dataesr/scientific-paragraphs-categorization huggingface dataset:
This method makes use of a tool from mathematical statistics, so called "copula". The copula enables us to construct a bivariate distribution function from given marginal distributions with prescribed correlation and/or dependence structure. With this new formulation and FUV and FIR univariate LFs, we analyze various FUV and FIR data with GALEX, Spitzer, and AKARI to estimate the UV-IR BLF. The obtained BLFs naturally explain the nonlinear complicated relation between FUV and FIR emission from starforming galaxies. Though the faint-end of the BLF was not well constrained for high-z samples, the estimated linear correlation coefficient ρ was found to be very high, and is remarkably stable with redshifts (from 0. 95 at z = 0 to 0. 85 at z = 1. 0). This implies the evolution of the UV-IR BLF is mainly due to the different evolution of the univariate LFs, and may not be controlled by the dependence structure.
To explore the bivariate properties of the SF in galaxies further, the proper tool is the UV-IR bivariate luminosity function (BLF). However, constructing a BLF from two-wavelength data is not a trivial task. When we have a complete flux-limited 1 multiwavelength dataset, we can estimate a univariate luminosity function (LF) at each band, but what we want to know is the dependence structure between luminosities at different bands. Mathematically, this problem is rephrased as follows: can we (re)construct a multivariate probability density function (PDF) satisfying prescribed marginals? Although there is an infinite number of degrees of freedom to choose the original PDF, if we can model the dependence between variables, we can construct such a bivariate PDF. A statistical tool for this problem is the so-called "copula" (see Sec.
2 for the definition). A copula has been extensively used in financial engineering, for instance, but until recently there were very few applications to astrophysical problems (e. g. ,  Koen 2009; Benabed et al. 2009; Scherrer et al. 2010; Sato et al. 2010 Sato et al. , 2011)). Takeuchi (2010)  introduced the copula to the estimation problem of a BLF. In this work, we apply the copulabased BLF estimation to the UV-IR two-wavelength dataset from z = 0 to z = 1, using data from IRAS, AKARI 2 , Spitzer 3 , and GALEX 4. The layout of this paper is as follows. We define a copula, especially the Gaussian copula, and formulate the copulabased BLF in Sec. 2. We then describe our UV and IR data in Sec. 3. In Sec. 4, we first formulate the likelihood function for the BLF estimation. Then we show the results, and discuss the possible interpretation of the evolution of the UV-IR BLF.
UV-IR data construction We have constructed a dataset of galaxies selected at FUV and FIR by GALEX and IRAS for z = 0. At higher redshifts, GALEX and Spitzer data are used for z = 0. 7 and EIS (ESO Imaging Survey) and Spitzer for z = 1. 0 in the Chandra Deep Field South (CDFS). We explain the details of each sample in the following. In the Local Universe, we used the sample compiled by  Buat et al. (2007). This sample was constructed based on IRAS all-sky survey and GALEX All-Sky Imaging Survey (AIS). This dataset consists of UV-and IR-selected samples. The UV-selection was made by the GALEX FUV (λ = 1530 Å) band with FUV < 17 mag (hereafter, all magnitudes are presented in AB mag). This corresponds to the luminosity lower limit of L FUV > 10 8 L ⊙. Redshift information was taken from HyperLEDA  (Paturel et al.
2007 ). The detection limit is S 90 > 0. 2 Jy. We first selected AKARI sources in the SDSS footprints to have the same solid angle with a forthcoming corresponding UV-selected sample we are preparing with GALEX-SDSS. 5  Then, to have a secure sample of galaxies with redshift data, we made a cross match of AKARI sources with the Imperial IRAS-FSC Redshift Catalogue (IIFSCz), a redshift catalog recently published  (Wang and Rowan-Robinson 2009) , with a search radius of 36 arcsec. Since about 90 % of galaxies in the IIFSCz have spectroscopic or photometric redshifts at S 60 > 0. 36 Jy, the depth of the sample is defined by this matching. This determines the redshift range of this dataset, to be approximately z < 0. 1. We measured the FUV and NUV flux densities with the same procedure as the IRAS-GALEX sample.
