dataesr/scientific-paragraphs-categorization huggingface dataset:
The KATRIN main spectrometer achieves a magnetic-field ratio B min /B max ≈ 1/6700 ≈ ∆E/E, corresponding to a filter width (energy resolution) of ∆E = 2. 8 eV at 18. 6 keV. The maximum acceptance angle θ max = arcsin B S /B max ≈ 50. 5°limits the range of pitch angles contributing to the integral spectrum. The magnetic fields and the retarding potential are provided by detailed field calculations using the Kassiopeia software  [85]. To compute the precise electromagnetic fields across the analyzing plane, we use an as-built geometry of the beamline magnets with a detailed three-dimensional model of the main spectrometer. The resulting transmission conditions can be included in the model individually for each active pixel. The detailed response function of the KATRIN apparatus is calculated from Eq.
The scaling factors of this background are additional nuisance parameters. We performed a combined fit to four TOF datasets measured at different column densities. Each dataset contains about 12 hours of data, resulting in ∼6 × 10 5 events surviving the TOF cut. The nine model parameters of interest are shared between all datasets, whereas each dataset has its own nuisance parameters as described above. The resulting best-fit parametrization is shown in Fig. 11  top and center for the integral and differential data, respectively. The same energy-loss function describes all four datasets well and the fit has a reduced χ 2 close to one. Uncertainties used in this work are of a statistical nature only. However, more advanced combined fits that also take into account the integral energy-loss measurements yield the same parameter values within their statistical uncertainties.
Due to the formation of a thin layer of H 2 O covering the baffle surface, the retention of 219 Rn was hampered such that R bg retains an observable non-Poissonian component during KNM1. In KNM1, the overall steady-state background rate, R bg , is continuously measured through the energyindependent part of the spectrum R( qU ). The whole spectrum is fitted, leading to a value over the 117 selected pixels of R bg = 0. 293(1) cps that is largely constrained by the 5 scan steps above the expected E 0. This value is consistent with data from independent background runs. Full fit results are given in Sec. IX D. The background is not distributed uniformly across the detector, as shown in Fig. 12. The decrease of R bg towards smaller radii can be explained by radiative deexcitation of the Rydberg atoms as they propagate inside the main spectrometer.
Analysis of the positions of the krypton L 3 -32 lines of unscattered and singly scattered electrons shows that a plasma-induced mutual shift of these positions cannot be larger than 70 mV. The corresponding additional m 2 ν -shift can be neglected for KNM1. We thus conclude that the effective L 3 -32 broadening parameters given above serve as a very conservative upper limit of plasma effects in the neutrino-mass analysis. In addition to the 83m Kr spectroscopy method, radial plasma inhomogeneities can be inferred directly from the neutrino-mass data by radial evaluation of E 0. The spectral fit from twelve separate detector rings (see Fig. 5  for detector structure) revealed a slope of -2(5) mV/ring, consistent with a slope of zero. A full propagation of the plasma model and its uncertainty was not included in the KNM1 analysis, primarily due to the immaturity of the plasma model as applied to the low KNM1 column density.
H. Stacking The averaging of the scan steps within the stacking techniques introduces a small bias on m 2 ν and E 0. In order to quantify these biases, we construct an Asimov dataset  [102]  by simulating 274 statistically unfluctuated "MC twin" spectra, incorporating the actual variation of slow-control parameters (including measured highvoltage values, isotopic compositions, and column densities) between scans. Later on, the MC spectra are combined into a single integral spectrum through the stacking procedure, as described in Sec. VII. As a last step, we fit this stacked MC spectrum. Comparing this fit result to the MC truth yielded a 1σ stacking uncertainty of 14 × 10 -2 eV 2 in one analysis approach (Sec. IX B), and 5 × 10 -2 eV 2 in the other (Sec. IX C), as shown in Table  IV  further below.
