dataesr/scientific-paragraphs-categorization huggingface dataset:
LAI Estimation Using SAVI with Negative Soil Adjustment Factor XSAVI We also conducted a linear regression analysis of SAVI and LAI by varying XSAVI from -0. 3 to 1 with a step of 0. 001 to validate SAVI's robustness using a negative soil adjustment factor. Both MODIS and field measured LAI data were used for the validation. The results of the linear regression are shown in Error! Reference source not found.
LAI Estimation Using SAVI with Negative Soil Adjustment Factor X SAVI We also conducted a linear regression analysis of SAVI and LAI by varying X SAVI from -0. 3 to 1 with a step of 0. 001 to validate SAVI's robustness using a negative soil adjustment factor. Both MODIS and field measured LAI data were used for the validation. The results of the linear regression are shown in Figure  6. From both LAI data, we can observe that the best results mainly appeared for X SAVI between -0. 18 and 0, indicating that negative X SAVI values were very satisfactory and behaved well compared to positive values. In the MODIS LAI data (Figure  6a ), the negative R 2 and slope appeared in the interval from 0. 225 to 1 for X SAVI , indicating that the correlation between SAVI and LAI was extremely weak. Besides, the negative slope meant a negative relationship between LAI and SAVI.
In our study (very dense vegetation areas), the vegetation isolines converged to one point in the first quadrant. As a result, the assumption based on the original point is much better than the point (-0. 5, -0. 5). This reason can also be seen in the TSAVI slightly better performance than SAVI because TSAVI assumes that the vegetation isolines' intersection lies between the origin and the point (-0. 5, -0. 5). Another possibility is that NDVI was used in the backup algorithm for inversion in MODIS LAI data when the radiative transfer inversion method fails, which might also influence the results.
However, although the method using negative soil adjustment factor achieves better advantages in LAI estimation than other VIs, the accuracy is still lacking compared to these LAI estimation methods. Our method is, therefore, more suitable for use in situations where sensor conditions are limited. For example, when only an optic sensor with a fixed viewing direction is available. In addition, we found that EVI and SAVI were negatively correlated with LAI in the MODIS LAI dataset. This negative correlation was not observed in the field measured data. It leads to inaccurate LAI estimation with errors that cannot be easily estimated. The negative correlation between VIs and the LAI is few reported in other VIs saturation studies, and we did not find a solid explanation for it. We simply assume that it is related to the insensitivity of reflectance values in the red band in the high LAI region.
Conclusions The saturation effects limit the use of VIs. In this study, we explored the possibility of using the negative soil adjustment factor to mitigate the saturation effects. Two data sets were used for the validation, including a long time series observation of MODIS LAI data in the Apiacás area and a global LAI dataset. The surface reflectance data derived from Sentinel-2, Landsat-8, and Landsat-5 were used for VIs calculation. An exhaustive search was employed to estimate the optimal soil adjustment factor for LAI estimation. Results show that LAI was highly cyclical with seasonal changes. Besides, VIs calculated using the positive soil adjustment factor had serious saturation effects, and the correlation between VIs and LAI was very low. In the MODIS LAI data, SAVI and EVI even showed a negative growth with LAI.
The exhaustive search suggested that a negative X SAVI (e. g. , -0. 148) in MODIS LAI and X SAVI (e. g. , -0. 183) in the field measured dataset might be better than the commonly used X SAVI (e. g. , 0. 5) in a high LAI area. So far, apart from the arid grasslands and simulated data that have been mentioned, the feasibility of using negative soil adjustment factors, our study indicates the feasibility of using a negative soil adjustment factor for saturation resistance in dense vegetation areas. Our study further confirms that the lower bound of the soil adjustment factor can be less than zero and reach negative regions. However, although the accuracy of using negative soil conditioning factors is much higher than positive values, the accuracy of our estimates still falls short compared to some other methods that are more demanding on the sensor.
The use of multiple sources of data helped us to increase the sampling frequency of the time series. Because of the similarity of satellite resolution of Sentinel-2 (10 m) and Landsat-8 (30 m), the VIs differences caused by the two sensors were neglected. First, the multispectral images were processed. All available Sentinel-2 and Landsat-8 Tier 1 surface reflectance data in GEE before 10 November 2020, were selected and then filtered by two rules: selecting images that cover the study area and an average cloud cover lower than 20%. Then, each remaining image was clipped to ensure that only the study area's pixels were retained. After that, two boundary masks were used in a preprocessing stage. The first was the cloud mask, and the second was the vegetation mask. GEE provides cloud masks of Sentinel-2 and Landsat-8.
