dataesr/scientific-paragraphs-categorization huggingface dataset:
With these considerations in mind, we aimed to investigate the effect of EVT (with or without IVT) versus IVT alone on outcomes in CeAD-patients with LVO (due to thrombus embolization from the dissection) treated since 2015 in participating centers of the EVA-TRISP (EndoVAscular treatment and ThRombolysis in Ischemic Stroke Patients) collaboration. 274 https://j-stroke. org national, multicenter studies on IVT in AIS patients and has now transformed to additionally include data on EVT-treated patients. As done in prior analyses, all data for the present study were collected locally in participating centers, anonymized, centrally pooled, and analyzed at the coordinating stroke center in Basel, Switzerland. 14, 15 e included data from all CeAD-patients (including both internal carotid artery dissection [ICAD] and vertebral artery dissection [VAD]) with intracranial LVO proven by computed tomography angiography (CTA), magnetic resonance angiography (MRA), or digital subtraction angiography (DSA) who had either EVT (with or without IVT, henceforward referred to as "EVT") or IVT alone (henceforward referred to as "IVT") between January 1, 2015 and December 31, 2019.
g. , V2-/ V3 segments of the vertebral artery with extension into the V4 segment of the dissected artery) were eligible for inclusion. CeADpatients receiving neither EVT nor IVT are not included in EVA-TRISP and thus were not included in the current analyses. Patients with missing data on the primary clinical outcome measure were excluded. All variables derived from the EVA-TRISP database for the present study are displayed in the results tables and were defined as in prior analyses. 14, 17 In brief, these included (1) baseline demographic data (e. g. , age, sex, and pre-stroke independency [prestroke modified Rankin Scale, mRS 0-2]), (2) medical history including vascular risk factors, (3) vital signs and baseline laboratory results, (4) information on the index event (i. e. , clinical characteristics including stroke severity as measured by the National Institutes of Health Stroke Scale [NIHSS] score  18  on admission and at 24 hours) and radiological characteristics (i.
e. , site of intracranial occlusion and presence or absence of early ischemic changes), and (5) outcome data as specified below (study outcomes). Regarding EVT-and IVT-procedures, the following time-based parameters were calculated: (1) onset-to-needle time (median, in minutes) defined as time from stroke onset to treatment ini-tiation of IVT in IVT-treated patients or EVT-treated patients who had bridging therapy; (2) onset-to-groin time (median, in minutes) defined as time from stroke onset to groin puncture in EVTtreated patients.
Patients were then matched in a 1:1 ratio with nearest-neighbor matching within a caliper of 0. 2 standard deviations of the propensity score. We report unadjusted comparisons of important baseline characteristics in matched EVT and IVT patients with calculation of respective standardized mean differences. We further performed unadjusted logistic regression analyses in the matched dataset to assess associations of EVT and IVT with the primary study outcomes. All analyses were performed using SPSS version 25 (IBM Corp. , Armonk, NY, USA). The propensity score matching (PSM) analyses were performed using "R" version 4. 0. 2 (R Foundation for Statistical Computing, Vienna, Austria).
Fifth, except for the presence of early ischemic changes on baseline imaging, other important imaging variables which are likely associated with outcomes (i. e. , Alberta Stroke Program Early Computed Tomography Score [ASPECTS], infarct volume on perfusion imaging, and collateral status) were not routinely assessed in the present dataset and therefore not included in the statistical models. We are aware that our key observations are in dissent to both clinical expectations and guidelines about the use of EVT in LVO in general. Therefore, our findings should not be interpreted as argument to refrain from EVT in CeAD patients in clinical routine. Still, our unexpected results are nevertheless worth being made publicly known and should stimulate future research about why some LVO-patients might benefit less than expected from EVT and why this might be particularly the case in CeAD patients.
, 2015; Wang et al. , 2016; Teixeira et al. , 2017; Pleijel et al. , 2021; Gerson et al. , 2022). As for other Hg species,  [MeHg]  in tree leaves and litter also largely depend on the forest type  (Witt et al. , 2009). As tropical forests are commonly rich in perennial broadleaved species with high litter production, relatively high atmospheric MeHg inputs are expected in these environments  (Zhou and Obrist, 2021). However, to our knowledge,  [MeHg]  has not yet been measured in tree leaves from tropical forests, and only one dataset of  [MeHg]  in litter samples from the Amazonian rainforest is available  (Roulet et al. , 2001). Forests can also provide favorable environments for Hg methylation processes  (Eklöf et al. , 2018) , enhancing the potential risk to the terrestrial biota  (Rieder et al.
