dataesr/scientific-paragraphs-categorization huggingface dataset:
scRNA-seq libraries. This work was supported by institutional grants from INSERM and Nantes Université to the Centre de Recherche en Transplantation et Immunologie, and from INSERM, CNRS and Aix-Marseille University to the Centre d'Immunologie de Marseille-Luminy. This work was carried out in the context of the IHU-Cesti project (ANR-10-IBHU-005) and of the LabEX IGO program (ANR-11-LABX-0016-01), managed by the Agence Nationale de la Recherche. The IHU-Cesti project was also supported by Nantes Métropole and the Région Pays de la Loire. This work was also supported by the patient association ALBI (Association pour la lutte contre les maladies inflammatoires du et des voies biliaires), by AFEF-Société Française d'Hépatologie, and by SNFGE-Société Française de Gastro-Entérologie. This work was granted access to the HPC resources of Aix-Marseille Université financed by the project Equip@Meso (ANR-10-EQPX-29-01) of the program "Investissements d'Avenir" supervised by the Agence Nationale de la Recherche.
Acknowledgements We thank the  Bioinformatics Core Facility of Centre d'Immunologie de Marseille-Luminy  for helpful discussions and comments. We acknowledge  HalioDX  and the  UCA Genomix platform  for sequencing (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprint this version posted January 7, 2020. ; https://doi. org/10. 1101/2020. 01. 06. 895938  doi: bioRxiv preprint.
Generalized Retrieval of Aerosol and Surface Properties (GRASP) GRASP is a versatile retrieval algorithm that can be used for a variety of remote sensing and in situ measurements to retrieve aerosol and surface properties  (Dubovik et al. , 2014). It is open-source software and is available free to noncommercial users for downloading from the website https: //www. grasp-open. com/ (last access: 8 September 2020). GRASP first demonstrated its overall capability in an aerosol retrieval test study  (Kokhanovsky et al. , 2010). It has gone on to prove itself in a variety of real-world applications  (Chen et al. , 2018 (Chen et al. , , 2019;; Frouin et al. , 2019; Li et al. , 2019; Schuster et al. , 2019; Torres et al. , 2017). GRASP has been successfully applied to measurements from many different types of instruments  (Benavent-Oltra et al.
, 2019; Espinosa et al. , 2017 Espinosa et al. , , 2018;; Román et al. , 2018; Titos et al. , 2019; Torres et al. , 2017) , but the most pertinent to AirHARP are the previous applications of GRASP to POLDER-3 on PARASOL  (Chen et al. , 2018 (Chen et al. , , 2019;; Dubovik et al. , 2011; Frouin et al. , 2019; Li et al. , 2019)  because of its familiar polarization, multi-wavelength, and multi-angle sampling characteristics. GRASP consists of two modules: a forward model and an inversion module. For the case of aerosol measurements, the forward model consists of a polarized radiative transfer (RT) code to calculate the radiance measured by the instrument, and it uses a precalculated spheroidal kernel to calculate the contribution of single scattering by the aerosol particles following the strategy described by  Dubovik et al.
4 in Dubovik et al. , 2011). For the RT calculations, GRASP uses a successive order of scattering (SOS) scheme. The RT module consists of different surface bi-directional reflectance distribution function (BRDF) and bi-directional polarized distribution function (BPDF) models for land and ocean. These models are briefly discussed in Appendix A, and further information about the RT code and single-scattering database that is beyond the scope of this paper can be found in  Dubovik et al. (2011)  and  Lenoble et al. (2007). Table  3. Five log-normal modes used for particle size distribution in GRASP retrieval for AirHARP; r v is the volume median radius and σ v the geometric standard deviation (SD). In this kernel, other particle properties are free to be retrieved. The particle single-scattering calculations that we employ for the AirHARP retrieval use one of two possible retrieval setups: (1) five log-normal distribution modes as described in Table  3  or (2) the aerosol assumed to be an external mixture of five aerosol components as described in Table  4.
