dataesr/scientific-paragraphs-categorization huggingface dataset:
Previous studies identified the ecological, behavior and physiological differences between these species, while very little is known on the genetic architecture underlying these traits [but see  [38] , on pheromonal pathways]. Our de novo transcriptomes offer a major contribution on deciphering the expressed sequences, with approximately 9,000 reconstructed unigenes for each sibling-species, with more than 4,600 unigenes newly identified for each species. The number of predicted genes for well-established insect genomes such as Bombyx morii genome size 530 Mbp: 14,623 genes  [33] , Drosophila melanogaster 165 Mbp: 14,039 genes  [33]  and Anopheles gambiae 278 Mbp: 13,683 genes  [39]  suggests that we did not reach the completion of the transcriptome for each species. This is also apparent through the assembly features, OHR values (annotated transcripts cover about 60% of the total length of the hits by BLAST alignment against the NR database) and CDS predictions (only 20% of the transcripts contain a complete CDS).
e. in an 82-97% identity range). Due to the limited current resources in public sequence databases most of these genes could not be functionally annotated. Among those that could be annotated were functions involved in development, defense against pathogens and sensory processes. These life history traits are good candidates in the divergence process among phytophagous insects in general  [44] , and in Ostrinia in particular  [7, 19]. No data were previously available in Ostrinia for the genes themselves involved in such traits. While validation studies are essential to resolve their pertinence in the divergence process between Ostrinia spp. , the present gene set enlarges the pool of available candidate genes.
Sequence cleaning The 454 adapter sequences located at the termini of the short reads were removed using the sffinfo application of Roche. Subsequently, we ran an in-house pipeline which applies Cross_match (Phrap/Cross_match/Swat package, http://www. phrap. org), SeqClean (http://sourceforge. net/projects/seqclean) and TrimSeq (EMBOSS package version 6. 3. 1,  [51] ) in order to clip those adapter sequences which were not correctly removed by the sffinfo program. In addition, this pipeline performed further filtering steps which removed species barcodes, too short sequence reads, sequences with a high ratio of undetermined nucleotides (N) and ribosomic RNA sequences representing eventual bacterial contaminations.
Assembly of 454 short reads The short reads were assembled with Newbler (version 2. 5. 3, Roche). Newbler has the advantageous option, in comparison to other assembly algorithms, to reconstitute alternative transcripts (designed as "isotigs" in Newbler outputs). A Newbler isotig represents a specific transcript composed of successive contigs connected by bridging sequence reads. This specific sequence of contigs is also called contig graph. A Newbler contig itself represents an exon. Alternative transcripts or splice variants are isotigs in which specific contigs are excluded (corresponding to cassette exons) from the contig graph. A Newbler "isogroup" comprises a set of all alternative transcripts that share the same contig subset. In consequence, an isogroup should represent, in theory, a unigene.
Functional annotation of assembled transcripts For each sibling species assembly, all transcripts were automatically annotated by blastx against the NR database (build of 06/12/2011), applying a threshold e-value of 1e-5. The same filter criterion was applied for the BLAST alignments of the O. nubilalis contigs of the assembly of Coates et al. (  [28] , see below for this latter data set). For all blast analyzes we applied a filter on the results in order to keep only the best hits to a specific transcript. More precisely, for each transcript we only retained hits of the best alignment quality defined by a combination of the sequence overlap and the sequence identity. High sequence overlap and identity among the aligned bp (at least 70% overlap and 50% identity) represented the best category, followed by high overlap (≥ 70%) and low identity (< 50%) (second category), low overlap (< 70%) but high identity (≥ 50%) (third category) and low overlap (< 70%) and low identity (< 50%) as fourth and lowest hit category.
Only those hits of the respective best-hit category were kept for each transcript for further functional analyzes such as determination of GO terms with Blast2GO and calculation of PDEGs and OHRs. For assignment of GO terms we applied a local installation of the Blast2GO pipeline b2g4pipe (version 2. 3. 5), using the default options, with access to a local Blast2GO MySQL database (version of june 2011, b2g_jun11). GO term enrichment analyzes were done by running the web application of Blast2GO (http://www. blast2go. com/webstart/makeJnlp. php) on the calculated GO terms.