We thus constructed the sample based on NUV-selection. Redshifts were taken from the COMBO-17 survey  (Wolf et al. 2004 ), and we have defined the z = 0. 7 sample as those with redshifts of 0. 6 < z < 0. 8. Data reduction and redshift association are explained in  Burgarella et al. (2006). We truncated the sample at NUV = 25. 3 mag so that more than 90 % of the GALEX sources are identified in COMBO-17 with R < 24 mag. We set the MIPS 24 µm upper limit as 0. 025 mJy for undetected sources. For the IR-selected sample, we based on the GOODS Spitzer/MIPS 24 µm sample and matched the GALEX and COMBO-17 sources. The sizes of the UV-and IR-selected samples are 340 and 470, respectively. In contrast to z = 0. 7, since NUV of GALEX corresponds to 1155 Å in the rest frame of galaxies at z = 1. 0, we cannot use NUV as the primary selection band as rest-frame FUV.
We adopted a formula L TIR = 2. 5νL ν @60 µm. This rough approximation is, in fact, justified by spectral energy distributions (SEDs) of galaxies with ISO 160 µm observations  (Takeuchi et al. 2006). Also, for the AKARI-GALEX sample, we have excellent flux density data at FIR. We used the following TIR estimation formula from AKARI two wide bands  (Takeuchi et al. 2005b (Takeuchi et al. , 2010)) , L TIR = ∆ν(WIDE-S)L ν (90 µm) +∆ν(WIDE-L)L ν (140 µm), (18) However, since deep FIR data of higher-z galaxies are not easily available to date, we should rely on the data taken by Spitzer MIPS 24 µm. we use conversion formulae from MIR luminosity to L TIR : log L TIR [L ⊙ ] = 1. 23 + 0. 972 log L 15 [L ⊙ ] , (20) log L TIR [L ⊙ ] = 2. 27 + 0. 707 log L 12 [L ⊙ ] +0. 0140 (log L 12 [L ⊙ ]) 2 , ( 21 ) which are an updated version of the formulae proposed by  Takeuchi et al.
We have already seen that the stepwise maximum likelihood estimation gave nonparametric LFs which reasonably agree with the assumed Schechter or Saunders function. Then, we can safely rely on the result for further discussion. However, we should note that the two-step estimation gives conditional errors of the parameters only for each step, not the marginal ones. Then, the errors for each parameters are significantly underestimated. If we want to discuss the error values more precisely, we need a larger dataset and must use the simultaneous estimation method. This will be done in the future works.
Second, if the ridge is curved upward, it means that the more active the SF in a galaxy is, the more luminous at the IR [dusty SF: (2) in Fig. 3 ]. Third, if the ridge is bent downward, the more active the SF is, the more luminous at UV ['transparent' SF: (3) in Fig. 3 ]. Now we show the estimated BLFs in Figs. 4-7. In the Local Universe, the BLF is quite well constrained. The estimated correlation coefficient ρ is very high: ρ = 0. 95 ± 0. 04 for IRAS-GALEX and ρ = 0. 95 ± 0. 006 for AKARI-GALEX datasets. The apparent scatter of the L FUV -L TIR is found to be due to the nonlinear shape of the ridge of the BLF. This bent shape of the BLF was implied by preceding studies  (Martin et al. 2005 ), and we could quantify this feature. The copula BLF naturally reproduced it. For the AKARI-GALEX sample, only the IR-selected sample is available at this moment.
