dataesr/scientific-paragraphs-categorization huggingface dataset:
3 to 2. 0 mM) every 300 s. Data were fitted with MicroCal Origin 7 software, according to standard procedures. Fitted data yielded the stoichiometry (n), the association contant (K a ) and the enthalpy of binding (DH). Other thermodynamic parameters (i. e. changes in free energy, DG, and entropy, DS) were calculated from the equation DG = DH-TDS = 2RT ln K a in which T is the absolute temperature and R = 8. 314 J mol 21 K 21. Two independent titrations were performed for each ligand tested. Crystallogenesis, data collection and structure determination. Crystals of BambL complexed with Le x tetrasaccharide were obtained by the hanging-drop vapor diffusion method using 2 ml drops containing a 50:50 (v/v) mix of protein and reservoir solution at 19uC. Lyophilized protein was dissolved in Tris/HCl buffer pH 7.
5, NaCl 150 mM at 1 mM and incubated for 1 h with 5 mM of sugar at room temperature prior to co-crystallization. Reservoir solution consisted of 200 mM Trisodium citrate, 100 mM sodium acetate pH 5. 0 and 28% Peg 6K. 10% ethylene glycol was added for cryoprotection prior mounting in a cryoloop and flash-freezing in liquid nitrogen. Diffraction data were collected at 100K, at the European Synchrotron Radiation Facility (Grenoble, France) on ID14-EH4 beamline using an ADSC Q4 CCD detector. The data were processed using MOSFLM  [39]  and scaled and all further computing was performed using the CCP4 suite unless otherwise stated  [40]. Data quality statistics are summarized in Table  S1. Molecular replacement was used to obtain the initial phases with the program PHASER  [41]  and one monomer of the native structure of BambL (PDB ID: 3ZW0) as searching model.
The structure was refined by restrained maximum-likelihood refinement using REFMAC 5. 5  [42]  with 5% of the data retained for cross-validation interspersed with model building and electron density inspection in Coot  [43]. The incorporation of the sugar moieties was performed after inspection of the the 2mFo-DFc and mFo-DFc weighted maps. Water molecules were introduced automatically using Coot and inspected manually. The coordinates have been deposited in the Protein Data Bank under code 3ZW1. All figures were drawn with The PyMOL Molecular Graphics System (Schro ¨dinger, LLC).
Modeling section All calculations were performed using the Schro ¨dinger Suite of programs through the Maestro graphical interface (http://www. schrodinger. com, Schro ¨dinger LLC, New York). The overall docking protocol and filtering procedure are schematized in Figure  2. Preparation of the ligands. Models of the seven fucosecontaining oligosaccharides displayed in Figure  1  have been built. Between two and four different conformations were considered for each structure, first one by extracting coordinates from the crystal structures of the complex when available and others one by selecting low energy conformations from the BIOLIGO database (http://glyco3d. cermav. cnrs. fr/bioligo). The dihedral angle values of the glycosidic linkages of the starting conformers are provided in Table  S2.
Preparation of the proteins. The crystal structures of two lectin/carbohydrate complexes, LecB/Lewis a (PDB ID: 1W8H)  [20]  and BambL/H type 2 (PDB ID: 3ZZV)  [14]  were taken from the Protein Data Bank  [44]. The four binding sites of LecB contain a conserved water molecule in close interaction with the protein and the fucose moiety. This structural water molecule is also found in other LecB crystal structures, and was thus explicitly included in all calculations. Since the lectin BambL possesses two similar binding sites per monomer, only the intra-monomeric site was selected for the calculations. The hydrogen atoms were added assessing a pH of 7. The histidine residues were treated as neutral. The selection of the histidine enantiomer and the orientation of asparagine and glutamine side-chains were performed so as to maximize hydrogen bond patterns.
