dataesr/scientific-paragraphs-categorization huggingface dataset:
Strain The other constituent nucleus (homokaryon H39) was de novo sequenced using PacBio with a coverage of 200Â (sequence submitted at NCBI; see supplementary Table  S1  for accession numbers). A total sequence length of more than 31 Mb was generated with an average read length of >560 kb resulting in 60 scaffolds. Using the linkage map of Horst U1 (see later), the number of scaffolds was reduced to 16. Chromosomes 6, 11 and 13 each consist of 2 non overlapping scaffolds whereas all other scaffolds were assembled to represent full chromosomes. The right end of chromosome 9 is not yet identified but is expected to be one of the smaller not mapped scaffolds. This part contains the ribosomal DNA cluster and this might have caused problems in the assembling process. All chromosomes contain the typical A.
That will reveal if DNA repair mechanisms of meiosis in the button mushroom will lead to gene conversion, a recombination with a nonreciprocal exchange of DNA resulting in a 3:1 segregation of the region involved  (Mercier et al. , 2015). All 10 homokaryons mentioned above were resequenced (Illumina HiSeq 2000) and reads were aligned to the reference genome H97 v3. 1. A first analysis showed that the constituent nuclei of the cultivars Sylvan A15 and Amycel 2200 were completely identical. For further processing of data we, therefore, excluded Amycel 2200. The genotype and position of each SNP was plotted for each half tetrade using an adapted R script (plotTetradeSeg described in  Anderson et al. , 2011)  resulting in a graphical presentation of the genotype of each homokaryon in each half tetrade (Fig.
Acknowledgments The research was in part financially supported by the governmental grant  TTI Green Genetics, National Institute of Horticultural and Herbal Science (NIHHS) of the Rural Development Administration (RDA) of the Republic of Korea, Sylvan Inc. , Amycel and Limgroup b. v. We are grateful for the technical support of  José Kuenen  for her technical assistance in strain handling,  Richard Finkers  for SNP selection and to  Caroline Anderson  for kindly providing of Rscript (originally part of ReCombine). We like to thank  Hans de Jong  for his useful comments on the manuscript.
The interviews were realized from 2008 to 2011 and were recorded and transcribed. Observations and interviews were eventually backed up with a documentary survey to exploit the many documents issued by the IPBES on its website (http://www. ipbes. net/). We used qualitative data analysis software (MAXQDA) to analyze our material to depict the meanings, tensions, and consensus elaborated by the actors in context, according to the principles of situational analysis  (Clarke 2005). Our approach is in line with a political science perspective that draws on Weberian sociology and considers that to "explain an outcome is to understand. how things came to be the way they were and not some other way"  (Hay 2011:172) : our role as social scientists is to bring into light a grounded understanding of how actors' understandings are themselves shaped by resources, interactions, and contingencies.
MAXQDA helped us to go through all the texts available (interview transcripts and conference observation notes: about 300 pages in total) in a systematic manner and to code themes relevant for the analysis, i. e. , mark the passages which referred to the same tension or technique of inclusiveness. Although the tensions could already be detected during the Ecology and Society 21(4): 11 http://www. ecologyandsociety. org/vol21/iss4/art11/ conference, the systematic analysis of the material enabled us to characterize them in details and also to identify and characterize the techniques of inclusiveness used by the chairman. With  Westley et al. (2013) , we think that the identification of transformative agency and skills should be carried out in relationship with the understanding of the type and context of SES change.
Analysis of Data Diversity was measured using Shannon and Pielou indices. In addition to the total dataset a second dataset was created excluding singletons, that is, species that were isolated only once during the study, and this dataset was used for analysis of endophyte assemblages. Species accumulation curves were computed for both datasets in EstimateS v. 9. 1. 0. (Colwell et al. , 2012)  (http://viceroy. eeb. uconn. edu/EstimateS) and compared with ACE and Chao1 curves. Variability in the endophyte assemblages in the two cultivars, at different stages of development and in different parts of plants (roots vs. aerial organs) was measured using permutational analysis of variance (PERMANOVA, 9999 permutations, use of Bray-Curtis dissimilarities) with the package vegan  (Oksanen et al. , 2013 ) and correspondence analysis with the package ade4  (Thioulouse et al.
