dataesr/scientific-paragraphs-categorization huggingface dataset:
These 16 PBs are the abstraction of local protein backbone structures. Each of the 16 PBs is defined by a vector of eight backbone torsion angles associated with five consecutive residues and represented by the alphabet characters from 'a' to 'p'. Hence, a protein structure can be transformed from a 3D to a 1D sequence of PBs. This ability to represent protein structure in 1D has led to the development of new approaches for protein structure analysis  (20). The Database of Structural Alignments (DoSA) is a result of our previous work on identification of structurally similar SVRs in homologous proteins by using a PB substitution matrix combined with the modified CLUSTALW  (22)  algorithm [for more details refer to  (17) ]. In our previous work, we clearly show that optimal residue-residue equivalences could be achieved on the basis of PBs leading to improved local alignments.
We also showed that this is particularly useful in comparative modeling of loop regions. Moreover, understanding of sequence-structure relationships can be enhanced through this approach  (17). DoSA provides improved structure-based sequence alignments of homologous proteins especially focusing on the SVRs. This database proposes a refined view of the SVRs, which may contain local similarity concealed in global alignment of homologous protein structures. DoSA provides the unique information about conformationally dissimilar and conformationally similar SVRs in pairwise structural alignments. It gives the refined structural alignment in terms of amino acid sequence, PBs and the 3D superimposition itself; the protein superimposition can be viewed through the Jmol applet (http://www. jmol. org/).
Database statistics The protein data set was obtained from the PALI v2. 7 database  (23) , which contains structure-based sequence alignments generated using DALI  (9)  for protein domain families defined by the SCOP 1. 73 database  (24). However, DoSA differs from PALI in a number of ways (see below). DoSA covers 6420 domains divided in 1867 protein domain families in PALI. A total of 62 730 pairwise alignments are featured in DoSA. These pairwise alignments were divided in 542 610 SCRs and 347 062 SVRs  (17). SCRs were identified using a C-C distance threshold of 3 A from all pairwise structural alignments in PALI. Similarly, SVRs were identified from all the pairwise structural alignments of homologous proteins from PALI using a C-C distance threshold of >3 A ˚for stretches of three or more contiguous residues.
The 347 062 SVRs correspond to 49% of the alignment positions in the database. These pairwise structural alignments were converted into alignment of PB sequences. PBs have been assigned using in-house software. Regions corresponding to SVRs were identified in PB alignments and were realigned (17) by a modified CLUSTALW  (22)  algorithm using a recently improved PB substitution matrix  (27). Identification of conformationally similar SVRs was based on PB alignment score. A PB alignment score threshold of À0. 42 was applied to distinguish between conformationally similar (score more than or equal to À0. 42) and conformationally dissimilar SVRs [score less than À0. 42, for more details refer to  (17) ]. In our analysis, a total of 215 920 complete SVRs with more than three aligned PBs were re-aligned by the modified CLUSTALW algorithm optimized for PB sequence alignments.
Access to DoSA DoSA can be accessed at http://bo-protscience. fr/dosa/. The database site has been optimized for Mozilla Firefox, Google Chrome and Internet Explorer (version 7 or later) web browsers. Improved pairwise and multiple alignments for 6420 domains can be browsed or searched using key words. Key word searching in DoSA can accept protein domain IDs (e. g. 1vpda1), PDB ID (e. g. 1vpd), incomplete or complete protein family IDs (e. g. a. 102. 1. 2 or a. 102. ) and incomplete or complete protein family names (e. g. hexokinase or kinase) as input. Protein IDs, family IDs and family names should correspond to PALI v2. 7. All the improved pairwise structure-based alignments are annotated to describe conformationally similar and dissimilar SVRs (Figure  1 ). As shown in Figure  1 , different scores to characterize the quality of superimposition for the SVRs are available as a mouseover event [see General features of DoSA (iii)].
Discussion DoSA is complementary to existing structural alignment databases, and it aims at identifying genuine conformationally similar substructures in regions that are otherwise tagged as structurally variable in these databases. This database would hence serve as a valuable resource to study the nature and extent of structural rearrangements in backbone conformations in structural alignments of homologous proteins. DoSA can thus aid in providing clues to model loop regions, for which a homologue of similar length is unavailable  (17). The effect of amino acid substitutions on the local structural alterations in the homologous protein structures could also be studied using the structural alignments provided in DoSA. This database can be used to identify equivalent regions in homologous protein structures that do not share structural similarity and in turn to understand the sequence-structure relationships.