A. Blinding strategy For the KNM1 analysis we enforced blind analysis procedures to fix data selection, analysis cuts, and model composition before the model was fitted to the data. This standard technique is designed to avoid observer's bias. For this first KATRIN m 2 ν limit, we employed model blinding rather than data blinding. The fit results are highly dependent on the molecular FSD (Sec. IV B); in particular, the value of m 2 ν depends on the width of the distribution of transitions to the electronic ground state of the daughter molecule 3 HeT +. Using an FSD with too large a width pushes m 2 ν towards higher values, while too narrow a width pushes it towards lower values. Indeed, historically, inaccurate FSD models were likely responsible for artificially negative m 2 ν results from the Los Alamos  [18]  and Livermore  [103]  experiments, a problem which is resolved by using the more modern theory described in Sec.
To prevent accidental unblinding, the adjusted FSD was provided as an independent software module synchronized with the main fitting software. The second measure to mitigate biasing is to perform the full analysis, including parameter fitting, using MCbased data sets first, before turning to the experimental data. For each experimental scan i we generate an MC twin (Sec. VIII H) from its averaged slow-control parameters to calculate the expected rate R β (E) i with the corresponding response function f (E -qU ) i and background rate R bg,i. Analyzing the MC twins allows us to verify the accuracy of our parameter inference by recovering the correct input MC values for m 2 ν. This MC dataset is used to assess statistical (σ stat ) and systematic (σ syst ) uncertainties and to compute our expected sensitivity.
The same results are obtained by combining the Markov chains of the individual fits into a single chain, and subsequently performing the same parameter-estimation procedure. Additional systematics will be analyzed in future work. The present dataset is strongly dominated by statistical uncertainties, and individual systematic effects are largely masked below 0. 1 eV 2 by numerical uncertainties. These uncertainties come from the finite number of Markov-chain Monte-Carlo samples and are on the order of 0. 006 eV 2 in the 1σ posterior width. Hence, the systematic budget was investigated with Asimov data, artificially increasing the amount of data and thus enhancing each included systematic effect with respect to statistical uncertainties. Taking these four explicitly included systematic uncertainties into account, the most probable m 2 ν value was found at -1.
ACKNOWLEDGMENTS We acknowledge the support of  Helmholtz Association, Ministry for Education and Research BMBF  ( 5A17PDA ,  05A17PM3 ,  05A17PX3 ,  05A17VK2 , and  05A17WO3 ),  Helmholtz Alliance for Astroparticle Physics (HAP) ,  Helmholtz Young Investigator Group  ( VH-NG-1055 ),  Max Planck Research Group (MaxPlanck@TUM) , and  Deutsche Forschungsgemeinschaft DFG  (Research Training Groups GRK 1694 and GRK 2149, Graduate School GSC 1085 -KSETA, and  SFB-1258 ) in Germany;  Ministry of Education, Youth and Sport  ( CANAM-LM2015056 ,  LTT19005 ) in the Czech Republic;  Ministry of Science and Higher Education of the Russian Federation  under contract  075-15-2020-778 ; and the  United States Department of Energy  through grants  DE-FG02-97ER41020 ,  DE-FG02-94ER40818 ,  DE-SC0004036 ,  DE-FG02-97ER41033 ,  DE-FG02-97ER41041 ,  DE-AC02-05CH11231 ,  DE-SC0011091 , and  DE-SC0019304 , and the  National Energy Research Scientific Computing Center.
, 2016) , and contributes to global warming  (Bond et al. , 2013; Myhre et al. , 2013; Wang et al. , 2014a). When deposited on snow, it reduces snow albedo  (Clarke and Noone, 1985; Hegg et al. , 2009) , accelerating melting. Since BC is both climate-relevant and strongly linked to anthropogenic activity, it is important to determine the effects of the COVID-19 lockdowns thereon. Here, we present a rigorous assessment of temporal and spatial changes BC emissions over Europe (including the Middle East and parts of North Africa), combining in situ observations from the Aerosol, Clouds and Trace Gases Research Infrastructure (ACTRIS) network and state-of-the-art emission inventories within a Bayesian inversion. We validate our results with independent satellite data and compare them to inventories' baseline and optimized emissions calculated for previous years.
