dataesr/scientific-paragraphs-categorization huggingface dataset:
Future assessments of temporal changes in the structure of the GLNs (which will be possible as data for a longer period of time becomes available) can identify whether English is gaining or losing influence with respect to the languages of rising powers such as India or China. Such changes, as well as the differences between GLNs based on traditional media (printed books) and new media (Twitter and Wikipedia), may help predict a language's likelihood of global importance, marginalization, and, perhaps in the long term, extinction. GLN centrality can therefore complement current predictions of language processes, which rely mostly on a language's number of speakers  (15).
Discussion We investigated standard yeast culture growth to define the typical cell cycle stages through which the cells progress. This allowed us to study cells at characterized physiological stages. We then evaluated different sample preparation protocols and several DNA-binding dyes to assess the cell cycle in S. cerevisiae by FCM through DNA content evaluation. Various combinations of cell washing, overnight incubation and the presence of the detergent Table  1. Signal intensity with different fluorochromes during the exponential cell growth phase (G1 and G2/M) or stationary phase (G0). Nonidet-40 (Sigma) were tested but did not improve the outcome (data not shown). However, analysis of the results revealed that there may be misinterpretations during such analyses of S. cerevisiae cultures.
Growth Stages To characterize the different cell growth phases, cultures were diluted (from 1 to 10 fold) in water. OD was measured on a Nanodrop ND-1000 Spectrophotometer (Thermo Scientific) at wavelengths from 550 to 700 mm. The means of at least three measurements from 586 to 616 mm were calculated and are reported to minimize low measure variations. Sample aliquots were quickly fixed for cytometry analysis, at various times from inoculation to 33 hours later. Growth phases were defined as time after inoculation by the linearity of the OD log signal. Parameters were adjusted by a Gompertz function using R statistical software version 3. 0. 1 and the package ''cellGrowth''  [10, 11].
Threshold level was set at 40,000 for FSC. For the identification of the region corresponding to yeast cells, data were collected to a total count of 20,000 cells. The flow cytometer was routinely operated at the Slow Flow Rate setting (14 mLsample/min) and yeast samples presented more than 95% singlets (on FSC-A/FSC-H representation). Cells were labeled by dilution of 25 ml aliquots of cells (1. 25610 6 cells) in 200 ml of fluorochrome-containing PBS to reach the final concentration indicated below and 1000 eps with a slow flow rate (14 ml. min 21 ). The concentrations used were based on the quantities of fluorochrome per cell described in the literature data  [12, 13] ; we verified that a stable signal was obtained, at least within a two-fold variation of the concentration. Labeled cells were kept at least 30 min in the dark at room temperature and signal stability was verified by reanalyzing the initial samples at the end of the full run.
Cytometry Data and Graphic Representation Cytometry data files are available in the Cytobank database at the following addresses: https://www. cytobank. org/cytobank/ experiments/25061 and https://www. cytobank. org/cytobank/ experiments/25066. Signal time courses were drawn using flow cytometry packages of the Bioconductor project running with R  [10, [14] [15] [16] [17]. The scale of the fluorochrome signal was set so that the G1 intensity peak pointed to the 200/1000 of the whole x scale. Population gates are optimized for static gating across the cell cycle. This induced some wrong gatings at the end of the culture, especially for G0 populations detected with SYBRH Green I labeling (Figure  6 ) : in these cases, some cells were counted as being in S-phase.
This paper describes a publicly available 3D database from the rue Madame, a street in the 6 th Parisian district. Data have been acquired by the Mobile Laser Scanning (MLS) system L3D2 and correspond to a 160 m long street section. Annotation has been carried out in a manually assisted way. An initial annotation is obtained using an automatic segmentation algorithm. Then, a manual refinement is done and a label is assigned to each segmented object. Finally, a class is also manually assigned to each object. Available classes include facades, ground, cars, motorcycles, pedestrians, traffic signs, among others. The result is a list of (X, Y, Z, reflectance, label, class) points. Our aim is to offer, to the scientific community, a 3D manually labeled dataset for detection, segmentation and classification benchmarking.
