dataesr/scientific-paragraphs-categorization huggingface dataset:
c. Invariant Unit Cell For Different Power/Ground Planes Sizes Another advantage of the MCTL Matrix method is that all Power/ Ground planes dimensions can be simulated by using the same unit cell topology and sizing which makes our method easy customizable in terms of the design requirements. For instance, let's consider the stack-up of the previous example while changing the dimensions to 10mm by 10mm. This can be translated by a simple modification of the number of horizontal and vertical elements in the Perl software. The structure was simulated up to 40GHz and the numbers of horizontal and vertical elements are assumed to be 100 MCTL on each side, having the same length and RLGC matrices definition as the previous example. From Fig. 11 , good agreement is evident up to 40GHz with some deviation in details above that frequency.
Th can be explained by higher edge radiation since the boundari are larger. From computation time cost point of view, MCT matrix method took 34. 66 seconds to simulate this structu however, Momentum simulated it in more than one hour wi a meshing density of 50 cells/wavelength. For the memo resources, only 305. 67 Mbytes are needed by our approac which corresponds to about 8 times lower than Momentu that used 2544 Mbytes. So the complexity has increas sharply with Momentum, while MCTL Matrix has kept near the same complexity, without forgetting the required chang and adjustments on the layout structure which can be arduo for complex packages. In addition a full substrate re-analy is needed before performing the electromagnetic meshin Fortunately, changing structure size in the MCTL Matrix c be performed by a simple update in the Perl software.
Another advantage of the MCTL Matrix method is that all Power/Ground planes dimensions can be simulated by using the same unit cell topology and sizing which makes our method easy customizable in terms of the design requirements. For instance, let's consider the stack-up of the previous example while changing the dimensions to 10mm by 10mm. This can be translated by a simple modification of the number of horizontal and vertical elements in the Perl software. The structure was simulated up to 40GHz and the numbers of horizontal and vertical elements are assumed to be 100 MCTL on each side, having the same length and RLGC matrices definition as the previous example. From Fig. 11 , good agreement is evident up to 40GHz with some deviation in details above that frequency. The deviation is important at high frequency comparing to the previous test structure.
The strategy combines two approaches : the first was based on the comparison of new grapevine genomic data with known grapevine sequences previously selected from pathogen-related studies to which were added genes deployed in two recently developed molecular diagnostic tools ("grapevine -qPFD", patent INRA FR 1055042/ WO/2011/161388, CT/FR2011/051470 -INRA Angers -Brisset MN,  [38]  and "BioMolChem" chip,  [26] ). The sequences used in RT-qPCR were blasted against the most recent Vitis vinifera sequences (taxid = 29,760,  [41] ) using the Blast resource from the National Center for Biotechnology Information (https://blast. ncbi. nlm. nih. gov/Blast. cgi). Nucleotide sequences were used and results were manually curated to find homologs to the original sequences used in the RT-qPCR experiments.
Homology was confirmed by aligning selected sequences with Clustal  [42]  and generating phylogenetic trees. Once recovered, the sequences to be included in the Fluidigm protocol were subjected to the primer-blast program  [43]  for specific primer design. The second strategy combined with the first involved the recovery of sequences from the model organism A. thaliana in the Genevestigator database  [44]. Microarray experiments involving foliar fungal pathogens deposited in Genevestigator were selected and the most differentially expressed plant sequences between control and treated samples were recovered. These sequences (N = 273) were identified on the NCBI website and aligned against the most recent Vitis vinifera sequences using the NCBI Blast resource. The mean percentage of Vitis vinifera protein sequence homology/ortology with those of Arabidopsis thaliana and/or Malus domestica was 53%, between 16 and 99 (Additional file 2).
Similarly, two major genes of the biosynthetic pathway of stilbene (PAL and STS) were strongly linked to resistance induced by BTH. These results are in agreement with data obtained in previous experiments conducted in controlled laboratory conditions  [16, 17]  and also in another where PAL and STS were reported to be major genes in the resistance of Vitis vinifera  [50]. Principal Component Analysis (PCA) was used to summarize the ways in which gene defense responses vary after elicitation. Most of the variance in the defense expression dataset was contained in the first two principal components which captured ~60% of the total variability (Additional file 3). Results of PCA based on the subset of the 24 Vitis vinifera defense-related genes common to the two technologies (Tables  1  and 2 ) summarized the 18 samples projected on the two principal Fig.
