dataesr/scientific-paragraphs-categorization huggingface dataset:
RNAComposer (64-bit Intel Xeon 2. 33 GHz processor-based platform with scalable 8 GB memory) predicts 10 3D models within <10 min. The resulting models were inspected for the total energy value calculated by RNAComposer and for the preservation of the 111GACUG115/148CAGUC152 and 28UC29/93GA94 interactions. Models showing lowest total energy were chosen for further analysis. Some fragment selections chosen by RNAComposer for the 3D structure assembly prohibited the formation of required contacts and such models were rejected at this stage. Subsequently, the selected models were investigated for the proximity of the 111GACUG115/ 148CAGUC152 pseudoknot region to the RNA termini. The mutate-and-map data (RNA Mapping Database) suggested that region hosting pseudoknot 111GACUG115/ 148CAGUC152 should be close to the molecule termini, namely to the internal loop 6GG7/182GA183.
Bujnicki group The Bujnicki group used a hybrid strategy similar to the one used in the previous editions of the RNA-Puzzles experiment  (Cruz et al. 2012) , which comprised template-based (comparative) modeling, global folding with restraints using a coarse-grained method for template-free folding, and highresolution refinement. First, for all target sequences they attempted to identify homologous families in the Rfam database  (Burge et al. 2013 ) and homologous RNAs with experimentally determined structures. For RNA sequences or sequence fragments that exhibited homology with RNAs with experimentally determined structures, initial models were constructed by template-based modeling and fragment assembly using ModeRNA  (Rother et al. 2011). Target-template alignments were prepared manually, with the aid of secondary structure information extracted from Rfam and corrected if needed with the use of predictions made with RNA metaserver (http://genesilico.
Here, they used a different method than previously, namely an in-house software tool QRNAS (J Stasiewicz and JM Bujnicki, unpubl. ) that extends the AMBER force field with energy terms explicitly modeling hydrogen bonds, idealizes base pair planarity and regularizes backbone conformation. As in their previous  (Cruz et al. 2012 ) modeling exercise, human intervention was relatively large. Most of the time was devoted to searching for additional information related to target RNA sequences and discussions within the group. Time used for alignment preparation and for selection of models for submission varied greatly depending on the difficulty of the Problem. Time used for template-based modeling was negligible. Time required for SimRNA modeling was typically a few days per target, and the final refinement was typically run overnight.
Another notable feature of the Vfold model at secondary level is the modeling of RNA loop free energy. By enumerating all the possible (sequence-dependent) intra-loop mismatches, the Vfold model partially accounts for the sequence-dependence of the loop free energy. Next, a 3D coarse-grained scaffold is constructed based on the predicted secondary structure  (Cao and Chen 2011). To construct a 3D scaffold, the predicted helix stems are modeled as A-form helices. For the loops/junctions, 3D fragments from the known PDB database were used. Specifically, a structural template database  (Xu et al. 2014 ) was built by classifying the structures according to the different motifs such as hairpin loops and internal/bulge loops, three-way junctions, four-way junctions, pseudoknots, etc. For each junction, the optimal (top 5) fragments were selected for the further structure assembly of the whole RNA.
2011) were created for threading structural templates into the desired sequence, such as the catalytic core of Problem 5, the lariatcapping ribozyme (see also below). Second, the Das group expanded fragment assembly of RNA with full-atom refinement (FARFAR)  (Das et al. 2010; Kladwang et al. 2011a) , whose interface for job setup was previously cumbersome, especially to solve subpieces of large RNAs (e. g. , the threeway P15/P8/P3 junction of Problem 5). The RNA puzzles inspired them to write a single python script (rna_denovo_ setup. py) for straightforward setup of FARFAR jobs, taking as input the full-model sequence and secondary structure, the residues of desired subdomain, and PDB models for any known subpieces of the subdomain. For Problem 6, the Das group also created a mode for setting up rigid-body "docking" of multiple RNA pieces including a placeholder sphere for the adenosylcobalamin ligand.
Dokholyan group The Dokholyan group at the University of North Carolina at Chapel Hill in collaboration with the Ding group from Clemson provided predictions for Problems 5, 6, and 10 using multiscale discrete molecular dynamics (DMD) method  (Proctor et al. 2011; Shirvanyants et al. 2012 ). The structure modeling was performed with coarse-grained folding simulations followed by an all-atom reconstruction. In the coarse-grained folding simulations, the three-bead RNA model, where each nucleotide is represented by three pseudoatoms corresponding to base, sugar, and phosphate groups  (Ding et al. 2008 ), was used. The interactions between the three beads are modeled based on information available from high-resolution RNA structure database. Bonded   i INF stack is the Interaction Network Fidelity taking into account only stacking interactions.
