dataesr/scientific-paragraphs-categorization huggingface dataset:
MIPAS operations and H O data The limb-viewing Fourier transform spectrometer MIPAS on ENVISAT (MIPAS-E) has been designed to operate in the mid-infrared spectral region covering five spectral bands between 685 and 2410 cm -1 with an unapodized full spectral resolution of 0. 025 cm -1  (Fischer et al. , 2008). The vertical instantaneous field of view (IFOV) is about 3 km. The instrument was launched into its sun-synchronous orbit by ESA on 1 March 2002. It passes the Equator in a southwards direction 14. 3 times each day at 10:00 local time. After the commissioning phase MIPAS was run predominantly in its nominal measurement mode from July 2002 until the end of March 2004. During each orbit approximately 72 limb scans covering tangent altitudes between 8 and 68 km were recorded (in steps of 3 km below 45 km) in the full spectral resolution mode.
In a first step, temperature and pressure at the measured tangent altitudes are retrieved simultaneously. In the next steps, volume mixing ratio (VMR) profiles of the primary target species H 2 O, O 3 , HNO 3 , CH 4 , N 2 O, and NO 2 are retrieved individually in the reported sequence. The H 2 O operational version 4. 61 data analysis has been carried out in the following four microwindows: 807. 850-808. 450, 946. 650-947. 700, 1645. 525-1646. 200, and 1650. 025-1653. 025 cm -1. A random retrieval error due to instrument noise is extracted from the diagonal elements of the error variance covariance matrix calculated during the retrieval process. Further error sources are estimated for day and night conditions and different seasons. The following parameter errors and forward model errors have been taken into account for the H 2 O profile retrieval: pressure/temperature random retrieval errors; spectroscopic data errors due to uncertainties in the intensity, width and position of emission lines; radiometric gain, instrumental line shape, and spectral calibration inaccuracies; uncertainties in assumed profiles of the contaminant species O 3 , ClONO 2 , and NH 3 ; uncertainty.
(2004)  and references   Stiller et al. , 2002; Höpfner et al. , 2002). A Tikhonov-Phillips regularization approach constraining with respect to the shape of an a priori profile was adopted. The resulting vertical resolution is typically between 2 and 4 km for the H 2 O retrieval and is therefore comparable to or better than the vertical resolution of MIPAS-E. H 2 O was analysed in MIPAS-B proven microwindows in the ν 2 band centred at 1595 cm -1. Transitions between 1210 and 1245 cm -1 and around 808 and 825 cm -1 have also been used for the data analysis. Spectroscopic parameters chosen for the MIPAS-B retrieval are consistent with the database taken for the MIPAS-E data analysis  (Flaud et al. , 2003)  and originate mainly from the HITRAN 2004 database  (Rothman et al. , 2005). A further overview on the MIPAS-B data analysis is given in  Wetzel et al.
However, when taking into account the increasing number of coincidences in the trajectory match, the deviations decrease to less than 10 %, which is clearly within the combined systematic error limits. This example illustrates the problem of validation of H 2 O in regions with very high spatial variability. The DLR airborne water vapour Differential Absorption Lidar (H 2 O-DIAL) was flown onboard the Falcon aircraft several times from Forli in October 2002. A system descrip-  tion together with an assessment of accuracy is given by  Poberaj et al. (2002)  and  Kiemle et al. (2008). Individual results of the comparison to MIPAS observations around Italy are shown in Fig. 10. Although in general not more than two MIPAS data points of the vertical profile overlap with the DIAL profile observations, the DIAL has a much higher resolution than MIPAS in the tropopause region and is thus well adapted to validate MIPAS data in this region where water vapour is difficult to measure due to strong gradients.
A single spectrum is measured every 10 to 15 s during the flight. About 20 of them are integrated for improving the signal-to-noise error. From these integrated spectra, altitude profiles of H 2 O VMR between about 15 to 60 km are retrieved along the flight track. A detailed description of the measurement method and instrument is given in  Feist et al. (2007)  and references therein. Retrieval method and error analysis are discussed in  Müller et al. (2008). ENVISAT validation flights were carried out in September 2002 covering a wide latitude range from the tropics to the Arctic, providing a large number of collocations (Fig. 12 ). The statistical analysis shows a mean deviation between the data sets in the order of 5 % between 20 and 25 km altitude, increasing to more than 15 % higher up.
