dataesr/scientific-paragraphs-categorization huggingface dataset:
The non-redundant Arabidopsis protein database (TAIR10, version 20110823, 35386 entries, http://www. arabidopsis. org) was locally queried using X!Tandem search engine (version 2013. 09. 01; http://www. thegpm. org/tandem/) with the following parameters: trypsin as enzyme, 1 allowed missed cleavage, carbamidomethylation of cystein as fixed modification and N-terminal acetylation of protein, deamidation of asparagine and glutamine, N-terminal-pyroglutamylation of glutamine and glutamate, oxidation of methionine, phosphorylation of serine, threonine and tyrosine, and methylation of glutamate and aspartate as variable modifications. Mass tolerance was set to 10 ppm on full scans and 0. 05 Da for fragment ions. Identified proteins were filtered and grouped using the X!Tandem pipeline v3. 3. 1 (http://pappso.
inra. fr/ bioinfo/xtandempipeline/). Identified proteins were filtered according to the following criteria: at least two different trypsin peptides with an E value below 0. 05 and a protein E value smaller than 0. 025 were required. Using the above criteria, the rate of false peptide sequence assignment and false protein identification as determined by the "decoy database" function implemented in X!tandem pipeline was lower than 1. 5 and 5%, respectively. The X!tandem grouping function allows to take into account redundancy and proteins with at least one common peptide were grouped. Within each group, proteins with at least one specific peptide relative to other members of the group were reported as subgroups. Relative label-free quantification was carried out with the MassChroQ software (version 2.
1) based on extracted ion chromatograms  (37). The detection threshold was set at a minimal and maximal value of 3000 and 5000, respectively. Data were filtered to remove (1) unreliable peptides for which standard deviation of retention time was above 15 s, (2) peptides shared within several proteins, and (3) quantified peptides in less than 3 biological replicates. Normalization was performed to take into account putative global quantitative variations between LC-MS runs. Normalized peptide areas were calculated by dividing the area value of each peptide by the sum of all peptide area values within each LC-MS run. The quantification of a protein relied on the quantification of the sum of its peptides. A Student t test (p value Ͻ 0. 05) allowed to determine the significant change in abundance.
Then, we also considered proteins identified with a unique peptide provided that this peptide showed a significant quantitative variation within treatments. The mass spectrometry proteomics data have been deposited in Proticdb  (38) : http://moulon. inra. fr/protic/pipinteractome (login: pipinteractome; password: review). Data Analysis and Protein Interaction Networks-The subcellular localization of interactants was predicted with SUBAcon server (http://suba. plantenergy. uwa. edu. au/) and the number of transmembrane domains was estimated with Aramemnon (http://aramemnon. botanik. uni-koeln. de/). The functional classification of interactants according to Gene Ontology (GO) annotation, and enrichment in interactants with regards to the Arabidopsis genome were performed with Panther server (http://pantherdb.
org/) (39). Protein-protein interaction data were obtained from plant interactome databases including results from a yeast two hybrid approach (18) (Arabidopsis Interactome Mapping Consortium, 2011) and from Split-ubiquitin approaches  (15, 16)  to build a network including PIP interactants identified in the present work together with the reported interactants of these PIP interactants. Protein-protein interaction networks were visualized using Cytoscape  (40). Networks were analyzed using the Cytoscape 2. 8. 2 Molecular Complex Detection (MCODE) plugin  (41)  to detect clusters (i. e. densely connected regions) that are predictive of functional protein complexes. The parameters used for MCODE to generate the clusters were as follows: loops included, degree cut-off of 2, deletion of single connected nodes from the cluster (haircut option disabled), expansion of the cluster by one neighbor shell (fluff option enabled), node density cut-off of 0.
Acknowledgments-We thank the  Mass Spectrometry Proteomics Platform (MSPP)  for its technical support regarding proteomics experiments,  Carine Alcon  and  Sylvain Rossi  for their technical help in FLIM measurements performed on MRI (Montpellier Rio Imaging platform, Montpellier), Cecile Fizames and  Olivier Langella  for bioinformatical assistance.
