dataesr/scientific-paragraphs-categorization huggingface dataset:
Experimental monoisotopic mass. 3 -At 5 ppm matching tolerance. Expectation value, calculated by ProSightPC software. The expectation value (e value) is the number of sequences in a database that are expected to have P-scores equal to or better than what was observed simply by chance. A P-score is the probability of obtaining at least as good a match between the observed fragment list and a sequence as by chance. Low E-values represent better matches (less likely to be false positives) than high E-values. 5, 6, 7, 8 -forms of the protein with N-terminal Met retained (5) or N-terminal NF (6), NFS (7) or NFSTN (8) residues cleaved. doi:10. 1371/journal. ppat. 1005312. t002.
Acknowledgments We thank  K. Anger  for her precious help in the BACTH experiments and  S. Aubert  for her help and interest in this work. We thank  J-P Flandrois  for stimulating discussions and  N. Taib  for sharing homemade scripts. We are grateful to  D. McGee  for the gift of vector pIR203C04. We thank the  Collection of the Institut Pasteur (CIP)  for providing us with the non-pylori Helicobacter strains. C. Brochier-Armanet  is member of the  Institut Universitaire de France (University French Institute ).
This work was supported by  Investissement d'Avenir grant  ( ANR-10-BINF-01-01  " Ancestrome "):  CBA , PhD fellowship of the  French Research Ministry: JG, Roux fellowship of the Institut Pasteur: FF, Projet transversal de Recherche PTR # 494  of the  Institut Pasteur: DV, Donation Janssen: HDR. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
2 - 4 - 24 Experimental monoisotopic mass. 3 -At 5 ppm matching tolerance. Expectation value, calculated by ProSightPC software. The expectation value (e value) is the number of sequences in a database that are expected to have P-scores equal to or better than what was observed simply by chance. A P-score is the probability of obtaining at least as good a match between the observed fragment list and a sequence as by chance. Low E-values represent better matches (less likely to be false positives) than high E-values. 5, 6, 7, 8 -forms of the protein with N-terminal Met retained (5) or N-terminal NF (6), NFS (7) or NFSTN (8) residues cleaved. doi:10. 1371/journal. ppat. 1005312. t002.
Clustering analysis For each file obtained per cell type, the list of differentially expressed genes was analysed using the eVenn package (version 2. 1. 6) to generate Venn diagrams and the common differentially expressed genes between all the tested cell types. The online David Bioinformatics Resource 6. 7 was used to define clusters of genes based on predicted functions. The R package FactoMineR was used to perform PCA on all 44k probes. For heatmap clustering and virtual Array, GSE38168 and GSE17984, GSE43398, GSE33953 from the NCBI GEO datasets (http://www. ncbi. nlm. nih. gov/geo/ ) provided data for the chicken and mouse respectively and were integrated in the comparison as described  (Heider and Alt, 2013). Anova analysis was performed on the 11708 common gene names found for the chicken arrays and on the 8749 found once the mouse ES and EpiSC data are integrated.
Relative Expression Figure  2  Pluripotency-associated genes are expressed in the different tested stem cell subtypes. The expression of pluripotency-related genes was analysed in the three cell types (cES, PGCs and cBC) by real time RT-PCR. Expression of NANOG, SOX3, ESRP2, TRIM71, POUV/OCT4, KLF2/KLF1, SALL4, LIN28A, TSPAN13, ACVR2B, ERBB3, MPLZ3, SOX7, DNMT3B, UCHL1, AP1S3, KRT8, ENS-1, LOC660611, is in the same range of expression for cES and cBC (from 23. 0 to 0. 5 fold), with more various expression level for the PGCs (from 2. 8 to 0. 15for KLF2). Expression was taken to be 1 in cES as a reference, and two independent samples were run, each in triplicate. Error bars indicate SD. cBC and PGC cells (Table  S3 -1). Analyzing this list using the David software, 82 genes were retained; several GO terms are enriched in this list: developmental process, regulation of biological process, regulation of gene expression, intracellular organelle and nucleus, nucleic acid binding and transcription regulator activity are particularly represented in pluripotent cells (cES and cBC) as compared to the two somatic cell types.