Methods This section gives a detailed description of all datasets and methods used for the calculation of COVID-19 impact. Section 2. 1 describes the instrumentation of the particle light absorption measurements from Aerosol, Clouds and Trace Gases Research Infrastructure (ACTRIS) and the networks European Monitoring and Evaluation Program (EMEP) and Global Atmosphere Watch (GAW). These measurements were used in the inverse modelling algorithm (dependent measurements) and to validate the optimized (posterior) emissions of BC (independent measurements). For each of the observations and stations, the source-receptor matrices (SRMs), also known as "footprint emission sensitivities" or "footprints", were calculated as described in Sect. 2. 2. The latter together with the observations were fed into the inversion algorithm described in Sect.
2. 3. To overcome classic inverse problems  (Tarantola, 2005) , prior (a priori) emissions of BC were used in the inverse modelling algorithm, calculated using bottom-up approaches (Sect. 2. 4). The optimized (a posteriori) emissions of BC were compared with reanalysis data from MERRA-2 (Modern-Era Retrospective Analysis for Research and Applications Version 2), which are described in Sect. 2. 5, while MERRA-2 Ångström exponent data, together with the absorption Ångström exponent from the Aerosol Robotic Network (AERONET) (Sect. 2. 6), were used to examine the presence of biomass burning aerosols in Europe. A description of the statistical tests and the country definitions used in the paper is given in Sect. 2. 7 and 2. 8, respectively.
Particle light absorption measurements The measurement sites contributing data to this paper are regional background sites (except for one site in Germany) and all contribute to the research infrastructure ACTRIS and the networks EMEP and GAW. The measurement data used for the period 2015-May 2020 consist of hourly averaged, quality-checked, particle light absorption measurements. The quality assurance and quality control correspond to the Level 2 requirements for ACTRIS, EMEP and GAW data, as described in detail in  Laj et al. (2020). All absorption measurements within ACTRIS and EMEP are taken using a variety of filter-based photometers: Multi-Angle Absorption Photometer (MAAP), Particle Soot Absorption Photometer (PSAP) Continuous Light Absorption Photometer (CLAP) and the Aethalometer (AE-31).
Information on instrument type at the various sites is included in Table  1 , and procedures for harmonization of measurement protocols to produce comparable datasets are described in  Laj et al. (2020)  in detail. Zanatta et al. (2016)  suggested that a mass absorption cross-section (MAC) value of 10 m 2 g -1 (geometric standard deviation of 1. 33) at a wavelength of 637 nm can be considered to be representative of the mixed boundary layer at European ACTRIS background sites, where BC is expected to be internally mixed to a large extent. Assuming an absorption Ångström exponent (AAE) is equal to unity, i. e. assuming no change in MAC for different sources  (Zotter et al. , 2017) , we extrapolated the MACs at 637 nm (MAC @λ1 ) to the measurement wavelengths of our study (MAC @λ2 ) using the following equation: MAC @λ2 = MAC @λ1 ( λ1 λ2 ) AAE yields ---→ MAC @λ2 = 10( 637 λ2 ) 1 , (1) following  Lack and Langridge (2013).
Bayesian inverse modelling The Bayesian inversion framework FLEXINVERT+ described in detail in  Thompson and Stohl (2014)  was used to optimize emissions of BC before (January to mid-March 2020) and during the COVID-19 lockdown period in Europe (mid-March to end of April 2020). To show potential differences in the signal from the 2020 restrictions, emissions were optimized with the same setup during the same period (January to April) in the previous 5 years  (2015) (2016) (2017) (2018) (2019). Note that the number of stations in the inversions of 2015-2019 was slightly higher (20 stations against 15 that were used in 2020), due to different data availability. The algorithm finds the optimal emissions which lead to FLEXPART-modelled concentrations that better match the observations considering the uncertainties for observations, prior emissions and SRMs.
