dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusion All in all, we consider the uncertainty on LYRA calibrated data to be of the order of 30 -40 % for all channels except Lyman-α, where it is about 120 %. In an attempt to validate those uncertainties, we have used alternative spectra in Equation (  4 ) and have seen how LYRA data were affected. Unfortunately, for the considered date (6 January 2010) and spectral ranges, we are not aware of any other measured spectrum than the TIMED/SEE and SORCE/SOLS-TICE ones. Even empirical models such as NRLSSI  (Lean et al. , 2005)  and SATIRE  (Krivova et al. , 2009)  are not provided for periods after 2006 and 2007 respectively. We have therefore tried to find a date with a similar solar-activity context as during LYRA first-light, for which those models produced spectra. We chose 10 November 2005.
Conclusion After more than two years of operations, LYRA demonstrated its ability to produce time series of solar irradiance in the XUV-EUV-MUV range with a very favorable sampling rate. Uncalibrated and calibrated data products, as well as various quicklooks and a list of flares, are distributed via the instrument website (http://proba2. sidc. be). Among others, the instrument appears to be particularly well adapted to the analysis of flares, for which its spectral channels and sampling rate are ideal. High sampling rate also benefits other fields of research, of which some have been mentioned in Section 4 (analysis of the center-to-limb brightness variation, determining the composition of Earth's upper atmosphere). Finally, LYRA finds its place in the long-term monitoring of solar irradiance in the EUV-UV range.
Acknowledgements The authors acknowledge the support from the  Belgian Federal Science Policy Office  through the  ESA-PRODEX programme. LYRA is a project of the  Centre Spatial de Liège , the  Physikalisch-Meteorologisches Observatorium Davos , and the  Royal Observatory of Belgium , funded by the  Belgian Federal Science Policy Offce (BELSPO)  and by the  Swiss Bundesamt für Bildung und Wissenschaft. The authors want to acknowledge this SOLA:  SOLA1866R3. tex;27 February 2013; 2:23; p. 23.
Affimetrix exon array One microgram of total RNA was processed with the GeneChip WT Sense Target Labeling kit and hybridized to GeneChip Human Exon 1. 0 ST arrays. Affymetrix exon-array data were normalized with quantile normalization. Antigenomic probes were used to perform the background correction. Only probes targeting exons annotated from full-length cDNA were retained for analysis. Cross hybridizing probes and probes with lower signals intensity than anti-genomic background probes showing the same GC content were removed. Only probes with a DABG P-value 0. 05 in at least half of the arrays were considered for further statistical analysis. Arrays were performed in four independent replicates. The strategies adopted to identify E2-, DHT-regulated and Ddx5/17dependent genes are described in Supplementary Figure  S1.
Sampling Taxonomic sampling was carried out at 80 localities throughout Grande Terre, Ile des Pins and Loyalty Islands. We gathered 153 specimens, representing 4 species (including 3 new species), at 17 localities from the centre-east (Houailou) to the south-east (Foret Nord) (Table  1 ). Most of the sampled sites were published in  [7, 21, 22, 23, 31, 32, 33, 34] , but some of them are new. We present these data in the results section to indicate the distribution pattern of Caledonula. 2. 1. 2. Molecular sampling. Only a few molecular phylogenies of grasshoppers at the genus level have been published since 1998  [35, 36, 37, 38, 39, 40, 41, 42, 43, 44]. We chose molecular markers according to these studies and we designed new primers for nuclear markers following studies on the Ensifera dealing with this taxonomic level (e.
g. ,  [45, 46, 47] ). Molecular sampling included sequences from 3 fragments of coding mitochondrial genes (Cytochrome b (Cytb, 346 bp), Cytochrome c Oxidase 1 (CO1, 670 bp) and Cytochrome c Oxidase 2 (CO2, 381 bp)) and 4 fragments of nuclear genes ((Elongation factor-1 alpha (EF1a, 367 bp), Histone Class 3 (H3, 331 bp), RNA of large ribosomal subunit (28S, 1100 bp), and Internal Transcribed Spacer 1 (ITS1, 350 bp)). The CO1 sequences showed full features of pseudogenes (high variability and many stop codons) and were thus discarded before phylogenetic analysis  [48]. Two markers (28S and ITS1) were also discarded because they showed no variation (0% for ,1450 bases, see Table  S1  for GenBank accession numbers). All newly generated sequences were deposited in GenBank and the molecular samples used in the analyses are presented in Table  1.
