dataesr/scientific-paragraphs-categorization huggingface dataset:
The same postures forming 11 classes are used in the comparative studies  [3, 4, 10, 40]. The images are taken from the database which is used in  [10]  and which was created within our laboratory in the past few years. Very similar images are also used in  [40]  though the hands seem to cover a wider part of the image, thus offering supposedly a better resolution. We enriched the existing database with some additional images of darkskinned hands and hands wearing black, yellow, blue, and red gloves.
Discriminative capability of the proposed signature For a given image of the test database, we aim at verifying the discriminative capability of the proposed signature. Let us assume this test image to belong to class h1 ∈ {1,. , 11}, and let Z h1 be the signature computed from this image. Let Zh2 be the mean signature obtained from all images of the learning database for class h2 ∈ {1,. , 11}. The proposed signature will be discriminative if these two conditions hold: Z h1 should be close to Zh2 when h1 = h2, and far from Zh2 when h1 = h2. We compute the normalized distance d h1,h2 Firstly, let us comment on some features concerning all postures: when h1 = h2, the mean of the distance values is 4. is 6. 5 10 -1 ; and whatever h1 ∈ {1,. , 11}, d h1,h2 is minimum when h1 = h2. The distance between two signatures is then the closest when these signatures correspond to images with the same postures.
This binary image is obtained using YC b C r mapping, a threshold applied to the C r component and mathematical morphology operations. Additionally, in  [4] , PCA is already used to reduce the dimensionality of the data, but the preselection of candidate postures is not performed with the proposed sphericity, but with a surface criterion. In Table  1 , we depict the results obtained with YC n bC r mapping and Fourier coefficients as invariant characteristics. This table shows that Fourier descriptors encounter difficulties with postures 4 (60. 8%), 8 (64. 8%), and 10 (74. 4%). This is due to the unability of Fourier coefficients We remind also that the results presented in Table  3  were obtained with hands of various colors, and therefore a somehow richer database than in  [10]  and  [4] , where only white hands are considered.
As mentioned in Subsection 7. 1, while applying the optical flow, it is necessary to wait 1 10 -1 s before calculating the velocity vectors for a couple of images t and t + 1 in order to work in appropriate conditions. The time required by the optical flow component to calculate the velocity vectors and the moving points is much shorter around 3. 3 10 -2 s, which is the time interval between two frames provided by the camera. The following computational times are obtained excluding the optical flow: The off-line methods require 102 s to process the 2,750 images of the learning database, reaching a mean rate of 27 frames/s. For the 1,650 images of the test database, the preprocessing step requires 410 s, that is, 4 images per second; the on-line recognition out of the binary images requires 22 s for the 1,650 images, that is, 27 images per second.
25 s. The optical flow program is written in C++. This can explain the fact that its computational load is not limitative. We can expect that transferring all our programmes from Matlab to C++ would decrease the required computational time. We can infer from the good segmentation results obtained http://asp. eurasipjournals. com/content/2013/1/167 with optical flow that this method may yield in the future a combined exploitation of movement analysis and hand posture recognition for a wide variety of touchless applications.
Acknowledgements This work was financially supported by the ' Conseil régional Provence Alpes Côte d'Azur' and by the firm 'Intui-Sense Technologies ,' to which we are very grateful. The authors would like to thank the anonymous reviewers for their careful reading and their helpful remarks, which have contributed in improving the quality of the paper. Received: 26 April 2013 Accepted: 22 October 2013 Published: 5 November 2013.
Introduction of realistic residual stresses The evaluation of the mechanical state in the rail due to the contact stress induced by the train traffic is crucial for the modeling of the rail resistance: plastic deformations occur in the region near the contact zone due to repeated rolling-sliding contacts between the wheels and the rail. To be realistic, it is necessary to take into account this phenomenon which may be very significant for crack initiation and propagation in the rail head. It is well known that under repeated rolling contacts, different asymptotic mechanical states could occur in the structure: elasticity, elastic shakedown, plastic shakedown or ratcheting. Determination of the stabilized state in the rail is performed by using sequentially VOCOLIN software and the stationary algorithm  [14].