It is an active research area. However, there is not a general consensus about the best detection, segmentation and classification methods. This choice is application dependent. One of the main drawbacks is the lack of publicly available databases allowing benchmarking. In the literature, most available urban data consist in close-range images, aerial images, satellite images but a few laser datasets  (ISPRS, 2013; IGN, 2013). Moreover, manual annotations and algorithm outputs are rarely found in available 3D repositories  (Nüchter and Lingemann, 2011; CoE LaSR, 2013). Some available data include Oakland dataset  (Munoz et al. , 2009) , which contains 1. 6 million points collected around Carnegie Mellon University campus in Oakland, Pittsburgh, USA. Data are provided in ASCII format: (X, Y, Z, label, confidence) one point per line, vrml files and label counts.
The training/validation and testing data contains 5 labels (scatter misc, default wires, utility poles, load bearing and facades). Ohio database  (Golovinskiy et al. , 2009)  is a combination of ALS and TLS data in Ottawa city (Ohio, USA). It contains 26 tiles 100 × 100 meters each with several objects such as buildings, trees, cars and lampposts. However, ground truth annotations only consists in a 2D labeled point in the center of each object. In that sense, segmentations results cannot be evaluated point by point. Enschede database  (Zhou and Vosselman, 2012)  contains residential streets approximatively 1 km long in the Enschede city (The Netherlands). Ground truth annotation consists in 2D geo-referenced lines marking curbstones. A well-defined evaluation method is available using buffers around each 2D line.
The drawback of this dataset is that no other objects are annotated. Paris-rue-Soufflot database  (Hernández and Marcotegui, 2009)  contains MLS data from a street 500 m long in the 5 th Parisian district. Six classes have been annotated: facades, ground, cars, lampposts, pedestrians and others. In this paper, we present a 3D MLS database for benchmarking detection, segmentation and classification methods. Each point in the 3D point cloud has been segmented and classified, allowing pointwise evaluations. Additionally, our annotation includes all available objects in the urban scene. Data have been acquired and processed in the framework of TerraMobilita project (http://cmm. ensmp. fr/ TerraMobilita/ ). Paper organization is as follows. Section 2 reminds some basic definitions. Section 3 describes Paris-rue-Madame database.
BACKGROUND A typical 3D urban analysis method includes 5 main steps: i) data filtering/down-sampling in order to reduce outliers and redundant data; ii) Digital Terrain Model (DTM) generation; iii) object detection in order to define object hypotheses and regions of interest; iv) object segmentation in order to extract each individual object; and v) object classification in order to assign a semantic category to each object. In the scientific community several definitions can be found for detection, segmentation and classification steps. For clarity, let us define these concepts in the way we intend they should be understood with this dataset: Detection: An object is considered detected if it is included in the list of object hypotheses, i. e. it has not been suppressed by any filtering/downsampling method and it has not been included as part of the DTM.
DATA DESCRIPTION Paris-rue-Madame dataset contains 3D MLS data from rue Madame, a street in the 6 th Parisian district (France). Figure  1  shows an orthophoto from the test zone, approximatively a 160 m long street section between rue Mézières and rue Vaugirard. The acquisition was made on February 8, 2013 at 13:30 Universal Time (UT). The dataset contains two PLY files with 10 million points each. Each file contains a list of (X, Y, Z, reflectance, label, class) points, where XYZ correspond to geo-referenced (E,N,U) coordinates in Lambert 93 and altitude IGN1969 (grid RAF09) reference system, reflectance is the laser intensity, label is the object identifier obtained after segmentation and class determines the object category. An offset has been subtracted from XY coordinates with the aim of increasing data precision: X 0 = 650976 m and Y 0 = 6861466 m, respectively.
The available files are "GT Madame1 2. ply" and "GT Madame1 3. ply", both of them coded as binary big endian version 1. This database contains 642 objects categorized in 26 classes, as shown in Table  1. It is noteworthy that several objects inside buildings have been acquired through windows and open doors, these objects have been annotated as facades. Other several "special" classes have been added because they are too different to be mixed with others. For instance, fast pedestrians and pedestrians+something have different geometrical features than a simple pedestrian. The idea of this annotation is including as much classes as possible, then each user may gather or exclude classes depending on the application. It is noteworthy that this database is different from others available in the state of the art since the entire 3D point cloud has been segmented and classified, i.
