dataesr/scientific-paragraphs-categorization huggingface dataset:
6c ) (PDB: 4ZG6)  [25]. To obtain a higher structural resolution, rATX-β was co-crystallized with both MEY-003 and MEY-002 (Fig. 6  and Table  S4 ). Superposition of both structures showed that the indole has a slightly different conformation in the hydrophobic pocket, relying on the ability of L213 to adopt two different conformations (Fig. 6 ). In the MEY-003 bound structure, L213 makes a side-on interaction with the indole while adopting a face-to-face conformation with MEY-002. The MEY-002 bound structure also revealed an important water bridge in the binding interface, linking the ligand with important residues, T209 and D171, to indirectly interact with the active site Zn ions (Fig. 6d ). We can expect this water bridge to be relevant for MEY-003 as well, but this is probably only visible in the MEY-002-rATX-β co-structure due to the higher resolution obtained (1.
9 vs 2. 5 Å) (PDB: 8C4W). This might explain the non-competitive inhibition of hATX-ɣ of MEY-003 (Fig. 4e ). Therefore, MEY-003 could be classified as a type 2 inhibitor since it binds in the hydrophobic pocket. However, it does not completely overlap with corresponding type 2 inhibitors like PAT-078 (Fig. 6c ) (PDB: 4ZG6). Partial overlap with type 1 inhibitors such as HA-155 (Fig. 6c ) (PDB: 2XRG) shows that chromone-indole hybrid inhibitors can be classified as type 2-like inhibitors, with indirect interactions with the active site through a water bridge.
4 ). Here, we also describe the structure of hATX-ɣ in complex with its natural end-product LPA18:1 and MEY-003 at a resolution of 2. 4 and 2. 3 Å, respectively (Fig. 5 ). Our results highlight that hATX-ɣ shares a very similar structural organization with hATX-β, as deduced from superposition with the PDB: 4ZG7  [25] , resulting in an RMSD of 0. 56 Å for 775 aligned Cα atoms. The deglycosylated mutant used for crystallization did not lead to a significant activity difference when compared to wild-type hATX-ɣ (Fig. S3 ). One major difference between the brain-specific hATX-ɣ and hATX-β is the presence of a 25 aa insertion at position 593 (EAETRKFRGSRNENKENINGNFEPRK), leading to a flexible loop at the surface of hATX-ɣ. The function of this modification is unknown and specific to the brain isoform.
7 Kev beam energy. The LPA 18:1 complex crystal structure data processing was performed using the Grenades parallelproc pipeline  [39]  and MEY-003 complex structure was processed using AutoPROC Staraniso  [40] , both using the XDS software suite  [41]. Molecular replacement was performed using PHASER  [42]  as implemented in PHENIX  [43]  with PDB:4ZG7 as an input model. Model building was iteratively performed using COOT  [44] , phenix. refine  [43] , and PDB-redo  [45]. Ligand restraints were created using ELBOW  [46]  and PDB validation was performed using MOLPROBILITY  [47]  before deposition. Maps were generated using phenix. refine and Table  S4  was generated with PHENIX. Graphical display was drawn with CCP4MG  [48]. All the mentioned software were managed by the SBgrid application  [49].
Acknowledgments We are thankful for the kind gift of ATX-expressing cell linesfrom the Perrakis laboratory at  NKI in Amsterdam. We thank the beamline staff from the  EMBL-ESRF Joint Structural Biology Group  for beamtime at the  European Synchrotron Radiation Facility (ESRF), Grenoble, France. The authors also wish to express their gratitude to the  Eukaryotic Expression Facility in Grenoble  for infrastructure access, especially  Alice Aubert  and  Martin Pelosse  for excellent technical support and fruitful discussions. MC Eymery  has been funded by the  EMBL International PhD program. AA McCarthy  has been funded by  EMBL. J Hausmann  was supported by a fellowship from the  EMBL Interdisciplinary Postdoc  ( EI3POD) program  under  Marie Skłodowska-Curie Actions COFUND  (grant number:  664726 ).
a b s t r a c t In this paper, three datasets are described. The first dataset is a complete set of GNSS-R (GNSS-R: Global Navigation Satellite System -Reflectometry) airborne data. This dataset has been generated with the data acquired with the GLObal Navigation Satellite System Reflectometry Instrument (GLORI) developed at Centre d'Etudes Spatiales de la Biosphère (CES-BIO), during the Land surface Interactions with the Atmosphere over the Iberian Semi-arid Environment (LIAISE) campaign in north-eastern Spain during the summer of 2021. It is the first time to our knowledge that a complete dataset of GNSS-R observables (reflectivity, incoherent component relative to the total scattering signal to noise ratio (SNR) for copolarized (right-right) and cross-polarized (right-left) measurements has been made available.