The partial atomic charges derived from the OPLS-2005  [45]  were assigned to the ligands, calcium ions and protein atoms. Finally, energy minimizations were performed using the Impref utility of the Schro ¨dinger package and the OPLS-2005 force-field. Docking Calculations. The docking program Glide  [46, 47]  has been shown to give good performances for docking carbohydrates  [37]. Rigid-receptor docking calculations were run in standard precision mode, with the OPLS-2005 force field, while the ligand was considered as flexible. The shape and physico-chemical properties of the binding sites were mapped onto cubic grid of 24 A ˚side centered on the ligand. During the grid generation, a scaling of van der Waals radii of 1 for atoms with partial charges less than 0. 25 e was used. Prior to docking, each conformer was submitted to an extensive conformational search protocol using the MM3 force field to generate several hundreds of conformers.
The docked solutions were ranked using Emodel, a composite scoring function, available in the Schro ¨dinger Suite and that is a combination of GlideScore, of the ligand receptor molecular mechanics interaction energy, and of the ligand strain energy  (Friesner et al. 2004 ). This score, which better takes into account electrostatic and van der Waals energy, is particularly well suited for comparing conformers but it should be used together with another scoring function while comparing different species. MMGBSA rescoring. The binding free energy of each best docked poses was estimated using the local optimization feature in Prime software (Schro ¨dinger) based on the molecular mechanics (MM) OPLS-2005 force field and the Generalized Born Surface Accessible (GBSA) continuum model. Such MM-GBSA rescoring protocol has proved to be successful  [49].
Specificity analysis by glycan array The fine specificity of the two bacterial lectins was analyzed using glycan array v4. 1 from the Consortium for Functional Glycomics that contains 465 glycoconjugates (http://www. functionalglycomics. org). The overall glycan array data for BambL was described previously  [14]  and those for LecB are available from the CFG website. Briefly, BambL only attaches to the oligosaccharides presenting at least one fucose residue, whereas LecB displays also some weak affinity towards mannose-containing glycans. In order to set up a comparison with the docking results, binding data were analyzed by selecting only glycans having one fucosylated epitope at the non-reducing position. This resulted in data for 53 glycans with two lectins at three concentrations (see Tables  S3  and S4 ).
As imposed with the filtering protocol, the fucose moiety always coordinated the two calcium ions through oxygen atoms O-2, O-3 and O-4. The shallow shape of the binding site allowed the oligosaccharides to adopt a variety of conformations. Since the present study aims at a medium throughput rationalization of glycan array data, only the pose with the best Emodel score is further discussed. It should be noted that this pose did not always originate from calculations starting with the lowest energy model from BIOLIGO database, i. e. conformer 1 in Table  S2. In 75% of the complexes, the best pose was obtained starting from other conformations taken from BIOLIGO database. The best poses are displayed in Figure  4  and their structural analysis is given in Table  1. Despite their structural similarities, H type 1 and H type 2 trisaccharides presented very different conformations (Figure  4a  and 4b ).
Discussion and Conclusion The development of glycan arrays allowed studying the fine specificities of glycan-binding proteins. Even though some authors have developed systematic analyzes of glycan array data to identify the lectin specificities  [51, 52] , there is a lack of automatic methods to extract structural information from glycan arrays. A docking protocol of bioactive oligosaccharides that would be common to fucose-binding lectins was investigated in this study, so as to structurally rationalize large amounts of experimental affinities, such as those arising from glycan array experiments. Among the different protocols evaluated, the refined molecular docking procedure combines Glide in standard precision for generating the poses and the Emodel scoring function for ranking them. Starting from several conformers derived from the BIOLIGO database greatly enhanced the conformational sampling, as up to four conformers for each molecule were introduced in the docking procedure.
Deviations between glycan array data and MM-GBSA calculations are expected, and among the numerous sources of discrepancies one could cite the fact that the glycans are methylated, and conformationally restricted as they are immobilized on a surface, usually through covalent interactions. On the contrary, the oligosaccharides used in the microcalorimetry experiments, have a free hydroxyl in C1, and can dynamically exchange between an open form, and a closed form, either with an axial or equatorial anomeric position, while only the equatorial structures were considered in the calculations. Also, one should keep in mind that trisaccharides were used for docking studies while experimental affinities were mostly performed on their tetrasaccharide analogs. Even though molecular dynamics simulations in explicit water environment is better suited for estimating the binding free energies, the combined use of database of oligosaccharide conformations and fast docking procedure appears as a mediumthroughput screening approach for the analysis of glycan array data.