5. 2. For the AERONET comparison, we make use of the second GRASP kernel that has reduced the number of aerosol parameters from 15 to 6. This reduced parameter option is the "GRASP/Models" kernel, where particle properties are assumed for each aerosol component given in Table  4. Complex refractive index, SF, and particle size distribution of each aerosol components are fixed for this kernel. Only the concentration (weight) for each aerosol component is retrieved. The inversion module in GRASP uses the multi-term least square method (LSM) to solve the following system of equations: f * = f (a) + f , 0 * = Ga + g , a * = a + a , ( 3a ) where a is a vector of unknowns and is called a state vector. f * is the vector which contains the instrument observations, f is the uncertainty in the observations, and f (a) is the forward model simulated observations.
b Retrieved spherical fraction includes a significant number of pixels with SF ∼ 99 %. resented as a Gaussian distribution with a mode value of 1. 55 for all wavelengths, while retrievals from the RSP and SPEX airborne instruments during the ACEPOL campaign produced values of RRI of 1. 56 and 1. 58, respectively, for a similar forest fire smoke  (Fu et al. , 2020). Fire Laboratory at Missoula Experiments (FLAME) records the real part of the refractive index in a range from 1. 55 to 1. 8, depending on the composition of the smoke particles  (Poudel et al. , 2017). FLAME 2 laboratory experiments reported RRI values in the range of 1. 54 to 1. 67, depending on the fuel source  (Mack et al. , 2010). The AirHARP retrievals of IRI range from 0. 01 to 0. 1 with a mean value of 0. 024 (Fig. 9d ), which compares well with the FLAME database range of 0.
01 to 0. 5 and the FLAME 2 database range of 0. 011 to 0. 217  (Mack et al. , 2010). Another smoke optical property study reports SSA in the range of 0. 78 to 0. 94 at 532 nm, depending on the age of the smoke  (Nicolae et al. , 2013). Our retrieved SSA from AirHARP in this work (see Fig. 9b ) ranges from 0. 6 to 0. 99 with mean values of 0. 87 ± 0. 06, 0. 86 ± 0. 07, 0. 84 ± 0. 08, and 0. 81 ± 0. 09 for 440, 550, 670, and 870 nm, respectively. Retrieved single scattering albedo values are well within the range measured during the FLAME 2 experiment using a photoacoustic spectrometer and a nephelometer and are close to the retrieved values from SPEX airborne and RSP by  Fu et al. (2020). Fine-and coarse-mode AOD calculated using the log-normal modes listed in Table  3  are plotted in Fig. 10a  and b.
5 Comparison of AirHARP GRASP retrievals with collocated data sets 5. 1 High Spectral Resolution LIDAR-2 (HSRL2) vs. AirHARP AOD comparison HSRL2, flying on the same aircraft with AirHARP during ACEPOL, provides the opportunity to compare the GRASP retrievals of AOD with an independent and collocated measurement. AirHARP lacks a wavelength channel identical to the wavelengths measured by HSRL2; therefore, for this study, we make use of the 440 and 550 nm channels on AirHARP to calculate the Angstrom exponent and then use that information to interpolate the AOD to HSRL2's wavelength of 532 nm. We collocate HSRL2 and AirHARP measures of AOD for the smoke plume shown in Fig. 4d  https://doi. org/10. 5194/amt-13-5207-2020 Atmos. Meas. Tech. , 13, 5207-5236, 2020 (9 November 2017; 19:30 UTC). The smoke plume in this image is a controlled fire started in the Kaibab National Forest, Arizona, USA, and is highly non-homogenous near the source fire.
For this study, we make use of the HSRL2 AOD at 532 nm, where an assumption of the lidar ratio is not required. All the GRASP retrievals for this comparison are done using the R I and DoLP measurements from AirHARP. Aerosol optical depth at 532 nm as a function of the collocated alongtrack pixels is plotted in Fig. 13a , and the scatter plot of the comparison is shown in Fig. 13b. In Fig. 13a  HSRL2measured AOD is denoted by the green diamond markers and AirHARP AOD by grey squares. Each square represents the mean of 28 pixels around the collocated HSRL2 ground pixel in the AirHARP image. We used seven pixels along-track (∼ 1. 93 km) and four pixels cross-track (∼ 1. 1 km) to find those mean values. The error bar in the AirHARP data points is the SD of AOD of all pixels within the ∼ 1. 93 km × 1.
