dataesr/scientific-paragraphs-categorization huggingface dataset:
ebi. ac. uk/Tools/pfa/iprscan/). For seed storage protein UniGenes, homology searches were performed on the whole UniGene build #55, using BLASTx and T. aestivum seed storage protein sequences as reference. No preliminary filter was applied to BLASTx results. Instead, all the alignments were carefully examined, and similarity in known conserved critical regions of seed storage proteins was given priority over e-value and BLAST score alone. In dbWFA, curated UniGene annotations are assigned to T. aestivum transcripts without any intermediate BLAST result. Following the recommendations of the International Wheat Genome Sequencing Consortium (IWGSC) for annotating T. aestivum genomic sequences (3), the percentages of coverage (with respect to the length of the orthologous proteins) and identity are used to assign functional annotations to a transcript.
In dbWFA, users can define the value of these two parameters, but we strongly recommend using the cutoff values suggested by the IWGSC, where BLAST results with an identity >45% and coverage >50% are assigned a 'putative function' and BLAST results with identity and coverage >90% are assigned a 'known function'. All data are stored in a MySQL database. The integration of the database allows one to assign the functional annotation from any of the systems described above to the transcripts of interest and vice versa. The dbWFA database thus provides a very powerful resource for the annotation of T. aestivum UniGenes. To find the most commonly sought types of information from dbWFA, simple yet pertinent queries with their parameters can be sent through a web-based interface (Figure  2 ). The results are delivered as html pages, and an export procedure is available to retrieve data in spreadsheet.
The html result pages provide links redirecting the user to websites of the different annotation systems, allowing a global analysis of the annotation results. The web interface can also be used to automatically create MapMan mapping files for the search results. Although the dbWFA web interface only allows data mining of common queries, specific queries can be performed using the SQL database, which can be downloaded from the dbWFA website. The modularity of the database will facilitate the integration of new T. aestivum data as transcripts are sequenced and annotated through different pipelines. 1. BLASTn analysis revealed that 12 478 full-length cDNA sequences matched a sequence in the UniGene set (build #60) with a coverage and identity threshold value >50 and 90%, respectively. Among these 12 478 correspondences, 10 996 and 5 932 full-length cDNA sequences and UniGene sequences, respectively, have a putative functional annotation in at least one of the annotation resources.
This result highlights the additional information brought by the full-length cDNA sequences. The quality of the annotation method is illustrated by comparing the MapMan bin annotation of all the transcripts of the T. aestivum NimbleGen 40 k microarray (developed with UniGene build #55) and the full-length cDNA sequences from TriFLDB with the annotation of A. thaliana and O. sativa imported from MapMan and recorded in the database. The MapMan bins were used here because this annotation system is available for the three species. Overall, there was no clear bias between the three species (Figure  3 ), and the percentages of genes    in the 26 categories for the three species were well correlated (T. aestivum versus A. thaliana: r = 0. 96, P < 0. 001; T. aestivum versus O. sativa: r = 0. 69, P < 0.
001), with no significant bias (P < 0. 001). The higher correlation found with A. thaliana compared with O. sativa is mainly because there are fewer annotated transcripts in the DNA bin for O. sativa than for A. thaliana and T. aestivum (r = 0. 90 for T. aestivum versus O. sativa when this bin is not considered). For the full-length coding sequences retrieved from TriFLDB and other public databases, the correlations between T. aestivum and A. thaliana and between T. aestivum and O. sativa were the same (r = 0. 90, P < 0. 001). The pairwise correlations between the four MapMan bin annotations presented were remarkably high, all >0. 9 when the DNA bin was omitted. Similar results were obtained for the PlantCyc pathway reactions and GO (data not shown). Unlike many annotation tools, dbWFA makes it possible to query multiple annotation systems simultaneously.