CONCLUSIONS We have presented a 3D MLS database manually annotated from rue Madame, a street in the 6 th Parisian district. Each 3D point has been labeled and classified, resulting in a list of (X, Y, Z, reflectance, label, class) points. The database has been acquired by the L3D2 vehicle, a MLS system from the Robotics laboratory (CAOR) at MINES ParisTech. A distinctive feature of this system is that it uses a Velodyne sensor aligned in a similar way to a Riegl sensor but providing much denser 3D point clouds. Annotation has been carried out in a manual assisted way by the Center for mathematical morphology (CMM) at MINES ParisTech. First, an automatic segmentation method is applied. Then, manual refinement and classification are done. This approach is faster than a completely manual approach and it vides accurate results.
To this end, the tool solves an optimization problem in which Pollution Indices (PIs) are minimized together with the implementation costs of measures needed to achieve these PIs. An additional key feature of such system is the substitution of the CTM with a suitable nonlinear surrogate model, allowing for a fast repetitive evaluation of the PIs with accuracy close to the one obtained with a full CTM  (Carnevale et al. , 2012a). The RIAT+ package requires an emission inventory, a set of feasible emission reduction measures and surrogate model(s) as input. It has already been successfully applied to various regions and is freely available on the LIFE+ OPERA project website (www. operatool. eu), since September 2013. In terms of application, this paper proposes the computation of optimal policies for air quality improvement in Alsace, focusing on nitrogen oxides and ozone exposure.
Chemical transport model simulations The air quality model simulations have been performed with the WRF/CHIMERE system. The configuration of the system has been based on the Atmo-RhenA System (MM5/CHIMERE) designed by ASPA, using Global Land Cover Facilities database to describe the land use, and MODIS data to estimate the vegetation fraction. WRF is driven by FNL-AVN NCEP global meteorological data for the reference year 2005. It has been set up on three nested domains (see Fig. 1 (a) ) with a horizontal resolution of 45, 15 and 3 km side-length centered on Western Europe, France and the Upper-Rhine area, respectively. The vertical resolution is stretched along 27 levels from surface up to 50 hPa. This Upper-Rhine area covers the Alsace Region, one part of Lorraine and Franche-Comté regions on the French side, Rhineland-Palatinate and west Baden-Württemberg on the German side and Basel region in Switzerland as illustrated in Fig.
1 (b ). The main physical WRF parameterizations (for details refer to NCAR (2012)) have been set up to: • Noah-LSM for the surface scheme; • RRTM-Duhia for the radiation scheme; • MM5 Monin-Obukhov for the turbulence scheme; • YSU for the mixing layer scheme; • Greel-Devenyi Ensemble for the cumulus scheme; • Graupel for the cloud microphysics scheme. A nudging has been used for temperature, relative humidity and wind speed. The CTM CHIMERE was run over the two nested grids centered on Western Europe and France, with emissions derived from the EMEP database. The vertical distribution of the emissions followed the work of  Bieser et al. (2011)  and  Terrenoire et al. (2013). Over the Upper-Rhine area, local emission inventories for year 2005 have been collected and gridded by ASPA using the Manag'Air tool, over the Alsace Region.
Having chosen these options, three different configurations have been considered, minimizing respectively: • population weighted average yearly NO 2 ; • average summer SOMO35; • a joint index composed by NO 2 and SOMO35 assuming, as an example, NO 2 "three times more important" than SOMO35. In terms of emission reduction measures (and relative removal efficiencies and costs), both the end-of-pipe technology datasets developed by IIASA for the GAINS EUROPE model, and some local non-technical measures, have been used. The GAINS dataset for France includes the measures available for the Gothenburg protocol revision ("CIAM1/ 2011 march, goth_nat_baseline_rev1" scenario, available at http:// gains. iiasa. ac. at/gains). In terms of local measures, four efficiency measures have been considered: • car sharing; • reduced road speed on highways; • eco driving; • thermal renovation of buildings.