profile observations were carried out within a ground-based measurement campaign for the validation of MIPAS temperature and water vapour data by the Istituto di Metodologie per l'Analisi Ambientale del Consiglio Nazionale delle Ricerche (CNR-IMAA) in Potenza (Italy) and the Department of Physics of the University of L'Aquila (Italy). Radiosondes measuring atmospheric pressure, temperature and relative humidity were launched at the University of L'Aquila (42. 4 • N, 13. 4 • E, 683 m above sea level). These profiles were measured with balloon-borne Vaisala RS80 sondes.
calibration/validation activities were supported by ESA/ESTEC contract No. 16040/02/NL/SF. The financial support received from Italian "Ministero dell'Ambiente e della Tutela del Territorio e del Mare" for the CETEMPS balloon sounding activities, is gratefully acknowledged. An acknowledgement goes to the work performed by the Quality Working Group established by ESA for verification and monitoring of MIPAS products. We are deeply saddened by the loss of our dear friend and colleague, Cornelius Schiller, who passed away on 3 March 2012. We acknowledge support by Deutsche Forschungsgemeinschaft and Open Access Publishing Fund of Karlsruhe Institute of Technology. The service charges for this open access publication have been covered by a Research Centre of the Helmholtz Association.
Acknowledgements. Financial support by the  DLR  (Project  50EE0020 ) and  ESA  for the MIPAS-B balloon flights is gratefully acknowledged. We thank the  Centre National d'Etudes Spatiales (CNES)  balloon launching team and the  Swedish Space Corporation (SSC) Esrange people  for excellent balloon operations, the  DLR  flight department and the  Russian Geophysica crew  for excellent campaign operation and the  Free University of Berlin  ( K. Grunow  and  B. Naujokat ) for meteorological support and trajectory calculations. Assistance by  C. Kiemle  ( DLR ) in retrieving the DIAL data is greatly acknowledged. Funding for the ACE mission was provided primarily by the  Canadian Space Agency (CSA)  and the  Natural Sciences and Engineering Research Council (NSERC)  of  Canada. The authors would like to thank  K.
H 2 O 2 profile observations were carried out within a ground-based measurement campaign for the validation of MIPAS temperature and water vapour data by the Istituto di Metodologie per l'Analisi Ambientale del Consiglio Nazionale delle Ricerche (CNR-IMAA) in Potenza (Italy) and the Department of Physics of the University of L'Aquila (Italy). Radiosondes measuring atmospheric pressure, temperature and relative humidity were launched at the University of L'Aquila (42. 4 • N, 13. 4 • E, 683 m above sea level). These profiles were measured with balloon-borne Vaisala RS80 sondes.
M2 plant phenotyping Ten seeds per M2 family were sown in pots to raise the nursery and to screen for visual developmental phenotypes. After 4 weeks, plants were transferred to the fields and about 95% of the M2 plants were recovered. The phenotypic scoring was based on the observation of each plant, using the untreated sunflower phenotype as a reference. The phenotypic screen included dwarfism, plant height, flowering pattern, the leaf architecture and sterility. The data collected are shown in Table  2. Photographs were taken at regular intervals for record purposes (Figure  1 ). A range of phenotype was observed and some are described below. Compared to the untreated sunflower, 10 and 4 plants showed dwarf and tall phenotype, respectively (Table  2 ). Some of these dwarf plants can be used in creating hybrids with enhanced yield and dwarf stature.
Acknowledgements We would like to thank the  Shroff  family, and  Shroff  family fund for financial support and encouragement. The authors wish to thank  JRF Directors  and  Mr. Sudhansu Gupta  and  Mr. Bipin Parmar  for administrative support. Mr. Lalman Paul  and  Mr. Sanjay Parmar  for their support in growing mutant population and caring of plants.
5% agarose gel (data not shown). Genomic DNA contamination was checked by PCR on a total RNA template using primers targeting the citrate synthetase sequence (data not shown). The A 260 /A 280 ratio of the RNA samples was 2. 08960. 017 (mean 6 SD), indicating the absence of protein and the purity of all the total RNA samples required for an accurate qRT-PCR analysis. First-strand cDNA was synthesized from 0. 8 mg total RNA using SuperScript III First-Strand Synthesis SuperMix (Invitrogen, Carlsbad, USA), with 1 ml of random hexamers in a 20-ml reaction mixture, according to the Manufacturer's instructions. cDNA samples were stored at 220uC.
