dataesr/scientific-paragraphs-categorization huggingface dataset:
1 ). Capped RNAs transcripts were generated and delivered into PLC3 cells by electroporation. Expression and subcellular localization of mutant ORF2 proteins. We first evaluated the expression and subcellular localization of mutant ORF2 proteins by indirect immunofluorescence. PLC3 cells electroporated with wild type and mutants of HEV RNAs (wt and mt PLC3/HEV cells) were fixed at 3 days post-electroporation (d. p. e. ) and processed for ORF2 staining. For all constructs, over 90% of cells were ORF2-positive indicating that robust transfection and expression of viral genome occurred in wt and mt PLC3/HEV cells (data not shown). As shown in Fig. 2a , the wt ORF2 protein displayed mostly a cytoplasmic localization but likely also a nuclear localization, as recently described  22. Although mutant ORF2 proteins were globally expressed as the wt ORF2 protein, N1 mutants showed a slightly more perinuclear localization (Fig.
In contrast, we observed a shift of migration between untreated (-) and treated (+) condition for the secreted wt and mutants N1S1, N2S1, N2S4 and N3S1 (Fig. 3f ). Importantly, no shift was observed for the Gly(-) double mutant that inhibit N-glycosylation on N1 and N3 sites, confirming that the N2 site is not occupied by N-glycans. In addition for the N2S4 mutant, we observed a higher migration shift between PNGaseF untreated (-) and treated (+) ORF2g and ORF2c proteins, as compared to wt (Fig. 3f ). To further confirm which sites of N-glycosylation are occupied by glycans on ORF2 protein, we next performed mass spectrometry analyses. ORF2g/ORF2c proteins immunoprecipitated with an anti-ORF2 antibody (4B2) were treated or left untreated with PNGaseF. Proteins were resolved by SDS-PAGE and Colloïdal blue-stained bands corresponding to ORF2g and ORF2c proteins in WB (data not shown) were digested in-gel with trypsin (Tryp) or the endoproteinase AspN and then analyzed by nano scale liquid chromatography coupled to tandem mass spectrometry.
5b ). We named this protein ORF2ni for nuclear ORF2intra. Among mutants of N-glycosylation, some differences in the ORF2ni detection (Fig. 5b ) and on the ORF2ni/ORF2intra ratio (Fig. 5c ) were observed. N1S3, N2S2, N2S3, Gly(-) and Gly(+) mutants showed reduced ORF2ni amounts whereas nuclear translocation was likely not affected for the other mutants, as compared to the wt protein. In order to quantify the effect of mutations of N-glycosylation sites on nuclear translocation, nuclear fluorescence intensity of ORF2 protein was measured on 50 cells for each mutant with the ImageJ software. A significant reduction of nuclear translocation was observed for all mutants excepted for the N2S1 mutant and the three mutants of the N3 site (Fig. 5d ). However, we did not observe any correlation between the status of N-glycosylation of ORF2 and its nuclear localization.
Methods Chemicals and cell cultures. PLC3  20  and Huh-7. 5 29 cells were grown in Dulbecco's modified Eagle's medium (DMEM) supplemented with 10% inactivated fetal calf serum and 1% of Non-Essential amino acids (Life Technologies) at 37 °C. Transfected PLC3 cells were maintained at 32 °C in a medium containing DMEM/ M199 (1 v:1 v), 1 mg/ml of lipid-rich albumin (Albumax I TM ), 1% of Non-Essential amino acids and 1% of pyruvate sodium (Life Technologies). plasmids and transfection. The plasmid pBlueScript SK(+) carrying the DNA of the full length genome of adapted gt3 Kernow C-1 p6 strain, (GenBank accession number JQ679013, kindly provided by S. U Emerson) was used as a template  19. The mutants of the ORF2 N-glycosylation sites were generated by site directed mutagenesis. Individual mutations were introduced by sequential PCR steps, as described previously  30  , using the Q5 High-Fidelity 2X Master Mix (New England Biolabs, NEB), then digestions with restriction enzymes and ligation were performed.
The double mutants of N-glycosylation sites were generated by exchanging the mutant fragments from single mutants using specific restriction sites. All the mutations were verified by DNA sequencing. To prepare genomic HEV RNAs (capped RNA), the wild type (wt) and mutant (mt) pBlueScript SK(+) HEV plasmids were linearized at their 3′ end with the MluI restriction enzyme (NEB) and transcribed with the mMES-SAGE mMACHINE ® kit (Ambion). Capped RNAs were next delivered to PLC3 cells by electroporation using a Gene Pulser Xcell TM apparatus (Bio-Rad)  20. patient samples. Patient samples were collected in France between 2014 and 2016. Samples were obtained only via standard viral diagnostics following a physician's order (no supplemental or modified sampling). Data were analyzed anonymously. The French Public Health Law (CSP Art L 1121-1.