One of the larger sets, sharing the GO term 'Regulation of transcription', comprises 17 genes, predominantly encoding transcription factors: CNOT2, DNMT3B, EAF2, IRF6, KLF1, NANOG, SOX3, CDX2, ID2, LIN28, NR6A1, OTX2, SALL1, SALL4, ACTR2B, TESC and TFAP2A. cPOUV/OCT4 (not recognised by the David software through its chicken name, POUV) should be added to this list. Genes encoding RNA binding proteins are also present a b Figure  3  Differential gene expression in different stem cell subtypes. a: Expression of PERP, FGF1, SYT8, LOC424593, PPL, UPK3BL, GALNT3, ENPP2, CLDN3, RBP5, CLDN3, and NR6A1 and SOX2 genes was analysed by real time RT-PCR in cES, PGC and cBC. Expression was taken to be 1 in cES as a reference and two independent samples were run, each in triplicate. Error bars indicate SD.
a: PCA analysis was performed on the 11708 common genes between the data from this study (GSE61221) (PGC, cBC, cES, BM2 and CEF) and those provided by the PGC and CEF samples (PGC 2, CEF 2) of the GSE38168 microarray data as described in Materials and Methods. CEF and PGC from the two studies clustered together. Each sample (circle) is plotted as well as the barycentre of the triplicate (square). b: The heatmap analysis was performed by ANOVA analysis of the 11708 common genes between the data from this study (GSE61221) and those of the GSE38168 set as listed in A. The heatmap presented was performed with the first 1000 ranked genes. c:. By incorporating the mouse ES and EpiSC cells in a virtual array approach as described in Materials and Methods, a clustering tree reveals that cES cells cluster more closely with mES cells than with EpiSC, whereas the latter are more closely related to PGC.
Acknowledgments This work was supported by grants from  ANR  ( AviRepro -ANR 09-BLAN-0141  and  Epibird  project  ANR-09-GENM-004-03 ) to BP and grants from the  Medical Research Council (MRC)  ( G0400559 ) and  BBSRC  ( BB/K007742/1 ) to CDS. SI was funded by a  Ph. D. scholarship from the  programme 'Strategic Scholarships for Frontier Research Network' of Thailand's Commission on Higher Education  ( CHE-SFR ),  Royal Thai Government. We thank  M. Mc Grew ,  Roslin Institute, University of Edinburgh ) for providing the first samples of  PGC RNAs  and  N. Aillioli  (IGFL,  UMR5242 ,  University of Lyon ) for her initial advice on microarray experiments.
V. S. Galligani et al. : Meso-scale modelling and radiative transfer simulations essential to prepare for the next generation of microwave to sub-millimetre observations, i. e. the upcoming ESA MetOp-SG satellites with sub-millimetre frequency channels. Robust methods have to be developed to retrieve ice/snow parameters from satellite measurements. These methods are often based on large data sets of simulated observations. The accuracy of the retrieval largely depends on the quality of the simulated database and its representativity. As a first step in the development of such simulated databases, this paper analyses the sensitivity of simulated passive and active microwave observations to the microphysical properties of the frozen phase. The objective is to assess our capacity to simulate passive and active microwave observations in a consistent way for snowfall situations.
A meso-scale cloud model (Meso-NH) is coupled with a radiative transfer model (the Atmospheric Radiative Transfer Simulator, ARTS) and run for a real snowfall case. The simulated brightness temperatures (TBs) and equivalent radar reflectivities (Z e ) are compared to the available microwave observations from the Microwave Humidity Sounder (MHS) and the Cloud Profiling Radar (CPR). This study is structured as follows. Section 2 presents a heavy snowfall case, and includes a description of Meso-NH model outputs and the coincident satellite observations. Section 3 briefly describes ARTS, along with the recently incorporated radar simulator module and a description of the microphysical properties to be analysed. The sensitivity study of consistent active and passive radiative transfer simulations on such hydrometeor characteristics is presented in Sect.