All of the analyzed data sets are freely available in a user-friendly web interface named FasterDB, which describes all known splicing variants of human and mouse genes and their splicing patterns across several dozens of normal and cancer cells as well as across tissues. Information regarding splicing factors that potentially contribute to individual exon regulation is also provided via a dedicated CLIP-seq and exon array data visualization interface. To the best of our knowledge, FasterDB is the first database integrating such a variety of large-scale data sets to enable functional genomics analyses at exon-level resolution.
FasterDB: An integrative bioinformatics platform dedicated to alternative splicing analysis In order to provide full support for performing functional genomics at exon-level resolution, the data sets presented above were organized in a freely available and user-friendly web interface. This novel database, named FasterDB (http://fasterdb. lyon. unicancer. fr/), provides researchers with information regarding the splicing variants generated for their genes of interest. Supplemental Figures S10-S21 demonstrate the use of FasterDB on the ENAH gene, which codes for the MENA protein that modulates cell adhesion and migration (Di  Modugno et al. 2012 ) and which has an exon (exon 12) that is specifically included in epithelial cells (Figs. 1C, 3B ). Thus, once the ENAH gene symbol is entered into the FasterDB search engine, FasterDB provides the description of the human and mouse genes as well as all known gene transcripts reported in public databases (Supplemental Fig.
S10 ). Information regarding alternative use of exons, various features of exons and introns, exon conservation, UTRs, and miRNA binding site prediction can be obtained as well (Supplemental Figs. S11-S16 ). FasterDB also provides information on the expression and splicing pattern of all protein-coding genes across a collection of 73 human cell lines (both normal and cancerous), 11 normal human tissues, nine mouse cell lines, and 11 normal mouse tissues corresponding to Affymetrix exon array data sets generated by The ENCODE Project Consortium and Affymetrix  (Thurman et al. 2012). Clicking on ''Expression'' in the main toolbar (Supplemental Fig. S10B ) reveals the relative level of gene expression across these panels (Supplemental Fig. S17 ) and provides the inclusion rate of any selected exon across the sample collection (Supplemental Fig.
S18A ), which can be helpful for selecting the adequate cellular model for functional studies of alternative splicing variants. To help with data mining, users can click on each cell line for a direct link to a dedicated visualization interface, named ELEXIR (Supplemental Figs. S18, S19 ). Finally, FasterDB provides some clues about the splicing factors that might be involved in splicing regulation of their favorite gene. Indeed, the ''Splicing factors'' button in the main toolbar (Supplemental Fig. S10B ) allows users to select different splicing factors and to access different kinds of information based on publicly available data sets  (Hung et al. 2008; Warzecha et al. 2009b; Xiao et al. 2009 Xiao et al. , 2012;; Xue et al. 2009; Yeo et al. 2009; Katz et al. 2010; Llorian et al. 2010; Wang et al.
2010; Grellscheid et al. 2011; Lebedeva et al. 2011; Mukherjee et al. 2011; Huelga et al. 2012; Lagier-Tourenne et al. 2012; Zarnack et al. 2013). For example, selecting hnRNPH/F on the ''Splicing factors'' screen (Supplemental Fig. S20 ) gives access to predicted binding motifs (Fig. 5A ) as well as to a dedicated CLIP-seq data visualization interface (Supplemental Fig. S21 ) based on the data set generated by Huelga and Katz  (Katz et al. 2010). From there, it is possible to zoom in and view the in cellulo binding sites of the selected splicing factor in the vicinity of the selected exon. For example, two hnRNPF binding sites were identified around ENAH exon 12, suggesting that hnRNPH/F might regulate ENAH splicing (Fig. 5B ). To further challenge this possibility, the ''Exon Arrays'' button (Fig.
5A ) gives access to Affymetrix exon array data obtained after hnRNPH/F depletion by Xiao and collaborators  (Xiao et al. 2009). As shown in Figure  5C  (upper panel), ENAH exon 12 probes appear red when comparing hnRNPH/F-depleted cells to control cells. This suggests that hnRNPH/F depletion favors exon inclusion, as validated by RT-PCR (Fig. 5C ). Meanwhile, analysis of the ESRP data set generated by  Warzecha et al. (2009b)  predicts that ESRP depletion induces skipping of this exon (Fig. 5C , lower panel), as validated by RT-PCR (Fig. 4F ). In sum, FasterDB will provide support for performing functional genomics at exon-level resolution by integrating publicly available large-scale data sets.