Stimuli were presented and button presses recorded using the E-Prime 2. 0 software (Psychology Software Tools, Pittsburgh, PA). Based on the participant's choices on the 128 regular trials, we calculated the individual degree of loss aversion, lambda (λ), by fitting a logistic regression to each participant's binary response (accept/reject). In contrast to  Tom et al. (2007) , we used the full gain/loss ratio of the mixed gambles as independent variable to derive the individual "decision-boundary" lambda in each participant. This was due to our focus on the full gamble ratio in the fMRI analyses, rather than the single gain and loss values. Lambda was estimated as the gain/loss ratio for which the probability of accepting a trial was equal to the probability of not accepting a trial (i. e.
Acknowledgments We sincerely thank all the participants for their time as well the  Danish Center for Ludomani  for establishing contact with the gambling community. We thank  Sid Kouider  for helpful comments on the manuscript and  Christian Buhl  for helping with data collection. This work was supported by the  Danish Council for Independent Research in Social Sciences  through a grant to  Dr. Ramsøy  (" Decision Neuroscience Project "; grant no. 0601-01361B ) and by the  Lundbeck Foundation  through a  Grant of Excellence  ("ContAct"; grant no. R59 A5399 ) to  Dr. Siebner. Work  performed by  Dr. Gelskov  at  Laboratoire de Science Cognitives et Psycholinguistique  is supported by  ANR  grants ( ANR-10-LABX-0087  and  ANR-10-IDEX-0001-02 ). The MR scanner was donated by the  Simon Spies Foundation.
Plasmids Plasmids used for diap1-GFP and diap3-GFP expression in HEK293 cells were pReceiver-M03-Diap1 (GeneCopoeia) and pEFmEGFP-mDia2 (Addgene), respectively (GenBank accession numbers: diap1, BC070412. 1; diap3, AF094519. 1). Diap2 coding sequence was subcloned from the donor plasmid pCMV6-Ac-GFP-diap2 (Origene) into the receiver plasmid pEGFP-C3 (Takara), using In-Fusion HD Cloning Kit (Takara; National Center for Biotechnology Information Reference Sequence for diap2, NM_172493. 2). Diap2 coding sequence was amplified from the donor plasmid using the primers diap2-PEGFP-3-F (5=-GTACTCAGATC-TCGAGATGGAGGAGCTCGGGG-3=) and diap2-PEGFP-2-R (5=-TAGATCCGGTGGATCCTTGGATGACATGGCTCC-ATTG-3=; Eurogentec). The receiver plasmid was digested with XhoI and BamHI restriction enzymes (Promega). Subcloning was performed following the instructions from the manufacturer (Takara).
Auditory brainstem response and distortion product otoacoustic emission recordings For ABR, the acoustical stimuli consisted of 9-ms tone bursts, with a plateau and a 1-ms rise/fall time, delivered at a rate of 11/s with alternate polarity by a JBL 2426H loudspeaker in a calibrated free field. Stimuli were presented to the left ear by varying intensities from 100 to 0 dB SPL, in 5-dB steps. Stimuli were generated and data acquired using Matlab (MathWorks) and LabView (National Instruments) software. The difference potential between vertex and mastoid intradermal needles was amplified (2500 times, VIP-20 amplifier), sampled (at a rate of 50 kHz), filtered (bandwidth of 0. 3-3 kHz), and averaged (100 to 700 times). Data were displayed using LabView software and stored on a computer (Dell T7400).
Electrocochleography A retroauricular incision of the skin was performed on anesthetized mice, and the left tympanic bulla was opened. Cochlear potentials were recorded with a silver positive electrode placed on the round window membrane. The acoustical stimuli were identical to those used to elicit ABRs except for the alternate polarity. Gross cochlear potentials were amplified (2500 times, VIP-20 amplifier), sampled (at a rate of 50 kHz), filtered (bandwidth of 0. 001-20 kHz), averaged (50 to 300 times), displayed with LabView software, and stored on a computer (Dell T7400). The signal was then digitally filtered using Matlab software with a low-pass filter (cutoff frequency 3. 5 kHz) to measure the compound action potential and the summating potential, and with a bandpass filter centered on the frequency of stimulation with a 4-kHz span to measure the cochlear microphonic.