Prior emissions As a priori emissions in the inversions, the ECLIPSE version 5 and 6 (Evaluating the CLimate and Air Quality ImPacts of ShortlivEd Pollutants)  (Klimont et al. , 2017)   (Lamarque et al. , 2013)  and PKU (Peking University)  (Wang et al. , 2014b)  were used (Fig. 3 ). All inventories include the basic emission sectors (e. g. waste burning, industrial combustion and processing, all means of transportation (aerial, surface, ocean), energy conversion and residential and commercial combustion; see references therein). Biomass burning emissions were adopted from the Global Fire Emissions Database, Version 4. 1s (GFEDv4. 1s) (Giglio et al. , 2013). Note that the a priori emissions used in the inversions of 2015-2019 period corresponded to year 2015 of ECLIPSEv6, and they were not interpolated for the years between 2015 and 2020, for which the ECLIPSEv6 emissions were calculated.
MERRA-2 (Modern-Era Retrospective Analysis for Research and Applications Version 2) The MERRA-2 reanalysis dataset for BC  (Randles et al. , 2017)   (Buchard et al. , 2017; Qin et al. , 2019; Randles et al. , 2017; Sun et al. , 2019). The Ångström exponent (AE), a measure of how the AOD changes relative to the various wavelength of light, is derived here from AOD469, AOD550, AOD670 and AOD865, by fitting the data to the linear transform of Ångström's empirical expression: τ λ = τ λ 0 ( λ λ 0 ) -a , (6) where τ λ is the known AOD at wavelength λ (in nm), τ λ 0 is the AOD at 1000 nm and α stands for AE  (Gueymard and Yang, 2020).
Uncertainty and validation of the posterior emissions One of the basic problems when dealing with inverse modelling is that changing model, observational or prior uncertainties can have drastic impacts on posterior emissions. We addressed this issue by finding the optimal parameters, in order to have a reduced χ 2 statistic around unity (see Sect. 2. 3). However, there are two other sources of uncertainty that, although not linked with the inversion algorithm, could affect posterior emissions drastically. The first is the use of different prior emissions; to estimate this type of uncertainty, we performed five inversions for January to April 2020 using each of the prior emission datasets (ECLIPSEv6 and v5, EDGAR_HTAPv2. 2, ACCMIPv5 and PKU). The uncertainty was calculated as the gridded standard deviation of the posterior emissions resulting from the five inversions.
Discussion The improved air quality that Europe experienced during the lockdown was also evident from the assimilated MERRA-2 satellite-based BC data. The latter are plotted in Fig. S3  (left axis) for 2015-2020, together with the posterior emissions calculated in the present study (right axis). For instance, weekly average concentrations of BC over Europe in MERRA-2 (Fig. S3 , bottom). Many of the ACTRIS stations reported increased light absorption at the beginning of the lockdown (e. g. Fig. 7 ); MERRA-2 data show the same patterns in France, Italy, UK and Spain and in all of Europe, in general. This can be explained by residential combustion, considering that the surface temperature during the lockdown was lower than in previous years (Fig. 5 ). The latter was confirmed by the MERRA-2 reanalysis Ångström exponent (AE) parameter at 470-870 nm, which shows higher values over central and Eastern Europe during the lockdown in 2020 than in the same period of the previous years (Fig.