Total Hg species (THg sp ) recovery was also calculated for IAEA-405, IAEA-336, and litter, ash, and soil samples as the ratio between [MeHg] + [iHg] and  [THg]. The precision was calculated as the relative standard deviation of three analytical replicates. The limit of detection and quantification were calculated based on the parameters of the analytical curves. For MeHg, LOD and LOQ were 0. 006 and 0. 020 ng g -1 , respectively. Recovery for IAEA-405 was 100. 4%, and the precision was <5%; detailed data are given in Supplementary Table  S2. For iHg, LOD was 0. 07 ng g -1 and LOQ was 0. 23 ng g -1. For THg sp , it was 95. 4% and 89. 1% for IAEA-405 and IAEA-336, respectively, and the precision was <5%. For litter and ash samples, THg sp recovery ([MeHg] + [iHg]) × 100/[THg]) was 73% ± 8% and 50% ± 12%, respectively (Supplementary Tables  S3, S4 ).
Statistical data treatment Data processing was conducted using PAST (Paleontological Statistics, version 4. 11)  (Hammer et al. , 2001) , GraphPad Prism version 10. 0. 0 (Boston, MA, United States), and XLSTAT ® software. The normality of the datasets was assessed using the Shapiro-Wilk test. To evaluate the statistical differences between normally distributed datasets, Student's t-test was employed. Furthermore, one-way ANOVA was used to assess the differences among more than three sets of normally distributed data. All statistical analyses were carried out with a significance level of 95% (α = 0. 05). Pearson's correlation coefficient (r) was also analyzed to assess the degree of dependence between pairs of variables (i. e. , combustion completeness (%), total biomass (kg m -2 ), OM lost from vegetation (%), THg lost from vegetation (%), MeHg lost from vegetation (%), OM lost from 0-1 cm soil (%), THg lost from 0-1 cm soil, and MeHg lost from 0-1 cm soil).
Total [THg] and methylmercury concentrations All the results presented a normal distribution, except the dataset of [MeHg] in the 0-1 cm soil layer AB, in which the CJ12  [MeHg]  (1. 18 ng g -1 ) appeared as an outlier (Grubbs' test for outliers; p <0. 05) (Figure  2 ). However, the rest of the data were normally distributed after removing this value. Paired Student's t-test was then applied to compare the different datasets (i. e. , litter and ash, soil BB or AB at different depths, and soil BB and AB at the same depth). [THg] in litter ranged from 25. 0 to 50. 0 ng g -1 (Table  1 ) and was significantly higher than that in ash (paired Student's t-test, p <0. 05). In soil, the overall average [THg] was 149 ± 12 ng g -1 (Table  2 ), and there was no difference among layers. After burning, a significant decrease was only observed in the 0-1 cm layer (p <0.
04 μg m -2 yr -1. This value is within the range of the previous estimations from 0. 08 to 0. 34 μg m -2 yr -1 reported by  Zhou et al. (2018)  for MeHg litterfall deposition in other remote regions. Nevertheless, as already pointed out, the litter exposed, for several months, to sun radiation and rain washout may have resulted in some loss of Hg and, consequently, to an underestimation of the initial net THg and MeHg depositions. Conversely, an estimate of Hg deposition based on a limited dataset obtained in the specific condition (after the vegetation was felled and dried) may be not representative of the Amazonian upland forest; however, it is paramount to highlight that this is the first dataset obtained on  [MeHg]  in litterfall in this ecosystem.
Funding This work was funded by the  São Paulo Research Foundation (FAPESP)  (projects  2008/04490-4 ,  2010/19040-4 ) and also partially supported by  The University of Campinas  ( UNICAMP/FAEPEX 2932/23 ), the  CNRS ,  French ANR , and  New Aquitaine Region council. LR received a fellowship from the  Brazilian Coordination for the Improvement of Higher Education Personnel, CAPES  (financing code  001 ). JV received a fellowship from  FAPESP  ( 2019/18124-4 ). The burning in Candeias do Jamari was conducted under permission 204/2014 from the  Secretaria de Estado do Desenvolvimento Ambiental de Rondônia (SEDAM)  and under permission from  CNPq.
