dataesr/scientific-paragraphs-categorization huggingface dataset:
Predictions for Selected Putative Effector Families In order to enlarge further our datasets containing signal peptides, we looked for phytoplasma gene families coding for putative effectors. Selection criteria were based on the number of members, presence within several phytoplasma phylogenetic groups, overall levels of conservation but also sequence variation in the region of the putative signal peptide. These criteria were designed to select for consistent families that allow to challenge signal peptide predictors and discriminate between them. We excluded sequences starting with a SVM motif. Among the few such gene families that were found, we selected three of them that were named after their member from AYWB (respectively AYWB_387, AYWB_376, and AYWB_042). The sequences and list of accessions used in this study are available in Supplementary Material M1 and Supplementary Table  8.
Unlike AYWB_042, AYWB_387 and AYWB_376 had already been identified as containing a putative signal peptide and were also designated respectively as SAP08 and SAP09  (Bai et al. , 2009). In a similar process as above, we performed signal peptide predictions for each of the family members and counted the number of putative signal peptides detected by the various software packages (Table  6 ). Similarly to the Tengu/SAP11/SAP54 effectors and SBP sequences, only Like in Tables  4, 5 , only unique signal peptides are counted. SignalP4. 1sensitive, TMHMM and Phobius_SP_TM predicted that 100% (n = 44) of these sequences included a signal peptide. SignalP4. 0 followed closely with 98%, and SignalP3HMM, SignalP5, and Phobius_SP respectively detected a signal peptide in only 66, 39, and 25% of the sequences.
Again, the sequences that were predicted to be devoid of a signal peptide varied depending on the software (Figure  4 ). Only eight accessions were predicted to contain a signal peptide by all of the software packages. A major part (8 out of 11 -73%) of positive predictions by Phobius_SP were also realized by all other software, the remaining (27%) being in contradiction with SignalP5 predictions. The cumulated positive predictions of SignalP3HMM, SignalP5 and Phobius_SP did not include the totality of the sequences (32 sequences out of 44 -73%). A not-yet-solved question is whether the sequences of this dataset do include a functional signal peptide that is processed by the secretion machinery. We currently have no experimental evidence of secretion for any of them, but the conservation of positively charged amino-acids before a core of hydrophobic residues, which are typical features of signal peptides, provides support for this hypothesis (sequence alignments in Supplementary Figures  3 4 5 ).
Whatever the true secretion status of these gene products, the most consistent predictors within each family are SignalP4. 1sensitive, TMHMM and Phobius_SP_TM. This dataset also provides examples of how predictors can be specifically affected by variations in primary sequence. We noticed that very similar N-terminal sequences sometimes resulted in different prediction outcomes, particularly with SignalP5, as shown in Figure  5. In the AYWB_376 family, the accessions WP_053521373. 1 and WP_011412494. 1 display a single amino acid variation within a hydrophobic stretch in their first 60 amino acids (leucine to phenylalanine at position 29), yet they have different SignalP5 prediction. A possible explanation could be an interference with cleavage site recognition, as cleavage is predicted at position 30.
The RmuC protein from E. coli is also predicted to contain a transmembrane domain at its N-terminus  (Lomize et al. , 2017). The second family focuses on the YneF gene, that codes for a short protein of about 70 amino acids of unknown function (uncharacterized protein family UPF0154, pfam PF03672). As yneF is widely conserved in phytoplasmas, but also in acholeplasmas and mycoplasmas, and in distant genera such as Bacillus that have a different lifestyle, it is likely to perform a conserved function. We had a similar case with the third family, referred to as AYWB_444 family. This family is shared among phytoplasmas but also with acholeplasmas and other bacterial taxons from Tenericutes. While its function is unknown, and no conserved functional domain has been identified, the conservation range suggests that it is involved in housekeeping functions.
