dataesr/scientific-paragraphs-categorization huggingface dataset:
Genome annotation The genome was annotated by Rapid Annotation using the Subsytem Technology (RAST) bioserver  [9]. BLAST2GO optimizes the function transfer from homologous sequences through an elaborate algorithm that considers the similarity, the extension of the homology, the database of choice, the GO hierarchy, and the quality of the original annotations  [10]. The resistome was analysed with the ARG-ANNOT (Antibiotic Resistance Gene-ANNOTation) database  [11]  and BLASTp in GenBank. The prediction of rRNAs was carried out using the RNAmmer tools  [12]. The exhaustive bacteriocin database available in our laboratories (Bacteriocins of the URMITE database; http://drissifatima. wix. com/bacteriocins) was performed by collecting all currently available sequences from the databases and from NCBI.
A motility test was positive. Acid production was observed for the following carbohydrates, with API 50 CH negative for D-arabinose, L-xylose, D-adonitol, methyl-β-D-xylopyranoside, L-sorbose, L-rhamnose, dulcitol, inuline, D-tagatose, D-fucose, L-arabitol, potassium 2-and 5gluconate; all other acid production was positive (Table  1 ). All activities were negative with API ZYM (Table  1 ). Further identification was performed using a 16S rRNA nucleotide sequence (GenBank accession number HF952919) gene closely related (99. 8%) to Actinomyces 152R-3 (GenBank accession number DQ278863. 1) (Fig. 2 ). This value was lower than the 95% 16S rRNA gene sequence threshold recommended by Stackebrandt and Elbers to delineate a new genus without carrying out DNA-DNA hybridization  [14]. The spectrum from MS2 was added to our MALDI-TOF mass spectra database.
Partial fragments of 16S rRNA gene sequences were combined in a complete sequence using Sequencher program (Version 4. 5). Complete sequences were compared with all bacterial 16S rRNA gene sequences available in the GenBank database, using multisequences comparison program BLAST. Multiple alignments of sequences and calculation of sequence similarity levels were carried out using Clustal W  (Thompson et al. , 1994). Neighbour-joining tree  (Saitou and Nei, 1987)  is constructed using MEGA version 4. 0. 2. The reliability of the tree topology was evaluated by bootstrap analysis of  Felsenstein (1985)  with 1000 resembling.
Isolation and identification An actinomycetes strain P. B. 373 was isolated from a pleural fluid specimen with gray dry and powdery colonies. Microscopic observation revealed Gram positive, non fragmented mycelium and long (up to 50) straight spore chains. Comparative analysis of an almost complete 16S rRNA gene nucleotide sequence (1459 pb) obtained from the isolate, with the corresponding sequences obtained from the Gen Bank/ EMBL/ DDBJ databases, assigned the strain P. B. 373 to the genus Streptomyces and found the most closely related species with a high degree of relatedness (up to 99 % of sequences similarity). The 16S rRNA gene sequence of the strain showed levels of similarity of 99. 85 % to that of Streptomyces cinereoruber ssp. cinereoruber JCM 4205 T and 99. 45 % to that of Streptomyces violaceorectus NBRC 13102 T.
Although this does not invalidate the conclusions drawn in the previous sections, it must be kept in mind when interpreting the results. By definition, the relative potentials for D AQP = 1, D AQP = 2, and D AQP = 3 represents averages over the 5-month period considered in this study. Given the incomplete time coverage within the 5-month period, the average potentials will therefore be based on different sets of points for their calculation. We therefore do not completely fulfill the conditions (described in the Annex) to ensure that meteorological effects are fully smoothed out during our analyses. However, the available days (spanning from January to May) cover high and low concentration days within each datasets, and differences in terms of average concentration levels remain low (see base case concentrations in Tables  2  and 3 ).
