dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGEMENTS The authors of the ANTARES collaboration acknowledge the financial support of the funding agencies:  Centre National de la Recherche Scientifique (CNRS) ,  Commissariat  à l'énergie atomique et aux énergies alternatives (CEA),  Commission Européenne (FEDER fund and Marie Curie Program) ,  Institut Universitaire de France (IUF) ,  LabEx UnivEarthS  ( ANR-10-LABX-0023  and  ANR-18-IDEX-0001 ),  Région Île-de-France (DIM-ACAV) ,  Région Alsace  (contrat CPER),  Région Provence-Alpes-Côte d'Azur, Département du Var and Ville de La Seyne-sur-Mer, France ;  Bundesministerium für Bildung und Forschung (BMBF) ,  Germany ;  Istituto Nazionale di Fisica Nucleare (INFN), Italy ;  Nederlandse organisatie voor Wetenschappelijk Onderzoek (NWO) , the  Netherlands; Council of the President of the Russian Federation  for young scientists and leading scientific schools supporting grants,  Russia ;  Executive Unit for Financing Higher Education, Research, Development and Innovation (UEFISCDI) ,  Romania ;  Ministerio de Ciencia, Innovación, Investigación y Universidades (MCIU) :  Programa Estatal de Generación de Conocimiento  (refs.
2019/10151-2 , No. 2010/07359-6  and No. 1999/05404-3 ;  Ministério da Ciência, Tecnologia, Inovações e Comunicações (MCTIC) ;  Czech Republic -Grant  No. MSMT CR LTT18004 ,  LM2015038 ,  LM2018102 ,  CZ. 02. 1. 01/0. 0/0. 0/16_013/0001402 , CZ. 02. 1. 01/0. 0/0. 0/18_046/0016010  and  CZ. 02. 1. 01/0. 0/0. 0/17_049/0008422 ; France -Centre de Calcul IN2P3/CNRS ;  Centre National de la Recherche Scientifique (CNRS) ;  Conseil Régional Ile-de-France ;  Département Physique Nucléaire et Corpusculaire  ( PNC-IN2P3/CNRS );  Département Sciences de l'Univers (SDU-INSU/CNRS) ;  Institut Lagrange de Paris (ILP)  Grant No. LABEX ANR-10-LABX-63  within the  Investissements d'Avenir Programme  Grant No. ANR-11-IDEX-0004-02 ;  Germany -Bundesministerium für Bildung und Forschung (BMBF) ;  Deutsche Forschungsgemeinschaft (DFG) ;  Finanzministerium Baden-Württemberg ;  Helmholtz Alliance for Astroparticle Physics (HAP) ;  Helmholtz-Gemeinschaft Deutscher Forschungszentren (HGF) ;  Ministerium für Innovation, Wissenschaft und Forschung des Landes Nordrhein-Westfalen ;  Ministerium für Wissenschaft, Forschung und Kunst des Landes Baden-Württemberg; Italy -Istituto Nazionale di Fisica Nucleare (INFN) ;  Istituto Nazionale di Astrofisica (INAF) ;  Ministero dell'Istruzione, dell'Universitá e della Ricerca (MIUR) ;  CETEMPS Center of Excellence; Ministero degli Affari Esteri (MAE) ;  México -Consejo Nacional de Ciencia y Tecnología (CONACYT)  No.
FUNDING PB acknowledges funding from the  French National Research Agency  grant  ANR-14-CE16-0011  project  DROPmAbs  and  ANR-18-CE15-0001  project  Autoimmuni-B, by the Institut Carnot Pasteur Microbes et Santé ( ANR 11 CARN 0017-01 ), the  Institut Pasteur  and the  Institut National de la Santéet de la Recherche Medicale (INSERM). MB is a recipient of a  CIFRE fellowship  from the  French Association Nationale de la Recherche et de la Technologie (ANRT). None of the sources of funding have an interest in the subject matter or materials discussed in the submitted manuscript.