Conclusions The impact of the COVID-19 lockdowns over Europe on the BC emissions, in response to the pandemic, was assessed in the present paper. Particle light absorption measurements from 17 ACTRIS stations all around Europe were rapidly gathered and cleaned to produce a high-quality product. The latter was used in a well-established Bayesian inversion framework, and BC emissions were optimized over Europe to better capture the observations. However, one should be careful not to overinterpret the emission changes at regional scales, due to the poor station data density used and the high-resolution time steps of the inversions (weekly posterior emissions). We calculate that the optimized (posterior) BC emissions declined from 63 to 40 kt (23 %) during the lockdowns over Europe, as compared to the same period in the previous 5 years  (2015) (2016) (2017) (2018) (2019).
org/10. 21336/gen. b5vj-sn33 (Evangeliou et al. , 2020)  or upon request to the corresponding author. All prior emission datasets are also available for download. ECLIPSE emissions can be obtained from http://www. iiasa. ac. at/web/home/ research/researchPrograms/air/Global_emissions. html (Klimont et al. , 2017) , EDGAR version HTAP_V2. 2 from http://edgar. jrc. ec. europa. eu/methodology. php# (Janssens-Maenhout et al. , 2015) , ACCMIP version 5 from http://accent. aero. jussieu. fr/ACCMIP_ metadata. php (Lamarque et al. , 2010)  and PKU from http:// inventory. pku. edu. cn (Peking University, 2021). FLEXPART is publicly available and can be downloaded from https://www. flexpart. eu (Pisso et al. , 2019)  and FLEXINVERT+ from https: //flexinvert. nilu. no (Thompson and Stohl, 2014). MERRA-2 reanalysis data can be obtained from https://disc.
Acknowledgements. We thank  Bernard Mougenot ,  Olivier Hagolle ,  Anatoli Chaikovsky ,  Philippe Goloub ,  Jersnimo Lorente ,  Ralf Becker  and  Matthias Wiegner  for their effort in establishing and maintaining the AERONET sites at  Ben Salem (Tunisia) ,  Minsk (Belarus) ,  Montsec (Spain) ,  MetObs Lindenberg  (Germany) and  Munich University (Germany). Financial support. This study has been supported by the  Research Council of Norway  (project  ID :  275407 ,  COMBAT -Quantification of Global Ammonia Sources constrained by a Bayesian Inversion Technique ). Nikolaos Evangeliou  and  Sabine Eckhardt  received funding from the  Arctic Monitoring & Assessment Programme (AMAP). John Backman  was supported by the  Academy of Finland  project  Novel Assessment of Black Carbon in the Eurasian Arctic: From Historical Concentrations and Sources to Future Climate Impacts (NABCEA ; project no.
Infarct Size Measurement At the end of the IR protocol, the heart was harvested from the apparatus. The left ventricle (LV) was embedded in agar (4% w/v), and transversely sliced (1 mm) with a vibratome. To reveal tissue viability, slices were incubated in a 1% solution of 2,3,5triphenyltetrazolium chloride (TTC, Sigma-Aldrich) for 15 min at 37 • C. After a fixation step (4% paraformaldehyde, 48 h), each slice was photographed from each side. The infarct area was quantified by planimetric measurements with ImageJ software.
Statistical Analysis Data expressed as mean ± SD values were compared among groups using non-parametric Mann-Whitney (two groups) and Kruskal-Wallis (multiple comparison) methods. P-values < 0. 05 ( * ), p < 0. 01 ( * * ), p < 0. 001 ( * * * ), and p < 0. 0001( * * * * ) were considered statistically significant. Analysis and graphical representation were performed using Graph-Pad Prism TM software (GraphPad).
ACKNOWLEDGMENTS The authors would like to thank the Biocampus facilities and in particular,  Julien Cau  and  Amélie Sarrazin  for assistance with microscopy (MRI),  Laura Gallot  for her expertise in ex vivo experiments,  Nelly Pirot  for histological preparations (RHEM) as well as  Denis Greuet ,  Luc Forichon  and  Karim Mesbah  for animal care (iExplore/RAM).
