dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2012). In addition, RNA polymerase (RNAP) beta subunit homologs were detected in the Global Ocean Sampling (GOS) expedition metagenomic database, representing novel branches, apart from those encompassing Bacteria, Archaea, and Eukarya and large viruses (only poxviruses being analyzed) and were considered to be possibly derived from unknown viruses  (Wu et al. , 2011). Moreover, we recently extended our 2010 study using DNA-dependent RNA polymerase subunit 2 (RNAP2) from a comprehensive, representative, and unbiased set of species from cellular organisms selected by using TimeTree, and from the Megavirales representatives, and the results strongly supported a four branch topology  (Sharma et al. , 2014). The name 'TRUC' (an acronym for "Things Resisting Uncompleted Classification") corresponds to a new classification that has been recently created to accommodate the division of currently known microbes in four branches, i.
Pandoraviruses have been identified as highly divergent viral entities. Indeed, only 16% of the predicted genes from P. salinus have significant matches in the NCBI GenBank sequence database, and more than half of these genes belong to families of paralogs  (Philippe et al. , 2013). In addition, only 14 of the 31 class I-III core genes of Megavirales members and 17 of the 49 inferred Megavirales ancestral genes have been identified in pandoraviral genomes  (Philippe et al. , 2013; Yutin and Koonin, 2013). Moreover, the size and shape of pandoraviruses are unique among viruses, and no capsid protein-encoding gene has been identified  (Philippe et al. , 2013). In the present study, we aimed to demonstrate that the giant pandoraviruses are members of the fourth TRUC.
The presence of these genes was assessed in the genomes of pandoraviruses. Despite having tremendous gene contents, pandoraviruses lack many ancestral genes and have been described to share only 17 of the 49 conserved genes assigned to the putative common ancestor of the Megavirales members using maximum likelihood (ML) evolutionary reconstruction  (Yutin et al. , 2009 ) and 5 of the 12 genes involved in DNA processing. Therefore, the gene markers used in the present study included RNAP1, RNAP2, DNApol, RNR, tyrosyl-tRNA synthetase, and TFIIB. The viral orthologous sequences were collected using the OrthoMCL procedure  (Li et al. , 2003)  with gene repertoires from 317 DNA viral genomes encoding proteomes comprising more than 100 protein sequences directly downloaded from the NCBI GenBank sequence database 1.
Recently available sequences from P. inopinatum were collected from the NCBI GenBank protein  sequence database 2. Comparative analysis of the gene repertoires from the three Pandoravirus isolates was performed using OrthoMCL and through the strategy of reciprocal best BLASTp hits (using as thresholds an e-value <1e-3, an amino acid identity >30% and a sequence coverage >70%) to identify the sets of bona fide orthologous genes  (Jordan et al. , 2002; Li et al. , 2003).
Search for Homologous Sequences in Cellular Life Forms Protein BLAST searches were performed using Standalone Blast 2. 2. 27  (Altschul et al. , 1990)  and giant viral protein sequences as queries against the NCBI GenBank non-redundant protein sequence database (nr) to collect homologous sequences from the members of the three cellular domains of life (Bacteria, Archaea, and Eukarya). The number of target sequences was limited to 20,000 per query.
Sequence Selection Criteria for Cellular Domains Conserved genes might possess multiple homologs in sequence databases, and random selection among these genes for phylogeny reconstruction might result in the selection of strongly biased non-representative sequence sets. Hence, we selected homologous sequences from various species corresponding to representatives from different phyla of Bacteria, Archaea, and 2 http://www. ncbi. nlm. nih. gov/genbank/ Eukarya using TimeTree, as previously described  (Sharma et al. , 2014). TimeTree is a public knowledge-base of divergence times among organisms estimated from molecular data in published studies  (Hedges et al. , 2006). This resource facilitated here the selection of sequences from species that diverged approximately 500 million years ago, which allowed obtaining an appropriate set of members from the three cellular domains of life through a good equilibrium between comprehensiveness and representativeness.
