dataesr/scientific-paragraphs-categorization huggingface dataset:
Methods A methodological flowchart is shown in Fig. 2 , including three steps. Firstly, RTMs were run forward for a specific range of the model parameters. These simulations built three LUTs from the model inputs parameters and reflectance outputs. Secondly, the backward inversion process tested different strategies to infer their influences on FMC retrieval. The optimal inversion strategy to produce FMC involved the investigation of (i) the selection of different spectral information to include in the computation of (ii) the selected cost functions, and (iv) the performance of different central tendencies in response to (iii) the selection of solutions. Thirdly, the exploration of temporal and spatial filtering rules removed low-quality field measurements from the validation database. Finally, the global FMC product was produced based on the optimal inversion strategy.
The PROSAIL script used in this study integrates a spectral library of background soil surface with a parameter, psoil (unit-less), to characterize the effect of moisture and roughness condition in the soil  brightness (e. g. , psoil = 0 represents a wet soil and psoil = 1 represents a dry soil). Additionally, the model script integrated six LIDFs: Planophile, Erectophile, Plagiophile, Extremophile, Spherical and Uniform. This script also offers an alternative way to characterize the LIDF using the average leaf angle ( ), where 0 ˚and 90 ˚indicate Planophile and Erectophile, respectively. GeoSail model simulated forest reflectance, as it provides a realistic representation of heterogeneous and discontinuous vegetation with a low computational cost  (Huemmrich, 2001). This model requires eight inputs: LIDF (unit-less), leaf-level spectral reflectance (µ) and transmittance (τ), LAI (m 2 /m 2 ), the spectral reflectance of the background, solar zenith angle (tts, ), the shape of the crowns (either cylinder or cone), height to width ratio of the crown (CHW, unit-less) and crown coverage (ccov, unit-less).
, 2014; Quan et al. , 2015; Quan et al. , 2017a; Yebra et al. , 2008)  and measurements from LOPEX1993  (Hosgood et al. , 1995)  and AN-GERS2003  (Feret et al. , 2008)  leaf optical properties databases. To simulate the canopy spectra of the understory layer with PRO-SAIL, a prior study from  Quan et al. (2017a)  applied LAI values of 0, 1 and 2 (where LAI = 0 indicates the forest background is bare soil); and EWT values of 0. 005, 0. 01 and 0. 02. The other parameters were set as fixed values given that (i) they are normally insensitive to the RTM outputs (Fig. S2 ) and (ii) the RTM inversion becomes unstable and illposed with too many free parameters  (Combal et al. , 2003a; Verhoef and Bach, 2007). Besides, the maximum and minimum values observed in the field (Table  S1 ) also constrained the FMC ranges for grassland (1% to 450%), shrubland (1% to 250%), and forest (1% to 250%).
The global FMC field measurements (Table  S1 ) provided the information to build the ecological rules together with LAI values extracted from the MCD15A3H product (LAI vs. FMC), as well as EWT, DMC and C ab values from the LOPEX1993 and ANGERS2003 databases. These databases all together include a large set of vegetation species. Fig. 3  and 4 demonstrate strong LAI vs. FMC (r = 0. 71, p < 0. 01) and C ab vs. DMC (r = 0. 58, p < 0. 01) relationships for grassland and EWT vs. DMC (r = 0. 66, p < 0. 01) and C ab vs. DMC (r = 0. 52, p < 0. 01) for forest but weak LAI vs. FMC (r = 0. 35, p < 0. 01) for forest and C ab vs. EWT (r = 0. 23, p < 0. 01) for shrubland. The rest variable combinations rendered no significant relation (p > 0. 01) and, therefore, they were not considered. The RTMs modeling phase introduced these relationships by sampling and simulating more likely variable combinations with higher correlations than the lower ones.
For this, it calculated the joint posterior probability distribution of the variable combinations using Eq. (3): f (x, y) = (1 - g(x, y) MAX(|g(x, y) | ) ) r×k (3) where the f(x,y) is the joint posterior probability distribution of x and y; g(x,y) is the fitted function in Figs. 3  and 4 ; r is the correlation coefficient and k is a controlling factor that a higher k value can generate more constrained variable combinations (Fig. S3  and k = 10 in this study). This approach removed unrealistic simulation scenarios. For example, higher LAI is unlikely to correspond to a lower FMC for grasslands (Fig. 3 ). Finally, a LUT for each fuel class was generated by running the RTMs forward using MATLAB software R2017a version (The Mathworks; Natick, MA, USA; www. mathworks. com).