Pixels in the cloud mask were removed in the subsequent data processing. The vegetation mask of Sentinel-2 was created through the classification of the NDVI and simple ratio NIR/Green Ratio Vegetation Index (GRVI) [29]  images using thresholds indicated in a technical report of Sentinel-2 [30] : pixels with (NDVI > 0. 40) or (0. 36 ≤ NDVI ≤ 0. 40 and GRVI > 2. 50) were marked as vegetation pixels. The same rule was implemented to create a Landsat-8 vegetation mask. VIs and reflectance values were calculated pixelwise, and the final VIs and reflectance of each satellite image were derived by aggregating VIs and reflectance calculated per pixel. In the next step, LAI was extracted for each corresponding satellite image. In this work, we used MODIS leaf area index 4-day global 500 m (MCD15A3H V6, https://doi.
Data Availability Statement: Landsat data are available at https://developers. google. com/earthengine/datasets/catalog/landsat, accessed on 11 March 2021. Sentinel-2 data are available at https:// developers. google. com/earth-engine/datasets/catalog/COPERNICUS_S2_SR#description, accessed on 11 March 2021. MODIS leaf area index 4-day global 500 m (MCD15A3H V6) are available at https://doi. org/10. 5067/MODIS/MCD12Q1. 006, accessed on 11 March 2021. The global leaf area.
Lack of detectable inflammatory responses in SARS-CoV-2 injected larvae We then tested if SARS-CoV2 inoculation in the swim bladder resulted in induction of a type I interferon response or inflammatory cytokines. For this, we performed qPCR on dT17primed cDNAs from whole larvae. Based on our previous results  (Kraus et al. , 2020; Levraud et al. , 2019) , we tested the main type I interferon genes inducible in larvae, namely ifnphi1 and ifnphi3; the strongly IFN-inducible gene MXA; the classical inflammatory cytokines il1b and tnfa; cytokines that reflect induction of type 2 or type 3 responses, il4 and il17a/f3, respectively, and chemokines ccl19a. 1 and ccl20a. 3. Although individual experiments suggested significant induction of ifnphi1 at 48hpi or il17a/f3 at 72h, this could not be replicated; as shown on Figure  8 , in which data from 4 independent experiments have been pooled, no significant change in expression of any of these genes can be observed compared to uninjected control larvae.
Dead cells were labelled with Sytox AADvanced (ThermoFisher). Cell suspensions were acquired on an Attune NxT flow cytometer (ThermoFisher) with blue and yellow lasers, and data analyzed with FlowJo. Cell culture Epithelioma papulosum cyprini cell line (EPC) was maintained in Leibovitz-15 media (L15, Gibco) supplemented with 10% fetal bovine serum (FBS, Gibco), 100 µg/l penicillin and 100 µg/ml streptomycin. EPC cells were cultured at 32 °C without CO2. Human ACE2 expressing constructs The hACE2 ORF was amplified from clone IOH80645 (Thermosfisher, GenBank NM_021804. 2) using primers hACE2NotStart3 and hACE2EndNot3 (table  2 ). The amplified PCR fragment was digested by NotI and inserted in the NotI site of the Tol2S263C:mC-F vector between the promoter of the zebrafish ubiquitous ribosomal protein RPS26 encoded by chromosome3, and the mCherry ORF.
In this construct, the RPS26 promoter drives the expression of a hACE2 protein fused at its C-term with farnesylated mCherry. In order for the ORF to drive the expression of two separated proteins (hACE2 and mCherry-F), primers hACE2NotStart3 and hAce2. 2ANot were used to amplify the hACE2 ORF from the IOH80645 clone. The amplified fragment was digested by NotI and cloned in the NotI site of Tol2S263C:mC-F between the promoter of the zebrafish ubiquitous ribosomal protein RPS26 encoded by chromosome3, and the mCherry ORF. Maps and sequences of plasmids are available at https://doi. org/10. 5281/zenodo. 4672028. For in vitro transfection of EPC cells, plasmid pcDNA3. 1-hACE2 (Addgene #1786) was directly used along plasmid pmEGFP-N1  (Chen & Reich, 2010). Cell transfection EPC cells were electroporated with the Neon transfection system (Invitrogen).
