dataesr/scientific-paragraphs-categorization huggingface dataset:
While contributions from ebullition appear lower from rivers, there are currently insufficient measurements from these systems to determine its role  (Crawford et al. , 2014; Stanley et al. , 2016). Ebullition fluxes are very challenging to measure, due to the high degree of spatiotemporal variability with very high fluxes occurring in parts of an ecosystem over the time frames of seconds followed by long periods without ebullition. Freshwater methane fluxes from streams and rivers were first estimated to be 1. 5 Tg CH 4 yr -1  (Bastviken et al. , 2011). However, this study had measurements from only 21 sites globally. More recently,  Stanley et al. (2016)  compiled a dataset of 385 sites and estimated a diffusive emission of 27 Tg CH 4 yr -1 (5th-95th percentiles: 0. 01-160 Tg CH 4 yr -1 ).
4 Tg CH 4 yr -1 (reproduced in Fig. 4 ). According to them, the grid maps do not represent, however, the actual global geological CH 4 emission because the datasets used for the spatial gridding (developed for modelling purposes) were not complete or did not contain the information necessary for improving all previous estimates. Combining the best estimates for the five categories of geological sources (from grid maps or from previous statistical and process-based models), the breakdown by category reveals that onshore microseepage dominates (24 Tg CH 4 yr -1 ), with the other categories having similar smaller contributions: as average values, 4. 7 Tg CH 4 yr -1 for geothermal manifestations, about 7 Tg CH 4 yr -1 for submarine seepage, and 9. 6 Tg CH 4 yr -1 for onshore seeps and mud volcanoes.
The re-analysis of termite emissions proposed in Saunois et al. (  2016 ) maintained the same approach, but the data were calculated using climate zoning (following the Köppen-Geiger classification) applied to updated climate datasets by  Santini and di Paola (2015)  and were adapted to consider different combinations of termite biomass per unit area and CH 4 emission factor per unit of termite biomass. Here, this analysis is extended to cover the periods  2000-2007 and 2010-2016. This latest estimate follows the approach outlined above for  Saunois et al. (2016). However, in order to extend the analysis to 2016, an alternative MODISbased measure of GPP from  Zhang at al. (2017) , rather than from  Jung et al. (2009), and Jung et al. (2011)  was used to estimate termite biomass. To have coherent datasets of GPP and land use, the latter variable, previously derived from  Ramankutty and Foley (1999) , was substituted for MODIS maps  (Channan et al.
, 2014; Friedl et al. , 2010). These new estimates covered  2000-2007 and 2010-2016 using 2002 and 2012  MODIS data as an average reference year for each period, respectively. Termite CH 4 emissions show only little inter-annual and inter-decadal variability (0. 1 Tg CH 4 yr -1 ), whereas there is strong regional variability, with tropical South America and Africa being the main sources (23 % and 28 % of the total emissions, respectively) due to the extent of their natural forest and savannah ecosystems (Fig. 4 ). Changing the GPP and land use dataset sources had only a minimal impact on the 2000-2007 global termite flux, increasing it from 8. 7 Tg CH 4 yr -1 as found in the first two reanalyses  (Kirschke et al. , 2013; Saunois et al. , 2016)  to 9. 9 Tg CH 4 yr -1 (present data), well within the estimated uncertainty (8.
, 2016). Methane emissions from estuaries were originally estimated by  Bange et al. (1994) ,  Upstill-Goddard et al. (2000) , and  Middelburg et al. (2002)  to be comprised between 1 and -3 Tg CH 4 yr -1. This range was later revised upwards by  Borges and Abril (2011)  to about 7 Tg CH 4 yr -1 based on a methodology distinguishing between different estuarine types and accounting for the contribution of tidal flats, marshes, and mangroves, for a total of 39 systems and a global "inner" estuarine surface area of 1. 1 × 10 6 km 2  (Laruelle et al. , 2013). The same methodology as in  Laruelle et al. (2013)  has been applied here to the same systems using an expanded database of local and regional measurements (72 systems) and suggests however that global estuarine CH 4 emissions were overestimated and may actually not surpass 3-3.