Genes Investigated in the Study Two series of genes were used in this study (Table  1 ). As the relevance of RT-qPCR analysis greatly depends on transcript normalization with stably-expressed reference genes (RGs), 6 candidates were selected from different functional classes. Four conventional candidate RGs were tested: 16S rRNA  [27] , rpoD  [28] , GAPDH  [27] , rsh  [27] , plus two RGs genes that had never so far been tested in prokaryotes: gltA and rpsL. The sequence of 16S rRNA is available in a database (GeneBank FJ184435. 1), but the other 5 RGs candidates were all isolated in this study. The second series were the target genes. They included nine hsp genes: hspA, hslO, hsp40, grpE, dnaK, hsp70 (1), hsp70(2), hsp70(3), and hsp70(4), which had previously been sequenced, and were kindly provided by Dr Quiblier (MNHN, Paris) plus four other genes: clpC (hsp100), htpG (hsp90), groEL (hsp60) and groES (hsp10)), which were isolated in this study.
Expression Stability of the Candidate Reference gene and Data Analysis In order to determine the true gene-specific variation, at least one stably-expressed RG is required to normalize the expression level of the target genes. The expression levels of the six candidate RGs were determined by RT-qPCR under the same experimental conditions as for the target genes (i. e. control condition+HL stress). To identify the genes most stably expressed during HL treatment, the C T values of these six candidate RGs were analyzed using three different mathematical algorithms: geNorm  [34] , Normfinder  [35]  and BestKeeper  [36]. In brief, GeNorm is the one most commonly used in the literature and it relies on the transformation of raw C T values (using the delta-C T method). The gene expression stability measure (M) for a candidate RG is computed by averaging pairwise variations of that gene versus all the other candidates tested.
The standard curve was obtained by plotting C T values against a logarithm of serial dilutions of the target nucleic acid. The efficiency of the reaction (E) was calculated from the slope value of a standard curve, as follows: E = 10 (21/sl u pe) -1. The relative quantity of each gene (Q), which was used in geNorm and NormFinder, was calculated as: Q = E (min C T 2 sample C T ) where Q = sample quantity relative to the sample with the highest expression; E = amplification efficiency; min C T = lowest C T value = C T value of the sample with the highest expression. To calculate the normalized relative gene expression levels, data were analyzed using Relative Expression Software Tool (REST) (http://gene-quantification. com/rest. html) [37]. Data of transcript expression levels were analyzed using oneway analysis of variance (ANOVA) at a confidence level of p,0.
Selection and Validation of Reference genes for RT-qPCR Normalization In order to compare the expression levels of hsp target genes in the WT and DmcyD mutant of P. agardhii CYA 126/8 under HL conditions, we normalized all the samples using the same RGs. The C T values were obtained for each candidate gene across all the samples (Fig. 1 ) and revealed the differences in transcript levels. The 16S rRNA gene gave the lowest C T (8. 81), corresponding to the highest expression level, whereas gltA and rsh showed the lowest expression levels, with mean C T values of 25. 66 and 25. 99, respectively (Fig. 1 ). The expression stability of the candidate RGs were analyzed using geNorm, NormFinder and BestKeeper, which provided complementary measures of the cDNA samples. Both geNorm and NormFinder classified 16S rRNA as the least stable gene (Table  3 ), regardless of the data series used (combined or single WT and mutant).
The transcript level of this candidate was also much higher than that of the others (Fig. 1 ). Because it is crucial to use RGs with ranges of expression similar to those of the target genes in the samples for analysis  [38] , we excluded the 16S rRNA gene from the BestKeeper analysis. The three most stable genes identified by all three programs were similar, especially for the first two in all the data sets (Table  3 ). Rsh, rpoD and gltA were identified as the best performing genes, whereas rpsL and 16S rRNA were always identified as the least stable genes. The optimum number of RGs required for an accurate normalization was provided by the pairwise variation (V n/n+1 ) calculation using the GeNorm program. The closest value to 0. 15 (the cut-off value for validation -  [35] ) in our analyses, was found for V 2/3 (0.
Acknowledgments We are very grateful to  Dr. Rainer Kurmayer  for providing us with the P. agardhii CYA 126/8 strain and its DmcyD mutant used in this study. We thank  Dr. Valerie Barbe  from the  Genomic Institut -Genoscope  (http:// www. genoscope. cns. fr),Evry, France for giving us 9 gene sequences. Many thanks to  Monika Gosh  for improving the English version of the manuscript.