This, however, does not bias the BLF estimation since the information from censored data points are incorporated properly in eq. (  24 ). This can be directly tested, for example, by using only one of the UV and IR-selected data for the z = 0 BLF estimation with IRAS-GALEX dataset. Both one-band estimations with UV and IR-selected data yield ρ = 0. 95 ± 0. 07. Obviously the error becomes larger because of fewer data, but the estimate itself remain unchanged. At higher redshifts (z = 0. 7-1. 0), the linear correlation remains tight (ρ ≃ 0. 91 ± 0. 06 at z = 0. 7 and ρ ≃ 0. 86 ± 0. 05 at z = 1. 0), even though it is difficult to constrain the  low-luminosity end from the data in this analysis (Spitzer-GALEX in the CDFS). The distribution of upper limits in Figs. 6 and 7 looks different from that in Fig.
Acknowledgments. This work is based on observations with AKARI, a  JAXA  project with the participation of  ESA. This research has made use of the NASA/IPAC Extragalactic Database (NED) which is operated by the  Jet Propulsion Laboratory, California Institute of Technology , under contract with the  National Aeronautics and Space Administration. TTT has been supported by  Program for Improvement of Research Environment for Young Researchers from Special Coordination Funds for Promoting Science and Technology , and the  Grant-in-Aid for the Scientific Research Fund  ( 20740105 ,  23340046 ,  24111707 ) commissioned by the  MEXT. TTT, AS, and FTY have been partially supported from the  Grand-in-Aid for the Global COE Program "Quest for Fundamental Principles  in the Universe: from Particles to the Solar System and the Cosmos" from the  Ministry of Education, Culture, Sports, Science and Technology (MEXT) of Japan.
The other 13 points were inaccessible due to crevasses. First, the old 1988 (Survey Of India (SOI)) and the new 2010 (UTM-Universal Transverse Mercator) coordinate systems were homogenized thanks to 6 geodetic benchmarks engraved in 1988 in rocks around the glacier. GPS measurements performed on these benchmarks in 2010 allowed us to calculate the local geometric transformation between the SOI and UTM coordinate systems. The residual error was less than 5 cm in horizontal and vertical components. From this transformation, the coordinates of the 104 points surveyed in 1988 were calculated in the UTM system. Second, we measured the elevations of the glacier surface at these points in order to obtain the thickness changes since 1988. Finally, these thickness changes are averaged by and applied to every 50 m altitude range area with data (67 % of the total area), and different thickness variations are tested for the unsurveyed glacier area (see Sect.
, 2009). Our study region includes 2110 km 2 of glaciers, 13 % of which is covered with debris. A complete description of the dataset and the methods we used here to adjust horizontally and vertically the DEMs and account for the C-band (SRTM) penetration into snow and ice can be found in  Gardelle et al. (2012a, b). Volume changes are calculated by integrating elevation changes over the whole glaciarized area or over individual glaciers, and a density of 900 kg m -3 is used for the volume-to-mass conversion. A seasonality correction, to cover 12 complete 12month periods from October 1999 to October 2011, must be applied between mid-October 1999 and mid-February 2000, a 4-month period when glaciers are accumulating mass. Given the lack of measurement of winter MB in the Lahaul and Spiti region our correction is based on a global mean winter MB of ca.
Regional representativeness of Chhota Shigri Glacier We used the 1999-2011 geodetic MB to assess the representativeness of the Chhota Shigri Glacier. With -0. 39 ± 0. 15 m w. e. yr -1 (a cumulative 12 yr MB of -4. 7 ± 1. 8 m w. e. ), the average glacier-wide MB for the Chhota Shigri Glacier is not statistically different from the region-wide (2110 km 2 of glaciers) MB of -0. 44 ± 0. 09 m w. e. yr -1. Also in favour of the regional representativeness of the Chhota Shigri Glacier, a good agreement was found between the glaciological and the regional (a 2 • latitude ×2 • longitude cell centered around the Chhota Shigri Glacier) ICESat-derived (Ice, Cloud,and land Elevation Satellite) cumulative MB between fall 2003 and fall 2008  (Kääb et al. , 2012 , their Supplementary Fig. S6 ). Those data indicate that the Chhota Shigri Glacier Table  5.