Predictions for Immunodominant Membrane Proteins Unlike membrane proteins described above, immunodominant membrane proteins are phytoplasma membrane proteins for which the bulk of the protein is extracellular and only a very short portion is intracellular  (Kakizawa et al. , 2006) , allowing them to interact with host molecules  (Konnerth et al. , 2016). They are associated with different gene products of various topologies. Among them, Amp was suggested to possess a cleaved signal peptide and a C-terminal transmembrane domain, based on experimental evidence  (Barbara et al. , 2002; Kakizawa et al. , 2004). We therefore gathered available Amp sequences to generate another dataset of signal peptidecontaining phytoplasma proteins. As Amp proteins are notoriously variable, we proceeded both by keyword search and BLASTP similarities to collect protein sequences.
1sensitive in all of the sequences, and SignalP3HMM and SignalP4. 0 had positive predictions for 37 and 28% of the sequences respectively. The transmembrane predictors TMHMM and Phobius_SP_TM detected a transmembrane region in 100% of the sequences, thus constituting obvious cases of false positives if all positive predictions of transmembrane regions are taken as an indication of signal peptides. As the signal peptide predictors tested in this study have been designed to discriminate between transmembrane regions and signal peptides, for the Imp dataset, the best performance will be associated with the lowest number of positive predictions. Following this principle, regarding the Imp dataset, the best performance comes from SignalP5 and Phobius, whereas the worst performance is produced by SignalP4.
Global Comparison of Signal Peptide Lengths and Scores The availability of the various sequence datasets detailed in this study opens up the possibility to outline conserved properties and possible variations of phytoplasma signal peptides. For instance, in their search of AYWB putatively secreted proteins,  Bai et al. (2009)  focused on candidates with predicted signal peptides longer than 20 amino acids and shorter than 50 amino acids. How does that rule of thumb fit with the Tengu, SAP11, SAP54, SBP and Amp sequence datasets? The analysis of signal peptide length distribution for these datasets shows that, whatever the predictor, a large fraction of predicted signal peptide sequences displays a length between 30 and 35 residues, and that the 20-50 amino acid length range captures the whole diversity of these datasets (Supplementary Figure  10 ).
Incidentally, the same type of analysis performed on various phytoplasma genomes revealed a similar distribution (data not shown). Notable exceptions came from SignalP4. 1sensitive and Phobius, which both predicted signal peptides shorter than 20 amino acids, that belong for some of them to likely housekeeping gene products such as ribosomal proteins (data not shown). Thus, dismissing predictions of signal peptides shorter than 20 residues seems currently to be a reasonable assumption. The various versions of SignalP also provide access to a score which allows the discrimination of signal peptides from non-signal peptides. This score can be interpreted as the confidence in the signal peptide prediction. Supplementary Figure  11  shows a comparison of these scores across the various datasets.
For all the SignalP versions there is a global trend to output lower values with transmembrane protein datasets (RmuC, YneF, AYWB_444, RNAseY). However, scores with moderate values can be achieved by sequences from datasets of different status, suggesting that such indicator values may not be helpful for an uncharacterized gene product. Furthermore, the prediction score, as a confidence score, may indicate the relatedness to signal peptide sequences of the software training set, which might not be fully relevant in the case of phytoplasma sequences. Finally, we also looked if there was any correlation between cleavage score values and the expected status of the sequence (signal peptide or transmembrane region) (Supplementary Figure  12 ). Like the predictions scores, we observed that cleavage scores were not highly informative, meaning that moderate values could correspond to sequences with either a signal peptide or a transmembrane domain.
, 2017). The theoretical definition of sediment connectivity has developed in parallel with the understanding of fluvial processes to represent the continuity of sediment transfer from a source to a sink  (Downs et al. , 2018). The concept of fluvial sediment connectivity is thus a powerful tool to link network scale processes to local morphology and to morpho-dynamics under natural and disturbed conditions. Over the last decade, emerging remote sensing technologies have fostered the generation of network-scale geomorphic datasets concerning, e. g. , hydrology  (Van Der Knijff et al. , 2010)  and channel morphology  (Bizzi et al. , 2019) , and functional characteristics such as channel confinement and width of the active channel  (Demarchi et al. , 2017; Fryirs et al. , 2019; Roux et al. , 2015).