TDCT was a PhD scholarship recipient of Vietnam. Financial support was provided through  ATM (''Biodiversiteet role des micro-organismes dans les ecosystemes actuels et passes '') fellowship from the  National Natural History Museum  (MNHN) from 2010 to 2011 and an annual grant from  Yves Rocher foundation  (grant number  660/09 ). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.
Genome sequencing of strains with fast and slow electrophoretic mobility To identify the genetic changes in the strains subjected to experimental evolution of electrophoretic mobility, we sequenced whole genomes of ϕ6-Fast1, ϕ6-Slow1, ϕ6-Fast2, ϕ6-Slow2, as well as the ancestor, ϕ6. We extracted genomic material from high titer lysates representing a population sample of each strain, using a QiaAMP Viral RNA extraction kit (Qiagen Inc. , Valencia, CA). We reverse transcribed extractions by RT-PCR with Superscript polymerase and random hexamer primers (Invitrogen, Carlsbad, CA). We purified PCR amplicons of the genome using gel extraction (QIAquick, Qiagen Inc. , Valencia, CA) and sequenced using standard Sanger sequencing methods. Sequences are available in Genbank under the following accession numbers: KF615858 -KF615869.
This strain was evolved under a high coinfection regime and acts as a "cheater", i. e. it outcompetes other coinfected viruses such that the vast majority of progeny viruses will bear the parental ϕH2 segments, to the exclusion of hybrids and the other parent. The known strategy of this strain served as further corroboration of hybridization estimation methods. We quantified electrophoretic mobility as described above, except that we diluted by serially poking the pipette tips directly on a plate with a Pp lawn. This modification was repeatable and increased throughput in the measurement of electrophoretic mobility. We generated mobility distributions using data from three independent gel runs of the same single burst lysate of each cross. We used finite Gaussian mixture models to determine the proportion of parental and hybrid progeny represented in the single burst lysate.
We used the package MIXTOOLS  [25]  in the R statistical programming environment  [26]  to estimate the proportions of mixture components. Because the distribution of parentals was known from control crosses, we input the mean migration value for parentals as prior information to fit the component mixture. The normal models in MIXTOOLS use an expectation maximization algorithm (EM) to find maximum likelihood estimates of parameters of incomplete observations. The EM algorithm has a stochastic element, so we run models with varying inputs to verify results. In particular, we tested an unsupervised classification (i. e. no prior information of means or variances of components) with three components to verify if the assigned components coincided with the parental controls and an intermediate hybrid.
We used the package MCLUST 3  [27]  which also uses an EM algorithm, but uses the Bayesian Information Criterion to determine the optimal number of mixture componentsas a second method to verify consistency of results. We considered the proportion assigned to the intermediate component to be the rate of hybridization for the environmental strain being tested. Crosses between strains may sometimes fail to yield hybrid progeny. Since hybrids may be present at low frequencies and may not be detected by our method, we adopted conservative criteria to conclude that the single-burst lysate was a two-component mixture, i. e. a hybridization rate of zero. We determined that a lysate from a cross was a two component mixture if: a) the proportion of a component in a mixture was lower than 0. 01 or the means of mixture components differed by less than 0.
Acknowledgements. This work was supported by funding from the ice2sea programme from the  European Union 7th Framework Programme , grant number  226375. Ice2sea contribution number  120. S. Price ,  M. Hoffman , and  M. Perego  were supported by the  US Department of Energy (DOE) Office of Science, Advanced Scientific Computing Research and Biological and Environmental Research programs. M. Hoffman  was partially supported by the  Center for Remote Sensing of Ice Sheets  at the  University of Kansas  through  US National Science Foundation  grant  ANT-0424589. Thanks to  Jonty Rougier  for helpful discussions. We thank  Fuyuki Saito  and two anonymous referees for their helpful reviews. Edited by:  E. Larour.
BLAST searches against the GenBank/EMBL Databases gave best hits with RanGAP2 (more than 97% identity with an e-value of 0) and RanGAP1 [AM411449. 1] (73% identity on average with an e-value lower than 10 -89 ), suggesting that, apart from RanGAP1, RanGAP2 does not have any close homologues in potato. All the RanGAP2 sequences obtained lacked introns and had a CDS of 1662 bp (553 amino acids), with the exception of one sequence obtained from accession 78S. 248. 1 of S. vernei (sequence VRN2-A1). This particular sequence variant has a nine nucleotide in frame deletion (nucleotides 1546 to 1554 corresponding to amino acids 516 to 518). All sequences encoded for a WPP domain at the 5'end (corresponding to amino acids 14 to 109) and 10 LRRs (corresponding to amino acids 213 to 493), a structure identical to that of the RanGAP2 orthologs sequenced in N.
