dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments This work was only possible thanks to the strong observation effort of the volunteers of the  Loire Basin Mammal Network  and the agents of the National Wildlife Office (ONCFS) since 1985. The river data collection in the hydro-morphology audit system SYRAH is financed by the  French Ministry of Research and Ministry of Environment and Sustainable Development. We are indebted to  Sandrine Ruette  (ONCFS) and  Charles Lemarchand  ( University Clermond-Ferrand ) for their constructive comments on the analysis approach and interpretation of the species' inventories. species requirements to the analysis of corridors over different landscape gradients and habitat components already proved successful for terrestrial species [9]. The complementary approach for different species integrating both habitat aspects and ecological networks offers perspectives to address more exhaustively ecological requirements for riparian corridor functioning, and challenges restoration by the diverse needs of different species [50].
Data Analysis General estimates of genetic diversity as well as genetic differentiation and phylogeographic analyses are reported in Buiteveld et al. [24]. We recalculated inbreeding coefficients (F IS ) taking into account the possible presence of null alleles by using the program INEst, running the individual inbreeding model with a Gibbs sampler of 10 5 iterations  [28]. Fine-scale within-population genetic structure was investigated following two approaches: classical spatial autocorrelation analysis and Bayesian clustering. Spatial autocorrelation analysis was performed using the multivariate method by Smouse & Peakall  [29]  implemented in the program GenAlEx 6. 5  [30]. It provides a multi-locus estimate of pair-wise relatedness between individuals (r) which minimizes the stochasticity found in single locus or single allele estimates of relatedness.
The rationale is that the four microsatellites have sufficient power to detect SGS if SGS estimates from realistic simulations fall consistently outside the no-SGS 95% confidence interval. ii) Comparing F 1 values observed in Fh and Fl with those obtained from the simulated extreme scenarios. The overlap of F 1 values from the observed vs. simulated scenarios will inform on the power of the four loci to discriminate between realistic and unrealistic SGS scenarios. iii) Comparing simulations with four and 20 loci. This comparison will help us assess the gain in accuracy and precision of SGS estimates when increasing the number of SSR loci. The spatially explicit Bayesian clustering algorithm implemented in the R package GENELAND v. 4. 0. 2 was used to assign individuals to putative sub-populations within each plot  [37].
Once reliable Bayesian clustering results were obtained, a hierarchical analysis of the genetic structure of all plots was performed to assess the importance of population substructure relative to structure at higher hierarchical scales. The hierarchical estimates of F-statistics were obtained by the R package HIERFSTAT  [41]  using the method by Weir & Cockerham  [38]  based on the estimation of variance components of gene frequencies. Two intermediate hierarchical levels were chosen: plot (Plot) and clusters (Clu). Therefore, in the following F PlotTotal refers to the correlation of genes within plots relative to the total, F CluPlot to the correlation of genes within clusters relative to the plots, and F IndClu to the correlation of genes within individuals relative to the clusters. F-statistics are related to each other by the following expression (12F IndTotal ) = (12F IndClu )6(12F CluPlot )6 (12F PlotTotal ).
Such high anthropogenic pressure has produced the loss of the spatial component and a simplification of the nonspatial component of genetic structure in this stand, as also reported analyzing a subset of our Italian datasets with different methods and markers  [25]. It is noteworthy, however, that this difference in the genetic structures does not correspond to a comparable reduction of within-population genetic diversity  [24]. Paffetti et al. [25]  reached similar results and the only difference in genetic diversity detected between managed and unmanaged stands was a slight reduction in the frequency of rare alleles in the managed stand. The shelterwood system produced weak to negligible genetic effects in the stand pairs analyzed (Al-Ah, Gl-Gh), with no difference in genetic diversity  [24]  and only a slight reduction in the intensity of genetic structure in the German plot pair.
This study was financially supported by the  European Commission  through the  Dynabeech project (5th Framework Programme ,  QLRT-1999-01210 ) and finalised based on the financial support by the  European Commission  through the  FP7 -project  FORGER: Towards the Sustainable Management of Forest Genetic Resources in Europe  ( KBBE -289119 ). AP was supported by the  Italian MIUR  project '' Approccio multitaxa allo studio delle risposte della biodiversitaitaliana al cambiamento climatico '' ( RBAP10A2T4 ). KK was in addition supported with co-funding by the  Dutch Ministry of Economics, Agriculture and Innovation  in the project ( KB-14-002-033 ) of the  Knowledge-Base Green Space and Biodiversity. This support is gratefully acknowledged. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
2b , Supplementary Table  S5 ). Interestingly, such domains include those composing proteins that are involved in metazoan multicellularity and development; for example the cell adhesion molecule integrin-b, and the transcription factors p53 and RUNX (Fig. 2b , Supplementary Table  S4 ). Several domains involved in transcriptional regulation were secondarily lost in M. brevicollis (Fig. 2c , Supplementary Table  S6 )  17. Domains involved in extracellular functions have been frequently lost in both Capsaspora and M. brevicollis. Our data indicate that 235 new domains emerged after the divergence of filastereans and choanoflagellates from the lineage leading to the Metazoa. These 'metazoan-specific  The number of Pfam protein domains that were gained or lost at each evolutionary period was inferred by Dollo parsimony, which does not consider multiple independent evolution of a domain.
