dataesr/scientific-paragraphs-categorization huggingface dataset:
Scale of the Putative Loss of the ENC3 Gene Our molecular phylogenetic analysis suggested the absence of the ENC3 ortholog in eutherians and possibly in lepidosaurs (fig. 1B ). Because of sparse sequence information in the lepidosaurian lineage (genome-wide information only exists for the green anole and the Burmese python  [Castoe et al. 2011] ), the absence of ENC3 in this taxon is highly speculative at this time point. The absence of ENC3 in eutherians was confirmed by exhaustive TBlastN searches in eutherian genome assemblies using nonmammalian ENC3 peptide sequences as queries. We aimed to determine whether this absence is best explained by a single-gene loss or a large-scale deletion involving substantial parts of the chromosome or even the whole chromosome. For this purpose, we examined whether gene orders are conserved between chicken chromosome 28 containing ENC3 and their orthologs in the human genome.
Discussion The ENC Gene Repertoire in Vertebrates Our survey in public databases (including databases derived from individual genome sequencing projects), as well as PCR screens, revealed the presence of three ENC subgroups (ENC1, -2, and -3) in jawed vertebrates, two ENC genes in the sea lamprey (ENC-A and -B), and one in a hagfish (ENC-A). An alignment of deduced amino acid sequences of ENC genes revealed a high level of conservation of some key residues (fig. 1A ). Therefore, we assume that the structure of ENC proteins is conserved among vertebrates. Our phylogenetic analysis clearly supported the individual clusters of three distinct gnathostome ENC subgroups, namely ENC1, -2, and -3 (fig. 1B ). These three subgroups show uniform rates of evolution indicated by comparable branch lengths.
Origin of the ENC Gene Family The ENC gene family is a member of the kelch repeat superfamily (supplementary fig. S1 , Supplementary Material online) and shares the conserved BTB/POZ domain and the kelch repeats with other members (fig. 1A ). Our database mining and molecular phylogenetic analysis did not identify any apparent ENC ortholog in invertebrates (fig. 2 ; supplementary table S4, Supplementary Material online). One possible explanation for the alleged absence of invertebrate ENC orthologs might be that they were secondarily lost in invertebrates. However, this assumption would require multiple independent gene losses in diverse invertebrate lineages. Alternatively, this absence can be explained by an elevated evolutionary rate of the ENC gene in the lineage leading to vertebrates erasing significant phylogenetic signals from their sequences (fig.
Acknowledgments This work was supported by the  Young Scholar Fund of the University of Konstanz  and the research grant ( KU2669/1-1 ) from the  Deutsche Forschungsgemeinschaft (DFG)  to S. K. , by the  University of Konstanz  to A. M. , and by the  International Max-Planck Research School (IMPRS) for Organismal Biology  to N. F. S. M. was supported by the  EVOLAX ANR  grant (number  ANR-09-BLAN-026201 ) and by  Region Bretagne (EVOVERT , grant number  049755 ). The authors thank  Ursula Topel ,  Adina J. Renz , and  Elke Hespeler  for technical support in cDNA cloning and in situ hybridization,  Dominique Leo  for supplying zebrafish embryos, and  Sven Tschall ,  Holger Kraus , and  Alexander Dressel  from the  Sea Life Centre Konstanz  for providing catshark embryos. A data set of predicted genes of the lamprey whole-genome sequence was produced by  Falk Hildebrand.
[11]  developed a numerical nesting system to simulate wintertime climate of the eastern South Pacific-South America-western South Atlantic region, using a limited area model (the Limited Area Hibu Model-LAHM) driven by the LMDZ GCM. Their study aimed at exploring the capability of the nesting system in reproducing mean climate conditions and, hence, it is the very first attempt in exploring the capability of a RCM in capturing the main climatic features of the observed climate. Due to computational constraints, they simulated 6 separated July months and evaluated the RCM climatology compared with the driving GCM, reanalysis, and gridded datasets for precipitation and temperature. Their model configuration was set using 100 km resolution for the RCM and close to 5 ∘ for the driving GCM. Though the authors found that the RCM generally improved the representation of the observed climate features compared with the driving GCM, this was not true for the 2-meter temperature.