In this study, x ranged from 0. 4 (most strict) to 3 (no filter). Notably, Eq. (  9 ) requires at least three FMC measurements in a site. For each coordinate in China, the FMC was only sampled once and therefore Eq. (  9 ) was not applied. The coefficient of variation (CV, %) provided in the global field measured FMC database by  Yebra et al. (2019)  assessed the site homogeneity. CV was calculated as the ratio of the standard deviation and mean NDVI of all the Landsat pixels within a 500 × 500 m buffer that matched the MODIS pixel size (CV Landsat , Eq. (  10 )) using Google Earth Engine  (Gorelick et al. , 2017).
Global FMC mapping The daily global FMC maps from 2001 to 2019 were produced based on the best inversion strategy for each fuel class using the highperformance parallel computing platform of the University of Electronic Science and Technology of China. This dataset can be accessed via https://firewatching. cn/world_FMC. Given storage limitations, the FMC product was stored in 8 days interval instead of daily. Fig. 7  shows the global three-month median composite FMC maps at 500 m from 2017 to 2018 as an example while Fig. 8  illustrates the average FMC curves for different latitudes, fuel classes and periods in terms of the maps in Fig. 7. Overall, the grassland FMC curves rendered the highest dynamics during the four periods, followed by the shrubland and forest curves. Over 33 • N latitude and between 8 • N and 5 • S, the highest grassland FMC was observed in the Jun-July-Aug period and the lowest in Dec-Jan-Feb (Fig.
01). That is, the combinations of RTM inputs with high posterior probability are more likely sampled and vice versa. Thus, this method can more realistically model the relationships between each one of the model inputs. The relations between model-free parameters are different between this work and previous studies. For example, Fig. 4  showed that there was no significant correlation (p > 0. 01) between EWT and DMC for grass and shrub species based on the LOPEX1993 and ANGERS2003 datasets. However, our previous studies found that there was a positive correlation between EWT and DMC (R 2 = 0. 46, p < 0. 01) for the alpine wetlands located in western China in the Summer season  (Quan et al. , 2017b) , with a similar degree as reported by  Feret et al. (2011). Therefore, a universal and representative way of characterizing the ecological information is preferred for reliable global-scale FMC mapping.
This is the reason to use LOPEX1993 and ANGERS2003 datasets, as they comprise diverse leaf properties from a large number of vegetation species. In the backward inversion process, the LUT algorithm was used to derive the FMC from the MCD43A4 product. A key aspect of this study was the selection of the best performing inversion strategy for different fuel classes. For the selection of spectral information, a single VI presented superior accuracy than a single band, whereas when all the bands and VIs were used together, the accuracy level did not improve significantly. Furthermore, this study confirmed previous researches that NDII can be a good indicator for FMC retrieval of these three fuel classes (Fig. 5 ) due to the strong absorption features of water in the near-and shortwave-infrared spectral regions  (Caccamo et al.
, 2016). Besides, the forest deep root system reduces the seasonal FMC variation in comparison to grassland and shrubland (see Table  S1 ), making it more difficult to validate. Moreover, forest can also have an understory grass and shrub layer  (García-Haro et al. , 2020). Hence, the reflectance from grass and shrub mixed with overstory forest canopy results in more impure mixed pixels. The coupled RTM was set to model this, but the higher dimensional unknown inputs of this coupled RTM, compared to the grassland and shrubland (Table  1 ), may aggravate the forest FMC retrieval uncertainty. Additionally, FMC field measurements used for the validation were biased towards grassland (31. 28%) and forest (49. 87%) with substantially less shrubland samples (18. 85%) (Table  S2  & S3 ). The sampling sites were not evenly distributed across the globe to represent the main plant functional types (being tropical sites underrepresented) nor the different countries (overall more data collected in the USA), and data were collected throughout different seasons (Except for China and Senegal) but generally more during the hot dry season, which is more prone to wildfires.
7 ) and no data available in this period. Between the 22 • N and 8 • N, the sharp drop in grassland and shrubland FMC is mainly explained because of the low FMC values in areas of North Africa closed to the Sahara Desert (Fig. 7 ). The shrubland FMC did not change much between 20 • S and 40 • S latitude, given the low FMC values of vast dry central Australia (Fig. 7 ). Overall, the global FMC dynamics obtained were consistent with vegetation drought seasonal patterns (SPEI Global Drought Monitor. Available online: http://sac. csic. es/spei/index. html).