3 , Supplementary Figs S12 and S13 and Supplementary Note 4). Our data show that these domains are, in metazoans, mainly involved in cell adhesion, intercellular communication, signalling, transcriptional regulation and apoptosis, which are relevant to multicellularity and development of metazoans. Most of these domains show clear enrichment exclusively in metazoans. However, the abundance of some of these domains is also increased in the genome of Capsaspora. Those that are particularly enriched include the laminin-type epidermal growth factor-like, Integrin-b4, Sushi, protein tyrosine kinase, Pleckstrin homology, Src homology 3, p53-like transcription factor DNA binding and Band4. 1 domain and leucine-rich repeat. These domains are not always similarly enriched in the M. brevicollis genome, as seen, for example, in the Integrin-b4 domain and LRR.
Discussion We have reported the first whole genome sequence of a filasterean, a close relative of metazoans. We show that the genome of Capsaspora encodes many proteins that are involved in cell adhesion, signalling and development in metazoans. Previously, the absence of a number of these proteins in the choanoflagellate M. brevicollis and in any sequenced fungi had misled inferences that they were metazoan-specific  12, 27, 28  , underscoring the importance of taxonomic sampling in comparative genomics. By adding the whole genome information of the filasterean Capsaspora, the sister group of choanoflagellates and metazoans, we have reconstructed a more robust picture of the unicellular ancestry of metazoans. This evolutionary scenario will be increasingly clarified as genome data from additional holozoan taxa (for example, ichthyosporeans) become available.
Resulting whole genome shotgun libraries were sequenced by Sanger chemistry, generating approximately eightfold paired-end raw reads: sixfold from the 4 kb library, 1. 6-fold from the 10 kb library and 0. 8-fold form the 40 kb library. Raw read sequences were submitted to NCBI's Trace Archive and can be retrieved with the search parameters CENTER_NAME ¼ 'BI' and CENTER_PROJECT ¼ 'G941'. Sequencing reads were assembled by the Arachne assembler  30  using the default parameters. After assembly, the AAImprover module (part of the Arachne assembler package) was run to improve assembly accuracy and contiguity. Finally, portions of the genome, which appeared to be misassembled, were manually broken to create the final assembly. The assembly was submitted to NCBI with accession number ACFS01000000, BioProject ID PRJNA20341.
RNA sequencing. Total RNA was isolated from two differently-staged C. owczarzaki cultures with Trizol (Life Technologies). Libraries were sequenced using GAII and HiSeq 2000 instruments (Illumina), which generated 76 base paired-end reads. The RNA-seq data were used for the protein prediction. Gene prediction. An initial protein-coding gene set was called with Evidence-Modeler 31 by the combination with three ab initio predictions by GeneMark. hmm-ES  32 , Augustus 33 , GlimmerHMM  34  , two sequence-homology-based predictions by Blast and GeneWise  35  and transcript structures built from ESTs by PASA package  36. The initial gene set was further improved by an incorporation of RNAseq data using PASA  36  and Inchworm 37 pipelines to obtain a final gene set. Synteny. We performed a synteny conservation analysis between C.
owczarzaki and M. brevicollis, A. queenslandica and N. vectensis using DAGchainer  38  with default parameters. Phylogenetic analysis. We analysed two independent data sets based on whole genome sequences: the mutual best hit (fMBH) data set used for assessing the phylogenetic position of the sponge A. queenslandica 3 and the data set containing 145 putatively orthologous proteins (145POP data set), which were chosen by OrthoMCL2 software  39. The collected protein sequences were aligned using the MAFFT program  40  , manually inspected and trimmed by the use of Gblocks program  41  with the default parameters. We inferred the maximum likelihood trees by using RAxML 7. 2. 8 (ref. 42) with the LG þ ! model. A nonparametric bootstrap test with 100 replicates for each topology was performed. We further tested topologies by the Bayesian inference using PhyloBayes 3.