Identification and Annotation of UniGenes Specifically Expressed During Either the Early or Late Stage of Grain Development A total of 39 029 transcripts from the UniGene set (build #55) and 1613 transcription factors from the wDBTF database not present in the UniGene set are spotted on the custom T. aestivum NimbleGen 40 k microarray  (36). Previous studies have shown that 18 140 (44. 6%) of these transcripts are expressed during T. aestivum grain development  (47). In dbWFA, 34-40% (depending on the build) of these transcripts have a putative functional annotation.
Box 1. Query Examples To demonstrate the usefulness of dbWFA, several biologically relevant queries that can be performed using the current system are presented. In these examples, the UniGene build #55 was used, with coverage and identity thresholds of 50 and 45%, respectively, as recommended by the IWGSC to assign a putative function to a transcript. The first committed step in the biosynthesis of carotenoids is the condensation of two geranylgeranyl disphosphate molecules by phytoene synthase to produce phytoene, which catalyses a rate-controlling step in the plastid-localized carotenoid pathway  (43). We could query the database for the PlantCyc pathway reaction 2. 5. 1. 32 using its web interface. The result of this query is shown in the above table. Two T. aestivum transcripts were annotated with a putative phytoene synthase activity.
In good agreement with this result, previous studies showed that Poaceae species possess a duplicated phytoene synthase gene  (44). A thorough analysis of the two annotated UniGene sequences confirmed that they correspond to the duplicated phytoene synthase gene found in Poaceae. A third phytoene synthase has been isolated in Z. mays and T. aestivum  (45, 46). Although the three O. sativa phytoene synthase genes are present in the database, the T. aestivum UniGene of this phytoene synthase gene was not found in dbWFA. The phytoene synthase activity also corresponds to GO:0016767 MapMan bin 16. 1. 4. 1. Searching dbWFA for this GO or MapMan bin yields the same results as above. It is possible to combine several overlying systems (e. g. PlantCyc pathway reaction and GO) in a single MySQL query when the database is installed on a local machine.
(continued) T. aestivum grain development comprises several distinct phases, starting with a syncytial then a cellularization phase (ca. 0-1008Cdays after anthesis), followed by a first differentiation phase of active endosperm cell division (ECD), expansion and differentiation (ca. 100-2508Cdays after anthesis), a second differentiation phase when storage polymers rapidly accumulate (ca. 250-7508Cdays after anthesis) and a maturation phase when grain rapidly desiccates (ca. 750-9008Cdays after anthesis)  (48, 49). The transitions between these phases are associated with major changes in the grain transcriptome  (5, 36, 50, 51)  and proteome  (52, 53). To validate the principle underpinning the database and provide another example of the usefulness of dbWFA, we analysed the functional annotation of the transcripts specifically expressed during either the ECD or SPA phase of grain development.
We used transcriptome data obtained with the custom T. aestivum NimbleGen 40 k microarray for The efficiency of the database stems from its multiple systems of annotation. The cross-system annotation feature of dbWFA is integrated in the web interface in the 'Transcript(s) annotation' search method. This type of query could be used to obtain information for a list of UniGenes of interest in the different annotation systems integrated in dbWFA. Querying the UniGene set for the first phytoene synthase transcript retrieved in Query 1 yields the annotation shown in the above table. On the web interface, the user can choose to display only the best hit (as in the above table) or the five best hits with percentages of coverage and identity greater than the thresholds set by the user. The user can also choose the systems of annotation to include in the query and the model species.
The results redirect the user to the web pages of the different annotation systems, which allows more detailed information to be obtained on the annotation of the list of transcripts of interest. In the search method 'MapMan mapping file generator', the user can select a metabolic pathway and automatically create a mapping file to visualise the results of transcriptomic experiences performed with the T. aestivum custom NimbleGen 40 k microarray using the -omic data viewing and analysing tool MapMan. The glycolytic pathway corresponds to the bin code 4. The first five lines of the table generated by dbWFA for this query are shown above. When the database is installed on a local machine, several pathways could be queried simultaneously to create a custom T. aestivum mapping file for MapMan. the T.
