dataesr/scientific-paragraphs-categorization huggingface dataset:
High genetic diversity exists between different strains of IAPV The complete genomes of IAPV strains collected in the US states of Maryland, California, and Pennsylvania were obtained by direct sequencing of overlapping RT-PCR fragments and partial sequences from both 59UTR and 39UTR and deposited in GenBank with accession numbers, EU224279, EU218534, and EU224280, respectively. Comparison of US, Chinese and Australian IAPV strains with the first reported Israeli IAPV strain at the genome level showed a significant genetic divergence among different strains, providing evidence of quasi-species dynamics in IAPV populations. The polymorphisms in IAPV were found more frequently in 59 UTR and functional protein coding regions compared to the capsid protein coding region and 39 UTR (Figure  4A ).
While strong colonies did not exhibit significant seasonal variation in IAPV infection, the infection rate of IAPV in adult bees in weak colonies increased from Spring to Summer and Fall and peaked in the Winter. All strong colonies survived through the cold winter months while the weak colonies collapsed before February. doi:10. 1371/journal. ppat. 1004261. g003 was significantly higher than error variance for both adult bees and brood (both p,0. 01) (Figure  S3C ). This confirmed that variation among samples was largely due to IAPV and suggested the good data quality for two ANOVA data analysis in both adults and brood. The distribution of differentially expressed genes in both adults and brood are presented by volcano plots (Figure  S3D ). All microarray data were deposited in the NCBI public database with accession number GSE46278.
melanogaster genes. The GO-enriched analysis of the genes that displayed fold-changes of more than 1. 5 (False Discovery Rate adjusted r value#0. 05) and had putative D. melanogaster orthologs by the Database for Annotation, Visualization and Integrated Discovery (DAVID) revealed major functional clusters including metabolism, host cell transcription, signal transduction, cell cycle, hormone synthesis, endocytosis, phagocytosis, autophagy, and innate immune response (Table  S1  and S2 ). The majority of genes with up-regulated expression were related to the regulation of signaling transduction and immune response, while the majority of those with down-regulated expression were involved with metabolic energy generation. Of the top functional clusters, genes that were related to immune response functions were of particular interest in this study.
The virus titer in bees fed siRNA-59IRES was 3. 360. 54, 4. 560. 33, 3. 960. 21 and 5. 260. 67 (Mean6SE, N = 3) fold higher than the group fed with siRNAsuppressor at Day in all additional sequences per a reference sequence segment. The length of the segment is equal to the length of the reference sequence divided by the width of its graphical representation (in pixels). The deletions, insertions and differences among the sequences are highlighted in blue, green and red-violet, respectively. If no significant alignment could be obtained for a particular sequence, no horizontal bar is shown. (B) Phylogenetic tree showing the relationship of IAPV strains from different geographic locations globally. Numbers at each node represent bootstrap values as percentages of 500. Individual sequences are labeled with their GenBank accession numbers.
The average infection rate each month for both strong and weak colonies was calculated by combining the date from five colonies each month and four years of the same month (N = 564). The infection rates of IAPV were compared for colony strength (healthy vs. weak), developmental stages (adult vs. brood) and months of the year. Because the data are binomial in nature (for each sample, the number of uninfected of 40 total), analysis was based on a generalized linear mixed model (because random effects were included), using the logit link and the R software (R Core Team 2012) with the lme4 package  [30]. The combination of lowest AIC and main effects retention (i. e. preserve main effects in the model even if not significant as long as higher order terms involving these main effects were significant) was used to select a model that captured the important features of the data.
The entire genome sequences of IAPV isolates from this study, as well as IAPV strains identified in Australia, China, and Israeli and previously deposited in GenBank were compared with the first reported strain of IAPV (GenBank Accession# NC_009025) in order to get a clear global picture of genetic diversity of IAPV strains. A phylogenetic tree was generated using all available complete genome sequences of IAPV. The sequence of KBV (GenBank Accession# NC_004807) was used as an outgroup to root the tree. Phylogenetic analysis was conducted in MEGA4  [36]. A tree was built using the Neighbor-Joining method and the reliability of the phylogenies was assessed by bootstrap replication (N = 1000 replicates). Node labels correspond to bootstrap support and those values. 50% were regarded as providing evidence of phylogenetic grouping.