Editorial notice The views expressed in this paper are those of the authors and in no way represent the Cochrane Library or Wiley. This article is based on a Cochrane Review previously published in the Cochrane Database of Systematic Reviews 2018, Issue 5, DOI:10. 1002/14651858. CD011680. pub2 (see www. cochranelibrary. com for information). Cochrane Reviews are regularly updated as new evidence emerges and in response to feedback, and Cochrane Database of Systematic Reviews should be consulted for the most recent version of the review.
, Tarrytown, NY, USA; Sanofi, Paris, France) and approved by Endocrinology and Metabolic Drugs Advisory Committee (EMDAC) of the US FDA in July 2015. These drugs have enabled a marked reduction in LDL-C levels in dyslipidemic patients. Several trials and meta-analyses showed a ca. 50-60% reduction in LDL-C levels using these mAbs in patients with a diet-based therapy, whether alone or in combination with different doses of statins, without any serious side effects  [119, 120, 122]. Evolocumab was clinically evaluated in combination with statin treatment in 27,564 highrisk patients with atherosclerotic cardiovascular disease (FOURIER study; ClinicalTrials. gov number: NCY01764633). In this clinical trial, the levels of LDL-C were decreased by 59% using the combination treatment along with 15% decrease in the cardiovascular events (cardiovascular death, strokes, myocardial infarction, hospitalization for unstable angina or coronary revascularization) after a 2.
2-year follow-up  [121]. On the other hand, alirocumab was also evaluated in clinical trials in 18,924 patients who had acute coronary syndrome and high LDL-C levels despite of receiving high doses of statins (ODYSSEY OUTCOMES; ClinicalTrials. gov number: NCT01663402). LDL-C levels were decreased by 61% compared to placebo after one year, although around 90% of patients were taking high doses of statin. Moreover, adverse cardiovascular events were also reduced by 15%, including CHD death, non-fatal myocardial infarction, non-fatal ischemic stroke and hospitalization for unstable angina  [123]. Taken together, both of these studies show the effectiveness of PCSK9 mAbs in high-risk patients with LDL-C > 70 mg/dL despite the use of high doses of statin. Aside from these promising results, there are still some limitations for the use of mAbs.
Although some cross-sectional studies have reported SARS-CoV-2 seroprevalence among HCWs being close to that of the general population  [2] [3] [4] [5] [6] [7]  , little is known about the dynamics of SARS-CoV-2 contamination related to the epidemic waves and the risk factors in this population. In the ascendant phase of the first COVID-19 wave in France, we initiated a multicenter prospective cohort study to estimate the cumulative incidence of laboratory-confirmed SARS-CoV-2 infection over the early phase of this outbreak among frontline public hospital HCWs in Paris and identify the risk factors (SEROCOV, Clini-calTrials. gov NCT04304690, first registered on 11/03/2020).
4 positive. When SARS-CoV-2 infection was suspected in an HCW during the study period, the COVID-19 diagnosis was established by RT-PCR on a nasopharyngeal swab with the Cobas SARS-CoV-2 kit (Roche Diagnostics) or RealStar SARS-CoV-2 RT-PCR kit (Altona), as previously reported  9. Outcomes. The primary outcome was laboratory-confirmed SARS-CoV-2 infection at M3 (i. e. , positive serology results [positive or weak positive result on SARS-CoV-2 IgG assay] and/or a positive RT-PCR result on an unplanned nasopharyngeal swab at any time during the survey). Secondary outcomes were positive serology for SARS-CoV-2 (considering weak positive serology as positive) at M0, M1 and M3. Covariates. Baseline characteristics collected were demographics, smoking status (current, former or nonsmoker), public transportation use, and job-related data (hospital, type of ward [emergency, infectious diseases, intensive care unit and virology laboratory]), professional category, number of years of experience in the job or department, and night or day shift.