We then use the Genetic Algorithm toolbox in Matlab to minimize the difference between the local transport capacity in a reach and the incoming sediment flux from the upstream network by altering the two parameters s and k of the Rosin distribution. Each set of s and k results in a different set of frequencies, Fi (see eqn. 1), for each grain size class to be used in calculating the local bed load transport capacity. Thus, we assume that the local GSD in a reach will change to accommodate sediment supply from upstream under local hydromorphologic conditions (gradient, width, discharge). Thus, network sediment flux only increases at confluences. However, changing the GSD implies that there can be erosion or deposition of specific size classes, resulting in specific morphodynamics. For example, if the optimization for a reach results in a GSD that is finer than the incoming GSD this fining could be related to either fine material being eroded from the channel or to deposition of coarse material.
River Network Hydrology The magnitude and frequency of discharges used to calculate sediment loads for each river reach were estimated using a hydrological model. The dataset was generated by the LISFLOOD model, a rainfall-runoff model which provides daily flow data across a 5km x 5km grid  (Forzieri et al. , 2014; Van Der Knijff et al. , 2010). Model simulations provided daily discharge data from 1990 to 2014. We assigned each reach in the CASCADE model to the grid cell of the hydrological model with which it had most overlap. From that cell, we then extracted the hydrologic time series and divided it into eight discharge classes corresponding to specific percentiles (0, 0. 1, 2. 3, 15. 9 ,50, 84. 1, 97. 7, 99. 9, 100). We also determined the frequency with which discharge was in each percentile.
7 where Q is the flow discharge, and Bw is the water width. The relationship calculated by interpolation of the data in Figure  4  is: 𝑟 = 𝐵 𝑎𝑡 𝐵 𝑤 = max(0. 2, (2. 36  * + 0. 09,1)) eqn. 8 where r is the ratio between active transport width (Bat) and water width (Bw). We took between 5 and 10 pictures of the bed at different locations on exposed gravel bars. We took pictures by using a digital camera positioned vertically and about 1. 5 m above the ground. We placed a scale bar in each frame to pinpoint the measurement scale. Picture resolution is 4032 × 3024 [px] resulting in an average pixel dimension of 0. 5 [mm/px] for the selected 1. 5 m distance from the ground. GSDs were calculated using Base Grain software  (Detert and Weitbrecht, 2013) , an object detection software tool for the analysis and extraction of granulometric information from images of non-cohesive gravel beds.
Despite the wide uncertainty in parameter values and scarce field data on grain size and sediment fluxes, the CASCADE simulations generate plausible and coherent patterns of sediment fining that match observed sediment size distributions along the Vjosa river and its tributaries (see Figure  5  and Table  1 ). The CASCADE-generated estimates of D50 and D84 are comparable to those observed in the field, whereas the model overestimates the size of the finer fractions. The bias in finer sizes is mostly a numerical effect related to the difficulty of resolving grain sizes finer than 8 mm with the Base Grain software. In the two most downstream reaches, Pocem and Kalivac, the modeled GSDs are overall finer than the measured GDS, but the mean distributions are similar. This effect for the downstream reaches is mostly due to two aspects of the modelling framework: i) CASCADE simulates transport across all sediment size classes defined in the network and finer sediment might be underestimated in our observed measures of surface grain sizes, e.
Acknowledgment This research was partially supported by the  EC Horizon 2020 Research and Innovation Programme, AMBER (Adaptive Management of Barriers in European Rivers) Project , grant agreement number  689682. The TanDEM-X DEM was provided under the  Tandem  project id  DEM_HYDR1516. We are thankful to  Dr Ad de Roo  from the  Joint Research Centre of the European Commission  for providing us the LISFLOOD hydrological data. We would like to thank  Prof. Guido Zolezzi  ( University of Trento ),  Prof. Walter Bertoldi  ( University of Trento ), and  Prof. Klodian Skrame  ( University of Tirana ) for their help in the field.
The concept There are currently no satellite observations sensitive to N 2 O in the lower troposphere and with sufficient precision and accuracy to be used to determine surface-atmosphere fluxes. Such measurements are more pertinent than ever to reduce uncertainties in the emissions and to monitor the evolution of this important GHG, which will very likely continue to increase in the future. The MIN 2 OS project intends to: 1) provide satellite measurements with a high sensitivity near the surface and unprecedented precision, 2) develop a Bayesian flux inversion tool based on a 4D-variational (4D-Var) assimilation method using state-of-the-art atmospheric chemistry transport models (at least one regional and one global model will be used), 3) use process-based modelling to estimate N 2 O surface fluxes on the local scale and aggregate them on the regional scale, 4) validate the inversion-based flux estimates against independent data (measurements and modelling), 5) distribute N 2 O flux data to external users (scientists, policy-makers, and other stakeholders).