Transfection efficiency was checked at 3 days post-transfection using a Zeiss Axio Observer Z1 widefield microscope with a 10X/NA 0. 25 objective. Phase and GFP channel were acquired on 5 field of view. Confocal acquisition of immunostained EPC cells was performed on a Leica SP8 upright microscope using a 25X/NA 0. 95 coverslip corrected objective. Endogenous GFP and Alexa 647 were excited with 488 nm and 638 nm respectively and detected with PMT. Fiji was used to adjust brightness and contrast of confocal images of immunostained EPC cells. Transfection efficiency was quantified using Fiji by manually counting total cells and GFP expressing cells, respectively. Statistical analysis Analysis were performed with GraphPad Prism. Methods used are indicated in Figure legends. Normality/lognormality tests of data distribution were performed to decide the most appropriate assays.
Acknowledgements We thank  Frédéric Sohm  and  Joanne Edouard  ( AMAGEN, Gif-sur-Yvette ) for the generation of the triple interferon mutants; our animal facility manager  Yohann Rolin  for ensuring fish health and fertility despite lockdowns; numerous members of  Institut Pasteur  for sharing materials ( Olivier Schwartz ,  Julian Buchrieser ,  Pierre Charneau ,  Viviana Scoca ,  Amandine Noirat ,  Nathalie Sauvonnet ,  Pierre lafaye ,  Christophe Zimmer ,  Christian Weber ), managing the  BSL3 lab  ( Ferdinand Roesch ,  Florence Guivel-Benhassine ,  Thomas Vallet ,  Heloise Mary ), occasional help in experiments ( François Huetz ,  Björn Meyer ,  Doris-Lou Demy ).
Funding This work was supported by the " Urgence COVID-19" fundraising campaign of Institut Pasteur , a dedicated grant co-funded by  Institut Pasteur and Institut du Cerveau -Paris Brain Institute , the "ZebraCorona" grant from the exceptional research program call of  Université Paris-Saclay , the  MUSE-University of Montpellier COVID program , the  Agence Nationale de la Recherche  (Grant  ANR-16-CE20-0002-03  « fish-RNAvax ») and the  "ImageInLife" Innovative Training Network  funded by  European Community 's  Horizon 2020 Marie-Curie Program  under grant agreement  n°721537.
Results Production and purification of SapL1. The hypothetical protein XP_016640003. 1 (EMBL accession number), encoded by SAPIO_CDS9261 and from now on referred as SapL1, was identified through data mining using the FleA (pdb entry 4D4U  34  ) sequence as bait into the genome of the reference strain S. apiospermum IHEM 14462  33. During the sequence analysis, we found that the first 74 amino acids of the putative protein (Uniprot A0A084FYP2) represent a very disordered region that is not present in any other related protein. We suspected that this peptide could be due to an error during the genome annotation. To verify it, we tried to get the gene from total mRNAs extracted from mycelium by 5' Rapid Amplification of cDNA Ends (5' RACE) using the following primers GSP1-SapL1 5'-TTA AGC AGG GGG CAG AAC AGC-3' and GSP2-SapL1 5'-CCA ACG ACC CAG CCA GAG TTCC-3' but we were unsuccessful.
Due to the impossibility to estimate the molecular weight of SapL1 by size exclusion chromatography on this resin, we performed measurements in solution using Dynamic Light Scattering (DLS). We obtained a monodisperse peak corresponding to a protein of 72 ± 29. 4 kDa corroborating that SapL1 forms dimers as FleA and other proteins of this family (monomer MW: 40 kDa, data not shown)  27. The range of the standard deviation also suggested an ellipsoidal shape, which is characteristic for the dimers in this lectin family  27, 35.
Overall structure of SapL1. SapL1 was co-crystallized with α-methyl-fucoside and the structure of the complex was solved by molecular replacement at 2. 3 Å resolution in the P2 1 space group using the coordinates of FleA (PDB code 4D4U  34  ) as the search model. The asymmetric unit contained two monomers, assembled as a dimer with all 295 amino acids visible apart of the N-terminal methionine. See data collection and refinement statistics in Table  1. SapL1 folds into the canonical six-bladed β-propeller with six-binding sites at the interface between blades typical for this family of lectin (Fig. 4 ). A fucose moiety was found in 5 and 4 of the six binding pockets of chains A and B, respectively while glycerol originating from the crystallizing solution was found in the other binding sites.