Multiple Sequence Alignments and Phylogeny Reconstructions The obtained sequences were aligned using the Muscle program  (Edgar, 2004). Alignment quality was manually analyzed, and phylogenetic reconstructions were performed using ML inference, including the WAG model, and confidence values were calculated using the Shimodaira-Hasegawa (SH) test through the FastTree program  (Price et al. , 2010). Phylogenetic trees were constructed using the FigTree software program 3.
Phyletic Pattern Analysis with Clusters of Orthologous Groups of Protein Clusters of orthologous groups of proteins corresponding only to the selected functional COG categories [J, A, K, L, B, and F], encoding proteins involved in information storage and processing and nucleotide transport and metabolism, were used for the analysis. BLASTp searches for the selected COG categories, with e-values <1e-3, were performed against the members from Bacteria, Archaea, Eukarya, and Megavirales. Using the BLASTp output results, presence (1) and absence (0) matrices were constructed. A hierarchical clustering dendrogram was constructed using the Pearson distance method with R package 'amap' 4.
Despite the biological importance of non-coding RNA, their structural characterization remains challenging. Making use of the rapidly growing sequence databases, we analyze nucleotide coevolution across homologous sequences via Direct-Coupling Analysis to detect nucleotidenucleotide contacts. For a representative set of riboswitches, we show that the results of Direct-Coupling Analysis in combination with a generalized Nussinov algorithm systematically improve the results of RNA secondary structure prediction beyond traditional covariance approaches based on mutual information. Even more importantly, we show that the results of Direct-Coupling Analysis are enriched in tertiary structure contacts. By integrating these predictions into molecular modeling tools, systematically improved tertiary structure predictions can be obtained, as compared to using secondary structure information alone.
INTRODUCTION Experimental work and genomic sequence analysis have revealed that RNAs have a widespread role inside the cell  (1) (2) (3) (4) (5). In addition to the transmission of genetic information, non-coding RNAs catalyze biochemical reactions and have a crucial role in a multitude of regulatory processes. While some functional RNA act essentially via their single-stranded information or in the context of RNA-protein complexes, in other cases function is directly tied to three-dimensional (3D) RNA structure  (6). Gaining such structural knowledge is important for understanding function. Experimental determination of RNA structure, however, remains challenging. Less than 6% of all structures in the RCBS Protein Data Bank  (7)  contain RNA. Thanks to advances in sequencing technology, many RNAs have been sequenced in different organisms and classified into homologous families in the Rfam database  (8).
Inference pipeline When applied to families of homologous RNA, DCA is able to detect secondary and tertiary structure contacts, which in turn can be integrated into existing tools for RNA structure prediction. To do so, we have developed a dedicated pipeline, illustrated in Fig. 1. Starting with a single RNA sequence • homologs are identified using the Rfam database; • coevolutionary analysis by DCA is performed; • secondary structure is predicted by integrating DCA results into a generalized Nussinov algorithm; or • tertiary structure is predicted by integrating DCA results into molecular modeling by Rosetta. This pipeline is completely modular, and improvements in each module -better alignments, more precise signal extraction by DCA, integration of alternative (e. g. experimental) knowledge into molecular modeling etc.
Selection of RNA families To test the basic pipeline, we have identified a set of test families. Specifically we concentrated on riboswitches, which have non-trivial structures. To generate a representative set of RNA families for both genomic analysis and structure prediction in our study, we systematically selected riboswitch families from the Rfam database by the following criteria: All riboswitches were included, which • belong to a family with more than 1000 sequences in the Rfam database to provide sufficient statistics for detecting nucleotide coevolution; • have a maximum of 100 nucleotides in the PDB structure to enable the efficient application of state-of-the-art molecular-modeling tools like Rosetta; • have a corresponding complete PDB X-ray diffraction structure (each included residue at last represented by a few atoms) with less than 3 Å resolution to be able to evaluate our approach.
In case of multiple PDB structures fulfilling these criteria, the one of highest resolution is chosen. We find that exactly the 6 families described in Table  S1  fulfill the selection criteria; no family meeting the criteria was excluded from our analysis. Interestingly, these riboswitches are common targets of many vigorous studies, e. g. (38--41). For these families, we have extracted multiple-sequence alignments and consensus secondary structures from the Rfam database (42) (version 11. 0). To be applicable for structure prediction of specific RNA sequences, the consensus secondary structures were curated by removing contacts, which do not satisfy base pairing in the specific sequence, and possibly by extending helices by adding base pairs adjacent to the consensus secondary structure, cf.