In this setting, we have made the analyzed data sets used in this report freely available through the user friendly web interface of FasterDB, which describes all known splicing variants of human and mouse genes and their splicing pattern across several dozen normal and cancer cells and tissues, as well as information regarding which splicing factors contribute to individual exon regulation. The aim of this database is to help researchers identify the different splicing variants of their favorite genes as well as the tissue, cell type, or cell line in which they are expressed, in order to facilitate further functional and/or mechanistic studies. For example, when researchers identify a splicing variant in physiopathological conditions, FasterDB will be useful in deciding which cellular model might be suitable for functional analysis.
The second aim of FasterDB is to help researchers characterize the splicing factors regulating the identified splicing variants. To the best of our knowledge, FasterDB is the first database integrating large-scale data sets focused on splicing, including CLIP-seq and splicing-sensitive microarray data sets  (Hung et al. 2008; Warzecha et al. 2009b; Xiao et al. 2009 Xiao et al. , 2012;; Xue et al. 2009; Yeo et al. 2009; Katz et al. 2010; Llorian et al. 2010; Wang et al. 2010; Grellscheid et al. 2011; Lebedeva et al. 2011; Mukherjee et al. 2011; Huelga et al. 2012; Lagier-Tourenne et al. 2012; Zarnack et al. 2013). Our next goal will be to include other data sets, such as RNA-seq data sets. We also aim to label the protein domains coded by each alternatively spliced exon in order to help users predict potential functional consequences resulting from alternative splicing.
FasterDB core database Human and mouse exons were collected from Ensembl (release 60, assemblies GRCh37 and NCBI m37)  (Flicek et al. 2013 ) and aligned against the NCBI transcript database using MEGABLAST (v 2. 2. 25). These exons were aligned against genomic sequences to define their chromosomal coordinates and then clustered by genomic position to define seven major events (e. g. , alternative first exon, alternative last exon, alternative 39 splice site, alternative 59 splice site, intron retention, exon deletion, and exon skipping). Scores were computed using MaxEntScan for each splice site. For each gene, a nonredundant repertory of untranslated regions was established using all corresponding transcripts. UTRs were more fully characterized by describing the motifs found in their sequences using PatSearch Tool  (Grillo et al.
2003). miRNA binding sites were predicted using PITA, miRanda, and PicTar  (John et al. 2004; Lewis et al. 2005; Kertesz et al. 2007 ). Conserved exons between human and mouse were identified by aligning each human exon against the exons of its orthologous mouse gene as provided by Ensembl. The ''in silico PCR'' tool is based on a multialignment of the transcript exons performed with ClustalW. FasterDB is built in Perl (v5. 14. 2) and runs on a Ubuntu server (v12. 04. 1) that hosts Apache (v. 2. 2. 16) and MySQL (v5. 1. 49) servers. More information is available in Supplemental Material and can be downloaded from http://fasterdb. lyon. unicancer. fr.
Exon array data set analyses Cell lines and tissues expression data were downloaded from the GEO and Affymetrix websites and corresponded to Affymetrix Exon Arrays data sets as listed in Supplemental Table  S10. The preprocessing pipeline is described in more detail in the Supple-mental Material (Part 2, Section 2. 2, page 9). Briefly, low-quality and cross-hybridizing probes were removed, and signals were summarized at the gene level by using the median over all remaining probes. Three different ways of computing the inclusion/exclusion rates of a given exon were computed by measuring the ratio of (1) its expression level versus the gene expression level (NI); (2) its NI in the condition of interest relative to the NI in the control condition (global SI); or (3) its expression level versus the expression level of the flanked exons (local SI).
Computation of the global and local SI is described in more detail in the Supplemental Material (Part 2, section 2. 2, page 10). Identification of cell type-specific alternative exons was performed after computing local SI and selecting exons with local SI above 1. 45 and with a P-value <0. 05. Heatmaps were generated using the multiexperiment viewer application of the TM4 package. Hierarchical clustering of Mev4 was also used to cluster cell lines that have similar regulation. Input data contains the SI of each of the regulated ASEs in each of the analyzed cell lines. The SI was computed for each cell line in comparison with all the different types of cell lines. Functional enrichment and KEGG pathway mapping were done using DAVID  (Huang da et al. 2009 ).