Acknowledgments The results used in this study come from research performed within the European Union's  Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement no. 218793  (MACCproject) and no. 283576  ( MACC-II  project) and  Horizon 2020 programme  under grant agreement  633081  ( MACC-III  project). Open Access This article is distributed under the terms of the Creative Comm ons Attribution 4. 0 International License (http:// creativecommons. org/licenses/by/4. 0/),which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
In head-restrained larvae, this readout was computed in real time to readjust accordingly the visual environment displayed around the larva. Our VR approach enabled the larva to interact meaningfully with its environment in different behavioral contexts. The larvae could change their swimming direction to follow a whole-field motion, and track small moving virtual prey. Perturbation of the delay of the visual feedback affected the success rate of capturing the virtual prey, suggesting that larvae rely on a continuous feedback for navigation. The behavioral database and the code required to infer the trajectory from the tail movements were written in a Python Jupyter Notebook  18. The code controlling the visual feedback was written in C+ +. They are all open source and can be downloaded and modified as required (www.
2b  and Materials and Methods). Thus, a simple regression is needed to fit the relationship between the tail deflection and the resulting trajectories. To assess the significance of our model, we predicted the trajectories in the test dataset of free-swimming larvae (a random set of 20% of the tail bouts in our library), based only on the changes in tail deflection. The resulting trajectories were then compared to the actual trajectories of the larvae. Figure  2c  shows that the trajectories resulting from different categories of tail movements can be fitted using the same model. Due to error accumulation, the trajectory predicted from the tail deflection may sometimes diverge from the observed trajectory, but the overall kinematics were similar. The quality of the prediction of the final orientation and position after a tail bout is shown in Fig.
2d. To compute the error between the predicted and the observed paths, we used a bootstrap between the test and the training datasets, in order to get a reliable estimate of our error. The mean square error (MSE) in the prediction of the direction of movements was 19. 4° (Fig. 2d. ii), a similar MSE of 23. 4° was observed in the prediction of the change in the direction of the larva's head (Fig. 2d. i). The MSE in the prediction of the larva's displacement was 0. 3 mm representing 1/10 of the body length of the larva (Fig. 2d. iii). Moreover, we showed that our method was able to generalize by predicting trajectories of tail movements absent in the training set (see Materials and Methods), thus confirming that the ARX method captures the most relevant dynamics of the larva's locomotion. To create the visual VR system, larvae were head-restrained in a drop of low-melting agarose and placed in a recording chamber (see Materials and Methods).
The VR software was based on a custom program written in C+ + , using OpenCV to process images and OpenGL to display the visual environment. The routine required for providing the visual feedback in real time is available at (www. zebrain. biologie. ens. fr/codes). Due to the flexibility of this method, we were able to study different types of visual behaviors. All routines required for the analysis of the library of movements and the generation of the ARX model were programmed in a Python Jupyter Notebook. This approach enables others to reproduce the data analysis, and to easily adapt the code according to their own needs. As a proof of principle, we tested the VR system using two different goal-directed visual behaviors: the optomotor response and prey-capture behavior. Optomotor response in a two-dimensional visual virtual reality system.
A center of curvature was defined as the intersection of the minor axis of the two ellipses. From the center, the deflection was defined as the inverse of the average distance between all the pixels in the fish and the center of curvature (1/R). The result was multiplied by the length of the fish at rest (L 0 ) in order to obtain a dimensionless value. The Python code required to extract the paths and tail deflection from the video of a larva is available in an Jupyter Notebook (https://github. com/ajouary/ VR_Zebrafish/blob/master/Code/FishTracking. ipynb) for reproducibility and reuse. Classification of tail bouts. This method uses fuzzy K-nearest neighbor to classify movements according to their similarity with respect to manually labeled tail bouts. We used the Dynamic Time Warping algorithm to compare the time series of tail deflections  35.
Prey-capture trajectories were recorded by introducing paramecia in one of the sessions. C bends and bursts occurred spontaneously, for instance when the wave generated by one larva triggered a startle response in a larva nearby. Only movements that started and finished inside the field of view were kept for further analysis. Although we did not identify individual larvae, we estimated that each larva would not contribute to more than ~5 bouts in the database. Supplementary Figure  1  shows the tail deflections of all the tail bouts in the library, classified according to the movement category they belong to.
The values obtained for the goodness of fit were =. R 0 67 Lateral 2 , =. R 0 79 Axial 2 and =. R 0 72 Yaw 2. We then tested the ability of the ARX model to fit unobserved trajectories. We split the dataset into two groups of equal size: large or small bouts, according to the maximal tail deflection in each bout. We assessed the goodness of fit of the ARX regression when generalizing between the small and large movements (see Table  1 ). Table  1  shows that the axial speed trained on small tail bouts poorly predicted the kinematics of large tail bouts, probably because the measurement of axial displacement is noisier for small tail bouts than for large ones. In the other configurations, the goodness of fit of the regression trained on small movements and evaluated on large movements (and vice-versa) was close to the goodness of fit obtained when training on a mix of small and large bouts.