The two other datasets are ground truth sets of measurements which have been acquired simultaneously with the flights. The in-situ measurements dataset consists in soil measurements (surface soil moisture, surface roughness, Leaf Area Index (LAI)) over 24 reference fields). The land use dataset provides a land use map (along with 385 ground truth plots) over the studied site for GLORI data evaluation. The combined datasets are particularly relevant for soil moisture and vegetation retrievals from GNSS-R observables, as well as studies for calibration and validation of bistatic empirical or physical models simulating coherent or incoherent components on agriculture sites, in the context of the preparation of future GNSS-R space missions, such as HydroGNSS, a European Space Agency mission, launch foreseen in 2024.
The entire database is archived in the AERIS LIAISE database. One DOI is available for each of the 3 datasets (airborne GLORI dataset, in situ measurements dataset and land use dataset). © 2023 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license ( http://creativecommons. org/licenses/by-nc-nd/4. 0/ ) Specifications Table  Subject  Earth and Planetary Science Specific subject area GNSS-R L1 polarimetric airborne data along with ground truth data (soil moisture, Land use) Data format GLORI airborne dataset : analysed and filtered data In-situ measurements dataset : analysed and filtered data Land use dataset : filtered raw and analysed data Type of data GLORI airborne dataset : one zip file containing three. csv data (one for each flight), one.
csv with metadata and one. txt for flights information In-situ measurements dataset : one zip file with shapefiles (EPSG4326 -WGS84) for the plot locations and a csv file which gives in situ measurement data. plot_id is a common field which makes the link between the shapefiles and the csv fil Land use dataset : a zip file with shapefiles (EPSG:32631 -WGS 84 / UTM zone 31N) and a csv file corresponding to the identified Land use for the 385 fields, and a land use map GTiff format, EPSG:32631 -WGS 84 / UTM zone 31N Data collection GLORI airborne dataset : GNSS reflectometry is a bistatic radar remote sensing technology (transmitters and receivers are not in the same place) that uses microwave signals of opportunity from radio navigation constellations. This dataset has been generated with the data acquired with the GLORI reflectometer developed at CESBIO  [1].
These data have been acquired during 3 flights carried out in July 2021 (flight 45/46/47 respectively on July 22nd, 27th and 28th) over the Urgell site. This dataset provides GNSS-R observables such as reflectivity, incoherent component relative to the total scattering, signal to noise ratio (snr) for copolarized (right-right) and cross-polarized (right-left) measurements. These products (Level 1-B) have been generated using a CESBIO software described with details in the data description section. Data provided in the delivered dataset are not normalized. They are then proposed for various incidence angles ranging between 0 and 60 °. The noise floor has been removed. Outliers have not been removed. In-situ measurements dataset : the field datasets consist of punctual surveys for soil moisture acquired using a Delta-T ThetaProbe MI2x, surface roughness acquired using a pin profiler), and Leaf Area Index (LAI).
The LAI was derived from hemispherical digital photography based on analysis of the canopy gap fraction with method proposed by Duchemin  [2]. Land use dataset: the survey was carried (one driver and one surveyor) in two different cars, following the dirt roads around two GLORI transect lines. The central coordinates of each plot were taken from the road with the smartphone GPS and google maps application. Using QGIS, google maps and NDVI map, polygons have been drawn around the points of the original shape file (E. Ayari, K. Dassas, V. Dehaye). Polygons have also been drawn on water and urbain areas. Classes have been attributed to the different land uses (OCS). [10]  ) have been published using these datasets, but we propose to choose the reference  [10]  as related research article. This article is related to the GLORI airborne dataset combined with the Land use dataset.
Value of the Data • These data are valuable for the GNSS-R community in order to prepare the analysis of future GNSS-R Space mission such as ESA HydroGNSS that should propose polarimetric data. The GLORI dataset make polarimetric reflectivities and SNR LHCP (Left Hand Circular Polarization) and RHCP (Right Hand Circular Polarization) available, but also variables which give information on the incoherent part of the signals (incoherent ratio variables). This information is valuable to validate or invalidate common hypothesis on the predominance of the coherent component in the GNSS-R signal over continental surface. • These data are valuable for electromagnetic modelling community to validate proposed scattering models for coherent and incoherent components and more generally simulators for GNSS-R measurements.