7 ). Fluorescence was observed all around the cell surface but also concentrated in some part of the nucleus. A strong fluorescence signal was already observed at the lowest concentration used (5 µg mL -1 ). The binding of SapL1 was inhibited in the presence of its cognate ligand: α-methyl-fucoside (Fig. 7E-G ). Some background signal was still observed in the first experiment (Fig. 7E, G ) whilst it was totally abolished in the duplicate (Fig. 7F, H ). These data show that SapL1 binding to the cells is dependent on the recognition of fucosylated carbohydrates.
Isothermal titration calorimetry (ITC). Experiments were performed using a Microcal ITC200 calorimeter (Malvern Panalytical) with 40 μL of L-fucose 5 mM in the syringe and 200 µL of protein 0. 05 mM in the sample cell. Both, the protein and sugars were dissolved in a buffer composed of 20 mM Tris HCl pH 8. 0 and 100 mM NaCl. A total of 26 injections of 1. 5 μL of ligand were added to the sample cell at intervals of 180 s while stirring at 850 rpm. Experimental data were adjusted to a theoretical titration curve by the Origin ITC Analysis software. All experiments were performed at least by duplicated and the stoichiometry was fixed to 1.
Acknowledgements We would like to thank to  Valérie Chazalet  and  Emilie Gillon  for their technical assistance. We are grateful to the  Consortium for Functional Glycomics  for providing the glycan array resource. We also would like to thank for access to the beamlines BM30A-FIP at the  European Synchrotron Radiation Facility (ESRF), Grenoble , France where initial tests were performed and Proxima 1 at SOLEIL Synchrotron,  Saint Aubin , France (Proposal Number  20170827 ), where final data were collected. Thanks to our local contacts  Jean-Luc Ferrer ,  Serena Sirigu , and  Pierre Legrand  for their assistance and technical support.
Funding This project has received funding from the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Skłodowska-Curie grant  ( H2020-MSCA-ITN-2017-EJD-765581 ), from the  French cystic fibrosis association Vaincre la Mucoviscidose. The project was also supported by  Glyco@Alps  ( ANR-15-IDEX02 ).
CNT/F occupational exposure induced a significant and diverse peptidomic response in the blood In this study, the serum peptidome was assessed for 24 workers at CNT/F facilities who were not active smokers and reported no active respiratory disease (mass spectrometry dataset available via the MassIVE repository, MSV000087305, of the ProteomeXchange Consortium, PXD025646). Subjects divided equally into two exposure groups based on their personal breathing zone levels of inhalable elemental carbon (EC): High exposure subjects had > 0. 5 µg/m 3 inhalable EC; Low exposure subjects had measures below the 0. 13 µg/m 3 limit of quantification for inhalable EC (three standard deviations from the limit of detection)  [19]. In this regard, subjects within the Low exposure group were indistinguishable from non-exposure.
Detected features spanned broadly across the available separation space (Fig. 1a ), demonstrating that these results were not artefactual from sporadic interference or insufficient separation. Given the serum peptidome's diverse response and the goal to identify group-discriminant peptides, the dataset was filtered further to retain the 3,532 peptides reproducibly detected in ≥ 75% of workers, which achieved an estimated 77% power to discriminate a twofold group-difference (see Additional file 1). Of those peptides, 934 were statistically significant between High and Low groups (Fig. 1b, c ), providing a reliable input dataset for model building. Overall, results here substantiate a significant and diverse serum peptidomic response conserved among workers with an average exposure exceeding the NIOSH 1.
0, with a Q 2 statistic of 0. 99916, demonstrating retained predictive power across permutations. Next, cross-validation assessment was performed on a validation subset of 6 subjects per group with data entirely separate from those use for topfive model building. Ideal discrimination was observed with no misclassification after 50 random sub-samplings between workers with > 0. 5 µg/m 3 inhalable CNT/F exposure from those with < 0. 1 µg/m 3 (Fig. 4a ). Unsupervised principal component analysis (PCA) was then performed to further affirm the discriminatory power of the serum peptidome and rule out overfitting of the supervised procedures used to build the model. When assessed across the full complement of 3,532 highly reproduced peptides, without any supervised testing applied (Fig. 4b ), the results clearly discriminated workers with High from Low CNT/F exposure across principal component 1, with 43.
Utilizing the R package Rcompanion  [68] , 237 possible models were fit between the mass spectrometry data and the worker metrics and then evaluated using the compareLM method. Akaike Information Criterion (AICc) scores were calculated per peptide across all models. A count of peptides exhibiting the lowest AICc score per model was tabulated, with the count's significance from random assessed using a one-sample z-test. Models with a significant number of low peptide AICc scores were further considered for their quantitative correlation using Pearson correlation. Adjusted R 2 values were assess across peptides for the selected models. Peptides exhibiting strong correlation with the selected model were used for further model refinement with PLS-DA, performed in Metaboanalyst v4. 0  [69]  using data from half of the subjects selected at random.