The research was funded by  Labex Arcane  ( ANR-11-LABEX-003 ) and also supported by  Centre national de la recherche scientifique, UniversiteJoseph Fourier  and  ANR  grant  NeoLect  ( ANR-11-BSV5-002-01 ) and  WallArray II  ( ANR-GENOM-BTV-010 ). The authors acknowledge the  COST action BM1003  and  CM1102  and the  Initial Training Network ''EuroGlycoArrays''. Support  from the  Association Vaincre la Mucoviscidose  and GDR Pseudomonas is appreciated. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Consequently, there are large areas of New Zealand currently covered by non-forest vegetation that could potentially support forest  [46, 47]. Large areas of marginal agricultural land have been recently abandoned or retired from production, providing great potential for afforestation by indigenous species  [48]. The latitudinal range spanned by the main islands of New Zealand generates a large amount of climatic variation, with climate types ranging from subtropical in the northern North Island to sub-Antarctic on Stewart Island. Also, a main axial range runs almost the entire length of New Zealand, providing large areas of mountain and subalpine habitat. New Zealand also has a vast database of vegetation survey plots  [49]  spanning various environment types and landscape contexts (e. g.
lesser or greater degrees of deforestation). This provides an excellent opportunity to understand the influence of abiotic constraints, local vegetation and proximity to seed sources on tree occurrence in non-woody vegetation. Finally, we have assembled a comprehensive database for key ecological strategy (potential height, leaf area and seed size) and dispersal (relating to dispersal adaptations and mode of dispersal) traits for all indigenous New Zealand tree species. This permits us to test whether trait data can improve our understanding of the processes driving afforestation.
g. temperature and water availability), local vegetation cover (i. e. vegetation type and local woodiness) and proximity to seed sources (i. e. distance to nearest forest and neighborhood forest cover) as predictor variables. We then used our functional trait data to test a suite of hypotheses on the processes by which abiotic environment, local vegetation cover and proximity to seed sources influence tree occurrence: H1: Relative frequency of tall species and species with large leaves will decrease with abiotic stress. Reason: Tall and large-leaved species are less tolerant of abiotic stress. H2: Relative frequency of tall species and species with large leaves will decline with declining local woody cover. Reason: Local woody cover reduces abiotic stress during tree establishment, favoring establishment of large-leaved species.
Vegetation survey dataset New Zealand's National Vegetation Survey Databank (NVS) contains records from approximately 77,000 vegetation survey plots encompassing forest, shrubland and non-woody vegetation  [49]  (http://www. givd. info/ID/AU-NZ-001). Plots using any one of three different data collection methods were considered for analysis. On the first plot type the abundance of each species present was recorded in seven fixed height tiers, using a modified Braun-Blanquet cover-abundance scale  [51]. On the second type, the frequency of each species was recorded using 50 circular subplots, 15 cm in diameter, centered every 40 cm along a transect 20 m long  [52]. On the third type, the frequency of each species was recorded using 50 quadrats (0. 25 m 2 ) centered every 2 m along a transect 100 m long  [53].
In view of the potential for differences in survey methods to introduce bias we conducted our analyses separately on plots, using only one of the three methods. The main results obtained for the entire dataset were also obtained for each of the three survey methods separately. Furthermore, survey method was only a very weak predictor of tree occurrence probability. Consequently, it is highly unlikely that the main findings of our study can be explained by artifacts associated with differences in survey method. From these candidates, we selected 10,061 plots for our analysis (Figure  1 ). Criteria for inclusion were (1) the plot location was recorded with a precision of at least 100 m (2), if there were multiple plot records for a given location, only the most recent plot measurement was retained (3), plots were not related to exclosures or other experimental treatments (4), plots were located below 1400 m elevation to exclude plots that were definitely in alpine zones (  5 ), woody species with potential height ≥ 5 m comprised <30% of the total cover or total frequency across the plot, to match the Kyoto Protocol definition of non-forest vegetation  [50]  (heights were taken from McGlone et al.