Acknowledgments The RIAT + tool and this work have been developed in the frame of the  OPERA  project ( Operational Pollution Emission Reduction Assessment ,  LIFE09 ENV/IT/092 ), funded by the  LIFE + programme of the European Commission DG-Environment. The Alsatian team thanks the  Mesocentre de Calcul de Strasbourg  for its technical support, the  REALISE (Réseau Alsace de Laboratoires en Ingénierie et sciences pour l'environnement)  and the  Alsace Region  for financial support. The authors thank INERIS, especially  E. Terrenoire  for interesting discussions about WRF/CHIMERE and support to improve the CHIMERE simulations, and acknowledge the other  OPERA  project partners and stakeholders:  ARPA Emilia Romagna  ( E. De Munari ,  M. Deserti  and  M. Stortini ),  Regione Emilia Romagna (E.
8X best matches) and each matched protein was then realigned using Genewise  40  on the same trout genomic region. 93% of these teleost proteins matched at 41,300 different genomic loci in the rainbow trout genome assembly. For building gene models, rainbow trout GenBank mRNA sequences were aligned onto the genome assembly using BLAT  39  and est2genome 41 resulting in Expression levels were normalized and centred across both ohnologues in the left panel, highlighting differences in relative levels of expression between both genes. Pearson's correlation coefficients between the expression levels of both ohnologues across all tissues are represented in the right panel. (c) Top functional enrichments for each class of ohnologues, compared with the remainder of the ohnologue set. Each class of ohnologues corresponds to a functionally distinct group of genes.
93% of mapping of these 421,414 mRNA sequences. Only the best matches with at least 90% of nucleotide identity were kept. On average, similarity level was 97. 8% and half of these alignments supported splicing evidence, with an average of 2. 5 exons per mRNA. We also used publicly available rainbow trout Roche 454 EST sequences available in SRA (accession number SRX007396) that were assembled using Newbler, and aligned using blat and est2genome with the same setting used for mRNAs. A total of 97% of these cDNA contigs were mapped on the rainbow trout assembly at 45,600 different genomic loci. In addition, we generated Illumina reads of different tissue transcriptomes (see below) that were also used to predict exon/intron structure on the genome assembly using gMorse  42. Using all these resources we predicted 69,676 transcripts with an average size of 4.
Rainbow trout transposable elements. Annotation of transposable elements was done using BES of O. mykiss and Salmo salar, 60 completely sequenced rainbow trout BAC, cDNA Unigene library and the rainbow trout genome sequence. Classification of TEs was based on Wicker's classification  49. A database of TEs was built combining both manual and automatic annotation (Supplementary Methods). Transposable elements account for B38% of the rainbow trout genome sequence (Supplementary Table  9 ). To evaluate the age of TE copies, Kimura distances were calculated based on the alignment (consensus from the TE library versus copy in the genome) generated by RepeatMasker. The Kimura calculation uses the rates of transitions and transversions. Those rates are then transformed in Kimura distances using the formula K ¼ À 1/2 ln(1-2p-q)-1/4 ln(1-2q), where 'p' is the proportion of site with transitions and 'q' the proportion of site with transversions.
We systematically compared the gene locations in rainbow trout with those of medaka, stickleback, tetraodon and takifugu using ad-hoc scripts to identify pairs of regions in the rainbow trout genome that are syntenic with single regions in non-salmonid species, and that correspond to DCS blocks. Pairs of paralogous trout genes on two different chromosomes (or non-anchored scaffolds) that belong to a DCS block are most likely duplicates originating from the Ss4R WGD event and are called ohnologues; there were 6,733 pairs of ohnologues. Genes that are inserted in a DCS block based on synteny with a non-salmonid species, but have no paralogous gene on the other chromosome or scaffold, are most likely former Ss4R duplicates in which one of the duplicated genes was lost, and are called singletons.
The rate of selective constraints on orthologues and ohnologues was calculated with PAML4. 4 (o ¼ dN/dS) using the method of Yang and Nielsen  58. A linear extrapolation from the dS comparison was used to infer the timing of the Ss4R. Transcriptome sequencing and data analysis. Tissues for transcriptome analyses were obtained from a homozygous clonal 1-year-old female sampled 3 weeks after spawning. These doubled haploid females were first produced after gynogenetic reproduction of standard females plus inhibition of first embryonic cleavage  59  , and further reproduced by a second round of gynogenesis (inhibition of second meiosis)  60. Homozygous clonal lines were further maintained during every generation by single within-line pair mating between one female and one hormonally sex-reversed male.