Availability of data and materials The peptidomic dataset generated and analyzed during the current study is available at the MassIVE repository, MSV000087305, of the ProteomeXchange Consortium, PXD025646 (https:// doi. org/ doi: 10. 25345/ C5RV4G). Individual metrics and demographics collected on the participating workers are not publicly available as they contain personally identifiable information protected by the U. S. Privacy Act of 1974.
Another physical characterization and surface sample retrieval mission, OSIRIS-REx by NASA  [14] , is ongoing with the asteroid Bennu as its target. The first attempt to reconstruct the interior structure of an small body, Comet Nucleus Sounding Experiment with Radiowave Transmission (CONSERT), was carried out in 2014 as a part of the European Space Agency's (ESA) mission Rosetta with the comet 67P/Churyumov-Gerasimenko as its target. The ongoing investigation of CONSERT's data has so far shown that the internal properties of a comet can be revealed via a bistatic radar configuration  [15]  of radiowave transmission between an orbiter and a lander by observing the travel time of the electromagnetic wave propagating through the body  [15] [16] [17]. Furthermore, recent numerical and experimental studies  [7, 18] , have suggested that a bistatic radar can detect deep interior electric permittivity anomalies and recover internal structural properties within an asteroid.
Wireframe construction The tetrahedral mesh for the object containing the mantle, interior and voids was created by Gmsh software 3 and then imported into Matlab (Mathworks, Inc. ) to create the wireframe structure, i. e. to replace the edges of the tetrahedral mesh with regular prisms. The edge width w was set to match with the filling ratio f p = 0. 66 and f p = 0. 90 for the mantle and interior compartment, respectively, accounting the    2 ) with radii r 1 , r 2 and r 1 ′, r 2 ′ before and after edge inflation, respectively. The effective radius r 1 (eff) has been selected so that the effect of the inflation is equal with respect to r 1 (eff) and r 2 , i. e. , r 1 ′/ r 1 (eff) = r 2 ′/r 2. effect of the inflation with respect to a volume of a 35 mm diameter sphere. The edge was placed on the longitudinal symmetry axis of the prism, and the length of the prism was set to be slightly larger with respect to that of the edge to create some overlap and, thereby, ensure the printability of the structure.
Each prism was constructed of eight triangles, i. e. , the minimum triangular configuration required to present a regular prism, to keep the size of the final triangular mesh of the volumetric model as low as possible. The eventual model, i. e. , a surface mesh describing the wireframe, was stored as an STL (stereolithography) file which can be read by the most extensively used 3D printing software such as the Prusa Slicer 4  application used in this study. The edge width corresponding to a given filling level f p was sought by optimizing the slicer's estimate for the filament volume for the spherical meshes (III) and (IV).
Stand design Developing a 3D printed stand was found to be necessary to allow accurate positioning of the model in the anechoic chamber. The Blender software  5  was applied to configure a cylindrical wireframe stand with an octagonal cross section of 190 mm diameter and 13. 7 mm height, and a triangulated cut-out part matching with a slightly expanded and coarse resolution asteroid shape model. The edge width of the stand was set to be around 2. 8 mm, i. e. , less than one fourth of the shortest wavelength in the planned measurement wavelength range (Section 2. 2) to ensure the invisibility of the stand in the actual measurement. The standard polylactic acid (PLA) filament (diameter 1. 75 mm, density 1. 24 g/cm3) was used, as it has a relatively low weight and permittivity (ε r ′ < 3), while providing a solid enough support for the measurement purpose.
Wireframe models The numerical wireframe models, their relative filling ratios, edge widths, and maximal aperture sizes have been described in Table  2. The complete models are illustrated in Fig. 5 , and a close view of the mesh structure with the two applied filling ratios in Fig. 6. The edge width was selected so that the final printable model (GCODE file) had the given relative filling ratios f r = 0. 66 and f r = 0. 90 in their respective compartments. The results show that the aperture diameter inside the printed analogue objects does not exceed 1. 4 mm. In addition to the aperture size, the overall structural accuracy of the models can be estimated to be determined by one half of the edge width, i. e. , w/2, which is also maximally 1. 4 mm. The edge length varies slightly within each wireframe as the mesh generator routine of Gmsh relates the tetrahedral grid to the slightly varying surface mesh size.