Simulating passive observations with ARTS Radiative transfer (RT) simulations were performed with ARTS  (Eriksson et al. , 2011). ARTS is a freely available, well documented, open source software package that is well validated  (Melsheimer et al. , 2005; Buehler et al. , 2006; Saunders et al. , 2007). ARTS handles scattering with a full and efficient account of polarization effects. It provides different methods to solve the radiative transfer equation and the reverse Monte Carlo method  (Davis et al. , 2007)  is used in this study. The RT simulations take full account of the 3-D description of the atmospheric state modelled by Meso-NH. In order to accurately simulate satellite observations of this real scene, a correct description of the surface properties is important, especially for microwave frequency channels away from the water vapour absorption line at 183.
The cloud radar simulator incorporated into ARTS The equivalent radar reflectivity factor (Z e ) is the main quantitative parameter measured by radar instruments. In the absence of attenuation, the equivalent radar reflectivity factor Z e is given by integrating the backscatter cross-sections of the individual particles over their size distribution: Z e = λ 4 π 5 |K w | 2 ∞ 0 σ b (D)n(D)dD, ( 6 ) where λ is the radar wavelength, |K w | 2 is the reference dielectric factor (a value of 0. 75 is generally used for Cloud-Sat), σ b is the backscatter cross-section and n(D) is the particle size distribution. Recently, a module has been added to ARTS that allows the simulation of cloud radar observations. The module is part of the open source ARTS development version described in Sect. 3. 1. Since Eq.
sat. ltu. se/arts/misc/arts-doc/ uguide/arts_user. pdf). Equation (  6 ) is also used in the Quickbeam radar forward model  (Haynes et al. , 2007). The main difference between ARTS and Quickbeam is that the latter runs with pre-defined Mie tables or on-the-fly calculations based on a number of in-built choices for size distributions, phase and mass-size diameter. In contrast, the ARTS radar module defines externally the single-scattering properties and the particle size distribution. Note that the radar simulations are calculated in reference to the CloudSat altitude bin resolution (125 vertical bins 240 m thick).
To test these two possibilities, the availability of coincident CloudSat observations can be exploited. CloudSat observations allow comparing its different retrieved ice water path (IWP) with those modelled by Meso-NH for the CloudSat footprint, as shown in Fig. 4. In order to carry out this comparison, the three frozen species from Meso-NH are summed (ice, graupel and snow) along the Cloud-Sat footprint and compared with three different retrieval algorithms. The ROIWP product is one of CloudSat standard products and is available from the 2B-CWC-RO data set. ROIWP is the radar only (RO) retrieved value of IWP, obtained by assuming that the entire profile is ice, and zeroing out cases where all cloudy bins are warmer than 273 K (assumed to be liquid). The IOROIWP, similarly available from the 2B-CWC-RO data set, assumes that the entire column is ice only.
The new simulations enhance scattering. Future work is planned in order to incorporate publicly available databases of DDA from  Liu (2004)  directly, in order to avoid the approximation used in this study and developed by the same author. A number of studies have started the complex task of using DDA databases directly, such as  Geer and Baordo (2014)  who implemented with success the original DDA tables provided by  Liu (2004)  and others in their calculations.
Conclusions Microwave passive and active radiative transfer simulations are presented for a specific snowfall mid-latitude scene, using outputs from the Meso-NH mesoscale cloud model and compared to the corresponding microwave observations available from MHS and CloudSat. The sensitivity of radiative transfer simulations to the microphysical characteristics of the frozen particles (size, density, dielectric properties) is the focus of this study. The radiative transfer simulations were performed by coupling ARTS and its recently incorporated active simulator module, to the mesoscale cloud model Meso-NH. Performing a comparison of simulations and observations over a large range of frequencies and exploiting the passive/active synergy is a challenging but useful task because it imposes strong constraints on the assumptions made to simulate the observations.