aestivum cultivar Recital grown under standard conditions in a greenhouse and sampled every 34-1178Cdays between 132 and 6868Cdays after anthesis  (35). Transcripts with different patterns of expression were classified with J-Express 2012 software package (54) using Euclidean distance-based k-means clustering. For this analysis, the number of clusters was empirically set at 25 because it allowed us to clearly discriminate between gene expression clusters specific to the ECD and SPA phases of grain development. One cluster of 238 genes contained genes expressed exclusively during ECD stages (Figure  4A  and B ). Two other clusters contained genes expressed exclusively during SPA stages. The latter two clusters were merged to form a single SPA cluster of 195 genes. dbWFA was then used to retrieve the functional classification of the transcripts from both clusters.
Finally, we note a substantial difference in the MIPS functional category 'protein with binding function or co-factor requirement', with more transcripts involved in DNA binding in the ECD cluster and more transcripts involved in RNA binding in the SPA cluster. All these data show great similarity to published results for T. aestivum and H. vulgare, reflecting the accuracy of the automatic annotation provided by dbWFA. Unlike several other T. aestivum transcriptome analyses where a complex process had to be carried out to assign a functional annotation to selected transcripts and/or proteins  (5, 57) , here only a single request was made to the dbWFA database to retrieve the functional classification of 45% of the transcripts of interest, and 40% of the 40 642 transcripts of the T. aestivum NimbleGen 40 k microarray.
Outlook The dbWFA database was created by integrating numerous data sources. As a result, it is a practical source of heterogeneous data for functional annotation of T. aestivum transcripts. The website grants access to the most common queries that can be applied to the database, and the freely available MySQL database is a powerful tool for more specific requests. Although further analyses are required to confirm the dbWFA annotation results, the database provides an efficient and fast solution for acquiring a wide range of functional information. cDNA resources are useful to predict exonic regions from genomic sequences; thus, efforts to annotate the UniGene resources will significantly contribute to the analysis of sequence data produced by ongoing T. aestivum initiatives and other genome sequencing projects.
The version of dbWFA presented here is operational, but the aim is not to restrict the database to storing O. sativa and A. thaliana annotations but to expand it to include data from other plant species genomes as their functional annotation becomes more consistent. Integration of InterProScan  (58)  in the workflow could be a valuable way of augmenting the process. Also, the integration of AFAWE  (59)  would provide an annotation workflow with different function prediction tools. However, the current version of AFAWE cannot be used independently from its web interface and would thus have to be implemented using the tools called in its workflow, which are available as web services. Finally, the upcoming integration of a BLAST program in the workflow will allow users to annotate their own sequences and will also make dbWFA applicable to other species.
ß The Author(s) 2013. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http:// creativecommons. org/licenses/by-nc/3. 0/), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. Page 1 of 12 (page number not for citation purposes) Database, Vol. 2013, Article ID bat014, doi:10. 1093/database/bat014.
Page 4 of 12 Database tool Database, Vol. 2013, Article ID bat014, doi:10. 1093/database/bat014. Using dbWFA: Percentage of Annotated UniGenes, Comparison of T. aestivum UniGene and A. thaliana and O. sativa Whole-Genome Annotation and Query Examples Thirty-four percent (13 713 transcript sequences), 40% (14 843), 35% (20 016) and 35% (20 034) of the transcript sequences of the UniGene builds #55, #58, #59 and #60, respectively, have a putative functional annotation in at least one of the annotation resources. Eighty-one percent of the 17 541 full-length cDNA sequences from TriFLDB have a putative functional annotation in at least one of the annotation resources. The number of transcripts and full-length cDNA sequences annotated in the different resources are given in Table.
(41)t Metabolic Network (PMN; http://www. plantcyc. org)(39),whichprovidesabroad network of curated databases on primary and secondary plant metabolism, including pathways, enzymes, genes, compounds and reactions from several plant species. dbWFAcontainsdatafrom AraCyc (version 9. 0) for A. thaliana and RiceCyc (version 3. 2) for O. sativa. MapMan (http://mapman. gabipd. org)(40),which is a user-driven tool for large datasets (e. g. gene expression data from microarrays) visualized in the context of diagrams of metabolic pathways or other processes. MapMan annotation data (bin tree version 1. 1) for both A. thaliana and O. sativa are stored in dbWFA. The dbWFA database also provides a function to automatically generate MapMan T. aestivum mapping files. Munich Information Center for Protein Sequences Functional Catalogue (MIPS FunCat; http://www.