Figure1. The pipeline for DCA-guided secondary and tertiary structure prediction. Starting from a target sequence, for which structure shall be predicted, we identify sequence homologs using the Rfam database. The corresponding MSA is analyzed by DCA. To predict secondary structure (left side), DCA results are processed by dynamic programming using a generalized Nussinov algorithm. To predict tertiary structure (right side), DCA results are used to predict tertiary structure contacts. The Rfam consensus secondary structure, after curation for the specific target nucleotide sequence, is fed into Rosetta, together with the DCA predictions. Rosetta predictions are clustered according to RMSD, and scored using standard Rosetta energy scores. Representative structures of the bestscoring clusters are returned as the final output of our structure prediction pipeline.
ACKNOWLEDGEMENT AND FUNDING The work of SC, RM and MW was partly supported by the  Agence Nationale de la Recherche  project  COEVSTAT  [ ANR-13-BS04-0012-01 ]. The work of BL, SR and AS was supported by the  Impuls-and Vernetzungsfond of the Helmholtz Association. BL acknowledges hospitality of the UPMC in the initial phase of this work. Funding for open access charge:  Karlsruhe Institute of Technology.
We present an extension to the Mathematica package SARAH which allows for Higgs mass calculations at the two-loop level in a wide range of supersymmetric (SUSY) models beyond the MSSM. These calculations are based on the effective potential approach and include all two-loop corrections which are independent of electroweak gauge couplings. For the numerical evaluation Fortran code for SPheno is generated by SARAH. This allows the prediction of the Higgs mass in more complicated SUSY models with the same precision that most state-of-the-art spectrum generators provide for the MSSM.
(2) We present here a fully automatised hybrid method implemented in the public tools SARAH and SPheno for the calculation of the scalar masses at the two-loop level in the DR renormalization scheme  [91] : while the one-loop corrections are calculated including the full momentum dependence, the two-loop corrections are derived in the effective potential approximation. In general, the setup is based on the following work distribution: the user implements their favourite model in SARAH. SARAH derives all analytical expressions for mass-matrices, vertices, renormalisation group equations as well as loop corrections and exports this information into Fortran source code. The Fortran source code is compiled together with SPheno and all numerical calculations are then performed by the new SPheno module.
Therefore, it is obvious that no gauge dependence has been introduced by including only Goldstone diagrams but no diagrams of the corresponding vector bosons. Of course, when including D-terms in the mass matrices the derivatives of the D-terms are still forced to vanish. However, we stress that the second method has to be used carefully as explained in sec. 2. 3. For the calculation of the two-loop effective potential we have translated the expressions of Ref. [90]  given in two-component notation into four-component language, see appendix A. All necessary generic expressions have been implemented in SARAH. SARAH uses these expressions to generate Fortran code for all two-loop diagrams which are possible in the considered model assuming the topologies SS, SSV , SSS, F F S, F F S, F F V and F F V.
We used m 0 = M 1/2 = 1 TeV, µ > 0, tan β = 10, A 0 = -2 TeV. The reference value is the one using the routines of Ref. [20, 21, 23, 24, 26]. As a next step, we compared the two-loop results of the SARAH routines for other models with existing references: these are the αs(α t + α b ) corrections in the NMSSM  [54]  and in the MSSM extended by Dirac gauginos, for which analytical results and an independent code will be presented in future work. Of course, we can use also the new routines to calculate the Higgs masses in these models including the other important corrections, but the presentation and discussion of these results is beyond the scope of this paper and will be given elsewhere  [99]. As example for the good agreement between our results and those of Ref. [54]  we show the light Higgs mass in Fig.
We also provide a "safe mode" for SPheno via flag 10: in this case SPheno starts with a large initial step size which is decreased automatically. It checks for what range of the initial step size the results are numerically stable by comparing the results obtained with different inital step sizes. If no stable range is found an error is returned. In the unlikely case that both methods suffer from numerical instabilities there is also the possibility to increase the numerical precision by passing from double to quadruple precision. For this purpose, the Makefile located in SPheno-3. 3. 0/src must be changed. The line PreDef = -DGENERATIONMIXING -DONLYDOUBLE should be replaced by PreDef = -DGENERATIONMIXING -DQUADRUPOLE Afterwards the entire SPheno code must be recompiled via make c l e a n a l l make Model=$MODEL This will slow down the numerical evaluation significantly.