, 1997)  in R software (http://www. r-project. org/). We performed an indicator species test  (Dufrene and Legendre, 1997)  using the package labdsv  (Roberts, 2010)  in R software to identify species characteristic of each stage of development and each type of organs. Indicator species are defined as the most characteristic species of each group, that is, those species most nearly confined to the group and present in most samples belonging to that group.
The advent of high-throughput sequencing techniques made many bacterial genome sequences available in public databases  [12]. Our laboratory recently developed a taxonogenomic approach based on a systematic comparison of phenotypic (especially the MALDI-TOF spectrum) characteristics and genomic characteristics with the phylogenetically closest bacteria found in the databases  [2] [3] [4] [5] [6]. Here we present an analysis of the characteristics that allowed us to describe Gabonia massiliensis strain GM3, a bacterium isolated from a stool specimen from a healthy Gabonese male youth and classified into the Porphyromonadaceae family, created in 2011 by Krieg  [13]. We describe the classification, biochemical features and complete genomic sequencing and annotation of G. massiliensis gen. nov.
If the confirmation was positive, the spectrum of the new bacterium was entered into the Bruker database. The 16S rRNA nucleotide sequence was corrected using Chromas Pro 1. 34 software (Technelysium, Tewantin, Australia), and the BLASTn searches were performed by the National Center for Biotechnology Information tool (http://blast. ncbi. nlm. nih. gov. gate1. inist. fr/ Blast. cgi). For phylogeny, sequences were aligned using CLUS-TALW, and phylogenetic inferences were obtained using the neighbour-joining method within MEGA software. Numbers at the nodes are percentages of bootstrap values obtained by repeating the analysis 1000 times to generate a majority consensus tree. The 16S rRNA for G. massiliensis strain GM3 was deposited in GenBank under accession number LN849789.
Genome annotation and comparison Using Prodigal (http://prodigal. ornl. gov/) with default parameters, we predicted open reading frames (ORFs). However, if  they spanned a sequencing gap region, the predicted ORFs were excluded. The predicted bacterial protein sequences were searched against the GenBank  [16]  and Clusters of Orthologous Groups (COGs) databases using BLASTP. Both tRNAs and rRNAs were predicted using the tRNAScan-SE  [17]  and RNAmmer  [18]  tools, respectively. Signal peptides and the number of transmembrane helices were predicted using SignalP  [19]  and TMHMM  [20] , respectively. Mobile genetic elements were predicted using PHAST  [21]  and RAST  [22]. ORFans were identified when their BLASTP E value was lower than 1e-03 for an alignment length greater than 80 amino acids.
If alignment lengths were smaller than 80 amino acids, we used an E value of 1e-05. We used such parameter thresholds in previous works to define ORFans. Artemis  [23]  and a DNAPlotter  [24]  were used for data management and visualization of genomic features, respectively. Finally, we used the Mauve alignment tool (version 2. 3. 1) for multiple genomic sequence alignments  [25]. The mean level of nucleotide sequence similarity at the genome level between G. massiliensis and other bacteria was estimated using the average genomic identity of orthologous gene sequences (AGIOS) homemade software  [6]. Overall, this software is combined with other software: Proteinortho  [26]  (to detect orthologous proteins between genomes compared two by two, and then retrieves the corresponding genes) and the Needleman-Wunsch global alignment algorithm (to determine the mean percentage of nucleotide sequence identity among orthologous ORFs).
Annotation and comparison analyses were performed using the multiagent software system DAGOBAH  [27]  that includes Figenix  [28]. G. massiliensis strain GM3 was compared with Bacteroides salanitronis strain DSM 18170, Alistipes shahii strain WAL 8301, Alistipes finegoldii strain DSM 17242, Barnesiella viscericola strain DSM 18177, Ornithobacterium rhinotracheale strain ORT-UMN 88, Bacteroides fragilis strain YCH46, Bacteroides dorei strain HS1 and Helicobacter pylori strain 26695.
MALDI-TOF and phylogenic analysis The spectrum resulting from the 12 clean G. massiliensis spots did not identify bacteria because there was no spectrum match with those of Bruker database. Using 16S rRNA sequence, the phylogenic analysis revealed that G. massiliensis gen. nov. , sp. nov. , strain GM3, exhibited 91% identification with Barnesiella viscericola (AB267809. 1) (Fig. 1 ). The similarity of 16S rRNA in these two bacteria led to classification of this new bacterium into the Porphyromonadaceae family, created in 2011 by Krieg (Table  1 )  [13]. However, this 16S rRNA nucleotide similarity to Barnesiella viscericola was lower than the threshold of 95% recommended by Stackebrandt and Ebers  [8]  to delineate a new genus. Consequently, G. massiliensis gen. nov. , sp. nov. , strain GM3, was classified as new genus called Gabonia.
