dataesr/scientific-paragraphs-categorization huggingface dataset:
3%. Thus, volume and AGB predictions from ALS data are highly dependent on the way field surveys were conducted as well as on the way lidar data were acquired. Both these groups of parameters can be, to some extent, set in order to optimize assessment of forest attributes from ALS data. There is a scarcity of studies assessing the relative significance of some of the parameters relating to lidar survey configuration and field inventory protocol that can be easily set and monitored. Indeed, most studies have addressed error evaluation using different parameter combinations but do not characterize the impact of each parameter, thus making it difficult to rank parameter significance. Another explanation lies in the way sensitivity analyses are conducted. Indeed, in most studies, the authors adjust the parameters manually, only considering a small number of alternative values for each of them, in a one-factor-at-a-time (OAT) approach.
Among the numerous parameters influencing AGB predictive model accuracy, only a few of them can be easily controlled, i. e. lidar pulse density and both field sampling protocols and measurements. Therefore, the main goal of this study was to provide À partly based on GSA results À technical guidelines to optimize lidar pulse density and field survey protocols in order to implement predictive models of AGB from ALS data. To achieve this, three specific tasks were performed. First, we assessed the influence of lidar pulse density on the performance of the model by comparing results obtained with several lidar data sets acquired on the same study site. Second, we identified field measurements most impacting model accuracy by comparing predictions obtained when parameters are changed one at a time (OAT approach) and all simultaneously in a GSA.
Field plot data Field measurements were collected on a series of sample plots for each study site. A stratified random sampling design has been used to define field plot positions at both sites. The stratification was based on stand age (young, intermediate and mature stands). As recommended by Laes et al. (  2011 ), for plots in a mixed condition, i. e. overlapping two or more stands, the plot centre was moved so the field measurements only represent a single condition. A different field protocol was used for each site. Hundred square plots (400 m 2 ) were measured at site 1 during the summer of 2012 where all the trees were identified and their species recorded in order to assess the total tree number per plot. In addition, the DBH for the 10 trees closest to plot center and H of the 5 trees closest to plot center were measured.
3. 1-7) and, the allometric equation from  Hounzandji et al. (2015)  was used for Pedunculate oak. Estimations at plot level were then rescaled to per-hectare values. Using the allometric equation from  Shaiek et al. (2011)  for Maritime pine, mean plots AGBs were estimated at 71. 8 Mg/ha in site 1 and 77. 5 Mg/ha in site 2. Lidar data ALS data were collected at both study sites using a small footprint lidar. Site 1 was surveyed in February 2013 using an ALTM 3100 (Optech, Canada) system. Four different acquisitions were carried out with different flight parameters and system configurations in order to produce ALS data at different pulse density: (flight A) 0. 5 pulse/m 2 , (flight B) 1 pulse/m 2 , (flight C) 2 pulses/m 2 , and (flight D) 4 pulses/m 2. Site 2 was surveyed using a LMS Q560 (Riegl, Austria) system in April 2011 with a pulse density of 8 pulses/m 2.
Additional data specifications for ALS data sets are given in Table  2. For both sites, lidar surveys were conducted at the same growing stage as field surveys. Data pre-processing was performed by the data providers, i. e. IGN and Sintegra for sites 1 and 2, respectively. Ground points were classified using the TIN-iterative algorithm  (Axelsson 2000)  in order to produce a digital terrain model (DTM). The DTM was then converted into a 1 m resolution grid. For each acquisition, aboveground heights were calculated by subtracting from each lidar point elevation the corresponding ground elevation given by the DTM, thereby removing topographic effects from lidar point clouds. From the resulting lidar point clouds, subpoint clouds corresponding to the spatial extent of each field plot were extracted.
Two sensitivity analysis approaches were used to investigate the role of these parameters in the prediction quality of AGB models. The first was a standard OFAT approach. When this approach was applied, regression analyses were carried out to assess the influence of key parameters individually on AGB accuracy. Each parameter was tested using a wide range of values in order to assess its specific impact on AGB prediction accuracy. The parameter value ranges were defined by gradually degrading the characteristics of the available data sets (section 3. 3) except in the cases of the choice of allometric equations (section 3. 3. 1-7) and pulse density (section 3. 2). The second approach used was a GSA based on Monte Carlo simulations. This approach aimed at identifying the parameters related to field protocols and measurements that explain most AGB prediction variance and also interactions between parameters (section 3.
