dataesr/scientific-paragraphs-categorization huggingface dataset:
In this way, HBM-data on glyphosate was achieved among children from five countries, Slovenia, Germany, France, Belgium and Cyprus [16], and adults from Germany, Switzerland, France, and Iceland  [15]. Data on pyrethroids and chlorpyrifos were obtained among children from Slovenia, Cyprus, France, Belgium, the Netherlands, and Israel and among adults from Germany, France, Switzerland and Israel  [14, 17, 18]. These HBM-data from the HBM4EU Aligned Studies is available for visualization in the EU-HBM dashboard: https://www. hbm4eu. eu/what-we-do/european-hbm-platform/ eu-hbm-dashboard/ (accessed on 18 10 2022) along with HBM-data from some of the studies included in this review. However, more HBM-data are needed to get an EU-wide picture of the exposure and to evaluate differences between countries and population groups, time trends, and age-related differences in exposure levels and sources.
River Discharge Inland waste mainly enters oceans through river systems. Consequently, relationships exist between the input rate of waste into the ocean and river discharge. The modeling of this relationship is detailed below in the Methods section. River discharges were estimated using the Global Flood Awareness System dataset provided by the Copernicus Emergency Management Service  [40]. This system combines a hydrological model and in situ measurements. The discharge rates of Indonesian rivers range from 50,000 to 150,000 m 3 /s, with a strong seasonal signal. For instance, a dry season can be observed on Java and the surrounding islands, whereas a more permanent flux characterizes the freshwater river flux on Kalimantan (Figure  2 ). Sea). ).
River Discharge Inland waste mainly enters oceans through river systems. Consequently, relationships exist between the input rate of waste into the ocean and river discharge. The modeling of this relationship is detailed below in the Methods section. River discharges were estimated using the Global Flood Awareness System dataset provided by the Copernicus Emergency Management Service  [40]. This system combines a hydrological model and in situ measurements. The discharge rates of Indonesian rivers range from 50,000 to 150,000 m /s, with a strong seasonal signal. For instance, a dry season can be observed on Java and the surrounding islands, whereas a more permanent flux characterizes the freshwater river flux on Kalimantan (Figure  2 ). [35]  for detailed coordinates of river mouths. Lower panel: time series of the sum of released particles for all 21 rivers (blue line), the 9 rivers of the islands of Java and Bali (orange line) and the 4 rivers from Kalimantan (green line).
Waste Observations Two datasets of waste observations were considered for comparison with our model results. The first dataset was extracted from visual surveys provided in Table  2  of  [41]. It is hereafter referred to as "GS". The authors of GS performed counts of floating debris by visual surveys in the coastal waters of three sites of interest: the Manta Bay close to Bali, Karang Makassar in the Komodo National Park close to East Nusa Tenggara and, finally, Pantai Bentar along the East Java coast south of the Madura Strait. These surveys were conducted between 2016 and 2018 and, for each site, the average counts and standard error results for the wet season (January to May) and dry season (August to October) were provided. A scientific protocol was similarly applied at each site to produce comparable field data.
The second dataset was derived from the work Dr. Sapta Putra Ginting and collaborators and was presented at the APEC Meeting of Bali in February 2020  [42, 43]  (Sapta Putra Ginting, personal communication). It is hereafter referred to as "PS". The authors of PS performed 26 beach cleaning sessions between 2017 and 2019 and recorded the weight of collected waste in metric tons. Seventeen beach cleaning sessions located on Java, Bali and East Nusa Tenggara were retained to be consistent with the locations from GS. It is noteworthy that no standard protocol was applied in this case and that the cleaned areas and sampling durations differed for each site and were not recorded. Furthermore, probable previous cleaning sessions conducted by local authorities or civil society actors were also not taken into consideration.
Lagrangian Drift To assess waste stranding and dispersion in the Indonesian region, a Lagrangian simulation was performed. The Lagrangian point of view allows multiple analyses of particle trajectories (e. g. ,  [44] ), including covered distance, drift time, stranding location, connectivity patterns between regions, backward analyses and probabilistic analyses. We used the Lagrangian software package Ariane, originally developed by B. Blanke and S. Raynaud  [45] , available at www. univ-brest. fr/lpo/ariane (accessed on 9 June 2022); it was released by the Laboratory of Ocean Physics and Satellite Remote Sensing (LOPS, France) and has been maintained since 2006. Ariane software computes trajectories from a 1D linear formulation using input 3D current fields and builds water mass transport diagnostics.