The spectrum resulting from G. massiliensis was thus added into the Bruker database (Fig. 2 ), and a gel view was performed in order to see the spectra differences between other close bacteria (Fig. 3 ). Then the 16S rRNA sequence of G. massiliensis was deposited in GenBank under accession number LN849789. Barnesiella intestinihominis strain YIT 11860  [32] , Coprobacter fastidiosus strain NSB1  [33]    Phenotypic and biochemical characterization G. massiliensis gen. nov. , sp. nov. , strain GM3 (= CSUR P1909 = DSM 100571), is a Gram-negative bacillus (Fig. 4 ). Strain GM3 is a non motile, non-spore-forming, oxidase-negative but catalase-positive bacterium. Growth was observed from 28 to 45°C, with optimal growth at 37°C, and colonies were obtained after 48 hours of culture. G. massiliensis grew in salinity conditions of 0 and at a pH of 7.
Genome properties The genome was 4,261,752 bp long with 37. 9% GC content. It was composed of 19 scaffolds (composed of 66 contigs) (Fig. 6 ). Of the 3,288 predicted genes, 3,227 were protein-coding genes and 61 were RNA genes (two genes were 5S rRNA, two genes were 16S rRNA, two genes were 23S rRNA and 55 genes were tRNA genes). A total of 1,807 genes (56. 00%) were assigned as putative function (by COGs or by NR blast), and 113 genes (3. 50%) were identified as ORFans (Table  3 ). The remaining genes were annotated as encoding hypothetical proteins (1,229 genes, 38. 08%). Nucleotide content and gene count levels of the genome are summarized in Table  3 , while the distribution of genes into COGs functional categories is presented in Table  4. The genome sequence has been deposited in GenBank under accession number CYPV00000000.
Diversity metrics Taxonomic diversity was measured by species richness (N0), inverse Simpson diversity (N2), and species evenness (N20 = N2/N0), based on R enyi general entropy and species number equivalents  (Hill 1973). Rao quadratic diversity with Jost correction was used to compare taxonomic, phylogenetic, and functional facets of diversity, as it corresponds to a generalization of the inverse Simpson index  (Jost 2007; de Bello et al. 2010). We compared alpha diversity metrics at the plot level and also the overall spatial gamma diversities and their additive partitioning at the survey level, that is, for old versus new datasets. The computation of phylogenetic diversity implies the construction of an ultrametric phylogenetic tree composed of the vascular plant species recorded during the field work.
To do so, we used sequences of two genes encoding chloroplast proteins (rbcL and matK) and calibrated branch length following the detailed procedure given in  Mauchamp et al. (2014). The computation of functional metrics was based on a set of functional traits, chosen according to previous studies that revealed their interest for studying vegetation variation along various gradients  (Pakeman 2004; Louault et al. 2005; Qu etier et al. 2007; Ansquer et al. 2009; Martin et al. 2009; Garnier and Navas 2012). We considered traits related to plant morphology (Hmax: maximum vegetative height; LD: leaf distribution), leaf characteristics (LDMC: leaf dry matter content; SLA: specific leaf area), and reproductive strategies (SM: seed mass; CGO: clonal growth organs). These traits were extracted from various databases: LEDA Traitbase for SM, LDMC, and SLA  (Kleyer et al.
2008) ; CLO-PLA for CGO (binary variables for 11 types represented in our dataset among the 17 forms contained in the database of Klime sov a and de Bello 2009); Biolflor for LD  (Klotz et al. 2002); and J€ ager et al. (2000)  for Hmax. LD is a qualitative variable classifying species according to the distribution of leaves on the stems: (1) rosette; (2) semirosette; (3) leaves distributed regularly along the stem; (4) shoot scarcely foliated; (5) tufts and crowns, leaves concentrated as a rosette at the top of taller shoot; and (6) other types  (Klotz et al. 2002). In addition to these true functional traits (sensu  Violle et al. 2007) , we also considered CSR strategies  (Grime et al. 2007 ), based on a tradeoff among resistance to stress (S), adaptation to disturbance (R), and competitive ability (C); C, S, and R numeric values represent the triangular coordinates, summing to 1 for each species.