Hotspots for the projected precipitation changes show for the summer season a consistent precipitation decrease over the southern Andes and the Amazon region and wetter conditions for central Argentina and the LPB region. For the winter season, the common features are wetter conditions over the tropical and southern Andes and drier conditions over subtropical Andes, eastern SA, and the Amazon region. Though the ensemble of opportunities may provide common results, the regional climate model simulations are sensitive to model configuration and experimental design  [23, 43, 44]. Consequently, results from uncoordinated modeling efforts may not provide robust results. It is important to bear in mind that all RCMs included in the ensemble of opportunities were driven by the same AGCM, except the RCA model  [42] , and it is a well-known fact that the climatic response to a given radiative forcing is model dependent, as shown in several intercomparison exercises using the CMIP3 database  [45].
The preliminary evaluation of the RCM ensemble can be found in Solman et al. [60]. This study showed that all RCMs are capaable of capturing the main features of the seasonal mean precipitation and temperature over SA. However, some systematic biases were identified, such as an overestimation of temperature and underestimation of precipitation over the La Plata Basin (LPB) region. Individual model's biases over tropical areas of SA were diverse, depending on the model. The Andean region was characterized by a strong underestimation of temperature and overestimation of precipitation mainly over the Andean slopes, a common shortcoming of every modeling effort for the region. However, it is important to bear in mind that also the quality of observational datasets over areas with complex topography is critical for evaluating model performance, as pointed out by several authors  [41, 61].
The lack of high-quality and high-resolution datasets is one of the major shortcomings for evaluating model performance over the South American continent. Though some areas over the eastern part of the continent are well covered by high-quality observations, the tropical area and the Andes are regions characterized by a very sparse data availability. Consequently, it is not possible to provide a robust assessment of model performance, at least over these areas. The availability of this set of coordinated RCM simulations allowed also exploring the degree of agreement or disagreement among individual RCMs and thus characterizing the uncertainty in simulating regional climate. Overall, it was found that the uncertainty in simulating the main characteristics of climate over the subtropical regions was generally lower compared with tropical regions of the South American continent.
Though several systematic biases were identified, the reliability of the simulations is generally good particularly for the LPB region. Moreover, another interesting result raised from this study was that the ensemble of RCMs systematically improves the quality of the simulated climate compared with any individual model, in agreement with similar exercises for other regions of the world. An assessment of the ERA-Interim driven simulations is summarized in Figure  4 , which shows the interannual variability of rainfall as depicted by each individual RCM participating in the CLARIS-LPB/CORDEX evaluation framework for several subregions of South America: the LPB region, the Amazon region, and the Andes region (indicated in Figure  3 ). Note that the temporal evolution of seasonal precipitation anomalies for the LPB region from the Climate Research Unit data set is well reproduced by every individual RCM.
Summary and Conclusions In this paper a review of the most outstanding efforts on regional climate modeling over the South American continent has been presented. The regional-scale climate modeling over the region has developed during the last 10 to 12 years, and an enormous progress has been made up to date. An example of this progress can be seen in Figure  6  which shows the number of research articles published on peerreview journals per year. This count has been made taking into account publications based on regional climate modeling studies over SA from the Scopus database. Early efforts, initiated during the beginning of the 2000s were conducted by isolated initiatives and a reduced number of modeling groups. These early efforts were mostly focused on evaluating the capability of a particular RCM on reproducing some basic aspects of the seasonal climate.
From 2002 to 2007, several groups started performing simulations with the different RCMs and focused on evaluating model performance and sensitivity of the RCMs on technical issues, such as model configuration and process studies. Though the modeling efforts were increased, there was still no coordination among the modeling groups. Scince 2008 most of the efforts have been focused on producing regional climate change scenarios. In these studies, the regional climate simulations were extended from 10 to 30 years, and the modeled climatologies were compared with observational datasets, focusing on different features of South American climate. The preliminary studies were based on results from a single RCM. The CLARIS and CLARIS-LPB projects represented an enormous progress in this regard, due to the coordinated framework designed to provide regional climate change scenarios over the South American continent.