, 2015; Pison et al. , 2018)  and  CO and NOx (Fortems-Cheiney et al. , 2019). The CIF is already interfaced with the CTMs: LMDz5 (global scale), FLEXPART (global and regional scales) and CHIMERE (regional scale). It includes the variational inversion method (among other methods) and can be used to assimilate satellite data, as well as surface measurements. It is currently used for projects dealing with GHGs (H2020 VERIFY (https://verify. lsce. ipsl. fr/) and CHE (https://che-project. eu/) projects and the French Agence Nationale de la Recherche (ANR) ARGONAUT project (https://anr. fr/Project-ANR-19-CE01-0007)) as well as reactive species (H2020 VERIFY, and the French ANR projects ARGONAUT and POLEASIA (http://www. lisa. u-pec. fr/projets/?id=1269)). An advantage of using different CTMs is the possibility to assess the impact of atmospheric transport errors in the posterior fluxes.
Insofar that spatial databases of soil properties, crop identification and associated management practices are available, they can provide explicit regional N 2 O emission predictions in response to climatic conditions. STICS crop model has a built-in capability of reinitializing missing spatial information such as seeding dates, seeding density and soil moisture at field capacity using leaf area index derived from satellite observations such as Landsat or Sentinel-2 (e. g. ,  Jégo et al. , 2012 Jégo et al. , , 2015)). Therefore, it can be run for large agricultural regions using relevant soil, crop, and climate databases.
g. , Khorat Plateau or Gulf of Tonkin, to validate the proposed method. Therefore, a calculation is performed on a test area in Central Eurasia where reliable seismic data are available to assess the efficiency of applying the sedimentary corrections to topography in computing the Moho and LAB depths. Then, the Moho and LAB depths will be determined for the study area including Vietnam using the high-resolution local gravimetric geoid model based on the proposed method. The validation of our models is performed using the seismic data as well as the global and local models available in the study region. A comparison with the Moho depth calculated using the classical approach from the inversion of Bouguer gravity anomalies is also presented. Finally, we further discuss the implications of this Moho and LAB depth model with respect to the regional geological structure of the study area.
The geoid and DTM data, which are used for deriving geoid height and (2) N = - π G g ρ w E 2 + z 2 c -E 2 ρ c + z 2 L -z 2 c ρ m + z 2 max -z 2 L ρ a -N 0 , (3) ρ m (z) = ρ a (1 + α[T a -T m (z)]), (4) ρ m = ρ a 1 + α 2 [T a -T mh ] , elevation data, respectively, contain signals representing all masses within the Earth. Therefore, to study the large scale lithospheric structure only (i. e. , Moho and LAB interfaces), a filtering procedure (low-pass filter) must be applied to the data. Thus, a MATLAB program was developed to filter geoid height and elevation data, and subsequently estimate the Moho and LAB depth.
Gravity data and anomalies The terrestrial gravity data used in our geoid and gravity anomaly computation were collected from the IGP-VAST, the Vietnam Institute of Geodesy and Cartography (VIGAC), and the Bureau Gravimétrique International [BGI;  (Bonvalot 2020) ]. The total number of gravity points is 31,102  (Vu et al. 2019)  and their distribution is shown in of free-air gravity anomalies is shown in Fig. 2b. They vary from -213. 0 to 175. 8 mGal with mean and STD of -12. 8 and 26. 9 mGal, respectively. Large negative values, -150 to -200 mGal, are seen in the northwestern region. The short wavelengths of the free-air gravity anomalies correlate strongly with topography. This map of free-air anomalies shows the same small details as seen in the topographic map (Fig. 1b ). The Bouguer gravity anomalies are obtained by removing the effect of the topographic and bathymetric masses from the free-air anomalies.