3% Triton X-100, and 450 NaCl and incubated for 2 h in the incubation solution with fluorescently labeled secondary antibodies, rhodaminephalloidin (Molecular Probes) and/or Hoechst dye (Invitrogen). They were finally rinsed three times for 10 min in wash buffer and mounted in Prolong Gold Antifade Reagent (Fisher Scientific). Tissues were examined with the Zeiss LSM780 or Leica SP8-UV confocal microscopes of the Montpellier RIO Imaging facility (Montpellier, France). Image stacks were then processed with ImageJ software (National Institutes of Health). For fluorescence analysis and synapse counting, image stacks were processed with Matlab software. The juxtaposed spots of the presynaptic ribbon component RIBEYE and postsynaptic glutamate receptors subunits GluA2/3 allowed the quantification of the ribbon-containing synapses per IHC.
Molecular correlates of cuticular plate remodeling The morphological distortion of the cuticular plate was then examined at the molecular level. Phalloidinrhodamine and spectrin immunostainings indicate a severe alteration in the shape of the cuticular plate. While the F-actin as well as spectrin networks appear homogenously distributed in the cuticular plate of WT IHCs, they tend to concentrate at the border of IHCs from both Tg lines, leaving F-actin-and spectrin-free spots in the middle of the cuticular plate (Fig. 7 ). In contrast, the F-actin and spectrin distribution were not affected in the cuticular plate of OHCs (data not shown). Thus, the drastic remodeling of the cuticular plate molecular components may account for the anatomical alteration observed in electron microscopy.
Mesozooplankton MAREDAT, MARine Ecosystem DATa,  (Buitenhuis et al. , 2013)  is a collection of global biomass data sets for major plankton functional types (e. g. diatoms, microzooplankton, mesozooplankton). Mesozooplankton is the only MAREDAT field covering the Indonesian archipelago. The      3 ). The flow across the Indonesian archipelago and the transformation of water masses simulated by the model result in realistic vertical distributions of nutrients and oxygen concentrations in the Banda Sea. The ITF leaves the archipelago and spreads into the Indian Ocean with a biogeochemical content in good agreement with the data available in the area. However, simulated vertical structures are slightly smoothed compared to data (Fig. 3 ). The vertical gradient of nitrate is too weak over the first 2000 m depth of the water column (North Pacific and Timor), and the area of minima oxygen concentrations is eroded (especially in North Pacific box).
Acknowledgements. The authors acknowledge financial support through the  INDESO  ( 01/Balitbang KP. 3/INDESO/11/2012 ) and Mercator Vert (LEFE/GMMC) projects. They thank  Christian Ethé  for its technical advice on NEMO-OPA/PISCES. Ariane Koch-Larrouy  provided INDOMIX data. We also thank our colleagues of  Mercator Océan and CLS  for their contribution to the model evaluation ( Bruno Levier ,  Clément Bricaud ,  Julien Paul ) and especially  Eric Greiner  for his useful recommendations and advice on the manuscript. Edited by:  S. Valcke.
Code and data availability The INDO12 configuration is based on the NEMO 2. 3 version developed by the NEMO consortium. All specificities included in the NEMO code version 2. 3 are now freely available in the recent version NEMO 3. 6 (http:// www. nemo-ocean. eu). The biogeochemical model PISCES is coupled to hydrodynamic model by the TOP component of the NEMO system. PISCES 3. 2 and its external forcing are also available via the NEMO web site. World Ocean Database and World ocean Atlas are available at https: //www. nodc. noaa. gov. Glodap data are available at http: //cdiac. ornl. gov/oceans/glodap/GlopDV. html. MODIS and MERIS ocean colour products are respectively available at http://oceancolor. gsfc. nasa. gov/cms/ and http://hermes. acri. fr/, primary production estimates based on VGPM, Eppley and CbPM algorithms at http://www.
This information is not only highly useful in all fields of applied microbiology such as food processing and safety, health sciences and waste processing, but also in fundamental research (e. g. , metagenomics, phylogeography, phyloecology). Currently, there is no centralized resource gathering the state of knowledge on habitats of bacteria in a comprehensive and normalized way. A large part of this knowledge is scattered in numerous scientific papers and databases, such as genomics databases (e. g. , GenBank 1  , GOLD 2  ), international microorganism culture collections ( e. g. , ATCC 3  , DSMZ 4  ), and biodiversity surveys (e. g. , GBIF 5  ). The information on bacteria biotopes is mostly expressed in free text (e. g. , articles or free-text fields of databases) describing very diverse locations (any physical location may be a bacteria habitat) in many different ways.