This kink-turn has a GA tandem and an asymmetric loop. We decided to assume its formation by adding it to the constraints using a mask in MC-Fold: GGGAGU-GCGAGGAUC ((((((-)). )))) The 3D model was built by using the T-loop crystal structure of a tRNA (PDB 1EVV). The kink-turn area was modeled after Kt-7 (PDB ID 3CC2 of the 23S rRNA of Haloarcula Marismortui). The remaining parts and the final assembly were modeled using MC-Sym (major. iric. ca/Web/ mctools). The models generated by MC-Sym were minimized up to the "brushup" level of the MC-Pipeline. The selection of the candidate models was based on "Score" values, a homemade all-atoms force-field which is part of the "Analysis" module of the MC-pipeline.
(). (). ()). This three-way junction had no 3D templates in the library but 3dRNA could automatically search the library and pick out a nearest three-way junction, whose secondary structure is (. (). (). ()). In this case, the loop was extracted from a ribozyme fragment 1GID. 3dRNA then made deletion and insertion operations on it to match the secondary structure needed, i. e. , (. (). (). ()). After that, the 3D templates of other secondary structural elements were automatically searched for by the searching module of 3dRNA and then assembled to a whole all-atom structure by the assembling module of 3dRNA. Finally, the structure was refined by AMBER energy minimization. All calculations were performed on an Intel S5500BC server (Intel(R) Xeon (R) CPU E5620 @ 2. 40 GHz). The template searching and assembling process took ∼2 min.
Unlike the numerous structures available for proteins, the number of RNA structures solved by crystallization is still limited and the available conformational space of RNA folding is far from complete. The prediction of non-WC pair and stacking could also be improved with the increase of known RNA structures and a complete search for RNA modules. Other than module prediction programs, easy and fast experiments can provide direct constraints in structure modeling. In the protein structure prediction trials CASP Round X  (Moult et al. 2014) , a new category of "contact-assisted" prediction was proposed. Experimental data such as NMR, chemical shift, cross-linking, and surface labeling have been proved to be instrumental. Previously, contacts inferred from evolutionary information also achieved success in protein structure modeling  (Morcos et al.
As an example, the dramatic hole in the ring structure formed by two helices in Problem 5 was not described at even nucleotide resolution by any prediction model. Current fast experiments can only help in detecting local and detailed interactions rather than global architectures determined by long-range contacts. These methods are mainly sensitive to detailed contacts but are largely uninformative as to global information such as holes. For consistent refinement to higher resolution 3D models that predict these striking features, we still need deeper understanding of RNA structure and/or new fast experimental tools. Currently, the major challenges in RNA structure prediction lie in (1) further improvement of algorithms that incorporate simple experimental data (contact-assisted data), (2) structure optimization to alleviate atomic clashes and improve accuracy, and (3) accumulation of comprehensive RNA structure knowledge with the help of database increases and automated structural bioinformatic tools.
ACKNOWLEDGMENTS This work (Z. M. and E. W. ) is supported by the  French Government  grant  ANR-10-BINF-02-02  <BACNET>. This work (E. W. ) has been published under the framework of the  LABEX :  ANR-10-LABX-0036_NETRNA  and benefits from a funding managed by the  French National Research Agency  as part of the  Investments for the future program. N. V. D. acknowledges support of the  National Institutes of Health (NIH)  grant  2R01GM064803-09  (PI:  K. M. Weeks ). The 3D structure modeling was supported by the  NIH  ( 5 T32 GM007276  to C. C. ;  R01 GM102519  to R. D. ), the  Burroughs-Wellcome Foundation  (CASI to R. D. ), Bio-X and HHMI international fellowships (F. C. C. ), a  Stanford Graduate Fellowship  (S. T. ), a  Conacyt fellowship  (P. C. ),  European Research Council  (grant  RNA+P=123D  to J.
Anatomy Microtomographic data were generated to assess the appendicular skeleton anatomy for most species of the sand-swimming ecomorph: all four species of Grandidierina, two of the three Voeltzkowia, and Paracontias minimus; and for Amphiglossus ornaticeps, a small-sized scincine related to Grandidierina, allegedly presenting the plesiomorphic state for Malagasy scincine morphology, with four functional pentadactyl limbs. A single specimen per species was scanned: G. fierinensis (ZSM 1636/2010), G. lineata (ZSM 1624/2010), G. petiti (ZSM 1620/ 2010), G. rubrocaudata (ZSM 1632/2010), V. mira (ZSM 867/0), V. mobydick (UADBA R70487), A. ornaticeps (ZSM 1603/2010 = ZCMV 13010) and P. minimus (ZSM 2253/2007). Scanning took place at the Museum für Naturkunde Berlin using a Phoenix|x-ray nanotom (GE Sensing & Inspection Technologies GmbH, Wunstorf, Germany) with a 180 kV highpower nanofocus tube and a tungsten target.