Statistical analysis. The number of eligible HCWs in participating services was estimated at approximately 1,000 at the time this study was designed. This planned sample size allowed for estimating a cumulative incidence of laboratory-confirmed SARS-CoV-2 infection at M3 of 5% (95% confidence interval [CI] [3. 7; 6. 5]), a cumulative incidence of 10% (8. 2; 12), and a cumulative incidence of 20% (17. 6; 22. 6). Staff characteristics are reported as percentages for categorical variables and median (interquartile range [IQR]) for continuous variables. Multiple imputation was used to replace missing serology testing at M0, M1 or M3. Briefly, 10 copies of the dataset were created with the missing values replaced by imputed values based on observed data including observed serology results and characteristics of participants (including age, sex, job-related data, smoking status, public transportation use, adherence to PPE recommendations and occurrence of ageusia and/or anosmia over the study period).
Each dataset was then analyzed by using standard statistical methods, and the results from each dataset using the statistical methods described below were pooled into a final result by using Rubin's rule  10. For binomial proportions, confidence intervals were estimated by the Wilson method for multiple imputation  11. Risk factors of laboratory-confirmed SARS-CoV-2 infection at M3 were assessed in the whole cohort by univariate and multivariate logistic regression. Baseline variables included in the multivariate model were defined a priori (age, sex, job-related data, public transportation use and smoking status), and no variable selection was performed. A second logistic model was developed for the same outcome among high-risk HCWs only (i. e. , excluding virology staff and jobs rated as "other") and accounting for adherence to PPE recommendations.
Outcomes. The estimated cumulative incidence of laboratory-confirmed SARS-CoV-2 infection at M3 after imputation of missing data was 14. 6% (95% CI [12. 5; 16. 9]), and the difference across professional categories was not statistically significant, although the incidence was highest for medical students (23. 5% [20. 8; 26. 4]) and higher in nurses than in care assistants (Table  2  and Fig. 3 ). The estimated seroprevalence after imputation of missing data at M0, M1, and M3 was 5. 9% [4. 7; 7. 5], 12. 9% [10. 9; 15. 1] and 13. 0% [11. 1; 15. 2], with the same trend across professional categories but not statistically significant. Of the 147 participants with laboratory-confirmed SARS-CoV-2 infection at M3, only 88 (59. 9%, 95% CI [51. 47; 67. 85]) reported symptoms suggestive of COVID-19 before or during the study period.
This result should not encourage smoking to limit the risk of COVID-19; indeed, 78,000 deaths per year are due to smoking in France  29. However, the nicotinic hypothesis is of interest, even in the era of an anti-SARS-CoV-2 vaccine, and is being investigating for prevention of COVID-19 (NCT04583410). The professional category was not a risk factor for laboratory-confirmed SARS-CoV-2 infection, even if medical students exhibited high prevalence as compared with senior physicians (23. 5%, 95% CI [20. 8-26. 4] vs 14. 2% [12. 2-16. 5]) (Table  2 ). This result may be explained by an insufficient practice or experience with PPE recommendations in medical students or confounded by age. Similarly, reporting high compliance with PPE was not protective (Table  4  and Supplementary Table  1 ). However, reported adherence to PPE was very good in the SEROCOV population.
Acknowledgements We sincerely thank all the  HCWs  for their participation. We specifically thank for their exceptional involvement in data management and collection of blood samples:  Ilaria Cherubini ,  Enfel Houas ,  Guillaume Payan ,  Sylvie Le Gac ,  Odile Fleurot ,  Elisabeth Bouvet ,  Christine Jestin ,  Justine Dorchies ,  Nathanelle Yeni ,  Olivia Da Conceicao ,  Lucie Touchar ,  Nicolas Mediamolle ,  Margot Dropy ,  Aida Zarhrate ,  Fatiha Bouchama ,  Marina Vaz ,  Mariem Ben Cheikh Souguir  and  Pr Vincent Calvez  for virology laboratory staff management. We thank  Laura Smales  for English editing.