Otideopsis kaushalii is combined in the genus Otidea. A key to the species of Otidea is given. An LSU dataset containing 167 sequences (with 44 newly generated in this study) is analysed to place collections and determine whether the named Otidea sequences in GenBank were identified correctly. Fourty-nine new ITS sequences were generated in this study. The ITS region is too variable to align across Otidea, but had low intraspecific variation and it aided in species identifications. Thirty type collections were studied, and ITS and LSU sequences are provided for 12 of these. A neotype is designated for O. cantharella and epitypes for O. concinna, O. leporina and O. onotica, along with several lectotypifications. The apothecial colour and shape, and spore characters are important for species identification.
concinna clade: ITS3 -ConcITS2midR (5´-GCCTGTAAATTTTAAAGACGAA-3´); ConcITS2midF (5´-CCAGGGTTGCTTTGGTA-3´) -ConcITS4 intR (5´-CACTGGGTAATTGGAGGTTT-3´); ConcITS2midF (5´-CCAGGGTTGCTTTGGTA-3´) -ITS4. PCR products were cleaned using ExoSAP-IT® (USB, Cleveland, OH, USA). The ITS was sequenced in both directions, using the primers ITS1 and ITS4 (and/or in a few instances ITS5, 5. 8S and ITS3) and the LSU using LR0R and LR5. For O. integra, the same primers as used for PCR, were also used for sequencing. PCR and sequencing conditions follows  Hansen & Olariaga (2015). Sequences were edited and assembled using Sequencher v. 4. 10 (Gene Codes Corporation Ann Arbor, Michigan, USA) and have been deposited in GenBank (Table  1 ). The sequences were aligned manually in Se-Al v. 2. 0a11 Carbon  (Rambaut 2002 ).
An all taxa LSU dataset was prepared. Monascella botry osa and Warcupia terrestris were used as outgroup, based on a higher level phylogenetic study of Pyronemataceae  (Hansen et al. 2013) , which supports these as the closest sister group. To explore inter-and intraspecific variation of the new species O. borealis, O. subformicarum and their closest relatives, two smaller datasets were prepared using ITS and LSU rDNA sequences, based on a more species-inclusive, multi-gene phylogeny of Otidea  (Hansen & Olariaga 2015). All three alignments are available from TreeBASE as accession no. S15887. The first dataset (the O. borealis dataset) contained O. borealis and related species in the O. concinna clade. The second dataset (the O. formicarum dataset) contained specimens of the O. formicarum clade.
Otidea caeruleopruinosa and O. nannfeldtii were used as outgroup for each of these datasets. Nucleotide diversity (e. g. Nei 1987, equation 10. 6) , as the average number of nucleotide differences per sites between two ITS sequences, was calculated within O. brunneoparva and O. subformicarum. One insertion or deletion, despite the length, was calculated as only one basepair difference.
Phylogenetic analyses Maximum Likelihood (ML) analyses of the all taxa LSU dataset were performed using the 'RAxML HPC2 on XSEDE' tool  (Stamatakis 2006 ) via CIPRES Science Gateway  (Miller et al. 2010) , employing mixed models of evolution and starting from a random tree. For the two smaller O. borealis and O. formicarum datasets, ML analyses were conducted using RAxML v. 7. 3. 1  (Stamatakis 2006 ) on the Bioportal, University of Oslo  (Kumar et al. 2009). A GTR-GAMMA model with four rate categories was assigned and all free model parameters were estimated by the program. For the ML bootstrap analyses (ML-BP) 1 000 rapid bootstrapping replicates from random starting trees were performed, followed by a subsequent ML search similarly using 1 000 replicates. As no strongly supported conflict was detected (ML-BP ≥ 75 %, PP ≥ 95 %), the ITS and LSU region were concatenated for the O.