• The 3 flights data available in the GLORI airborne dataset correspond to 3 periods with different pluviometry (dry, after inundation event, and one during the drying process) as presented in Table  1. This is of great interest to use the data for SSM estimation. Other researchers can reuse this data to validate soil moisture model using the GNSS-R dataset along with the variable SSM in the situ measurement dataset. This data could also be proposed for inter-comparison of different SAR or passive microwave soil moisture products with GNSS-R estimations. • The Land use map can be used by any other researchers working on the Leida region (typical of the semi-arid region, of great interest for climate variables studies) but it can also be reused by researchers working on Land use models to compare their results using the available ground truth data.
Context and study area 2. 1. 1. LIAISE campaign All data needed to generate the L1b GLORI dataset, the Land Use dataset and the in-situ measurements dataset have been acquired during the Special Observation Period (SOP) of the last field campaign from the GEWEX (Global Energy and Water EXchanges)-supported LIAISE international project  [11]. LIAISE project focuses on the Ebro basin in north-eastern Spain as its study domain. The basin is geographically delimited by the Pyrenees in the north and the Iberian System in the south. Over time, the presence of human society has led to increased surface heterogeneity in the region. This is primarily attributed to intensive agricultural practices, which have significantly impacted both the hydrological cycle and the landscape. The SOP took place in July 2021, when contrasts between irrigated and rainfed agricultural fields are at their maximum.
The climate in this region falls under the Koppen-Geiger classification, characterized as a cold, semiarid climate bordering the Mediterranean Sea. It experiences mild winters, very dry and warm summers, and two rainy seasons in autumn and spring. The site exhibits a diverse land use pattern, encompassing both annual crops such as corn, cereals, and alfalfa, as well as orchards (apple and pear. ). The specific locations of the land cover dataset and in situ measurements dataset can be observed in Fig. 1.
GLORI airborne dataset 2. 2. 1. Structure In the downloaded LIAISE_GLORI_V1. zip file: -one csv file with a time serie (L1b file) with a temporal resolution of 200ms (the incoherent integration time) for each flight on the GLO zone: LIAISE_GLORI-2021_SAFIRE-ATR42_Cesbio_05ms_200ms_as210045_v1. csv LIAISE_GLORI-2021_SAFIRE-ATR42_Cesbio_05ms_200ms_as210046_v1. csv LIAISE_GLORI-2021_SAFIRE-ATR42_Cesbio_05ms_200ms_as210047_v1. csv -GLORI_transects_infos_flights45_46_47. txt file with all flights information, summarized in Table 1 -LIAISE_GLORI-2021_SAFIRE-ATR42_Cesbio_05ms_200ms_metadata. csv gives information on the flights data variables, available in Table 2.
GLORI flights This airborne dataset has been generated with airborne raw data from 3 flights with French research ATR-42 aircraft carried out in July 2021 (flight 45/46/47, respectively, on July 22 nd , 27 th and 28 th ) over the Urgell site. Fig. 1  (left panel) shows the flight paths over the Urgell site, with the location of the GLO transects zone. These transects (9 by day) have been specially planned for the GLORI instrument study to fly both over an irrigated surface and over a dry surface. The altitude over ground of the flight over the GLO zone is approximately 1150m. The flights were carried out using 9 transects to ensure optimum coverage of the entire study site, taking into account the altitude of the aircraft and the incidence angles considered by the GNSS-R measurements. Choosing 200ms as incoherent integration time also respect the objective of having data at the scale of the agricultural plot with a good signal-to-noise ratio.
Variables description The in-situ measurements dataset consists of punctual surveys for soil moisture, surface roughness, and vegetation Leaf Area Index (LAI) over 24 reference fields (bare soil, alfalfa, maize, apple trees), cf Fig. 1. The LAI is defined as the total one-sided area of leaf tissue per unit ground surface area, thus it is a dimensionless quantity characterizing the vegetation cover development. The field plot_id is a common field which makes the link between the shapefiles and the csv file.