These tracks will certainly allow improving the quality of the simulations and fostering the use of RCM outputs for impact and adaptation studies. Another pending issue is related to the availability of reliable datasets of high-quality observations covering the whole South American continent. Though several gridded databases have been compiled at high-resolution, namely, 0,5 ∘ lat-lon, these are available mostly for precipitation on both daily and monthly bases. To date, the unique highresolution daily dataset for temperature is only available for the LPB region  [62]. The need for higher-resolution-gridded datasets of daily precipitation and temperature covering the whole continent remains a challenge for assessing model performance. The availability of high-quality observational datasets is of major relevance particularly for evaluating the models over regions of complex topography, such as the Andes.
Acknowledgments This paper has been made under the support of the  European Community 's  Seventh Framework Programme  ( FP7/2007-2013 ) under Grant Agreement N ∘  212492  ( CLARIS LPB A Europe-South America Network for climate change assessment and impact studies in La Plata Basin ). This work has also been supported by  UBACyT  Grant  X160 ,  UBACyT  Grant  Y028 , and  CONICET  Grant  PIP 112-200801-00195. The author wishes to acknowledge the anonymous reviewers whose comments and suggestions led to improve the original manuscript.
Indeed, out of 36 targets of miR30a identified in the first screening, 26 (72%) were also identified after silencing miR30 family in the second screening. Moreover, this last screening included 8 experimentally validated targets of miR-30  [5] , such as P4HA2 and CBFB, that had not been revealed by the previous whole genome expression analysis. These results suggest that miR-30 family members display target redundancy, and the downregulation of all of its members is necessary for growth under non-attachment conditions. To define functional enrichment of miR30 targets, we used the Database for Annotation, Visualization and Integrated Discovery (DAVID ) v6. 7. First, we used the final list of 118 putative targets of miR30 family of miRNAs. However, no significant enrichment was found for any functional category or pathway when using the whole human transcriptome as a reference.
Statistical analyses BRBArrayTools v3. 8. 1 was used for bead array analysis, as described above. For other comparisons, means and differences of the means with 95% confidence intervals were obtained using GraphPad Prism (GraphPad Software Inc. ). Two-tailed student t test was used for unpaired analysis comparing average expression between classes. Pearson's correlation was used to study the association between mammosphere formation and metastatic behavior. P values < 0. 05 were considered statistically significant. Raw miRNA and transcriptome data were background subtracted, quantile normalized, and further analyzed by BRB-Array Tools Version 3. 8. 1 (developed by Dr. Richard Simon and the BRB-ArrayTools Development Team). For quantile normalization we used the median array as the reference array.
The normalization is performed by computing a gene-by-gene difference between each array and the reference array, and subtracting the median difference from the log-intensities on that array, so that the gene-by -gene difference between the normalized array and the reference array is 0. Gene ontology analyses were performed with the Database for Annotation, Visualization and Integrated Discovery (DAVID) v6. 7 using the whole human genome as reference. In addition, geneset enrichment was done in BRB-Array Tools for Gene Ontology categories, and biological pathways (BioCarta and KEGG). For class comparison analyses p value < 0. 001 and false discovery rate (FDR) < 0. 15 were used as cut-offs. Genes significantly distinguishing the classes were further analyzed with the miRecords resource (http://mirecords.
Lyssaviruses (family Rhabdoviridae) have negative-sense, single-stranded RNA genomes approximately 12 kb long. Traditionally, partial gene sequence (usually the N-gene) was utilized for viral speciation and phylogenetic analysis. However, limitations on the information obtained from these partial sequences and a requirement to use full genome sequence as part of the ICTV (International Committee for Taxonomy of Viruses) criteria to propose new lyssavirus species, in combination with recent advances in high-throughput sequencing, have resulted in an increase of full genome sequences available on Genbank. From the submission of the first full length lyssavirus genome sequence of RABV (prototype) in 1988  [20]  to the completion of representative genomes from each of the established twelve lyssavirus species (RABV, LBV, MOKV, DUVV, EBLV-1, EBLV-2 and ABLV, ARAV, KHUV, IRKV, WCBV and SHIBV) in 2010, the commonly used methodology for obtaining the genomes was relatively unchanged, utilizing reverse transcription then either cloning the cDNA directly, or cloning amplified PCR products, and Sanger sequencing  [2, 5].