A small nuance can be noted on Fig. 7(d) : with the actual threshold method, the noise alert map can report small residual values (see red arrows) of a strong artifact highlighted by ϵ d and ϵ u (see yellow arrow on the right Figs. 7(b), 7(c)). However, this approximation does not affect the interpretation of the artifacts since they are correctly pointed out by red and blues alerts. The confidence map is really a qualification of the results given by the C(x, y) calculation, not linked to an a priori evaluation of the performance of the set-up, but directly evaluated from the data themselves. Therefore, an image interpreter can be directly alerted to the fact that the saw-tooth shape on the edges of the canonical sample or the dark gray level lines on the CFRP sample, are truly artifacts.
AAZTA 5 -SA-PSMA was also radiolabeled with 64 Cu (10 nmol vector, 0. 2 M acetate buffer pH 5. 5, RT) and 177 Lu (10 nmol vector, 0. 25 M HEPES pH 4. 4, RT) in excellent RCY (>95%) within only 1 min. Both 68 Ga and 44 Sc were stable in human serum; 177 Lu complex showed a slightly decreased stability after 48h (86% RCP), while the 64 Cu-labeled derivative was poorly stable after 12 h (40% RCP). This work highlighted the highly versatile labeling properties of AAZTA 5 -SA-PSMA for several radiometals, suggesting the significant value of the AAZTA-SA building block in the design of new radiopharmaceuticals candidates. A DATA 5m analogue of this latter molecule, named DATA 5m -SA-KuE, was also synthesized and radiolabeled with 68 Ga  [76]. Interestingly, reaction heating was necessary to reach high RCYs, especially when small amounts of vector were used.
Imaging was performed before the second cycle of 5 GBq [ 90 Y]Y-DOTATOC. Compared with pretreatment imaging, post-treatment [ 68 Ga]Ga-DOTATOC demonstrated partial remission with a 65% decrease in the uptake in the primary pancreatic tumor. [ 68 Ga]Ga-DATA 5m -TOC imaging performed 24 h later at the same time post-injection (50 min) showed a similar and very intense uptake in the primary pancreatic tumor. More importantly, notably lower uptake of [ 68 Ga]Ga-DATA 5m -TOC in the normal liver was observed compared to [ 68 Ga]Ga-DOTATOC (SUV max = 9. 1 vs. 23. 1; tumor-to-liver ratio = 5. 2 vs. 3. 1) (Figure  6 ). Overall, this first-in-human evaluation showed that the chelator switch from DOTA to DATA 5m did not compromise the biological properties of the peptide vector and allowed practical advantages of DATA for single-vial cold-kit formulation.
Recent climate change mitigation strategies rely on the reduction of methane (CH 4 ) emissions. δ 13 C CH4 and δ 2 H CH4 measurements can be used to distinguish sources and thus to understand the CH 4 budget better. The CH 4 emission estimates by models are sensitive to the isotopic signatures assigned to each source category, so it is important to provide representative estimates of the different CH 4 source isotopic signatures worldwide. We present new measurements of isotope signatures of various, mainly anthropogenic, CH 4 sources in Europe, which represent a substantial contribution to the global dataset of source isotopic measurements from the literature, especially for δ 2 H CH4. They improve the definition of δ 13 C CH4 from waste sources, and demonstrate the use of δ 2 H CH4 for fossil fuel source attribution.
Results from previous studies were combined in a common database that we updated with our new measurements, as well as with additional literature. We found that microbial sources are generally well characterised. The large variability in fossil fuels isotopic compositions requires particular care in the choice of weighting criteria for the calculation of a representative global value. The global dataset could be further improved by measurements from African, South American and Asian countries, as well as more measurements from pyrogenic sources. The final version of the European methane isotope database coupled with a global inventory of fossil and non-fossil δ 13 C CH4 and δ 2 H CH4 source signature measurements, is available at:.