The Cy5-or Cy3-labeled cRNA were mixed in the same amount and hybridized to honey bee oligonucleotide microarray slides fabricated at the University of Illinois. Slides were hybridized, washed, dried, and scanned using methods previously described  [37]. The signal intensities were normalized based on the mean signal intensity across all genes on the arrays. The signal-to-noise ratio (SNR = ,signal mean -background mean,/,background standard deviation. ] was then calculated for each spot to discriminate true signals from noise. Only spots with an SNR equal to or greater than 2. 0 were considered positive. All negative, poor and empty spots were flagged and discarded. The normalized data were analyzed using Partek Genomics Suite Version 6. 4 (Partek Inc. , St. Louis, MO). Principal component analysis (PCA) and hierarchical clustering analysis were conducted with Partek with default settings.
The Fisher's exact test was used to calculate a rvalue to determine the probability that the association between the gene in the dataset and the predefined pathways and functional categories in the Ingenuity Pathway Knowledge Base is due to random chance alone. A list of 20 genes involved in host immune responses were validated by SYBR Green real-time qRT-PCR in IAPV infected adult bees. The primers used in qRT-PCR are included in Table  2. The DDCt method was chosen for interpretation of gene expression in response to IAPV infection following the same procedures described above. The approximately equal amplification efficiencies of the RT-qPCR assay for housekeeping gene b-actin and target immune genes were confirmed individually (the slope of normalized Ct vs. log input RNA#0. 1). The data output of each gene was expressed as a foldchange indicating whether the expression of the target gene in IAPV infected bees was up-regulated or down-regulated compared to the expression of the same gene in uninfected bees.
Effect of silencing putative viral suppressor of RNAi Complete predicted protein sequences of IAPV (NC_009025. 1), along with other honey bee viruses, including KBV (NC_004807. 1), ABPV (NC_002548. 1), CrPV (NC_003924), and DCV (NC_001834) were retrieved from GenBank and scanned for the DvExNPGP sequence motif where the upstream sequences of the DvExNPGP motif was reported to encode a RNAi suppressor  [13]. A DvExNPGP sequence motif was identified in IAPV and the upstream sequences of the DvExNPGP motif at the 59 terminus of the IAPV genome was therefore assumed to be a putative IAPV-encoded suppressor (Figure  S1 ). siRNA corresponding to upstream sequences of DvExNPGP was designed using online siRNA design tool siDirect version 2. 0 (http://sidirect2. rnai. jp/). The sequences of the siRNAs used in this study are as follows: 59-UACAACUUAUUCAAGAAUCCA-39 and 59-GAUUCUUGAAUAAGUUGUACC-39.
5% polyethylene glycol in TBST. The grids were then washed in TBS and counterstained for electron microscopy with saturated aqueous uranyl acetate followed by lead citrate. Ultrastructural analyses were performed in a Jeol-1010 electron microscope. Randomly selected areas were then photographed from the selected ultrathin sections at a final magnification of 50,000×. Then, the spatial distance between immunoparticles for A 2A R and D 2 R was measured using appropriate software (ImageJ; NIH). To this end, we measured the nearest neighbour distances (NNDs) between the 10-nm gold particles (A 2A R) and the 15-nm gold particles (D 2 R). Distances between the two particles were then compared between normal striatum and 6-OHDA-lesioned striatum using cumulative frequency plot and statistical analysis.
Funding This work was supported by grants  SAF2011-24779 , Consolider-Ingenio  CSD2008-00005  and  PCIN-2013-019-C03-03  from  Ministerio de Economía y Competitividad  and ICREA Academia-2010 from the  Catalan Institution for Research and Advanced Studies  to  F. C. Also , V. F. -D. , J. J. T. , M. G. -S. , M. L. -C. and F. C. belong to the " Neuropharmacology and Pain" accredited research group  ( Generalitat de Catalunya ,  2014 SGR 1251 ). Also, this work was supported by research grants from the  Centre National de la Recherche Scientifique ,  Institut National de la Santé et de la Recherche Médicale , and by the  Plateforme de Pharmacologie-Criblage of Montpellier  and the  Region Languedoc-Roussillon. V. F. -D. was awarded with a short-term  EMBO  fellowship to visit the  Institut de Genomique Fonctionnelle , in Montpellier.
