dataesr/scientific-paragraphs-categorization huggingface dataset:
75 mg) was incubated with 100 mg of purified His 6 -CwlA in 50 mM Tris-HCl, pH 8. 0, overnight at 37°C. A control sample of PG was incubated without enzyme under the same conditions. After incubation, soluble and insoluble fractions were separated by centrifugation at 20,000 Â g for 15 min. The insoluble fraction was further digested with mutanolysin from Streptomyces globisporus (Sigma) at 2,500 U•ml 21 for 19 h at 37°C in 25 mM sodium phosphate under shaking. The soluble muropeptides were reduced with sodium borohydride and analyzed by RP-HPLC and mass spectrometry as described previously  (56). Phase-contrast, SNAP labeling, and fluorescence microscopy. For phase-contrast microscopy, C. difficile strains were cultured for 5 h in TY (with antibiotics and inducers when needed) at 37°C. Cells were visualized using a Zeiss Axioskop microscope and analyzed using the software ImageJ and the plugin MicrobeJ (80) for quantitative analysis.
ACKNOWLEDGMENTS This work was funded by the  Institut Pasteur , the  Université de Paris , and grants from the  ITN Marie Curie, Clospore  ( H2020-MSCA-ITN-2014 642068 ), and the  ANR DifKin  ( ANR-17-CE15-0018-01 ). E. C. and T. G. G. are the recipients of an  ITN Marie Curie  and an  ANR  fellowship, respectively. The anti-Cwp66 antibody was a gift from  N. Fairweather. This work has benefited from the facilities and expertise of  Christine Longin  and  MIMA2   MET ,  INRA-UMR GABI.
In this work, we present the first dynamic calculations performed with the Monte Carlo neutron transport code TRIPOLI-4 R with thermal-hydraulics feedback. For this purpose, the Monte Carlo code was extended for multi-physics capabilities and coupled to the thermal-hydraulics subchannel code SUBCHANFLOW. As a test case for the verification of transient simulation capabilities, a 3x3-assembly mini-core benchmark based on the TMI-1 reactor is considered with a pin-by-pin description. Two reactivity excursion scenarios initiated by control-rod movement are simulated starting from a critical state and compared to analogous simulations performed using the Serpent 2 Monte-Carlo code. The time evolution of the neutron power, fuel temperature, coolant temperature and coolant density are analysed to assess the multi-physics capabilities of TRIPOLI-4.
INTRODUCTION Multi-physics simulations of nuclear reactor cores involve the coupling of neutron transport to other disciplines, such as thermal-hydraulics or thermo-mechanics. So far, such problems have mostly been analyzed using deterministic neutron-transport codes  [1] [2] [3]. In recent years, intensive research efforts have focused on multi-physics calculations with Monte Carlo neutron transport codes in view of constructing high-fidelity simulation tools for the simulation of reactivity-induced transients in PWRs  [4] [5] [6]. The goal is to provide reference solutions for neutron transport with multi-physics feedbacks. In this context, that we have implemented an external coupling scheme between the Monte Carlo code TRIPOLI-4 R  [7]  and the thermal-hydraulics subchannel code SUBCHANFLOW  [8].
Note that the TRIPOLI-4 and SUBCHANFLOW meshes must have the same bounding box, but they do not need to superimpose exactly or to be numbered in the same way, as long as spatial interpolation is performed between them. The power distribution scored by TRIPOLI-4 is transferred to SUB-CHANFLOW using the interpolation tools provided by the MEDCoupling library. The subchannel code computes the updated properties of the fuel (temperatures) and the moderator (temperatures and densities), which are transferred back to TRIPOLI-4. Again, the MEDCoupling interpolation tools are used to transfer the SUBCHANFLOW results to the corresponding cells of the TRIPOLI-4 mesh. The TRIPOLI-4 material temperatures and densities are then updated; note that this requires the TRIPOLI-4 geometry to be discretized in such a way that each mesh cell corresponds to an individual volume.
CONCLUSIONS We have performed the first dynamic simulations with the coupling scheme between the Monte Carlo neutron transport code TRIPOLI-4 and the thermal-hydraulics subchannel code SUBCHAN-FLOW. For this purpose, we have considered a 3x3 mini-core based on the TMI-1 reactor. The initial reactor state was calculated once with a criticality calculation including feedback. We reused it for the two dynamic calculations. We have studied two reactivity-insertion scenarios, with the control rods being extracted by respectively 30 cm and 40 cm. For both scenarios, the feedback mechanisms make the power decrease and ultimately reach a new equilibrium state. We compared our results to the published Serpent 2/SUBCHANFLOW results and generally found good agreement. The simulation of the peak power is very challenging because of the large variations on the population size.