Direct measurements of the isotopic signature of CH 4 sources allow us to characterise them well, and a lot of data is available in the literature. Several review articles on CH 4 isotopic source signatures were previously published  (Rice and Claypool, 1981; Cicerone and Oremland, 1988; Bréas et al. , 2001). The most recent one presented by  Sherwood et al. (2017) , and recently updated in  Sherwood et al. (2021) , gathered values from 13 489 locations (10 778 fossil fuel, 2711 non-fossil) from 347 published references. The 2017 study focused on (fugitive) fossil fuel sources, and allowed to re-evaluate the global δ 13 C CH4 value assigned to this emission category towards more depleted values  (Schwietzke et al. , 2016). A disadvantage of this database is that it is rather US-centered, and that the dataset is strongest for fossil fuel sources, but less robust for non-fossil sources.
Therefore the database can be completed by more studies, especially concerning non-fossil sources. The MEMO 2 project (MEthane goes MObile -MEasurements and MOdeling) was a H2020 MSCA European Training Network 1 with the goal to use innovative mobile measurement and modelling tools to improve the quantification of CH 4 emissions in Europe  (Walter et al. , 2019). An important component of MEMO 2 was the isotopic characterisation of CH 4 sources. Two laboratories involved in MEMO 2 , at Utrecht University, The Netherlands, and at the Royal Holloway University of London, UK, carried out a large number of high-precision measurements with isotope ratio mass spectrometry (IRMS). Another method, using cavity ring-down spectroscopy (CRDS) was developed for the mobile measurements of ambient CH 4 isotopic composition.
Several research groups were involved in field work with mobile measurements that targeted specific sources or environments in several European countries. Using this network, numerous CH 4 sources could be sampled for isotopic measurements. The resulting isotopic source signatures were gathered in a publicly available database: The European Methane Isotope Database. This article presents the data collected within MEMO 2 , and the implications for the global understanding of CH 4 source isotopic composition. To place the new data in context, we analyse it together with an updated version of the  Sherwood et al. (2017 Sherwood et al. ( , 2021) )  global database of measured CH 4 source signatures.
Sampling The data was collected by the research teams of 8 universities and research institutes: Utrecht University (UU), the Royal Holloway University of London (RHUL), the Laboratoire des Sciences du Climat et de l'Environnement (LSCE), Heidelberg University (UHEI), AGH University of Science and Technology (AGH), Lund University (LU), the University of Groningen (UG), and the Netherlands Organisation for Applied Scientific Research (TNO). They participated in several campaigns in the Netherlands, the United Kingdom, France, Germany, Poland, Sweden, Romania and Turkey. Several other teams collaborated in two intensive campaigns: the CoMet 2 campaign in the Upper Silsian Coal Basin (USCB) in Poland  (Fiehn et al. , 2020; Gałkowski et al. , 2021) , and the ROMEO campaign in Romania 3  (Röckmann, 2020).
02 and 0. 04 ‰. They were within the analytical error reported by the two laboratories, so that the isotopic results obtained within the MEMO 2 project are consistent across the laboratories. The inter-comparison exercise is presented in detail in a MEMO 2 deliverable report publically availabe 4. The UHEI and LSCE groups performed isotopic measurements using CRDS instruments (G2201-i, Picarro inc. , USA). Their methods were described in  Hoheisel et al. (2019)  and  Defratyka et al. (2021). In the database, the method of isotopic measurements is specified by the "Measurement type" parameter, as either 'IRMS' or 'CRDS'. The laboratory where the measurements were performed is specified in the column "Measurement lab".
Both methods involve a linear regression model to fit the observed data. Different models were used: ordinary least squares (OLS) minimising the difference in the y-axis coordinate, bivariate correlated errors and intrinsic scatter (BCES)  (Akritas and Bershady, 1996) , and ordinary distance regression (ODR)  (Boggs and Rogers, 1990). Zobitz et al. (2006)  compared different regression methods when applied in Keeling plots. The ODR can induce a bias towards lower values in the case the data points cover a relatively small range on the x-axis, so the OLS and BCES methods were usually prefered. All the mass balance and regression methods are statistically valid. Therefore we did not work towards a uniform procedure, but the different approaches are specified for each entry of the database by the parameters "Mass balance approach" and "Regression method".
