dataesr/scientific-paragraphs-categorization huggingface dataset:
To facilitate the assessment of convergence speed, we have added the number of iterations (out of a maximum of 3000 for all runs) needed before the minimumenergy structure was found. Length in nucleotides and the number of different secondary structure elements are also indicated to facilitate assessment of the structure's complexity (table  2 ). For better comparison we used ROSETTA software, with postfiltering by CRYSOL to improve on otherwise poor average decoy scores, which suggest that indeed producing good models is much harder than estimated by equation used to compute P-value  [10]. For each target, a 100 decoys were generated, and then one with the best fit to simulated SAXS data was chosen (and included in File S6). It is worth noting that the method presented in this paper does usually achieve a slightly better result using just a single annealing run.
This comparison proves that while ROSETTA computed for a longer time, and produced more decoys, it didn't propose better models, even after filtering by fit to the SAXS data. The comparison of the accuracies and expected RMSD values from random modeling (table  2 ) clearly indicate the size and complexity of molecules that may be successfully modeled without further restraints. Although the models for the two molecules with the simplest topologies (1L2X and 1DDY) could theoretically have models of similar quality generated by chance, provided secondary structure restraints, the three other models show significant predictions, as indicated by lg 10 P(RMSD)  [10] , in particular for the largest molecule -a guanine-responsive riboswitch with a PDB ID of 1U8D, Figure  6. The comparison of the number of secondary structure elements and the SAXS fit seem to indicate a limitation on the maximum complexity of the modeled structure, at least when conformational space is not further restrained.
Comparison of the HP4 Model to the NMR-derived Structure SAXS data were measured for stem loop 4 (HP4) of 7SK RNA. The sequence used for the SAXS analysis (shown in Figure  7 ) is similar to that used for the NMR analysis (deposited in PDB as 2KX8  [51] ). Both are parts of the Homo sapiens 7SK sequence. We used a construct encompassing nucleotides 302-332. For the NMR study, a longer 296-331 sequence was used, with an addition of 3 G-C base-pairs forcing the structure into a hairpin. We compared the parameters inferred from the data, our model, and the NMR structure after cutting the outermost helix which was added to increase stability of the structure in  [51]  (see File S1 for secondary structure of HP4). Model shows not only a good x~1:07 fit to data, but also a very consistent R g  (17.
HP3 Model Proposal We also used our method to propose two models for the stem loop 3 (HP3) of 7SK RNA (fig. 9 ), in accordance with two secondary structures of 7SK proposed by either  [12]  and  [13]. The latter model  [13] , was based upon evolutionary analysis of 7SK which suggested a more symmetric secondary structure model than the earlier study based upon chemical probing experiments  [12]. Both base-pairing schemes give similar stability. Both models of this longer element closely match SAXS data (first with x~0:75, and second with x~0:93, see model and data in File S4 ), and corresponding secondary structures (see model on Figure  9 , and fit on Figure  4 ). Both models strongly suggest an extended conformation, with only slight bends in absence of protein partners. The resolution of SAXS data does not permit us to see loop details.
Discussion We report a novel method for modeling RNA structures using pre-established secondary structure predictions, SHAPE-based determination for improved accuracy, and low-resolution tertiary structure reconstruction using SAXS data. Used together, these methods show great potential to overcome the difficulties currently seen in determining RNA structures using crystallography and NMR. The accuracy of the method is mostly limited by the discriminative power of available SAXS data and may be enhanced by gathering multiple data sets for components of a larger structure. The gray envelope is a shape reconstruction using SAXS data, the red is a model using SAXS data, and the green is an NMR-based structure deposited in PDB. Tails of the model and the native structure that do not share the common secondary structure, are marked with shades of grey.
Acknowledgments MJG and ACDB would like to thank  Dmitry I. Svergun  for discussions, and reading draft of the paper. MJG would like to thank  Eckart Bindewald  for providing the RNAJunction database in an SQL format [33], which was essential for the initial parts of the research and also gave us an excellent ''gold standard'' of a manually curated database for the most difficult parts of the structure. MJG thanks  Naomi Ruff  of  RuffDraft Communications  for help on improving readability of the manuscript.