These results demonstrate the ability of our method to predict the kinematics of trajectories outside the training dataset. The Python code required to extract the parameters Θ from a library of tail movements is available in the Jupyter Notebook (https://github. com/ajouary/VR_Zebrafish/blob/master/Code/ PredictionTrajectory. ipynb) for reproducibility and reuse. 1. Each column contains the value of the goodness of fit for the three kinematic parameters evaluated using the ARX model. For the last column, the dataset was split randomly in a train and test dataset containing respectively 80% and 20% of the tail bouts. The goodness of fit was evaluated on the test dataset. This procedure was iterated 100 times leading to an estimate of the mean and the standard deviation for the goodness of fit.
(i) Tail deflection corresponding to different categories of movement. (ii) The axial speed. (iii) The lateral speed. (iv) Yaw angle. For each case, the observed kinematic parameter is in red, and the predicted in blue. (d) Distribution of the errors between the predicted position and orientation, and those observed, for: (i) change in head orientation; (ii) direction of movement; (iii) amplitude of the tail bout. The results presented in (c) and (d) were taken from the test dataset.
(iii) A trial was considered successful if the larva got at least 400 μm from the virtual prey. (c) Percentage of trials that ended in successful capture of the virtual prey. Only trials where larvae executed at least one tail bout were considered. Left: Each dot represents the performance of individual larvae. Right: The performance obtained by shuffling the angular positions of the virtual prey in each dataset. The red segment depicts the average. (p = 1. 4*10 -5 , Wilcoxon signed-rank test). (d) Examples of paths of a larva towards the virtual prey. The paths are color-coded according to the position of the virtual prey at the onset of the first tail bout (color bar). Upper panel: Individual paths for one larva. Left: Paths leading to capture. Right: paths failing to capture the virtual prey.
Acknowledgements This work was supported by  EraSysBio+ Zebrain ,  ERC  stg  243106 ,  ANR-10-LABX-54   MEMO LIFE ,  ANR-11-IDEX-0001-02  and the  PSL Research University. A. J acknowledges support from the  Fondation pour la Recherche Médicale  and the  ENS Cachan. We thank  P. Gongal  for editorial assistance.
, 2016)  with red circles, nonneoplastic cell sequences (from  Metzger et al. , 2016)  with black circles, sequences from GenBank with light gray diamonds and sequences from GenBank from  Layton et al. (2014)  in dark gray diamonds was much stronger (data not shown). We therefore sequenced the mtCOI gene following  Metzger et al. (2016) 's protocol and analysis.
Now that we became aware of the possibly high prevalence of contagious cancers in the wild, let us keep it in mind when interpreting weird genotypes and develop routine procedures to track genetic chimerism in our dataset. 3 | EXAMPLE WITH OUR OWN DATA IN MYTILUS MUSSELS Metzger et al. (2016) 's publication appeared while we were having difficulties to interpret weird genotypes in our Mytilus mussel dataset. Among 938 mussels sampled along the European Atlantic coasts (from the Netherlands to France), we found what we thought to be five hybrid genotypes between M. edulis and M. trossulus, one in the Wadden F I G U R E 2 (a) Examples of SNP cluster plots using the KASPar ® assay technology for six markers. Green dots: homozygotes for the trossulus allele (allele more frequent in M. trossulus reference samples), blue dots: homozygotes for the edulis/galloprovincialis allele (allele in higher frequency in M.
ACKNOWLEDGEMENTS We thank  Julien Normand ,  Ambre Ribardiere ,  Myriam Rumebe , and  Petr Strelkov  for providing samples. We also thank one anonymous referee for useful comments on an earlier version of this manuscript. We are grateful to the platform  Génomique Environnementale  of the  LabEx CeMEB. This work was supported by a  Languedoc-Roussillon Region "Chercheur(se)s d'avenir 2011" grant  to  NB  ( Connect7  project) and a  LabEx CeMEB postdoctoral fellowship  to FR.