ACKNOWLEDGEMENTS This work was done within the  McSAFE  project which is receiving funding from the  Euratom  research and training programme  2014-2018  under grant agreement N o 755097. TRIPOLI-4 R is a registered trademark of CEA. We wish to thank  Electricité de France (EDF)  for partial financial support of the code TRIPOLI-4 R. We express our gratitude to  D. Ferraro  ( KIT, Germany ) for providing the numerical data of the Serpent 2 calculations.
12 to 4. 0°. The raw data  40  was first reduced by subtracting background, normalizing the reflected intensity to that of the direct beam I 0 , and correcting for overillumination of the sample, all using pySAred software. 41 The data was thereafter analyzed using the software GenX,  42  which uses Parratt recursion to simulate specular reflectivity by modeling indepth organization of a sample. In this way, the neutron specular reflectivity profile provides information about the number and order of sublayers in the sample film, as well as parameters of each sublayerthickness, roughness, and chemical composition. The latter can be derived from a parameter called scattering length density (SLD), which can be expressed as ∑ ρ = • N b M SLD A i i c,i i (2) where N A is the Avogadro constant, ρ is the density, b c is the neutron bound coherent scattering length, which is a unique parameter for every isotope,  43, 44  and M is the molar mass of the substance, summarized for all constituent elements i.
An optical slab model was fitted to the data using the GenX software package  42  for obtaining the thickness and SLD profile of layers. Pattern Transfer into Si. Samples for etching were cut into 7 × 8 mm 2 pieces and then mounted on a sapphire carrier using a doublesided thermal tape (REVALPHA, Nitto Denko Co. , Japan). The thermal tape was used to improve the thermal contact to the carrier. To remove the polymer from the AlO x infiltrated BCP pattern, oxygen plasma ashing was performed for 75 s at a pressure of 3 mTorr using an O 2 flow of 30 sccm, 25 W of RF power, and an ICP power of 10 W in an Apex SLR ICP-RIE (Plasma-Therm). To dismount the sample after ashing, the sapphire carrier was placed on a hot plate, heated to 150 °C. The sample was then mounted on a Si carrier using the thermal tape.
Funding This study was supported by " GreeNanoFilms " project, the  European Union   Seventh Framework Program  ( FP7/2007-2013)  under grant agreement  603519 , and by the  Swedish Foundation for Strategic Research , project  SSF   RIF14-0090. M. M. , R. B. , and W. -C. C. acknowledge support from  CNRS ,  Univ. Grenoble Alpes, NTU , and the  PolyNat Carnot Institute. A. L. ,  R. J. J. , and I. M. acknowledge support from  NanoLund at Lund University and MyFab. The Super-ADAM project at  Institut Laue-Langevin (ILL, France)  was supported by the  Swedish Research council.
Data Collection From the Literature We collected data on GHG emissions from the literature  (Barros et al. , 2011; Hertwich, 2013; Deemer et al. , 2016) , and the latest measurements of the GHG emissions from reservoirs were also added based on published papers (e. g. ,  Almeida et al. , 2016; Bevelhimer et al. , 2016; Jiang et al. , 2017; Li et al. , 2018) , in particular those from North and South America and Asia. The reservoirs in our dataset are mostly distributed in North and South America, Europe, and Asia, but they are limited in Africa (Figure  1 ); the records of GHG emissions are from 1988 to 2018. Data on GHG emissions and the reservoirrelated parameters (e. g. , water quality, reservoir morphology, location, precipitation in the catchment, and usage) were gathered from these studies.
, 2013). The classifications of hydrobelts and climatic groups were determined using a geographic information system (GIS; ArcGis 10. 5), and the Kruskal-Wallis nonparametric test was used to identify the differences in GHG fluxes between the different groups mentioned above using R language, version 3. 5. 1. According to the amounts of GHG emissions reported in the gathered dataset, we finally compared the differences in CH 4 and CO 2 fluxes in five hydrobelt groups (BOR, NML, NDR_NST, EQT, and SST_SDR_SML), while three hydrobelt groups were used for N 2 O fluxes (BOR, NML, and Others).
Estimating Present and Long-Term Evolution of Greenhouse Gas Emissions at the Global Scale In order to generalize the GHG emissions documented from the literature, we used the Global Reservoir and Dam Database (GRanD, v. 1. 3, http://globaldamwatch. org/grand/), which selected 7,320 reservoirs and their respective surface area (3. 6 × 10 5 km 2 ) (with a storage capacity of >0. 1 km 3 ). The regulated natural lakes Victoria, Baikal, and Ontario, totaling 0. 12 × 10 6 km 2 , in the original GRanD database census were excluded from our estimate of GHG emissions from reservoirs. The average areal GHG fluxes were derived from the reclassified hydrobelt groups based on the results of the Kruskal-Wallis nonparametric test (see above) and were then multiplied by the corresponding reservoir surface area in each hydrobelt group.