1 km region around the HSRL2 ground pixel, representing the spatial variability of the smoke plume within the averaging rectangular box. For this heterogenous smoke plume case, we had to apply χ 2 norm < 20, to filter out bad pixels/fits. Nonhomogeneity of the smoke makes the retrieval complicated since we see different parts of the smoke plume when we scan through the different viewing angles of AirHARP data. So, using a higher χ 2 norm value for filtration helps to catch the higher AOD values. A scatter plot of these two data sets is shown in Fig. 13b  and the solid black line is the 1 : 1 line, whereas the yellow error bar represents the spatial variability of AirHARP AOD, similar to that in Fig. 13a. A comparison of HSRL2-measured AOD at 532 nm with the AirHARP AOD retrievals at 532 nm shows a strong, positive correlation and only deviates when the plume is thick and heterogeneous.
GRASP was applied to AirHARP observations of two heavy aerosol loading situations, both of smoke plumes near fire sources. It is in one of these situations that optical and mihttps://doi. org/10. 5194/amt-13-5207-2020 Atmos. Meas. Tech. , 13, 5207-5236, 2020 crophysical characteristics of the smoke, in addition to AOD, are retrieved using the GRASP software. Retrievals of smoke properties on 27 October 2017 T18:16 UTC show that particles are fine with the real and imaginary refractive indexes of 1. 55 and 0. 024, respectively. Single scattering albedos of 0. 87, 0. 86, 0. 84, and 0. 81 are retrieved for the smoke at 440, 550, 670, and 870 nm, respectively, with a fine-mode volume median radius of 0. 157 µm and SD of 0. 55 with a mixture of spherical and non-spherical particles. The isolated location of these local smoke plumes prevents any validation with AERONET observations.
Acknowledgements. We thank the ACEPOL organizing team for supporting the involvement of the AirHARP instrument in the project. We thank the PIs of the AERONET stations used in this study for maintaining and providing easy access to data. We also acknowledge the engineering contributions from  Roberto Fernadez-Borda  ( ESI, UMBC ) and  Dominik Cieslak  ( ESI, UMBC ) in designing and manufacturing the AirHARP instrument. We acknowledge the former Aerosol,  Cloud, Ecosystem (ACE) program  at  NASA's Earth Science Division  as a sponsor for ACEPOL flights. The Dutch contribution to the ACEPOL flight campaign hours was funded by the  SRON Netherlands Institute  for SPACE Research and by  NSO/NWO. The authors acknowledge the support of  Anton Lopatin  ( GRASP-SAS ) and  Tatsiana Lapionak  ( LOA Université Lille ) in providing the GRASP kernels for this study.
The authors acknowledge the work by the  Python Software Foundation  (http://www. python. org,last access: 8 September 2020) and  Anaconda Inc. (https://www. anaconda. com/,last access: 8 September 2020) in developing and maintaining the Python programming language and packages used for analyzing and visualizing the data used in this study. Financial support. This research is primarily supported through  NASA projects HARP CubeSat  (grant no. NNX13AN11G ),  AirHARP  (grant no. NNX16AK36G ), and  JCET cooperative agreement  (grant no. NNX15AT34A ). This research is also supported by the  National Key R&D Program of China  (grant no. 2016YFE0201400 ). Oleg Dubovik appreciates the support from the  Chemical and Physical Properties  of the  Atmosphere  Project funded by the  French National Research Agency  through the  Programme d'Investissement d'Avenir  (contract no.
