dataesr/scientific-paragraphs-categorization huggingface dataset:
Output files are visualized using the guppy program (a companion program of pplacer). Guppy (http://matsen. github. io/pplacer/generated_rst/guppy. html #) generates the phylogenetic tree showing either the probability of placements (fat visualization) or the best placements (tog visualization) of query sequences. The Galaxy framework provides interoperability mechanisms to dynamically call external viewer. Trees are generated in the phyloXML format and displayed using the Archaeopteryx phylogenetic tree viewer java applet (41) (Figure  3h ). To provide an intuitive overview of the geographic distribution of current data, the Map page displays for each location, the number of sequences available in MicRhoDE, the actual number of superclusters and clusters according to phylogeny and the dominant ones as well as the proportion of predicted spectral variants.
Conclusion MicRhoDE is a specialized database devoted to the study of microbial rhodopsins, which are functionally versatile proteins of crucial importance in the ecology of terrestrial and aquatic photic environments. As microbiologists from all fields use molecular, genomic and metagenomic methods to look at microbial diversity in the biosphere in more breadth and depth, we anticipate that the release of MicRhoDE will help comprehensive ecological and evolutionary analyses of these cosmopolitan genes.
In a database, the diameter (D) of the knot was recorded at an interval of 1 cm from the stem's pith in the radial direction. Similarly, the position of the central axis of the knot along the longitudinal stem axis (Z, referred to as the 'trajectory') was recorded at an interval of 1 cm from the stem's pith. This way, we obtained a representation of the geometric profiles of 5,377 knots. A more detailed description of the knot reconstruction method was presented by  Duchateau et al. (2013a). The demarcation between stem and knot xylem cannot be considered as perfectly discrete. Knot profiles were therefore extracted from the CT images by manually delineating high density wood corresponding to a knot and the surrounding lower density stem wood. Although the transition was generally clear enough to ensure accuracy (Fig.
" Knot development at a given radial position (l) was reconstructed using the diameter (D l ) and trajectory data (Z l ). The same Weibull equation with an additional linear term was used to model both series of D l and Z l measurements: y l = α  1 -e  -β  1 Rmax-l  + µ • l (0 ≤ l < R max ) (1) where y l represents either the D l or Z l values (mm), l is the distance from the stem's pith in the radial direction (mm), R max is the total length (mm) of the knot along the stem's radial direction and α, β and µ are parameters to be estimated empirically. The functions were fitted to each knot independently using the nls function of the nlme library in the R statistical programming environment  (R Core Team, 2014). The models for both D l and Z l converged for 95% of the knots in the database.
Knot-level models Table  4  shows the fixed effects parameter estimates and standard errors for each section of the final knot diameter model (Eq. (  4 )). To evaluate the model, knot diameter profiles were predicted and compared to observations in the evaluation dataset. Plots of the raw residuals (observed minus predicted values) showed that, on average, knot diameter was slightly underestimated in the middle section of the knot profiles, but overall the model was unbiased (Fig. 8A ). The mean absolute error was 0. 031 and the root mean square error (RMSE) 0. 054. When the profile of each knot in the database was reconstructed by adding successive annual diameter predictions, the absolute value of 50% of the residuals was less than 2. 6 mm along the pith-to-bark profiles, while the absolute value of 90% of the residuals was less than 9.
7 mm. Table  5  shows the fixed effects parameter estimates and associated standard errors for each section of the final model of knot trajectory (Eq. (  5 )). Again, predictions of knot trajectory profiles were compared to observations in the evaluation dataset. On average, the model was unbiased along the knot profile up to ring 75, with a slight overestimation beyond this point (Fig. 8B ). The mean absolute error for this model was 0. 118 and the root mean square error (RMSE) 0. 189. When the profile of each knot was reconstructed by adding successive annual trajectory predictions, the absolute value of 50% of the residuals Table  4  Fixed effects parameter estimates and standard errors for each section of the knot diameter model given by Eq. (4). Section 1, knot initiation (1-3 years); Section 2, growth phase (4-25 years); Section 3, stabilisation and death (>25 years).
