dataesr/scientific-paragraphs-categorization huggingface dataset:
The Spike-Furin Molecular Docking Simulations 2. 3. 1. 1. Blind ab-initio docking in Cluspro 2. 0 Subsequent to filling up for the structural voids in the trimeric SARS-CoV-2 Spike (section 2. 2), an ensemble docking approach was adapted (using 'blind docking' in ClusPro 2. 0  [65] ) wherein Furin (PDB ID: 1P8J, ligand) was docked ab initio onto each unbound 'all atom' Spike atomic model (receptor) belonging to the disordered FLCS Spike ensemble. The ClusPro web interface performs ab initio blind docking in successive steps, combining course grain sampling and fine grain refinements. First, a grid-based rigid-body docking is performed between the receptor (static, fixed at the center of a cubic box) and the ligand (dynamic, placed on a movable grid) in PIPER  [66]  using its fast Fourier transform (FFT) correlation approach, sampling billions of conformations.
To have such disordered loops appears quite characteristic of the SARS-CoV-2 Spike trimer which contains a total of four missing stretches of roughly similar lengths at strategic positions, adding up to 42 amino acids in PDB ID: 6XR8. This is perhaps reasonable given the extended internal packing involved in the trimerization of the monomeric S units. The highly localized positive charge cloud concentrated over the arginine-rich 681 PRRAR 685 region of the loop further boosts the said probability as this would instigate electrostatic self-repulsion of the loop adding to its conformational instability. The presence of Proline (P681), the well-known helix breaker, within the SARS-CoV-2 FLCS Spike , plausibly adding to the loop-disorder, has further been suggested to improve the protease active site accessibility for Furin as well as for other proteases [100].
A representative set of Spike structures (CoV/CoV-2) were culled (resolution ≤ 3 Å), accumulated (see subsection 2. 1, Materials and Methods), and their UNIPROT sequences (in FASTA format) derived from proteomics data  [63] , were extracted from corresponding entries in the Protein Data Bank  [39]. The full-length Spike sequences were aligned using MUSCLE [106] and those containing gap(s) at their aligned position(s) homologous to the 681 PRRAR 685 pentapeptide motif (FLCS Spike , SARS-CoV-2) were removed. The final set consisted of all unique and non-redundant pentapeptide sequence motifs (PSGAG, PGSAS, PASVG, PSRAG, PSRAS, PRRAA, PRARR) to be found within the FLCS Spike spanning the entire plethora of coronavirus Spikes. The full-length sequences were then run in the PrDos web-server [102] -which combines local sequence information and homology templates using iterative (psi) BLAST.
The collective dynamics of the Spike-Furin interaction (SARS-CoV-2) would thus naturally lead to a conformational selection of the disordered FLCS Spike in its Furin-bound state. To sustain such an energetically costly 'conformational selection', an energy-source is hence required to compensate for such a high entropy-loss of the disordered FLCS Spike ensemble. Given the physico-chemical nature of long-and short-range forces sustaining the native bio-molecular environment, such compensating energies would naturally be enthalpic in nature. Thus, an enthalpy -entropy compensation seems necessary. So, the obvious question that follows next would be as to what might be the source of this enthalpic energy to compensate for such a high entropy-loss? To address this, first and foremost, the missing disordered stretches in the representative SARS-CoV-2 Spike pre-fusion structure (PDB ID: 6XR8) were ensemble-modeled (see, section 2.
82) even for the short (101 atoms) modeled FLCS Spike -loop (resi. 677-688) compared to the 'all-atom' Spike structures (2. 93 Å; SD: 0. 54 for 26940 aligned atoms). This further confirmed the disorder, intrinsic to the FLCS Spike in SARS-CoV-2. To serve as a baseline, a similar approach was adapted to model the homologous missing patch ( 664 SLLRSTS 670 ) in SARS-CoV Spike (see, section 2. 2, Materials and Methods) that was originally missing in the representative experimental structure (PDB ID: 7AKJ), albeit with a lower degree of conformational sampling proportional to a lower degree of disorder (than that of SARS-CoV-2) in its FLCS Spike (see, section 2. 2, Materials and Methods).