Observe that this is not an artificial requirement. The significance of a host switch distance has already been pointed out in several studies  [21, 22]. Indeed, if parasites switched only between closely related hosts, this would lead to a higher degree of congruence between the parasite and host trees. When this information is available, it should thus be taken into account in the reconciliation process. Moreover, it can happen that for some datasets and cost vectors, there is no optimal time-feasible solution. One way to overcome this problem can be by varying the length of the farthest allowed switch until at least one time-feasible solution is obtained. On the contrary, in the case where the number of optimal time-feasible reconciliations is high, one can decrease their number by selecting a subset of them.
Datasets To test EUCALYPT, we selected 12 datasets from the literature. As we are mostly interested in host-parasite systems, the first 10 datasets concern such relations: EC -Encyrtidae & Coccidae  [23] , GL -Gopher & Lice  [24] , SC -Seabirds & Chewing Lice  [25] , RP -Rodents & Pinworms  [26] , SCF -Smut Fungi & Caryophillaceus plants  [27] , PLML -Pelican Lice ML  [28]  (the trees are generated through a maximum likelihood approach), PLMP -Pelican Lice MP  [28]  (the trees are generated through a maximum parsimony approach), RH -Rodents & Hantaviruses  [29] , PP -Primates & Pinworm  [30] , and FD -Fishs and Dactylogyrus  [31]. In addition, we used a dataset of our own which corresponds to arthropod hosts and a bacterium genus, Wolbachia, living inside the cells of their hosts  [32, 33].
The datasets were chosen to provide a variety in terms of size of the host and parasite trees: those from the literature are relatively small (from 7 to 100 leaves), while our own data provide an example of much bigger host and parasite trees, each having 387 leaves. Moreover, we were careful that the selected datasets cover, as much as possible, a range of situations in terms of coevolution and of the expected frequencies of each event. Finally, since EUCALYPT can be applied to any type of datasets compatible with the model, we also tested it on a genes-species dataset from  [17]  that had previously been used by  [20]. The dataset has 3983 unrooted gene trees. To be able to run our algorithm on this dataset, we rooted the trees using an approach similar to  [20] : for each possible rooted version of the unrooted gene tree, we consider the optimal cost of the reconciliation and choose the rooting that has minimum cost among all.
Comparison with CORE-PA and NOTUNG In Table  1 , we compare EUCALYPT to CORE-PA and NOTUNG for some cost vectors that commonly appear in the literature. As mentioned in the introduction, CORE-PA does not always enumerate all solutions, as shown in Table  1. In the Additional file 1, we give an explicit example from one of the datasets used ("Smut Fungi & Caryophillaceus plants"  [27]  with cost vector 0, 1, 1, 1 ) where indeed EUCALYPT finds more (correct) solutions, some of which are acyclic, that are time-feasible. The same result is observed for other datasets (examples not shown). Notice that sometimes when enumerating optimal reconciliations, CORE-PA outputs the same one more than once. In all our tests, we were never able to obtain more than 1000 different reconciliations with CORE-PA.
This explains the presence of the value 1000 twice in Table  1  and Table  2  (each time indicated by a * ) while EUCALYPT for the same datasets and cost vectors finds many more solutions. NOTUNG generates only time-feasible reconciliations and their number coincides with the result of EUCALYPT for all the datasets used. However, NOTUNG imposes some restrictions on the cost values. Indeed, the cost of a cospeciation is always assumed to be equal to 0 and the cost of a loss positive.
Results of EUCALYPT and discussion The results obtained by EUCALYPT and presented in Tables  1  and 2  are striking for various reasons. First, we observe that when the size of the tree increases, in most cases the total number of optimal solutions also increases. However, this does not hold for the number of time-feasible optimal solutions. For instance, according to the results given in Table  1 , for the dataset EC having 7 and 10 leaves, the number of time-feasible optimal solutions is much higher than for the case of dataset COG4964 (having 100 and 27 leaves). Even for the same dataset, this number can be reduced significantly depending on the cost vector. In particular when the cost of the losses is 0, the number of optimal solutions can be huge even for relatively small datasets, such as for example FD  (20-51 leaves).