ACKNOWLEDGEMENTS The authors are grateful to the  Natural Sciences and Engineering Research Council of Canada (NSERC)  for the financial support for this project through the  ForValueNet strategic research network on  forest management for value-added products. We are also grateful to  Amélie Denoncourt ,  Louis Gauthier ,  Fabien Lanteigne ,  Vanessa Joly ,  Alice Bernier Banville ,  Eugénie Arsenault , and  Caroline Hamelin  for their assistance in collecting the samples. Two anonymous reviewers provided helpful comments on an earlier version of the manuscript. The UMR 1092 LERFoB is supported by the  French National Research Agency through the Laboratory of Excellence ARBRE  ( ANR-12-LABXARBRE-01 ).
In silico prediction of gonadotropin receptor genes Eel gonadotropin receptor genes. Genes encoding gonadotropin receptors were sought by Blast searches  [34]  using the Japanese eel (Anguilla japonica) fshr and lhcgr cDNA sequences (AB360713, EU635883, AY742794, AY742795,  [27] [28] [29] ), against the European and Japanese eel genomic sequence database (Eelgenome. org; NCBI)  [24] [25]. Exons and introns were annotated using the package CLC Main Workbench (CLC Bio, Qiagen, Denmark). Gonadotropin receptors from other teleosts. Using the FSHR and the two LHR protein sequences identified in the European eel in this study as queries (tBlastn), fshr and lhcgr genes were sought and annotated (if not yet predicted) in the increasing number of teleost genomes available in Ensembl or NCBI (see S1 Table ).
cDNA cloning of European eel gonadotropin receptors The predicted European eel fshr, lhcgr1 and lhcgr2 CDS sequences were completed and confirmed by cDNA cloning. Specific cloning primers were designed on both extremities and inside the predicted CDS sequences, using Primer3 browser  [35]  (S2 Table ). First strand cDNAs were synthesized from total RNA samples previously prepared from testis and ovary of silver European eels  [26, 33]. One μg was reverse transcribed using superscript III (Invitrogen, Carlsbad, CA, USA), random primers (50 ng) (Invitrogen) and oligo(dT) 18 (50 μg) (Roche, Mannhein, Germany), at 50°C for 60 min, after an initial step at 25°C for 10 min. PCR were performed using GoTaq mix (Promega, Fitchburg, WI, USA) on ovary and testis cDNAs. PCR amplifications were performed on the Life Express Thermal Cycler (Hangzhou Bioer Technology Co.
Prediction of gonadotropin receptor protein sequences and domains Nucleotide sequence translation and first methionine were predicted using the package CLC Main Workbench. The signal peptide cleavage site was determined using SignalP  [36]  and putative transmembrane helices were predicted using TOPCONS  [37]. Searches for motifs and sequence patterns were performed by comparison with protein families databases on ExPASy server  [38]. Leucine-rich repeats were identified using Pfam database  [39]  and the recent crystallography study of human FSHR extracellular domain  [40] [41]. Potential tyrosine sulfation sites were predicted by sulfosite browser with 80% prediction sensitivity  [42]  and palmitoylation sites in the ICD were predicted using PalmPred browser  [43].
Phylogeny analysis and domain comparison of gonadotropin receptors Seventy eight gonadotropin receptor sequences of representative vertebrates were collected from the available databases of NCBI and Ensembl, including 33 sequences defined in the present study and 45 sequences previously published (S1 Table ). Multiple alignments were performed using ClutstalW and were manually adjusted taking into account the receptor topology to improve the alignment accuracy. The phylogeny of gonadotropin receptors was inferred by Maximum Likelihood using PhyML with the WAG model of substitution  [44]  combined to the neighbor-joining interchange (NNI) method on Seaview browser  [45]. A tree was generated and robustness of the phylogeny assumption was evaluated by the approximate likelihood test (aLRT) SH-like branch support and by bootstrapping procedure from 500 data set replicates.