P was calculated from the lidar data as the ratio of the number of first returns below 2 m to the total number of first returns. Lastly Cv LAD was calculated as the ratio of the standard deviation to the mean of the leaf area density profile. The density profile was computed by assessing a transmittance profile and then using the Beer-Lambert law to retrieve vegetation density at each height interval  (Bouvier et al. 2015). l CH , r 2 CH , P and Cv LAD were assessed considering only returns above 2 m for each of the point cloud subsets corresponding to the areas covered by each of the field plots. The metrics were included in a multiplicative power model with the coefficients fixed through a regression models based on these metrics. In order to make an unbiased assessment of the predictive capacity of a model, a reference data set is generally created independently of the calibration or training data set  (Snee 1977).
Influence of field data characteristics on the predictive performance of the model A-Sensitivity analysis: One-factor-at-a-time (OAT) Each parameter was varied within a range of values or choices in order to assess its effect on AGB prediction accuracy, while other parameters were set to their nominal value in the available field data sets. The ranges of values were defined by gradually degrading the characteristics of the data sets. Three parameters of the inventory protocol were first investigated: the number and size of field plots, and the DBHmin defining trees to be included in the database. Next, the accuracy of field plot measurements was also investigated considering respectively: H and DBH measurements, plot center positioning, and the choice of allometric equations used to predict reference AGB.
For each studied parameter, evolutions in accuracy values were presented as a rate of change in RMSE considering the RMSE value obtained with the original and complete field data set. This reference RMSE changes depending on the site used to study the impact of each parameter. Influence of the number of field plots. The more field plots, the higher prediction accuracy is likely to be. The optimal number of field plots is thus a tradeoff between the quality of AGB predictions and the cost of field data acquisition. This optimal value also depends on field plot size, methodology used, and stand characteristics  (Gobakken and Naesset 2009). Hundred field plots were collected in site 1, which made it possible to evaluate the influence of a lower numbers of field plots on model accuracy. Therefore, AGB was predicted using a subset of field plots from 100 to 20, in decrements of 1.
5 m. The point cloud subsets were clipped accordingly to compute the metrics used to build and validate the models. Influence of the minimum DBH threshold. Various DBHmin values have been used in forest inventories to determine if a tree should be included or not in the measurements made in the plot  (Tomppo et al. 2010). We investigated impact of DBHmin on AGB prediction accuracy using the plots of site 2 where all trees with DBH ! 7. 5 cm were collected. DBHmin ranged from 7. 5 cm -value dictated by available field data -to 17. 5 cm -maximum threshold value used in traditional field inventory  (Duplat et al. 1981 ) -with a 0. 5 cm step. Influence of field plot position accuracy. The exact location of the field plot center is usually measured using differential GPS. Differential corrections are applied using the closest fixed antenna for position accuracy, which provide sub-meter accuracy.
5 cm were collected. We focused on allometric equations for the Maritime pine, as it is by far the most dominant species ($99% of all the trees in the inventory). Table  3  lists the sources of the allometric equations used. Equations 1, 2a and 2 b are explicitly AGB equations. Equations 2a and 2b were combined during the analysis to cover the whole range of stand ages observed in the study sites. Indeed, equation 2a was developed for smaller trees (1. 5 < DBH < 16 cm) and equation 2b was developed for larger trees (29 < DBH < 52 cm). Equations 3 and 4 are total wood volume equations, including stumps and branches. A database providing expansion factors from total wood volume to AGB was used to predict the reference AGBs of plots in site 2  (Zanne et al. 2009). AGB prediction accuracy levels were compared when using the 4 allometric equations to obtain the reference data sets.
Finally, in the third and last step of the GSA, first-order variance-based sensitivity indices are estimated for each studied parameter, which are derived from the results of each model run. These sensitivity indices are based on the decomposition of the total output variance into conditional variances. First-order sensitivity indices measure the individual contribution of each uncertain parameter to the total variance of the AGB prediction. The remaining part of variance of the AGB prediction is explained by the inter-parameter interactions. The GSA approach was implemented in R, using sobol2007 function from the package 'Sensitivity'. A detailed description of this Monte Carlo estimation of Sobol' indices can be found in the references that are given in the reference manual of this package  (Looss et al.