The widely used NCBI taxonomy 6   (Federhen, 2012)  is the resource used for Bacteria entity categorization. The OntoBiotope ontology 7  , which is dedicated to the description of microorganism habitats, is used for biotope categorization. Previous work has shown the relevance of OntoBiotope for bacteria habitat detection  (Ratkovic et al. , 2012). The first two editions of the task  (Bossy et al. , 2012; Bossy et al. , 2015)  used general-purpose documents, mostly web pages of genomics projects that can be understood by non-specialists. However, scientific literature is the major source of detailed and accurate information on bacteria for biologists. This edition focuses then on scientific paper abstracts from the PubMed database, which offers a twofold advantage, open access and easier readability than full-text.
Corpus Selection The BB corpus consists of titles and abstracts of PubMed entries. We followed a four step procedure to build a representative reference corpus for the task from the whole PubMed database. It started from the set of all PubMed references and successively selected a subset of references while preserving the distribution of bacteria and habitat categories. In the first step, we selected PubMed entries relevant to bacteria, relying on the MeSH index provided by the NLM. We selected all entries that were indexed by any term of the Organisms/Bacteria subtree (B03). PubMed contained 27,872,481 entries, of which 1,156,824 indexed by a term in the Bacteria subtree (4%). In the second step, we automatically annotated Bacteria, Geographical and Habitat entities in the title and abstract of these selected entries (see the corpus annotation subsection for details about this automatic approach).
Corpus Statistics Tables 3, 4 and 5 provide descriptive statistics of the corpus for the three subtasks respectively. They show the distributions of entities, categories, and events among the different datasets (training, development and test) of each subtask. We analyzed these statistics in order to study the characteristics of the BB corpora with respect to the tasks. Each distinct entity surface form has only two occurrences on average in the corpus, which makes the recognition task more difficult than with highly repeated mentions: there are 1,489 and 1,466 distinct entity mentions (i. e. , strings or surface forms) out of a total 2,887 and 2,842 annotated entity mentions in the BB-cat and BB-cat+ner datasets, respectively (see Table  3 ). In comparison, there is less variety in entity categories, since the number of distinct categories is only 519 out of a total of 3,189 occurrences.
The combination of these two observations indicates that there is quite a lot of variation in the surface forms of entities, i. e. , the same category can be expressed in several different ways in the text. This is particularly true for Habitat entities for which there is a higher proportion of distinct surface forms than for Bacteria names (59% vs. 38% in the combined BB-cat datasets). Additionally, we computed the proportion of direct mappings ( i. e. , exact string matches) between Habitat surface forms from the training and development datasets of BB-cat and BB-cat+ner and the ontology labels. We found that respectively 24% and 27% Habitat entity occurrences exactly matched with an ontology label. As expected, proportions were similar in the test sets of these two tasks, with respectively 25% and 27% exact matches.
This finding emphasizes the fact that there is much variation in the expression of Habitat entities, and thus simple methods based on exact string matching are not sufficient to automatically categorize entities with high quality. Multiple categories may be assigned to a given entity mention, as can be seen in Table  3 , which is more challenging than single categories. This is the case mainly for Habitat entities, since there is a total of 1,921 distinct Habitat entities for a total of 2,221 assigned Habitat categories in the BB-cat datasets. The number of Geographical entities in the BB-event+ner sets is much lower than the other entity types with 101 Geographical entities only in total, which may make machine-learning approaches less efficient for this type of entity. Not surprisingly, the majority of Lives in events links Bacteria entities to Habitat entities and only a small number of events involves Geographical entities in the BB-event datasets (e.
Shared Task Organization The BB task schedule was divided in a training period of two months and a test period of twelve days. After the test, detailed evaluation of the system performances was provided to the participating teams and published on the BioNLP-ST 2016 website. Supporting resources were made available to the participants. These resources are the output of state-of-the-art automated corpus analysis tools applied to the BB datasets. They were generated in the same way as for the SeeDev task of BioNLP-ST (see  Chaix et al. (2016)  for further details). In addition to the information available on the website, we maintained a set of community web tools. They included a dedicated forum that allowed participants to interact directly with each other and with the organizers, and an online evaluation service the participants could use to evaluate their predictions during the training phase.