NanoLC-MSMS analyses of the protein digests were performed as described in Montpellier et al. 20  MS/MS data was interpreted using search engine Mascot (version 2. 4. 0, Matrix Science) with a tolerance on mass measurement of 10 ppm for precursor and 0. 02 Da for fragment ions, against a composite targetdecoy database (40584 total entries) built with the sequences of ORF2 (H9E9C9_HEV) and the PNGaseF-deglycosylated ORF2 protein in which the three N-glycosylated sites were replaced by Asp(D) residues, fused with a Swissprot homo sapiens database (TaxID = 9606, 20 May 2016, 20209 entries) and a list of classical contaminants (119 entries). Carbamidomethylation of cysteine residues, oxidation of methionine residues and protein N-terminal acetylation were searched as variable modifications. Up to three trypsin or Asp-N missed cleavage were allowed.
(d) wt and mt PLC3/HEV cells were fixed at 3 d. p. e. , and ORF2 protein was stained by using the 1E6 antibody. Cells (n = 50) were analysed by LSM 800 confocal laser-scanning (Zeiss) using x40 oil immersion lens. The nuclear fluorescence intensity of ORF2 protein was determined using the ImageJ software. For clarity and conciseness concerns, blots were cropped. Full-length blots are presented in Supplementary Fig. 5.
Acknowledgements This work was supported by the  French "Agence Nationale de la Recherche sur le Sida et les hépatites virales" (ANRS). M. A. was supported by a fellowship from the  ANRS  and the  University of Lille/Pharmacy college  ( A. Goffard ). I. M. S. was supported by PhD-grants from  Ghent University  and the Egyptian Government. P. M. was supported by an  'Excellence of Science ' grant of the  Research Foundation -Flanders (FWO-Vlaanderen)  under grants n°  EOS-30981113 ; and  FWO  project  G0D2715N. We thank  Charline Camuzet  and  Sophana Ung  for their technical contribution. We thank  Suzanne U. Emerson  ( NIH, USA ) for providing us with reagents. We thank the imaging core facility of the  BioImaging Center Lille-Nord de France  for access to the instruments.
7 subtype. Only peak P1 was observed to strongly inhibit the hNa V 1. 7 current, with peak P2 and peaks P3 and P4 showing moderate and no activity, respectively (data not shown). Therefore, peak P1 was further analyzed in detail by analytical RP-HPLC chromatography. The results revealed the existence of two peaks, P1' and P1", of similar areas and separated by retention times of more than 3 min (Figure  1Bc ). Peaks P1' and P1", injected separately, generated again the two same peaks (data not shown). This demonstrates that the synthetic product equilibrated between two structural organizations, having different interaction properties with the column phase. The influence of temperature (from 0 to 60 • C) was studied on the equilibrium between the two forms, P1' and P1", of peak P1 (Figure  1Bd ).
(B) Backbone peptide folding of PhlTx1 and toxins with PDB entries and showing the highest percentage of amino acid sequence identity with PhlTx1 (PDB entries of CcoTx1: 6BRO, CcoTx2: 6BTV, F8A-Pre1a: 5I1X, Hs1a: 2MT7, HnTx-III: 2JTB, and HwTx-I: 1QK6). All of the toxins sharing between 45 and 76% of sequence identity with PhlTx1 were Na V , K V , and Ca V channel ligands (Table  2 ). All of them possess three strictly conserved amino acids (G4, P11, and W28). Some of these toxins, such as Cd1a, CcoTx1, and HnTx-III, were described to target the Na V 1. 7 subtype with nanomolar affinities (IC 50 between 2 and 232 nM, Table  2 ) and, thus, to have potential implications in the pain therapy field  [28] [29] [30]. Table  2. Comparison of amino acid sequences between PhlTx1 and the 14 most similar or well-documented toxins described in the literature.
The sequence alignment was performed with ClustalIW (version 12. 1 from Emboss programs, EBlosum62 matrix for two pair alignment). The cysteine scaffold and conserved residues are indicated in blue and red, respectively. The Ala-substituted amino acids producing a complete loss of variant affinity for the hNa V 1. 7 subtype are colored red (W24A, K25A, and Y26A), those resulting in a less than 2. 8-fold change variant affinity are colored orange (A1Z, S8A, K12A, and K15A), and the one (D7A) resulting in a gain of variant affinity is colored green (see also  Table 1). The percentage of sequence identity with PhlTx1, the known targets, and the mean IC 50 value (in nM) reported for the hNa V 1. 7 subtype are described in the right-hand columns, according to data sourced from Pubmed and ArachnoServer  [31].