Viral heterogeneity analysis Due to the processes involved, each read is obtained from a single cDNA strand, and therefore where multiple reads cover a region viral heterogeneity can be detected within the reads indicating the presence of a heterologous viral population. Although this methodology is not optimized for investigation of low level viral populations, which would require viral read depths of over 10,000, it is still possible to observe dominant or high level single nucleotide polymorphisms (SNPs). Even with this dataset, we were able to observe minimum SNPs (equating to one read with a SNP) at 1% of the population in cultured material, and 3% in brain tissue samples (Table  3 ). In general, cell cultured samples had more SNPs than tissue samples, where some tissue samples (RV2417, RV2627, RV2516) had no detectable SNPs (Table  3 ).
Of the 28 nucleotide substitutions observed, 22 (79%) were conservative (pyrimidine to pyrimidine or purine to purine). Substitutions were identified throughout the genome, although only one was identified outside of a coding region (RV20 at position 3143 in the M-G untranslated region). At the amino acid level, 8 (29%) were synonymous substitutions resulting in no amino acid change (Table  3 ). Two stop codon substitutions were identified in this dataset, both in the RV20 tissue culture supernatant samples, and both at a low level (2%) equating to a single read (Table  3 ). Furthermore, one of these (Ngene position 276) had two reads which differed from the consensus, one encoding a stop codon, resulting in a truncated Nucleoprotein transcript, the other a synonymous change (Tyr/Tyr 92 ) (Figure  2a ).
RV61, an Arctic-like RABV, had one SNP detected in the 454 read data at genome position 3503 (Figure  2b ). This SNP is located in the glycoprotein gene resulting in an amino acid substitution at position 70 Ile/Thr at   2b ). RV50, a US isolate (isolated from Eptesicus fuscus), was genome sequenced using Sanger sequencing as part of a larger dataset  [6] , providing a unique opportunity to analyze the same virus isolate, propagated and sequenced independently. Alignments of the published RV50 genome (JQ685956) and the consensus sequence obtained in this study showed 100% identity. However, the 454 data identified a SNP at genome position 11,349 (L-gene), resulting in a nonsynonymous coding change at residue 1982 resulting in conservative substitution Asn/Asp 1982 (Figure  2c ). The frequency of this variant in the read data was 27% (Table  3 ).
Obtaining genomic termini The genomic termini sequences from 454 data were obtained with varying success. The 5′ UTR was usually represented by reads more often than the 3′ UTR, the reasons for which are unclear (Table  2  and Figure  1 ). Often de novo assembly failed to incorporate reads which contained the genomic termini. These reads were only incorporated after splicing the missing sequence, from a published similar genomic sequence, to the consensus sequence deduced from the de novo assembly and subsequently mapping reads against the spliced reference using GS mapper. Subsequent to this dataset, in an attempt to increase the population of genomic end viral reads, the 454 sample preparation methodology was modified by the addition of 1pmol of N165-146 and LRACEF2 at the hexamer cDNA synthesis stage, to enhance the cDNA population containing the genomic termini.
This modification was trialed on a genome sample highly related to RV2772, where the genomic ends were obtained (data not shown). This simple modification will be used for future genome sequences. For samples where the genomic termini were still absent, RT-PCR was performed on the depleted RNA, using primers designed against the highly conserved genomic termini to obtain PCR products which were sequenced directly using Sanger sequencing. Although this approach resulted in a genome sequence still lacking the sequencing relating to the primer sites, for known lyssavirus species isolates this is acceptable. However, for novel lyssavirus species prototypes the ends must be deduced either by RACE  [1]  or circularization of the RNA, followed by RT-PCR  [2]. To this end IKOV genomic ends were obtained using circularization of the genomic RNA  [22].