, 2008; Luo et al. , 2019; Nolan et al. , 2016; Yebra et al. , 2018a). The produce developed here provides near-real-time, high temporal resolution (daily) and moderate spatial resolution (500 m) highly accurate (R 2 = 0. 71, RMSE = 32. 36%, p < 0. 01) FMC estimates on a global scale. This dataset is encouraging for operational wildfire danger assessment. Since the FMC characterizes the status of water in vegetation, this product also has potential applications in monitoring drought and vegetation health and guiding forest cultivation. Another dominant feature of this global FMC product is its generation from the RTMs inversion techniques. Empirical models require calibration only valid for a local region. The generalization power of RTMs makes them applicable to the global scale. This is important as most prior work to estimate FMC from remote sensing data was carried out and concentrated in Europe, the USA and Australia  (Yebra et al.
, 2013). Other fire-prone regions such as the Amazon rain forest, Africa and East Asia lack such information. Since the launch in 1999 (Terra) and 2002 (Aqua), MODIS has successfully operated for>18 years. However, the expected lifetime of the Terra and Aqua satellites has already been exceeded, and at some point, in the not-too-distant future, they will become inoperative. Yebra et al. (2018b)  evaluated the feasibility and relative benefits of data sources including the geostationary Japanese Himawari-8 satellite (10 min, 2 km), the European Sentinel-2 (5 days, 20 m), the Landsat (16 days, 30 m) and Visible Infrared Imaging Radiometer Suite (VIIRS) (daily, 750 m) satellites for FMC retrieval in Australia. They reported a similar accuracy of these satellite data for FMC retrieval when applied the RTM-based methodology.
Conclusions This study presented the first global FMC product based on RTMs and MODIS MCD43A4 reflectance data. The IGBP landcover type classified the vegetation into grassland, shrubland and forest fuel classes. For each fuel class, available prior information on model parameters carefully parameterized the RTMs, analyzing different inversion strategies. Selecting the VIs most sensitive to FMC variations obtained the optimal inversion strategy for each fuel class with EVI-NDVI-NDII-MSI-G ratio , NDII-EVI-VARI-G ratio , and NDII-EVI-GVMI-G ratio for grassland, shrubland and forest, respectively. The cost functions were RMSE f for grassland and shrubland; and LAE for forest. The median value of the FMC values from 1% of the ranked f(v, w) solutions rendered the final retrieved FMC. A large number of FMC measurements (3,034) from a global database assessed the accuracy of the FMC estimates.
Acknowledgements This work was supported by the  National Natural Science Foundation of China  (Contract No. 41801272  and  U20A2090 ) and  Sichuan Science and Technology Program  (Contract No. 2020YFS0058 ). The authors are grateful to the  Land Processes Distributed Active Archive Center (LPDAAC) at the U. S. Geological Survey (USGS) Earth Resources Observation and Science Center (EROS)  (http://lpdaac. usgs. gov)for providing the MODIS dataset.
Study Procedure The device was inserted during CPR in the catheterization laboratory via the femoral artery under vascular ultrasound guidance when possible and as specified by the manufacturer. Coronary angiography was conducted in all patients after Impella insertion and start. If required, revascularization was performed, depending on the clinical context. Chest compressions were continued until sufficient Impella flow could be achieved or until the patients was considered deceased. The neurological status was assessed using the cerebral performance category score (CPC score) collected from the medical health records. The weaning of the hemodynamic support was conducted according to each center's protocols. Clinical data and survival at one month were collected retrospectively from electronic medical records in a dedicated database.
Statistical Analysis Statistical analyses were performed using IBM SPSS Statistics 20. 0 (IBM Inc. , New York, NY, USA). Continuous data are reported as the mean ± standard deviation. Categorical data are reported as absolute counts (percentages). Continuous data were compared using a Mann-Whitney test. Categorical data were compared using an χ 2 test or a Fisher's exact test. The Kaplan-Meier method was used to describe the probability of survival over time in the whole population. All tests were two-sided. Differences were considered to be statistically significant when the p value was less than 0. 05. Figures were drawn using the GraphPad Prism 8 (GraphPad Software Inc. , San Diego, CA, USA) software system.