Acknowledgements The authors thank  Dr Etienne Paux  and  Dr Catherine Feuillet  ( INRA, UMR1095 GDEC, Clermont-Ferrand, France ) for useful discussions and advice, and  Mr Sebastien Reboux ,  Ms Claire Viseux  and  Mr Michael Alaux  ( INRA, URGI, Versailles, France ) for installing and maintaining the database on the URGI server.
helmholtz-muenchen. de/en/mips/projects/funcat)(41), which provides a hierarchical scheme for the functional description of proteins of prokaryotic and eukaryotic origin. MIPS FunCat annotations for A. thaliana (MAtDB version 2. 1) are stored in dbWFA. Database, Vol. 2013, Article ID bat014, doi:10. 1093/database/bat014 Database tool. A. thaliana Gene Family Information (TAIR version 10; http://www. arabidopsis. org/browse/genefamily).
Presentation The motor is controlled using a dSPACE system with a DS1005 PWM card and a DS2202 acquisition card. A Mitutoyo LGF-550L position sensor with the EH10P signal conditioner is used to obtain the real position of the mover. The power supply of the prototype has been developed by the Novatem SAS Company and consists of four independent H-bridges (one H-bridge per phase). Figure  4  shows the test bench of the Pulsamag prototype. In this pump application, the functioning of the motor requires a back and forth motion. The motor is to be controlled in position with a sinusoidal variation of the position: ft X t X x S Z 2 sin ŝin ˆ (18) where X ˆ is equal to half the desired stroke and f is the operating frequency equal to the desired cardiac frequency (cf. Equation (  1 )). The control of the motor has been developed using Matlab Simulink software in discrete mode with a sampling period: T e = 100 μs.
The real position of the mover, called x m , measured by the sensor position, will be compared to the reference sinusoidal signal, x, of Equation (18). Figure  5  shows the open loop control of the motor. Concerning the use of Matlab Simulink and dSPACE tools for the control of SRM, we can cite the works  [11, 12]  for variable speed pumping applications. The elements that are inside the dotted box in Figure  5  are the material elements of the control. In the "PWM & power supply" block, the DS1005 pulse-width modulation (PWM) card generates the PWM signals needed for the Novatem SAS power supply. This provides voltages, U 1 to U 4 , to the motor. In the "Sensor position" block, the DS2202 card is used to retrieve the real position of the mover, x m , measured by the Mitutoyo sensor position (analog to digital conversion).
"Current Signal Generator" Block The generation of currents, i 1 to i 4 , is performed by the "Current signal generator" block. This block is an "S-function" of Matlab Simulink. The inputs are the desired total force, F, and the reference signal position, x. Figure  6  illustrates the current supply sequence required for a reference signal position. We can see that when the reference position increases (positive speed), the current supply sequence generated is in the order i 1 , i 2 , i 3 , i 4 allowing movement of the mover in the direction of increasing x. On the contrary, when the reference position decreases (negative speed), the current supply sequence generated is in the order i 4 , i 3 , i 2 , i 1 , allowing movement of the mover in the direction of decreasing x. The current amplitude is function of the desired force and is determined by: dx dL F i i dx dL F 2 1 2 1 2 (20) It is assumed here that only one phase is energized at a time, and only the phase supplied contributes to generating the desired force.
e. , the content of the block "Mechanical LSRM model" of Figure  8 , is given in Figure  12. The "Matlab Function" calculates the values of g k (x) according to the position, x (cf. Table  4 ), and determines the effort of each phase, according to Equation (  22 ). The entire modeling of the motor can be improved by taking into account the values of g k (x) in the electrical modeling of the motor phase. For an inductance profile that is not linear without saturation, the modeling approach remains valid by giving the non-linear equation of the inductance profile with respect to the position.