Structure of the database We used the same parameters as in the database of  Sherwood et al. (2017 Sherwood et al. ( , 2021) )   The categories are grouped into the three main CH 4 formation pathways: modern microbial, pyrogenic, and fossil fuels. The "modern microbial" CH 4 is formed by microorganisms in surface ecosystems or in animals through enteric fermentation, and are refered simply as "microbial" throughout the paper. Microbial CH 4 formations in the subsurface related to petroleum systems belongs to the "fossil fuels" category. Compared to  Sherwood et al. (2017 Sherwood et al. ( , 2021)) , we extended the biomass burning type to include emissions from all combustion sources, such as traffic or industry (Table  1 ).
Reported variables The analytical parameters reported in the database are δ 13 C CH4 and δ 2 H CH4 , which are defined as: δX = ( R sample R standard -1) with R = 13 C 12 C for X = 13 C or R = 2 H 1 H for X = 2 H The unit is in per mille (‰), and values are relative to the international standard materials Vienna Pee Dee Belemnite (VPDB) for δ 13 C, and Vienna Standard Mean Ocean Water (VSMOW) for δ 2 H.
Results and discussion The data on isotopic source signatures from the measurement campaigns carried out within the MEMO 2 project (2017-2020) were compiled into one database: The European Methane Isotope Database. The first version was made accesssible 5 on October 1 st 2020, and described in a publicly available report 6. The European data was used in several publications over the past 2 years by  Menoud et al. (2020 Menoud et al. ( , 2021b, in review), in review) ;  Maazallahi et al. (2020) ;  Defratyka et al. (2021) ;  Bakkaloglu et al. (2021, in review) ;  Fernandez et al. (in review). The final version of the global database, including the MEMO 2 measurements and the additional literature are available at: https://doi. org/10. 24416/UU01-4PO56T.
The European Methane Isotope Database The isotopic signatures obtained within the MEMO 2 project concern 734 locations over 8 countries, with δ 2 H source signatures being measured at 54 % of the sites (Table  2 ). Figure  1  shows the geographical distribution of the sampled sites in the different countries, according to the type of source. The number of sources we sampled does not necessarily represent the emission magnitudes. We characterised 376 locations by both δ 13 C and δ 2 H values, and we compared the results to ranges reported in the literature in Fig. 2. The fossil fuel sources partly overlap with the range of thermogenic CH 4 , but also spread towards lower δ 13 C or higher δ 2 H. This is due to the presence of natural gas of microbial origin in the coal reservoirs of Silesia, in Poland  (Kotarba, 2001; Kotarba and Pluta, 2009; Menoud et al.
, 1998; De Visscher, 2004; Conrad, 2005; Gebert and Streese-Kleeberg, 2017; Obersky et al. , 2018; Bakkaloglu et al. , 2021) , which increases the range of possible isotopic signatures of the emitted CH 4. The maps in Fig. 3  emphasise the similarities between δ 13 C source signatures from modern microbial and fossil fuel sources in Poland and Romania. The average δ 13 C CH4 of fugitive emissions from coal, oil and gas extraction sites in Poland and Romania was -48. 5 ± 0. 6 ‰ (n=235), compared to -38. 9 ± 0. 3 ‰ (n=154) from gas leaks in only the UK and the Netherlands (and -40. 4 ± 0. 3 ‰ (n=217) including France and Germany). This distinction is also visible in the histograms of the European Methane Isotope Database in Fig. 5. A. In western Europe, δ 13 C allows for a good separation between microbial and fossil fuel sources, which is well-established in the literature  (Levin et al.