Synteny analysis of gonadotropin receptors Flanking genes of the three gonadotropin receptors in European and Japanese eel draft genomes were sought by Blast searches. Only a limited number of flanking genes could be annotated due to the current size of the scaffolds. The Spotted gar, a non-teleost actinopterygian, which we showed to possess fshr, lhcgr1 and lhcgr2, was chosen as reference to study the genomic regions of the gonadotropin receptors. At the beginning of our study, the spotted gar genome was assembled at the level of chromosome but was non-annotated. The flanking genes of the spotted gar gonadotropin receptors were identified by blasting the contigs (AHAT01001660 to AHAT01001713) against the zebrafish protein dataset, using CLC Main Workbench. In the course of our study, an annotated version of the spotted gar genome became available in Ensembl and NCBI and was in agreement with our gene synteny predictions.
A single fshr and a single lhcgr are present in sarcopterygians. In a basal sarcopterygian, the coelacanth, one partial lhcgr could be predicted on scaffold JH127072 (S1 Table ). This partial sequence consisted of 10 exons giving a 632 aa protein, the first exon encoding for the signal peptide being missing. In addition, we could identify one fshr gene (S1 Table ) on scaffold JH127072, positioned in tandem with the lhcgr. The fshr sequence consisted of 10 exons and encoded for a 690 aa receptor. As in the coelacanth, a single fshr and a single lhcgr are present in the other sarcopterygians, including tetrapods. No additional lhcgr could be found in any sarcopterygians. In the course of our investigation of genome databases, we observed that some sauropsid species possess fshr but may have lost lhcgr.
Acknowledgments We thank  Drs. R. Dirks ,  C. Henkel  and  G. van den Thillart  ( ZF-Screen, University of Leiden ) for kindly providing us contigs and scaffolds of the draft genomes of the European and the  Japanese eels , and for their training for CLC Workbench. We are grateful to  Dr. Bruno Quérat  ( CNRS, University Paris-Diderot ) for his constructive discussion. Some of these results were presented in the following international conferences: Maugars,  Eel genome Symposium ,.
The work was supported by the  MNHN, CNRS-INEE  and a grant from the  European Community  ( ProEEL EU-FP7-KBBE-2009-3 No. 245257 ) to SD. MG is a recipient of post-doctoral fellowships from the  MNHN  and  CNRS-INEE. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
The sensitivity parameter (c) is a measure of overall discriminability. The determinism parameter (φ) specifies the consistency of responding. Two learning rates (λ w and λ a ) determine how quickly the exemplar-category associations and attention weights are learned. For the present simulations, c varied from. 5 to 6. 5 with increments of 1. 0, φ varied from. 5 to 4. 5 with increments of 1. 0, λ w varied from 0 to. 15 in steps of. 01, and λ a varied from 0 to. 15 in steps of. 01 for a total of 8,960 parameter combinations (7 X 5 X 16 X 16). These parameter ranges were drawn from published literature and are representative of parameter values that have been used to simulate human behavioral data  [15, 23, 24]. All simulations were performed using Matlab and the code is available as supporting information.
These parameter ranges were drawn from published literature and are representative of parameter values that have been used to simulate human behavioral data  [15, 23, 24]  All simulations were performed using Matlab and the code is available as supporting information. COVIS assumes that the salience of each stimulus dimension and the decision criteria on each dimension are adjusted during the course of learning. Thus, the numerical values of these salience and criteria define the network state of the model and are known at the end of the preand post-criterion blocks. As with ALCOVE, this network information can be used to find the underlying competence of COVIS in that state. Just as we did with ALCOVE, we saved the network state of COVIS at the end of the pre-and post-criterion blocks. Next COVIS was simulated for an extended time (100 blocks), with the learning mechanisms disabled, using the saved pre-or post-criterion weights.
Because of noisy data, neural networks used to model natural phenomena are sensitive to overfitting; the use of regularisation methods to deal with the bias-variance trade-off is thus mandatory (cf. Sect. 3. 1. 2). Kong-A-Siou et al. (  2014 ) compared neural network models and VEN-SIM software to simulate flooding or drought; they concluded that neural modelling performed better for extreme events, whereas VENSIM worked better for intermediate, more complex events. This statistical approach has been used to propose some interesting hydrological models. Artigue et al. (2012)  has proposed a combination of linear and nonlinear modelling in the same model. Corzo and Solomatine (2007)  have proposed a combination of specialised neural networks to represent isolated processes involved in flood genesis.