Therefore, and despite the relative simplicity of the environment, a minimum of 40 plots is recommended. Plot positioning efforts should be performed with great care and position accuracy of center plots of below 5 m is highly recommended. It might be even more important to follow these recommendations when working in more complex forest environments, as the error related to plot positioning might be higher. In addition, it is also recommended to inventory field plots at least 13 m in radius when plots contain trees with a DBH equal or greater to 17. 5 cm. Special attention should be paid to the choice of the allometric AGB equation. contribution to the development of the package « lidR » was partly supported by the GRAINE program of ADEME (Agence de l'Environnement et de la Maîtrise de l' Energie) (PROTEST project [1703C0069]) and by  ONF (convention TETIS-2015-227).
Interestingly 6 out of the 8 cysteines are gathered in the C-terminal region between Cys39 and Cys69, following a spacing (Cys-X2-7-Cys-X3-10-Cys-X0-7-Cys-X1-17-Cys-X4-19-Cys) typical of cystine-knot structures, (i. e. , three intricate disulfide bridges that confer very high stability to proteins; Figure  4 ;  (Daly et al. , 2011) ). Hence, it is likely that rigidity originates from the structure formed by these cysteines that are highly conserved in the protein family, as indicated by a Consurf analysis (Figure  S1 , Figure  S4 ). Thus, we sought to determine whether these disulfides are formed and whether they influence the stability and/or the oligomerization state of the protein by covalent bonds. A single peak corresponding to the theoretical mass of MLP124266 monomer was obtained by mass spectrometry (data not shown).
To identify potential structural homologs of MLP124017, we performed structural similarity searches using the Dali server  (Holm and Rosenström, 2010). Queries identified SBAL_0622 (PDB code 3BLZ) and SPO1084 (PDB code 3FKA) as the closest structural homologs with the highest Z-score of 6. 0 and 6. 3, respectively, and a RMSD of 4. 1 and 3. 5Å, respectively (Figure  S6 ). These two proteins, which are from the bacteria Shewanella baltica and Ruegeria pomeroyi, have no known function, but share a common Nuclear Transport Factor 2-like (NTF2) fold. The NTF2 superfamily comprises a large group of proteins that share a common fold and that are widespread in both prokaryotes and eukaryotes  (Eberhardt et al. , 2013). Taken together these results show that although MLP124017 do not share sequence similarities or domain with other proteins in sequence databases, its structure is similar to proteins of the NTF2 folding superfamily.
lini effectors AvrL567 and AvrM have predicted disordered Nterminal regions that are susceptible to protease degradation  (Catanzariti et al. , 2006; Wang et al. , 2007). Flexible folds are known to be adaptable linkers that favour the ability to bind partners  (Chouard, 2011). As the N-terminus of many cytoplasmic effectors is anticipated to mediate protein entry into host cells  (Petre and Kamoun, 2014) , it is tempting to speculate that this flexible part may bind a target important for cell entry. MLP124266 belongs to a rust multigene family reported in the Melampsoraceae for which population data has been collected. The survey of synonymous and nonsynonymous nucleotide substitutions in isolates of M. lini, M. larici-populina and in several Melampsora spp. identified positions under positive selection in between strictly conserved cysteines in the C-terminus region of the proteins  (Barrett et al.
EXPERIMENTAL PROCEDURES Sequence analyses and names Alignment and phylogenetic analyses were performed on the phylogeny website (www. phylogeny. fr) with default parameters. Alignments were corrected and edited manually, and phylogenetic trees were generated with FigTree v1. 4. 3 (http://tree. bio. ed. ac. uk/software/figtree/). Physical and chemical parameters of proteins were estimated using protparam tool (http://web. expasy. org/protparam/). Common names and JGI ID of genes described in this study are as follow: Mlp124266,  Mlp124499, Mlp124111, Mlp124478, Mlp124530, Mlp124561, Mlp37347, Mlp109567, Mlp124017, Mlp107772, and Mlp124202. The mapping of the family-wide conservation pattern of amino acids onto MLP124266 structure was performed with Consurf (http://consurf. tau. ac. il/2016/).
For MLP124266 and MLP1124499, NMR spectra were acquired on a Bruker DRX 600 MHz spectrometer equipped with a TCI cryoprobe. For MLP124266 and MLP124499, COSY, TOCSY (mixing time of 60 ms) and NOESY (mixing time of 150 ms) experiments were run at 298 K, respectively. For MLP124266, HNHA, HNHB, R1 and R2 15 N relaxation rates, 1 H-15 N heteronuclear NOE, HNCA (with 24 ( 15 N) x 28 ( 13 C) complex points and 192 transients per increment) standard experiments were recorded. Spectra were processed using Topspin® 3. 0 software (Bruker) and analysed with NmrViewJ  (Johnson and Blevins, 1994) , CcpNmr  (Vranken et al. , 2005)  and ARIA2  (Rieping et al. , 2007).