Identification of Homologs of DNA Replication Components Homologs of each archaeal DNA replication component were retrieved from the reference sequence database at the National Center for Biotechnology Information (NCBI) using the BlastP  (Altschul et al. 1997)  program with different seeds from each archaeal order. The top 100 best hits for each order were then used to create hidden Markov model (HMM) profiles  (Johnson et al. 2010 ; http://www. hmmer. org, last accessed January 16, 2014) that allowed an iterative search of a local database of 142 archaeal genomes including 98 plasmid sequences and a local database of 56 complete archaeal virus genomes downloaded from the Viral Genomes database of NCBI (as of June 20, 2013) (supplementary table  S3 , Supplementary Material online). The absence of a given homolog in a specific genome was verified by performing additional tBlastN  (Altschul et al.
Phylogenetic Analysis Multiple alignments were performed with MUSCLE v3. 8. 31  (Edgar 2004 ) and manually inspected using the ED program from the MUST package  (Philippe 1993)  to verify that all sequences retrieved at the first step were homologous. Final single protein data sets were trimmed using the software BMGE  (Criscuolo and Gribaldo 2010)  with default parameters and subjected to phylogenetic analyses by Maximum Likelihood and Bayesian methods. Maximum likelihood analyses were performed with Treefinder  (Jobb et al. 2004; version of 2008). For each protein data set, the best-fit parameters and model of amino acid substitution were chosen using the Akaike information criterion with a correction (AICc) for finite sample sizes  (Hurvich and Tsai 1989)  as implemented in Treefinder  (Jobb et al.
PhyloBayes 3. 3b  (Lartillot et al. 2009 ) was used to perform Bayesian analysis using the CAT + GTR model and a gamma distribution with four categories of evolutionary rates was used to model the heterogeneity of site evolutionary rates. The concatenated datasets were also recoded using Dayhoff 6 and Dayhoff4 recoding schemes as implemented in PhyloBayes 3. 3b  (Lartillot et al. 2009 ) and analyzed with the same model parameters. For each data set, two independent chains were run until convergence (max diff < 0. 01). The first 25% of trees were discarded as burn in and the posterior consensus was computed by selecting one tree out of every two to compute the 50% majority consensus tree. Maximum likelihood analysis was carried out by PhyML  (Guindon et al. 2010 ), the LG model and a gamma correction with four categories of evolutionary rates.
1998). Methanosarcina acetivorans encodes a homolog of eukaryotic RPA70 called MacRPA1, along with two divergent homologs, MacRPA2 and MacRPA3, each able to self-assemble into a homomultimeric complex  (Robbins et al. 2004; Skowyra and MacNeill 2012). In addition, many archaeal genomes encode proteins that are not homologous to RPA but are found close by and therefore were called RPA-associated proteins  (Berthon et al. 2008 ) (hereafter referred to as RAP). In H. volcanii these RPA-associated proteins are thought to be cotranscribed with the adjacent RPA2 and RPA3 genes  (Skowyra and MacNeill 2012)  and have been shown to interact with them  (Stroud et al. 2012). We found that homologs related to Methanosarcina RPA1 are largely distributed in archaeal genomes (in yellow in fig. 5 , see also supplementary table  S2  [Supplementary Material online] for full accession numbers) and their phylogeny, although not completely resolved, is consistent with the archaeal tree (not shown).
Therefore, these likely represent the core RPA component and are likely essential. In fact, among the three RPA copies present in H. volcanii, the copy that we assigned to the core is the only one that is essential  (Skowyra and MacNeill 2012). A number of late emerging euryarchaeal lineages also display one or two additional and divergent RPA homologs that we classified as RPA2 and RPA3 according to their sequence similarity to Methanosarcina acetivorans MacRPA2 and MacRPA3 (in green in fig. 5 , see also supplementary table  S2  [Supplementary Material online] for full accession numbers). Their specific distribution in late emerging euryarchaeal lineages and phylogenetic analysis (not shown) indicates that RPA2 and RPA3 are paralogs that arose via gene duplication in Euryarchaeota, after the divergence of Thermococcales, Methanococcales, and Methanobacteriales.