It is also noteworthy that beach cleaning session data from PS are also a best-we-have proxy for data on strandings; collected waste may come not only from offshore but also from land or beach activity (commercial, recreational, etc. ). Here again, we decided to include this study because of the scarcity of eligible studies. To perform this comparison, we summed the simulated particles that were stranded for a given period before the relevant site's observation date and within a given distance of the site's location. For both waste observations datasets, the threshold distance was set to 20 km. This roughly corresponds to the length of two grid cells in the SMOC model grid. On the other hand, time thresholds were adjusted to the particularities of each dataset. The cleaning sessions that yielded the PS data only lasted one day, and strandings that occurred in the 7 days prior to the cleaning session date were considered.
This 7-day limit corresponded to an approximative period of debris accumulation that occurred before the cleaning session. In contrast, GS provided data gathered during periods of 1 to 3 months in the wet or dry seasons of the years 2016 to 2018. Their Komodo site was not sampled in 2018, and their Pentai Bentar site was only sampled during the 2017 wet season. Considering these limitations, the simulated strandings were summed when they occur in the 60 days prior to the end of the 2017 wet or dry seasons. An error estimate was also computed and corresponds to the 5th and the 95th percentiles of the values obtained when taking into account small changes in the site location (within 10 km for both datasets) and date (within minus 30 days for GS and within plus or minus 7 days for PS).
The correlation between the simulation and observations was not as striking as with the GS dataset (lower right panel of Figure  8 ), and the specific conditions of each site need to be analyzed. Most sites on Java Island (S301, S302, S303, S304 and S307) reported low collected weights (616, 668, 404, 69 and 10 kg, respectively) consistent with data showing no strandings in our simulations (0 [0-11], 0 [0-0], 0 [0-3], 4 [0-4], 0 [0-0] stranded particles, respectively; the numbers in square brackets indicate the corresponding 5th and 95th percen-   [43]  at the APEC Bali workshop and (right panel) visual surveys presented in  [41]. In the left panels, the units are in metric tons (MT), and in the right panels, units are in 10 3 item/km 2. Maps in the upper row show the different site locations.
The second set of comparisons was performed using observations from PS. The purpose of the PS study was not to compare the relative amounts of waste between monsoons but to clean beaches and estimate the weight of the waste collected. It appears that, apart from on Bali, no beaches were cleaned during both the wet and dry seasons; thus, the monsoonal variability in strandings could not be compared in this case. The correlation between the simulation and observations was not as striking as with the GS dataset (lower right panel of Figure  8 ), and the specific conditions of each site need to be analyzed. Most sites on Java Island (S301, S302, S303, S304 and S307) reported low collected weights (616, 668, 404, 69 and 10 kg, respectively) consistent with data showing no strandings in our simulations (0 [0-11], 0 [0-0], 0 [0-3], 4 [0-4], 0 [0-0] stranded particles, respectively; the numbers in square brackets indicate the corresponding 5th and 95th percentiles).
These sensitivity tests to resolution are especially important as this first study shows that strandings mainly occur close to river sources. Assessing the performance of strandings simulated by a numerical experiment proved to be complex, as mentioned in  [53]. To assess this performance, we compared simulated stranding results with different datasets assembled based on observations. We encountered three main difficulties. Firstly, among the seven available field studies that have been evaluated  [41, 43, 50, [54] [55] [56] [57] , only two were sufficiently relevant to allow performing a comparison  [41, 43] , for reasons that are explained in the following paragraph. Secondly, comparisons had to be conducted in a nuanced fashion depending on the particularities of each field study. Finally, the comparison results had to be interpreted with caution due to the limitations stemming from the simulation assumptions.
g. , a site located inside a narrow strait might not be explicitly resolved by the present model). We could not aggregate field data from different studies because field methods are numerous and rarely comparable: the cumulated times of observation, sampling methods, types of sampled sites and units often differ from one dataset to the other. In particular, for the datasets used in our study, the observations from the PS study were not homogenized to a surface area, and previous cleanings were not reported; in the GP study, the exact periods of sampling for observations were missing. Things are evolving; several protocols have been recently published aid in the homogenization of plastic and waste measurements (e. g. ,  [58] [59] [60] [61] ). Newly implemented studies tend to adopt a similar protocol  [62] , and future comparisons should be facilitated.