15 mL of Taq polymerase and 1 mL of DNA. The PCR consisted of an initial denaturing step at 94uC for 4 min, 40 amplification cycles (denaturation at 94uC for 30 s, annealing at between 48 and 55uC (Table  3 ) for 40 s, and extension at 72uC for 40 s), and a final step at 72uC for 7 min. PCR products were checked on agarose gels and sequenced in both directions with the same primers at Genoscope (Evry, France). Sequences were cleaned, and coding sequences were translated using the invertebrate mitochondrial genetic code to check for the absence of stop codons using Sequencher v. 4. 8 (GeneCodes Corporation, Ann Arbor, MI, USA). All genes were screened for potential contamination using the BlastX algorithm on GenBank.
Analyses were performed with MrBayes 3. 1. 2 [61]. Four Markov chains were run simultaneously for 20 million generations, sampling every 100 generations to ensure independence of samples. The first 20,000 trees generated were determined empirically from the log-likelihood values using TRACER V1. 4 and discarded as burn-in. [62]. The remaining trees were used to construct 50% majority-rule consensus trees. Two independent runs were performed to check whether convergence on the same posterior distribution was reached and whether the final trees converged on the same topology. The statistical confidence of each node was evaluated by posterior probabilities. Since pseudogenes are known to be prevalent in grasshoppers  [48, 63, 64] , we conducted separate analyses for each data set and for different subsets of data to estimate the informative content of each kind of data and to check for problematic amplifications.
Geographical distribution, soil diversity and niche modelling We aimed to test whether certain environmental parameters (soil, climate) could have influenced the distribution of the genus. Ecological niche models (ENM) were constructed using the maximum entropy niche modelling approach implemented in MAXENT  [73, 74]. This is one of the best-performing programmes for species distribution models, which it builds based on presence only  [75]. Data on absences were therefore not used in the analysis, but we show them to illustrate all the investigated areas (see results section). Environmental data layers were constructed for the 19 BIOCLIM variables (at 30 arc-seconds resolution) in the worldclim dataset. These variables were derived from the interpolation of monthly readings for precipitation and minimum, maximum, and mean temperatures for the period 1950-2000  [76].
Geographical distribution and soil diversity Caledonula fuscovittata has a large distribution with twelve distant localities, always on non-metalliferous soils (Figure  2 ). Specimens of this species collected on Mont Mou and Mont Koghis (ultramafic massifs) were collected on non-metalliferous soils at the base of these mountains and the species was not found on the ultramafic higher elevations. Mont Do has a heterogeneous soil composition: it is non-metalliferous at the bottom and metalliferous at the top. Except for one specimen showing contradictory information on the labels (GPS data indicates the top of Mont Do, whereas it is labelled as being from the base of Mont Do), all specimens identified as C. fuscovittata are from non-ultramafic soils only. All other species were located on ultramafic soils in the South, and each is known only from one or two close localities (Figure  2 ), suggesting that they are highly microendemic.
Acknowledgments We are grateful to  Shepherd Myers  and  Keith Arakaki  ( Bishop Museum, Honolulu ),  George Beccaloni  (The  Natural History Museum, London ), for loans of specimens, to  Gae ¨l Kergoat (CBGP)  for specimen collection in Mont Kouakoue, and to  Gilbert Hodebert  for the habitus drawings. We sincerely thank  Christian Mille  for loans of specimens from  IAC  and for providing valuable help in New Caledonia. We also thank  Hannah ter Hofstede  ( Dartmouth College, Hanover, USA ) and  Mark Judson  ( MNHN ) for their critical reading of the paper and for improving the English. Finally, we want to remember that this work was initiated under the critical training in acridid morphology and taxonomy of RN by the late  Christiane Ame  ´degnato,.
Global analysis of gene expression during embryonic development Using the newly developed "INRA-BF2I_A. pisum_Nim blegen-ACYPI_4x72k_v1" microarray (ArrayExpress design ID: A-MEXP-1999), built on the pea aphid genome v1. 0 assembly  [23] , we obtained gene expression profiles of aphid embryos belonging to three distinct groups, namely early (EE), intermediate (IE) and late (LE) embryos, collected according to their developmental stage (see Table  1  and Figure  1A ), together with aphids at their first larval stage (L1). Three biological replicates were used for each experimental group (see Methods section for further details). To check the overall quality of the data, after nor malization, we performed an unsupervised cluster analysis using the 50% of genes showing the highest SD in all the samples (i.