Predicted 3D-Structure of PhlTx1 Computational modelling was performed, using the PyMOL software, to deduce the 3D-structure of PhlTx1 from those of closely related toxins archived in the protein data bank (PDB): ceratotoxin-1 (Cm1a, PDB: 6BRO), ceratotoxin-2 (Cm1b, PDB: 6BTV), F8A variant of Pre-1a (PDB: 5I1X), peptide Hs1a (PDB: 2MT7), hainantoxin-III (HnTx-III, PDB: 2JTB) and huwentoxin-I (HwTx-I, PDB: 1QK6).
Acknowledgments: The authors wish to thank  Meena Murmu  ( SIMOPRO, CEA, Gif sur Yvette, France ) and  Andy McCourt  ( Sahlgrenska Academy, Gothenburg University, Sweden ) for critical reading of the manuscript,  Andrees Bohme  and  Brigitte Schombert  ( Sanofi R&D, Vitry-sur-Seine, France ) for their help in experiments regarding cardiosafety issue, and the staff of the Montpellier Biocampus animal facilities.
Funding: This research was funded by operating grants from the  Agence Nationale de la Recherche  to  E. Bourinet  ( ANR Pain-T, Labex ICST ) and to  N. Gilles  ( ANR PEPTOPAIN ) and from "la  Fondation pour la Recherche Médicale " (équipe FRM 2015). T. C. Gonçalves was supported by a doctoral  CIFRE  fellowship from  Sanofi , and  P. Lesport  by a  LABEX ICST  fellowship.
Such endeavours have been greatly hampered by the fact that, up until the launch of the Global Ecosystem Dynamics Investigation (GEDI) instrument (see below), there has never been any spaceborne sensor designed to measure biomass, so space-based estimates of biomass have relied on opportunistic methods applied to non-optimal sensors, with the limitations this implies. In the tropics, the most significant developments have been based on forest height estimates derived from the Geoscience Laser Altimeter System (GLAS) onboard the Ice, Cloud and land Elevation Satellite (ICESat) before its failure in 2009  (Lefsky, 2005 (Lefsky, , 2010)). Combining GLAS data with other EO and environmental datasets and in situ biomass measurements has led to the production of two pan-tropical biomass maps  (Saatchi et al.
2010; Baccini et al. 2012 ) at grid scales of 1 km and 500 m respectively; differences between these maps and differences between the maps and in situ data are discussed in  Mitchard et al. (2013 Mitchard et al. ( , 2014)). Refinements of these maps have been produced by  Avitabile et al. (2016)  and  Baccini et al. (2017)  based on essentially the same satellite datasets. For boreal and temperate forests, methods have been developed to estimate Growing Stock Volume (GSV, defined as the volume of wood in all living trees in an area with diameter at breast height above a given threshold) from very long time series of C-band Envisat satellite radar data  (Santoro et al. 2011). Multiplying these GSV estimates by wood density allowed  Thurner et al. (2014)  to estimate the carbon stock of forests north of 30°N.
Pol-InSAR and TomoSAR require the combination of multi-temporal stacks of data; this imposes very strong conditions on the BIOMASS orbit pattern, with significant consequences for the production of global biomass products (Section 4). The orbit pattern also imposes strong requirements on the ability of the AGB and height inversion techniques, discussed in Section 5, to adapt to changing environmental conditions. Section 6 deals with the use of BIOMASS data to estimate severe forest disturbance, while Section 7 describes the development of the reference datasets to be used for algorithm calibration and product validation. In Section 8 we discuss developments in how BIOMASS data can be used to estimate key carbon cycle and climate variables. Section 9 addresses a range of secondary objectives.
, 1997; Mokany et al. , 2006; Thurner et al. , 2014). In particular,  Ledo et al. (2018)  used an extensive tropical, temperate and boreal forest dataset to develop a regression, with just tree size and mean water deficit as predictor variables, which explains 62% of the variance in the root-to-shoot ratio. Therefore, throughout this paper, 'biomass' denotes 'above-ground biomass'Below-ground biomass cannot be measured by BIOMASS (or any other remote sensing instrument), but can be inferred from above-ground biomass using allometric relations combined with climate data  (Cairn et al. , 1997; Mokany et al. , 2006; Thurner et al. , 2014). In particular,  Ledo et al. (2018)  used an extensive tropical, temperate and boreal forest dataset to develop a regression, with just tree size and mean water deficit as predictor variables, which explains 62% of the variance in the root-to-shoot ratio.