It is noteworthy that although DoSA is derived from the PALI database, it is yet different in a number of ways. Most significantly, PALI is broad based with a few general features, whereas DoSA is a structure-based alignment database that specializes on getting clarity on apparent SVRs. DoSA is specialized in identifying SVRs (often loops) with genuine conformational differences and SVRs that are conformationally similar although not superimposable in a global superposition because of rigid-body orientational differences. In the future, DoSA will be updated with the new releases of the PALI database.
, Matt. 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 License (http://creativecommons. org/ licenses/by/3. 0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. page number not for citation purposes) Database, Vol. 2013, Article ID bat048, doi:10. 1093/database/bat048.
http://www. jmol. org/) (Figure2). The aligned coordinate files can also be downloaded as text files. (v) Database searching: DoSA can be searched by protein domain ID, Protein Data Bank (PDB) ID, protein family name and protein family ID available in PALI v2. 7 (23) using the keyword search option. (vi) Multiple structure-based sequence alignments: Even if the focus of our previous study (17) was on pairwise alignments, for each protein family defined by SCOP 1. 73, multiple structure-based sequence alignments obtained using MUSTANG (29) and their corresponding multiple PB sequence alignments are also.
Page 1 of 5 ( 5 , Matt. 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 License (http://creativecommons. org/ licenses/by/3. 0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. page number not for citation purposes) Database, Vol. 2013, Article ID bat048, doi:10. 1093/database/bat048.
Acknowledgements The authors thank  William Harris  for discussion in the early stages of the project,  Paul Martin  for ongoing support of the zebrafish work in Bristol and  Brooke Morriswood  for critical reading of the manuscript. This work was supported by the  BBSRC  ( BB/ H008462/1  to F. C. and  E019633  to K. S. and D. J. S. ), the  MRC  (F. C. ) and the  Wellcome Trust  ( WT089227/Z/09/Z  to F. C. and  WT077988/Z05/Z  to Y. F. ).
After analysis with the psRNATarget program, five 20-21 nucleotide motifs complementary to the PvmiR399 isoforms were identified, located at positions 1084, 1175, 1345, 1399, 1453 in BAT477 and 1094, 1185, 1355, 1409 and 1463 in DOR364 PvPHO2 5' UTRs (Figure  4 ). For comparison, these predicted PvmiR399 sites will be referred to as 1 to 5, starting with the site at the 5' end. BAT477 ---------G   BAT477 ---------G   ---------G   Our results agree with those from the Arabidopsis PHO2 gene that contains five binding sites complementary to miR399 in its 5' UTR region, approximately 200-400 nucleotides upstream of the start codon; miR399-dependent PHO2 transcript cleavage at these sites has been experimentally verified  [51]. Putative PHO2 orthologs containing five miR399 binding sites in their 5' UTRs have also been identified in rice, Medicago truncatula and poplar (Populus trichocarpa)  [31].
1-TOPO TA vector (Invitrogen, California, CA, USA). Plasmid DNA from at least two clones from each genotype was purified and sequenced at least three times (replicates). The 1. 6 kb sequence from the PvPHO2 5' UTR from each of the bean genotypes was analyzed to identify putative target sites complementary to miR399. The program psRNATarget  [54]  was used for such analysis. This program also allows predicting the level of matching (score) from each of the microRNA complementary sites  [55].
Amplification products were resolved in a 6% polyacrylamide gel in 1xTBE and EtBr stained for visualization. Only after the second round of nested PCR, we obtained specific fragments after 30 or 35 cycles. For PCR product quantitation, the amplification products (after 30 cycles, in Figure  5 ) were analyzed using the ImageQuantTL software (GE Healthcare Bio-Sciences, Uppsala, Sweden). The PCR fragments obtained were purified by elution and ethanol precipitation from the gel and were cloned using the TOPO-TA system (Invitrogen, California, CA, USA). Several independent clones were sequenced to confirm their identity (Unidad de Secuenciación, IBT, UNAM).