2014). The strategy developed in these studies is based on the preliminary identification of parental species-specific polymorphisms. The NGS data set obtained for the polyploid is assembled using parameters adapted to optimize the recovery of paralogous and homoeologous copies. The constructed contigs are then compared with the diploid parental genomes using specific polymorphic sites  (Flagel et al. 2008; Salmon et al. 2009; Ilut et al. 2012). Pipelines such as PolyCat  (Page, Gingle et al. 2013) , SNiPloid  (Peralta et al. 2013) , HyLiTE  (Duchemin et al. 2014)  and SWEEP  (Clevenger and Ozias-Akins 2015)  were designed to detect homeologs in allotetraploids, using their diploid parents as reference. These tools align diploid species reads (or sequenced ESTs) for detecting interspecific polymorphisms at homologous genomic regions.
The detected polymorphisms are then considered as putative SNPs between homoeologous regions ("homeoSNPs") in the allotetraploid. The sequences from hybrid or allopolyploid species are then aligned or coaligned to the parental homologous regions and the putative homeologs can be assigned to the corresponding parental genome according to the detected homeoSNPs. The POLiMAPS pipeline  (Tennessen et al. 2014 ) associates homeolog-specific sites with genetic linkage maps, when a diploid genome reference is available. However when the diploid parents are unidentified or extinct, detection of homoeologous copies requires the development of adapted tools  (Salmon and Ainouche 2015). In this study, we aim at reconstructing reference transcriptomes from NGS data sets and detecting the various expected duplicated gene copies in the polyploid genus Spartina Schreb.
Sampling, cDNA Preparation and Sequencing This study focused on five Spartina species: the two hexaploid parents S. maritima and S. alterniflora, the sterile F1 hybrids S. x townsendii and S. x neyrautii and the allododecaploid species S. anglica. S. x townsendii was collected in Hythe (Hampshire, England). Samples from S. x neyrautii were collected in Hendaye (Pyrenees Atlantiques, France) and S. anglica was sampled in Roscoff and l'Anse de Goulven (Finistere, France). RNAs were extracted from leaves and roots, from plants grown in same conditions in the greenhouse as indicated in Ferreira de  Carvalho et al. (2013). Roche-454 data were sequenced at the Genoscope Platform (Evry, France) and at the Environmental and Functional Genomics Platform of the University of Rennes 1 (Biogenouest, OSUR, France).
Both normalized (two libraries for S. maritima only) and nonnormalized (two libraries for each species) data were pyrosequenced to enhance the number of assembled contigs as previously published (GenBank accession: SRP015701 and SRP015702; Ferreira de  Carvalho et al. 2013). Roche-454 data of the hybrids and the allopolyploid were obtained using the same protocol as used by Ferreira de  Carvalho et al. (2013). Illumina libraries were prepared from cDNAs of the same samples as those used for the 454 pyrosequencing for each five species and Illumina (Hi-Seq 2000) sequencing and read-quality trimming (Phred score = 20) were performed at the Genoscope Platform (Evry, France). The number of cleaned reads obtained for each species is indicated in table 1. This project has been deposited at Genbank under the accession SRP081066 and at https://spartina-genomics.
Strategy for Assembling Roche-454 and Illumina Reads For each species we independently assembled Roche-454 and Illumina data using the most reliable approaches (fig. 1):  (1) Roche-454 reads were first assembled using the GS de novo assembler Software v. 2. 6, Roche (ml = 80 bp; mi = 90%;  Margulies et al. 2005) ; (2) the Trinity algorithm  (Grabherr et al. 2011 ) commonly recommended for Illumina RNA-seq assemblies  (Clarke et al. 2013; Liu et al. 2013; Chopra et al. 2014 ) was used for assembling Illumina reads with the following parameters: k-mer size of 25 and minimum contig length of 48; (3) Roche-454 and Illumina separately assembled contigs with a length higher than (or equal to) 100 bp (to avoid the formation of chimeric contigs), were then co-assembled using the Newbler software (ml = 40 bp; mi = 90%).
The different contigs obtained after the co-assembly step and Roche-454 contigs and Illumina contigs which were not considered during the co-assembly step (with a length ranging from 40 to 100 bp) were post-processed by deleting redundant contigs and self-blasted in order to maximize the length of overlapping contigs. Contigs overlapping on 50 bp or more with an identity percentage !90% were then assembled using custom python scripts. Redundancy of the contigs was checked again using a SELFBLAST (minimum length: 40 bp and minimum identity percent: 90%).