dataesr/scientific-paragraphs-categorization huggingface dataset:
Materials and methods All statistics were done using ANOVA method with the Graphpad Prism V6. 0 software. Some figures were done using FigureJ  49. Immunohistochemistry of mouse and monkey pancreatic islets of Langerhans. All animal procedures were approved by the Institutional Animal Care and Use Committee (IACUC, protocol number 2013/ SHS/816) of the SingHealth system. Experiments were carried out in accordance to experimental guidelines established by the Singhealth Experimental Medicine Center (SEMC), which is an AAALAC accredited facility. Immunohistochemistry of mouse or monkey pancreas was performed as previously described  23. Briefly, the pancreas from a 5-week-old wild type C57 mouse was dissected, rinsed in phosphate buffered solution (PBS) and fixed in 4 paraformaldehyde for 12 hours.
Confocal microscopy and image acquisition. Single photon confocal microscopy images of islets of Langerhans were acquired using an upright Leica SP8 confocal microscope (Leica Microsystems, Germany) equipped with a white light laser and hybrid detectors (HyDs). The following excitation (Ex) and emission (Em) parameters were used for image acquisition: DAPI Ex-405nm and Em-432/481 nm; AlexaFluor 488, Ex-498nm and Em-507/559 nm; AlexaFluor 568 Ex-577nm and Em-588/681 nm; AlexaFluor 647 Ex-647nm and Em-656/762 nm. Image processing and analysis. All implementations were done in Java as ImageJ plugins using our home-made tools and library for 3D processing and analysis. Datasets and source code are available on demand. A wiki page, with detailed instructions on how to use the toolbox along with a sample dataset was created at: http://imagejdocu.
tudor. lu/doku. php?id= plugin:3d:tissue_organization_toolbox:start. Nuclei segmentation. Images corresponding to nuclear labeling (DAPI) were first processed by 3D median filter with a radius of 4-4-2 in X-Y-Z to reduce noise and homogenize intensities inside the nuclei. Images were then filtered by a 3D band pass filter  50  with specific size interval of 20-28 pixels corresponding approximately to 6-7 μm. This is the approximate diameter of the average nucleus in our dataset. The resulting image is a 32-bit image; which is then scaled to a 16-bit image. Our plugin 3D Spot Segmentation  33, 51  was used to segment the nuclei with a local maxima in a radius of 4 voxels extracted from the band pass filtered image. A threshold is set to only detect local maxima corresponding to nuclei regions (threshold value was set to 30,000 for both mouse and monkey dataset images  33  ).
Acknowledgments T. B. 's international mobility was partly funded by the  programme Investissements d'avenir   ANR-11-IDEX-0004-02. R. A. D. is supported by a postdoctoral research fellowship from the  AXA  research foundation and the  Nanyang Institute of Structural Biology (NISB). P. O. B. is supported by a start-up grant from the  Lee Kong Chian School of Medicine, Nanyang Technological University.
Discussion Nuclei segmentation. Although nuclei segmentation has been studied for many years, no definite tools or algorithms are available, especially for thick tissue samples. One of the most efficient tools is FARSIGHT  9  , which did not present a better performance in comparison to our algorithm (Supplementary Fig. 3 ). The main originality of our approach is the use of a filtering to enhance structures based on their size with a high degree of specificity (Fig. 1  and Table  2 ). Moreover, this filtering allows us to discard any eventual intensity variation between nuclei and within the nuclei and contributes to a high detection efficiency observed in our dataset. Computation of cell zones. The computation of marked cell zones is an essential part in the analysis of the spatial organization of any given tissue.
In our dataset, the membranes of islet cells were unlabeled and thus we used watershed approach to separate nuclei and compute plausible cell zones around each nucleus (Fig. 2D ). Nevertheless, our observed results indicate a high accuracy in cell phenotype identification (Table  3 ). Subsequent analysis of cellular interactions confirmed previous results describing homo-and heterotypic cellular interactions in mouse and monkey islets (Fig. 3  and Supplementary Table  2 ). Moreover, 3D analysis of an intact islet made possible by tissue clearing yielded the largest number of possible cell-cell interactions (mouse4 and mouse6, Supplementary Table  1 ) when compared to single or serial sections datasets. This suggests that analysis of thin tissue sections is insufficient and does not provide a real account of a tissue spatial organization, thus strengthening the need for real 3D imaging and analysis.