Acknowledgments This work was supported by grant  PAPIIT IN200411  from  Dirección General de Asuntos del Personal Académico-UNAM  and partially supported by grant  152776  from  Consejo Nacional de Ciencia y Tecnología (CONACyT). We thank  Maheswara Pallavolu Reddy  for critical review of this manuscript and  Víctor Bustos  and  Waldo Díaz  for technical assistance.
Gallus gallus 2 1 Homo sapiens 3 1 Presence of Dicer and RIG-I family proteins in selected animals. Data were obtained from Pfam (version 26. 0)  (Finn et al. , 2010) (pfam. sanger. ac. uk). RIG-I family proteins were identified by having both helicase domains and the RIG-I C-terminal domain (Pfam: PF11648). Available sequence data is sparse for some clades and absence of data might not be sufficient evidence for absence of genes. DOI: 10. 7554/eLife. 00994. 019 10 ng/µl of fosmid DNA, 5 ng/µl of the co-marker transgene myo-3::gfp::unc-54, 85 ng/µl of 1 kb DNA ladder (Invitrogen), 20 mM potassium phosphate pH 7. 5, and 3 mM potassium citrate pH 7. 5. The transgene was then integrated via X-ray irradiation as described  (Fire, 1986). We controlled that the transgenic copy of drh-1 was transcriptionally active in transformed animals by qRT-PCR on a portion of the mRNA that is deleted in the wild JU1580 strain.
The lysis mixtures were pooled for each replicates and the qRT-PCR was performed as indicated above for the F2 families. Small RNA sequencing Library preparation and sequencing Preparation of RNA used for small RNA libraries is described above. For 5′ independent libraries, 3-5 μg of total RNA was pre-treated with 5′ polyphosphatase (Epicenter) following the manufacturer's instructions. Small RNA libraries were generated from either polyphosphatase treated or total RNA (3-5 μg) using the TruSeq Small RNA kit (Illumina) following the manufacturer's instructions. Small RNA libraries were sequenced using the Illumina MiSeq. Small RNA sequence data were submitted to the Gene Expression Omnibus (GEO) under accession number GSE41693.
Sequencing analysis Small RNA libraries were sequenced using the Illumina MiSeq. Fastq files generated by the machine had adaptors removed using the program Cutadapt v1, and were converted into Fasta files using a custom Perl script. For alignment to the viral genome, Fasta files were trimmed to leave only reads of between 15 and 30 nucleotides using a custom Perl script and were aligned using Bowtie (version 0. 12. 7) to RNA1 and RNA2 of the Orsay virus genome  (Félix et al. , 2011) , reporting only the best single alignment with up to one mismatch allowed. Sam files from Bowtie were converted into bam files using the SAMtools utility  (Li and Durbin, 2009)  and bam files were converted into bed files using the BedTools utility  (Quinlan and Hall, 2010). Bed files were read into the R environment and plots of the length and first nucleotide as well as read distribution along RNA2 were generated using custom scripts written in R.
Molecular evolution analysis of the 6 Mb central region of chromosome IV For each of the 97 isolates, 97 SNPs included in the 6 Mb central region of chromosome IV associated with Orsay virus sensitivity were extracted from RAD-sequencing data  (Andersen et al. , 2012). DNAsp allowed us to classify all isolates in 28 different haplotypes for this region. The neighbor-net network  (Bryant and Moulton, 2004 ) was then drawn using the SplitTree software  (Librado and Rozas, 2009). The average number of differences per polymorphic RAD site along chromosome IV was calculated using DNAsp  (Huson and Bryant, 2006)  using a sliding window of 25 SNPs every 10 SNPs. The linkage disequilibrium (D′ ) between drh-1 alleles and polymorphic RAD sites on chromosome IV was calculated by DNAsp.
Acknowledgements We thank  M Tanguy  and  J Rehwinkel  for critical insights into viral biology,  C Bradshaw  and  G Allen  for Bioinformatics support and  S Moss ,  I Nuez  and  A Richaud  for laboratory management. We are grateful to  C Franz  and the  D Wang laboratory  for viral filtrate preparations, to  E Andersen  for help with the whole-genome association, and to  M De Bono  for reagents. Some strains were provided by the  CGC , which is funded by  NIH Office of Research Infrastructure Programs  ( P40 OD010440 ). Some deletion mutations were provided by the  International C. elegans Gene Knockout Consortium  which is funded by the  National Institute of Health , the  Canadian Institute for Health Research, Genome Canada ,  Genome BC , and the  Michael Smith Foundation. Fosmids were provided by  D Moerman  laboratory.
