanatory algorithm based on a succession of regression trees (RTs) to identify observed subgroups of individuals potentially affected by such violations. Since an RT consists of a set of nodes (i. e. , binary decision rules), themselves subdivided into other nodes (and so on, see Figure  S1 ), using the exposure as the outcome and one or several covariates as predictors enables one to obtain an estimate of the exposure probability (i. e. , the relative frequency) in each subgroup represented by the nodes  [18]. In the presence of a node with an exposure's extreme relative frequency, we would therefore suspect a violation of the positivity assumption in the corresponding subgroup. To facilitate the use of this method, we implemented it as a function in the R package RISCA  [19].
How are the RTs learned? The algorithm learned the RTs using the R package rpart to predict the exposure on different combinations of predictors  [20]. We define the first hyperparameter γ, which refers to the maximum number of predictors used to define the nodes. We stress that the predictors set must match the variables used for the adjustment since positivity is only relevant for them  [2]. In contrast, including pure predictors of the exposure in the adjustment set increases the risk of positivity violations. For instance, if γ = 1, each RT predicted the exposure on one different predictor. If γ = 2, each RT predicted the exposure on one different predictor and then on a different combination of two predictors, and so on (Figure  1 ). Three other hyperparameters, specific to rpart, must be defined.
The default values of the hyperparameters To the best of our knowledge, there is no consensus on the precise definition of a subgroup presenting a positivity violation. Nevertheless, one can set α = 5% as several authors suggest trimming the weights at this level in inverse probability weighting analyses  [21] [22] [23]. Following D'Amour et al. , one can also set β = 5%  [3]. Note that this value is consistent with the propensity score literature  [4, 24]. By default, the last parameter is set to γ = 2 as positivity violations defined by three or more variables may not represent real practices. However, this could change depending on the field of study. Note that, to build the RTs, we used the default values defined in the R package rpart (version 4. 1-15): at least 20 individuals for intermediate nodes, at least six individuals for leaves, and a maximum depth of 30  [20].
Discussion The proposed PoRT algorithm uses three hyperparameters to identify subgroups affected by a positivity violation in the sample: α is the minimum size of the subgroup expressed as a percentage of the whole sample size, β is the maximum probability of being exposed or unexposed in the subgroup, and γ is the maximum number of predictors for the discrimination of subgroups. PoRT builds RTs using the classification and regression trees (CART) algorithm implemented in the rpart R package with its own hyperparameters  [20]. CART has the advantage of providing an exposure relative frequency in each node, unlike alternative RT algorithms, such as C4. 5  [18]. Other packages implementing CART could be used in PoRT, but the hyperparameters may differ. In a reanalysis of data from four published studies, the PoRT algorithm confirmed two subgroups with a plausible violation, as stated by the authors  [26, 27].
It also confirmed the absence of problematic subgroups in one study  [29]. Furthermore, PoRT identified nine new problematic subgroups, including six judged plausible by domain experts. These subgroups were initially missed by the authors but were validated by the same persons in light of these reanalyses. PoRT also invalidated the a priori choice in one study to restrict the population  [28]. Note that making an accurate description of the problematic subgroups and their potential overlap (which may represent an even more problematic subpopulation) can help the investigators better define the eligibility criteria and, thus, a better-suited target population. For instance, the application to the dataset of Léger et al. [26]  highlighted the advantage of increasing the level of γ to identify structural violations when β is close to the exposure prevalence.
Previously, we emphasized that the predictors must match the intended adjustment set. Nevertheless, we can consider as additional predictors the center when a center effect is expected  [31] , and the calendar time when the individuals are included over an extended period. As shown in the application to the dataset of Foucher et al. [28] , the probability of experiencing the exposure can change over time with the evolution of practices. In addition to these concordant and discordant results illustrating the helpful support offered by the PoRT algorithm, we reanalyzed the data in consideration of these alternative targeted populations. Although the clinical conclusions were unaffected, we reported nonnegligible differences in the effect sizes, underlying the importance of the targeted population.
Third, King and Zeng identify an overlapping multivariate space by a convex hull and suggest removing individuals outside this space  [34]. Similarly, Fogarty et al. proposed an algorithm constructing a hyperrectangle around an identified overlapping multivariate space  [35]. However, these approaches fail to identify subgroups inside this space  [36]. In contrast, Oberst et al. proposed an algorithm that suggests rules to redefine the target population, albeit the rules seem unnecessarily complex  [36]. Comparing this last approach to PoRT on synthetic and real datasets is challenging and needs further work  [37]. We focused on in-sample violations, which can appear for two reasons. First, due to the inclusion of individuals who theoretically could never receive one of the exposure values at the population level, one then speaks of structural violations.