[54] )  (6) , plot mean canopy height was ≤5 m. Each plot was scored as to whether any tree species were present (termed 'tree occurrence' hereafter). The tree species present in these plots may either be post-deforestation recruits or individuals that have survived deforestation. The criteria we used to select plots should exclude adult forest-dwelling trees, so that almost all of our occurrences will be contributed by new recruits or saplings that have survived deforestation. Plot surveys were conducted between 1982 and 2008. Over 80% of the plots were surveyed between 1982 and 1990, with around 10% surveyed between 1990 and 2000 and around 10% surveyed after 2000. These plot data are publicly available through the NVS website. In most parts of New Zealand tree line naturally occurs below 1400 m, with mean annual temperature of 5.
9°C generally defining the tree line  [48]. Consequently, our dataset encompasses plots that naturally would not support forest (mainly due to climatic stress) and plots where forest has been removed by human or natural disturbance (mainly landslides associated with extreme rainfall or earthquakes). Eighty-seven per cent of plots have mean annual temperature of >5. 9°C, based on climate maps summarizing over 30 years of temperature data  [55]  (Figure  S1b ). Consequently, extreme low temperatures will not be the sole reason for the absence of forest in most of our dataset. We deemed it advantageous to include some plots above tree line since this gives us more power to detect the effect of abiotic stress on tree species occurrences.
Functional trait data To determine seed mass, seeds of different species were sourced from the seed collection housed at the Allan Herbarium, Lincoln, New Zealand (CHR). For large-seeded species, mass was determined for five replicates of 10 seeds each; for small-seeded species, from five replicates of 50 seeds each. Replicates were placed in glass vials and dried overnight at 80°C. They were then placed in a desiccator and subsequently weighed on a Precisa 40SM-200A Weddenburn balance. Weights were recorded to the nearest 0. 01 mg. Seed mass data are available from the Landcare Research ecological traits database (http:// ecotraits. landcareresearch. co. nz) We sourced data on tree height (m) and leaf size (mm 2 ) from McGlone et al. [54]. Dispersal trait data were sourced from Thorsen et al.
[56]. From this dataset we used four traits to describe dispersal: (1) nature of morphological dispersal adaption (none, aril, fleshy, hooked, minute, pappate, viscid or winged); (2) principal dispersal mode (ballistic, 'mobile' -i. e. epizoochorous, by frugivory, water or wind); (3) number of dispersal modes; and (4) whether or not the species is biotically dispersed. There was no evidence of strong correlations between either ecological strategy or dispersal traits (all pairwise Pearson correlations <0. 5). Height and dispersal data are available from published appendices linked to McGlone et al. [54]  and Thorsen et al. [56]  respectively.
Environmental and land cover predictor data We used a variety of complementary environmental and land cover variables to predict tree occurrence. The climatic variables were taken from the GIS climate surfaces described by Leathwick et al. [55]. The variables we used were: mean and minimum annual temperature (°C), mean annual rainfall (mm), mean annual solar radiation (kJ. day -1. m -  ), October soil water deficit and vapor pressure deficit (kPa), mean wind speed for the month of October (when prevailing westerly winds are most active, km. h -1 ), soil particle size, phosphorus, and drainage (all ordinal rankings). Our land cover variables were derived from either Land Cover Database 2 (LCDB2  [57] ; http://koordinates. com/layer/1072-land-cover-databaseversion-2-lcdb2/) or Ecosat  [58]. LCDB2 documents aerial extent estimates of 61 classes of land cover or land use derived from a classification of Landsat satellite imagery acquired in the austral summers of 1996/7 and 2001/2.
Distance to nearest forest gives an indication of proximity to seed sources, while percentage forest cover within a 100 m radius gives a more fine scale (< 100 m) measure of seed source availability in the local neighborhood. Percentage woody cover within a 25 m radius quantifies woodiness in the immediate vicinity of the plot, while the dominant Ecosat vegetation type within 100 m indicates neighborhood vegetation type, and provides more detail on the variation in forest composition than does LCDB2. Figure  S1  presents histograms and bar charts showing plot frequency distributions with respect to the predictor variables. All environmental and land cover data were taken from GIS grids with 100m x 100m pixel size, so that the values assigned to each plot were those for the 100m grid square in which it occurred.