ANR-11-LABX-0 0 05-01 ), the  Regional Council "Hauts-de-France", and the "European Funds for Regional Economic Development". Henrique M. J. Barbosa  is grateful for the support of  Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)  (processo no. 2016/18866-2 ) and  Conselho Nacional de Desenvolvimento Cien tífico e Tecnológico (CNPq)  (project no. 308682/2017-3 ). The Dutch contribution to the ACEPOL flight campaign hours was funded by the  SRON Netherlands Institute  for SPACE Research and by  NSO/NWO  (project no. ALW-GO/16-09 ). Review statement. This paper was edited by  Sebastian Schmidt  and reviewed by  Otto Hasekamp  and one anonymous referee.
Data availability. The AirHARP L1B and HSRL2 data for the ACEPOL campaign can be accessed from the website https://www-air. larc. nasa. gov/cgi-bin/ArcView/acepol (last access: 21 September 2020, ACEPOL Science Team, 2017). Quick look images for each of the AirHARP flight legs can be accessed through the website https://sites. google. com/view/airharp-acepol/ home (last access: 21 September 2020, AirHARP science team, 2017). AERONET data used for this study can be downloaded from https://aeronet. gsfc. nasa. gov/ (last access: 21 September 2020, AERONET Team, 2020).
CrRPI1 Structural Folds We solved the crystal structure of CrRPI1 in the P2 1 space group at 1. 40 Å resolution (Table  1 ). Model building of the two subunits of the asymmetric unit achieved the placement of 235 amino acid residues in the first subunit and 237 in the second (Figure  1A ). The residues of each subunit were numbered according to uniProtKB entry A8IRQ1. The high resolution of the dataset further allowed us to position eight sulfates, two sodiums, and 511 water molecules in the crystal asymmetric unit.
CrRPI1 dimer Each monomer is composed of six alpha helices and twelve beta strands distributed into two domains. Domain 1 spans residues 29 to 160 and 246 to 264, folds according to the Rossmann topology, and is part of an unnamed structural superfamily (CATH classification 3. 40. 50. 1360). Domain 2 is inserted in between the two parts of domain 1, spans residues 161 to 245, and is classified as an ACT domain (CATH classification 3. 30. 70. 260) (Figure  1B ). Our model aligns with 62 entries of the Protein data bank with an average RMSD lower than 1. 8 Å. The closest similarity is with the Ribose 5-phosphate isomerase from Plasmodium falciparum (PDB 2F8M, RMSD = 0. 852 Å over 229 aligned Cα). CrRPI1 secondary structure content is distributed as follows: α helix h1 (residues 33-45), β strand s1 (52-56), h2 (60-75), s2 (81-85), h3 (88-97), s3 (112-115), s4 (118-121), s5 (124-127), h4 (135-143), s6 (146-152), s7 (167-171), h5 (176-185), s8  (193) (194) (195) (196) , s9 (211-216), h6 (225-233), s10 (238-240), s11 (249-254) and s12 (257-262) (Figure  1C ).
CrRPI1 dimer was aligned with Plasmodium falciparum RPI dimer (PDB entry 2F8M) with an RMSD = 2. 115 Å over 438 equivalent atoms. Both dimers have the same quaternary organization, with corresponding subunits positioned in virtually identical positions. The only significant difference lies in the 3 10 helix formed by residues 104-106 of CrRPI1 that is structured as a loop in PfRPI. The interface between two subunits of CrRPI1 is calculated from the arrangement of the two subunits forming the asymmetric unit (Figure  3A ) by the PDBe-PISA server v1. 52  [25]. The subunit A-to-subunit B interface is 1165 Å 2 wide and has a computed ∆ i G = -19. 3 kcal/mol (p-value = 0. 027). In comparison, PfRPI dimer interface is more extended with a surface of 1340. 8 Å 2 and an equivalent computed ∆ i G = -19.