The work of MJG was partially funded by the  SAXIER  ( RIDS-011934 ) grant from  EU. ACDB and DMZ were funded by  French Agency for Research  (grant TrscrREGsnRNP  ANR-06-BLAN-0072 );  CNRS, INSERM, University of Strasbourg  and the SPINE-2 European project (Contract  LSHG-CT-2006-031220 ). DMZ received a doctoral fellowship from  CONACYT ,  Mexico , and  Association pour la Recherche sur le Cancer (ARC). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
We took advantage of our study to address this question by comparing our data with data freely available on public databases, in order to extend our analysis to a variety of stimulation conditions and to other mouse and human DC subsets. We identified, for the first time to the best of our knowledge, a core set of NF-κB-or IFN-stimulated genes induced in DCs upon their maturation irrespective of the stimulus they received, irrespective of the subset they belong to, and conserved in evolution.
1A ). In particular, pDCs did not undergo a cell-fate conversion into cDCs at MCMV infection leads to a profound and convergent genetic reprogramming of all splenic DC subsets in vivo. Genome-wide expression analysis was performed on quiescent (q) versus activated (a) immune cells isolated from the spleen of untreated versus d1. 5 MCMVinfected C57BL/6 mice. (A) Unsupervised hierarchical clustering of splenocyte subsets from control or MCMV-infected animals was performed. Unsupervised hierarchical clustering with complete linkage was performed on the 28 707 ProbeSets selected as having at least one Log 2 expression value >5 in the dataset and a fold change >2 across all conditions. Boxes highlight clusters of q_DC subsets (gray) versus a_DC subsets (black). (B) Principal component analysis on all Probe-Sets present on the microarrays.
The legend is the same as for Figure  1C. The GEO datasets used are listed in Table  1. For each condition, data are shown from pooled replicates of at least two independent experiments. specific membrane markers, Siglech and Ccr9. Similarly, a CD11b + cDCs and a CD8α + cDCs also kept the expression of their respective specific gene signature defined under steady-state conditions  [4]. Thus, contrary to what had been proposed  [27] , the fate of DC subsets during viral infection in vivo cannot be strongly opposed: the changes induced upon maturation are as profound in cDCs as in pDCs, and at least as important as ontogeny in defining the identity of activated DC subsets. In other words, cDCs are as versatile as pDCs since both subsets undergo major and overlapping changes in their gene expression programs upon maturation.
Microarray experiments and analyses Microarray and real-time PCR experiments were performed, processed, and analyzed as described  [33]. Genes differentially expressed between quiescent and activated DC subsets (Fig. 2A  and B ) in a statistically significant manner (false discovery rate ≤0. 05) were identified using VAMPIRE (http://sasquatch. ucsd. edu/vampire/), a procedure based on a mathematical modeling of the data allowing robust statistical analysis of duplicates  [35]  and further selected based on a fold change ≥2 as previously published  [33]. Datasets have been deposited in the GEO database under reference numbers GSE21491, GSE45365, and GSE39556. The core gene signature modulated in >80% of the 18 activation conditions was generated as follows. We applied GSEA to the 18 activation conditions, using as GeneSets the lists of genes modulated in DC subsets during MCMV infection (Fig.
Acknowledgements: We thank  Pierre Grenot  and  Atika Zouine  for technical support on cell sorting,  Violaine Alunni  and  Christelle Thibault-Carpentier  from the  Plate-forme Biopuces  ( Strasbourg, France ) for performing the microarray experiments (www. microarrays. ustrasbg. fr),and  Dr. George S. Yap  ( Center for Immunity & Inflammation, New Jersey Medical School ) for his kind gift of STAg. The gene chips experiments using Ifnar1 -/-/WT mixed bone marrow chimeric mice were funded by  SANOFI. This work received funding from  CNRS ,  Inserm ,  Agence Nationale de la Recherche (ANR)  (pDCphysiology,  ANR-07-MIME-018-01 ;  EMICIF ,  ANR-08-MIEN-008-02  and  PhyloGenDC ,  ANR-09-BLAN-0073-02 ), and the  European Research Council  under the European Community's  Seventh Framework Programme  (FP7/2007-2013 Grant Agreement number  281225 ).
Online Self-Assessment In the course of ALS, patients need alternative ways of communicating, especially because of dysarthria and progressing physical impairment. An increasing number of patients rely on novel methods, such as the Internet, for communication; therefore we chose the Internet self-assessment method for completion of questionnaires. The Internet portal ALShome was created as a safe Web application for collecting patient-related data and has been described previously  [17]. Patients had controlled access to this website using an automatically generated username and password. Participants were asked to perform online self-assessments once a week over a period of 6 months. The study period was based on the ability of the CNAQ to predict weight loss after 6 months. We used monthly average CNAQ scores for data analysis.