borealis and O. formicarum datasets. Each combined dataset was analysed using ML analyses under the same settings as specified above. Relationships were likewise constructed using Metropolis-coupled Markov Chain Monte Carlo (MCMCMC) and 'model-jumping' as implemented in MrBayes v. 3. 2. 1 (Ronquist et al. 2012 ). The substitution model was sampled across the GTR space by the MCMC analysis  (Huelsenbeck et al. 2004 ). Four parallel searches, each with four chains, were run for ten and three million generations, respectively, for the all-inclusive LSU dataset and the O. borealis and O. formicarum datasets, initiated with random starting trees. The chains were sampled every 100 generations from the posterior distribution. The first 25 % of the trees sampled was discarded as the 'burn-in', and the remaining trees were used to calculate the posterior probabilities (PP) of the clades.
Alignment and ITS minisatellites Fourty-nine ITS and 44 LSU sequences were newly generated in this study (Table  1 ). In total 146 ITS sequences were utilised, including 34 obtained from GenBank and 63 from  Hansen & Olariaga (2015). The ITS sequences were too variable to align across all of Otidea, due to a highly polymorphic part in ITS1 and large length variability (insertions and deletions), and therefore were not included in phylogenetic analyses of the entire genus. The ITS sequences were aligned among closely related species or species groups, and used as an aid to verify identifications. The ITS region was especially useful in cases were the protein-coding genes (RPB1, RPB2 and EF1) failed to amplify, due to poor quality DNA from old or poorly treated material. ITS sequences of the type specimens of O.
mirabilis and O. kauffmanii that could not be amplified for the multiple genes are provided here. The all taxa LSU alignment consisted of 167 LSU sequences, including 57 from GenBank and 68 from  Hansen & Olariaga (2015)  (Table  1 ) and 956 bp including inserted gaps, of which 263 bp were parsimony informative characters. The O. borealis dataset consisted of 31 taxa, represented by 24 complete ITS-LSU and six ITS sequences, and 1 555 bp including inserted gaps (ITS 614 bp; LSU 941 bp), of which 216 were parsimony informative characters. The O. formicarum dataset consisted of 14 sequences and 2 531 bp (ITS 1 695 bp; LSU 836 bp). Part of the ITS1 (805 bp) in the O. formicarum dataset was omitted from the analyses, due to a long insertion and tandem repeats in O. subformicarum and the two Mexican specimens, and the combined dataset thus included 1 726 bp, of which 145 were parsimony informative characters.
All taxa LSU phylogeny The ML analysis of the all taxa LSU dataset resulted in a single best ML tree of -lnL = 6511. 68. Bayesian analyses reached an average standard deviation of split frequencies of 0. 004 after 10 M generations. A majority rule consensus tree was constructed from the 300 000 trees sampled from the four runs, each consisting of 75 000 trees sampled from the station- 1 Sequences from  Hansen & Olariaga (2015). 2 The voucher specimen for AF086573 is mistakenly given as OSC 56823 in GenBank. This voucher (OSC 56823) is O. smithii based on morphological re-examination and the ITS sequence (AF072061) deposited by the same authors. The LSU sequence AF086573 is O. leporina (Fig. 1 ). 3 The voucher specimen for AF086588 is mistakenly given as OSC 56825 in GenBank. This voucher (OSC 56825) is O.
leporina based on morphological re-examination and the ITS sequence (AF072078) deposited by the same authors. The LSU sequence AF086588 is O. tuomikoskii (Fig. 1 ). 4 ITS: Internal transcribed spacers (ITS1 and ITS2) and the 5. 8S gene of the nrDNA; LSU: 28S large subunit of the nrRNA gene. ary tree distribution (the first 25 % discarded as the burn-in) (Fig. 1 ). The ML and Bayesian tree topologies were congruent and recovered the same moderate to strongly supported clades (Fig. 1 ). The terminal clades that constitute species we recognise  (Hansen & Olariaga 2015)  have moderate to strong support in both analyses, except for O. leporina and O. mirabilis. A few synonymies inferred from sequences of type collections from which we were not able to obtain the protein-coding genes, and clear misidentifications of sequences deposited in GenBank are evidenced by the LSU phylogeny (see.