, 2006b). This procedure allows simulation of the conversion of biomolecules into their corresponding diagenetic geomolecules  (Stankiewicz et al. , 2000; Gupta et al. , 2006 Gupta et al. , , 2007)). Aims of this study are (1) to determine the common palaeochemotaxonomic (diagenetic) signatures of all extant Araucariaceae species, (2) to evaluate the inter-and intra-generic differences within the family, (3) to highlight the molecular characteristics which should allow their distinction from other conifer families in ancient sediment samples. This contribution should serve as a database for future palaeoenviroennemental, archaeological and environmental research.
Gas Chromatography-Mass Spectrometry (GC-MS) and identification of compounds Aliphatic and aromatic fractions were analysed using an HP 5890 Series II gas chromatograph coupled with an HP 5971 mass spectrometer (GC-MS). Polar fractions were silylated using N, O-bis-(trimethylsilyl)trifluoroacetamide (BSTFA) at 60 • C for 15 min before analysis. The capillary column was a DB-5 J&W (60 m × 0. 25 mm i. d. , with 0. 1 µm film thickness). The temperature programme was 70-315 • C at 15 • C min -1 to 130 • C, and then 3 • C min -1 followed by an isothermal stage at 315 • C for 15 min. Helium was the carrier gas (1 mL min -1 flow rate). The MS operated in the electron impact mode (EI) at 70 eV ionization energy, and mass spectra were scanned from 50 to 500 Da using a quadrupole detector. Data were acquired and processed using the Agilent ChemStation software.
4a ). In the aromatic fractions, the cadalane-type compounds are repre-sented by calamenene and calamene (C 15 H 22 , both partially aromatized), cadalene (C 15 H 18 , completely aromatized). One norcadalene (probably the 1-isopropyl-7-methylnaphthalene according to the spectrum from  Singh et al. , 1994)  is detected in all species (Fig. 4d ). Its formation is the result of the demethylation of cadalanetype compounds, and no more precision to its structure was proposed. In addition, cadalane-type compounds show a high abundance in most species' samples and represent also one of the major Araucaria sesquiterpenoids, except for A. araucana and A. cunninghamii; -eudesmane-type compounds. 4α(H)-eudesmane and 4β(H)-eudesmane are identified according to Wiley275 database and the spectrum published by  Alexander et al.
Chamazulene elutes after cadalene and its spectrum, according to the Wiley275 database, is quite similar to that of norcadalene, but with a molecular ion (m/z 184) more intense than its base peak at m/z 169 (Fig. 4e ). Due to its carbon skeleton composed of a ring composed of seven carbon atoms and a pentenic ring, it is probably derived from precursor of similar carbon skeleton, like aromadendrenes, alloaromadendrene and spathulenol, which are widespread in the fresh species  (Brophy et al. , 2000; Olawore, 2005). Pentamethyl-2,3-dihydroindene, identified according to Wiley275 database, is characterized by a spectrum with a molecular ion at m/z 188 (C 14 H 20 ) and a base peak at m/z 173 (M + -15) (Fig. 4f ). Pentamethyl-2,3-dihydroindene and chamazulene show a higher abundance than other  aromatic bicyclic sesquiterpenoids with the exception of cadalene; -furthermore, 1,3,4-trimethyl-2-(4-methylpentyl)benzene (C 15 H 24 ) with a molecular ion at m/z 204 and a base peak at m/z 133 was detected in all Araucaria species, but with a low abundance.
robusta. Despite the presence and the absence, these tetracyclic compounds represent the major components within the Agathis species. The unidentified compounds, like those of Fig. 8a  to 8e, are also detected with significant abundances in Agathis species. Furthermore, some peaks show relatively high abundance and their spectra are respectively characterized by molecular ions at m/z 264 and 274. A base peaks at m/z 109 and 259 are observed in the aliphatic fraction of Ag. australis and Ag. moorei. Their chemical formulas are, respectively, C 20 H 24 and C 20 H 34 , and their spectra are given in Fig. 9i  and 9j. Since these spectra are neither referenced in the literature nor in our spectral database, their molecular structures are not yet clear. However their frequent, significant and also unique presence (Fig.