Data Analysis Relevant data was recorded via the Web-based database and analyzed with PASW Statistics version 19. 0 for Windows. Regarding the CNAQ independent two-sample t tests with between subject factor group (patients with "mild to severe" symptoms vs patients "not affected") and within subject factor symptoms (swallowing impairment, dyspnea, orthopnea, and respiratory insufficiency) were performed at baseline. For further analysis a multiple linear regression was applied. For analyzing the BMI data and mean CNAQ scores (baseline vs follow-up) we used the dependent t test for paired samples. The significance level was tested using a two-tailed test at P=. 05. Mean values and SD are given.
This result may be explained by early satiety after eating small amounts due to ALS-related weakness of the patients' diaphragm  [26] , supported with evidence from patients with paralysis of the diaphragm who developed peri-or postprandial dyspnea and fatigue  [26]. More speculatively, the known change of inflammatory status related to respiratory failure may reduce appetite  [12, [27] [28] [29] [30]. The observed effect of respiratory disturbances is unlikely to be related solely to modifications of patients´ diet due to bulbar dysfunction, since dysphagia was not a risk factor for loss of appetite. In our study, 12 patients using NIV were enrolled. Severe loss of appetite occurred less frequently in the NIV group (42%, 5/12) as compared to patients without NIV (49%, 19/39). Although it is well-known that NIV may reduce energy expenditure and prevent negative effects of dyspnea on satiety, the data of our study did not reach statistical significance and was limited by small sample numbers.
Acknowledgments The authors thank the study nurses  Kerstin Krause ,  Dorit Strassenburg , and  Birgit Koch , the  institute for Biometry and Epidemiology  under the leadership of  Dr. Peter Martus , and the  ALS  patients for their participation and support. This study received funding from the  German Federal Ministry of Education and Research and the AirBerlin Funds for ALS Research.
Acknowledgements. The authors acknowledge useful discussion with  David J. Wilman ,  Massimo Dotti , and  Emanuele Ripamonti. The authors thank the anonymous referee for a thorough report, which greatly improved this manuscript, and  Astrid Peter  for his helpful and detailed language revision. The authors would like to acknowledge the work of the entire ALFALFA collaboration team in observing, flagging, and extracting the catalog of galaxies used in this work. G. G. acknowledges financial support from  Italian MIUR PRIN  contract  200854ECE5. Support for M. Fumagalli was provided by  NASA  through  Hubble Fellowship grant   HF-51305. 01-Aawarded by the  Space Telescope Science Institute , which is operated by the  Association of Universities for Research in Astronomy, Inc. , for  NASA , under contract  NAS 5-26555.
R. G. and M. P. H. are supported by  US NSF  grants  AST-0607007  and  AST-1107390  and by a  Brinson Foundation  grant. This research has made extensive use of the GOLDmine database (Gavazzi et al. 2003) and of the NASA/IPAC Extragalactic Database (NED), which is operated by the  Jet Propulsion Laboratory, California Institute of Technology , under contract with the  National Aeronautics and Space Administration. Funding for the Sloan Digital Sky Survey (SDSS)  and  SDSS-II  has been provided by the  Alfred P. Sloan Foundation , the Participating Institutions, the  National Science Foundation , the  U. S. Department of Energy , the  National Aeronautics and Space Administration , the  Japanese Monbukagakusho , and the  Max Planck Society , and the  Higher Education Funding Council for England.
Sequencing and assembly We sequenced a 43-year-old root-grafted copy of the P. abies clone Z4006, which originated from a tree in Ragunda, central Sweden, collected in 1959. Many copies of this clone are available in clone archives and seed orchards, and it has been extensively used in Swedish breeding programs. We estimated its genome size to be 19. 6 Gb (C 5 20. 02 6 0. 95 pg (mean 6 s. d. ); Supplementary Information 1. 1), in accordance with previous reports  10. De novo sequencing and assembly of large, repeat-containing, heterozygous genomes remains challenging. To assemble the P. abies genome, we developed a hierarchical strategy combining fosmid pools  11  with both haploid and diploid whole genome shotgun (WGS) data, and RNA sequencing (RNA-Seq) data  [12] [13] [14]  (Supplementary Information 1.