RPI1 Catalytic Site CrRPI1 crystallographic model includes a sulfate ion, a sodium ion and water molecules located in a shallow pocket exposed to solvent (Figure  4A ). Structural superposition of CrRPI1 with RpiA from Legionella pneumophila (LpRPI, PDB: 6MC0, RMSD = 0. 734 Å), which is co-crystalized with ribose-5-phosphate bound to the active site, reveals extended similarities (Figure  S2 ). Surface charges were calculated on Pymol using the APBS electrostatics plugin  [27, 28]. For both enzymes, the active site is an analog cavity with an electronegative charge at the center, surrounded by electropositive charges at the rims (Figure  5A, B ). Electrostatic surface potentials are similarly distributed on both active sites, suggesting an identical electrostatic complementarity toward the substrate.
C149 and C250 side chains are modelled as free thiols (Figure  1A  and S3 ). Both side chains are buried in the protein hydrophobic core where TRX or GRX should be excluded once RPI1 is folded. The two other cysteines of CrRPI1 are closer to the surface but separated by 20 Å, precluding the formation of an intramolecular disulfide bridge. C192 has a solvent exposed backbone and its sidechain points toward the hydrophobic core. C175 has its sulfur atom exposed to solvent and is the most likely to be the target of redox post translational modifications, such as the formation of an intermolecular disulfide bridge with another dimer of CrRPI1 or with a partner protein. Indeed, CrRPI1 is predicted to interact with ten other proteins in Chlamydomonas reinhardtii by the STRING database  [31]  and a disulfide bridge may stabilize or condition these interactions.
C149 and C250 side chains are modelled as free thiols (Figure  1A  and Figure  S3 ). Both side chains are buried in the protein hydrophobic core where TRX or GRX should be excluded once RPI1 is folded. The two other cysteines of CrRPI1 are closer to the surface but separated by 20 Å, precluding the formation of an intramolecular disulfide bridge. C192 has a solvent exposed backbone and its sidechain points toward the hydrophobic core. C175 has its sulfur atom exposed to solvent and is the most likely to be the target of redox post translational modifications, such as the formation of an intermolecular disulfide bridge with another dimer of CrRPI1 or with a partner protein. Indeed, CrRPI1 is predicted to interact with ten other proteins in Chlamydomonas reinhardtii by the STRING database  [31]  and a disulfide bridge may stabilize or condition these interactions.
Cloning The amino acid sequence of Chlamydomonas reinhardtii ribose-5-phosphate isomerase (Cre03. g187450, UniRef100 entry A8IRQ1) was analyzed with TargetP2. 0 [40] , ChloroP  [41]  and Predalgo  [8]  to predict the transit peptide cleavage site. The subsequent mature sequence of chloroplastic protein coding for amino acids 23 to 269 was amplified by PCR using a forward primer introducing an NcoI restriction site (bolded) at the start codon: 5 -CGCACCATGGCCGCGCCGGTCTCAA-3 and a reverse primer introducing a BamHI restriction site (bolded) downstream of the stop codon: 5 -GCACGGGATCCTTAGTGCTTCTTGGGGTTGGG-3 as previously reported  [30]. The PCR was performed using the Chlamydomonas reinhardtii EST index  [42]  database plasmid AV390085 obtained from the Kazusa DNA Research Institute (Chiba, Japan).
Protein Crystallization and Structure Determination Purified CrRPI1 was tested for crystallization on commercial sparse-screening conditions (Qiagen, Hilden Germany) of the Joint Center for Structural Genomics screens  [43]  and JBScreen classic 1-8 (Jena Bioscience, Jena, Germany) with a mixture of 50 nL of protein and 50 nL of precipitant solution equilibrated against 30 µL of reservoir solution at 20 • C. Two monocrystal grew after one year in conditions JBScreen 6-C8 (3 mmol. L -1 ammonium sulfate) and JBScreen 6-D9 (3 mmol. L -1 ammonium sulfate, 20% glycerol). Crystals were flash frozen in liquid nitrogen for diffraction experiments at Proxima-1 beamline of SOLEIL synchrotron (Gif sur Yvette, France)  [44]. A 99. 63% complete dataset at 1. 40 Å resolution was obtained from 3600 images over 360 • of phi rotation and indexed in the P2 1 space group, integrated, scaled and converted with XDSME  [45].