GLORI airborne dataset 4. 1. 1. GLORI instrument description The GLORI instrument is derived from the conventional GNSS-R (cGNSS-R) family. It is a 4channel GNSS-R polarimetric receiver which allows simultaneous acquisition of reflected Left Hand Circular Polarization (LHCP) and Right Hand Circular Polarization (RHCP) signals ( Fig. 6  ). For GLORI 2021, measurements of direct and reflected GNSS signals are realized by two passive hemispherical GPS L1 (L1 band = 1575. 42 MHz) dual-polarization antennas. The zenith antenna (ZR) is looking up to the direct signal, and the nadir antenna is looking down to the nadir LHCP (NL) and RHCP (NR) as illustrated in Fig. 7. The main and detailed technical specifications of the instrument are similar to the GLORI 2015 described by E. Motte  [12]  , except for the zenith antenna which is a passive Cobham antenna (Cobham DS1563, the same model as the nadir antenna).
GLORI processing chain The GLORI data have been processed after the flights using the GLORI pipeline developed at CESBIO. This pipeline includes 3 main steps described in Fig. 9  , starting with the raw data acquired with the GLORI instrument described in the previous section. Step 1 is the processing of the GNSS-R data from level RAW (RF signals down-converted to IF) to Level 0 (complex waveforms tagged in time and metadata). The direct and reflected GNSS signals, u d,p and u r,p ,respectively, were cross-correlated with pseudo-random noise (PRN) code replicas a (t). The cross-correlated waveform can be written in the following form:  (1)  where t t coh = 5 ms in the present study) is the coherent integration time. For the direct signal, τ corresponds to the signal propagation delay from transmitter to receiver, f c is the GPS L1 frequency (1575,4 MHz) and f corresponds to the Doppler shift resulting from the velocity of the aircraft relative to that of the GNSS satellite.
p stands for polarization and is equal to r for RHCP and l for LHCP. For the direct signal, p is always equal to r. Y ( τ, f ) = 1 t t coh t t coh u d,p a( t -τ ) e i( fc + f ) t dt Step 2 is the processing step which yields to the following datasets variables: noise, gamma, incoherent_ratio, and snr. When estimating the reflectivity (gamma), we utilize a calculation method that relies on the interferometric complex field (ICF). The ICF is defined as the ratio between the peak time series of the reflected waveform and the direct waveform. The tracking of the waveform peaks, which correspond to signal distributions generated by the area surrounding the specular point, and especially the direct signal mitigation algorithm employed in the GLORI pipeline, is described by E. Motte  [12]. The general expression of ICF is as follows: ICF corr = | Y r,max | -noise f ix | Y d , max | -noise f ix e j( ϕr,max -ϕ d,max ) G d G r ( 2 ) where noise f ix  (3)  is the instrumental noise as a function of the coherent integration time, G d is the antenna gain for the direct channel, G r is the antenna gain for the reflected channel, ϕ r,max and ϕ d,max are the phases, and |Y r,max | and |Y d,max | are the moduli of the reflected and direct waveform peaks, respectively.
The instrumental noise is defined as: noise f ix = noise 1 ms /t coh ( 3 ) where noise1ms is the empirical noise value over 1 ms equal to 0. 098 and t coh is the coherent integration time in seconds. B f ix is a good estimation of the direct and reflected waveform noise levels. In the L1b dataset, the noise_fix variable is provided as well as the computed noise floor, noise_nr, noise_nl , and noise_zr (for comparison to noise_fix). The noise floor has been computed using the peak waveform mask. Reflectivity : to estimate the reflectivity p , we eliminate the incoherent contribution from the ICF, as proposed by Egido et al. [13]  : p = | ICF p,corr | 2 -σ 2 ICFp,corr (4) where σ 2 ICFcorr is the variance of the ICF over the incoherent averaging period (200ms), and p stands for polarization (right or left).
Incoherent component: in order to estimate the percentage of the incoherent component relative to the total scattering power, the incoherent ratio, α p , is computed: α p = σ 2 ICFp,corr | ICF p,corr | 2 (5) In the L1b dataset, l and r are provided as gamma_l and gamma_r variables, α l and α r are provided as the incoherent ratio_l and incoherent_ratio_r variables. The total signal to noise-fix ratio is also provided for direct signal, for LHCP and RHCP reflected signals, respectively, as snr_zr, snr_nr and snr_nr. Step 3 : L1a files are then aggregated for each PRN. Footprint shapes are computed the following way: -compute the ellipse corresponding to the start of incoherent integration time -compute the ellipse corresponding to the end of incoherent integration time. -join the min and max footprints two by two -compute convex hull as an approximation of the footprint shape.