This makes it practically impossible to check the time-feasibility of each of them (this explains the presence of a * in some cells of Tables  1  and 2 ). Thus, it seems that the cost vector and the topology of the trees together with the mapping of the leaves play a more important role in the total number of time-feasible solutions. We also tested EUCALYPT on the much bigger trees of Wolbachia and the arthropods. Due to limitations in space and time, we could not enumerate all optimal solutions because their number is huge: 1. 01 × 10 47 for cost vector -1, 1, 1, 1 , 3. 87 × 10 136 for cost vector 0, 1, 1, 0 , 3. Number of solutions found by each one of the programs CORE-PA, NOTUNG and EUCALYPT for each dataset and each cost vector c c , c d , c s , c l. For EUCALYPT and CORE-PA the columns represent: #T = total number of optimal solutions, #C = total number of cyclic solutions and #A = total number of acyclic solutions.
In all cases #A is always equal for both NOTUNG and EUCALYPT. For EUCALYPT the column #CA denotes the number of event classes in the set of acyclic solutions. CORE-PA limits to 1000 the total number of enumerated solutions and these cases are denoted by the symbol *. Bold numbers indicate the cases where the number of solutions produced by CORE-PA differs from the one found by EUCALYPT. Number of solutions found by the programs CORE-PA and EUCALYPT for each dataset and each cost vector c c , c d , c s , c l. The columns represent: #T = total number of optimal solutions, #C = total number of cyclic solutions and #A = total number of acyclic solutions. For EUCALYPT the column #CA denotes the number of event classes in the set of acyclic solutions. CORE-PA limits to 1000 the total number of enumerated solutions and these cases are denoted by the symbol *.
The results confirm once again that the number of optimal reconciliations can be huge. Moreover, the problem remains even if we restrict the results to only time-feasible solutions. In order to deal with this huge set of solutions, we propose to group them in classes depending on the number of events observed. As shown in Tables  1  and 2 , the number of classes in a set of time-feasible optimal solutions is significantly smaller compared to the size of the set itself. For some datasets (SFC, RH, COG3715 and, COG4964), the number of optimal time-feasible solutions is zero when reconciliations are obtained by using a given cost vector and unbounded k. After identifying k start (minimum k whose optimal cost o is equal to the optimal cost obtained for unbounded k), we decremented k until k A (maximum k which generates acyclic solutions) is found.
For each pair (dataset, cost vector), the following values are given: the decrement of the bound (from k start to k A ), the new optimum found (from o to o A ) and the new number of acyclic solutions (#A). For instance, for dataset EC and vector 0, 1, 1, 1 , the 10 optimal time-feasible solutions are split in 5 classes # c , # d , # s , # l as follows: 1 for 4, 1, 4, 1 , 3 for 4, 0, 5, 1 , 2 for 5, 0, 4, 2 , 2 for 3, 1, 5, 0 and, 2 for 3, 0, 6, 0. For dataset RP and vector 0, 1, 1, 1 , the 16 optimal timefeasible solutions are split in 3 classes: 4 for 7, 0, 5, 3 , 2 for 4, 0, 5, 1 and, 10 for 6, 0, 6, 2. Even more interestingly, for dataset SFC and vector 0, 1, 1, 1 , the 144 optimal time-feasible solutions belong to a unique class: 4, 0, 11, 0. For more details on this type of analysis, we refer to Additional file 1.
Finally, we want to call attention to the fact that, in many cases of datasets and cost vectors, there is no optimal solution that is time-feasible. Indeed, in the case of the datasets from  [17] , from 3983 trees we choose 429 with between 20 and 50 leaves for which the total number of optimal reconciliations is less than 10000. Among these 429 trees (rooted according to the one that leads to a minimum total cost of the reconciliation) and using the vector 0, 2, 3, 1 , 233 (i. e. more than half ) have no timefeasible solutions. To deal with this problem, we consider the restriction when the host switches are constrained to have bounded distance.