SEC-SAXS Analysis Here, 50 µL of pure CrRPI1 at a concentration of 10 mg. mL -1 were injected on BioSEC-3 300 size-exclusion chromatography column (Agilent Technologies, Santa Clara, CA, USA) equilibrated in buffer C, in series with the small-angle X-rays scattering (SAXS) exposure capillary at the synchrotron beamline SWING (SOLEIL, Gif sur Yvette, France). Collected scattering images were analyzed on the application Foxtrot 3. 3. 4 (Xenocs, Sassenage, France) and the ATSAS 2. 8. 3 suite  [54, 55]. PrimusQT calculated a radius of gyration of 28. 16 ± 0. 18 Å and estimated SAXS CrRPI1 molecular weight at 52,781 Da (Qp), 53,991 Da (MoW), 49,221 (Vc), and 55,257 Da (size and shape). This molecular weight allows us to fit between 1. 78 and 2. 00 times the molecular weight of monomeric recombinant CrRPI1 estimated with Expasy ProtParam at 27,551 Da.
Structural Data Crystallographic data are registered at the Protein Data Bank under accession code 6ZXT. Supplementary Materials: Supplementary materials can be found at http://www. mdpi. com/1422-0067/21/20/ 7787/s1. Figure  S1. Sequence alignment made by Clustal Omega of RPI A from different species. Residues with more than 50 % conservation are colored in red, strictly conserved residues are in white with a red background. Numbering is according to Chlamydomonas reinhardtii. RPI sequences were retrieved from uniprotKB database for the following species: Chlamydomonas reinhardtii (A8IRQ1), Glycine Max (I1NC83), Arabidopsis thaliana (Q9S726), Plasmodium falciparum (Q8I3W2), Deinococcus radiodurans (Q9RW24), Streptococcus pneumoniae (B2INU5), Mus musculus (P47968), Homo sapiens (P49247), Gonium pectorale (A0A150H4N9), Volvox carteri f.
Acknowledgments: We thank the  FR 550 Institut de Biologie Physico-Chimique  for the access to the crystallization platform and to the data visualization wall. We acknowledge SOLEIL beamlines Proxima-1 and Proxima-2a for providing the setup of X-ray diffraction experiments and beamline SWING for SAXS experiments. We thank  Laure Michelet ,  Fernanda Borrega , and  Sandrine Hoang  for help with the preparation and characterization of the recombinant protein.
Classes PC1-PC3 suitable for MYI and second-year ice were excluded from our study, because it was hard to find enough training data for the no-go class. The method is robust and applicable to other delay-Doppler radar altimeters, most importantly those onboard Sentinel-3. An example RIO chart is shown in Figure  3. The RIO decision classes are given in 25 km resolution along the CryoSat-2 orbit ground track for the winter months of October-April (see RIO charts at http://ice. fmi. fi/). The results were validated for January-April 2017 by comparing them with RIO decision classes calculated from the ice charts of the Russian Arctic and Antarctic Research Institute (AARI). For PC5 the results from the classifier were especially good and up to 0. 95 correlations were found with the AARI ice charts in January, although this grew worse over time, reaching a minimum of 0.
Other studies have shown that MR measurements at low-frequency L-band from ESA's SMOS mission can be used to retrieve SIT of up to 0. 5-1. 0 m  [24] [25] [26] , where the maximum retrievable SIT depends on ice temperature and salinity. Since 2015, NASA's SMAP mission has been providing data at the same frequency. Within the framework of SPICES, combining data from SMOS and SMAP for SIT retrieval was investigated  [27]. The SIT retrieval follows  [25]  and is based on the brigthness temperature intensity which is the average of the horizontally and vertically polarized brightness temperatures, equal to 0. 5. For the combined use of SMOS and SMAP data, the multi-angular SMOS measurements were fit to a SMAP incidence angle of 40°. We found The current RIO product can be used in the ship navigation.