2 (ref. 43) with the CAT þ ! evolutionary model  44. The Monte Carlo Markov Chain sampler was run for 10,000 generations, and then burned-in the last 8,000 saving every 10 generations. Protein domain gain and loss analysis. We ran the Hmmscan program from HMMER 3. 0 package  45  against the Pfam-A version 25 database using protein sets from 35 species: Amphimedon queenslandica, Arabidopsis thaliana, Aspergillus oryzae, Branchiostoma floridae, Brugia malayi, Caenorhabditis elegans, Capitella teleta, Capsaspora owczarzaki, Chlamydomonas reinhardtii, Coprinopsis cinerea, Cryptococcus neoformans, Daphnia pulex, Dictyostelium discoideum, Drosophila melanogaster, Homo sapiens, Hydra magnipapillata, Laccaria bicolor, Lottia gigantea, Monosiga brevicollis, Naegleria gruberi, Nematostella vectensis, Neurospora crassa, Physcomitrella patens, Phytophthora sojae, Rhizopus oryzae, Schizosaccharomyces pombe, Strongylocentrotus purpuratus, Tetrahymena thermophila, Thalassiosira pseudonana, Tribolium castaneum, Trichoplax adhaerens, Trypanosoma brucei, Tuber melanosporum, Ustilago maydis and Volvox carteri.
Hits with the scores above the gathering threshold values were considered significant. Dollo parsimony criterion was used to infer the Pfam domains gained and lost along the branches of the phylogenetic tree. The Pfam domains were mapped to GO terms by the use of the Pfam2GO mapping (July 2011). The Ontologizer 2. 0 program  46  was used for the GO term enrichment analysis. We evaluated whether a GO functional category evolved in a certain evolutionary position using a P-value calculated by the topology-weighted algorithm  47. Domain enrichment analysis. Protein sets for 12 genomes (H. sapiens, D. melanogaster, C. elegans, H. magnipapillata, N. vectensis, T. adhaerens, A. queenslandica, M. brevicollis, C. owczarzaki, N. crassa, L. bicolor and D. discoideum) were first filtered by removing short proteins less than 30 amino acids.
For genes that have multiple alternatively spliced isoforms, only the longest protein product was retained for each gene. Protein domain search was performed by the use of InterProScan  48  against InterPro database  25. The InterProScan results on the complete proteomes of other eukaryotes (E. histolytica, A. thaliana, C. reinhardtii, P. falciparum, L. major, P. tetraurelia, and E. siliculosus) were retrieved from the Uniprot (http://www. uniprot. org/) database. Protein domains that are enriched in metazoans compared with all the other non-metazoans except C. owczarzaki and M. brevicollis were selected by the use of Fisher's exact test (Po1. 0e À 20). The number of genes containing such domains, but not the number of domains themselves, was considered. Values were normalized by the numbers of the protein-coding genes in the whole genome.
The results were depicted in a heatmap by the R and its Bioconductor package  49. Intergenic distance analysis. We approximated the intergenic distance by calculating the distance between two protein-coding sequences. We then ran two sided t-tests on these distances at upstream (or downstream) regions of genes in each functional category against all other genes in the same genome. Genes were classified by Gene Ontology (GO)  50  annotations, which were generated by the use of Blast2GO 51 and InterPro2GO 52 pipelines. Gene family analysis. We chose several gene families that are particularly interesting in the context of the evolution of multicellularity. For each gene family, we inferred the presence and absence of the gene or protein domains in chosen taxa using the HMMER  45  package, mutual Blast and phylogenetic analyses based on maximum likelihood trees inferred by RAxML  42.
Acknowledgements We thank  Lora Lindsey  for providing the differential interference contrast microscopic picture of C. owczarzaki. We thank  Joshua Levin  and  Lin Fan  for generating RNA-Seq libraries and the  Broad Institute Genomics Platform for Sanger and Illumina sequencing. We thank  Terrance Shea  for genome assembly,  Qiandong Zeng  for genome annotation and  Lucia Alvarado-Balderrama ,  Narmada Shenoy  and  James Bochicchio  for data release and NCBI submission. We thank  Nicole King  for providing useful insights to the manuscript. H. S. was supported by the  Marie Curie Intra-European Fellowship  within the 7th  European Community Framework Programme. Genome sequencing, assembly and some supporting analysis was supported by grants from the  National Human Genome Research Institute  ( HG003067-05  through  HG003067-10 ), as were C.