-create a geopanda geoserie compute the specular point ellipse centered variables: s_lat, s_lon, s_dem, azim, elev, h_msl provided in the L1b dataset are variables which corresponds to the center of the footprint shape, shown on Fig. 10. Each ellipse corresponds to the first Fresnel zone as described in  [14]  and  [15]. At the end of the processing chain, the L1b files are time series with a temporal resolution of 200ms (the incoherent integration time), available in the GLORI airborne Dataset.
In situ measurements dataset 4. 2. 1. Soil moisture For each of the dates specified in Table  4  , around 20 handheld measurements (Delta-T ThetaProbe Ml2x) were conducted in both the irrigated and non-irrigated reference fields. These measurements were taken at a depth of 5 cm and were collected from multiple locations within each reference field, within a four-hour time window coinciding with the airborne data acquisitions. To ensure accuracy, the thetaprobe ( Fig. 11 a )) measurements were calibrated using gravi-metric measurements obtained during previous campaigns (  [16]  ). Given a homogeneous hypothesis for agricultural fields, these in situ measurements are expected to provide a 5 cm volumetric soil moisture content (SSM) mean accuracy of better than 0. 02 m3/m3 (water fraction by volume).
Surface roughness The soil roughness is characterized from measurements taken by a pin-profiler ( Fig. 11b  ) with a 1m length and a spacing between needles of 2cm. We carried out between three and six measurements for each reference plot. We limited ourselves to three profiles for the smoothest plots. The profiles are digitized using a Matlab code which generates the height profile cantered around zero. From each profile, we calculate the height correlation function to deduce the two statistical parameters, the root mean square of the height (HRMS) and correlation length (Lc)  [17]. These parameters are then averaged for all considered profiles. In the absence of any change in the surfaces, a single campaign of roughness measurements is carried out during the GLORI campaign, on July 23, 2023.
Land use map generation We provide here more details on the method used to generate the Land use map with the Orfeo Tool Box with the 385 ground truth plots previously cited and Sentinel 2-data. Sentinel-2 data are downloaded from the Theia site. Among the available products, only the products without clouds are used. To monitor the land use changes over time, the classification features include spectral indices and reflectance bands. Three indices are calculated, namely, the normalized difference vegetation index (NDVI), the normalized difference water index (NDWI) and the brightness index (BI). To better distinguish among the crop classes, the optical red edge bands are identified. The random forest classifier has been chosen to perform a supervised classification, with a training and a validation dataset, corresponding respectively to 70% and 30% of the reference data for the land covers (randomly divided).
Acknowledgements This work was supported by the  French National Research Agency  ( HILIAISE ANR-19-CE01-0017  project) and the  French Space Study Center (CNES/SCOMAG  project). (Corresponding author: Mehrez Zribi. ). We would like to thank all farmers of the  Urgell  site who have allowed the GLORI team to collect data on their fields. The authors also wish to thank the technical teams from the SAFIRE, as well as the SAFIRE pilots, for their support and contributions to the successful airborne campaigns. They also would like to thank the  SEDOO team  (Data Service at the  Observatoire Midi-Pyrénées)  in charge of SAFIRE + and of the LIAISE database. Finally, Theia is acknowledged for providing free Sentinel-2 products (corrected from atmospheric effects).
3. 3. 3 Land use dataset 3. 3. 1. Structure In the downloaded the LIAISE_SAFIRE_ATR42_LAND_USE_V1. zip file: -HILIAISE_Leida_landcover_transect_25_07_2021_poly. zip is a zip file with shapefiles (EPSG:32631 -WGS 84 / UTM zone 31N) and a csv file corresponding to the identified Land use for the 385 fields. -LeidaJuly2021LandUseMap_fromOTB. tif is a land use map (GTiff format, EPSG:32631 -WGS 84 / UTM zone 31N) generated with the Orfeo Tool Box (CNES, 2018) using the 385 ground truth samples previously cited and Level-2A (please refer to section Land use map generation to have more details on the method used).
Design, population, data collection This observational retrospective study was conducted in the Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland. We used the previously described LAUS'AKI cohort in which all adult (>18 years old) patients admitted to a medico-surgical ICU between January 1st, 2010, and June 15th, 2020 could be included  [13]. Patients with documented or expressed wishes of nonparticipation in clinical research, those with end-stage renal disease, those who stayed less than 6 h in ICU, and those with missing KDIGO defining data (i. e. , less than 6 h of UO measurements, or absence of sCr measurement) were excluded. In the present study, we included only patients admitted to ICU with sepsis as the main diagnosis. In the case of multiple eligible admissions during the study period, only the first one was considered.