The O. formicarum and O. concinna clades in separate ITS-LSU phylogenies The ML analysis of the O. formicarum dataset recovered a single tree of -lnL = 3579. 93 (Fig. 2 ). Bayesian analyses reached an average standard deviation of split frequencies of 0. 003 after 3 M generations. A majority rule consensus tree was constructed from the 90 004 trees sampled from the four runs, each consisting of 22 501 trees sampled from the stationary tree distribution (the first 25 % discarded as the burn-in). The four collections of O. subformicarum from Spain and Italy form a distinct, strongly supported monophyletic group (ML-BP and PP 100 %). Likewise, the five collections of O. formicarum from Fennoscandia, and the two collections of O. aff. subformicarum from Mexico, each form separate, strongly supported clades (ML-BP and PP 100 %; ML-BP 84 %, PP 100 %, respectively).
Phylogenetic analyses of the combined ITS-LSU dataset fail however, to resolve relationships among these three clades with any certainty. The combined ML analysis of the O. borealis dataset resulted in a single best ML tree of -lnL = 4230. 82 (Fig. 3 ). Bayesian analyses reached an average standard deviation of split frequencies of 0. 005 after 3 M generations. A majority rule consensus tree was constructed, as for the O. formicarum dataset (above). The supported topology (PP ≥ 95 %) did not differ from the supported topology recovered by ML analysis. The placement of the single O. borealis collection is unresolved, but the ITS-LSU phylogeny confirms it is genetically divergent from its sister species (Fig. 3 ). Other species with a yellow outer surface of the apothecia, O. concinna, O. minor, O.
Otidea subformicarum Two collections from Mexico, FH301035 and FH301036 (here referred to as O. aff. subformicarum), belong to the O. formicarum clade based on our molecular phylogenetic analyses (Fig. 1, 2 ). Although analyses of the combined ITS-LSU dataset did not resolve the relationships among these three lineages (Fig. 2 ) and the LSU phylogeny place FH301035 as a strongly supported sister taxon to two collections of O. subformicarum, our three-and four-gene phylogenies  (Hansen & Olariaga 2015)  suggest the two European species share a most recent common ancestor, and the Mexican collections diverged earlier. The spore size and shape of the Mexican collections conform to those of O. subformicarum (Fig. 20 ), but the apothecial colours differ in these collections. Fresh material has not been available to us, but the photo of fresh apothecia of FH301035 shows eggyellow apothecia, clearly distinct from O.
Otidea bufonia-onotica clade Apothecia dark brown or ochre yellow. Basal tomentum with brown or yellow tones, especially when dried. Spores fusoid or ellipsoid, smooth. Resinous exudates on the ectal excipulum converting into reddish particles or melting into amber drops. Species   (Zhuang 2006). Our study of the O. brevispora holotype confirmed the smaller spores (9. 5-10. 5 × 5. 5-6 µm; L m = 9. 9 µm, W m = 5. 6 µm, Q m = 1. 75). Otherwise O. brevispora shares all macro-and microscopic features with O. onotica, including the yellow reaction of the basal mycelium in KOH. Our ML analyses, with an LSU GenBank sequence of the Chinese holotype, show O. onotica var. brevispora is a sister taxon to a clade of O. onotica specimens (Fig. 1 ). Based on this and the smaller spores we consider it to be a distinct species.