Microarray data validation by quantitative RT-PCR To validate the data obtained from the microarray analysis in our first experiment, we repeated the experiment for the same four stages and quantified the expression of eight A. pisum genes, belonging to four functional classes (3 developmental genes, 3 amino acid pathways genes, 1 cuticular gene and 1 transporter gene), using quantitative reverse transcription-PCR (qRT-PCR). The whole experiment was performed again using three new biological replicates for each stage group and for these samples total RNA was not amplified (see Methods for details). We compared the data obtained from qRT-PCR with that obtained in the independent microarray experiments and found very good concordance, with a Pearson's correlation C) The Venn diagram of the 3945 genes showing significantly differential expression in the three comparisons (comparison colour code as in Figure  1A ).
Developmental genes expression analysis Among the 387 pea aphid developmental genes annotated by Shigenobu et al. [55] , using homology with Drosophila melanogaster, 368 were present in our microarrays and were analysed. In our dataset, 118 genes (32%) showed significant differential expression in at least one of the three comparisons we performed (Figure  2A ). In the comparisons EE-IE, IE-LE and LE-L1 we found, respectively, 30, 67 and 44 significant variant genes (Figure  2B  and Additional file 3: Table  S3 ). Twenty-one genes increased their expression during development, while 80 genes showed decreasing levels of expression. Among these 80 genes, 45 were found only in the comparison IE-LE, i. e. the last of the embryonic developmental stages analyzed in this study. We performed a detailed analysis of two gene classes, as defined in the Shigenobu et al annotation paper  [55] : the homeobox-containing genes and the signalling-pathway genes, which are involved in the establishment of anatomical patterns and in the regulation of developmental processes in all the metazoans.
Metabolism gene expression: the amino acid pathways To explore the general metabolic changes that take place during the parthenogenetic development of the pea aphid, we analyzed the relevant genes (Additional file 1: Table  S1 ) using the annotations available in the AcypiCyc database, which contains the global reconstruction of the metabolic network of the pea aphid  [56]. At least one enzyme-coding gene showed significant differential expression for 43, 74 and 90 pathways in the EE-IE, IE-LE and LE-L1 comparisons, respectively (Additional file 5: Table  S5 ). We classified all these pathways, in AcypiCyc, into five broad groups of compounds: lipids, amino acids, sugars, nucleotides, and others. An analysis of the distribution of genes within these classes, compared with the total number of genes in the pea aphid genome, did not reveal any significant changes that would indicate a class of particular interest.
For example, 16. 8% of the total number of differentially expressed enzyme-coding genes in our dataset forms part of the amino acid metabolism group and, in the pea aphid genome, the percentage of genes coding for this class is 16. 4%. So, the number of detected genes is not significantly different from the number that would be expected by chance. The same is true for the other classes, but we decided to carry out further gene-by-gene analysis for those classes supposed, a priori, to play an important role in symbiosis or in developmental processes. In particular, we analyzed in more detail the gene expression profiles of the 135 genes involved in the amino acid biosynthesis or degradation pathways  [29] , which were present in our overall analysis and which represent one of the key functions in the physiology of pea aphid/ B.
Embryo development specific regulation of the gene ACYPI004243 The matching profiles of the enzyme-coding gene expression for tyrosine synthesis and the accumulation of this amino acid in the LE and L1 embryo groups prompted us to perform a more detailed analysis of the genes involved in this pathway (Figure  3B  and 3C). To gain a better understanding of the differences between the four genes (ACYPI000044, ACYPI003009, ACYPI004243, and ACYPI 006213), annotated in the AcypiCyc database as coding for the enzyme aspartate transaminase (EC 2. 6. 1. 1), we performed a detailed enzyme gene annotation using the dedicated PRIAM tool  [58]. This analysis revealed that, for all of the corresponding proteins, after the primary annotation as enzymatic activity aspartate transaminase (EC 2. 6. 1. 1), two other complementary annotations follow: the enzymatic activity tyrosine transaminase (EC 2.
