dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2010). A higher r. m. s. d. value of 2. 46 A ˚is encountered upon comparison with the mean structure of the hC5a NMR structure  (Zhang et al. , 1997)  owing to a distortion of the region between Val705 and Ala716 in the NMR model compared with other C5a structures (Fig. 3a ). In hC5a, the short sequence preceeding the core region (residues Tyr690-Ser693) forms a loop that allows the N-terminal segment to pack back against the C5a core in an H1 helical motif antiparallel to H2. In contrast, residues Tyr690-Ser693 of hC5a-A8 form an additional helical turn prior to H2, allowing the latter to merge with the N-terminal helical segment into the long helix H1 0. This arrangement is reminiscent of that observed for hC5a-desArg  (Cook et al. , 2010). Indeed, monomer B of hC5a-desArg (PDB entry 3hqa) displays a similar structural arrangement to hC5a-A8, with a long N-terminal helix and an overall three-helix bundle architecture, and the two structures superimpose well with an average r.
In both panels, the residues delimitating the conserved core between all structures, Val694 and Ile742, are indicated with stars. (a) Superimposition of hC5a-A8 (purple) with the structure of hC5a from human C5 (green; PDB entry 3cu7;  Fredslund et al. , 2008)  and from the NMR model (orange; PDB entry 1kjs;  Zhang et al. , 1997). The stretch of residues corresponding to the region encompassing the most significant structural differences within the conserved core of all three proteins is indicated in blue. (b) Superimposition of hC5a-A8 (purple) with the two conformations observed for hC5a-desArg (grey and salmon for monomers A and B, respectively, from PDB entry 3hqa;  Cook et al. , 2010). 1180 A ˚2). The other monomer-monomer interactions that are observed in the hC5a-A8 crystals do not represent biologically relevant interfaces according to PISA.
The purpose here is not to list the bioactive molecules that have been discovered, elucidated, synthesized, nor to detail their bioactivities. Excellent reviews are regularly updated on the subject, presented by natural products chemists and pharmacologists. Interactive databases include standard spectral information, and sophisticated algorithms are available as aids for structure elucidation and as dereplication tools. Two recent and outstanding information sources are  [31]  and its updated version  [33] , and  [34]. Well over 20,000 secondary metabolites have been screened so far, but only a handful is now legally approved for use as prescription drugs or treatments by the Food and Drugs Administration (FDA) and by the European Union since 1974 (see  [36]  and updates thereof in: http:// marinepharmacology.
How most of us are doing depends not on fluctuations of the stock market, nor on the price of the latest hi-tech widget, nor on whether the next crime scene is going to take place in our neighborhood, but on how fast our natural environment is going to degrade. Why not redirect our priorities and efforts to better understand how our biosphere functions, how species interact, how biomes are connected, how biodiversity is maintained in marine and terrestrial environments? We generate enormous amounts of data of genomic, chemical, taxonomic, pharmacological, biotechnological value that can be better analyzed. Dealing with environmental issues, the first question is the preparation of environmental databases, our "clouds"-the crucial initial step. What "standard" environment will be suitable to monitor long-term changes? Climatic changes being global (temperature rises, ocean acidification etc.
) and heavily influenced by human activities, one can only define a study zone as representative of a "standard environment" if it is not locally impacted by direct human interference (habitat destruction, introduction of alien species, chemical and microbiological pollution…). Fluctuations in parameters of interest must not be subjected to amplitudes higher than those of experimental (read: impacted) study zones, and sampling must be large and statistically significant for the "standard" database to be robust and reliable enough to evidence mild to severe changes in same parameters from experimental study zones. This applies not only to physico-chemical parameters, but also to genomics, transcriptomics or metabolomics metadata (e. g. microbial metagenomics). We need markers within variables, sentinel species within populations, signatures within metabolomes, early responses within complete transcriptomic repertoires, and so on.
Acknowledgements. We thank  J. Roulle  (for cold room operation),  X. Ravanat  and  G. Pulfer  (for help with collecting snow material),  C. Carmagnola ,  C. Chandiou , and  N. Calonne  (for help with experiments),  P. Hagenmuller  (for discussions), and  E. A. Hardwick  (for improving the English in the paper). The research leading to these results was possible because of funding from the  People Programme  ( Marie Curie Actions ) of the European Union's  Seventh Framework Programme  ( FP7/2007-2013)  under  REA  grant agreement # 298672  ( FP7-PEOPLE-2011-IIF, "TRIME "). Financial support was also provided by Project # 144 ,  MAP 3 ("Monitoring for the Avalanche Prevision Prediction and Protection") ,  Obiettivo Cooperazione Territoriale Europea Italia/Francia  (Alpi) 2007-2013 Alcotra. This work has also been supported by a grant from  LabEx OSUG@2020  (Investissements d'avenir - ANR10 LABX56 ).