Discussion The MB series of the Chhota Shigri Glacier measured since 2002 using the glaciological method has been extended back to 1988 using geodetic measurements. Over the whole period 1988-2010, the cumulative MB of the Chhota Shigri Glacier was -3. 8 ± 1. 8 m w. e. , corresponding to a moderate mass loss rate of -0. 17 ± 0. 08 m w. e. yr -1. In fact, this www. the-cryosphere. net/7/569/2013/ The Cryosphere, 7, 569-582, 2013 glacier experienced first a slightly positive or near-zero cumulative MB between 1988 and 1999 followed by a period of ice wastage, confirming the presumption of  Azam et al. (2012). Azam et al. (2012)  could only infer indirectly and qualitatively a steady state of the glacier before 2002, based on a dynamical approach. Here, we are able to quantify this near-zero or slightly positive MB based on space-borne and field data of elevation differences available since 1988.
Acknowledgements. This work has been supported by the  IFC-PAR/CEFIPRA  under project no. 3900-W1  and by the  French  GLACIOCLIM observation service as well as the  Department of Science and Technology (DST)  and the  Space Application Centre of the Government of India. The  French National Research Agency  through  ANR-09-CEP-005-01/PAPRIKA  provided DGPS devices to perform field measurements. We thank our field assistant  Mr. B. B. Adhikari  and the porters who were involved in successive field trips,  Jawaharlal Nehru University  for providing all the facilities to carry out this work,  P. Chevallier  for providing the map and  A. Racoviteanu  for commenting this manuscript. E. Berthier  acknowledges support from the  French Space Agency (CNES)  and from the  Programme National de Télédétection Spatiale (PNTS).
This paper deals with the validation of the multiple scattering corrections developed in the CONRAD code for the capture yield calculations in the Resolved Resonance energy Range (RRR). In order to calculate the capture yields, analytic and stochastic calculation schemes implemented in CONRAD are described and compared with the analysis code SAMMY/SAMSMC. The results are in excellent agreement for a variety of samples. We concentrate the discussion here on 238 U, 197 Au and 55 Mn.
Introduction CONRAD  [1]  is dedicated to nuclear reaction cross section evaluation and related variance-covariance matrices from thermal up to the fast energy range. The aim of this note is to validate the capture yield calculations in the Resolved Resonance energy Range (RRR). The cross section calculations performed with CONRAD are compared with the reference code NJOY  [2]. Transmissions and capture yields are compared with the Resonance Shape Analysis codes REFIT  [3]  and SAMMY/SAMSMC  [4]. The nuclear reaction models implemented in the code for the RRR are the Reich-Moore and the Multilevel Breit-Wigner approximations of the R-Matrix theory. The details of the validation of cross section and transmission calculations can be found in Refs. [5] -  [7]. In general, the calculation with CONRAD are in perfect agreement with NJOY using the Multilevel Breit-Wigner or the Reich-Moore nuclear reaction model.
The plant phylogeny (A) is based on a trnL-F/trnK-matK combined dataset, and was built by parsimony with TNT1. 1 (default parameters, on www. phylogeny. fr) using the Jobson & Luckow  [30]  dataset (downloaded from Treebase, study number S1763, and amended with the P. trisperma from this study). The % of a and b-rhizobia per legume host (in bold) from field sampling  (B)  or from the coinoculation experiment (C) are represented as white (Burkholderia) and black (a-rhizobia) squares, with the number of strains sampled within each square. *: statistics of symbionts from  [44]  and  [11]. The grey colored square for Stryphnodendron indicates that % of a-rhizobia originates from a trapping experiment on soil (see Material & Methods section). doi:10. 1371/journal. pone. 0063478. g001 using uninoculated negative controls randomly placed between treatments.