This determines the number of parameters mechanically; model complexity increases with the number of parameters. The general equation of the function calculated by the feed-forward multilayer perceptron is the following: y k = g NN y k-1 p ,. , y k-w y p , u k ,. , u k-w u -1 , C , (1) where the estimated value of the output at the discrete time k is y k ; the observed value of this variable is y k p ; the input vector is u k ; the non-linear function implemented by the neural network is g NN ; w u and w y are the width of windows used to apply the input time series, they are linked to the length of the vectors u and y p ; and C is the matrix of parameters of the model, also called "weights". As statistical models, neural networks are designed in relation to a database. This database is usually divided into three sets: a training set, a stop set, and a test set.
The training set is used to calculate parameters through a training procedure that minimises the mean quadratic error calculated on output neurons. The training is stopped by the stop set (cf. Sect. 2. 1. 2), and model quality is estimated by the third part of the database: the test set, which is separate from the training and stopping sets. The model's ability to be efficient on the test set is called generalisation. However, the training error is not an efficient estimator of the generalisation error: the efficiency of the training algorithm makes the model specific to the training set. This specialisation of the neural network on the training set is called overfitting. Overfitting is exacerbated by large errors and uncertainties in field measurements; the model learns the specific realisation of noise in the training set.
Regularisation methods In the context of this study, the goal of regularisation methods is to minimise output variance. To this end, crossvalidation  (Stone, 1974)  was used as explained in Kong-A-  Siou et al. (2012)  to empirically select input variables and the number of hidden neurons. Cross-validation thus minimises model complexity and therefore output variance  (Schoups et al. , 2008). Another regularisation method is commonly employed: early stopping  (Sjöberg et al. , 1995). This method stops training before overtraining occurs. A dedicated set, called a stop set, is considered separately from the database. Working also on the Lez aquifer but considering only underground water at the Lez spring, Kong-A-Siou et al. (2011)  applied multilayer perceptron to perform forecast at Lez spring and validated cross-validation as a useful method to select the complexity of the model.
Moreover, Kong-A-Siou et al. (  2012 ), for the same basin, focussed on regularisation methods (early stopping and weight decay). They conclude that early stopping used in conjunction with cross-validation was efficient. Nevertheless these results, obtained with a 16-year daily database cannot be applied directly in the present study because the flash-flood database is too limited to extract definitively another set from the database (the stop set). Thus, to apply early stopping without stop set, a pre-defined maximum number of training iterations were selected to stop training before the complete convergence and, by this way, avoid overtraining. Nevertheless, for this purpose, the selection of the optimal number of training iterations is done using a stop set. Then afterwards, the model is run without the stop set using this pre-defined optimal number of training iterations.
In the first stage, the database, not including the test set, was divided into S subsets corresponding to flashflood events. Training was performed on S-1 subsets with 50 different parameter initialisations. The remaining subset was used as a stop set. Each subset was used in turn as a stop set. For each trial the training iteration with the minimum mean quadratic error over the stop set is set aside. The median of these numbers of iterations was calculated for all stop sets and all initialisations and selected as the optimal number of training iterations. In a second stage, this optimal number of training iteration (12 iterations) is used in all the following without further utilisation of a stop set. In this study, parameters are iteratively calculated using the Levenberg-Marquardt algorithm  (Hagan and Menhaj, 1994).
Monitoring network Hourly rainfall data are available at five rain gauges: Saint-Martin-de-Londres, Prades-le-Lez, Sommières, Vic-le-Fesq and Saint-Hippolyte-du-Fort. The French Weather Forecasting Service (Météo France) manages the first two gauges, and the Flood Forecasting Service of the Grand Delta (SPCGD) manages the last three gauges. Only the Prades-le-Lez rain gauge is inside the Lez system, but as pointed out in introduction, it is essential to make use of spatialised rainfall information. In addition, no data at the considered time step is available further south than the Prades-le-Lez rain gauge. Spatial rainfall variability is thus not correctly described in the southern part of the basin. This will limit the reliability of this study regarding the southern zone of the basin. Unfortunately, it is not convenient to use weather radar information in this basin because, due to the distance of the Nîmes radar (50 km), this information is not robust from one event to another and generally underestimate the rainfall value compared to the rain gauge measurements  (Marchandise, 2007; Visserot, 2012) ; also radar information is not available for all events in the database.