ACKNOWLEDGMENTS The authors are grateful to all their colleagues who kindly answered, whether they contributed to the poll or not, and to  Editors John McNeill ,  Gerry Moore  and  Franz Stadler. Thanks are due also to  Howard Falcon-Lang  about the publication dates of The  Fossil Flora of Great-Britain , to  Michael Krings  for information about Hopfgarten, the type-locality for Agathoxylon, and to  Eckhard Mönnig ,  Ralf Werneburg ,  Gerd-Rainer Riedel ,  Norbert Hauschke ,  Stephan Schultka  and  Volker Wilde  for information about collections in their care. Five anonymous reviewers kindly helped to improve a first version of this paper. This research got support from  National Natural Science Foundation of China  ( 41172006 ).
Detection of migration within the Drosophila simulans species complex The statistics R F , W x1s2 and W x2s1 were calculated for a D. simulans complex dataset (Table  1 ), with the aim of testing for gene flow, and assessing direction, between these closely related species. In all pairwise comparisons, the two populations of D. mauritiana were considered separately because of their high differentiation  [27] , and intraspecific comparisons were done for this species. Table  2  gives estimates of the mutation and recombination parameters for each locus and each species. For all loci, D. simulans showed the highest estimates for both parameters, the estimates were much lower for D. sechellia and intermediate values were found for the two populations of D. mauritiana. Compared to the parameters used in the simulations we can see that the estimated rate of recombination is high enough ( ρ >10 for most locuspopulation combinations) to provide some power for the detection of gene flow with the statistics proposed in this work (Figure  3 ).
Conclusions This works confirms the prediction that the spatial arrangement of polymorphisms along a recombining stretch of a genome is affected by the presence and direction of gene flow. Two summary statistics are described that are sensitive to the spatial clustering of polymorphic sites generated by gene flow, and which can be used in a test to reject the Isolation model as null hypothesis. A third statistic is described that has a distinct behaviour in the presence of migration, but that was not adequate for the test-based approach described in this work. The interest of these statistics is that they are applicable to datasets with characteristics that prevent their analysis with current available programs such as IM  [41] , because of the presence of intragenic recombination, or MIMAR  [8] , because of the absence of fixed differences and shared polymorphisms at the same time.
However, these statistics should not be seen as a substitution of those methods but rather as a complement. They allow the extraction of information from data that derives from linkage disequilibrium among alleles from recombining loci. This is information that is usually lost when IMa is applied to short sequences (length selected to assure lack of recombination within them) or in likelihood-free methods (such as approximate Bayesian computation, ABC) that do not use heavy computing LD summary statistics. These statistics can be applied to an entire dataset as a previous step before applying IMa to a reduced dataset. They may also be useful as summary statistics in the ABC framework; like the variance of pairwise differences that have low power when applied as a test  [3]  but have proved useful for ABC  [42].
Hey's Sites software (http://bio. cst. temple. edu/~hey/software/software. htm). Sites software numerically solves the equations of the expected number of F, S, X 1 and X 2 sites in function of θ 1 , θ 2 , θ A and T for the observed number of F, S, X 1 and X 2 sites. Power and the false positive rate of the proposed statistics were evaluated on datasets generated with coalescent simulations. Six demographic models were used for this evaluation: the isolation (I), migration (M), unidirectional migration (UM), isolation-with-migration (IM), isolationwith-unidirectional-migration (IUM) and panmictic population (P) models (see Additional file 1: Figure  S1 ). The isolation model is as described above. The migration.
Acknowledgements We thank  M. Debiais-Thibaud  for collecting Drosophila simulans flies from  South Africa  and  C. Biémont  for making lines of D. simulans from Madagascar and Comoros available. We thank  B. Emerson  for correcting and improving the English of this article. The input from several reviewers (in particular one that revised three different versions of the manuscript) contributed to improve the text and the analyses. This work was supported by  Agence Nationale de la Recherche ,  Biodiversity program   ANR-06-BDIV-003. DL benefited from membership of the " Laboratoire d'Excellence "  TULIP  ( ANR-10-LABX-41 ). MN has also been funded by the  Jeune Équipe INRA   2010  " Inférence en Génétique et Génomique des Populations ".