Splicing factor analyses The regulation of cell type-specific ASEs by specific splicing factors was tested using three different sources: microarray data, literature RNA-seq, and CLIP-seq data sets. Chromosomal coordinates of exons regulated by ESRP1, PTBP1, and/or RBFOX2 identified by RNA-seq were retrieved from previously published work  (Warzecha et al. 2009b; Xiao et al. 2009; Xue et al. 2009; Yeo et al. 2009; Katz et al. 2010; Llorian et al. 2010; Huelga et al. 2012). ASEs predicted to be regulated by a splicing factor using exon array or RNA-seq data sets were compared to ASEs predicted to be cell type-specific. For each cell type, we paid attention to the ASE regulation sense. For example, PTBP1 is up-regulated, while ESRP1 and RBFOX2 are down-regulated, in endothelial cells; thus, an endothelial ASE is predicted to be regulated by one of these factors if it is skipped upon PTBP1 depletion or included by ESRP1 or RBFOX2 depletion.
Experimental validation RNA from different cell types were purchased as indicated in Supplemental Table  S1. Primers used for RT-PCR are described in Supplemental Table  S9. RT-qPCR analyses were performed using primers described in Supplemental Table  S9. Epithelial MCF-7, fibroblast-like MDA-MB-231 cells, and endothelial HUVEC cells were transfected using RNAiMax (Invitrogen) with control siRNAs or siRNAs against ESRP1 and 2, RBFOX2, or PTBP1 and 2 (Supplemental Table  S9 ) 48 h before RNA extraction. Electrophoretic gels were analyzed with ImageJ software.
Acknowledgments We thank the researchers who deposited their data sets in public libraries. We hope that our website will be welcomed by both users and the researchers who generated the data sets and that we did not forget to acknowledge any of those researchers in Supplemental Table S10 and on the FasterDB website. This work was supported by the  Fondation pour la Recherche Medicale (FRM) ,  INSERM Plan Cancer   2009-2013 ,  Institut National du Cancer (INCa) ,  Agence Nationale de la Recherche (ANR) , and  Association Franc xaise Contre les Myopathies (AFM). P. M. was supported by  Lyon Science Transfert , J-P. V. and H. M. by  FRM , F-O. D. by the  Association pour la Recherche sur le Cancer , M. P. E. by  AFM , S. S. by the  Ligue National Contre le Cancer , L-C. T. and E. C. by  INSERM ''Plan Cancer   2009-2013.
The development of efficient algorithms for designing RNA sequences with predefined secondary structures is thus a milestone to enter the synthetic biology era. RNAinverse pioneered RNA secondary structure design algorithms. It has been developed and distributed with the Vienna RNA package  (Hofacker et al. , 1994). However, only posterior experimental studies revealed the potential and practical impact of these techniques. Thereby, during the past 6 years, many improvements and variants of RNAinverse have been proposed. Conceptually, almost all of existing algorithms follow the same approach. First a seed sequence is selected, then a local search strategy is used to mutate the seed and find, in its vicinity, a sequence with desired folding properties. Using this strategy, INFO-RNA  (Busch and Backofen, 2006) , RNA-SSD (Aguirre-Herna´ndez et al.
, 2007) and NUPACK:Design  (Zadeh et al. , 2011)  significantly improved the performance of RNA secondary structure design algorithms. (Bodini and Ponty, 2010 ) that enables us to control, for the first time, explicitly the GC-content of the solution. This functionality is essential because wild-type sequences within living organisms often present medium or low GC-content, presumably to offer better transcription rates and/or structural plasticity. Previous programs do not allow to control this parameter and tend to output sequences having high GC-contents  (Lyngsø et al. , 2012). We demonstrate the performance of our algorithms on a set of real RNA structures extracted from the RNA STRAND database  (Andronescu et al. , 2008). To complete this study, we develop a hybrid method combining our global sampling approach with local search strategies such as the one implemented in RNAinverse.