Adjustment factors Data on potential confounders were collected during clinical exams as well as thought questionnaires administered by field workers or selfcompleted by the parents. Adjustment factors were chosen a priori and included variables that caused the outcome without being a consequence of the exposure, or that caused both the outcome and exposure. This included child age at blood collection (linear term), child sex (boy / girl), delivery mode (vaginal / caesarean), maternal age (linear and quadratic terms), maternal body mass index (BMI) before pregnancy (continuous), parity (nulliparous / parous), maternal education (≤ 2 years / between 3 and 4 years / ≥ 5 years after high school), maternal smoking during pregnancy (yes / no), maternal urinary iodine and serum selenium concentrations (continuous).
We also plotted the estimated effect of each chemical individually, to detect potential non-linear associations. BKMR was run for the whole population and in boys and girls separately, to explore sex-specific effects. We did not explore effect modification by iodine because of the small number of women in the group with iodine concentrations above 150 μg/L. Statistical analyses were performed using R version 4. 0. 4. Several packages were used including haven to open SAS dataset, aod to perform Wald test, Tidyr, tidyverse for data manipulation, questionr, dplyr to perform descriptive analysis, stats for regression models, ggplot2 for graphical visualization, Hmisc for Spearman correlation, mice for multiple imputation and bkmr for the BKMR models.
Acknowledgments We thank the SEPAGES study group:  E. Eyriey ,  A. Licinia ,  A. Vellement  ( Groupe Hospitalier Mutualiste, Grenoble ),  I. Pin ,  S. Bayat ,  P. Hoffmann ,  E. Hullo ,  C. Llerena  ( Grenoble Alpes University Hospital, La Tronche ),  X. Morin  ( Clinique des Cèdres, Echirolles ),  A. Morlot  ( Clinique Belledonne ,  Saint-Martin  d'Hères),  J. Lepeule ,  S. Lyon-Caen ,  C. Philippat ,  I. Pin ,  J. Quentin ,  V. Siroux ,  R. Slama  ( Grenoble Alpes University, Inserm, CNRS, IAB ). We acknowledge  Karine Supernant ,  Anne Boudier ,  Sarah Lyon-Caen  and  Matthieu Rolland  for data-management as well as  Paulina Jedynack  for the review of the R codes. SEPAGES biospecimens are stored at  Grenoble University Hospital (CHU-GA ) biobank (bb- 0033-00069 ). We thank clinical research assistants; nurses; midwives; fieldworkers; the staff from  Grenoble Center for Clinical Investigation (CIC) , clinicians from  Grenoble University Hospital , midwives from the four maternity wards of Grenoble urban areas and ultrasound practitioners from Grenoble urban area for their support in the recruitment and follow up of the study volunteers.
5406 Å from a generator operating at 40 keV and 40 mA (operating at Θ-2Θ configuration). The scanning rate was 0. 03° 2Θ per minute from 20° to 85°. The Diffraction patterns were interpreted using MAUD software. The Thermal Gravimetry Analysis (TGA) and Differential Scanning Calorimetry (DSC) experiments were performed using a Mettler Toledo TGA/DSC 1 Stare System analyser with a horizontal reaction chamber. About 10-20 mg of sample were placed in a cylindrical Al 2 O 3 crucible. The TGA furnace was constantly purged with Ar gas. The samples were annealed from 25 to 900 °C at a heating rate of 10 °C/min in a stream of N 2 or Ar "reactive gas" provided directly above the sample with a flow rate of 50 mL min -1. A baseline, obtained under the same conditions with an empty Al 2 O 3 crucible, was subtracted from the measured thermograms.
Acknowledgements We are grateful to the various supporting institutions including, the  University of South Africa ,  iThemba LABS (ITLABS) , the  National Research   Foundation of South Africa (NRF) , the  African Laser Centre (ALC) , the international  Organization of Women in Science (OWSD) , the  Abdus Salam International Centre for Theoretical Physics  (The Abdus  Salam-ICTP) , the  Royal Society-London  and naturally the  United Nations Education, Sciences and Culture Organization (UNESCO)  as well as the  French Foreign Ministry  and the  ADESFA program. Likewise, the  Centre for High Performance Computation (CHPC)  is acknowledged.
3. The study takes a cradle-to-grave perspective and considers all life cycle stages. The decision context for the study is defined as 'accounting environmental impacts' (situation C1) (EC-JRC, 2010), and the LCI modeling framework is attributional. Multifunctional processes are handled using allocation. All background data are based on Ecoinvent v3. 6  (Ecoinvent, 2021 ) and all scenarios are modeled using the software SimaPro 9. 1. 1. 7 (PRé Sustainability, 2020). While data can be available for different geographies depending on the technology, a single dataset was chosen for each technology using the following hierarchy for the choice of the geography of the background data; European average (RER) was placed at the top of the hierarchy, but as it was generally not available for the assessed activities, a German (DE) technology was chosen in most cases.