Finally, the total GHG emissions from global  The ebullition of CH 4 is considered an important component of CH 4 emissions from lakes and reservoirs; however, this pathway is not usually measured. There were 50 records of CH 4 diffusion and associated ebullition in the dataset of  Deemer et al. (2016). In order to derive more records of total CH 4 emission, we also used the dataset of  DelSontro et al. (2018) , i. e. , 96 pairs of data from lakes and reservoirs, and established a relationship between the total CH 4 emission and diffusive CH 4 emission for global lakes and reservoirs (Figure  2 , p < 0. 01, n 146). Thereby, we were able to complete the missing CH 4 ebullition emissions in our dataset (Supplementary Table  S1 ) using this relationship, leading to total CH 4 emissions for  33, 68, 31, and 38 records (vs 9, 28, 11, and 27 records in Deemer et al.
, 2016) ) in boreal, temperate, subtropical, and tropical zones, respectively. Therefore, our collected dataset contained more total CH 4 emissions than previous studies (∼2. 3 times more than in  Deemer et al. (2016) ). To evaluate the long-term evolution of GHG emissions from global reservoirs since 1950, we computed the cumulative number and surface area of reservoirs in the eight aforementioned hydrobelts. The average GHG fluxes were multiplied by the total reservoir surface area in each hydrobelt group, resulting in total GHG emissions per hydrobelt. In addition, previous studies have described the impact of eutrophication on GHG emissions from inland waters (e. g. ,  DelSontro et al. , 2018; Beaulieu et al. , 2019). Herein, we also considered the potential impact of reservoir eutrophication on GHG emissions based on the regression equations from DelSontro et al.
Global Estimates of Greenhouse Gas Emissions From Reservoirs The results of the nonparametric Kruskal-Wallis test showed significant differences in CH 4 and CO 2 areal fluxes between the BOR, EQT, and SST_SDR_SML hydrobelt groups (p < 0. 01), while no significant difference was found in the NML and NDR_NST hydrobelts. Therefore, four hydrobelt groups (BOR, EQT, SST_SDR_SML, and NML_NDR_NST) were reclassified for the global estimation (Table  1 ). The results of a Kruskal-Wallis test for N 2 O emissions did not show any significant difference between hydrobelts owing to the limited measurements in the present dataset, and N 2 O emissions were thus considered as a whole (Table  1 ). Globally, annual GHG emissions from reservoirs were approximately 12. 9 Tg CH 4 -C, 50. 8 Tg CO 2 -C, and 0. 04 Tg N 2 O-N (Table  1 ).
Statistical Analysis of Factors Influencing Greenhouse Gas Fluxes From Reservoirs Based on the data collected here, the relationships between GHG fluxes and the characteristics of the reservoir watersheds revealed that there was a significant linear relationship between CH 4 and CO 2 fluxes and the surrounding terrestrial net primary production (NPP) and precipitation (Figure  4 , p < 0. 01), although the rather low variation coefficient indicated that other factors can influence GHG emissions. N 2 O fluxes had no significant relationship with NPP. We also analyzed the impact of the reservoir age and residence time on GHG emissions. Surprisingly, we found no significant relationship between GHG emissions and the age and residence time of reservoirs (p > 0. 05), in contrast to results from previous studies of hydroelectric reservoirs.
Estimating Long-Term CO 2 Eq Emissions From Global Reservoirs A prerequisite for the estimation of long-term GHG emissions was to calculate the cumulative reservoir number and surface area (Figure  5 ). Thus, GHG emissions (expressed as CO 2 Eq) were calculated for the period 1950-2017 for different hydrobelts. Here, we used the averaged GHG fluxes in each hydrobelt group in agreement with the above analysis (see Global Estimates of Greenhouse Gas Emissions From Reservoirs Section). In the GRanD database, there are 357 reservoirs with unknown construction date, with a total surface area of 7,849 km 2 , representing 2. 2% of the actual total surface area of global reservoirs taken into account in our evaluation. Hence, these reservoirs were not included in the cumulative number, surface area, and evolution of CO 2 Eq emissions.