We considered cover-weighted mean values for the defoliation tolerance (defol), the importance of allochthonous flora (neo, based on the type and time of immigration of the flora), and the degree of artificialization (artif, influence of man on site conditions, that is, "urbanity" sensu  Wittig et al. 1985) , based on Flora indicativa  (Landolt et al. 2010). As Landolt indicator value of defoliation tolerance is mixing mowing and herbivory (grazing and browsing), we also considered separate indicators for mowing (MowTol), grazing (GraTol), and trampling (TraTol) tolerance, based on Biolflor database  (Klotz et al. 2002). This set of community indicators was complemented by the pastoral value (PV), based on the agronomic forage quality of the species  (Daget and Poissonet 1971).
between years 1990 and 2000, in order to have a time interval of at least 10 years before the new plot records. All retained plots have been projected on recent aerial orthophotographs to keep those that are nowadays located in a parcel still used as grassland (excluding situations of agricultural conversion). Of this dataset, 150 plots were finally randomly selected to conduct the second survey in spring 2012 (Fig. 1). In this sample, 50 relev es belonged to the Arrhenatherion elatioris W. Koch 1926 (lowland mesic hay meadows), 45 to the Triseto flavescentis-Polygonion bistortae Braun-Blanquet & T€ uxen ex Marschall 1947 (mountain mesic hay meadows), and 55 to the Cynosurion cristati T€ uxen 1947 (mesic pastures) (Ferrez 2007).
Acknowledgments This research project was supported by the  Conseil r egional de Franche-Comt e and the Comit e Interprofessionnel de Gestion du Comt e (CIGC)  and took place in the  Long-Term Ecological Research site Jurassian Arc  (http://zaaj. univ-fcomte. fr/?lang=en). We thank  Jean-Claude Monnet  and  Florence Compagnon  for the provision of old relev es and  Julien Moreaux  for his contribution to data analysis. We are grateful to the anonymous reviewer for his/her constructive comments.
Our algorithm does not require the adjustment of tissue-specific parameters, and scales in sub-quadratic time with tissue size. It does not rely on precise positional information, permitting large cell movements between frames and enabling tracking in datasets acquired at low temporal resolution due to experimental constraints such as phototoxicity. To demonstrate the method, we perform tracking on the Drosophila embryonic epidermis and compare cell-cell rearrangements to previous studies in other tissues. Our implementation is open source and generally applicable to epithelial tissues.
Introduction Live-imaging microscopy is a powerful, and increasingly quantitative, tool for gaining insight into fundamental processes during embryonic development  [1] [2] [3]. Quantitative information on cell growth, proliferation, death, shape changes and movement extracted from live-imaging reveals how such processes are regulated to give correct tissue-level behaviour. This approach has been particularly successful in characterizing the growth and patterning of embryonic epithelial tissues in a number of model organisms  [4] [5] [6] [7] [8] [9]. A common experimental technique for visualizing cell shapes in an epithelial sheet is to fluorescently tag a molecule marking cell boundaries, such as E-cadherin (figure  1a ). The analysis of time-lapse microscopy data obtained from such tissues is extremely challenging  [2, 3] , especially in cases of imaging data of rapidly evolving tissues, and when limitations of, for example, microscope speed, imaging resolution or phototoxicity prohibit the creation of datasets with high temporal and spatial resolution.
Optical flow (warping) between successive frames can be used to guide cell tracking as well as segmentation  [19]. It is also possible to combine segmentation and tracking of two-dimensional microscopy videos by interpreting time as a third spatial dimension and employing three-dimensional segmentation techniques  [20]. The nearest-neighbour method associates two cells in consecutive frames with each other if their respective centroids have minimal distance within the field of view  [10] , or if their overlap in pixels within the field of view is maximal  [21, 22]. Particle image velocimetry, a technique originally developed to analyse fluid flow  [23] , has also been employed to track cells in epithelial tissues  [24]. Software implementations and computational tools for cell tracking include FARSIGHT  [25]  (segmentation only), SeedWaterSegmenter  [10]  (nearest-neighbour tracking), ilastik  [18]  (chain-graph models), Tufts Tissue Tracker  [11]  (min-cost max-flow algorithm), Tracking with Gaussian Mixture Models  [26]  (nearest-neighbour tracking), Packing Analyzer  [27]  ( particle image velocimetry) and EpiTools  [13]  (nearest-neighbour tracking).