, 2002; Prinn et al. , 2000) , the Commonwealth Scientific and Industrial Research Organisation (CSIRO;  Francey et al. , 1999) , the University of California Irvine (UCI;  Simpson et al. , 2012) , and in situ and flask measurements from regional networks, such as the ICOS (Integrated Carbon Observation System) network in Europe (INGOS, 2018; ICOS-RI, 2019; https://www. icos-ri. eu/, last access: 29 June 2020). The combined datasets provide the longest time series of globally averaged CH 4 abundance. Since the early 2000s, CH 4 columnaveraged mole fractions have been retrieved through passive remote sensing from space  (Buchwitz et al. , 2005a, b; Butz et al. , 2011; Crevoisier et al. , 2009; Frankenberg et al. , 2005; Hu et al. , 2018). Ground-based Fourier transform infrared (FTIR) measurements at fixed locations also provide timeresolved methane column observations during daylight hours and a validation dataset against which to evaluate the satellite measurements such as the TCCON network (e.
6 to ∼ 1800 ppb since pre-industrial times. Accounting for the decrease in mean lifetime over the industrial period,  Prather et al. (2012)  estimated from these data a total source of 554 ± 56 Tg CH 4 in 2010 of which about 64 % (352 ± 45 Tg CH 4 ) was of anthropogenic origin, consistent with the range in our top-down estimates. For wetlands in 2008-2017, the top-down and bottomup estimates of 181 Tg CH 4 yr -1 (range 159-200) and 149 Tg CH 4 yr -1 (range 102-182), respectively, are statistically consistent. Bottom-up mean wetland emissions for the 2000-2009 period are smaller in this study than those of  Saunois et al. (2016). Conversely, the current 2000-2009 mean top-down wetland estimates are larger than those of  Saunois et al. (2016)  (Table  3 ). The reduction in wetland emissions from bottom-up models is related to an updated wetland extent dataset (WAD2M; see Sect.
-More systematic measurements from sites reflecting the diverse lake morphologies will allow us to better understand the short-term biological control on ebullition variability, which remains poorly known  (Wik et al. , 2014 (Wik et al. , , 2016a)). -Extending monitoring of methane fluxes year round from the different natural sources (wetlands, freshwaters) complemented with environmental meta-data (e. g. , soil temperature and moisture, vegetation types, water temperature, acidity, nutrient concentrations, NPP, soil carbon density) will allow us to enrich the FLUXNET-CH 4 observation dataset and to better constrain methane fluxes and their isotopic signatures in land surface models  (Glagolev et al. , 2011; Turetsky et al. , 2014; Ganesan et al. , 2019). 2. Towards a better assessment of uncertainties for global methane sinks in top-down and bottom-up budgets.
g. Simpson et al. , 2012; Turner et al. , 2019). -Improve the availability of in situ data for the scientific community, especially ones covering poorly documented regions such as China  (Fang et al. , 2015) , India  (Lin et al. , 2015; Tiwari and Kumar, 2012)  and Siberia  (Sasakawa et al. , 2010; Winderlich et al. , 2010) , which have not been included so far in international databases. Over the long run, integrating more measurements and regional studies will help to improve the top-down systems, and further reduce the uncertainties. -Integrate global data from future satellite instruments with intrinsic low bias, such as active lidar techniques with MERLIN  (Ehret et al. , 2017) , that are promising to overcome issues of systematic errors  (Bousquet et al. , 2018)  and should provide measurements over the Arctic, contrary to the existing and planned passive missions.
Data availability The data presented here are made available in the belief that their dissemination will lead to greater understanding and new scientific insights into the methane budget and changes to it and help to reduce its uncertainties. The free availability of the data does not constitute permission for publication of the data. For research projects, if the data used are essential to the work to be published, or if the conclusion or results largely depend on the data, co-authorship should be considered. Full contact details and information on how to cite the data are given in the accompanying database. The accompanying database includes one Excel file organized in the following spreadsheets and two NetCDF files defining the regions used to extend the anthropogenic inventories. The file Global_Methane_Budget_2000-2017_v2.
As a second priority, the uncertainty on the chemical loss of methane needs to be better assessed in both the top-down and the bottom-up budgets. Our work also suggests the need for more interactions among groups developing emission inventories in order to clarify the definition of the sectoral breakdown in inventories. Such an approach would allow easier comparisons at the subcategory scale. Building on the improvement of the points detailed in Sect. 6, our aim is to continually update this budget synthesis as a living review paper regularly (∼ every 2-3 years). Each update will produce a more recent decadal CH 4 budget, highlight changes in emissions and trends, and incorporate newly available data and model improvements. In addition to the decadal CH 4 budget presented in this paper, and following former studies (e.