Conclusions In this international registry, in patients undergoing Impella CP insertion during on-going CPR for CA, the 30-day survival rate was 45. 7% overall and 37. 1% without neurological impairment. Impella CP insertion may be lifesaving in patients with CA younger than 75 years old with a time from arrest to CPR ≤ 5 min. Prospective studies are required to confirm these encouraging but preliminary data and should help to accurately define patient selection criteria.
Crystal structure of AdeS DHp-CA domain and EM assembly of AdeS cytoplasmic domain To further elucidate the molecular mechanism and assembly of AdeS, we carried out a combined X-ray crystallography and electron microscopy study of recombinant AdeS. The crystal structure of apo AdeS DHp-CA was solved by molecular replacement using a trimmed version of the catalytic domain of histidine kinase CpxA (PDB ID; 5FLK)  (Mechaly et al. , 2017) , which has an overall sequence identity of 25% with AdeS (Figure  S3 ), and an I-TASSER model of the DHp domain of AdeS as search templates. The AdeS DHp-CA domain consists of a symmetric homodimer, in which the a1-a2 DHp domain forms a central four helix bundle with a large parallel dimerization interface, which is flanked by CA domains on each side (Figure  4A ).
In total 619 and 319 micrographs were collected for the apo and ATP-bound states, respectively. After contrast transfer function estimation, 566 and 299 micrographs were screened out for the particle-picking procedure. About 37,000 particles for each individual dataset were used to do the 2D classification. Comparing the two class averages, more compact density distribution within each blade was observed in the ATP-bound state (Figure  4C ). The gold-standard Fourier shell correlation curves at 0. 143 criterion indicate a final resolution of 25 A ˚(apo state) and 22 A ˚(ATP-bound state), respectively (Figure  S4 ). The projections obtained from certain Euler angles of the final model matched well with the experimental 2D class averages, with the three-blade propeller-like orientation corresponding to the top view of the 3D model (Figure  4C ).
We then manually docked our crystal structure of DHp-CA dimer into the two electron microscopic (EM) models by using the Fit-in-map routine of UCSF Chimera software. The crystal structures occupied the three blade-like densities outside well with the central joint-density vacant. It demonstrates that the DHp-CA dimers assemble further into a trimer-of-dimers mediated by the centrally located N-terminal HAMP domain. The arrangement requires greater flexibility between the HAMP domains and DHp domains than was observed in CpxA, and this may have contributed to the difficulty in crystallizing the full-length cytoplasmic AdeS construct. Comparison of the coordinates of the docked crystal structures shows a slightly clockwise rotation of the CA domains in the ATP-bound model relative to the apo model.
ACKNOWLEDGMENTS We thank the staff from the  BL18U1/BL19U1  beamline and BL19U2 small angle X-ray scattering beamline for technical support during data collection at the  National Center for Protein Sciences Shanghai (NCPSS) at Shanghai Synchrotron Radiation Facility. We also appreciate the  Electron Microscopy Center of Lanzhou University , especially  Dr. Deng Xia , for technical support. This work was supported by  National Natural Science Foundation of China  ( NO. 82072237 ,NO. 31870132 , NO. 81741088  to  Y. Wen , NO. 31600593  to Z. L. ) and  Shaanxi Province Natural Science Funding  (NO. 2020-JQ084  to  Y. Wen ).
The spectra (not shown) were simulated with SIMNRA  [19]. The doses of arsenic were computed by simulating the MEIS spectra (figure  3 ) by using the MEISanalyser. exe software developed by Denis Jalabert. The electrostatic detection system used in MEIS is only capable to detect charged projectiles. For accuracy in the calculations, the fraction of the singly charged ions exiting the sample has been taken into consideration, based on the work of Zalm et al  [20]. According to table 2, one can say that the dose implanted in S1 by using the implanter method is as targeted. The discrepancy between the measured and targeted ones in S2, verified by the two experimental techniques, can be explained by the complex interactions between neutral species and energetic ions, with the contribution of non selection in mass in the PIII process  [17, 21, 22].
Acknowledgments This work carried out on the  Nano Characterization Plat-Form (PFNC)  of  CEA-Leti & Minatec  european campus, was supported by the  'Recherches Technologiques de Base' Program of the French Ministry of Research. We are thankful to  F. Mazen  for providing the samples and to  A. Jannaud  for their preparation for microscopy analyses. The 3D simulations were performed via the PowerMEIS server of UFRGS in Brasil. Last,.