The aforementioned limitations should be kept in mind when switching to global simulations and/or systematic comparisons with global databases  [63]. Other means of Lagrangian performance assessment exist and may be worth considering. In particular, drifters' trajectories can be compared with simulated trajectories. However, comparisons with real trajectories should be interpreted with caution: they are often more sensitive to windage, a process that can significantly affect trajectories and that has not been accounted for in the present study. Adding more ocean physics, such as windage or the specific transformation of any one type of plastic, would represent another full study; such an endeavor definitely deserves more attention in the future. As an additional difficulty, very few drifters travel through the Indonesian seas.
Acknowledgments: We would like to thank  Nicolas Grima  and  Bruno Blanke  from the  LOPS  for providing the Ariane Lagrangian software package and updates to match the needs of the present study, as well as the two anonymous reviewers. We also thank  Sapta Putra Ginting  and his collaborators for agreeing to let us use the dataset they presented at the APEC Bali meeting about the waste collected during their beach cleaning sessions. This study benefitted from the support of the  French National Research Institute for Sustainable Development (IRD).
Availability of data and materials Anonymous aggregated data for the first round are available online. The EpiCov dataset is available for research purpose concerning the first round, and will be available by March 2022 concerning the second round for research purpose on CASD (https://www. casd. eu/), after submission to approval of French Ethics and Regulatory Committee procedure (Comité du Secret Statistique, CESREES and CNIL). Access to anonymized individual data underlying the findings may be available before the planned period, on request to the corresponding author, to be submitted to approval of ethics and reglementary Committee for researchers who meet the criteria for access to data. 3. The sampling design is taken into account for the estimation of prevalence, confidence intervals (logit transformation), crude and adjusted odds ratios, confidence intervals and statistical tests, with the SAS procsurvey procedure.
IRIS, INSERM, EHESS, CNRS Aubervilliers, France 10 INED, France 11 Université Paris Dauphine, France 12 CNRS, France The EPICOV study group : J Warszawski, N Bajos (Co-PI), G Bagein, F Beck, E Counil, F Jusot, , N Lydié, C Martin, L Meyer, P Raynaud, A Rouquette, A Pailhé, D Rahib, P Sillard, This research was supported by research grants from Inserm (Institut National de la Santé et de la Recherche Médicale) and the French Ministry for Research, by Drees-Direction de la Recherche, des Etudes, de l'Evaluation et des Statistiques, and the French Ministry for Health, and by the Région Ile de France. Dr. Bajos has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No. [856478]) This project has also received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 101016167, ORCHESTRA (Connecting European Cohorts to Increase Common and Effective Response to SARS-CoV-2 Pandemic).
Acknowledgements We sincerely thank all the participants in the EpiCoV study. We warmly thank the INSERM staff, including, in particular,  Carmen Calandra ,  Karim Ammour ,  Jean-Marc Boivent ,  Jean-Marie Gagliolo ,  Frédérique Le Saulnier , and  Frédéric Robergeau , who worked with considerable dedication and commitment to make it possible to develop, in record time, and to maintain all regulatory, budgetary, technical, and logistical aspects of the EpiCov study. We warmly thank the staff of Santé publique France, and especially  Lucie Duchesne , who played a major role in organisation and quality assurance for the seroprevalence component of the EpiCov study. We thank the  CRB  biobanks staff, and especially their heads,  Dr Isabelle Pellegrin , and  Julien Jeanpetit  ( Centre Hospitalier Universitaire Robert Pellegrin, Bordeaux, France), Pr Edouard Tuaillon Centre de Ressources Biologiques du CHU de Montpellier ),  Dr Yves-Edouard Herpe  ( Centre de Ressources Biologiques Biobanque de Picardie ),  Pr Jacqueline Deloumeaux  ( Centre biologique du CHU de la Guadeloupe ),  Dr Rémi Neviere  (  CeRBiM, Centre de Ressources Biologiques de la Martinique ),  Julien Eperonnier ,  Estelle Nobecourt  ( Centre de Ressources Biologiques de la Réunion ) for the quality of DBS sample management of the EpiCov study.