Examination of 59A THI3 sequence compared to S288c revealed several single nucleotide polymorphisms (SNPs), of which three are non synonymous (Y14C, L344I and D592N). We also observed an insertion of one adenosine in a polyA chain in the C terminal region, causing a frame-shift that altered the protein from amino-acid 605 to the stop codon (Additional file 1). The S288c Thi3p sequence is 609 amino-acids long whereas the 59A version contains 621 amino-acids. We compared the THI3 sequences of the published genomes from the Sanger Institute  [21]  or in Genbank. Most S. cerevisae strains possess the THI3 allele found in strain 59A, whereas strain S288c and other lab strains (W303 and BY4742) possess the 609 amino-acids version. S. paradoxus THI3 sequence display the same number of adenine as 59A and most of S.
Yeast strain and culture condition The two parental strains used in the global analysis are S288c (MATα; SUC2, gal2) and 59A (MATa; ho), which is a haploid derivative of EC1118 (Lalvin EC-1118, Lallemand, HO/ho), an industrial wine strain  [17]. Mutations between the two parental strains are accessible in the GenYeasTrait database previously described (http:// genome. jouy. inra. fr/genyeastrait/ [17] ). The fermentations were performed for the global analysis, as presented in  [16] , in a synthetic SM425 medium (with 425 mg/L of assimilate nitrogen pH3. 3 and previously describes as SM300  [28] ) which mimics natural grape must. Except when indicated, this synthetic medium contained 250 μg/L of thiamine (Sigma T-4625). The same medium and method was used to analyze the constructed strain (59A, 59A THI3-S288c and 59A thi3Δ).
eQTL linkage analysis Details on the transcriptomic profiles for the global analysis were provided in a previous publication  [16]. This profiles were obtained at 70% of the fermentation progress. The Cy3-labeled cRNAs were hybridized on Agilent gene expression microarrays 8x15k with the one-color method (Agilent Technologies, Santa Clara, CA, USA). Array design is based on ID 016322 completed with the 39 genes from the new regions of EC1118  [30]  and available on GEO under the accession number GPL16012. The array pictures were analyzed on a GenePix 4000B laser Scanner (Axon Instruments) with the GenePix PRO7 software. The complete array data set is available on the Gene Expression Omnibus database (www. ncbi. nlm. nih. gov/ geo, global analysis: GSE41025). Data normalization and statistical analyses were performed using R 2.
13. 1 software and the limma package (  [31] [32] [33] [34] ). Normalization was done using the quantile method considering the whole array data set (55 arrays). The normalized LOG 2 of the spot-median intensity was used as the quantitative evaluation of gene expression (5. 3 corresponding to background signal, 16 to spot saturation). The individual genotyping method is described in a previous publication  [17]  on Affymetrix YGS98 microarrays. The marker map was designed according to a selection inspired by  Brem et al. 2002 [15]. This resulted in a map of 2140 markers that we completed with 46 markers localized in areas of low markers density  [16]. These markers were based on single nucleotide polymorphism detection using Illumina veracode technology. The linkage analysis was performed using the R/qtl package  [35].
The qPCR was performed in three technical replicates. Expression levels were calculated in LOG 2 using a calibration range of gDNA for each primer pair then measured relative to UBC6 expression for each condition. A global correction of the data was performed, taking the lower replicate expression level as 0, for THI11 expression in 59A thi3Δ, as this strain is not able to express this gene. The good stability of the results was controlled by the SCR1 expression level which did not change between conditions (data not shown).
Experimental Crystal data  Symmetry codes: (i) Àx; y þ 1 2 ; Àz À 1 2 ; (ii) Àx; Ày; Àz À 1; (iii) Àx; Ày; Àz. Data collection: APEX2  (Bruker, 2006) ; cell refinement: SAINT  (Bruker, 2006) ; data reduction: SAINT; program(s) used to solve structure: SIR97  (Altomare et al. , 1999) ; program(s) used to refine structure: SHELXL97  (Sheldrick, 2008) ; molecular graphics: ORTEP-3 for Windows  (Farrugia, 2012)  and DIAMOND (Brandenburg & Putz 2005); software used to prepare material for publication: WinGX  (Farrugia, 2012)  and CRYSCAL (T. Roisnel, local program). Supporting information for this paper is available from the IUCr electronic archives (Reference: BG2529).