Determination of assembly completeness CDSs were predicted with the FrameDP (version 1. 2. 0) application  [26] , applying the default parameters. This way, we allowed similarity searches against well-established Swiss-Prot proteins and self-training in order to optimize the predictions. As suggested by O'Neil et al. [27] , we calculated an OHR for each assembled transcript with at least one hit by dividing the length of the ungapped alignment by the total length of the hit sequence. In the current analysis, an OHR (value between 0 and 1) indicates how well a transcript reconstitutes a hit sequence in a known reference set (e. g. NR database or known Ostrinia gene sets). An OHR of 1 indicates that the assembled transcript covers the entire hit sequence. For transcripts with more than one hit sequence, the hit with the best OHR value was kept as reference for further OHR interpretation.
Calculation of sequence identity Transcripts common to both Ostrinia species were determined by bi-directional inter-species blastn alignment. As these species are closely related and poorly divergent, we applied stringent criteria to determine the true positive homologs: reciprocal best hits were determined with an e-value of at least 1e-10 and a minimum alignment sequence overlap of 90%. The sequence identity percentage was retrieved from the blastn alignment output features. [28, 29]  and 454 NGS of RNA extracted from antenna of young adults  [30]. After an in-house cleaning procedure, Coates et al. [28]  used Newbler for the assembly with default parameters, i. e. a minimum overlap-length of 40 bp and a sequence identity of 90%. Their resulting set of 7,413 non-redundant contigs displayed a mean contig length of 360 bp (n50 = 392 bp, [157-2,886 bp]) and a total size of the assembly of 2.
Additional files Additional file 1: Figure  S1. Homopolymer length distributions in O. nubilalis reads. The distributions of the longest homopolymer in the O. nubilalis reads are given. For each transcript the longest homopolymer had to be composed of at least two successive identic nucleotides. The X axis lists the homopolymer length and the Y axis shows the counts of transcripts with homopolymers of the specific length. The figure shows the homopolymer distributions for the nucleotides A, C, G and T, respectively. Additional file 6: Figure  S6. GO term distribution. Standard configuration of the Blast2GO web application (http://www. blast2go. com) was applied to generate level 3 graphs for GO-term distributions to the categories molecular function (Figure  S6A ), biological process (Figure  S6B ), and cellular component (Figure  S6C ).
No significant differences were observed between the Ostrinia GO-terms distributions (Kolmogorov-Smirnov test, P = 0. 661, 0. 581 and 0. 979 for Figures S6A, S6B and S6C, respectively). Abbreviations: NTP: nucleoside-triphosphatase, transmem. : transmembrane, substr. -spec. : substrate-specific (Figure  S6A ). cell. macromol. : cellular macromolecular (Figure  S6B ). mem. : membrane (Figure  S6C ). Additional file 7: Table  S1. Ortholog Hit Ratios (OHR) for best hits in NR. Table  S2  Recovery level of NR Ostrinia ESTs. Table  S3  Recovery level of a reference and public dataset of O. nubilalis contigs. Table  S4  Distribution of Ortholog Hit Ratios (OHR) of similar transcripts. Table  S5  Distribution of Ortholog Hit Ratios (OHR) of PDEGs. Table  S6  Blast2GO Fisher enrichment test results for PDEGs versus complete transcripts set GO terms.
Acknowledgements We thank  Jérôme Gouzy  and  Sébastien Carrere  from the  Laboratoire Interactions Plantes Micro-organismes (INRA/CNRS Toulouse, France , http:// www. toulouse. inra. fr/lipm)for their technical advices on data cleaning and assembly. We thank  Kevin Wanner  and collaborators for helpful comments on their Ostrinia transcriptomic data. We are grateful to the bioinformatics platforms  CNRS-UPMC ABiMS  (http://abims. sb-roscoff. fr)and GenoToul (http://bioinfo. genotoul. fr)for providing computational resources and support. In addition, we would like to thank  Jeffrey Sanger  for reviewing the written English of this article. This study was supported by an  INRA -CIRAD grant  ' SDIPS' (Mechanisms of Speciation and Molecular Diagnosis of Insect Pest Species Complexes ), and in part by an  INRA -SPE department grant  ' SCOOP ' (Super Contigs in Ostrinia spp.