DE was placed second in the hierarchy because it is the country with the largest population in EU-28. For a few technologies, a DE dataset was not available and other geographical scopes were chosen. This concerns hydropower (reservoir) which is represented by French technology (FR), and Concentrated Solar Power (CSP) which is represented by an average global production (Rest-of-the-world, RoW). A full overview of all unit processes used for modeling is found in Section S2. 3. A sensitivity analysis was made to identify the geographical influence of the background data chosen on the results (on climate change). The results of this can be found in Sections 3. 3 and S6. 4.
Life cycle inventory The Life Cycle Inventory (LCI) includes the quantity of future electricity generation and the development of the electricity mix for which data is delivered by EnerFuture (Enerdata©) (for scenarios Base and Green) and IEA (scenario NZE). Illustrations of the electricity mix in each scenario can be found in S2. 3. Most technologies are represented in the Ecoinvent database; however, a few technologies have been adjusted or added manually. This concerns electricity generation from coal with CCS which is based on  Iribarren et al. (2013) , natural gas with CCS based on  Petrakopoulou et al. (2015) , Bioenergy (wood chips) with CCS (BECCS) based on  Singh et al. (2011) , and Photovoltaics (PV) which is based on a French research project, INCER-ACV  (Pérez-López et al. , 2020).
In the case of CCS the transport and storage of carbon is modeled based on  Kim et al. (2019). A description of the modeling of these technologies can be found in Section S2. 3. As the study takes a cradle-to-grave perspective and considers all life cycle stages, the construction, maintenance, and end-of-life of the necessary infrastructure (e. g. power plants) is also included. This is contained in each dataset as a yearly average and is therefore not placed at the year of installation only. As the performance of nearly all existing technologies is expected to improve over the coming years (indicated e. g. in a recent study by  Singh et al. , 2021) , improvement rates are applied for all energy production technologies. In the present study, the improvement rates reflect the efficiency of the considered power plants.
, 2009). The assignment of SoSOS is most frequently based on direct consumption or direct impacts only, and assignments including the indirect consumption or impacts have, to our knowledge, not been reported. Especially in industries delivering products or services not only to end consumers (B2C) but also to other companies (B2B), the indirect consumption or emissions might constitute a significant proportion of the total consumption or emissions. To include both direct and indirect consumption and impacts, this study calculates the SoSOS using a script developed by  Oosterhoff et al. (2023) , which uses Environmentally Extended Multi-Regional Input-Output (EE-MRIO) models from Exiobase v3. MRIO modeling is based on vectors and matrices which give an overview of the economic flows between economic sectors across the world (Den Europaeiske Unions Publikationskontor, 2011).
The best mix is observed for Green in 2050, where the scenario consists of about 74 % renewable energy sources (wind, solar, hydro, bioenergy, and geothermal), 21 % nuclear, 3 % CCS attached to coal and natural gas, and 2 % BECCS. With this mix, the Green scenario manages to stay within 7 out of 15 boundaries defined by the SoSOS 1 and 13 out of 15 boundaries defined by the SoSOS 2. The main reason why this mix achieves such good results is that it is mainly composed of renewable energy and that it uses CCS for a remaining part of carbon-based plants as well as for bioenergy. Especially the deployment of BECCS lowers the impacts, as it sequesters around 40 % of the emissions. A sensitivity analysis was made to identify the influence of the geography for the technology datasets chosen on the results (on climate change).
70-0. 88) 3 days after symptom onset  [5]. In a pooled analysis of studies assessing the accuracy of RT-PCR and serological tests for Covid-19 diagnosis, Kostoulas and colleagues  [6]  estimated a sensitivity of 0. 68 (95% PrI=0. 63-0. 73) and a specificity of 0. 99 (95% PrI=0. 98-1. 00) for RT-PCR. The real-world performance of RT-PCR and antigen testing has also been estimated in a further study using data from the Danish national registries. The estimated specificity of RT-PCR was greater than 0. 997%, while the sensitivity was estimated to be 0. 957% (95% PrI=0. 928-984). [7]. This study found no evidence that RT-PCR sensitivity differed between vaccinated and unvaccinated individuals, although the number of test-positive vaccinated individuals was small. In this paper, we demonstrate the implementation of simple Bayesian models for addressing potential misclassification bias in TND studies that seek to evaluate the effectiveness of Covid-19 vaccines.