When indicated, films were scanned and immunoblot-derived signals quantified using the ImageJ software (National Institutes of Health). Full size images of blots are shown in Supplementary Figs. 11 12 13 14 15 16 17. Animal studies. For long-term tumorigenic experiments with cell lines, MDA-MB-231-Luc cells were trypsinized, resuspended in complete medium, counted, pelleted by centrifugation, washed once with ice-cold PBS, re-pelleted and resuspended (2 Â 10 7 cell per ml) in a an ice-cold 50:50 solution of Matrigel (Growth Factor Reduced and Phenol Red-free; reference: 356231; BD Biosciences) and PBS. Fifty microlitres of the final cell suspension (1 Â 10 6 cells) were introduced into the right inguinal mammary gland of anaesthetized, age-matched 4-6-week-old female NOD/SCID mice using a 25-gauge needle.
4T1 (usually 3,000 cells per implant, except in Supplementary Fig. 3f  in which we used 500,000 cells per implant), 4TO7 (500,000 cells per implant) and 168FARN (500,000 cells per implant) were introduced in mammary fat pads as above. Tumour growth in each case was quantified by taking measurement of the tumour length (L), width (W) and depth (D). These parameters were then used to calculate the total tumour volumes at experimental time point using the mathematical formula: tumour volume ¼ pLWD/6. Alternatively, tumour growth was assessed using as read-out the bioluminescence generated by the luciferase stably expressed by MDA-MB-231-Luc cells. To that end, orthotopically transplanted mice were injected intraperitoneally (150 mg g À 1 of body weight) at the indicated time points with a D-luciferin (Promega) solution in PBS, anaesthetized with isofluorane and imaged using an IVIS system (Xenogen) coupled to Living Image Acquisition and Analysis software (Xenogen).
Tissues were rehydrated through successive incubations with 70% ethanol followed by distilled water and then stained with Red Carmine overnight. Mammary glands were analysed with a stereomicroscope (SMZ800, Nikon) and acquired with a digital camera (Digital Sight DS-Fi1, Nikon). The mammary gland area was obtained by calculating the area occupied by a lane drawn around the gland, including the nipple. The mammary gland length was measured from the end of the longest duct to a reference median point located between the nipple and the adjacent lymph node. In both cases, the analyses were carried out using ImageJ software (National Institutes of Health). Number of terminal end buds and branching points were counted using photomicrographs of the entire gland. Analysis of mRNA abundance. Total RNAs were extracted using the RNeasy mini kit (Qiagen) and expression levels of transcripts determined by qRT-PCR using the QuantiTect SYBR Green RT-PCR kit (Qiagen) and a qRT-PCR apparatus (StepOnePlus Real-Time PCR System cycler, Applied Biosystems).
Primers used for mRNA quantitation are described in Supplementary Table  5. For normalization purposes, we measured in parallel the abundance of the endogenous transcript for human and mouse GAPDH (Supplementary Table  5 ). Raw data were analysed using the StepOne software v2. 1 (Applied Biosystems). In vitro proliferation and survival determinations. We used the MTT (3-(4,5dimethylthiazol-2-yl) 2,5-diphenyltetrazolium bromide) assay (Sigma) to determine the growth properties of cancer cells under study. To this end, cells were plated onto 24-well dishes (7,000 cells per well) and cultured in RPMI supplemented with 10% calf serum for the indicated periods of time. At the indicated time point, the culture medium of each well was discarded and replaced by 250 ml of an MTT solution (0. 5 mg ml À 1 ) in PBS.
5% FCS) or, alternatively, in standard MEC primary growth medium supplemented when needed with indicated drugs. After 10 days, bright-field pictures of cell colonies were acquired using an inverted microscope (Axiovert 135, Zeiss) coupled to a digital camera (C2724-95, Hamamatsu). After measuring the diameter (D) of the cell colonies using ImageJ software, the volume of colonies (V) was calculated using the following mathematical formula: V ¼ 4. p. (D/2) 3 /3. When indicated, 0. 2 mM wortmannin (Calbiochem) or 40 mM PD98059 (Calbiochem) were included in culture media. For cell survival determinations, cancer cells were seeded in either complete or serum-free media using both standard (at 25 Â 10 4 cells per well) and ultra-low attachment surface six-well plates (5 Â 10 5 cells per well) obtained from Corning.