This block is an "S-function" of Matlab Simulink and has x, x m * and the control speed, V c , for inputs. The flow chart of the S-function is presented in Figure  16 (a). In principle, the speed error should not continue to be integrated to inflate the command when the dynamics of this error are such that it tends to disappear naturally, otherwise the extra control contributes to overshooting. To do this, signal V c was compared to the estimated speed, V est. When the estimated speed, V est , is too rapid compared to V c , then the switch is open (out = 0); otherwise, it will be closed (out = V c ). Figure  16 (b) shows the results of the closed loop control for the same input waveform, x, as in Section 4. 1. 4. We can see that the corrector cancels the steady state error. We can see also that the phase shift is very small between the two waveforms.
Conclusions In this paper, we have presented a tubular linear-switched reluctance motor for a medical application. This actuator must provide a pulsatile pump operation for a ventricular assistance device. After describing the operating principle of this actuator, we presented the characteristics of the prototype actuator and the principle of position control. This control is based on a dSPACE control system associated with Matlab Simulink. A dedicated power supply based on a H-bridge inverter for the four phases of the machine has been developed. A simplified model of the actuator was proposed, which can be generalized to a linear-switched reluctance motor for all applications. This modeling takes into account the variation of inductance with respect to the position and assumes an independence of the phases (and a linear behavior of the magnetic materials).
After this modeling, we present the open-and closed-loop position control of the actuator in Matlab Simulink. For the closed-loop control, a controller with an integral action and anticipatory action, called feed-forward, is implemented. We provide the implementation of a magic switch that prevents the integral action to inflate the command and that avoids overshoots of the output. The results obtained show that the use of a simplified model is satisfactory. In the perspective of this work, a real control of the prototype under dSPACE based on the Simulink models already developed must be carried out.
Data integration is a key challenge for modern bioinformatics. It aims to provide biologists with tools to explore relevant data produced by different studies. Large-scale international projects can generate lots of heterogeneous and unrelated data. The challenge is to integrate this information with other publicly available data. Nucleotide sequencing throughput has been improved with new technologies; this increases the need for powerful information systems able to store, manage and explore data. GnpIS is a multispecies integrative information system dedicated to plant and fungi pests. It bridges genetic and genomic data, allowing researchers access to both genetic information (e. g. genetic maps, quantitative trait loci, markers, single nucleotide polymorphisms, germplasms and genotypes) and genomic data (e.
Introduction A second green revolution is needed to meet the challenges of the 21st century. Crop production of sufficient quantity is required (for both food and biofuel) while taking into account environmental consequences and climatic changes. Plant genetics and genomics have progressed, and there are now powerful tools for investigating the molecular basis of phenotypic variation, accelerating breeding and the exploitation of genetic diversity. Large volumes of crop genetic and genomic data have been integrated. However, these data have been produced by many different groups, and thus are heterogeneous and may even appear unrelated. The challenge for an efficient information system (IS) is to integrate these data by identifying their relationships both within the database and to publicly available material.
The throughput of nucleotide sequencing technologies is becoming ever faster, and there is increasing need for powerful ISs, which are able to store, manage and explore large-scale data such that it can be fully exploited in the fields of genomics and genetics. Several ISs have been developed. Some are dedicated to one species: TAIR  (1)  for Arabidopsis, MaizeGDB  (2)  for maize and Flybase  (3, 4)  for Drosophila. Others, such as Gramene  (5, 6) , Ensembl  (7, 8) , NCBI genome   (9) , Phytozome  (10) , PlantGDB  (11) , MIPSPlantDB  (12)  and Tropgene-DB  (13) , incorporate information for several species. Few databases offer an integrated multispecies system where navigation offers intuitive user interfaces for data browsing. By reducing the number of windows and databases required for a search, data exploration may be improved by limiting the risk of user confusion.