In this case, one cluster corresponds to Halobacteriales and Methanogens class II, and the other to Thermoplasma/DHEV2/Archaeoglobales/ ARMAN-2. This would indicate that two independent   S1  (Supplementary Material online) for accession numbers and taxonomic information. horizontal gene transfers from bacteria are at the origin of DNA gyrases in the two groups of archaea. However, we speculate that the second transfer would have been possible only because the newly introduced DNA gyrase replaced an already present bacterial-type enzyme. The two alternative scenarios (a single transfer or two successive transfers) remain possible, as statistical tests showed that the data do not reject either of the two topologies (P > 0. 48 for all tests, see Materials and Methods for details). DNA gyrase is likely essential in all species that harbor it, suggesting that it may be difficult to lose this enzyme once acquired.
8 ). Finally, all of the nanosized archaea (Nanoarchaeota, ARMAN-5, ARMAN-4, and the three Nanohaloarchaea), except for ARMAN-2, form a monophyletic clade that emerges after the divergence of Thermococcales and Methanogens class I (fig. 8 ). Considering the very fast evolutionary rate of these lineages, it cannot be excluded that this grouping is due to a tree reconstruction artifact. To test this possibility, we created several versions of the concatenated dataset containing different combinations of taxa (i. e. , we removed all nanosized lineages from the concatenation and reintroduced them one by one) and we recoded the amino acid supermatrix using Dayhoff 6 and Dayhoff4 recoding schemes, a procedure known to alleviate certain artifacts due to fast evolutionary rates  (Delsuc et al. 2005 ).
Considering the small number and taxonomic coverage of viral sequences presently available in public databases (supplementary table  S3 , Supplementary Material online) our analysis suggests that the world of archaeal extrachromosomal entities may be particularly enriched in genes encoding for replication proteins. Moreover, the presence of highly divergent and related components in Thermococcales and Methanococcales, such as their DNA primase and the RPA three-gene cluster, may indicate potential avenues of gene sharing through a common pool of plasmids and viruses  (Soler et al. 2010). Archaeal plasmids and viruses rarely encode components of the transcription machinery and, to our knowledge, no translation components. The targeting of DNA replication by virus/plasmid entities to hijack the host machinery provides a strong advantage and is a well-known phenomenon.
Distribution of homologs of 22 main replication components in 142 archaeal genomes. Filled circles represent homologs that we assigned to the core replication machinery, whereas gray circles represent homologs assigned to the shell component (see text for details). Split genes are indicated by half circles, and the fused primases by a box (see text for details). Letters in first column indicate the phylum (A, Aigarchaeota; T, Thaumarchaeota; C, Crenarchaeota; K, Korarchaeota; N, Nanoarchaeota; E, Euryarchaeota). Asterisks indicate classes instead of orders. Full accession numbers are given in supplementary table S1, Supplementary Material online.
Acknowledgments The authors acknowledge the article by Makarova and Koonin (2013) that was published while this manuscript was under review and which presents a comparison of DNA replication in Archaea and Eukaryotes. The authors thank the  PRABI (Pole Rhone-Alpes de Bioinformatique)  for providing computing facilities. K. R. is a scholar from the  Pasteur-Paris University (PPU)   International PhD program  and received a stipend from the  Paul W. Zuccaire Foundation. C. B. A. is member of the  Institut Universitaire de France. This work was supported by the  Investissement d'Avenir grant  " Ancestrome " ( ANR-10-BINF-01-01 ).
ComplexFIG FIG. 7. -Bayesian phylogeny of a concatenation of archaeal DNA gyrase small and large subunits and a selection of bacterial homologs (1,083 amino acid positions). The tree was calculated by MrBayes (MIX model + gamma4). The scale bar represents the average number of substitutions per site. Supports at nodes indicate posterior probabilities. Colors correspond to archaeal orders according to those used in figure 2. The tree is collapsed for clarity. See supplementary table S1 (Supplementary Material online) for accession numbers and taxonomic information.
Distribution of homologs of 22 main replication components in 142 archaeal genomes. Filled circles represent homologs that we assigned to the core replication machinery, whereas gray circles represent homologs assigned to the shell component (see text for details). Split genes are indicated by half circles, and the fused primases by a box (see text for details). Letters in first column indicate the phylum (A, Aigarchaeota; T, Thaumarchaeota; C, Crenarchaeota; K, Korarchaeota; N, Nanoarchaeota; E, Euryarchaeota). Asterisks indicate classes instead of orders. Full accession numbers are given in supplementary table S1, Supplementary Material online. Complex Evolutionary History of DNA Replication in Archaea GBE within nonconserved genetic contexts and/or display more restricted taxonomic sampling and inconsistent phylogenetic affiliations.