, 1999) , was examined. Thus, the switch between the mitotic cycle and the endocycle may not be regulated by miR396 in roots, at least in M. truncatula. The impact of miR396 de-regulation on cell proliferation was further investigated using two approaches. First, we determined the expression levels of genes homologous to several currently used markers of the cell cycle. Homologues of A. thaliana H4, CYCB1;1, CYCB1;3, CYCB2;1 and CYCD3;1 genes were identified in M. truncatula genomic and EST databases (Medtr7g106110. 1, Medtr8g038460, Medtr7g106110, Medtr5g023790 and  Favery et al. , 2002; respectively). Quantitative RT-PCR experiments showed (a) Accumulation of pre-miR396a, pre-miR396b and mature miR396 was analysed by quantitative RT-PCR in 3-week-old roots expressing the 35S: pre-miR396a or miR396b constructs (miR396a-OE and miR396b-OE, respectively).
truncatula  (Kuppusamy et al. , 2009). Second, we estimated the number of replicating cells in the RAM using 5-ethynyl-2'-deoxyuridine (EdU) staining combined with flow cytometry to determine quantitative changes in replicating cells from root apices. These experiments revealed that the percentage of replicating cells (EdU-positive) was significantly lower in miR396-OE roots (6. 41%) and higher in MIM396 roots (18. 96%) when compared with their appropriate controls (14. 3% and 11. 6% respectively, Figure  6c, d ). Together, these data strongly suggest that miR396-dependent modulation of root growth may essentially be due to a restriction of cell division activity. The increase in expression of miR396 genes in root tips from the proximal meristem to the transition zone where cell division gradually stops is consistent with these results.
, 2011). We showed that over-expression of both miR396 isoforms in transgenic roots led to downregulation of the MtGRF transcripts, although different efficiencies were observed depending on the target. A similar variability has already been reported in A. thaliana leaves (a) The expression of mtr-miR396a (396a), mtr-miR396b (396b) precursors, mature miR396 (miR396), target genes (MtGRF1-6 and MtbHLH79a and b) and other non-related miRNA precursors (miR167c, miR390a and miR2658) was analysed using quantitative RT-PCR in roots treated for 1, 3 or 5 h with epibrassinolide (BR) at a concentration of 1 nM. Expression was normalized to the values of untreated roots. Three references genes (defined using Genorm software, see Experimental procedures) were used for normalization. (b) Levels of GUS transcript in roots expressing the prom-miR396b:GUS construct (grey bars) or the prom-GRF5:GUS construct (black bars) treated or not treated with 1 nM BR for 3 h were measured using quantitative RT-PCR.
Bioinformatic analyses Sequences of the miR396 precursors and mature isoforms in M. truncatula and other plant species were retrieved from the miRBase database version 19 (November 2012, http://microrna. sanger. ac. uk). To identify M. truncatula homologues of A. thaliana GRF, bHLH74, bHLH79, CYCB1;3, CYCB2;1, CYCD3. 1 and histone H4 proteins (Table  S1 ), tBLASTx searches were performed for ESTs on the Institute for Genomic Research database, release 10. 0 (http://compbio. dfci. harvard. edu/cgi-bin/tgi/Blast/index. cgi), and for available genomic sequences on the National Center for Biotechnology Information (http://www. ncbi. nlm. nih. gov/BLAST), Hapmap (http://www. medicagohapmap. org) and Legoo (Mt genome version 3. 5. 1,. http://www. legoo. org/) sites. MtbHLH79 homologues in land plants were researched using the tBLASTx program on the National Center for Biotechnology Information database.
To evaluate the complementarity between miR396 and their putative targets, alignments of 21 bp between the mature miRNA variants and the target mRNAs were examined. Penalty scores were calculated as described by in Jones-  Rhoades and Bartel (2004). lM. truncatula gene expression profiles obtained using the Affymetrix Medicago GeneChip â were retrieved from the Medicago truncatula Gene Expression Atlas database (MtGEA, http:// mtgea. noble. org/v2/, Benedito et al. , 2008; He et al. , 2009).
The PCR products were first cloned into the pENTR/D-TOPO vector (Invitrogen) and then recombined into the pFRN destination vector (derived from pFGC5941; National Center for Biotechnology Information accession number AY310901) using Gateway technology (Invitrogen). An empty pFRN vector was used as a control  (Gonzalez-Rizzo et al. , 2006).