Next, we used the classical watershed separation approach to compute a Voronoi diagram around the segmented nuclei. We however modified the watershed algorithm to constrain the Voronoi zones not to extend farther than a predefined radius from the nuclei boundaries. Finally, to process the three different labels altogether, we observed that the supervoxels clustering approach  34  was the most robust and allowing a correct clustering for all the three channels. It is worth noting that while this approach was successful for our datasets, it is possible that for other applications investigating other cell types (e. g. neurons) a different combination of tools may be necessary  20.
This COG group is the biggest in proportion (11% of the whole genome). We also noticed that the S. proteamaculans MFPA44A14-05 genome contains biogenic amine production activity arising from amino acid catabolism, which involves two genes encoding a lysine decarboxylase to produce cadaverine (cadA and ldcC genes) and all genes of the polyamine superpathway II (speA to speF) to produce agmatine, putrescine, and spermidine from arginine, ornithine, and S-adenosylmethionine. Accession number(s). This whole-genome shotgun project has been deposited in ENA BioProject number PRJEB20089 and assembly under the accession numbers FWWG01000001 to FWWG01000080. The version described in this paper is the first version.
ACKNOWLEDGMENTS Lysiane Fougy  was the recipient of a Ph. D. fellowship by the  Association Nationale de la Recherche et de la Technologie (ANRT). The LABGeM ( CEA/IG/Genoscope & CNRS UMR 8030 ) and the  France Génomique National  infrastructure (funded as part of  Investissement d'Avenir program  managed by  Agence Nationale pour la Recherche , contract  ANR-10-INBS-09 ) are acknowledged for support within the MicroScope annotation platform. We are grateful to the  INRA MIGALE bioinformatics platform  (http://migale. jouy. inra. fr)for providing computational resources and data storage.
The Andean mountains of South America are the most species-rich biodiversity hotspot worldwide with c. 15% of the world's plant species, in only 1% of the world's land surface. Orchids are a key element of the Andean flora, and one of the most prominent components of the Neotropical epiphyte diversity, yet very little is known about their origin and diversification. We address this knowledge gap by inferring the biogeographical history and diversification dynamics of the two largest Neotropical orchid groups (Cymbidieae and Pleurothallidinae), using two unparalleled, densely sampled orchid phylogenies (including more than 400 newly generated DNA sequences), comparative phylogenetic methods, geological and biological datasets. We find that the majority of Andean orchid lineages only originated in the last 20-15 million yr.
Taxon sampling, DNA sequencing and phylogenetic analysis To generate solid phylogenies of the tribe Cymbidieae and subtribe Pleurothallidinae, we newly generated a total of 420 sequences of the nuclear ribosomal internal transcribed spacer (ITS) and a c. 1500-bp fragment of the gene ycf 1 of underrepresented lineages of key biogeographical importance. DNA amplification, PCR product purification and sequencing were conducted as described previously in  Irimia et al. (2014) and P erez-Escobar et al. (2016a). Voucher information and GenBank accession numbers are provided in Supporting Information Tables  S1  and S2. We merged our novel dataset with previously generated data from the studies of  Blanco et al. (2007) ,  Whitten et al. (2014) ,  Karremans et al. (2016a ,b), P erez-Escobar et al.
(2017 ), and Ram ırez et al. (2011) , using the R-package MEGAPTERA v. 1. 0 (available at https://github. com/cran/megaptera. git). We retrieved 3541 sequences of nuclear (ITS) and plastid (matK, trnL-F region, psbA, ycf1). We selected outgroup taxa representing the old and new world subtribes Polystachyinae, Aeridinae and Laeliinae. Trees were rooted on Calypso bulbosa (for Cymbidieae) and Arpophyllum giganteum (for Pleurothallidinae) following  Whitten et al. (2014). Poorly aligned positions were excluded from the alignments using GBLOCKS v. 0. 9  (Talavera & Castresana, 2007). To statistically detect potential incongruences between plastid and nuclear DNA phylogenies, we used the tool PROCRUSTES APPROACH TO COPHYLOGENY (PACo; http://www. uv. es/cophylpaco/)  (Balbuena et al. , 2013; P erez-Escobar et al.