Structure calculation NOE peaks identified in 3D 15 N-NOESY-HSQC and 2D NOESY experiments were automatically assigned during structure calculations performed by the program CYANA 2. 1  (Güntert, 2004). The 15 N, HN, 13 C', 13 Cα, Hα, and 13 Cβ chemical shifts were converted into φ/Ψ dihedral angle constraints using TALOS+ (v. 1. 2)  (Shen et al. , 2009). Hydrogen bonds constraints were determined according to 1 H/ 2 H exchange experiments of backbone amide protons (HN). Each hydrogen bond was forced using following constraints: 1. 8-2. 0 Å for HN,O distance and 2. 7-3. 0 Å for NH,O distance. Final structure calculations were performed with CYANA (v. 2. 1) using all distance and angle restraints (Table  S3 ). 600 structures were calculated with CYANA 2. 1, of which the 20 conformers with the lowest target function were refined by CNS (v.
1. 2) using the refinement in water of RECOORD  (Nederveen et al. , 2005)  and validated using PROCHECK  (Laskowski et al. , 1993). For MLP124266 structure calculation, 783 NOE peaks identified in 3D 15 N-NOESY-HSQC or 2D NOESY spectra, 75 φ/Ψ dihedral angles generated by DANGLE  (Cheung et al, 2010)  and 10 hydrogen bonds were input as unambiguous restraints in ARIA2. Covalent disulfide bonds between Cys39-Cys55, Cys44-Cys58 and Cys50-Cys64 were also introduced. Among the 400 structures generated by ARIA2, 20 models of lowest energy were refined in water (Table  2 ). NMR assignment and structure coordinates have been deposited in the Biological Magnetic Resonance Data Bank (BMRB code 34298 and 34423) and in the RCSB Protein Data Bank (PDB code 6H0I and 6SGO), respectively. 2. 8 RMSD (Å) (b)  Backbone 1.
ACKNOWLEDGMENTS The  UMR1136  is supported by a grant overseen by the  French National Research Agency (ANR)  as part of the  "Investissements d'Avenir" program  ( ANR-11-LABX-0002-01 ,  Lab of Excellence ARBRE). This work was supported by the  French Infrastructure for Integrated Structural Biology  ( ANR-10-INBS-0005 ).
It has also been shown that the coexistence of different genomes in allopolyploid cultivars can induce Homoeologous Exchanges (HE), i. e. recombination between chromosomes contributed by different species taking place when allotriploidy was established. In Brassica napus, HEs have been shown to cause changes on expression dosage affecting the phenotype of allotetraploid plants  [12]. In banana, it had been suggested that intergenome translocations were common  [13, 14]  and it has been shown that chromosome pairing between A and B chromosomes at meiosis can occur and translocations between A and B genomes has taken place  [15]. However, the relationship between genome structure in allotriploidy and gene expression has not been explored. In this study, we used data sets obtained from root apexes of 12 banana cultivars covering auto and allotriploids (AAA, AAB and ABB).
Read mapping and single-nucleotide polymorphism (SNP) calling For this study we first used a published RNAseq dataset  [16]  on roots of three Musa cultivars (two AAA and one ABB ('Cachaco')) that we re-mapped on the latest version (v2) of the reference M. acuminata sequence (A genome). The number of high-quality reads mapped increased from 81 to 92% by using the new reference, highlighting the improvement on gene annotation in the reference assembly  [17]. Subsequently, a second RNAseq dataset was produced with ten cultivars to broaden our observations with a larger representation of allopolyploids including additional five ABB, one AAB and three AAA genomes in addition to Musa ABB 'Cachaco' (Table  1 ). In total, we obtained an average of 24 million single-end reads per genotype. Between 13.