Geoid height and filtering An accurate local gravimetric quasigeoid, called GEOID_LSC, was determined using the available gravity data in Vietnam by  Vu et al. (2019). Geoid undulations are used to determine Moho and LAB depth, so first the quasigeoid must be converted to the geoid. The grid of Bouguer gravity anomalies and the mixed SRTM model were used to compute the differences between height anomaly and geoid undulation for converting the quasigeoid to the geoid (Hofmann-Wellenhof and Moritz 2006), called GEOID_LSC_C. This conversion procedure was not applied in recent studies using the geoid height inversion approach despite the fact that GGMs provide the height above the quasigeoid (Hofmann-Wellenhof and Moritz 2006). The geoid is an equipotential surface of the Earth's gravity field, while the quasigeoid is not.
e. , 100 km), will be used in the section "Results" to determine Moho and LAB depth. However, in this study region, no seismic data are available on the sedimentary basins, e. g. , Khorat Plateau or Gulf of Tonkin, to validate the proposed method. Therefore, a calculation is first performed on a test area where reliable seismic data allow to assess the efficiency of applying the sedimentary correction to topography in computation of the Moho depth. Consequently, the Moho and LAB depth will be determined for Vietnam using the proposed method.
Validation from seismic data Tele-seismic results obtained from a total of 50 seismic stations were also compiled to validate our model. They include 24 stations from  Nguyen et al. (2013) , 2 stations from  Li et al. (2008) , 1 station from  Noisagool et al. (2014) , 13 stations from  Yu et al. (2017) , and 10 stations of MRF, Vietnam from  Su et al. (2018). Four of the stations are in Thailand, 2 stations in southern China, and 1 station on Hainan Island, China. To evaluate the possible improvement of the models developed in this study, these seismic data were also used to compare with existing Moho depth models in the study region. Moho depths derived from the receiver function are calculated beneath the topographic elevation, instead of referenced to sea level, so the elevation of seismic stations had to be subtracted before comparing the results.
Conclusion We have determined new models of Moho and LAB depth for Vietnam and its surrounding areas at 1′ resolution based on the high-accuracy and -resolution GEOID_LSC_C recently developed  (Vu et al. 2019)  and mixed SRTM models. Sedimentary thickness data derived from CRUST1. 0 model were used to correct the elevation data. The topography-corrected sedimentary basin model is employed to refine the inverse model presented by  Fullea et al. (2007). The results obtained for a test area demonstrated that a better consistency with seismic data is reached when elevation corrected for the sedimentary effect is used. The average discrepancies between the Moho depth derived from seismic data with those inferred from this study using elevation corrected for sediment and elevation data only are 4.
2 and 5. 0 km, respectively. This elevation-corrected model was used to provide new insights in the lithospheric structure beneath Vietnam. It was determined using a linear density increase with depth in the crust and a thermal analysis for the lithospheric mantle density under the assumption of local isostasy. High-pass filtering with a 1D-Gaussian function was used to remove the long wavelengths in the geoid height using GOCE DIR-R5 up to d/o = 9. 2D-Gaussian low-pass filtering was used to remove the short wavelengths (i. e. , < 100 km) from the mixed SRTM-corrected sedimentary thickness. These filtered data were used to determine the Moho and LAB depth models. The Moho depths derived from our model vary across the study region from 15. 3 to 37. 8 km with mean and STD of 28. 0 and 4. 0 km, respectively.
The lithospheric depths vary from 82. 3 to 144. 7 km with mean and STD of 123. 1 and 12. 3 km, respectively. These results were compared to the seismic data as well as to global and local models of the study region. A set of 50 seismic stations distributed over Vietnam was used to validate locally the proposed model of Moho depth. This comparison indicates an agreement within 1. 5 km in STD for the entire country. This new Moho depth model thus represents a significant improvement over the global models CRUST1. 0 and GEMMA, which have STDs of 3. 2 and 3. 3 km, respectively, when compared to the same seismic data. Regarding the geoid-derived LAB depth model also inferred from this study, it is consistent with a recent global model of S-wave velocity model from this region. However, the current uncertainties on the structure of the lithosphere beneath Vietnam and its surrounding areas do not allow a more thorough evaluation.