FUNDING This work was supported by  Inserm , the  University of Montpellier  (FD, SB-L, and CJ) and by the  CNRS  (SB-L). We thank the  Fonds Marion et Elisabeth Brancher  for the financial support of this project ( CS ) and also the  PHC program of Campus France  (project number  33858WM ; SK and SB-L) as well as the  Agence Nationale de Recherche  for the  LabEx ICST ANR  ( ANR-11-LABX-0015 ; SB-L, JN, AV, CB, and CP) and for the  PPAROA ANR  ( ANR-18-CE18-0010-02 ; FD, CJ, and RC) grants,  SATT  AxLR n • 19/0150 contract, the  ECOS-Sud  (action ECOS n • C18S03) and  La Fondation Arthritis. We also thank the  University of Naresuan  for the staff development travel grant (NN).
8 ± 4. 1 μg of Hg per gram, Fig. 1 ) due to atmospheric deposition from neighboring industries and bioaccumulation of mercury by the microbial mats  18. A total of 30 metagenomic datasets, covering the spatial and temporal variability of the microbial mats, was generated, leading to 389 GB of sequences, then co-assembled, resulting in the recovery of 407 high-to medium-quality MAGs. Mercury cycling populations were identified and protein sequences of these microbial MAGs were compared to 300 GB of rRNA-depleted metatranscriptomic sequences produced from the same samples. The expression profiles of hgcA, merA, merB genes, and energetical metabolic pathways were investigated at the population level, revealing a larger variety of metabolic activities than usually presumed for mercury cycling microorganisms.
Microbial community composition The microbial community composition was determined using all 16S rRNA genes recovered from the metagenomic datasets (2. 1 ± 0. 9 × 10 4 16S rRNA genes per sample; Fig. 2 ). A discontinuity of the activity in the oil refinery during our 2012 sampling campaign disrupted the microbial diversity of the EDB mat, leading to a divergent microbial community profile (Fig. 2 ). Excluding these samples, the microbial community composition was relatively similar in both sites (Bray-Curtis similarity index: 71. 44%) and stable over the three sampling campaigns. Bacteria strongly predominated the prokaryotic community with on average 98. 67 ± 0. 48% of the 16S rRNA reads whereas a maximum of 2. 02% of the 16S rRNA reads was affiliated to Archaea. Bacteroidetes, representing on average 18.
Metagenome assembled genomes of the microbial mat and mercury cycling populations Binning of the contigs obtained after coassembly of all metagenomic reads resulted in the recovery of 407 high-to medium-quality MAGs (completeness > 50%, contamination < 5%). Taxonomic affiliation of these MAGs, inferred from 16S rRNA genes and ribosomal genes phylogenetic analysis (Fig. 3 ) indicated that MAGs from both dominant and rare lineages of the microbial mats were reconstructed. Bacteroidetes (72 MAGs), Cyanobacteria (25 MAGs), Alphaproteobacteria (42 MAGs), Gammaproteobacteria (68 MAGs), Desulfobacterota (26 MAGs), Patescibacteria (19 MAGs), and Verrucomicrobia (14 MAGs) were the most represented phyla (Fig. 2 ). Mercury methyltransferase gene hgcA, mercury reductase gene merA, and alkylmercury lyase gene merB were screened in the metagenomic datasets.
Up to 45 different hgcA genes (binned and unbinned) were identified in the metagenomic dataset (Fig. 4 ). Based on taxonomic analysis of hgcA sequences, 82% of these genes were affiliated to the Desulfobacterota phylum. Binning of the contigs recovered 10 MAGs with hgcA gene, representing most of the hgcA gene diversity identified in the samples, with 8 MAGS affiliated to Desulfobacterota (Desulforhopalus, Desulfobulbaceae, Desulfatiglans, and Desulfobacteraceae (Fig. 4 ). In addition, one MAG was affiliated to Roseimarinus (Bacteroidetes) and another one to the Aminicenantes phyla (Table  1  and Figs. 3  and 4 ). All of the binned hgcA genes were followed on their contigs by the hgcB gene. Unbinned hgcA sequences without binned references were related to archaeal methanogen (1 sequence), Geobacter (1 sequence), and Desulfovibrio (3 sequences) groups (Fig.