N. , C. R. , B. H. and Z. C. B. F. L. and A. J. R. acknowledge financial support through the  Canadian Research Chair program. This study was supported by an  ICREA  contract, a  European Research Council   Starting Grant  ( ERC-2007-StG-206883 ) and a grant ( BFU2011-23434 ) from the  Spanish Ministry of the Economy and Competitiveness (MINECO)  awarded to I. R. -T. M. V. was supported by  CNRS , the  Agence Nationale de la Recherche  ( ANR  grant  BLAN-0294 ) and the  Institut Universitaire de France.
1%. During the experiment, stock solutions were added to the perfusate at the desired concentration. Data analysis and statistics. Data are reported as mean Ϯ SEM. Statistical analyses were performed using Student's t test for two group comparisons, one-way ANOVA followed by appropriate post hoc tests for three or more group comparisons, and 2 test for distributions. Cumu- lative distributions were compared using Kolmogorov-Smirnov (K-S) test or one or two-way appropriately designed ANOVAs and Bonferroni post hoc tests. Differences were considered statistically significant if p Ͻ 0. 05. Spontaneous synaptic currents were analyzed off-line using the automatic detection protocol within the Mini Analysis Program (Justin Lee, Synaptosoft, 1999) and subsequently checked manually for accuracy. Event analyses were performed blind to genotype.
3ЈUTR sequences were PCR-amplified from IMAGE clone cDNAs when available: nefma (549 nt), stmn2b (884 nt), tubb5 (911 nt) or from genomic DNA: zebrafish actb1 (521 nt), actb2 (564 nt), huC/elavl3 (1762 nt), neuroD (1025 nt), mouse ␤-actin (733 nt), ␥-actin (701 nt), and chicken ␤-actin (539 nt), flanked with EcoRI and NotI sites, and cloned into a single UAS:myr-Venus or UAS:myr-mCherry vector using these restriction enzymes. When a gene had ambiguous or multiple 3ЈUTRs, the longest sequence annotated in the UCSC database was used. Reference coding sequences of mCherry  (Shaner et al. , 2004), and Venus (Nagai et al. , 2002)  were used. Detailed sequences are available upon request. Zebrafish strains, maintenance, and injections. Zebrafish were raised and maintained as described previously  (Kimmel et al.
Peroxidase activity was detected with FITC-or Cy3-coupled tyramide (10 g/ ml), prepared as described by  Davidson and Keller (1999) , in the presence of 0. 001% H 2 O 2. For joint immunodetection of protein, primary antibodies to GFP/Venus (Aves Laboratories, GFP-1020; 1:500) or acetylated-tubulin (Sigma-Aldrich, T-6793; 1:500), were included in the incubation mix. They were detected after the tyramide staining, using appropriate dye-conjugated secondary antibodies: anti-chicken IgY (Jackson Immunoresearch; 1:500), anti-mouse IgG (Molecular Probes; 1:500). mbp  (Lyons et al. , 2005)  and neuroD  (Lecaudey et al. , 2007)  probes were published previously. All probes used are available upon request. Data analysis. Processed embryos were flat-mounted on microscope slides, then imaged on a Nikon Eclipse E800 epifluorescence microscope or on a Leica Sp5 confocal microscope.
Results Because no instance of axonal mRNA transport has yet been reported in the zebrafish, we started our study by selecting candidate genes whose mRNA could be detected in axons in at least one of the large-scale screens conducted on rat  (Willis et al. , 2007; Taylor et al. , 2009; Gumy et al. , 2011) , mouse, and Xenopus  (Zivraj et al. , 2010)  cultured neurons. Among the top-listed genes, we selected nefm, stathmin-like 2b, and tubulin␤5, which have been repeatedly detected in these screens (Table  1 ) and whose orthologues showed neuronspecific expression profiles in the early zebrafish embryo, ac- cording to the Zebrafish Model Organism Database (ZFIN; zfin. org). To test the axonal localization of candidate mRNAs in the whole embryo context, we used sensitive fluorescent in situ hybridization detection combined with immunolabeling of axons.
Acknowledgments The authors would like to thank  Marc Zelsmann  for his help in the deposition of thick aluminum. Special thanks go to the  BM2-D2AM  beamline staff of  ESRF  for their technical support. This work was financially supported by the  French Ministère de la Défense-Direction Générale de l'Armement  and by the  Region Rhône-Alpes Scientific Research Department via Clusters de Micro et Nanotechnologies.