This groove serves as a potentially attractive docking site evolutionarily for FLCS Spike patches. A detailed independent survey of the twodomain Furin structure (PDB ID: 1P8J, chain A, see section 2. 1. 1, Materials and Methods) further reveals that it has an abundance of anionic residues (30 aspartates and 25 glutamates) which are spread rather homogeneously all over its catalytic and P domains (see Introduction). While the majority (58%) of these residues are either partially or completely exposed to the solvent (bur≥0. 05) (see section 2. 3. 2. 1, Materials and Methods), those that are part of the catalytic domain (Figure  2 ) are of interest to the given context. Note that even partially exposed anionic residues with lesser exposure (say, 0. 05<bur≤0. 15) in vicinity of the triad can, in principle, seldom flip into a more extended and exposed conformation during the course of the Spike-Furin binding (till the Spike-S1/S2 cleavage).
Acknowledgment and Funding We convey our sincerest gratitude to  Prof. Raghavan Varadarajan ,  Molecular Biophysics Unit, IISc, Bangalore, India  and  Prof. Dhananjay Bhattacharyya ,  Computational Science Division, Saha Institute of Nuclear Physics, Kolkata, India  (retired) for their constant motivations and helpful discussions during the work. The project was self-funded.
Accurate knowledge of the seasonal snow distribution is vital in several domains including ecology, water resources management, and tourism. Current spaceborne sensors provide a useful but incomplete description of the snowpack. Many studies suggest that the assimilation of remotely sensed products in physically based snowpack models is a promising path forward to estimate the spatial distribution of snow water equivalent (SWE). However, to date there is no standalone, open source software dedicated to snow data assimilation. Here we introduce a new data assimilation toolbox, the Multiscale Snow Data Assimilation System (MuSA). MuSA was developed to fuse remotely sensed information with the energy and mass balance Flexible Snow Model (FSM2). MuSA was designed to be user-friendly and scalable.
We demonstrate the wide capabilities of MuSA through two snow data assimilation experiments. First, 5 m resolution snow depth maps derived from drone surveys are assimilated in a distributed fashion in the Izas catchment (Central Pyrenees). Furthermore, we conducted a joint assimilation experiment, fusing MODIS land surface temperature and fractional snow-covered area with FSM2 in a single cell experiment. In light of these experiments, we discuss the pros and cons of assimilation algorithms, including their computational cost.
Overview of the data assimilation system The core of MuSA is the energy and mass balance of the snowpack model, the Flexible Snow Model (FSM2  Essery, 2015). FSM2 has from two to three levels of representation of different key processes related to the energy and mass balance of the snowpack. The most complex configuration is chosen by default in MuSA, leading to a more detailed simulation of the internal snowpack processes. Albedo is computed from the age of the snow, decreasing its value as snow ages and increasing it with fresh snowfalls, instead of diagnosing it as a function of the surface temperature. Thermal conductivity of the snowpack is computed as a function of the snow density, instead of using a fixed value. Snow density is computed considering overburden and thermal metamorphism, instead of using an empirical estimation increasing the density as function of age or using a fixed value.
Turbulent energy fluxes are computed as a function of atmospheric stability. Melt water percolation in the snowpack is computed using the gravitational drainage, instead of a bucket model. Although this is the default configuration, it is possible to choose any other FSM2 setup, which may result in slight performance differences both in terms of the computational cost and accuracy of the model  (Günther et al. , 2019). MuSA was designed as a Python program encapsulating the FSM2 Fortran code (Figure  1 ). It handles the forcing and initial files as well as the FSM2 runs and outputs, by internally generating the needed ensemble of simulations from simple configuration (config. py) and constants (constants. py) files that should be filled by the user. Then, it solves most of the challenges of ensemble-based snow data assimilation frameworks for the user.
Meteorological forcing The variables required as forcing to run MuSA include downwelling (i. e. , incoming) shortwave and longwave radiation In the current version of MuSA, the forcing must be provided in the netCDF format at an hourly time step in a grid based geometry, without any specific requirement on the native spatial resolution. The most likely candidates to be used as meteorological forcing for MuSA will be the outputs of atmospheric simulations or automatic weather station data. These include atmospheric reanalyses, such as ERA5  (Hersbach et al. , 2020)  or MERRA-2  (Gelaro et al. , 2017)  and regional climate model outputs  (Alonso-González et al. , 2021)  as well the outputs of different downscaling approaches driven by the aforementioned datasets (e. g. Fiddes and Gruber, 2014; Gutmann et al.