Agrobacterium rhizogenes root transformation and root growth measurements Genetic constructs and the corresponding control empty vectors were introduced into the A. rhizogenes Arqua1 strain and then used for stable root transformation of M. truncatula. A17 seeds were surface-sterilized and germinated as previously described, and transgenic roots of composite plants (e. g. bearing wild-type aerial parts) were obtained as described by  Boisson-Dernier et al. (2001). For each construct, 30 transgenic roots were obtained in at least three independent experiments. Root length was measured using ImageJ software (http://imagej. en. softonic. com/) 6 days after plant transfer to a non-selective medium, using the initial position of the root apex at the time of transfer as a baseline. At this point, roots were also dried over a period of 4 days at 70°C, and weighed to estimate the global biomass of the root system.
ACKNOWLEDGEMENTS We thank  C. Roux  and  F. Frugier  for valuable discussions, and  J. Gouzy  and  E. Sallet  ( LIPM, Toulouse, Centre national de la recherche scientifique, France ) for the miR396 target search. Microscopy and flow cytometry experiments were performed using the Imagif platform (https://www. imagif. cnrs. fr/),with the kind assistance of  Spencer Brown  and  Michael Bourge. This study was supported by grants from the  MIRMED  project ( Genoscope ,  Centre National de la Recherche Scientifique ) and the  Saclay Plant Sciences program  ( ANR-10-LABX-40 ). P. B. was supported by a  Marie Curie Postdoctoral Intra-.
Testing the human speech learning and recognition model Learning speech. Before speech can be recognized, it has to be learned  [48, 49]. We, therefore, first tested whether the model could learn to recognize words. For this, we used the sound waves of the words for digits zero to nine spoken by one speaker. We took the stimuli from a speech database (TI-46, www. ldc. upenn. edu), which is a standard benchmark test for speech recognition algorithms  [50, 51]. We first put each module into learning mode, which is characterized by very high precision at the second level states and relatively lower precisions at the first level states (Figure  3A ). This makes each module expect sequential dynamics at the second level and adapt states at the first level accordingly, using prediction error. In addition, at the first level, we set each module's precision for the sensory states, i.
In general, we found that precisions deviating from these settings will lead to either slower learning rates or no learning at all. To illustrate the quality of learning, we read out the internal model of each module by using the learned parameters to generate cochleagram dynamics and compared it with the actual stimulus that was learned. In Figure  4 , we show a typical sample where the dynamics generated using the learned parameters (dashed lines) follow the cochleagram dynamics (solid lines) closely. Qualitatively, all words have been learned similarly well. Word recognition task. After learning has concluded for each module separately and backward connections are fixed, we tested whether the agent showed high performance in a word classification task. We tested classification performance on a subset of the TI-46 speech database, which contained ten samples of ten words for digits (zero to nine) spoken by five female speakers, adding up to a total of 500 speech samples.
Western blotting analyses on cell lysates revealed that, in NHLF, in Jurkat cells, IDE intracellular content under stress conditions is significantly increased in a way similar to HSP70 (Fig. 1D  and for WB on PBL results (see supplemental Fig. S3 ). Similar to SHSY5Y, NHLF cells were further analyzed by fluorescence microscopy after 24 h of recovery. Even in this case, a broad cytoplasmic distribution of the enzyme was observed (Fig. 1E ). It is worth pointing out that other neuroblastoma cell lines (namely LAN-5, CAN, and SK-N-BE) have been tested for IDE levels in our laboratory under both normal and stressed conditions showing: (a) high IDE concentrations in unstressed cell lines and (b) a pattern of stress induction of IDE clearly overlapping with that of SHSY5Y in all cell lines (data not shown).
Finding (i) is clearly demonstrated following the expression of the enzyme in different human cell lines (i. e. SHSY5Y, NHLF cells, Jurkat cells, and human peripheral blood lymphocytes, PBL are discussed in detail, see above). Our results clearly show that both malignant and not-malignant cells, once exposed to various stressful conditions, trigger IDE gene transcription, strengthening the hypothesis for a role of IDE in the HSR. A bioinformatic analysis of the IDE gene promoter (Gen-Bank TM database, accession number: NG_013012)(Ϫ4799/Ϫ1 bp upstream of the first ATG) seems to suggest the presence of multiple heat shock elements (HSE) in the region between 4010/Ϫ4700 bp. More specifically, TESS and TF search software (which can be found at: www. cbrc. jp/research/db/TFSEARCH. html) make it possible to identify several 5Ј-nGAAn-3Ј sequences, known as high affinity binding sites for the heat shock factor (HSF).