We reasoned that the first category represents components that were primarily vertically inherited during ar-chaeal diversification and form what we called the conserved core replication components (fig. 2, filled circles; for full ac-cession numbers see supplementary table S1, Supplementary Material online), whereas the second category represents hor-izontally transferred genes, decaying paralogs, or homologs arising from integration of extrachromosomal elements that form a variable pool of proteins that we called the shell rep-lication components (fig. 2, open circles; for full accession numbers see supplementary table S1, Supplementary FIG. 2. -phylogeny, as opposed to 2) highly divergent copies that lie Material online). Genome Biol. Evol. 6(1):192-212. doi:10. 1093/gbe/evu004 Advance Access publication January 6, 2014.
Analyse in three dimensions An analysis of the building vibration was performed in 3D by the FDD technique. Fig. 7  shows examples of 3D mode shapes obtained, data from a "deformed" configuration. The lines on the horizontal plane are the projections of the structure deformation on this plane, which indicate more clearly the vibration directions in each mode. For example, the first mode at 1. 25 Hz evidences a vibration direction which makes an angle θ 1 approximately 40 • from the longitudinal direction of the building (NS). The mode at 1. 54 Hz shows a vibration direction forming an angle θ 2 about 130 • from the longitudinal direction of the building (NS), i. e. perpendicular to the first mode (at 1. 25 Hz).
Fixed-base structure Modelling was done with the SAP2000 software, which was successfully applied in the study of  Liu et al. (2005)   [18]. The FE model was firstly built for the superstructure corresponding to the fixed base (Fig. 10 ). The materials' characteristics were calibrated in modelling to reproduce the in-situ dynamic behaviour. Indeed, the current value of the Young's modulus of concrete in France is 32 GPa (Eurocode 2  [9] ), but this corresponds to a concrete tested at 28 days. This value decreases in the times due to the creep phenomena. Following the empirical formula gave in Eurocode 2, this Young's modulus of concrete can decrease until 12 GPa. Therefore, a calibration of concrete modulus was necessary in an interval from 12 to 32 GPa. For Young's modulus of masonry, to surmount the inhomogeneity of masonry panels, the in-situ values found in the study of  De Sortis et al.
A runtime code generator is generated at static compilation time, embedded in the application binary code. The polymorphic production of code variants involves register renaming, instruction shuffling and the production of semantically equivalent code fragments. The overhead of code generation is significantly high since each new code generation is reported to execute in 90 ms (11, 970. 10 6 cycles at 133 MHz) on an ARM926 processor running GNU/Linux, in order to generate polymorphic versions corresponding to the 64 xor operations in the AES kernel. This approach is the closest to the approach that we envision in this paper, but as we will illustrate below, we expect our approach to better fit with the constraints of resource constrained embedded systems.
Sketching deGoal deGoal is a framework for runtime code generation, whose initial motivation is the use of runtime code specialisation to improve program performance, e. g. execution time, energy consumption or memory footprint. In this section, we first sketch the characteristics of deGoal and then elaborate about its use in the context of security. (Couroussé et al. , 2013; Aracil and Couroussé, 2013; Charles et al. , 2014)  further elaborate about the use of deGoal in contexts or application domains that originated its design. In classical frameworks for runtime code generation such as interpreters and dynamic compilers, the aim is to provide a generic infrastructure for code generation, bounded by the syntactic and semantic definition of a programming language. The generality of such solutions comes at the expense of an important overhead in runtime code generation, both in terms In deGoal, a different approach is used: code segments (thereafter called kernels) are generated and tuned at runtime by ad hoc runtime code generators, called compilettes.
Each compilette is specialised to produce the machine code of one kernel. Syntactic and semantic analyses are performed at the time of static compilation, and compilettes embed only the processing intelligence that is required for the runtime optimisations selected. As a consequence, compilettes offer very fast code generation (10 to 100 times faster than typical frameworks for runtime code interpretation or dynamic compilation), present a very low memory footprint, can run on very small microcontroller architectures such as 8/16-bit microcontrollers  (Aracil and Couroussé, 2013) , and are portable  (Charles et al. , 2014). In COGITO, we re-target the original purpose of deGoal in order to focus on security aspects: we exploit the flexibility brought by deGoal for runtime code generation to achieve code polymorphism.