Postprocessing unpaired regions: a local/global (glocal) hybrid approach Owing to our simplified energy model, unpaired regions are not subject to design constraints other than the GC-content, leading to modest probabilities for refolded design candidates to match the targeted structure. To improve these performances and test the complementarity of our global sampling approach with previous contributions based on local search, we used the RNAinverse software to redesign unpaired regions. We specified a constraint mask to prevent stacking base pairs from being modified and, whenever necessary, reestablished their content a posteriori, as RNAinverse has been witnessed to take some liberties with constraint masks. As shown in the Supplementary Material, this postprocessing does not drastically alter the GC-content, so the glocal approach reasonably addresses the constrained GC-content design problem.
Implementation Our software, IncaRNAtion, was implemented in Python 2. 7. We used RNAinverse from the Vienna Package 2. 0  (Hofacker et al. , 1994). All-time benchmarks were run on a single AMD Opteron(tm) 6278 Processor at 2. 4 GHz with cache of 512 kb. The penalty , associated with invalid base pairs, was set to 15. Figure  3  presents the average times spent running IncaRNAtion þ RNAinverse to generate one sequence with the required GC-content. As expected, the time grows linearly in function of the length of the structures for IncaRNAtion.
Dataset To evaluate the quality of our method, we used secondary structures from the RNA STRAND database  (Andronescu et al. , 2008). Those are known secondary structures from a variety of organisms. We considered a subset of 50 structures selected by  Levin et al. (2012) , whose length ranges between 20 and 100 nt. To ease the visualization of results, we clustered together structures having similar length, stacks density and proportion of free nucleotides in loops, leading to distributions of structures shown in Figure  4.
Design We ran our method as follows. First, we sampled approximately 100 sequences per structure. Then, we use these sequences as seed in RNAinverse. Finally, we computed the Minimal Free-Energy (MFE) with the RNAfold program from the Vienna Package 2. 0  (Hofacker et al. , 1994). Before starting our benchmark, we asses the need for our methods and performed an analysis of the GC-content drift achieved with state-of-the-art software. Using our dataset of 50 structures, we generated 100 samples per structure with classical softwares that do not control the GC-content. Namely, RNAinverse, INFO-RNA, NUPACK:Design and Frnakenstein. We show the distribution of the GC-content of the sequences produced with these softwares in Figure  5. As anticipated, we observe a clear bias toward high GC-contents and a complete absence of sequence with 530% of GC.
Next, the Boltzmann frequency is defined for a structure S and a sequence s as follows: e ÀEðs, SÞ RT =Z s Frequency ð8Þ where Z s is the partition function of sequence s. This measure tells us how dominant is a structure S in the Boltzmann ensemble of structures over a sequence s. A high value implies a stable structure. We compute this frequency with RNAfold from the Vienna Package 2. 0  (Hofacker et al. , 1994). Figure  8  shows the number of solutions generated (i. e. sequences with a MFE structure identical to the target structure). Here, we note that low GC-contents have a strong (negative) influence on the number of sequences generated, and in parallel also affect negatively the sequence diversity. This observation emphasizes the difficulty to design sequences with low GC-content. Once again, large percentages of free nucleotides increase the difficulty of the task.
Global sampling versus Local search versus Glocal approach To conclude this study, we estimate the impact of the design methodology on the performances. More precisely, we aim to determine the merits of a global sampling approach (IncaRNA tion), compared with a glocal procedure (IncaRNAtion þ RNAinverse) and a local search methodology (RNA-SSD). To the best of our knowledge, RNA-SSD, beside IncaRNAtion, is the only software that implements an explicit control of the GCcontent. Here, we compare the running time and the sequence diversity of the solutions produced by each software. In addition, we focus on the design of sequences with low GC-contents ( 30%) as they are almost impossible to design with classical software (Figure  5 ). Figure  3  shows the running time of each software. These data demonstrate the efficiency and scalability of our techniques.
We evaluated the method on a benchmark composed of target secondary structures extracted from the RNA STRAND database. We observed good overall success rate, with the notable exception of very low targeted GC-content (10%), and a good to excellent entropy within designed candidates. Finally, we implemented a hybrid approach, using the RNAinverse software as a postprocessing step for unpaired regions. This approach greatly increased the success rate of the method, allowing for the design of highly diverse candidates for almost all of the structures in our benchmark, while largely preserving the targeted GC-content. In the future, we would like to complement this study by further investigating the potential of hybrid local/global-or glocal-approaches. A global sampling approach would capture the positive aspects of design, optimizing affinity toward a given structure while allowing the specification of expressive systems of constraints.