Acknowledgments: The authors would like to thank the members of the  AtlanREA and DIVAT groups  for their involvement in the study, the physicians who helped recruit patients, and all patients who participated in this study. We also thank the clinical research associates who participated in the data collection. The analysis and interpretation of these data are the responsibility of the authors. Funding information: This work was partially supported by a public grant overseen by the  French National Research Agency (ANR)  to create the  Common Laboratory RISCA (Research in Informatic and Statistic for Cohort Analyses , www. labcom-risca. com, reference:  ANR-16-LCV1-0003-01 ). The funder had no role in the study design, the analysis and interpretation of data, the writing of the report, or the decision to submit the report for publication.
Materials and methods The methodological challenge was to adapt the EFA calculator framework to consider permanent grassland impacts on ESs without distorting it, thus maintaining its scientific validity, simplicity and ability to aid decision making at the European scale. The first step was to study the initial framework (section 1) to understand key challenges and the choices necessary to adapt it for permanent grasslands (section 2). Next, data were collected to relate permanent grasslands to parameters and their associated classes that impact each ES (section 3). The initial framework was then adapted to calculate impact scores (section 4) and combined with novel approaches to estimate final scores of impact of permanent grasslands and their management on the provision of ESs (section 5).
Based on the initial framework, spatial and ecological parameters were used to estimate permanent grasslands' scores of impacts on ESs. Novel approaches were then developed to estimate impacts of management. Data were collected using a literature review and expert judgement. The latter helped to organise data to identify parameters of permanent grasslands and their associated classes that contributed to impacts on the ES. Finally, the approaches of the initial framework were combined with the novel approaches to estimate impacts of permanent grasslands on five regulation and maintenance ESs. According to CICES nomenclature (Haines-Young and Potschin, 2018), these were global climate regulation by reduction of greenhouse gas (GHG) concentrations, maintenance of chemical condition of freshwaters, mass stabilisation and control of erosion rates, pollination and seed dispersal, and pest and disease control (hereafter, GHG mitigation, water-quality maintenance, erosion control, pollination, and pest control, respectively).
Data collection The literature was reviewed and compiled into a database to understand impacts of grasslands and their management on ESs (Table  S1 ). This was further supplemented and refined with the expert judgement of five internationally renowned authorities collected during interviews. These scientists, who were specialists in livestock science and/or agronomy, had knowledge of research topics related to permanent grasslands (e. g. permanent grassland ecology, impacts of livestock and grassland management on permanent grasslands) (Table  S2 ). The experts helped to relate scientific evidence to parameters and quantify the relative influence of each parameter and its classes. Experts also helped to relate impacts on biodiversity mentioned in the literature  (Allan et al. , 2015)  to impacts on ESs, especially pest control, whose relationship to permanent grassland parameters was not clearly described and always passed through the intermediary of biodiversity (e.
g. habitat, food supply). The literature review and expert judgement were used to collect data to identify an indicator to use as a proxy for each of the five ESs and then to estimate the impacts of permanent grasslands on these proxies. As permanent grasslands can partially compensate for GHG emissions by sequestering carbon  (Rumpel, 2011; Soussana and Lemaire, 2014) , the annual rate of carbon sequestration was used as proxy for GHG mitigation. As nitrate leaching has one of the largest impacts on water quality in agroecosystems  (Scherer-Lorenzen et al. , 2003; Vertès et al. , 2007) , nitrate leached was used as proxy for water-quality maintenance. A potential amount of soil loss was used as a proxy for erosion control  (Tzilivakis et al. , 2016). Finally, the capacity to host robust populations of pollinators or natural pest enemies was used as a proxy for pollination and pest control, respectively  (Scohier and Dumont, 2012; Allan et al.
Wider perspectives The novel approach has been integrated into a new software application based on the EFA Calculator that is being developed as part of the EU H2020 project FRAMEWork (2022) to use clusters of farmers to manage agrobiodiversity across ecosystems. It aims to "develop and promote biodiversity-sensitive farming to conserve native biodiversity, support  [ES]  provision and maintain reliable agricultural output…in Europe" with "the integration of diversity into farming practices and incentives for wider biodiversity management including native biodiversity"  (AERU, 2022). This new software application will combine ES calculations with biodiversity monitoring data and routines to assess habitat suitability, connectivity and zones of influence to provide a tool that facilitates holistic landscape management to deliver ES and biodiversity benefits.
Acknowledgements We thank  INRAE  scientists  P. Carrere ,  L. Delaby ,  B. Dumont ,  A. Farruggia  and  F. Vertès , who helped us to relate scientific evidence to parameters and quantify the relative influence of each parameter class to estimate impacts on ES, and the farm teams of  Trévarn and Saint Laurent de la Prée. We thank  ADEME , the  Bretagne region, Olga Triballat Institute, EGAAL doctoral school and Agreenium  for funding to perform this study.