Forest AGB and height estimation techniques BIOMASS will exploit properties of all three SAR techniques, PolSAR, Pol-InSAR and TomoSAR, to estimate biomass, while both Pol-InSAR and TomoSAR will provide estimates of forest height. However, because BIOMASS will be the first spaceborne P-band SAR, the experimental data needed to support the development and testing of these techniques is based on limited airborne and groundbased measurements. Six major ESA airborne campaigns were carried out (BioSAR-1, -2 and -3 in the boreal zone, and three in tropical ecosystems: TropiSAR in French Guiana, AfriSAR in Gabon and Indrex-2 in Indonesia) using the E-SAR and F-SAR (DLR, Germany) and SETHI (ONERA, France) P-band SARs (see.
(2011)  where mean backscatter differences between the boreal and tropical data were ascribed to calibration errors and removed by shifting the data. The careful calibration of the datasets shown in Fig. 2  indicates that the difference is real and that different physical and biological factors (such as forest structure) are at play in the different forest types. -Fernandez et al. , 2012) , those from La Selva, Costa Rica, in 2004 by the AIRSAR system  (Antonarakis et al. , 2011)  and those from Remningstorp, Sweden, by the E-SAR system in 2007  (Sandberg et al. , 2011). The regression lines indicate that in natural units the HV backscatter is approximately related to biomass, W, by a power law relationship, i. e. ( ) 2 where c and p are parameters. Analysis in  Schlund et al. (2018)   2 ) clearly shows that topography severely affects the power law relationship given by eq.
, 2017) ; b) about 35% of the total volume tends to be concentrated at approximately 24-40 m above the ground  (Tang, 2018). However, tomographic data will only be available in the first phase of the mission. In addition, exploiting the relation between AGB and the 30 m tomographic layer requires knowledge of how the regression coefficients vary in time and space, hence substantial amounts of training data. In contrast, ground cancellation can be carried out with both tomographic and Pol-InSAR data (so throughout the mission). This allows the volume scattering term (eq. (  3 )) to be isolated and hence AGB to be estimated using the scheme described in  Soja et al. (2018) , which makes much less demand on the availability of reference data. The value of tomography for estimating AGB in boreal and temperate forests is less clear, since (a) these forests in general have smaller heights than in the tropics (so it is more problematical to isolate the signal from a canopy layer without corruption by a ground contribution, given the roughly 25 m vertical resolution of the tomographic product from BIOMASS), and (b) the double bounce mechanism appears to be important in recovering the AGB of boreal forests.
Severe forest disturbance The BIOMASS disturbance product aims to detect high-intensity forest disturbance (effectively forest clearance) occurring between satellite revisit times. This is a natural extra use of the data gathered for biomass and height estimation, rather than a driver for the BIOMASS mission, and will contribute to the overall capability to measure forest loss from space using optical (e. g. ,  Hansen et al. , 2013)  and radar sensors (e. g. , the pair of Sentinel-1 C-band radar satellites). Changes in the polarimetric covariance matrix caused by deforestation are relatively large; for example, Fig. 1  indicates that changes by 5 dB as biomass decreases from 500 t ha -1 to nearly zero, while a change in AGB from 100 to 200 t ha -1 causes to change by only ~1 dB. Hence change detection is less affected by the statistical variability inherent in the radar signal, allowing the disturbance product to be produced at a spatial resolution of ~50 m, instead of 200 m, as for the biomass and height products.
ESA are addressing challenge (i) and (ii) by working with existing networks to develop suitable extensive in situ reference data before launch through the Forest Observation System (http://forestobservation-system. net/). A further encouraging development is the ESA-NASA initiative to collaborate in developing the in situ data requirements for GEDI, BIOMASS and NISAR. Cooperation along these lines is already in evidence from joint contributions to the AfriSAR campaign by ESA and NASA. As regards (iii), for the temperate zone, southern hemisphere sites, e. g. in Tasmania, would be suitable, while Siberia is the most desirable region for the boreal zone. However, concrete plans to gather in situ data in these regions are not currently in place. An important complement to in situ data that helps to address challenge (ii) is airborne lidar data.