Discharge data are provided by the Lavalette gauging station managed by an office of the French ministry of ecology and sustainable development (DIREN). Both rainfall and discharge data are available at an hourly time step, which is convenient for flash-flood modelling. The data suffer from high noise and uncertainty. The uncertainties of discharge measurements have been estimated at around ±20 % for flash floods. The uncertainty of rainfall measurements, can be as high as ±10 to 20 % (Marchandise, Fifteen flood events whose peak discharges exceed 80 m 3 s -1 were selected (Table  2 ). Events 7 and 8 were the most intense; contrary to other intense events, events 13 and 8 occurred on dry soils. 4 Application of the KnoX method to flash flooding at Lavalette.
Model selection As presented in Sect. 2. 1. 2, model selection is done using cross-validation and pre-definite number of training iterations. Ranges of investigation and chosen values of various window width and hidden neurons numbers are provided in Table  3. One can note that the complexity of the model is moderate (small number of hidden neurons). To make the model assessment more reliable on the most intense events 7 Figure  4. Hydrographs of major events in the database: events 7 and 8. Simulated discharge is the median of outputs coming from the 50 run models (differing by their initialisation parameters). Uncertainty on the observed value is the measurement 20 %. Uncertainty on the simulated value is represented by simulations coming from the 50 run models (differing by their parameters initialisation).
However, significant losses occur through numerous faults in the southern part of this zone (cf. Fig. 2 ). As in the south-western zone, two contributions play a role: surface (rapid) and underground (slow) (recall that the contribution reflects the behaviour of the entire training database; thus, this schematic behaviour can be assumed). Nevertheless the Lez spring, which drains the underground north-eastern zone, has a smaller discharge than Lirou spring, during flood events, and thus softens the underground flooding. The daily discharge of the north-eastern zone to Lez spring (50-54 %) can be explained only by infiltration through numerous faults, not only limited to the surface watershed but also in the extreme northern part of the under- ground basin. Indeed, a dye tracing experiment demonstrated water circulation between sinkholes in river tributaries of the Vidourle (east of Lez basin, cf.
Limits of the study The KnoX method is a novel tool for investigating the behaviour of heterogeneous basins. Because this method is currently under discovery and development, the sensitivity of the provided estimations to noise, uncertainty, and small database size have not yet been fully assessed. Nevertheless the overview of the Lez aquifer that this method has provided appears to be quite consistent with the current knowledge. Based on the proposed behaviour of the Lez aquifer, several fieldwork projects are currently in progress to assess karst and non-karst contributions at the Lavalette station.
Conclusion Mediterranean flash floods and mountain floods are responsible for numerous casualties and major property damage. These floods occur in heterogeneous basins, which are difficult to observe and thus to model. For this reason this paper investigates the ability to obtain information on a complex aquifer through global systemic modelling using neural networks. For this purpose we chose as a case study flash flooding at the entrance to the great city of Montpellier (southern France) where large potential losses are at stake. After recent trends in flash flooding and karst modelling, this paper focuses on hydrological modelling with neural networks and presents the basics of neural network modelling. It was shown that these statistical models can efficiently model unknown relationships using only databases.
On a more interesting note, several time responses were identified and associated with surface circulations or underground contributions. The lag between these two different response times, estimated at 3 h, may thus correspond to a synchronisation difference between surface and underground flooding. This information may help flood warning services anticipate the size of a flood in case of a rain event composed of two rain peaks separated by 3 h. This is a generic method that can be applied to any heterogeneous basin as long as a sufficient database is available. Author contributions. T. Darras, L. Kong-A-Siou and A. Johannet designed the experiments and T. Darras conducted them. V. Borrell Estupina, B. Vayssade and S. Pistre provided hydrological and meteorological data and their expertise.