Study design This was a prospective, multicentered, clinicalepidemiological study executed in 15 participating centers (13 Respirology and 2 Allergology departments) across Spain. Data were collected during regular clinical practice and medical procedures. The study was designed to compare the efficacy of FeNO in addition to ACQ-7, versus ACQ-7 alone, to determine the level of asthma control upon treatment adjustment in patients with previously not well controlled asthma.
Sample size calculation and statistical analysis Sample size was calculated for the comparison of two related proportions on the basis of prior data on the detection of controlled asthma using ACQ-7 and FeNO  [19] , and resulted in 333 subjects to achieve a 95% statistical power with a level of significance of 5% for bilateral contrast, assuming a 10% proportion of discordant pairs. A 20% loss to follow-up was predicted since the study was prospective, therefore leading to a total of 424 subjects to be included in visit 1. Subjects were included in the study on consecutive random sampling basis, and each participating center had a recruitment compromise of 30 patients minimum completing visit 2. Categorical variables are expressed as absolute and relative frequencies, and quantitative variables as mean and standard deviation.
Comparisons between visit 1 and visit 2 data sets were analyzed using paired-data student's t-test or nonparametric analysis for quantitative or ordinal variables as appropriate, or chi-square or McNemar test for categorical variables. Sensitivity, specificity, positive predictive value and negative predictive value were calculated for the ACQ-7 plus FeNO combination versus ACQ-7. A Receiver Operating Characteristics (ROC) was generated, and the area under the curve estimated. Correlation was evaluated with Spearman's coefficient. A P value of less than 0. 05 was considered for statistical significance. Analysis was performed with SPSS software version 18. 0 (SPSS Inc. , Chicago, IL, USA).
Results Four hundred and ninety subjects were considered for the study, of which 109 were excluded due to ACQ-7 or FeNO missing data, or not fulfilling inclusion criteria. Three hundred and eighty one assessable subjects with complete data were included in the statistical analysis. Table  1  shows the demographics and asthma characteristics for the subjects analyzed. Table  2  shows the FeNO and ACQ-7 (including FEV 1 ) values collected to evaluate asthma control on visits 1 and 2, and the numbers and percentages of subjects categorized as having not well controlled or controlled asthma. Following maintenance therapy adjustment on visit 1, the FeNO and ACQ-7 values were significantly reduced on visit 2. The proportion of not well controlled patients was also significantly decreased on visit 2 for whichever classification criterion employed, whether ACQ-7, FeNO or both combined.
On visit 2, the combination of ACQ-7 and FeNO detected 39 additional cases as not well controlled in comparison with ACQ-7 alone, which corresponds to a 14. 8% increase in the detection of not well controlled patients. Table  3  shows patient distribution as controlled or not well controlled asthma on visit 2, classified as per FeNO plus ACQ-7 score versus ACQ-7 alone. The data were also analyzed on the patients classified as atopic versus non-atopic. This analysis showed that the addition of FeNO to ACQ-7 yielded a 10. 8% increment of patients classed as not well controlled asthma on visit 2 in the atopic group and, similarly, 8. 7% in the non-atopic group. FeNO values and ACQ-7 scores correlated weakly on both visit 1 (r=0. 150, P<0. 01) and visit 2 (r=0. 180, P<0. 01). However, the decrease of FeNO and ACQ-7 from visit 1 to visit 2 showed a stronger correlation (r=0.
Acknowledgements María Teresa Ramírez  ( Hospital Infanta Sofía, Córdoba );  Ingrid Solanes García (Hospital de la Santa Creu i Sant Pau, Barcelona) ;  Luís Lores Obradors (Hospital Comarcal Sant Boi, Barcelona) ;  Xavier Muñoz Gall (Hospital Vall d´Hebron, Barcelona) ;  Fernando Sánchez-Toril López  ( Hospital Arnau de Vilanova, Valencia );  Eva Martínez Moragon  ( Hospital de Sagunto, Valencia );  José Nieto Serrano  ( Hospital de Jaén );  José Daniel Alcázar Ramírez  ( Hospital Torrecárdenas, Almería );  Luís Sanjosé Valverde  ( Hospital General de la Defensa, Madrid );  María Ángeles Fernández Jorge  ( Complejo Hospitalario Asistencial de Palencia, Palencia );  Ramón Agüero Balbín  ( Hospital Marqués de Valdecilla, Santander );  Juan Manuel Barreiro Barreiro  ( Hospital Xeral Cíes, Vigo, Pontevedra );  Fernando Molina Prieto  ( Hospital Modelo, A Coruña );  Pedro Ojeda Fernández  ( Clínica de Asma y Alergia Ojeda, Madrid );  José Ángel Carretero Gracia  ( Hospital Miguel Servet, Zaragoza ).