More than 86% of the elements remained as singletons, indicating that LTR-RTs are quite divergent and that there are several low-abundance families. We searched three LTR-RT families for signatures of unequal intra-element recombination events in scaffolds. 50 kb and 20 complete fosmids  33. For families ALISEI, 3K05 and 4D08_5 we identified 21, 22 and 39 complete elements, and four, five and no solo LTRs, respectively (Supplementary Information 3. 10). Although this data set is limited, the analysis suggested that LTR-RT-related sequences might be removed less frequently by unequal recombination than in other plant genomes. The ratio of solo-LTRs to complete elements in P. abies is ,1:9, whereas in A. thaliana, rice and barley it is 1:1 (ref. 33), 0. 6:1 (ref. 34) and 16:1 (for the abundant BARE 1 element  35  ), respectively.
Acknowledgements The authors would like to acknowledge support from the  Knut and Alice Wallenberg Foundation. Additional funding was provided in particular by the  Swedish Research Council (VR) , the  Swedish Governmental Agency for Innovation Systems (Vinnova) , the  Swedish Research Council for Environment, Agricultural Sciences and Spatial Planning (Formas) , the  Swedish foundation for Strategic Research (SSF) , the  Government of Canada through Genome Canada , by  Genome British Columbia , and by  Genome Quebec, Science for Life Laboratory  and the  National Genomics Infrastructure (NGI), Sweden. We also acknowledge  Skogforsk  for the P. abies genetic material,  UPPMAX  for computational infrastructure,  CLC Bio  for assembly software development and Lucigen for fosmid-pool method development.
Acknowledgments Authors would like to thank  RIIDFCM CYTED  ( 209RT0380 ) network for supporting collaborative research on development of bioactive metal-based compounds and  RIDIMEDCHAG CYTED , and also the financial support of  Fundação para a Ciência e Tecnologia (FCT, Portugal ),  PEst-OE/QUI/UI0100/2011  and  CIÊNCIA2007 , and the  Portuguese NMR and Mass Spectrometry Networks (IST-UTL Centers). The crystallographic work was supported by  CONICET  ( PIP 1529 ), and by  ANPCyT  ( PME06 2804  and  PICT06 2315 ) of Argentina. G. A. E. and O. E. P are research fellows of CONICET. M. A. C. acknowledges the support of the  Agencia Nacional de Investigación e Innovación (ANII ;  Innova Uruguay , Agreement No. DCI-ALA/2007/19. 040 between Uruguay and the  European Commission ). AFM studies were supported by  Ministerio de Economía y Competitividad (MINECO ,  CTQ2011-27929-C02-01 ) and performed by  Rosa Brissos.
Angel1, a member of the CCR4 family, acquired an eIF4E-binding motif in vertebrates To find new eIF4E partners, our first approach was to look for sequences in protein databases that contained an eIF4E-binding consensus motif, YxxxxLÈ. However, scanning databases with the webserver Prosite (http:// www. expasy. org/tools/scanprosite) did not produce any significant hits because the probability of finding this consensus motif randomly in databases is too high. To define a more accurate consensus sequence, we performed a structural analysis using the crystal structures of the complexes formed between eIF4E and peptides derived from well-known 4E-IPs: 4E-BP1 and eIF4GI [PDB ID: 1EJ4 and 1EJH,  (8) ]. We evaluated the change in the 3D structure after substituting each residue of the peptides from positions À3 to +6 (annotated from the conserved tyrosine of the consensus motif) using Turbo-Frodo software  (28).
The resulting set of sequences that were tolerated by the peptides and that could still interact with eIF4E constituted the refined eIF4E-binding motif matrix (Figure  1A ). Scanning the multi-species UniProtKb/Swiss-Prot database with Prosite revealed that the refined eIF4E-binding motif was present in 582 sequences (out of 5. 10 5 protein sequences in the database, data not shown). As expected, we found orthologs of already known eIF4E-interacting proteins, such as eIF4Gs or 4E-BPs. Because functionally relevant sequences are expected to be conserved throughout evolution, orthologs that share the refined eIF4E-binding motif therefore have a greater probability of effectively binding eIF4E. By pairwise alignment of the 582 sequences containing the consensus motif, we found 19 groups of at least three orthologs that shared a coverage rate of >75% of their lengths and a percentage of identity >50%.