Phylogenetic and Sequence Analyses Nucleotide sequences from 16 S rDNA, recA, nodC and nifH genes were corrected with CHROMAS PRO v1. 33 (Technelysium Pty Ltd), aligned using Muscle3. 6  [52] , and alignments were manually curated with GENEDOC  [53]. Screening and classification of each 16 S rDNA haplotype (unique sequence in our dataset) was performed using MOTHUR using the unique. seqs command  [54]. Phylogenetic trees were constructed by neighbor joining and likelihood methods using MEGA5  [55]  and PAUP4  [56] , or by Bayesian analyses using Mr Bayes  [57]  using priors from a GTR+I+G model (with parameters previously estimated by ML under PAUP4). Parsimony analyses on trnL-F+trnK-matK plant markers were performed on TNT1. 1 (default parameters, www. phylogeny. fr) using the Jobson & Luckow  [30]  dataset (downloaded from Treebase, www.
Building a Collection of Rhizobia from Piptadenia Group Species We sampled at least one representative species of the different clades and genera in the Piptadenia Group (see species in bold in Figure  1A ): Piptadenia sensu stricto (the ''Eupiptadenia'' clade e. g. P. gonoacantha, P. paniculata, P. adiantoides), the Pityrocarpa clade (Py. moniliformis), the P. viridiflora clade, and the Anadenanthera, Parapiptadenia, Stryphnodendron and Pseudopiptadenia clades. The species assignment of each plant host was confirmed by the Botanical garden of Rio de Janeiro (R. Ribeiro). For five species (Stryphnodendron adstringens, P. trisperma, P. paniculata, P. gonoacantha and A. peregrina), the taxonomic position was confirmed by sequencing the trnL-F marker, and comparing it to the Jobson & Luckow dataset  [30].
Among all sampled legume species, we confirmed that 17 of them are nodulated, and one, Piptadenia trisperma is a new report for nodulation (according to nodulation data in GRIN database, http://www. ars-grin. gov/,sbmljw/ cgi-bin/nodulation. pl and  [58] ). The rhizobial collection was composed of 196 isolates and details are presented in Table  1 , where they are classified according to original host legume, £ number of isolates from the same host, location and 16 S haplotype per representative strain listed in the first column. # positive nodulation tests obtained from the representative strain. representative strain and site of isolation. Representative strains for each location and legume host were chosen according to their 16 S rDNA sequence haplotype (named ribotype in Table  1 ): one strain per legume host and per sampling location with a unique 16 S rDNA sequence based on a 800 bp alignment was kept for further analyses, and designated as representative strains (for a total of 63 for strains from this work, and 79 when including reference strains from other studies).
Taxonomic Characterization of the Piptadenia Symbionts Collection To further characterize the rhizobial collection at the taxonomic level and assign a putative species to each strain, a phylogenetic analysis of all representative strains based on a partition of two neutral markers (643 bp of the variable part of the 16 S rRNA gene, and a 644 bp fragment of the recA gene), was built following a Bayesian analysis (see Mat&Methods). The recA gene is a more highly resolved phylogenetic marker than the 16 S rDNA gene, and has proven to be a valuable tool for discriminating species within the Burkholderia genus  [11] ,  [12] ,  [59]. We also included type strains of the most closely related species in the genera Burkholderia, Rhizobium and Bradyrhizobium. A phylogenetic tree of the 16 S-recA dataset is presented in Figure  2A  (only for the genus Burkholderia, as a-rhizobia are presented in Figure  S3 ); and individual gene marker phylogenies are available as Figure  S4.
50%). Abbreviations used: B. : Burkholderia, C. : Cupriavidus, BPL: Burkholderia phenoliruptrix, BT: B. tuberum, BSa: B. sabiae, BD: B. diazotrophica, BSy: B. symbiotica, BM: B. mimosarum, BN: B. nodosa, BSP1,2,3,4: Burkholderia sp. 1 to 4, T indicates species type strains. Accession numbers are listed in TableS3. doi:10. 1371/journal. pone. 0063478. g002.