Echocardiographic recordings Echocardiography was carried out with the subject lying in the left lateral decubitus position by Vivid ultrasound systems (GE Healthcare, Horten, Norway) using a 3. 5-MHz transducer (M4S probe). Cine loops were recorded in parasternal long-axis and apical (5, 4, 3, and 2 chamber) views and saved for blinded offline analysis (EchoPac, BT203 version, GE Healthcare). Grayscale images were saved at a frame rate of 80-90 frames/s, and color tissue velocity images were saved at a frame rate of 120-140 frames/s. 2D echocardiographic measurements were performed in accordance with the guidelines of the American Society of Echocardiography  (23). All echocardiographic data were averaged from measurements obtained on 3-5 cardiac cycles.
Then, EchoPAC data were exported as ". txt files" to be processed by a specific toolbox (Scilab version 4. 1; Consortium Scilab, INRIA-ENPC, Paris, France). To adjust all strain parameters for intersubject differences in heart and frame rates, the time sequence was also normalized to the percentage of systolic duration (i. e. , time was 100% at end-systole). LV twist was calculated as the difference between apical and basal rotations at each percentage of systolic duration. We considered GLS, CS, and LV rotations, and peak twist as indexes of myocardial systolic function. Regional analysis was carried out at the level of the longitudinal strains (basal, median, apical LS) and circumferential (basal and apical CS).
Statistical analyses Statistical analyses were performed using SPSS 25 statistical software. All values were expressed as mean ± SD. Before the study, we calculated the size of the groups to differentiate the GLS with a statistical power of 90% and an effect size of 1, which correspond to a difference of the GLS of 2% with an SD of 2% and taking into account the Bonferroni correction when including three groups in the analyses. The analysis indicated a minimal size group of 26 patients in each group. One-way analysis of variance (ANOVA) was used to compare groups after checking the normality of distribution of each variable by a Shapiro-Wilk test. The non-parametric Kruskal-Wallis test was used when the variables do not meet the criteria of normal distribution. Correlations were determined between BMI z-scores and hemodynamic, biological, and cardiac ultrasound variables by linear regression analysis and Pearson's correlation coefficient.
As a result, European countries retain sovereignty in this matter and can develop independent national vaccination policies. This policy can facilitate politically and culturally motivated decision-making and may lead to divergent vaccination strategies  [21]. During the COVID-19 pandemic, several policy trackers were established to monitor the evaluation of COVID-19 policies across countries. For instance, the Oxford Government Response Tracker captures government policies related to closure and containment, as well as health and economic policy developments  [22]. The COVID-19 Government Response Event Dataset (CoronaNet) is a daily-updated dataset that categorizes COVID-19 policies into over 100 sub-categories with detailed text descriptions  [23]. Both datasets focus predominantly on the non-pharmaceutical interventions with only a few vaccine-specific measures being included.
The included vaccine policy data comprises country vaccine allocation prioritisation lists, eligible groups, cost of vaccination to the individual, and the presence of a vaccine mandate for the Oxford Tracker and distribution, regulatory approval processes, purchasing, and resources spent on research and development for Corona-Net. While these datasets are useful for retrospective analyses to determine the effects of policies within national boundaries, they fall short when trying to understand how policy environments differ across countries and what the broader public health implications of such divergent policies can be. In this manuscript, we aim to perform an in-depth analysis of the active COVID-19 vaccine policies in effect by February 1, 2022, across Europe and capture policy divergences between European countries.
Questionnaire development and data collection We developed a questionnaire designed to chart and analyze key vaccination policies across the aforementioned ten countries in Europe. This questionnaire was largely based on the framework established by the United States Operation Warp Speed (OWS) Strategy for Distributing a COVID-19 Vaccine  [26] [27] [28] ; four of the six critical components of our questionnaire came directly from the OWS Strategy. Three online meetings were held and several feedback rounds took place to consistently improve the form of the questionnaire. All co-authors were involved in the development of the questionnaire and the subsequent collection of materials using the questionnaire. The selection of questions in the questionnaire was intended to be broad enough to cover multiple dimensions of COVID-19 vaccine policy, yet remain focused enough to maintain feasibility.
The full list of critical components and associated questions is included in the Supplementary Materials. This effort was complemented by a rapid review of official national policy repositories (see eTable 1 in Supplementary Materials), PubMed, and Google (Scholar). The keywords used consisted of ''COVID-19", ''coronavirus", ''policy", ''law", ''vaccine", ''vaccination" combined with the studied countries. These findings were ultimately merged into a single dataset based on categories and themes included in the questionnaire. The initial data collection took place between February 1 and February 28, 2022. The date of February 1, 2022 was chosen as the COVID-19 vaccine infrastructure had stabilised by this point and sufficient evidence was available on the effectiveness of COVID-19 vaccines  [29] [30] [31].
Data analysis The data collected were open-ended, long-form responses to a series of questions focused on six critical components of vaccine implementation, including (1) authorization, (2) prioritization, (3) procurement and distribution, (4) data collection, (5) administra-tion, and (6) mandate requirements. The scope of the data collected is shown in Table  1. Because no standardized reporting guidelines exist for this type of research, we followed the Standards for Reporting Qualitative Research (SRQR) as closely as possible  [33].