Uncertainties and Challenges in Estimating Total Greenhouse Gas Emissions From Reservoirs Indeed, there is still uncertainty regarding the estimation of GHG emissions at the global scale due to the numerous interacting factors. First, the total surface area of global reservoirs varies from 0. 26 × 10 6 to 1. 5 × 10 6 km 2 (St. Louis et al. , 2000; Downing et al. , 2006). Based on the Global Reservoir and Dam Database (GRanD, v. 1. 1),  Lehner et al. (2011)  estimated that reservoirs with more than 0. 1 km 3 storage capacity cover 0. 3 × 10 6 km 2 (excluding regulated natural lakes, see Estimating Present and Long-Term Evolution of Greenhouse Gas Emissions at the Global Scale Section), which was considered a reliable area of global reservoirs  (Li and Lu, 2012) , and was also used by  Bastviken et al.
(2011)  and  Deemer et al. (2016). In the present study, 0. 36 × 10 6 km 2 of reservoir surface area was used based on the updated database (GranD, v. 1. 3), which adds new dams and reservoirs that were built between 2000 and 2016. In other words, reservoirs currently under construction (and planned) also affect GHG emissions  (Zarfl et al. , 2015). Not surprisingly, the different total areas used by various studies are therefore one of the important reasons for the variations in the GHG estimation results, i. e. , St. Louis et al. (2000)  estimated the highest GHG emissions from global reservoirs with a surface area of 1. 5 × 10 6 km 2. Also, the more GHG measurements in reservoirs are collected, the more robust global estimations will be obtained. While we 1) expanded the dataset provided by  Deemer et al.
(2016)   2016 ) datasets), and 3) used a classification that is conceptually fully adapted to reservoirs (hydrobelts;  Meybeck et al. , 2013) , we obtained comparable GHG estimations (Table  1 ). CH 4 pathways also act as a major source of variation when estimating total GHG emissions from reservoirs, especially for ebullitive CH 4 flux, which has been described as being rather stochastic with high spatial and temporal heterogeneity  (Delsontro et al. , 2011 (Delsontro et al. , , 2015;; Deemer et al. , 2016). Therefore, ebullitive CH 4 flux was reported to be highly unpredictable and would greatly limit accurate estimations of CH 4 emissions from reservoirs  (Prairie et al. , 2018) , while CH 4 ebullition was found to vary more temporally than spatially  (Linkhorst et al. , 2020). We have also emphasized the scarcity of data on N 2 O emissions (1/3 and ¼ of CH 4 and CO 2 records, respectively).
Influencing Factors of Greenhouse Gas Emissions From Reservoirs Existing studies suggest that the influencing factors of GHG emissions from reservoirs are multifaceted. CH 4 and CO 2 fluxes from hydroelectric reservoirs declined with age  (Barros et al. , 2011). We also attempted to analyze the correlations between GHG flux and reservoir age on the basis of our dataset. Unfortunately, no significant correlation was found between GHG flux and reservoir age (p > 0. 05), partly because the reservoirs in our dataset are used not only for hydroelectric energy but also for flooding control, water supply, as well as irrigation (Supplementary Table  S1 ). It has been reported that reservoir age affects GHG fluxes, probably due to flooded vegetation and/or organic matter accumulation after impoundment, which provides a large amount of substrate for GHG production, especially in hydroelectric reservoirs  (Abril et al.
Long-Term Greenhouse Gas Emissions From Reservoirs Based on the GRanD database in the long term and on our own approach for estimating the GHG emissions by hydrobelts, we have identified two periods of GHG emissions: from 1950 to the 1980s (period I), and from the 1980s to the present (period II). Period I corresponds to post-World War II. In these 3 decades the world population doubled (2. 5 vs Five billion people in 1950 and 1980, respectively, https://ourworldindata. org/worldpopulation-growth), requiring modernization (e. g. , industries, agriculture) to meet society's basic needs but also the demand for more wellbeing (domestic goods, household equipment). Although this period of continuous growth was characterized by cheap fossil energy  (Dyer and Desjardins, 2009) , most of the reservoirs were constructed for water irrigation, drinking water production, health and personal hygiene, and for producing energy to power this modernization.