Field study of fosetyl aluminum and BTH effects on downy mildew disease The progress of the disease was observed several times throughout the epidemic. The downy mildew disease severity (average percentage of attack) was assessed 6 times, beginning on 9th June after 6 treatments, 4 weeks after artificial inoculation, and around 18th July (Fig. 5 ) by assessing the level of attack of 30 leaves per block according to the CEB method No. 007: Method of practical effectiveness for fungicide tests designed to fight against downy mildew (Plasmopara viticola (B. C) Berl and Tomi). Data from a disease progress curve were transformed into a single value by a formula developed by Simko and Piepho  [48]  which calculates the area under the disease progress curve (AUDPC): AUDPC ¼ X n-1 ð Þ i¼1 ð Þ y i þ y iþ1 2 Â t iþ1 -t i ð Þ All clusters of each experimental block were collected on 11th October 2011, counted and weighed to assess the yield.
PCR primer pairs The most recent molecular information available for designing a Biomark assay can provide valuable results in terms of pathways involved in the response of the plant to the pathogen. The mRNA sequences of the genes studied were taken from the National Center for Biotechnology Information (NCBI) Gene Database or from the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway Database with Vitis vinifera (Wine grape) as reference genome. For primer design, the Primer3 free software (http://bioinfo. ut. ee/ primer3-0. 4. 0/) was used. The specificity of the primer pairs was checked by melting curve analysis and gel electrophoresis of the amplified product (data not shown). PCR efficiencies of the assays were determined with a 5-point dilution series of a pool of samples from the experiment in qPCR triplicates, in agreement with Bustin et al.
Real-time qPCR with Stratagene Mx3005P system The expression of the selected genes was assessed by using a Stratagene Mx3005P qPCR system (Agilent technologies) with SYBR Green to detect dsDNA synthesis. For each reaction, 1 μL of each primer at 1 μM and 7 μL of 2 × MESA BLUE qPCR MasterMix Plus for SYBR® Assay Low ROX (Eurogentec) including Hot start DNA polymerase, dNTP and MgCl 2 and 5 μL of cDNAs, were used according to the manufacturer's instructions (350-750 ng of cDNA by well). Each PCR reaction was done in duplicate. The PCR was performed at 94 °C for 15 min, followed by 40 cycles at 95 °C for 10 s, 55 °C for 20 s and 72 °C for 20 s. Data were analyzed with MxPro QPCR Software (Agilent technologies) as the cycle of quantification (Cq), where the fluorescence signal of the amplified DNA intersected with the background noise.
Acknowledgements We are grateful to the  Toulouse Genomics Platform  (http://get. genotoul. fr)for access to the  BioMarkTM HD System , to  N. Ladouce  ( LRSV ) for technical assistance, to  L. Druelle  for technical assistance in vineyard experiments, to  L. De Bastard  from  Syngenta  for supplying acibenzolar-S-Methyl, and to  Prof. R Cooke  for reading the manuscript.
RNAcentral is a database of non-coding RNA (ncRNA) sequences that aggregates data from specialised ncRNA resources and provides a single entry point for accessing ncRNA sequences of all ncRNA types from all organisms. Since its launch in 2014, RNAcentral has integrated twelve new resources, taking the total number of collaborating database to 22, and began importing new types of data, such as modified nucleotides from MODOMICS and PDB. We created new species-specific identifiers that refer to unique RNA sequences within a context of single species. The website has been subject to continuous improvements focusing on text and sequence similarity searches as well as genome browsing functionality. All RNAcentral data is provided for free and is available for browsing, bulk downloads, and programmatic access at http://rnacentral.
INTRODUCTION Non-coding RNAs (ncRNAs) are a critical component of cellular machinery of all organisms. For example, the ribosomal RNA has been shown to be a ribozyme responsible for peptide bond synthesis  (1) , and the activity of the eukaryotic spliceosome is mediated by ncRNAs  (2). Apart from being the main player in those central processes, ncR-NAs provide additional layers of subtle regulation of gene expression. MicroRNAs have been shown to regulate the expression of the majority of mRNAs in animals and plants  (3) , and the range of regulatory roles of lncRNAs, including by genomic scaffolding and chromatin remodelling and modification  (4) , is becoming clearer. There is an intense scientific interest in ncRNAs resulting in a large number of ncRNA databases, but until recently searching and comparing them was challenging, and there was no uniform way to access or reference ncRNA sequences.