Reconstructions were performed in datos|x-reconstruction software (GE Sensing & Inspection Technologies GmbH phoenix|x-ray) and data were visualized in VGStudio Max 2. 0 (Volume Graphics GmbH, Heidelberg, Germany). The upper and lower body of the specimen were scanned separately for 1000 projections each, resulting in a magnification ratio of 5. 5x and 6. 4x, and a voxel size of 9. 2 μm and 7. 8 μm, respectively. To visualize skeletal features in three dimensions, such as the pectoral and pelvic girdles, the osteoderms were digitally isolated and rendered transparent. For the remaining elements of regressed limbs we formulated tentative homology hypotheses based on their relative position and size, although embryological evidence have shown that digit identity frame shift may occur as a consequence of digit reduction in skinks  [27].
Molecular analyses DNA sequences. We collected DNA sequence data for three fragments of mitochondrial (mtDNA) genes: NADH-dehydrogenase subunit 1 (ND1) with adjacent tRNAs (tRNAMet, tRNAGln and tRNAIle genes), 12SrRNA (12S) and 16S rRNA (16S), and for four proteincoding nuclear genes (nDNA): brain-derived neurotrophic factor (BDNF); recombination activating gene 2 (RAG2); oocyte maturation factor (CMOS) and phosducin (PDC). Standard polymerase chain reactions were performed in a final volume of 12. 5 μl containing 0. 3 μl each of 10 pmol primer, 0. 25 μl of total dNTP 10 mM (Promega), 0. 1 μl of 5 U/ml GoTaq, and 2. 5 μl of GoTaq Reaction Buffer (Promega). Primers and PCR conditions followed Crottini et al. [1]. The successfully amplified products were purified using the ExoSAP-IT purification kit according to the manufacturer's instruction.
Purified PCR templates were sequenced using dye-labeled dideoxy terminator cycle sequencing on an ABI 3130 automated DNA sequencer. The data matrix is 94. 3% complete. Sequences were aligned using the ClustalW algorithm and subsequently refined manually in BioEdit 7. 0  [28]. We used GBLOCKS  [29]  with stringent settings (no gaps allowed) to determine and exclude uncertain positions in the alignment. A total of 249 newly determined sequences were deposited in GenBank under accession numbers KM057077 to KM057324 (see S1 Table for a detailed list). Phylogenetic analyses. We conducted partitioned Bayesian inference (BI) and maximum parsimony (MP) analyses based on the full concatenated and unphased dataset. BI was carried out using MrBayes 3. 1. 2 [30]. The best-fit substitution models and partition were calculated using Partition Finder v1.
MP analyses were performed using PAUP Ã 4. 0b10  [35]  with 100 random addition sequence replicates, equal character weighting, tree bisection and reconnection (TBR) branch swapping, and gaps coded as missing data. Nodal support was obtained by bootstrapping, with 2000 replicates, 10 random addition sequence replicates and TBR branch swapping. We used two hierarchical outgroups: Tiliqua and ''Eumeces" sensu lato. Among the non-Malagasy skinks, previous more inclusive studies  [22] ,  [36]  suggested that species of the genus Eumeces sensu lato are relatively close to the Malagasy radiation. For both outgroup taxa, concatenated chimera datasets were compiled by combining sequences of different species from GenBank (see Crottini et al. [1] ). Time tree reconstruction. To estimate divergence times within the Malagasy Scincinae, we constructed a time calibrated phylogenetic tree using BEAST 1.
29 x 10 -9 per site per year. To cross-validate these two priors we carried out two independent preliminary analyses: (i) we fixed the mitochondrial substitution rate at 0. 0053/site/MYr and estimated divergence times both on the mtDNA dataset and on the whole dataset (mtDNA+nDNA) to see if the estimated median divergence between Madascincus and Amphiglossus would fall within the confidence interval reported by Crottini et al. [39] ; ii) similarly, we fixed divergence between Madascincus and Amphiglossus at 47 Ma and estimated the whole dataset substitution rate in order to evaluate if it approximated the one reported by Eo & DeWoody  [40]. As both analyses showed that the two priors were compatible, iii) we proceeded with the time tree estimation on the whole dataset using the two prior values as calibrations.