Data acquisition and data-treatment Data acquisition and data-treatment were performed as described by  Mézière, Cariou, et al. (2020)  with a liquid chromatographyhigh resolution mass spectrometry (HRMS) coupling fitted with an electrospray ionisation source (Q-Exactive, Thermo Fischer Scientific, San José, CA, USA). Briefly, chromatographic separation was achieved using a Hypersil Gold analytical column (100 mm × 2. 1 mm, 1. 9 µm) (Thermo Fischer Scientific). Mobile phase consisted of 70% to 100% ACN in water. A mixture of DCM/ACN (1:1, v/v) was added post-column to enhance the formation of the targeted ions. HRMS data were acquired in the negative mode and in full scan mode over the m/z range [300-1500], at a resolving power set to 140,000 full width at half maximum at m/z 200. The open source programming R environment was used to extract and integrate specific [M + Cl] -adducts signals from CP homologue groups (±5 ppm tolerance) within C 8 -C 36 chain length and Cl 4 -Cl 30 chlorine number (excluding homologue groups with n Cl > n C + 2).
A least-square approximation with a non-negative constraint was calculated using the function lsqnonneg of the pracma package in the open-source programming R environment. The exposure mixture perfectly matched the samples when the parameter a of the equation [S] = a × [M] +b was equal to 1 (±0. 1), where [S] and [M] are the vectors of the homologues detected in the sample and the corresponding technical mixture, respectively. Based on a preliminary data examination, a first-kinetic model was adjusted to the experimental egg concentrations using a non-linear regression with the function nls of the package stats in the R environment. The kinetics equation was expressed as in equation (  1 ): C CPssubcat. (t) = C CPssubcat. (egg, ∞) × (1 -e -kt ) (1) where C CPssubcat. (t) and C CPssubcat. (egg, ∞) are the calculated concentration of the CPs subcategories in eggs (ng/g lw) for the exposed group eggs at day t and at steady-state, respectively, and k is the accumulation rate constant.
Recently, the EFSA proposed a first decision on CPs risk assessment, emphasising that it was based on relatively few data and thus acknowledging the relatively low robustness of the evaluation  (EFSA CONTAM panel, 2020). According to this assessment, benchmark doses were estimated at 2. 3 and 36 mg/kg bw/day for SCCPs and MCCPs, respectively. The margin of exposure calculated based on available European occurrence data at the time indicated no health effect for both types of CPs. In our study, we exposed hens to 5 × 26 µg/day of CPs in total, which is equivalent to 0. 13 mg/kg bw/day, and observed a physiological change between the exposed and the control hen eggs. Although we do not know whether this change is related to one CP type in particular or to the global exposure as a whole, nor if it reflects a toxic effect, it does indicate an incidence of CPs exposure in the hen metabolism.
Acknowledgements The authors acknowledge the  French Ministry of Agriculture and Food, General Directorate for Food (DGAL)  for its financial support. The authors are grateful to (i)  Juliane Glüge  and  Lena Schinkel  for kindly providing the I-42 technical mixture, (ii) to the technical staff of the experimental unit PEAT ( INRAE, Nouzilly, France ) and particularly  Nicolas Besné  and  Philippe Didier  for the preparation of feed, rearing hens and collecting eggs, (iii)  Thierry Bordeau  and  Pascal Chartrin  for their implication in sample collection, as well as (iv) to  Lucie Ravot  and  Daniela Magalhães  for help in sample preparation.
Study Population A retrospective analysis was performed of all adult (>18 years of age) TBI patients admitted to the ICU of Erasme Hospital, Brussels, Belgium, between 10 January 2018 and 30 December 2020. Eligible patients were those having a NPi assessment on hospital admission, which was recorded into the patient data management system (PDMS, Picis Critical Care Manager, Picis Inc. , Wakefield, MI, USA), as part of routine care. Exclusion criteria were the absence or incomplete NPi data as well as any ocular damage that could affect the pupillary examination (i. e. , direct ocular trauma, cataract, blindness, glaucoma, previous eye surgery, severe periorbital edema). The study was approved by the ethical committee of the Erasme Hospital (Comité d'Ethique Hospitalo-Facultaire Erasme-ULB; P2021/035), which waived the need of informed consent given the retrospective observational design of the study.