, 2016b). Maximum likelihood (ML) tree inference was performed using RAXML-HPC v. 8. 0  (Stamatakis, 2014) , under the GTR + G substitution model with four gamma categories (best model for both datasets as inferred via the Akaike information criterion (AIC) in JMODELTEST v. 2. 1. 6; Darriba et al. , 2012) , with 1000 bootstrap replicates and data partitioning by genome compartment. All phylogenetic and dating analyses were performed in the CIPRES Science Gateway computing facility  (Miller et al. , 2015).
Ancestral range estimation Species ranges were coded from the literature  (Pridgeon et al. , 2009)  and from herbarium specimens through a survey of virtual collections and loans of several herbaria (AMES, COL, F, MO, SEL, US, M), as well as the Global Biodiversity Information Facility (GBIF) repository. To query the GBIF database, we relied on the function occ of the R-package SPOCC  (Chamberlain et al. , 2016). A total of 19 486 distribution records were compiled for the Cymbidieae, and 9042 records for the Pleurothallidinae. Protocols for distribution maps and species richness pattern analyses are detailed in Methods S1. Distribution maps for Cymbidieae and Pleurothallidinae (summarized in Figs  S1, S2 ) and extant distribution patterns identified for other plant lineages (e. g. Rubiaceae,  Antonelli et al.
To infer the ancestral range of all examined lineages in Cymbidieae and Pleurothallidinae, we used the R-package BIOGEOBEARS v. 0. 2. 1  (Matzke, 2013 (Matzke, , 2014)). In addition, in order to estimate the number of migrations, dispersals, extinctions and within-area speciation events from our phylogeny, we used biogeographical stochastic mapping (BSM)  (Matzke, 2014)  under the best-fit model, as implemented in BIOGEOBEARS (for detailed settings, see Methods S1).
Rates of species diversification To infer the diversification dynamics of the Cymbidieae and Pleurothallidinae, we first used a time-dependent model implemented in BAMM v. 2. 5. 0  (Rabosky, 2014)  to estimate the rates of extinction and speciation across the phylogenies. Incomplete taxon sampling was accounted for by assigning a sampling fraction of 25% of the extant orchid diversity of Cymbidieae, and 13% of Pleurothallidinae (sampling fractions of every genus sampled were incorporated according to  Chase et al. , 2015). We performed three runs with 1 million Markov chain Monte Carlo (MCMC) generations, sampling parameters every 10 000 generations. Diversification rates and rate shift configurations were plotted using the R-package BAMMTOOLS  (Rabosky et al. , 2014). We checked the convergence of the runs by plotting the loglikelihood across MCMC generations sampled in the 'mcmc_out' file.
Geographical state-dependent analyses We used GEOSSE  (Goldberg et al. , 2011) , an extension of the BiSSE model that allows lineages to occur simultaneously in two areas and to test whether one area has overall higher speciation rates, as implemented in the R-package DIVERSITREE v. 0. 9-7  (Fitzjohn, 2012). To test whether lineages restricted to the Northern Andes ('A') had higher diversification rates than lineages absent from the Northern Andes (collectively called 'B' here), we used Bayesian MCMC GEOSSE analyses of 1 million generations on the maximum clade credibility tree from BEAST (in the particular case of Cymbidieae, only Neotropical representatives were included). Implemented models in GEOSSE and settings of tailored simulations to account for Type I error biases in GEOSSE are provided in Methods S1.
Mapping speciation rates in the Neotropics Based on the speciation and extinction rates inferred for orchid lineages, and their geographical occurrence, it is possible to identify important areas of diversification as plotted on a heat map  (Condamine et al. , 2013). For this purpose, we designed a novel method that involves retrieving speciation rates from BAMM analyses using the function GETTIPSRATES in BAMMTOOLS v. 2. 1  (Rabosky et al. , 2014)  and to link them to species occurrences. Rates were further associated to known distribution records of Cymbidieae and Pleurothallidinae and interpolated to a polygon representing the currently known distribution of Cymbidieae and Pleurothallidinae species, using the inverse distance weight method implemented in the software ARCMAP v. 9. 3 (Esri).
Palaeo-elevation-dependent diversification We tested the effect of past environmental change on the diversification of Cymbidieae and Pleurothallidinae using birth-death models that allow speciation and extinction rates to vary according to a quantitative, time-dependent, environmental variable  (Condamine et al. , 2013) , here the palaeo-elevation of the Northern Andes  (Hoorn et al. , 2010; Lagomarsino et al. , 2016). The R-package PSPLINE  (Ramsey & Ripley, 2010)  was used to interpolate a smooth line for Andean palaeo-elevation. This smooth line was sampled during each birth-death modelling process to give the value of the palaeo-elevation variable at each time point. Speciation and extinction rates were then estimated as a function of these values along the time-calibrated phylogenies, according to the parameters of each model.