5 mins -20% B; 2 mins -45% B; 7. 5 mins -64% B; 8 mins -100% B. This gradient was followed by a 3. 5 minute hold at 100% B, followed by a reduction to 20% B and a 1 minute hold to re-equilibrate the column. The eluent was passed directly to the electrospray source of an Amazon ETD ion trap mass spectrometer (Bruker) operated in +ve ion, multiple reaction monitoring mode. MS conditions for each analyte are provided (Supplementary Table  S7 ). At least 6-point standard curves of pre-labelled analytes in solvent A were built and samples were run in at least duplicate, in 7 batches. Data processing was performed using the Bruker Compass plugin, QuantAnalysis. Any effect of matrix on measurement of neurochemicals in the brain samples was corrected for by use of measured concentration of the internal standard, caffeic acid.
Each sample was injected simultaneous detection in positive ion and negative ion using polarity switching mode, the spray voltage was 4. 5 kV and 3. 5 kV respectively. Full scan MS detection was acquired for the range 70-1400 m/z at 50000 (at 400 m/z) resolution mode for each polarity. Raw mass spectrometry data was processed using our standard pipeline, consisting of XCMS (for peak picking), MzMatch (for filtering and grouping) and IDEOM (for further filtering, post-processing and identification). Core metabolite identifications were validated against a panel of unambiguous standards using accurate mass and retention time. Additional putative identifications were assigned by accurate mass along with a retention time prediction algorithm. LC-MS/MS. Samples (10 μ l) were separated by a SeaQuant ZIC-pHILIC 4.
A maximum of 5 ions selected from the precursor ion list were selected for MS/MS and were isolated using a 2 m/z window and activated with HCD at 60. 0 NCE. Fragment ion spectra were acquired using a resolution of 35000 (at m/z 400). Fragment ion spectra were searched against online public databases, Massbank (http://www. massbank. jp/), mzCloud (https://mzcloud. org/) and Metlin (http://metlin. scripps. edu) and in silico fragmentation using MetFrag (http://msbi. ipb-halle. de/MetFrag/) searched against Kegg database. When no compounds were found from Kegg then PubChem and Chemspider was used as an alternative. All databases were searched using 20 ppm mass window for the fragmentation data.
Acknowledgements We thank  Mrs Ailsa Travers , and  Mr William Mungall  for their excellent technical support at the  Biomedical Research Resources at the University of Edinburgh ;  Dr Karl Burgess  at  Glasgow Polyomics  for advice;  Mrs Yvonne Crawford  at  Easter Bush Pathology ;  Professor Austin Smith  and  Dr Kathryn Blair  for providing the DAK31 cell line;  Dr Michael Clinton  for his editorial input; and  Professor David Hume  for encouraging discussions. This work was supported by funding the  Biotechnology and Biological Sciences Research Council   Institute Strategic Programme Grants   BB/J004316/1 ,  BB/J004332/1 :  BBSRC Response mode  grant  BB/H012478/1 , and  European Community 's  Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement No. HEALTH-F4-2010-241504  (EURATRANS).
Discharge data Five discharge gauging stations within the studied zone are used to analyse the observed changes and also as input or validation for the model simulations. Ansongo (Mali) at the head of the study zone is needed to analyse the Guinean flood before any influence of the red flood on the discharge. Ansongo data are also needed as input to the hydrological model (see below). The discharge in Niamey is the main focus of this work, and is the best quality data record of all five stations. It is also the only station used to validate the model output. The discharge at the three right bank tributary outlets (Alcongui, Kakassi and Garbey) is used to quantify the locally generated runoff and its variability over the years. All discharge data are provided by the Niger Basin Authority (ABN) database.
Rainfall data record Rainfall data are used in Sect. 3 to analyse the observed changes in the climatic and hydrological signals. It is also needed as forcing for the hydrological model. Rainfall over the Niger basin is associated with the West African monsoon and occurs mainly between June and October. In the studied area, like in overall Sahel, 90 % of rainfall comes from propagating mesoscale convective systems (MCSs)  (Laurent et al. , 1998; Mathon et al. , 2002; Lebel et al. , 2003). Although MCS patterns vary, they often organise in a curved convective line followed by a stratiform region  (Houze, 1993). The resulting rain fields are characterised by strong space-time variability, with intense rain rates when the convective front is passing through (typically during less than an hour in a given point at ground) followed by a few hours of less intense rainfall in the stratiform part.