CONCLUSION In this study, a novel classification method based on the hydrobelt concept was used to estimate GHG emissions from global reservoirs. Based on the dataset gathered from reservoirs at the global scale from the literature and from new data that we have added, we estimated that annual GHG emissions were approximately 12. 9 Tg CH 4 -C, 50. 8 Tg CO 2 -C, and 0. 04 Tg N 2 O-N. In terms of total carbon emissions (CH 4 -C and N 2 O-N, calculated as CO 2 eq), annual carbon emissions from global reservoirs amount to 2. 3% of total carbon emissions from inland freshwaters. Areal fluxes of CH 4 and CO 2 showed significant differences within hydrobelts, a spatial pattern that should be taken into account when estimating the amount of GHG emissions from global reservoirs. In addition, the characteristics of reservoirs (e.
g. , geographical location) and their catchments (e. g. , surrounding terrestrial NPP, and precipitation) may influence GHG emissions. Our results showed that updating the number of GHG measurements and the surface area of all impounded reservoirs, including small reservoirs not documented in the database and all those being planned, would help anticipate future GHG emissions from reservoirs. Tracing back the GHG emissions of reservoirs, albeit by simply linking the long-term change of the reservoir surface areas with the present areal GHG emissions, provides a trend of these global "new" emissions from reservoirs and allows us to anticipate their future impact with further impoundments.
Statistical Analyses An independent, unblinded third party conducted analyses using the SAS System (SAS Institute Inc, Cary, NC) version 9. 2 or higher. The intent-to-treat (ITT) population (all randomized patients) was used to analyze efficacy, patient characteristics, and disposition. The To qualify for study entry as stage 2 hepatic encephalopathy, both asterixis and !1 error in the 5 sentinel questions must have been present at screening and baseline. For recording hepatic encephalopathy response after starting study drug infusion, patients were classified as improved to stages 0/1 only if asterixis resolved and all 5 questions were answered correctly. a Three or more flaps/30 s indicates asterixis. safety population included ITT patients who received any study treatment (see the Supplementary Methods section for more detail).
Supplementary Results Low diastolic blood pressure was observed in both groups throughout the study. TEAEs related to abnormal electrocardiogram findings were QT prolonged in 3 patients (3%) in the placebo treatment group and in 2 patients (2%) in the OP treatment group, and electrocardiogram findings were abnormal (elongation of QT) in 1 patient (1%) in the OP treatment group. A QTcF increase of more than 60 msec was observed in 11% and 7% of patients who received OP and placebo, respectively (P ¼. 360); an absolute QTcF of more than 500 msec was observed in 15% and 8% of patients who received OP and placebo, respectively (P ¼. 144). No correlation between PAA concentration and QTcF was observed (data not shown). The mean improvement (decrease from baseline) in the Model for End-stage Liver Disease score, indicating lessening severity of disease, was À4, À1, and À1 in the OP 10-, 15-, and 20-g/24 h groups, respectively, compared with 0 in the placebo group at 3 hours after the end of the infusion or early hospital discharge/early termination; the differences were not statistically significant.
In terms of model parameterization, previous studies at a regional scale proved that FMC retrieval from RTM improves when using prior knowledge to constrain the model inputs  (Casas et al. , 2014; Wang et al. , 2013; Yebra and Chuvieco, 2009a, b). Research to validate this model parameterization at the global scale to retrieve FMC is still lacking to date. The generation of FMC using RTM requires proper model inversion. Current popular inversion algorithms include numerical optimizations  (Bicheron and Leroy, 1999; Combal et al. , 2003b) , look-up tables (LUT)  (Quan et al. , 2017a; Yebra and Chuvieco, 2009a; Yebra et al. , 2008) , and machine learning methods  (Fang et al. , 2003; Quan et al. , 2021; Schlerf and Atzberger, 2006). Compared to other algorithms, the LUT is straightforward and computationally efficient to handle large global databases.
FMC field measurements A total of 3,034 FMC field measurements taken in grasslands (9 5 5), shrublands (5 6 6), and forests (1,513) from 2001 to 2017 over 120 sites in Australia, China, Italy, Senegal, Spain, South Africa, Tunisia, and the USA validated the FMC retrieval methodology (Fig. 1 ). Table  S1  in the Supplementary Material presents detailed information on these field sites while Table  S2  and S3  illustrate the percentage of measurements in different seasons and fuel types for each country, respectively. The data is a subsample of  Yebra et al. (2019) 's database and contains only sites with more than three consecutive FMC weekly measurements or only one time but at several nearby sites (e. g. , Chinese sites), capturing the spatial instead of the temporal FMC variability.
Landsat products The surface reflectance Landsat 5 TM, 7 ETM+, and 8 OLI data integrated in Google Earth Engine  (Gorelick et al. , 2017)  tested the homogeneity of each sampling site. Pixels marked as snow, cloud, and cloud shadow were not considered in the analysis. Each of these Landsat satellites has 16 days temporal resolution at a spatial resolution of at least 30 m. For the Jan 2001-Nov 2011 period, Landsat 5 TM was preferred over Landsat 7 ETM + unless Landsat 5 TM had poor quality due to cloud cover. Landsat 7 ETM + was used from Dec 2011 to Apr 2013, and afterward, the Landsat 8 OLI was used.