Exploiting BIOMASS data in carbon cycle and climate analysis Although the primary objectives of BIOMASS are to reduce the major uncertainties in carbon fluxes linked to Land Use Change, forest degradation and regrowth and to provide support for international agreements (UNFCCC & REDD+), its products will also play a key role in advancing fundamental knowledge of forest ecology and biogeochemistry. For example, BIOMASS data will help in constraining critical carbon cycle parameters, initialising and testing the land component of carbon cycle and Earth System models (ESMs), and quantifying the forest disturbance regime. Differences between ESM forecasts of the carbon cycle are currently significant, and lead to major uncertainties in predictions  (Exbrayat et al. , 2018 ). These differences have been linked to variations in the internal processing of carbon, particularly in the large pools in biomass and soil organic matter  (Friend et al.
8 ). Another component of uncertainty in ESMs is in their initialisation of biomass stocks, arising from the paucity of data in the tropics, Land Use Change and internal model steady states. Data from BIOMASS will provide the modelling community with a compelling resource with which to understand both steady state and transient forest carbon dynamics. Observations of the disturbance regime will constrain modelling of both natural processes of disturbance and mortality and the role of humans  (Williams et al. , 2013). The potential for BIOMASS to monitor degradation (partial loss of biomass) will be critical for modelling the subtle and slow processes of carbon loss associated with forest edges, fires and human communities  (Ryan et al, 2012; Brinck et al. , 2017). Fig. 8. The relative reduction in the size of the 95% confidence interval of estimated vegetation carbon turnover times when using a prior value for biomass at each pixel compared to a run without a biomass prior.
Turnover times were estimated using the CARDAMOM system. The darker areas show where reduction in relative uncertainty is largest. Repeated measurements of biomass will allow significant improvements in global monitoring of forest dynamics, and analysis of associated carbon cycling at fine spatial scales. Current biomass maps (e. g. ,  Saatchi et al. , 2011)  provide maps of stocks at a fixed time (or combine observations from several times). While such data help to constrain the steady state biomass, relevant at regional scales (~1º), they give little information on the dynamics of forests at finer (ha to km 2 ) scales over time. BIOMASS will allow detailed, localised, and temporally resolved analyses of forest dynamics to be constrained. The value of such detailed information has been illustrated in a site level analysis for an aggrading forest in North Carolina  (Smallman et al.
The primary advantage of BIOMASS is the higher coherence and longer coherence time resulting from the lower frequency of BIOMASS compared to all other space-based SAR systems. Its longer wavelength with deeper penetration into the firn ensures less sensitivity to snowfall, surface melt and aeolian processes  (Rignot, 2008). This is seen when comparing L-band and C-band results  (Rignot, 2008; Boncori et al. , 2010) , and explains the long coherence time observed in airborne P-band data acquired by the Danish Technical University POLARIS SAR in the percolation zone of the Greenland ice sheet  (Dall et al. 2013 ). The range and azimuth components of the ice velocity field will most likely be measured with differential SAR interferometry (DInSAR) and offset tracking, respectively. At lower latitudes two velocity components might instead be obtained by combining DInSAR from ascending and descending orbits, since the range resolution of BIOMASS is too coarse for offset tracking to provide the range component  (Dall et al.
2013). Generally DInSAR ensures less noisy results, and phase unwrapping is facilitated by the fact that the fringe rate of BIOMASS DInSAR data will be 1/12 of that of Sentinel-1 data, assuming a 6-day baseline in both cases. The very low ice velocities in the interior of Antarctica call for a long temporal baseline, but a 70-day baseline has been successfully used at C-band  (Kwok et al. , 2000) , and therefore sufficiently high P-band coherence is not unlikely with the 228-day baseline provided by the BIOMASS observation cycle. However, ionospheric scintillation is severe at high latitudes, and without accurate correction will corrupt the ice velocity maps, possibly prohibitively. Assessment of whether proposed correction techniques  (Kim et al. , 2015; Li et al. , 2015)  are sufficiently accurate will only be possible when BIOMASS is in orbit.
BIOMASS will focus on tropical and sub-tropical woodlands at 4 ha resolution (though will also cover the temperate and boreal forests of Asia and the southern hemisphere), NISAR will give global coverage at 1 ha resolution but with AGB estimates limited to areas where AGB < 100 t/ha, and GEDI will cover the full range of AGB, but with sample footprints limited to lie within 51. 5 latitude. Hence without the data from all three missions, wall-to-wall estimation of global forest biomass will not be possible. There will, however, still be lack of temporal and/or spatial coverage in regions where BIOMASS cannot operate because of SOTR exclusions and where AGB exceeds the 100 t/ha threshold for NISARThere will, however, still be lack of temporal and/or spatial coverage in regions where BIOMASS cannot operate because of SOTR exclusions and where AGB exceeds the 100 t/ha threshold for NISAR.