The geometry of the observations must match the geometry of the forcing, i. e. both datasets must share the same grid extent and resolution. The time step, however, does not have to be the same so that the observations can be irregularly spaced in time. Also, it is not necessary that the observations of the different variables occur at the same time step, allowing the assimilation of products from different satellite platforms with different orbits. Optionally, a binary mask can be provided in a separate file to delineate the basin of interest. If provided, MuSA will skip the cells that are not covered by the mask. Again, the mask must be provided on the same grid as the forcing and the observations. Many remotely sensed products exhibit gaps due to the presence of clouds or failures in the close sensing devices.
Here we present the equations for both the stochastic EnKF (van Leeuwen, 2020) and the ES (van Leeuwen and  Evensen, 1996) , as well as their iterative versions based on the multiple data assimilation (MDA) scheme of  Emerick and Reynolds (2012, 2013) , in the form that they are used in MuSA. Let N a denote the number of assimilation cycles (iterations) performed in a pseudo (rather than model) time. For the standard EnKF and ES we set N a = 1 while for their iterative variants N a > 1, typically with N a = 4  (Emerick and Reynolds, 2013; Aalstad et al. , 2018). The superscript ℓ is used to index these iterations. Let U (ℓ) = u (1)(ℓ) ,. , u (i)(ℓ) ,. , u (N )(ℓ) denote the m p × N parameter matrix containing the ensemble (i = 1,. , n) of parameter vectors u (i)(ℓ) for iteration ℓ. Recall that the subset of these parameters that are physically bounded have undergone the relevant analytic transformations for Gaussian anamorphosis, and the corresponding inverse transforms are applied back to physical space when these are passed through the dynamical model (see  Aalstad et al.
Drone data assimilation experiment For the first experiment, we ran MuSA to assimilate snow depth retrievals obtained from a fixed wing drone (?  Revuelto et al. , 2021a) , over two different snow seasons (2018/2019 and 2019/2020) in the Izas catchment using different data assimilation algorithms. The drone surveys were not equally distributed in time along the seasons, and the surveys present spatial gaps in some of the cells. The whole drone dataset is composed of 18 temporally distributed snow depth maps retrieved at 5m spatial resolution. Due to the high-resolution of the observations, we expect MuSA to be able to reproduce the wind redistribution patterns by inflating the uncertainty of the forcing to compensate for the deposition and removal of wind-blown snow. The simulation ensemble was composed of 200 particles.
Acknowledgements. Esteban Alonso-González  has been funded by the  CNES  postdoctoral fellowship. Data from Izas experimental catchment was collected in the frame of HIDROIBERNIEVE-CGL2017-82216-R funded by the  Spanish Ministry of Economy and Competitiveness  project. Kristoffer Aalstad  was funded by the  Research Council of Norway  through the  Spot-On  project (# 301552 ), and acknowledges.
In the present paper, we characterize a major ozone outbreak across Europe in July 2017 by using the multispectral satellite approach IASI+GOME2, tropospheric chemistry reanalyses and other observations. We analyse the sources that have most probably contributed to ozone photochemical production along transport of major ozone plumes travelling from Southwest to Eastern Europe. To the authors' knowledge, such detailed identification of the sources of ozone precursors of a complex event over Europe has not yet been performed using satellite observations. Section 2 describes the observational and modelling data used for the analysis. Results and discussions on the evolution of the European ozone outbreak and the associated precursor sources are presented in section 3. A summary and conclusions are given in the last section.
Satellite observations of lowermost tropospheric ozone from IASI+GOME2 The multispectral satellite approach IASI+GOME2 was designed for observing lowermost tropospheric ozone by synergism of TIR atmospheric radiances observed by IASI and UV earth reflectances measured by GOME-2  (Cuesta et al. , 2013; 2018). Both instruments are onboard the MetOp satellite series, and they both offer global coverage every day (for MetOp-A around 7 November 2022). We use an updated version of the IASI+GOME2 product, merging the IASI+GOME2/MetOp-A and IASI+GOME2/MetOp-B products for improving the spatial coverage. For reducing random errors, the dataset is averaged in a regular horizontal grid of 1° × 1°. Ozone concentrations from the surface to 3 km of altitude (a. s. l. ) are provided as volume mixing ratio in ppb (parts per billion), calculated as the ratio of lowermost tropospheric partial columns (in molecules per square centimetre) of ozone and air.