After autoradiograph exposure of filters at À 70 °C, the total amount of radiolabelled proteins was determined indirectly by densitometry analysis of autoradiography films using the ImageJ software. Polysome analyses were performed adapting a procedure previously described for Sachharomyces cerevisiae 52. To this end, 6-8 Â 10 7 cells were grown in 150 mm dishes to 80% confluency and, immediately before harvesting, treated with cycloheximide (0. 1 mg ml À 1 , Sigma) for 5 min at 37 °C. Cells were washed twice with ice-cold PBS containing 0. 1 mg ml À 1 cycloheximide and scraped off from plates using 2 ml of the same solution and a rubber policeman. Cells were collected by centrifugation and lysed by vortexing in 400 ml of polysome-profile lysis buffer (10 mM Tris-HCl (pH 7. 5), 60 mM NaCl, 15 mM mgCl 2 , 0.
PCR products were sequenced at the Genomics and Proteomics Unit of our Center. Image processing. Images were assembled and processed for final figure presentation using Canvas 9. 0. 4 (Deneba Systems). Statistics. Statistical analyses were performed using the two-tailed Student's t-test for paired and impaired data versus control values or, in other cases, the analysis of variance. Sample size was chosen to obtain statistically significant conclusions. No samples were excluded from analyses. With the exception of immunohistochemical data, score of experiments was not done blindly. Results with a P-value r0. 05 were considered as statistically significant.
Acknowledgements We thank  A. Abad ,  M. Bla  ´zquez and personnel of the  CIC Histology, Genomics/Proteomics and Bioinformatics units  for technical assistance. This work has been primarily supported by a cooperative grant from the  Spanish Association Against Cancer (AECC)  to both X. R. B. and B. A. Additional funding includes grants from the  Castilla-Leon Autonomous Government  to X. R. B. ( CSI039A12-1 ) and the  Spanish Ministry of Economy and Competitiveness  to X. R. B. ( SAF2009-07172 ,  SAF2012-3171 ,  RD06/0020/0001  and  RD12/0036/0002 ), E. d. A. ( RD12/0036/0017 ), B. A. ( RD06/0020/1002  and  RD12/ 0036/0075 ) and M. D. ( BFU2011-23668 ).
© 2014 Núñez et al. ; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons. org/licenses/by/4. 0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http:// creativecommons. org/publicdomain/zero/1. 0/) applies to the data made available in this article, unless otherwise stated.
L egionella anisa was first isolated from an environmental sam- ple, in drinking and cooling-tower water  (1). Some strains were also implicated in several cases of Legionnaire's disease  (2, 3). We isolated a strain of L. anisa, Linanisette, using an amoebal coculture procedure. Common bacterial detections and culture methods, such as those used for Legionella spp. , did not allow its isolation (4). This is a Gram-negative and Gimenez-positive bacillus, classified in the genus Legionella. Regarding the mip gene (macrophage infectivity potentiator), the strain Linanisette exhibits 100% similarity using BLASTn  (5)  with the type strain of L. anisa (Genbank accession no. U91607). After cocultivation with the amoebic species Acanthamoeba polyphaga, L. anisa Linanisette is unable to grow on Columbia with 5% sheep blood agar and is able to grow after 3 days on buffered charcoal yeast extract (BCYE) medium, under a 2.
5-to-5% CO 2 atmosphere. This bacterium is oxidase, catalase, and gelatinase positive. The genome was pyrosequenced with a 454 GS FLX Titanium platform (Roche, Branford, CT) (6) using paired-end and shotgun techniques. A total of 437,769 reads were assembled with Newbler software version 2. 6 (Roche). Scaffolding was improved using Opera software version 1. 4  (7) , combined with GapFiller version 1. 10 (  8 ) and manual curation. The draft genome of L. anisa Linanisette consists of 20 scaffolds of 56 contigs containing 4,319,808 bp and an estimated size, including gaps, of 4,341,831 bp. The GϩC content of this genome is 37. 97%, which is similar to those of the other closed Legionella spp. Using BLASTn, Aragorn (9), and RNAmmer (10), the draft genome was shown to contain 39 RNA genes, including 3 rRNAs in a single operon and 36 tRNAs.