Akihiko Ito  ESA GHG-CCI Robert Parker European Research Council (ERC ; grant no. 725546 ,  METLAKE) David Bastviken European Union 's  Horizon 2020 research and innovation programme  under grant agreement ( VERIFY  project) no. 776810  Pierre Regnier  and  Glen P. Peters F. R. S. -FNRS  for postdoctoral funding at the  ULB Goulven Laruelle German Federal Ministry of Education and Research (BMBF)  through the  PalMod  project, grant no. 01LP1507B   Thomas Kleinen Gordon and Betty Moore Foundation  through grant  GBMF5439  " Advancing Understanding of the Global Methane Cycle " to  Stanford University Rob Jackson ; co-P. I. s  Philippe Bousquet ,  Marielle Saunois ,  Yuanhong Zhao ,  Josep Canadell ,  Gustaf Hugelius , and  Ben Poulter Gordon and Betty Moore Foundation  through grant  GBMF 2995  "the  Global Landscapes Initiative" at the University of Minnesota Kimberly M.
The PCR product was purified on QIAGEN MinElute columns and primers dimers were removed from the 3C library by using AMPure XP beads following the manufacturer's protocol (Beckman Coulter). Finally, libraries were subjected to paired-end sequencing on an Illumina sequencer (NextSeq500). Processing of sequencing data PCR duplicates from each 3C library sequence dataset were discarded using the 6 Ns of custom-made adapters  (Marbouty et al. , 2015). Reads were aligned independently using Bowtie 2 in very sensitive mode  (Langmead and Salzberg, 2012). Only reads with mapping quality > 30 were kept to establish contact maps.
Marker Frequency Analysis MFA was performed on gDNAs extracted from cells grown in the same conditions as for the chromosome capture experiments, using the Sigma GenEluteâ bacterial genomic DNA kit. Libraries for sequencing were prepared following Illumina TruSeq protocol (Westburg). Libraries were sequenced on an Illumina NextSeq500 instrument, following manufacturer's protocol. Approximatively 15 millions of reads were recovered for each sample. We used the Bowtie2 software to perform the mapping in the local mode, and the mpileup software from Samtools to calculate the coverage for each genome position. Then, as described in  Galli et al. (2019) , enrichment of uniquely mapping sequence reads was calculated over 1kb and 200kb sliding windows. Local 1kb-window values deviating by more than 15% from the local 200kb-window values were discarded from the analysis.
ACKNOWLEDGMENTS We thank  S. Duigou ,  J. -L. Ferat , and  Y. Yamaichi  for careful reading of the manuscript and members of the F. B. laboratory for fruitful discussions. We also thank  V. V. Rybenkov  for sharing unpublished results,  C. Pourcel  for providing the PAO1_orsay strain, and  S. Duigou  for the gift of the pBAD::muk-FEB plasmid. We thank the I2BC genomic facility for high-throughput sequencing. We thank  F. -X. Barre  for sharing MATLAB functions. This research was supported by  CNRS  and  UniversiteParis-Sud.
Second, we accounted for specific traits that are known to mediate the sensitivity of marine mammals to climate change, such as traits related to reliance on pack ice  25. Several traits specific to habitat requirements were obtained through analysis of the IUCN geographical range data (http://iucnredlist. org) and the Bio-ORACLE raster environmental spatial data (http://www. oracle. ugent. be; see Supplementary Method 1 for more details). To provide a quantitative ranking of species sensitivity to global warming, we extended the methodology proposed by  25  , in which each considered trait was associated with specific quantitative ranking criteria that were evaluated on a three-point scale (with 0 being the least sensitive and 2 being the most sensitive). The ranks of the quantitative traits were evaluated by dividing the ranges of the values into three equal parts.
To assess the uncertainties in the models and scenarios, we used 11 different Earth system models that participated in the Coupled Model Intercomparison Project phase 5 (CMIP5  53  ) and that were run for the 1861-2100 period under the historical, RCP8. 5 and RCP2. 6 scenarios  54. We regridded the model output data onto a regular 1° × 1° grid (∼10,000 km²) by using the Krige function of the R package gstat. We selected the following CMIP5 ESMs: MRI-CGCM3, IPSL-CM5A-LR, GFDL-ESM2G, GFDL-ESM2M, IPSL-CM5A-MR, MIROC-ESM, MPI-ESM-LR, GFDL-CM3, CSIRO, and CanESM2. We selected high and low greenhouse gas emission scenarios among the set of RCPs that were run with the CMIP5 models to explore the impacts of different socioeconomic climate policies on the climatic projections  55. RCP8. 5 is a high-carbon emission, business-as-usual scenario with a net radiative forcing of 8.