Our algorithm does not require the tuning of parameters to a specific application, and scales in sub-quadratic time with the number of cells in the sheet, making it amenable to the analysis of large tissues. We demonstrate here that our algorithm resolves tissue movements, cell neighbour exchanges, cell division and cell removal (for example, by delamination, extrusion or death) in a large number of in silico datasets, and successfully tracks cells across sample segmented frames from in vivo microscopy data of a stage-11 Drosophila embryo. We further show how our algorithm may be used to gain insight into tissue homeostasis by measuring, for example, the rate of cell rearrangement in the tissue. In particular, we find a large amount of cell rearrangement within the observed dataset despite the absence of gross morphogenetic movement.
Material and methods In this section, we provide a conceptual overview of the core principles underlying our cell tracking algorithm. We focus on providing an accessible, non-technical description rather than including all details required to implement the algorithm from scratch. A comprehensive mathematical description of the algorithm is provided in the electronic supplementary material. The input to the algorithm is a set of segmented images obtained from a live-imaging microscopy dataset of the apical surface of an epithelial cell sheet. For each image, the segmentation is assumed to have correctly identified which cells are adjacent and the locations of junctions where three or more cells meet. Various publicly available segmentation tools can be used for this segmentation step, for example, SeedWaterSegmenter  [10]  or ilastik  [18].
Code availability The code used in this article is publicly available under the 3-clause BSD licence as the MCSTracker project (https:// github. com/kursawe/MCSTracker). The project is implemented in pure Python, employs unit testing  [37]  and is fully documented. Graphs in our code are represented using the Python package NetworkX  [38]. , where the black cell is paired correctly, the local MCS is larger than in (c), where the selected cell is not considered for mapping. Hence, the pairing of black cells is added to the MCS. rsif. royalsocietypublishing. org J. R. Soc. Interface 13: 20160725 (a) (b) ( c) x x x x x.
Generation of in silico datasets To test the algorithm, we generate in silico datasets that include examples of cell divisions, removals and neighbour exchanges, as well as tissue movement. These datasets are generated using Voronoi tessellations modified using Lloyd's relaxation, which resemble cell packings in a variety of epithelial tissues  [33, 39]. To generate polygonal patterns of size m Â n, where m and n are natural numbers, (m þ g) Â (n þ g) Voronoi seeds are distributed uniformly at random in a two-dimensional domain V of width m þ g and height n þ g (figure  5a ). Here, g denotes the size of a boundary region that is introduced to reduce the impact of the Voronoi boundary on the patterns. The domain V is surrounded by two additional rows of evenly spaced seeds on each side. The inner row is a distance of 0.
In silico testing of the algorithm To assess the performance of the algorithm, we begin by applying it to in silico datasets that include cell neighbour exchanges, tissue movement, cell removal and cell division. In each case, we compare the outcome of the tracking algorithm to the ground truth. We begin by assessing the ability of the algorithm to resolve permutations in otherwise identical tissues (figure  6a ). In this test, a random tessellation of size nine by nine cells is created as described in the Material and methods section, and integer identifiers c i are assigned to each cell. Next, an identical copy of the tissue is created in which the integer identifiers are randomly shuffled. A ground truth mapping from the first to the second integer identifiers is We design four further tests of tissue rearrangements (figure  6b-e ).
We run 100 realizations of each test case, and compare the tracking outcome to the ground truth. In all cases, we find that cells are tracked correctly, with at most three unmatched cells at the boundary of the sheet. In figure  6 , all cells identified after the cleaning step, in which weakly connected cells are removed from the MCS, are coloured green, whereas cells that are identified by the postprocessing algorithm are coloured red. Note that the exact number of cells that are identified by the post-processing algorithm varies between individual realizations of the tests. In many cases, the cells identified by the post-processing algorithm include cells that are adjacent to those undergoing division, removal or neighbour exchange. We next analyse the extent to which the success of our tracking algorithm depends on the number of Lloyd's relaxation steps, n L , used to generate the in silico datasets.