Percentage woody cover within a 25 m radius was converted to 100m coverage by applying the value of the 25-m pixel at the top left corner of the 100-m pixel to the whole 100-m pixel. This is a very common practice in GIS analyses, because different types of variables -climate, land cover, topography -are typically mapped at different scales. Obtaining values for each variable at each plot location requires that they all be mapped at the same resolution. All GIS data are publicly available on request to the corresponding author. Some care must be exercised around our use of local woody cover and local vegetation type to predict tree occurrence. Many of our plots were surveyed prior to the remote sensing used in Ecosat and LCDB2 (the land cover classifications we used). Consequently, there is a risk of circularity since local woodiness might be influenced by the presence of tree species in our plots -with plots containing tree species perhaps more likely to have high woody cover due to the presence of tree species.
Cross validation was performed by randomly removing 10% of the plots from the dataset, fitting the BRT model on the remaining plots and then assessing the goodness of fit on the removed plots. In machine learning methods like BRT, the use of cross validation in assessing goodness of fit is vital to prevent over-fitting. To further reduce the chance of over-fitting, we conducted a stepwise model simplification process that sequentially removes the predictor variable whose removal causes the smallest decrease in goodness of fit. We performed this model simplification using the gbm. simplify function implemented in R  [63]  provided in the supplementary material of Elith et al. [61]. Following the simplification procedure we fitted a new model containing only the variables whose removal caused a nonnegligible decrease in goodness of fit.
Dispersal is a highly stochastic process, and this stochasticity often makes it difficult to detect dispersal patterns. The fact that we obtained such clear evidence for the influence of dispersal limitation on traits emphasizes that large datasets, such as the one used in this study, may overcome the stochasticity caused by rare, random dispersal events, such as long distance dispersal, to reveal dispersal-driven patterns  [75]. Relative frequency of biotically dispersed species, particularly those dispersed primarily by frugivory, increased with local woodiness and decreased with distance to forest. By contrast, frequency of abiotically-dispersed species, species with no obvious dispersal adaptations and more than one mode of dispersal decreased with local woodiness and increased with distance to forest.
A new scheme for the delineation of raining and non-raining cloud areas applicable to mid-latitudes from daytime and nighttime multispectral satellite data is developed. The technique is based on optical and microphysical cloud properties using an artificial neural network. The tests have been conducted during the rainy season of 2006/2007. The proposed algorithm uses the spectral parameters of SEVIRI (Spinning Enhanced Visible and Infrared): brightness temperature T IR10. 8 and brightness temperature differences DT IR10. 8-IR12. 1 , DT IR8. 7-IR10. 8 , DT IR3. 9-IR10. 8 and DT IR3. 9-WV7. 3 during the nighttime and reflectances R VIS0. 6 , R NIR1. 6 , brightness temperature T IR10. 8 , brightness temperature difference DT IR8. 7-IR10. 8 and DT IR10. 8-IR12. 0 during the daytime. The algorithm is calibrated by instantaneous meteorological radar using multilayer perceptron.
The technique is based on information about optical and microphysical cloud properties from MSG/SEVIRI. It is calibrated by instantaneous meteorological radar data using multilayers perceptron. Artificial neural networks (NNs), widely used in precipitation remote sensing  (Bellerby et al. 2000; Grimes et al. 2003; Tapiador et al. 2004) , are the statistical tool chosen to define the correlations between satellite measurements and classes of ground precipitation as estimated by weather radars.
Presentation of study region and datasets Algeria is located on the South shore of the Mediterranean region; it is bordered on the East by Tunisia and Libya, on the South by Niger and Mali, South-West by Mauritania and Western Sahara and West by Morocco. This region has a particular orographic structure and special characteristics of the sea-land coast. Due to these geographical properties, its climate has a very complex spatio-temporelle feature  (Lionello et al. 2006). Indeed, it is influenced by both the subtropical climate and the climate of mid-latitude systems  (Trigo et al. 2006; Alpert et al. 2006 ). The spatial distribution of precipitation is characterized by a very marked North-South gradient and a very low East-West gradient. The rainy season extends from October to March, with maximum rainfall occurring during November-December.