Representations and Formulation of the Problem The recovery of shallow meaning, and semantic role labels in particular, has a long history in linguistics  (Fillmore 1968). Early attempts at systematically representing lexical semantics information in a precise way usable by computers, such as Levin's classification or WordNet, concentrated on defining semantic properties of words and classes of words in the lexicon  (Miller et al. 1990; Levin 1993). But only recently has it become feasible to tackle these problems by using machine learning techniques, because of the development of large annotated databases, such as VerbNet  (Kipper et al. 2008)  and FrameNet  (Baker, Fillmore, and Lowe 1998) , and corpora, such as PropBank  (Palmer, Gildea, and Kingsbury 2005). OntoNotes  (Pradhan et al. 2007 ) is a current largescale exercise in integrated annotation of several semantic layers.
The most notable specificity of NomBank is the use of support chains, marked as SU. Support chains are needed because nominal long distance dependencies are not captured under the Penn Treebank's system of empty categories. They are used for all those cases in which the nominal argument is outside the noun phrase. For example, in a support verb construction, such as Mary took dozens of walks, the arcs linking walks to of , of to dozens, and dozens to took are all marked as support. The data we use for English are the output of an automatic process of conversion of the original PTB, PropBank, and NomBank into dependency structures, performed by the algorithm described in  Johansson and Nugues (2007). These are the data provided to participants to the  CoNLL-2008 and CoNLL-2009  shared tasks (http://ifarm.
Planarization of Dependencies Without including the Swap action, the derivations described above could only specify planar syntactic or semantic dependency graphs. Planarity requires that the graph can be drawn in the semi-plane above the sentence without any two arcs crossing, and without changing the order of words. 3 xploratory data analysis indicates that many instances of non-planarity in the complete graph are due to crossings of the syntactic and semantic graphs. For instance, in the English training set, there are approximately 7. 5% non-planar arcs in the joint syntactic-semantic graphs, whereas summing the non-planarity within each graph gives us only roughly 3% non-planar arcs in the two separate graphs. Because our synchronized derivations use two different stacks for the syntactic and semantic dependencies, respectively, we only require each individual graph to be planar.
Monolingual and Multilingual Experiments To test the design of the syntax semantic interface and the use of a latent variable model, we train and evaluate our models on data provided for the CoNLL-2008 shared task on joint learning of syntactic and semantic dependencies for English. Furthermore, we test the cross-linguistic generality of these models on data from the CoNLL-2009 shared task for seven languages. 9 n our experiments, we use the measures of performance used in the CoNLL-2008 and CoNLL-2009 shared tasks, typical of dependency parsing and semantic role labeling. Syntactic performance is measured by the percentage of correct labeled attachments (LAS in the tables). Semantic performance is indicated by the F-measure on precision and recall on semantic arcs plus predicate sense labels (indicated as Semantic measures in the table).
Monolingual Experimental Set-up We start by describing the monolingual English experiments. We train and evaluate our English models on data provided for the CoNLL-2008 shared task on joint learning of syntactic and semantic dependencies. The data is derived by merging a dependency transformation of the Penn Treebank with PropBank and NomBank  (Surdeanu et al. 2008). An illustrative example of the kind of labeled structures that we need to parse is given in Figure  3. Training, development, and test data follow the usual partition as sections 02-21, 24, and 23 of the Penn Treebank, respectively. More details and references on the data, on the conversion of the Penn Treebank format to dependencies, and on the experimental set-up are given in  Surdeanu et al. (2008). We set the size of the latent variable vector to 80 units, and the word frequency cut-off to 20, resulting in a vocabulary of only 4,000 words.
The data and set-up correspond to the joint task of the closed challenge of the CoNLL-2009 shared task, as described in  Hajič et al. (2009). 14 The scoring measures are the same as those for the previous experiments. We made two modifications to reflect differences in the annotation of these data from the experiments reported in the previous section (based on CoNLL-2008 shared task data). The system was adapted to use two features not provided in the previous shared task: automatically predicted morphological features  15  and features specifying which words were annotated as predicates. 16 Both these features resulted in improved accuracy for all the languages. We also made use of one type of feature that had previously been found not to result in any improvement for English, but resulted in some overall improvement across the languages.