Size-exclusion chromatography multi-angle light scattering The experiments were performed using a Viskotec TDA305 triple detector array with an integrated GPC max VE 2001 system (Malvern, France). The protein samples were injected at a concentration of 2 mg/ml (&40 mM) on a Superdex TM 200 HR 10/300 (GE Healthcare) column equilibrated in 20 mM Tris-HCl (pH 7. 5), 100 mM NaCl. The OmniSEC software of the manufacturer was used for acquisition and analysis of the data. Bovine serum albumin (Sigma-Aldrich) was used as standard reference protein for detector calibration.
Dynamic light scattering Dynamic light scattering (DLS) measurements were carried out at 20 C on a Wyatt DynaPro light scattering instrument with both protein forms, NprR and NprR(ÁHTH), at 20 mM alone or in complex with 200 mM NprX7. The samples were centrifuged at 15 000g for 10 min at 4 C just before use. For each sample, 20 acquisitions (5 sec each) were recorded. The data were analyzed using the Dynamics V6 software (Wyatt Technologies Corp. ).
Isothermal titration calorimetry Isothermal titration calorimetry (ITC) experiments were performed at 20 C with an ITC200 calorimeter (Microcal Õ ). The protein concentration in the microcalorimeter cell and the peptide concentration in the syringe were set at 25 mM and 250 mM, respectively. A first injection of 0. 4 ml was followed by 20 injections of 2 ml at intervals of 180 s. The data were analyzed according to the one binding-site model using the MicroCal Origin software provided by the manufacturer.
Crystallographic analysis The diffraction data sets used for the resolution of the structure were collected at 100 K on beamline Proxima-I at the synchrotron SOLEIL (Gif-sur-Yvette, France). Diffraction data from selenomethionine labeled crystals were collected at a single energy (12. 6615eV) corresponding to the maximum of the anomalous dispersion factor f 00. The native data set was collected at a wavelength of 0. 980 A ˚. Both data sets were processed using the XDS package  (28). Phasing was performed by the single-wavelength anomalous diffraction (SAD) method. The sub-structure determination was performed with the SHELX program suite  (29). Initial phasing was performed by the program PHASER  (30) , and density modification was carried out with RESOLVE  (31). An initial model covering $80% of the NprR(ÁHTH) sequence was automatically built using BUCCANEER  (32).
This partial model was then used as input for phasing using the program PHASER, and completion of the sub-structure was performed by a new run of RESOLVE and BUCCANEER. Subsequent model building was done manually with COOT (33) alternating with refinement cycles using BUSTER  (34). Non-crystallographic symmetry was applied in the first steps of refinement. The final refinement cycles were performed using PHENIX  (35)  against the 3. 2 A ˚resolution native data set. The B-factors were refined individually. The final model was evaluated using COOT validation tools and the MolProbity software  (36). Final statistics are listed in Table  2. The atomic coordinates and structures factors of the NprR(ÁHTH)/NprX8n complex have been deposited in the Protein Data Bank under the ID code 4GPK.
SAXS analysis SAXS data were recorded on beamline Swing at SOLEIL Synchrotron (Gif sur Yvette, France) at a wavelength of 1. 003 A ˚on a 17 cm Â 17 cm low-noise Aviex chargecoupled device detector positioned at a distance of 1850 mm from the sample, with the direct beam offcentered. The useful Q-range was 0. 018-0. 4 A ˚À1 , where Q = 4psiny/ is the momentum transfer, and 2y is the scattering angle. 40 ml of NprR(ÁHTH) sample at 14 mg/ml were injected into a size-exclusion column (SHODEX KW403) using an Agilentß High Performance Liquid Chromatography system cooled at 15 C and eluted directly into the SAXS flow through capillary cell at a flow rate of 150 ml/min. SAXS data were collected online throughout the whole elution time, with a frame duration of 2 s and a dead time between frames of 1 s.