New Expert Databases RNAcentral aggregates ncRNA sequence data from an international consortium of RNA resources that we call Expert Databases. In the past two years, 12 additional Expert Databases have been integrated into RNAcentral (see Table  1 ). Among the newly imported resources were two major ribosomal RNA databases, SILVA  (7)  and Greengenes  (8) , which complement rRNAs from ENA and Rfam, as well as a high quality subset of rRNA sequences from RDP  (9). Ribosomal RNAs represent the majority of sequences in RNAcentral due to their use in environmental sampling. Sequences from five Model Organism Databases (Dicty-Base  (10) , PomBase  (11) , SGD  (12) , TAIR  (13)  and Worm-Base (  14 )) have been imported into RNAcentral. Long noncoding RNA (lncRNA) coverage was extended by the addition of NONCODE  (15)  and LNCipedia  (16)  datasets.
The inclusion of PDB  (17)  as an Expert Database helps to map between the worlds of ncRNA sequence and structure. Small nucleolar RNAs (snoRNAs) play an important role in guiding the modification of other ncRNA and mRNAs  (18) , and snoPY  (19)  provides to RNAcentral a dataset of snoRNAs found in human, fly, worm, yeast, and thale cress. An up-to-date list of all RNAcentral Expert Databases is available at http://rnacentral. org/expert-databases.
Database growth RNAcentral currently holds 10. 2 million distinct ncRNA sequences (an increase of 2. 1 million since release 1) with about 28 million cross-references (up 11 million since release 1) to 22 Expert Databases (Figure  1 ). The sequences come from over 720 000 organisms from all domains of life, with half of all sequences deriving from bacteria and ∼40% from eukaryotes.
Species-specific identifiers Since its inception, RNAcentral has provided unique identifiers for each distinct RNA sequence. For example, URS00004C905 is the identifier for the sequence UU UGGUCCCCUUCAACCAGCUG, which is the miRNA miR-133a-3p. The exact same sequence is observed in more than 10 species. These unique and stable identifiers are useful for a number of tasks, such as unambiguously referring to an RNA sequence, reducing redundancy in sequence datasets, and keeping track of cross-references. However, it is essential to be able to refer uniquely to a ncRNA sequence annotation in a particular species. In order to address this issue, we have introduced species-specific RNA sequence identifiers. The new identifiers are composed of a unique RNA sequence identifier and a NCBI taxonomic identifier separated by underscore.
Modified nucleotides Modified nucleotide residues play important roles in functions of many ncRNAs. For example, modifications of ribosomal RNAs are essential for the assembly and stability of ribosomes  (21) , and tRNA modifications can influence protein gene expression  (22). In order to begin capturing information about modified residues in RNA molecules and enable comparison of different datasets, we imported modified rRNA and tRNA sequences from MODOMICS  (23) , a database of RNA modification pathways, as well as all ncRNA modifications from Protein Data Bank. So far RNAcentral contains over 170 different chemical modifications found at over 8000 positions in about 600 unique sequences. Figure  2  shows a web interface that provides a unified view of the modifications from different databases.
Text search RNAcentral text search provides a flexible way for exploring RNAcentral data using a faceted interface powered by EBI search  (26). In the past two years, the search functionality was improved both in terms of user interface and searchable data. For example, publication metadata (such as paper titles, PubMed identifiers or author names) associated with ncRNA sequences can now be searched, which makes it possible to look up ncRNA sequences submitted to sequence archives when new papers are published. For example, a recent paper describes TRM10 (  27 ), a mRNA-derived small RNA that acts as ribosome inhibitor. By searching RNAcentral with PubMed identifier 24685157 the sequence is easily found (see entry URS00007E15D1) and can be used for further analysis, such as sequence similarity search.