6. 1. 5) and the enzymatic activity aromatic amino acid transaminase (EC 2. 6. 1. 57) (Additional file 2: Table  S9 ). This additional in silico analysis confirmed the hypothetical role of these four genes in coding the enzymes catalyzing the aspartate, tyrosine and aromatic amino acids transamination reactions. The two more specific annotations of PRIAM are not included in the AcypiCyc database as they did not pass the cut-off point applied in the generation of the database using CycADS  [56]. Finally, we analyzed the gene structure and the genome organization of the four genes encoding for the enzyme aspartate transaminase (E. C. 2. 6. 1. 1) in the pea aphid. Among these genes, ACYPI000044 shows a two-exon structure, with the coding sequence restricted to exon 2, while all the other genes have an 8-exon structure (with the coding sequence spread out between exon 1 and exon 8).
It is worth noting that the genes ACYPI004243 and ACYPI003009 mapped to the same contig in the pea aphid genome. An analysis of the protein sequences was performed and the alignment of the four proteins revealed the expected conservation, with the exception of the ACYPI000044-PA protein sequence that has a unique N-terminal portion which is not aligned to the other three proteins with the enzymatic activity aspartate transaminase (EC 2. 6. 1. 1) (Additional file 8: Figure  S1 ). A detailed phylogenetic analysis of the four aspartate transaminases, expanding the protein information available in PhylomeDB  [59]  and using the UniProt database  [60] , was performed (Additional file 9: Figure  S2 ). The evolution of the aspartate transaminases, also called aspartate amino transferases (AAT) family, revealed five major duplications separating bacterial AAT (TyrB and AspC) from mitochondrial AAT (AATM or GOT2) and cytoplasmic eukaryote AAT families (AATC or GOT1).
Discussion In this study, the transcriptome analysis of parthenogenetic development in embryonic pea aphids enabled us to characterize the gene expression profiles at key stages in the development of this insect. A comprehensive transcriptome dataset, such as this, is important for the ongoing genome annotation effort as gene expression profiles at specific steps of pea aphid development, or in specific tissues, can lead to an initial characterization of the role of different genes in aphid physiology. After an unsupervised clustering analysis, that indirectly demonstrated the good quality of our dataset, we identified 3945 genes that showed significant changes in expression level in the developmental groups analysed here (Figure  1 ). We examined this high number of variant genes, combining unsupervised and hypothesis driven analyses.
A comprehensive analysis of the genes involved in metabolism, performed using the AcypiCyc database annotations  [56] , did not reveal any significant global change in gene expression in the specific pathways. Nevertheless, the known central role of amino acid metabolism pathways in this symbiotic relationship  [29]  and, in particular, during pea aphid development  [48] , together with the induction of expression of the amino acid transporter genes observed in our experiments, prompted us to analyse, in more detail, all the genes involved in the amino acid pathways  [29]. Most genes coding for enzymes involved in amino acid metabolism showed strong expression levels (Figure  3 ), as expected given the central role of amino acids in embryonic development  [49] [50] [51] [52]. A significant induction of gene expression during pea aphid development, in the stages under study, affects several amino acid pathways (Additional file 6: Table  S6 ).
Microarray experiments and data collection The "INRA-BF2I_A. pisum_Nimblegen-ACYPI_4x72k_v1" microarray for the pea aphid was developed in collaboration with NimbleGen using the pea aphid genome v1. 0 assembly  [23]. This NimbleGen 385 K 4-plex (4 × 72,000 probes) high-density array can accommodate four samples that are hybridized onto a section of the array containing 72,000 60-mers oligonucleotide probes, representing 24,011 pea aphid transcripts (corresponding to 23,855 genes). The microarray design is deposited in the ArrayExpress database (http://www. ebi. ac. uk/arrayexpress/) and the array is available to the International Aphid Genomics Consortium community. On the arrays, 185 probe sets recognise more than one gene: these ACYPI probe sets are labelled with an asterisk (*) in all of the Results tables and they are listed in full in Table  S1E.
Labelling (using the NimbleGen One-Color DNA Labelling Kits and Cy3 Random Nonamers), hybridization on the arrays (at 42°C for 16-20 hours) and scanning (using MS 200 Microarray Scanner and the MS 200 Data Collection Software) were carried out by Roche NimbleGen, as described in the NimbleGen arrays user's guide for gene expression arrays, and they provided the final data files. All the transcriptomic data obtained are available in the ArrayExpress database (http://www. ebi. ac. uk/arrayexpress/).