5a ); 2) Ma11_g14590 whose unique paralog (Ma00_t02460) has consistent expression with Ma11_g14590 (Fig. 5b ). For the remaining 32 DEGs, the paralog expression was globally higher than the sampled gene, overruling or reducing the impact of the expression difference between 'Cachaco' and AAA cultivars (Ma05_g02000 and Ma11_g20920 as example in Fig. 5c  and d ; Additional file 6). In the larger dataset including six ABB and three AAA cultivars, differential expression of almost all the 15 validated DEGs, over-or under-expressed in 'Cachaco' vs AAA cultivars, was confirmed to be associated with the presence of the B genome (Fig. 6 ). In the second dataset, DEGs were identified between each allopolyploid and each of the three AAA genotypes. For each allopolyploid, DEGs detected in all the three comparisons were selected (Table  2 ).
For instance, around 9% of the Musa annotated genes lie in genomic regions where the 'Cachaco' genome ratio is 3B:A0, but, when only the DEGs between 'Cachaco' and both AAA cultivars were considered (3674 genes), the 3B:A0 gene fraction is significantly higher (14. 7%, p < 2. 1 2e-36 in first dataset and results reported in Table  2  for the second one). We can conclude that the substitution of A homeoalleles increases the probability of significant changes in gene expression and, consequently, in phenotype. Similar dosage-dependent effects on expression of genes in genomic regions that underwent homoeologous exchanges were already observed in Brassica napus  [12].
Allotriploid genomic structure Our dataset of genotypes 'Mbwazirume' , 'Cachaco' and 'Grande Naine' was used as a starting point for further analysis. All the reads were realigned to the newest version of the Musa genome (derived from the genomic sequence of M. acuminata DH Pahang v2  (Martin et al. , 2016) ). The allotriploid genomic structure was verified by checking the 2/3 expected contribution of B homeoallele variants (1/3 for 'Pisang Ceylan' , AAB) of SNPs in the expressed genes along the 11 chromosomes. In order to reduce the bias introduced by SNPs due to the A sequence polymorphism, only SNP positions monomorphic in available A genomes (DH Pahang and RNA-Seq data of AAA cultivars 'Grande Naine' and 'Mbwazirume') were taken into account. At each retained SNP position, the alternative nucleotide to the reference variant was considered the B genome variant (Fig.
Multivariate analysis To find genotype specific gene expression patterns, a Partial Least Squared analysis was performed with the NIPALS algorithm on the normalized read counts (Statistica 13, Non Sigma) of the larger sample dataset. 35,307 annotated genes were considered as X variables and the number of B chromosomes as Y variables (0, 1, 2). Normalized reads were subjected to ANOVA analysis and Benjamini correction (33,667 variables).
Gene enrichment To analyse possible gene enrichment, the Benjamini corrected variables were selected according to their ABB or AAA profile and their accession numbers were converted to entrez gene IDs using the conversion tool (http://banana-genome-hub. southgreen. fr/convert). Entrez gene IDs were submitted to DAVID  [33]. Significant enrichments (EASE threshold 0. 1) were exported and visualized using Cytoscape  [34].
Paralog detection and paralog inclusive expression (PIE) analysis Identification of Musa specific paralogs on the whole set of differentially expressed genes (DEGs) was performed using Orthofinder v2. 2. 1 with protein sequences from Musa acuminata v2, Oryza sativa v7, Arabidopsis thaliana v10 and from Vitis vinifera v1. The number of paralogs was defined by counting the number of gene occurrence of Musa within each orthogroup. The presence of lineage specific paralogs (in-paralogs) in the Musa genome for a limited number of selected DEGs was verified by BLASTp analysis on Non-redundant protein database at NCBI  [38]. Musa genes, having higher scores than genes from any other species, were considered in-paralogs. Even if orthology/paralogy is defined based on phylogenetic analysis, we considered our simplified approach based on sequence similarity appropriate to find paralogs and the possible bias due to lack of identification of paralogs with high sequence divergence as negligible.
In this work, a multi-parameter inter-comparison of diverse ocean forecast models was conducted at the sea surface ranging from global to local scales in a twophase stepwise strategy. Firstly, a comparison of CMEMS GLOBAL and the nested CMEMS IBI regional system was performed against satellite-derived and in situ observations. Results highlighted the overall benefits of both the GLOBAL direct data assimilation in open water and the increased horizontal resolution of IBI in coastal areas. Besides, IBI (Iberia-Biscay-Ireland) proved to capture shelf dynamics by better representing the horizontal extent and strength of a river freshwater plume, according to the results derived from the validation against in situ observations from a buoy moored in NW Spain. Secondly, a multi-model inter-comparison exercise for 2017 was performed in the Strait of Gibraltar among GLOBAL, IBI, and SAMPA (Sánchez-Garrido et al.