9 IRIS, INSERM, EHESS, CNRS Aubervilliers, France 10 INED, France 11 Université Paris Dauphine, France 12 CNRS, France The EPICOV study group : J Warszawski, N Bajos (Co-PI), G Bagein, F Beck, E Counil, F Jusot, , N Lydié, C Martin, L Meyer, P Raynaud, A Rouquette, A Pailhé, D Rahib, P Sillard, This research was supported by research grants from Inserm (Institut National de la Santé et de la Recherche Médicale) and the French Ministry for Research, by Drees-Direction de la Recherche, des Etudes, de l'Evaluation et des Statistiques, and the French Ministry for Health, and by the Région Ile de France. Dr. Bajos has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No. [856478]) This project has also received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 101016167, ORCHESTRA (Connecting European Cohorts to Increase Common and Effective Response to SARS-CoV-2 Pandemic).
Background: Germline variants explain more than a third of prostate cancer (PrCa) risk, but very few associations have been identified between heritable factors and clinical progression. Objective: To find rare germline variants that predict time to biochemical recurrence (BCR) after radical treatment in men with PrCa and understand the genetic factors associated with such progression. Design, setting, and participants: Whole-genome sequencing data from blood DNA were analysed for 850 PrCa patients with radical treatment from the Pan Prostate Cancer Group (PPCG) consortium from the UK, Canada, Germany, Australia, and France. Findings were validated using 383 patients from The Cancer Genome Atlas (TCGA) dataset. Outcome measurements and statistical analysis: A total of 15, 822 rare (MAF <1%) predicted-deleterious coding germline mutations were identified.
Optimal multifactor and univariate Cox regression models were built to predict time to BCR after radical treatment, using germline variants grouped by functionally annotated gene sets. Models were tested for robustness using bootstrap resampling. Results and limitations: Optimal Cox regression multifactor models showed that rare predicted-deleterious germline variants in ''Hallmark'' gene sets were consistently associated with altered time to BCR. Three gene sets had a statistically significant association with risk-elevated outcome when modelling all samples: PI3K/AKT/mTOR, Inflammatory response, and KRAS signalling (up). PI3K/AKT/mTOR and KRAS signalling (up) were also associated among patients with higher-grade cancer, as were Pancreas-beta cells, TNFA signalling via NKFB, and Hypoxia, the latter of which was validated in the independent TCGA dataset.
Sequencing of DNA from PrCa patients Whole-genome sequencing (WGS) data derived from whole blood samples were collated for PrCa patients from member countries of the Pan Prostate Cancer Group (PPCG, http://panprostate. org; Australia n = 133, Canada n = 288, France n = 15, Germany n = 230, and UK n = 184; Table  1 , further characteristics in Supplementary Table  1 ). The study presented here combines data from patients following RP and a small subset of samples with radical radiotherapy (RT; 8%) from the Canadian study group. We refer to the samples collectively as having radical treatment. Samples were collected according to the criteria outlined in the Supplementary material. Collection was subject to the International Cancer Genome Consortium (ICGC) standards of ethical consent. Collection and E U R O P E A N U R O L O G Y 8 2 ( 2 0 2 2 ) 2 0 1 -2 1 1 202 Please visit www.
eu-acme. org/europeanurology to answer questions on-line. The EU-ACME credits will then be attributed automatically. analysis of the Australian samples received institutional review board approval (Epworth Health 34506; Melbourne Health 2019. 058). WGS was performed using Illumina technology to !30Â depth. The Burrows-Wheeler Aligner  [6]  was used to align sequencing data to the GRCh37 human genome (human_g1k_v37) with polymerase chain reaction duplicates removed  [7]. Sequencing data have been deposited at the European Genome-phenome Archive (https://ega-archive. org; dataset IDs in Table  1 ) and are available upon request.