Microarray data analysis Microarray data were normalized, using the RMA method  [68] , and then transformed into log2. A one-way between groups ANOVA analysis was performed using the Limma package in the R software  [69] : two by two comparisons were performed to identify any differentially expressed genes between the different stages of development (EE-IE; IE-LE; LE-L1). The non-parametric p-values were estimated using 1000 sample permutations and further adjusted using the Benjamini and Hochberg method  [70] , to limit the number of false positives by a control of the False Discovery Rate (FDR). For this study we chose an FDR lower than 0. 05. A gene was considered significant if its adjusted p-value was lower than 0. 05 and if it showed a two-fold change in the considered contrast (see Additional file 1: Table  S1  for a detailed list of significantly expressed genes).
Validation of microarray data by qRT-PCR Total RNA was reverse-transcribed in cDNA using the SuperScript ™ III First-Strand Synthesis System for RT-PCR (Invitrogen, Paisley, UK), with random primers, according to the manufacturer's instructions. This protocol involved three principal steps: (1) an incubation for 5 min at 65°C, (2) a reverse transcription using 1 μg of total RNA and including three incubations (25°C, 50°C and 85°C), (3) an RNA matrix degradation using RNase H. Primers to target transcripts (Additional file 2: Table  S12 ) were designed with the Oligo7 software  [74] , except those used for the genes ACYPI009127 and ACYPI001858 which were taken from Brisson et al. [75]. Real-time PCR was performed in 96-well plates with a LightCycler 480 instrument (Roche diagnostics, Meylan, France).
For the data normalization, two genes were tested in the different developmental stage groups analysed here: actin (ACYPI000064) and rpl32 (ACYPI000074). Real-time RT-PCR data were analysed using the BestKeeper © software tool  [76]  and the actin gene was retained as the best candidate for data normalization. An analysis of the quantitative RT-PCR data was performed using the REST software (  [54] ; http://rest. gene-quantification. info/). The relative expression ratio of each target gene was calculated by comparing the tested condition against the control condition, and also relative to the normalization gene. More precisely, this ratio (R) was calculated taking into account the real-time PCR efficiency of each gene (E) and the crossing point difference (ΔCP) of a test condition (IE, LE or L1), as compared to the reference condition (EE, IE or LE according to the comparison (EE-IE, IE-LE, LE-L1)), and expressed in comparison to the normalization gene (actin) using the following model  [77].
Sequence analyses The four protein sequences (ACYPI006213-PA, ACYPI 003009-PA, ACYPI004243-PA and ACYPI000044-PA) for the aspartate transaminase enzymatic activity (E. C. 2. 6. 1. 1) were obtained from PhylomeDB  [59] , together with their corresponding arthropod orthologs and paralogs. The data set was completed with other homologous proteins, extracted from the UniProt database  [60] , using a stringent e-value (= 10 -6 ) for the BlastP search. Amino acid sequences were then aligned using the software MUSCLE  [79]  embedded in the phylogeny software SeaView 4. 0  [80]. A phylogenetic tree was drawn in order to remove non-homologous proteins and redundant sequences and to reduce the species number in some taxonomic groups of the tree. The alignment was then recalculated and manually corrected.
Acknowledgements Funding: This work was supported by the  Agence National de la Recherche (ANR, France) , the  Biotechnology and Biological Sciences Research Council (BBSRC, UK)  «  MetNet4SysBio  » project grant ( 2008-2010 ), and an  INSA-Lyon BQR program  grant ( 2008-2009 ) to SC. AR was the recipient of a PhD fellowship from the  French Ministry of Research  ( 2008-2011 ). The authors would like to thank  Pierre Masselot , who worked on the data during his statistics training internship and contributed useful feedback. The authors are grateful to  Alain Clavel  for plant production and to  Valerie James  for the English language editing of the final manuscript.
General DNA techniques Oligonucleotides were designed, and in silico analysis was carried out with the afa-8 sequence of E. coli 239KH89 (accession number AF072900). The sequences of the oligonucleotides (Eurogentec) used in this study are listed in Supplementary Table  S1. All polymerase chain reaction (PCR) products were amplified from the chromosomal DNA of E. coli strain 239KH89 (99. 7% nucleotide sequence identity to strain AL862). Enzymatic reactions were performed in accordance with the manufacturer's instructions.