PRJNA227065). Total RNA was extracted as described for transcriptome sequencing. Because of the high egg yolk content of vitellogenic ovaries, ovarian samples were subsequently purified using a NucleoSpin miRNA kit (Macherey Nagel, Germany). RNA integrity was checked using an RNA 6000 Nano chip (Agilent). Small RNA libraries were constructed according to the Illumina Small RNA v1. 5 sample preparation guide (Supplementary Methods) and were sequenced with a 36-bp chemistry on an Illumina HiSeq-2000 sequencer. A total of 3,484,155,614 reads were generated from the 38 sequenced libraries. Lowquality sequences were filtered and adaptors removed from raw small RNA sequence data using the FastQC and Cutadapt programs  66  , respectively. Intra-and inter-condition redundancy was eliminated and annotations of known miRNAs were performed as follows: reads were blasted to miRBase database  67  , Rfam database  68  , rRNA-silva database  69  and tRNA database  70.
Reads with hits in miRBase and Rfam but not in rRNA or tRNA database were kept as potential miRNAs. We only kept reads with more than 1,000 hits (among all conditions) to build strong loci. Reads were subsequently aligned to the trout genome with the following filters: exact match or maximum 95% suboptimal best hit with a maximum of 15 localizations within the genome sequence. miRNA-5p/miRNA-3p loci were built as follows: sequences belong to the same locus if there is no base difference among sequences and if miRNA-5p and miRNA-3p are at least 30 nucleotides distant from each other. This allowed the identification of 495 miRNA loci corresponding to 84 different families and 164 mature sequences (Supplementary Data 1). Gene ontology analyses. GO analyses were performed in two steps. Statistically enriched functional annotations were obtained in the sample set using a random sampling procedure (10,000 iterations, custom Perl script) with corrected false discovery rate for multiple testing (Benjamini-Hochberg FDR correction, with a 10% FDR threshold).
Acknowledgements This work was supported by the  ANR  ( Genotrout  project,  ANR-09-GENM-001  )Genomique et Biotechnologies Vegetales*). We thank  J. Brunelli  ( Washington State University (WSU ),  C. Hervet  and  C. Ciobotaru  ( INRA UMR GABI ) for DNA extraction from fin tissue and microsatellite control analyses; L. Labbe, the staff of PEIMA experimental facilities and  N. Dechamp  ( INRA UMR GABI ) for production and management of the B57 clonal fish;  P. Wheeler  ( WSU ) for production and management of the Swanson clonal fish; and  T. Nguyen  ( INRA LPGP ) for RNA extraction. D. C and A. J. PhD fellowships were, respectively, supported by the 'Ministere  Franc¸ais de la Recherche et de l'Education' and 'l'Institut National de la Recherche Agronomique '.
Introduction Members of the genus Paxillus (Agaricomycota, Boletales, Paxillaceae) are ectomycorrhizal fungi associated with various woody host plant species including hardwood and coniferous trees. They are commonly encountered in a wide range of ecosystems and habitats through the northern hemisphere. Paxillus is a taxon of importance for academic research, as shows a bibliographic search done in early 2013 through the international database ISI Web of Science (http://www. isiknowledge. com/) among the scientific literature dealing with fungi. The item Paxillus was recorded in the title of 152 scientific publications and in the topic of at least 690 different published works originating from about 60 different countries. The species Paxillus involutus accounted for most of these records.
(2008)  and  Vellinga et al. (2012). This paper aims to describe the species corresponding to PS IV/P. involutus II and to study various aspects (genetic, morphological, and ecological) of the diversity of the P. involutus complex as exhaustively as possible, based on our collections and on data from the literature, in order to establish a framework to delineate these Paxillus species. The objectives were  (1)  to assign specimens to lineages and to study genetic variation within and among the lineages in order to determine a phylogenetic cut-off for species delimitation  (2)  to identify key differentiating morphological features between species (3) to reveal the ecological preferences of each species.