Hereinafter, this amount is designated as IASI+GOME2 LMT ozone concentration. For assessing the contribution of IASI+GOME2 for capturing near surface ozone evolution, we compared IASI+GOME2 with the surface observations of ozone in Europe. We consider hourly surface data from stations in rural areas, obtained from the Global Atmosphere Watch (GAW, http://ebas. nilu. no, last access: 28 January 2020), the European Monitoring and Evaluation Programme (EMEP, http://ebas. nilu. no, last access: 28 January 2020), and the European Air Quality e-Reporting (https://eea. europa. eu, last access: 27 January 2020). We identify rural stations based on the Tropospheric Ozone Assessment Report (TOAR) station category  (Schultz et al. , 2017). TOAR is a research community with an up-to-date scientific assessment of tropospheric ozone's global distribution and trends from the surface to the tropopause initiated by the International Global Atmospheric Chemistry (IGAC).
All comparisons show significant correlations (p < 0. 01). IASI+GOME2 over the Iberian Peninsula has a slight positive difference of 0. 9 ppb. Over Southern and Central Europe and Central and Eastern Europe, the satellite retrievals are lower than in situ measurements by -4. 7 ppb and -10. 8 ppb, respectively. The root mean squared (RMS) difference over the Iberian Peninsula is smaller than those in the other two regions. The values of correlation coefficients around ∼0. 45 are not very high, but they may reflect differences in the spatio-temporal representativity of surface in situ measurements and satellite data at daily scale and this particular event. Vertical gradients of ozone concentrations near the surface may also be responsible of the apparent differences between satellite and surface data.
Lower ozone abundances retrieved by the satellite approach may be induced by high ozone concentrations near the surface, as typically remarked during ozone outbreaks of anthropogenic origin. Previous comparisons of IASI+GOME2 data with surface in situ measurements showed relatively larger values of correlation coefficients, being ∼0. 69 for a major ozone outbreak over Eastern Asia (11 surface stations over Japan,  Cuesta et al. , 2018)  and ∼0. 55 for differences of 15-day averages over Europe  (Cuesta et al. , 2022). In this last case, ozone satellite retrievals over Europe are also lower than surface concentrations (by -8. 6 ppb over the whole continent).
Tropospheric Chemistry Reanalysis version 2 (TCR-2) Chemical reanalyses provide comprehensive information on the evolution of atmospheric composition, offering the spatiotemporal full coverage of chemistry-transport models and enhanced precision by assimilation of various satellite observations. TCR-2 is a tropospheric chemical reanalysis based on an assimilation of multi-constituent observations from diverse satellite sensors  (Miyazaki et al. , 2020). TCR-2 data is publicly available for the period 2005-2019 (https://tes. jpl. nasa. gov/tes/chemical-reanalysis/). In the present study, we use 2-hourly TCR-2 reanalysis data  (Miyazaki, personal communication, 2020)  of ozone and two species representative of ozone precursors: nitrogen dioxide (NO2) and   -Maenhout, et al. , 2015). For biomass burning emissions, the monthly Global Fire Emissions Database (GFED) v4  (Randerson et al.
Other satellite observations and emission inventories For analysing the origin and evolution of ozone plumes, CO retrievals from IASI/MetOp-B are used for identifying the location of CO plumes. This total column CO product is publicly available at the French data centre AERIS (https://iasi. aeris-data. fr, last access: 7 November 2022). It is derived from IASI radiance using the FORLI algorithm (Fast Optimal Retrievals on Layer for IASI;  Hurtmans et al. , 2012) , from the Université Libre de Bruxelles (ULB) and the Laboratoire Atmosphères, Milieux, Observations Spatiales (LATMOS). It is the same product used jointly with IASI+GOME2 data by  Cuesta et al. (2018). For reducing random errors, the dataset was averaged in regular grid of 1° × 1° in the same way as IASI+GOME2. For consistency with the other datasets, we derive CO mixing ratios assuming that all CO molecules within the column are located in the lowermost troposphere.
7b ). In addition, TCR-2 shows ozone plumes originating from North America that are transported across the North Atlantic for arriving to North-western Iberian Peninsula on 13-15 July (not shown). However, those ozone plumes transported to the Iberian Peninsula are only partially depicted by IASI+GOME2 observations. These differences could be associated with misrepresentation of these ozone plumes in the simulations or lack of sensitivity (or spatial coverage) in the satellite data (particularly over the ocean). The 7-day backtrajectories confirm that the air masses come from the west over the North Atlantic and offshore transport from Western Africa in the middle troposphere (Fig. 7f ). It suggests that the link between those ozone plumes and emissions in North America is unclear. Pay et al.