We used a normal prior for the divergence between Madascincus and Amphiglossus setting the mean at 47 Ma and the standard deviation (S. D. ) at 12 MYr in accordance to Crottini et al. [39] , and another normal prior for the mitochondrial substitution rate (mean: 0. 0053/site/ MYr, S. D. : 0. 003, quantiles 5% = 3. 6E-4, 95% = 0. 01) in accordance to Eo & DeWoody  [40]. Although these calibrations are not ideal (i. e. , not primary fossil-based  [38] ), they provide a first objective approximation of divergence times of Malagasy skinks to be refined in future studies. The best-fit substitution models and partition were calculated using Partition Finder v1. 1. 1  [31]  (see above). Consequently, for the molecular clock model we divided the dataset into two partitions, one for the mitochondrial and another for the nuclear data, both with a relaxed molecular clock model with lognormal distribution  [41].
Thus, in an attempt to reach mixing and acceptable ESS we simplified the substitution models for the two mitochondrial partitions (we arbitrarily set both to HKY+I+G), and excluded two species (Pseudoacontias menamainty and Amphiglossus crenni) that had missing data for all nuclear genes. As a result, the simplified analysis showed good mixing and ESS values larger than 700, reaching stationarity in the beginning of the burn-in (first 10% of all iterations). We summarized a maximum credibility clade tree using Tree Annotator from Beast v1. 8 package  [37] , discarding 10% as burn-in. The tree was visualized and edited using Figtree v1. 4. 1  [42]. Population genetic analyses. Population genetic structure was assessed within the clade containing Grandidierina fierinensis and individuals assigned by Glaw & Vences  [19]  to an undescribed candidate species named G.
sp. "pallida" on the basis of their morphological differentiation. The PHASE algorithm  [43]  implemented in DnaSP v5  [44]  was used to infer haplotypes from each available sequence of each nuclear gene, in a data set containing all Grandidierina species. Haplotype networks were reconstructed using statistical parsimony  [45] , as implemented in the program TCS v1. 21  [46]  with a connection limit of 95%. Analyses of population structure within the G. fierinensis clade were based on the four nuclear loci using STRUCTURE v2. 2  [47]  under a model assuming two populations (K = 2) in order to verify if the genetic data support differentiation of the two phenotypic groups found. This analysis assigns individuals probabilistically to clusters based on their multilocus genotype. We performed five replicates with two million MCMC generations of which 50% were discarded as burnin.
Phylogenetic relationships The BI tree based on the complete concatenated dataset is highly congruent with previously published phylogenies  [1] ,  [7] ,  [26]. It retrieves the two main clades A and B (cf. [36] ) within Malagasy scincines (Fig  2 ). The poorly supported clade A is formed by the two monophyletic sister genera Paracontias (Posterior probability (PP): 1. 00) and Madascincus (PP: 0. 73), which together are sister to Pseudoacontias menamainty, a taxon of unstable relationships (but probably belonging to clade A) for which sequences of only 3 out of 7 loci are available. Clade B (PP: 1. 00) is composed of the paraphyletic genus Amphiglossus, the Grandidierina clade (PP: 0. 95), the Pygomeles clade (PP: 1. 00), and the Voeltzkowia clade (1. 00) (see taxonomic accounts in Discussion).
We recognize four described species belonging to the genus Grandidierina, which are: (  1  Pygomeles Grandidier, 1867. This genus currently contains two species, the type species Py. braconnieri and the subsequently described Py. petteri  [51]. The phylogeny of Crottini et al. [1] included only Py. braconnieri, a species from south-western Madagascar. This species was found to be the closely related sister group of another southern species with reduced limbs classified as Androngo trivittatus. However, because Py. petteri was missing from their data set, Crottini et al. [1]  refrained from taxonomic conclusions. Our analysis (Fig 2 ) suggests that An. trivittatus is the sister taxon of Py. braconnieri and these two taxa form the sister clade to Py. petteri, thus rendering Pygomeles sensu lato paraphyletic.
Acknowledgments The work was carried out in collaboration with the  Département de Biologie Animale, Université d'Antananarivo. We are grateful to the Malagasy authorities, in particular the  Ministère de l'Environnement et des Eaux et Forêts , for research permits, and to  Annemarie Ohler  and  Ivan Ineich (MNHN)  for providing us access to specimens under their care. Bill Love provided unpublished photographs for examination.