Sequence similarity search RNAcentral sequence search is the first online tool that enables sequence similarity searches against a comprehensive set of ncRNAs. The service is powered by the nhmmer software, which has a comparable speed to BLAST but is more sensitive  (30). The web interface supports searching using an RNA or DNA sequence as a query and displays pairwise sequence alignments for each match. The results can be sorted by E-value, sequence identity and other criteria. If an exact match for a query sequence already exists in the database, its RNAcentral identifier is retrieved using the RNAcentral API without having to wait for the full search results to become available.
Genome browser Viewing sequences in their genomic context can provide important biological insights. For example, one can visualise snoRNAs found in introns of lncRNA GAS5 (31) using a built-in genome browser (see RNAcentral entry URS00008B3C85). In a recent update, we extended this functionality to enable browsing RNAcentral starting with a genomic location. The embedded genome browser, powered by Genoverse (http://genoverse. org), currently supports 13 key species, including human, mouse, fly, worm, and yeast (Figure  3 ). RNAcentral sequences are displayed alongside genes and transcripts from Ensembl  (32)  and Ensembl Genomes  (33)  with links to fully featured genome browsers, such as UCSC  (34)  and Ensembl. The genomic data are also available via a programmatic interface and downloadable files in BED/GFF formats.
TRAINING AND OUTREACH We continuously engage in outreach activities and provide user support by email and on GitHub. We have delivered over 20 presentations to date at scientific conferences and research institutes worldwide and organised a training event at the Wellcome Genome Campus. We also developed an online training course and recorded a live webinar (available on YouTube). All training materials can be accessed at http: //rnacentral. org/training. We are open to suggestions from our user community by email, on GitHub and on Twitter. The contact information can be found at http://rnacentral. org/contact.
FUTURE DIRECTIONS The main goal of RNAcentral is to provide a comprehensive set of ncRNA sequences, so integrating new Expert Databases and importing more data will remain a priority. For example, our goal is to integrate the remaining Model Organism Databases, such as FlyBase  (40)  and RGD  (41) , in order to provide uniform access to high-quality ncRNA sequence and annotations from key species. More than 20 participating ncRNA resources still need to be integrated and new Expert Databases are continually joining the consortium. We welcome relevant databases to contact us about membership. In the second phase of development, we will begin to integrate new types of annotations that can provide insight into the functions of ncRNA sequences found in RNAcentral. We will work on importing secondary structure information from Comparative RNA Website  (42) , GtRNAdb  (43)  and Rfam  (44)  databases.
Work is underway on integrating miRNA-mRNA interactions from TarBase  (45)  and miRNA-lncRNA interactions from LncBase (46) into RNAcentral. We will enrich existing annotations by importing ontology terms from external resources and assigning ontology terms automatically where possible. We also plan to use sequence alignment-based mapping to connect more RNAcentral sequences to reference genomes. RNAcentral is a young and fast-growing resource, but it has already proved useful for many applications, and its utility will be increased as more data are integrated and the associated services mature.
ACKNOWLEDGEMENTS RNAcentral has been prepared by  Anton I. Petrov ,  Simon J. E. Kay ,  Ioanna Kalvari ,  Kevin L. Howe ,  Kristian A. Gray ,  Elspeth A. Bruford ,  Paul J. Kersey ,  Guy Cochrane ,  Robert D. Finn ,  Alex Bateman  at the  European Bioinformatics Institute (EMBL-EBI) ;  Ana Kozomara ,  Sam Griffiths-Jones  ( University of Manchester );  Adam Frankish  ( Wellcome Trust Sanger Institute );  Christian W. Zwieb  ( University of Texas ),  Britney Y. Lau ,  Kelly P. Williams  ( Sandia National Laboratories );  Patricia P. Chan ,  Todd M. Lowe  ( University of California Santa Cruz );  Jamie J. Cannone ,  Robin R. Gutell  ( University of Texas at Austin );  Magdalena A. Machnicka ,  Janusz M. Bujnicki  ( International Institute of Molecular and Cell Biology and Adam Mickiewicz University );  Maki Yoshihama ,  Naoya Kenmochi  ( University of Miyazaki );  Benli Chai ,  James R.