5 W/m 2 by 2100 and a simulated global atmospheric surface temperature change of approximately 3. 7 ± 0. 7 °C relative to the temperature from 1986-2005. RCP2. 6 is a strong emission mitigation scenario with a global mean net radiative forcing of 2. 6 W/m 2 by 2100 and a simulated global atmospheric surface temperature change of approximately 1. 0 ± 0. 4 °C56 relative to 1986-2005. The Earth system model output was regridded onto a regular 1° × 1° grid (∼10,000 km²) using the Krige function of the R package gstat, and we extracted the average value of the SST for both the baseline records and the projected SST from each model and for the two emissions scenarios. We then calculated the changes in the SST at the global scale within the geographical range of each species from the baseline to the two future periods for both the RCP2.
Acknowledgements This work was funded by the  French National Research Agency (ANR   MORSE  Project  11-CEPL-006 ). We thank  Bastien Mérigot  and  Christine Meynard  for the helpful comments during preliminary analyses. TLF acknowledges funding from the  Swiss National Science Foundation  under Grant  PP00P2_170687  and the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  820989.
Context. The small bodies of the Solar System, and especially their internal structures, are still not well-known. Studies of the interior of comets and asteroids could provide important information about their formation and also about the early Solar System. Aims. In this paper, we investigate the possibility of obtaining information about their inner structure from their response to an incident electromagnetic field in preparation for future space radar missions. Our focus is on experimental measurements concerning two analog models with the shape of 25143 Itokawa, a small rubble pile asteroid monitored by the Japanese space agency's (JAXA) Hayabusa mission in 2005. Methods. The analog models prepared for this study are based on the a priori knowledge of asteroid interiors of the time. The experimental data were obtained by performing microwave-range laboratory measurements.
2007 (Kofman et al. , 2015)). Among the most significant future missions planned in the area of low-frequency radar investigation is the HERA mission  (Michel et al. 2018) , the European component of Asteroid Impact and Deflection Assesment (AIDA), which is a shared effort between ESA and NASA  (Hérique et al. 2019). A radio frequency radar measurement is one of several potential approaches to obtaining direct measurement data The measurement data obtained for the asteroid analog models are freely available on this web page https://www. fresnel. fr/ 3Ddirect/index. php following a basic registration. (Herique et al. 2018 ) from the subsurface structures of asteroid. Due to the absence of liquid water, a low-frequency and low-power radio wave can penetrate a significant distance from several hundred meters to a few kilometers inside an asteroid regolith  (Kofman 2012).
Namely, it distinguishes the overlapping direct and indirect wavefronts scattering from different parts of the target and, thereby, allows for the detection of otherwise weakly detectable internal details. For this capability, we are also able to predict multiple scattered and multi-path wavefronts (i. e. , data corresponding to higher order scattering zone) which can potentially be utilized in the observation of the interior structures. As the present experiment setup and permittivity modeling approach are applicable with various measurement configurations, asteroid shapes, and interior structures, they can potentially be applied in the examination of subsequent asteroid models in the future.
Acknowledgement. The authors acknowledge the opportunity provided by the  Centre Commun de Ressources en Microonde  to use its fully equipped anechoic chamber and the  Mesocentre of Aix-Marseille University  for its support for these numerical simulations. L. -I. S. , M. T. and S. P. were supported by the  Centre of Excellence in Inverse Modelling and Imaging (Academy of Finland   2018-2025 ). L. -I. S. was also supported by an  Emil Aaltonen Foundation  research grant for young researchers. L. -I. S. and S. P. acknowledge  CSC -IT Center for Science Ltd. , for providing computing services on the Puhti supercomputer. Premix Oy  is acknowledged for providing support for the permittivity-controlled 3D printable filament materials.
ACKNOWLEDGEMENTS The authors are especially grateful for the valuable input and guidance provided by  Judit Berényi Üveges (Hungary) ,  Valérie Dermaux (France) ,  Dirk Nemitz (UNFCCC) ,  Zitouni Ould Dada (FAO) , as well as several members of YOUNGO (official youth constituency at the UNFCCC). The authors would also like to express their thanks for the continuous engagement of all agriculture negotiators. The authors would like to acknowledge the  German Government , whose funding to the  FAO Mitigation of Climate Change in Agriculture (MICCA)  programme.