Confirming correct sequence and differentiating between indels/homopolymeric repeats Whenever possible, consensus sequences where aligned together with genome sequences from the same lyssavirus species. Any potential insertions or deletions (indels) were investigated by analysis of the flowgrams in the GS program suite. A limitation of pyrosequencing processing of raw data is runs of homopolymers such as the GAAAAAAA poly A termination signal found at the end of each lyssavirus gene sequence (Figure  3a  and b ). Figure  3  illustrates the discrepancies between individual reads from the same sample in a homopolymeric run, where the signal strength for each base is plotted for each read. Detecting the correct signal in a homopolymer of four or more bases can be problematic, resulting in incorrect base calling.
In general, the more reads covering the genome the more accurate the resulting consensus sequence (represented by contig00001 in Figure  3a ). Where discrepancies cannot be resolved, or a new virus species is being sequenced, confirmation of the correct sequence using PCR products spanning the region in question sequenced using Sanger technologies may be required. In this dataset, only the proposed new lyssavirus species IKOV (RV2508) required additional PCR confirmation. Each intergenic region length and sequence was confirmed by RT-PCR, with 100% concurrence between the 454 consensus sequence and the Sanger sequence derived from PCR products (data not shown).
Furthermore, extraction from the cell monolayer (often discarded when clarifying harvested supernatant) is an excellent source of viral RNA. The only disadvantage is the potential presence of non-functional virus variants presumably present in the cells as partially synthesized viral derivatives. In this study however, it was virus in the supernatant which appeared to contain defective virion particles, albeit at a low level. The platform used in this study (Roche 454) has been shown to be reliable and consistent, however, the RNA can be prepared for any NGS platform. Indeed we have obtained full genome sequences from a small number of RNA samples prepared as described here from brain material on the Illumina platform successfully (data not shown). The application of NGS sequencing of viruses has begun a new era of virus discovery and characterization of novel viruses and will revolutionize phylogeographic studies in all fields of virology in the coming years.
Sequence data analysis Initial assembly of the 454 reads was achieved with Newbler (V2. 6) using the GS de novo assembly software (Roche). All contigs were exported from the software and were aligned with a reference genome using Seqman (DNAStar) whenever possible. The resulting consensus sequence was subsequently used in GS Reference Mapper (Roche) to obtain further sequence reads from the original raw data. If a suitable reference sequence was available, this was used in the GS Reference Mapper program with the 454 reads to obtain a consensus sequence without the initial use of the GS de novo assembly software. Where no suitable genome reference sequence was available (RV2508) a BLAST search with the contigs provided evidence for which contigs were viral sequence, using the megablast algorithm on the nucleotide database (http://www.
ncbi. nlm. nih. gov/blast/Blast. cgi?CMD=Web&PAGE_TYPE=BlastHome). Due to the increased difficulty of mapping reads at the ends of contigs, it was occasionally necessary to splice the 3′ and/or 5′ UTR sequences from a reference genome sequence onto the termini of the contig sequence of interest. Using the spliced consensus sequence in the GS Reference Mapper program resulted in extra reads being mapped to the contig resulting in a complete, or almost complete genome sequence.
Acknowledgements The authors would like to thank  Saira Crawthraw  and  Colin Black  for technical assistance,  Don King ,  Dirk Höper ,  Thomas Mϋller ,  Sylvia Grieson ,  Bhudipa Choudhury ,  Chad Fuller  and  Brandon Londt  for fruitful discussions. We are grateful to our collaborators in the  US, Estonia, Morocco, Tanzania, Iraq and Denmark , for providing the lyssavirus positive samples. This work was funded by the  Department for Environment, Food and Rural Affairs (Defra), UK  (grant ROAME  SE0427 ) and by the  EU   FP7-funded Research Infrastructure Grant European Virus Archive  (no. 19 228292 ).