One needs to evaluate the functional implication at a task level of some retinal properties. Examples include the asymmetry between ON and OFF cells  [248]  and the irregular receptive field shapes  [190]. One question is whether we would still need inspiration from the retina to build new descriptors, given the power of machine learning methods that provides automatically some optimized features given an image database? What the FREAK-based models show is that it is not only about improving the filters. It is also about how the information is encoded. In particular, what is encoded in FREAK-based models is the relative difference between cell responses. Interestingly, this is exactly the same as the rank-order coding idea proposed as an efficient strategy to perform ultra-fast categorization  [359] , and which has been reported in the retina  [268].
g. ,  [238, 138, 139] ). The basic structure of the encoder component of segmentation networks is similar to the hierarchical networks trained for object recognition  [168]. For example, the AlexNet has been trained by learning a hierarchy of kernels in the convolutional layers to extract rich feature sets for recognition from a large database of object classes. Segmentation networks  [238, 138]  have been designed by adding a decoder scheme to expand the activations in the category layers through a sequence of decon-Inria volutions steps such as in autoencoder networks  [134]. Even more extended versions include a mechanism of focused attention to more selectively guide the training process using class labels or segmentations  [139]. The hierarchical structure of such approaches shares several features of cortical processing through a sequence of areas with cells that increase their response selectivity at the size of their receptive fields over different stages in the cortical hierarchy.
Promising bio-inspired solutions. A modern trend in bio-inspired models of motion integration is to use more form-motion interactions for disambiguating information. This should be further exploited in computer vision models. Future research will have to integrate the growing knowledge about how diffusion processes, form-motion interaction and multiplexing of different cues are implemented and impact global motion computation  [348, 280, 213]. Despite the similarities in the biological and artificial approaches to solve optical flow computation, it is important to note that there is only little interaction happening between computer vision engineers and biological vision modellers. One reason might be that biological models have not been rigorously tested on regular computer vision datasets and are therefore considered as specifically confined to laboratory conditions only.
It would thus be very interesting to evaluate models such as  [320, 22, 42, 339]  to identify complementary strengths and weaknesses in order to find converging lines of research investigations. Figure  8  illustrates work initiated in this direction where three bio-inspired models that have been tested on the Middlebury optical flow dataset  [15]. Each of these models describe a potential strategy applied by the biological visual systems to solve motion estimation problem. The first model  [326] , demonstrates the applicability of a feedforward model that has been suggested for motion integration by MT neurons  [301]  for estimation of optical flow by extending it into a scale-space framework and applying a linear decoding scheme for conversion of MT population activity into velocity vectors.
Inria 6 Conclusion Computational models of biological vision aim at identifying and understanding the strategies used by visual systems to solve problems which are often the same as the one encountered in computer vision. As a consequence, these models would not only shed light into functioning of biological vision but also provide innovative solutions to engineering problems tackled by computer vision. In the past, these models were often limited and able to capture observations at a scale not directly relevant to solve tasks of interest for computer vision. More recently, enormous advances have been made by the two communities. Biological vision is quickly moving towards systems level understanding while computer vision has developed a great deal of task centric algorithms and datasets enabling rapid evaluation.
However, computer vision engineers often ignore ideas that are not thoroughly evaluated on established datasets and modellers often limit themselves to evaluating highly selected set of stimuli. We have argued that the definition of common benchmarks will be critical to compare biological and artificial solutions as well as integrating recent advances in computational vision into new algorithms for computer vision tasks. Moreover, the identification of elementary computing blocks in biological systems and their interactions within highly recurrent networks could help resolving the conflict between taskbased and generic approach of visual processing. These bio-inspired solutions could help scaling up artificial systems and improve their generalisation, their fault-tolerance and adaptability.
Acknowledgements NVK. M. acknowledges support from the  EC   IP  project  FP7-ICT-2011-8 no. 318723  (Math-eMACS) H. N. acknowledges support from  DFG  in the  SFB/TRR 'A Companion Technology for Cognitive Technical Systems. P. K. acknowledges support from the  EC   IP  project  FP7-ICT-2011-9  no. 600847  (RENVISION). GSM is supported by the  European Union  ( Brainscales ,  FP7-FET-2010-269921 ), the french  ANR  ( SPEED ,  ANR-13-SHS2-0006 ) and  CNRS.