Illustrative example A TND study conducted in Ontario, Canada, investigated the effectiveness of mRNA Covid-19 vaccines (bnt162b2 and mrna-1273) against symptomatic SARS-CoV-2 infection  [14]. The study used data combined from provincial SARS-CoV-2 laboratory testing, Covid-19 vaccination, and health administrative datasets. All Ontarians aged at least 16 years, eligible for provincial health insurance, not living in long term care, and who were tested for SARS-CoV-2 between 14 December 2020 and 19 April 2021 were included. Those who tested positive for SARS-CoV-2 before 14 December 2020 and recipients of the ChAdOx1 vaccine were excluded from the analysis. The analysis was restricted to individuals who had at least one relevant Covid-19 symptom (based on self-report or observation, such as measured temperature), at the time of testing.
Of the test-positive subjects (cases), 57 were vaccinated and 51 220 were unvaccinated. In subjects testing negative (negative-controls) 3 817 were vaccinated and 251 541 were unvaccinated (Table  2 ). The estimated unadjusted OR was 0. 07 (95% CI=0. 06-0. 09), which translates to a VE of 93% (95% CI=91-94). An adjusted analysis was performed with a multivariable regression logistic model including covariates that were selected a-priori based on their presumed associations with SARS-CoV-2 infection and receipt of a Covid-19 vaccine. The adjusted VE estimate was 91% (95% CI=89-93). In order to use our Bayesian model to analyse this dataset, some prior information is required for the diagnostic test characteristics. Kostoulas and colleagues  [6]  used a Bayesian latent class model to estimate the diagnostic accuracy of RT-PCR and lateral flow immunoassay tests for Covid-19.
However, using previously developed PPK models in the obese patient population requires validation of their predictive ability, as they were not initially developed in this population. Therefore, we sought to determine whether current knowledge about the PK of apixaban and rivaroxaban, as formalized in published PPK models developed in obese and non-obese patient populations treated for various indications, was applicable to obese patients treated for VTE. To this end, we performed a systematic review of published apixaban and rivaroxaban PPK models and a subsequent external evaluation on an independent dataset to validate their predictive performance in the population of obese patients treated for VTE.
Review of Published PPK Models The Scopus, MEDLINE, and Embase databases were searched to identify published PPK analyses of rivaroxaban or apixaban using the following keywords: "rivaroxaban" [AND] "pharmacokinetics" for rivaroxaban, and "apixaban" [AND] "pharmacokinetics" for apixaban. The Cochrane database was searched less stringently, using only the keywords "rivaroxaban" or "apixaban". The reference lists of identified articles were manually screened for additional relevant studies. The search included studies published in English or French between the inception of the databases and 1 November 2020. The included PPK models were those developed using the following: (1) human DOAC data from adult patients; (2) a compartmental, parametric modeling approach. The PPK models were excluded if (1) the model description was insufficient/inadequate to fully reproduce the model, or (2) the model was a physiological-based pharmacokinetic (PBPK) model.
Independent External Validation Data Set DOAC PK and demographic data were collected from adult patients with obesity (BMI ≥ 30 kg/m 2 ) receiving apixaban or rivaroxaban for VTE treatment and enrolled in a prospective multicenter observational study  [16] , conducted in the outpatients' thrombosis clinics of Rennes and Brest University Hospitals between August 2017 and January 2019. Any patient with obesity with VTE followed by the thrombosis center could be included in the study, whatever the time since the initiation of anticoagulation. Apixaban or rivaroxaban plasma concentrations were measured just after the inclusion visit, whatever the time since the last intake, as part of routine care. The exact time between the DOAC intake and blood sampling was strictly recorded, as well as the DOAC dose.
External Predictive Performance Evaluation of Apixaban and Rivaroxaban PPK Models The MonolixSuite 2020R1 (Lixoft SAS, Antony, France) was used for external evaluation. The R language and environment (version 4. 1. 3, R Foundation for Statistical Computing) was used to postprocess the Monolix output and generate graphics. The published PPK models were employed using reported model equations, parameter values, covariate relationships, interpatient variability, parameter covariance, intra-patient variability, and unexplained residual variability. For each model, the apixaban and rivaroxaban concentrations, respectively, were simulated using dosing regimens, sampling times, and covariate information from the external validation dataset (EVD). To assess steady-state concentrations, ten doses were simulated before the first observation.