Phylogenetic analyses Whatever the method used (maximum likelihood, maximum parsimony or minimum evolution), no clear evidence of phylogenetic structure was observed, as illustrated with the maximum likelihood analysis (Figure  1 ). Thus sequences from species with the genome formulae AA (diploid) or AAAA (tetraploid) were not structured according to the species origin. Nonetheless sequences derived from the diploid species with the genome formula BB  [25]  and from the di-and tetraploid species from Mexico and the USA tend to group together. Findings from the phylogenetic tree were then confirmed with factorial analysis. As can be seen in Figure  2 , axis 2 and axis 3 explained 12. 6% and 11% of the total observed variability, respectively, based on the RanGAP2 dissimilarity matrix. The dataset was divided into the two clades, A and B according to the genome formula, the clade A correspond to the species with the genome AA or AAAA.
Identification of positions of functional relevance in RanGAP2 Despite the low nucleotide diversity (1. 3%) observed along the Solanum RanGAP2 sequence dataset, we previously noted that nearly 50% of the mutations observed correspond to non synonymous mutations. Overall, 178 positions distributed along the entire protein sequence appear to be affected by non synonymous mutations, however only 28 of them appear to affect more than two Solanum species and 19 out of these 28 (ie nt positions 98,  175, 191, 309, 316, 709, 712, 787, 965, 1056, 1112, 1122, 1129, 1198, 1201, 1316, 1340, 1401, 1427)  are located in either the WPP or LRR domains. To identify functionally relevant variants among these 19 positions, we investigated whether evidence for positive selection pressure could be detected in our data, and carried out evolutionary analyses using either neutrality or dN/dS tests.
Neutrality tests (Tajima's D) were conducted at the intra-specific level in each of the investigated species, while dN/dS tests were conducted at the inter-specific level using SLAC, FEL and PAML (M1, M2, M7 and M8 models) methods. Values showing an excess of rare variants were obtained for the Tajima'D statistic (Table  1 ), but none of these neutrality tests provided significant results that allow a clear distinction from neutrality. In dN/dS methods, first using a reduced sequence dataset consisting of only one sequence per Solanum species (ie 18 RanGAP2 aligned sequences), we compared the four evolutionary models implemented in the CODEML program: M1 vs M2 and M7 vs M8. None of the positive selection models appeared to be better adapted than the null model (2Δl = 9. 84; NS p = 0,001). However, using the total sequence dataset (the 147 RanGAP2 aligned sequences), the positive selection models appear better adapted (2Δl > 69.
ADG1-A2 : Genome AA / AAAA : Genome BB : Genome AABB Figure  1  Phylogenetic analysis of Solanum section Petota RanGAP2 sequences using maximum likelihood methods. Phylogenetic analyses were conducted on the entire RanGAP2 sequence dataset with 1000 bootstraps. All branches with bootstraps lower than 50% were collapsed. Genome formulae AA or AAAA, BB and AABB of the species studied are reported according to Matsubayashi  [25]  and highlighted by colored areas. The PF variants which present a proline residue at position 106 and phenylalanine residue at position 237 are represented in red. (data not shown). Thus, natural variation at positions 106 and 237 also has no effect on Rx recognition of the "KR" variant of the PVX CP. Despite the detection by immunoblotting of similar RanGAP2 levels of all variants (Figure  3B ) it appeared that the intensity of the HR observed with the positive control Rook-6 varied depending on the RanGAP2 variant expressed.
Furthermore RanGAP2 also appears to be less variable than the housekeeping gene nitrate reductase, whose variability was evaluated in various Solanum species  [28]. The estimated pi value for two S. stoloniferum accessions (eight sequences) and one S. sparsipilum accession (two sequences) is at least twice as high for the nitrate reductase gene  [28]  than that estimated in these two species for the RanGAP2 gene (two S. stoloniferum and one S. spegazzinii accession were also represented in our dataset). The phylogenetic tree generated with the 147 RanGAP2 sequences correlates well with several phylogenies obtained with neutral markers or using DNA sequences of single copy housekeeping and chloroplast genes  [28] [29] [30] [31] [32]. We can also note that RanGAP2 sequences of the three allo-tetraploid species S.