Acknowledgements. We thank  Alain Rouiller ,  Fredéric Lannuzel ,  Bastien Farre  for technical assistance. We thank  Frederic Thévenard  ( UMR 5276 LGLTPE ,  University Claude Bernard, Lyon I) ,  Anne Allaria  ( Botanical Garden of the Villa Thuret, INRA, Antibes ),  Denis Larpin  ( Museum Nationale d'Histoire Naturelle, Paris ),  Romaric Pierrel  and  Marc Rémy  ( Botanical Garden of Montet, Vandoeuvre-lès-Nancy ) for their help for providing samples. We also thank the  Wollemi Pine France  (http://france. wollemipine. com)and the Kientzler society for providing branches of the Wollemi Pine in 2006 during the propagation programme of this species in several botanical gardens around the world. We also wish to thank the handling editor of Biogeoscience (X. Wang),  Dr. Sawada  and a second anonymous reviewer for their appreciation, as well as for their numerous helpful suggestions, which improved the quality of our manuscript.
Acknowledgments The authors thank  Centre National de la Recherche Scientifique (CNRS-France) ,  Agence nationale de la recherche  ( ANR -project ECORUM -ANR 11-BSV7-019 ),  Fédération Nationale des Producteurs de Plants de Pomme de Terre  ( FN3PT/RD3PT ) and  Association Nationale de la Recherche et de la Technologie  ( ANRT-CIFRE n°1282/2011 ) for financial supports. Potato tubers were kindly supplied by  CNPPT/SIPRE.
The procedure is based on single-trial task decoding from muscle synergy activation features. The task decoding based metric evaluates quantitatively the mapping between synergy recruitment and task identification and automatically determines the minimal number of synergies that captures all the task-discriminating variability in the synergy activations. In this paper, we first validate the method on plausibly simulated EMG datasets. We then show that it can be applied to different types of muscle synergy decomposition and illustrate its applicability to real data by using it for the analysis of EMG recordings during an arm pointing task. We find that time-varying and synchronous synergies with similar number of parameters are equally efficient in task decoding, suggesting that in this experimental paradigm they are equally valid representations of muscle synergies.
In practice however, not only the identity of these synergies, but also their number and their contribution to task discriminating variations, is unknown a priori. To select the number of synergies, the most widely used criteria rely on the dependence of the amount of variance explained upon the number of synergies. However, criteria solely based on VAF depend substantially also on factors not related to task execution, such as noise (neural variability, measurement fluctuations etc. ) and preprocessing (filtering, averaging of EMG signals etc. ) and cannot distinguish synergy that describe task-to-task variations from synergies that do not. We argue that synergy model selection should reflect not only the reconstruction of the dataset but also include a way to assess the reliability of the associated mapping from synergy recruitment to motor task identification.
To address these needs, here we propose, implement and validate on EMG data a method (schematized in Figure  1 ) for predicting, on a single-trial basis, the motor task from the synergy activation parameters. The method is based on quantifying the task discriminability afforded by one or more synergies, and then using this for an automated objective selection of the minimal set of synergies containing all information about such salient task-related differences in synergy activation patterns. We validate the robustness and applicability of the method using simulated EMG datasets. We finally apply it to real EMG recordings during a reaching task to illustrate how this method may be used for individuating synergy sets relevant for the execution of a given set of tasks, and for evaluating in task space the effectiveness of different types of muscle synergy decompositions.
Synchronous synergy model We used the Non-negative Matrix Factorization (NMF) algorithm  (Lee and Seung, 1999)  to extract synchronous synergies. In this model, the EMGs are represented as a linear combination of a set of time-invariant activation balance profiles across all muscles activated by a time-dependent activation coefficient: m s (t) = N i = 1 c s i (t)w i + ε s (t) (1) where m s (t) is again the EMG data of all muscles at time t; w i is the synergy vector for the i-th synergy; c s i (t) is the scalar coefficient for the i-th synergy at time t; N is the total number of synergies composing the dataset; and ε s (τ) is the residual (e. g. , noise). N is an input to the NMF algorithm, so we varied the number of extracted synergies from 1 to 8. In this case, the sample-independent muscle synergies are time-invariant vectors and the parameters that have to be modified in each sample s are the time-varying waveforms c s i (t)  (Cheung et al.