Acknowledgements The authors would like to thank all the meningitis experts* for participating in the development of the interview guide and their useful comments during the development of this study. They would also like to thank  Véronique Loncle-Provot  and  Céline Pribil  for their support and valuable contribution to this manuscript. Finally, they would like to thank  Edusanté  for interviewing the participants. (*Experts: Patricia Merhant-Sorel, president, Association Petit Ange -Ensemble contre la meningitis;  Christophe Philippe , paediatrician;  Catherine Weil-Olivier , paediatrician;  Myriam Winance , sociologist). Contributors ES and LF conducted the review of the literature. ES was the main coder for the thematic analysis. LF was the second coder for the thematic analysis. ES and LF analysed the data and edited the manuscript.
To test whether this was also the case in SARS-CoV-2-infected hamsters, we used Illumina deep sequencing to determine the virus variants in lung samples after favipiravir treatment. The data showed a 3. 2-fold increase in mean variants for favipiravirtreated hamsters compared with untreated hamsters (SI Appendix, Fig. S1B ). Oral treatment with 300 mg/kg favipiravir caused over 5% weight loss at days 3 and 4 pi, which is slightly more than that of the untreated animals (Fig. 2D ). This could be due to the effect of administering a relatively high volume of compound per os (which was at the limit of 10 mL•kg -1 •d -1 ) or due to some toxicity of the molecule. On the other hand, i. p. administrations of favipiravir were better tolerated, with the high dose of 1,000 mg/kg resulting in only 2% weight loss, on average, at day 4 pi.
Potent reduction of SARS-CoV-2 replication in hamsters has been demonstrated by a single dose with a singledomain antibody from a llama immunized with prefusionstabilized coronavirus spikes  (15, 30) , thereby validating the use of hamsters to evaluate treatment options against SARS-CoV-2. In addition, our data also indicate that hamsters are highly amenable for studying the potential antiviral effect of small molecules on virus transmissibility in a preexposure and postexposure setting. In an effort to contribute to the debate on the efficacy of HCQ and favipiravir in COVID-19 patients, we evaluated both repurposed drugs in our hamster infection and transmission model. Treatment with HCQ or combined treatment with azithromycin was not efficacious in significantly lowering viral RNA levels and infectious virus titers in the lungs of SARS-CoV-2-infected hamsters.
Materials and Methods SARS-CoV-2. The SARS-CoV-2 strain used in this study, BetaCov/Belgium/GHB-03021/2020 (EPI ISL 407976|2020-02-03), was kindly provided by Piet Maes, Katholieke Universiteit Leuven (KU Leuven), Leuven, Belgium. This strain was recovered from a nasopharyngeal swab taken from an RT-qPCR-confirmed asymptomatic patient who returned from Wuhan, China, in the beginning of February 2020  (44). A close relation with the prototypic Wuhan-Hu-1 2019-nCoV (GenBank accession number MN908947. 3) strain was confirmed by phylogenetic analysis. Infectious virus was isolated by serial passaging on HuH7 and Vero E6 cells  (15) ; passage 6 virus was used for the studies described here. The titer of the virus stock was determined by end-point dilution on Vero E6 cells by the Reed and Muench method.
Deep Sequencing and Analysis of SARS-CoV-2 in Lung Samples. Genome sequences from all samples were obtained using SureSelectXT target enrichment and Illumina sequencing. Reads generated were trimmed with Trim Galore  (45). Duplicated reads were removed using Picard (46). A consensus genome for the inoculated virus was obtained by mapping the reads from the inoculation sample to the SARS-CoV-2 reference genome (NC_045512) from GenBank using the Burrows-Wheeler Alignment tool with maximal exact matches. The mapping quality was checked using Qualimap, and the consensus whole genome sequence for the inoculated virus was generated using quantification and annotation of short reads in R  (47, 48). Reads from the hamster lung samples were mapped against the consensus inoculate sequence to identify variants.
The scored parameters, to which a cumulative score of 1 to 3 was attributed, were the following: congestion, intraalveolar hemorrhage, apoptotic bodies in bronchial epithelium, necrotizing bronchiolitis, perivascular edema, bronchopneumonia, perivascular inflammation, peribronchial inflammation, and vasculitis. Micro-CT and Image Analysis. Micro-CT data of hamster lungs were acquired in vivo using dedicated small animal micro-CT scanners, either using the X-cube (Molecubes) or the Skyscan 1278 (Bruker Belgium). In brief, hamsters were anesthetized using isoflurane (2 to 3% in oxygen) and installed in prone position into the X-cube scanner using a dedicated imaging bed. A scout view was acquired, and the lung was selected for a nongated, helical CT acquisition using the High-Resolution CT protocol, with the following parameters: 50 kVp, 960 exposures, 32 ms/projection, 350-μA tube current, rotation time 120 s.