Original pictures published in the present paper have been deposited in Morphobank (available at www. morphobank. org) under the project number 1195 (n = 49 high definition pictures numbered from M332080 to M332129). A total of 249 newly determined sequences were deposited in GenBank under accession numbers KM057077 to KM057324 (available at www. ncbi. nlm. nih. gov/genbank/).
Ovine ITGB4 gene sequence and annotation Because of its association with human JEB  [17, 22, 34] , the ITGB4 gene was identified as the most promising candidate. Our first aim was to identify the causal mutation via Sanger sequencing of the ITGB4 gene in four JEB cases and four unrelated controls. Through the Sanger approach for genomic sequencing, we were able to obtain 84% (29,936 bp) of the complete genomic sequence of the ITGB4 gene (ENSOARG00000009764), in the four cases and four controls. By comparing the sequences of the unaffected and the JEB-affected lambs, we identified eight single nucleotide polymorphisms (SNPs) that had have different genotypes in the cases (g. 54857417T>G. g. 54857293C>T, g. 54856642A>G, g. 54853969C>G, g. 5485255C>A, g. 54851357C>T, g. 54850814C>T and g. 54848707G>A).
All of the identified SNPs were located in non-coding ITGB4 regions. A search for the SNPs in the International Sheep Genomic Consortium (ISGC) database (https://data. csiro. au/dap/landingpage?execution = e1s2) allowed us to discard these mutations as causal mutations, as all of them were already published on healthy sheep from other breeds. Because of the difficulties in performing Sanger DNA sequencing of the whole gene using the ITGB4 reference sequence available in Ensembl (ENSOARG00000009764), we isolated the ITGB4 sequence from three ovine BAC clones (CH243-344E15, CH243-306J7 and CH243-504K6)  [35]. The BAC clone sequencing allowed us to obtain the complete genomic sequence of ITGB4 (GenBank: KP025765). Pairwise alignment between the sheep ITGB4 reference obtained via BAC clone sequencing and the ITGB4 reference available in Ensembl (ENSOARG00000009764) revealed an identity of 83.
To determine the gene structure of the ITGB4 sequence, Augustus software was used  [37, 38]. In total, of 5360 reads that were overlapping the ITGB4 region in the control sample were used to generate a hints file for Augustus. A complete CDS of 5256 bp was found. This transcript is distributed into 38 exons. The sheep ITGB4 CDS sequence encodes a predicted protein of 1751 amino acids. The BLAST tool (http://blast. ncbi. nlm. nih. gov/Blast. cgi) was used to compare the predicted amino acid sequence from the ovine ITGB4 gene with several available sequences, including the cow (DAA18168. 1), camel (EQB77252. 1) human (aai43743. 1) and mouse (NP_598424. 2) sequences. The predicted ovine integrin β4 protein sequence showed 92% homology with the bovine protein, 92% homology with the camel sequence, 89% homology with the human, and 88% homology with the mouse sequence.
Following the assumption that JEB in the Churra sheep is caused by a homozygous recessive mutation, the variants were sifted to retain those present at a homozygous stage in the affected case (1/1) and absent in the control animal (0/0). Fourteen variants matched this filter: nine intron variants, three synonymous variants, one missense variant, and one frameshift variant and feature truncation. We discarded the mutations located in introns, which had average DP (coverage) values of 3. 2 in the case and 5. 6 in the control, and the synonymous variants. The missense variant detected in the RNA-seq variant analysis is a G>A substitution located at OAR11, which was 54,868,894 bp of the sheep Oar_v3. 1 assembly. Regarding the ITGB4 sequence (GenBank: KP025765) the mutation is positioned at 9,381 bp of the ITGB4 genomic sequence.
In the predicted CDS, the SNP is located at 1337 bp (c. 1337G>A), in exon ten of the ITGB4 gene (Fig  3 ). The mutation leads to an arginine-to-glutamine residue change at position 446 of the integrin β4 protein (G>A p. Arg446Gln). The genotype quality (GQ) value for c. 1337G>A was 99 for the control and 42 for the case. In general, the determined coverage (DP value) for the ITGB4 variants was higher in the control lamb than in the case; for this SNP the DP value was 75 and 14, respectively. This non-synonymous SNP has been observed at a homozygous stage in other sheep breeds (ISGC SNP database) and was predicted to be non-deleterious by SIFT  [40]  and PolyPhen-2  [41]. Therefore, the c. 1337G>A variant was discarded as a putative causal mutation of JEB. The frameshift variant detected in the RNA-seq variant analysis is a four-bp deletion located at OAR11:54,849,767-54,849,770 bp of sheep Oar_v3.