Variant calling Variant calling was performed with the Genome Analysis Toolkit pipeline (GATK v4. 0)  [8]  following GATK best practice recommendations for germline single nucleotide variant and indel calling (Supplementary material)  [9, 10] , apart from for the German samples that were called using FreeBayes v1. 1. 0 [11]  and processed as described by Gerhauser et al  [12] , normalised with vt v0. 5 (Supplementary material)  [13]. This analysis was restricted to variants within protein-coding transcript sequences according to GENCODE v29  [14].
Quality control, variant annotation, and prioritisation Low-quality variants and samples were removed based on established quality control (QC) protocols  [15] [16] [17]. We excluded samples from related individuals (using R package SNPRelate method identity by descent  [18] ) or those with non-European ancestry (using Principal Component Analysis relative to 2504 samples from the 1000 Genomes Project  [19] ). We used Picard tools v2. 23. 8  [20]  to remove samples with a mean insert size of <250 bp, AT or GC dropout >5%, <95% aligned reads, >5% mismatch rate, <80% with !20Â coverage, or >5% missing call rate. Using verifyBamID v1. 1. 2 [21] , we removed samples with >3% sample contamination. We excluded variants with a missing call rate in >5% of the samples, those with monomorphic loci, those in repetitive regions (simple repeats, segmental duplications, and centromeric regions), and those for which the ExAC minor allele frequency in any population was >1%.
Software and libraries All statistical analyses were applied using Python v3. 8  [25]. Data in VCF format were converted using PyVCF v0. 6. 7  [26]  and processed using pandas v1. 3. 0  [27] , SciPy v1. 4. 1  [28] , NumPy v1. 18. 3  [29] , IPython v7. 14  [30] , and Scikits. bootstrap v1. 1  [31]. Survival analysis for Cox's proportional hazard (PH) model and Kaplan-Meier estimates were performed using the Lifelines v0. 25 package  [32]. Tables and graphs were output using Matplotlib v3. 3. 4  [33] , to_precision  [34] , and Maftools v2. 6. 5  [35].
Multifactor Cox regression Analyses were performed on the combined post-QC dataset (   [36]. For the 72 Canadian samples with RT, BCR was defined as a rise in PSA concentration of >2. 0 ng/ml above the nadir, backdated to first PSA >0. 2 ng/ml if PSA continues to rise  [37]. We performed a sensitivity analysis on a subset that excluded RT samples, which did not affect the significant risk-elevating gene sets observed (Supplementary Table  3 ). Variables included in the final models were selected by performing Cox regression with penalisation based on the least absolute shrinkage and selection operator (LASSO)  [38]. The optimal penalty factor (lambda) was determined as within 1 standard error of the optimum from the mean of 100 ten-fold cross-validation models. Only features with a nonzero coefficient were retained.
Kaplan-Meier analysis A Kaplan-Meier plot measuring time to BCR in the event of a relapse was used to visualise the impact of mutations within significant gene sets on the risk of BCR. This was applied separately to the whole dataset and the high-Gleason subset, and reported alongside log-rank test p values. We performed a combined analysis, considering mutations in any of the gene sets significant for the corresponding analyses and subdivided to ascertain potential additive effects upon a patient's time to relapse.
Results We analysed germline WGS data from 850 patients across five studies in the PPCG consortium (Table  1  and Supplementary Tables  1  and 2 ) for germline predictors of PrCa progression measured by BCR after radical treatment. This analysis was restricted to variants within protein-coding transcript sequences, resulting in 15, 822 rare variants identified as deleterious or likely deleterious, jointly categorised as PD. No individual variants or genes demonstrated a significant association with time to BCR (Cox regression analysis; p > 0. 05), although the available sample size of 850 cases is underpowered for such an analysis. Therefore, we focused on finding gene sets or pathways with significant associations to identify potential biological mechanisms linked with progression. To this end, we determined whether there was at least one PD germline alteration in 52 gene sets, including 50 Hallmark gene sets from the MsigDB database  [24] , containing over 4000 genes with sets varying in size from 30 to 200, the DRG panel containing 175 DNA repair genes  [16] , and the extended BROCA gene panel containing 65 genes (Supplementary Table  4 )  [17].