To tackle this shortcoming, we added functionality to the widely used PSI-MI interchange format for molecular interaction data by defining new controlled vocabulary terms that allow annotation of different aspects of cooperativity without making structural changes to the underlying XML schema. As a result, we are able to capture cooperative interaction data in a structured format that is backward compatible with PSI-MI-based data and applications. This will facilitate the storage, exchange and analysis of cooperative interaction data, which in turn will advance experimental research on this fundamental principle in biology.
Alternatively, adjacent interaction sites can mediate multivalent binding  (3). These features allow the interactions of proteins, RNA or DNA to be regulated by the two basic mechanisms underlying cooperative binding: allostery, where the functional properties of a molecule at one site are altered by a perturbation at a distinct site  (7, 25) , or pre-assembly, where pre-formation of a complex affects interactions of its components through different non-allosteric mechanisms  (3, 5, 7). Despite the importance of cooperative interactions in regulating biological molecular systems, they are currently not adequately captured in bioinformatics resources, for instance, interaction databases such as IntAct  (26) , the Database of Interacting Proteins (DIP)  (27)  and the Molecular Interaction database (MINT)  (28) , but also pathway databases like Reactome  (29).
Although these resources provide a large amount of useful data in great detail, the true biological complexity of the interactions and processes they describe can in many cases not be represented. Moreover, the lack of annotation of cooperativity between different binding events can even lead to misinterpretation of the data. One aspect that contributes to the discrepancies between the complexity of in vivo biological interactions and their in silico annotation is the lack of a computer-readable standard data format to represent these interdependencies in full detail. The previously defined Biomolecular Interaction Network Database (BIND) data specification enabled representation of interaction interdependencies and ordered binding events  (30) ; however, it was never adopted as a standard molecular interaction data format.
In this article, we illustrate the basic mechanisms that mediate cooperative binding, which were defined based on a literature survey, and describe how the current PSI-MI XML format (version 2. 5. 4) can capture cooperative interaction data by using new controlled vocabulary (CV) terms that were added to the PSI-MI CV (version 2. 5. 5). In combination with previously defined PSI-MI CV terms, such as those used to specify the type of interaction, these new terms enable detailed description of distinct binding events, their interdependencies and the underlying molecular mechanisms. This strategy avoids having to make any structural changes to the PSI-MI XML schema, keeping it backward compatible and the existing data viable, and allowing additional annotation of cooperative effects for interactions already curated in available databases.
Its Src Homology 2 (SH2) domain binds to phosphorylated LAT, while its Pleckstrin Homology (PH) domain interacts with phosphoinositides in the plasma membrane. Either of these two interactions will preorganize the other domain for binding and thereby facilitate its interaction. Both discrete binding events will then stabilize each other and increase the enthalpic stability of the complex (69) (Figure  1C ). This mechanism generally mediates the assembly of meta-stable signalling platforms whose subunits associate with higher affinity than the   58 ), which has a higher affinity for FliM compared with the inactive form (red) (PDB:1E6K) (59) owing to burial of the Y106 residue. (B) Recognition of p27 Kip1 (green) by the SCF Skp2 ubiquitin ligase requires pre-assembly of the Skp2 (red)-Cks1 (blue) complex because these two proteins form a continuous composite binding site required for binding of p27 Kip1 (PDB:2AST) (  60 ).
The PSI-MI format The PSI-MI data standard was developed to facilitate the exchange, comparison and verification of molecular interaction data  (32, 33). Use of the PSI-MI XML schema ensures that all molecular interaction data representations are consistent in form. To ensure consistency in content, an extensive list of CV terms has been defined  (32, 33). In addition, a community guideline called the Minimum Information about a Molecular Interaction Experiment (MIMIx) has been defined to advise users on how to describe a molecular interaction experiment and specify the minimum information that is required to unambiguously report an interaction  (70). The XML schema, CV terms and MIMIx guidelines can be found at the HUPO PSI-MI website (http://www. psidev. info/groups/molecular-interactions).