Conclusions We have presented a detailed study of the daily evolution and associated sources of precursors of a major ozone outbreak transported across Europe in July 2017, by using IASI+GOME2 multispectral satellite observations and tropospheric chemical reanalysis TCR-2. The multispectral satellite approach offers the currently unique capacity of observing the ozone distribution in the lowermost troposphere (below 3 km a. s. l. ), which is in fair consistency with surface observations. The major European ozone outbreak analysed here is associated with several sources of ozone precursors: biogenic, anthropogenic and biomass burning emissions. We describe the sources of precursors and the transport pathways of these ozone plumes (see scheme in Fig. 12 ), using IASI+GOME2, TCR-2 and other datasets.
Acknowledgements Authors acknowledge the financial support of  Centre National des Etudes Spatiales (CNES , the  French Space Agency)  via the  SURVEYPOLLUTION  project from  TOSCA (Terre Ocean Surface Continentale Atmosphère) , the  Université Paris Est Créteil (UPEC) , and the  Centre National des Recherches Scientifiques-Institut National des Sciences de l'Univers (CNRS-INSU) , for helping in achieving this research work and its publication. We thank the French atmospheric data centre AERIS (https://www. aeris-data. fr, last access: 10 June 2022) for supporting the production of IASI+GOME2, data access and computing resources provided by the  ESPRI IPSL  mesocentre (https://mesocentre. ipsl. fr/, last access: 1 April 2022) and the  European Air Quality e- Reporting for in situ surface measurements of ozone,  EUMETSAT  for GOME-2 level 1 data (provided by the  NOAA CLASS data portal).
IASI is a joint mission of  EUMETSAT  and  CNES. We thank the  Institut für Meteorologie und Klimaforschung (Germany)  and  RT Solutions (USA)  for licenses to use, respectively, the KOPRA and VLIDORT radiative transfer models. We also thank  Zhaonan Cai  from the  Chinese Academy of Sciences (China ) and  Xiong Liu  from the  Harvard-Smithsonian Center for Astrophysics (USA ) for their help to produce IASI+GOME2 data. The  Programme National de Télédétection Spatiale (PNTS)  and the  Agence Nationale de la Recherche (ANR)  also contributed in supporting the development of the IASI+GOME2 satellite product. Part of this work was conducted at the  Jet Propulsion Laboratory, California Institute of Technology , under contract with the  National Aeronautics and Space Administration (NASA). We warmly acknowledge all datasets provided for this study: CO satellite retrievals from IASI by  ULB/LATMOS (Université Libre de Bruxelles/Laboratoire Atmosphères, Milieux, Observations Spatiales) laboratories  (special thanks to  C.
NOX and NMVOCs emissions were obtained from CAMS Global anthropogenic emissions (CAMS-GLOB-ANT v4. 2-S1. 1; Granier et al. , 2019). The emissions are based on the emissions provided by the Emissions Database for Global Atmospheric Research (EDGAR v4. 3. 2; Crippa et al. , 2018)  and the Community Emissions Data System (CEDS; Hoestly et Anthropogenic Compounds and Compilation of Ancillary Data (ECCAD, https://eccad. aeris-data. fr/, last access: 26 May 2021). al. , 2018), and provided as monthly averages with a horizontal resolution of 0. 1° × 0. 1°. Soil NO emission was obtained from CAMS Global soil emissions provided as monthly averages with a horizontal resolution of 0. 5° × 0. 5° (CAMS-GLOB-SOIL v2. 3; Granier et al. , 2019;  Simpson and Darras, 2021). Soil NOX is mainly emitted as NO.
The Model of Emissions of Gases and Aerosols from Nature (MEGAN) is one of the most widely used biogenic emission model (Guenther et al. , 2012). We obtained biogenic isoprene and monoterpenes emissions developed under the Monitoring Atmospheric Composition and Climate project (MACC), MEGAN-MACC Biogenic emission inventory (MACC-MEGAN, Sindelarova et al. , 2014). The emissions are provided as monthly averages with a horizontal resolution of 0. 5° × 0. 5°. The locations of fires are derived from the Terra and Aqua MODIS (Moderate Resolution Imaging Spectrometer) active fire products (MCD14ML Collection 6; Giglio et al. , 2016)   distributed by the Fire Information for Resources Management System (FIRMS, https://earthdata. nasa. gov/firms, last access: 13 March 2020). This dataset provides the values of Fire Radiative.