Atmospheric forcing ISBA needs a classical atmospheric forcing (precipitation, temperature, pressure, humidity, radiance and wind) to compute the water balance for each grid cell. Here the atmospheric forcing, except for rainfall, is provided as a climatological mean value for each day. The daily mean value was computed from the 2003-2012 period based on the WATCH (Water and Global Change) Forcing Data methodology applied to ERA-Interim data (WFDEI;  Weedon et al. , 2011 Weedon et al. , , 2014)) , re-analysed by Météo France (B. Decharme, personal communication, 2013). Sensitivity tests (not shown) have been run to verify the sensitivity of the model output to this atmospheric forcing. The tests showed that using a climatological mean does not impact the simulated discharge and does not change the characteristic of the simulated red flood.
Vegetation map ISBA computes energy and water balance based on empirical equations and vegetation and soil properties. In the reference simulation the vegetation map (vegetation type and fraction, Fig. 4a ) is based on the Ecoclimap data  (Masson et al. , 2003; which covers the 2002 -2006 period)  and the soil texture (sand, silt proportion) is based on FAO data. These data sets are considered representative of the current conditions in the area. The bare soil proportion on the basin ranges from 20 to 100 % and follows the rainfall latitudinal gradient with less vegetation in the north than in the south. Within ISBA, the vegetation cover of the Ansongo-Niamey reach basin is composed mainly of Sahelian savannah and bush Sahelian savannah, which present low coverage (LAI < 2 and at least 20 % of bare soil).
Acknowledgements. We warmly thank the  Niger Basin Authority (ABN) , for providing the Niger River discharge data and for their collaboration. We would like also to thank all the teams that provided the various rainfall data sets online. We also are grateful to  L. Gal  for her precious help and advice for the maps. This work has been co-funded by the  French Centre National d'Etudes spatiales (CNES)  and the  Midi-Pyrénées region. It was also partly supported by the  French national programme EC2CO-LEFE "Evolution récente  de l'aléa hydro-climatique au Sahel: détection et éléments d'attribution". Edited by: R. Uijlenhoet Reviewed by:  N. Wanders  and one anonymous referee.
: a research product based on a rich set of operational gauges (available since 1950)This regional product provided by the Laboratoire des Transfert en Hydrologie et Environnement (LTHE), hereafter named the KRIG product, is a gauge-based rainfall estimate. Panthou et al. (2014) on a database first built by LeBarbé et al. (2002)and updated byPanthou et al. (2014). The KRIG product is based on rain gauge records from different institutes: the Centre Inter-Etats d'Etudes Hydraulique (CIEH), the AGRometeorology, Hydrology, METerology centre (AGRYMET) and the National Weather services from several African countries (DMN in French). The available network density is variable during the period and inside the basin. Over the studied area and after quality control, the available number of stations ranges from 60 to 15 gauges between 1950 and 2012, and since 2006 the network has been sparser.
0x10 -8 ), or a total of 18 loci  [10] [11] [12] [13] [14] [15]. Imputation has proven to be a powerful tool in genome-wide association studies (GWAS) by facilitating investigation of variants not directly assessed on genotyping arrays, the merging of GWAS datasets genotyped on different arrays, and fine-mapping of ABSTRACT Genome-wide association studies (GWAS) have identified common pancreatic cancer susceptibility variants at 13 chromosomal loci in individuals of European descent. To identify new susceptibility variants, we performed imputation based on 1000 Genomes (1000G) Project data and association analysis using 5,107 case and 8,845 control subjects from 27 cohort and case-control studies that participated in the PanScan I-III GWAS. This analysis, in combination with a two-staged replication in an additional 6,076 case and 7,555 control subjects from the PANcreatic Disease ReseArch (PANDoRA) and Pancreatic Cancer Case-Control (PanC4) Consortia uncovered 3 new pancreatic cancer risk signals marked by single nucleotide polymorphisms (SNPs) rs2816938 at chromosome 1q32.