Phylogenetics, age and biogeography of Andean orchids Analyses of phylogenetic incongruence detection identified 259 and 125 potential conflicting tips in Cymbidieae and Pleurothallidinae, respectively (Figs S3, S4), all of which clustered in weakly to moderately supported clades (< 75% bootstrap support, BS) or in clades with extremely long branches. These analyses indicated the absence of supported phylogenetic incongruence  (Mason-Gamer & Kellog, 1996; P erez-Escobar et al. , 2016b). In the absence of supported phylogenetic conflicts, nuclear and plastid partitions of Cymbidieae and Pleurothallidinae were concatenated. For the Cymbidieae, our molecular dataset consisted of 6. 6 kb DNA (five markers) for 816 species, and yielded the first strongly supported phylogeny of the tribe (Fig. S5 ).
The Pleurothallidinae dataset was composed of 2. 4 kb DNA (two New Phytologist (2017) 215: 891-905 Ó 2017 The Authors New Phytologist Ó 2017 New Phytologist Trust www. newphytologist. com markers) and 684 terminals, including, in total, 420 newly generated sequences (Fig. S6 ). Both orchid phylogenies are strongly supported at most important nodes, with 618 nodes (76%) with BS > 75% for the Cymbidieae, and 321 nodes (47%) with BS > 75% for the Pleurothallidinae (Figs S5, S6). Ages obtained on our wide orchid-dated phylogeny were very similar to those of other recent orchid dating studies  (Chomicki et al. , 2015; Givnish et al. , 2015). A chronogram for the orchid family showing absolute ages and 95% CIs for every node is provided in Fig. S7  Our dating and biogeographical analyses identified the Dispersal-Extinction-Cladogenesis model with founder speciation event (DEC + J) as the best fitting model for both Cymbidieae and Pleurothallidinae (Tables  S3, S4 ).
The diversification rate shifts are all located at clades that already inhabited the Northern Andes, and temporally match with periods of accelerated Andean uplift in this region (Cymbidieae, Fig. 1 ; Pleurothallidinae Fig. 2 ). To further explore this apparent correlation with either accelerated Andean uplift or presence in the Northern Andes and fast diversification, we used a traitdependent approach (GEOSSE) that estimates region-dependent speciation rates. Here, a model with free rates fitted best our Cymbidieae and Pleurothallidinae datasets (Table  S9 ), indicating significant differences in speciation (sA À sB) and diversification (dA À dB) rates highly if not maximally supported (0. 99 and 1 Bayesian posterior probabilities, respectively). GEOSSE analyses further indicated that speciation rates in Northern Andes are consistently higher than in any other biogeographical region (Figs  3c, 4c ) in both Cymbidieae and Pleurothallidinae datasets.
Species name and voucher information for material used to build the Cymbidieae dataset Table S2 Species name and voucher information for material used to build the Pleurothallidinae dataset Table S3 Comparison of different biogeographical models fitted on the Cymbidieae dataset Table S4 Comparison of different biogeographical models fitted on the Pleurothallidinae dataset New Phytologist (2017) 215: 891-905 Ó 2017 The Authors New Phytologist Ó 2017 New Phytologist Trust www. newphytologist. com.
Acknowledgements We thank  M. Whitten ,  G. Gerlach  and  E. Pansarin  for plant material;  G. Gerlach ,  M. A. Blanco  and  G. Salazar  for providing information on pollination;  C. Bernau  for dedicated IT support;  A. Mulch  for the compilation of surface uplift data from the Andes;  J. Heinrichs ,  R. Camara ,  W. J. Baker  and  L. Lagomarsino  for discussions; and  F. Pupulin  and  B. Gravendeel  for their support on Cymbidieae and Pleurothallidinae research. O. A. P-E. is supported by a  Colombian National Science Foundation (COLCIENCIAS)  scholarship and G. C. is supported by a  German Science Foundation  grant ( RE 603/20 ). F. L. C. is supported by a  Marie Curie grant  ( BIOMME  project,  IOF-627684 ) and has benefited from an  'Investissements d'Avenir ' grant managed by  Agence Nationale de la Recherche  (CEBA, ref.
ANR-10-LABX-25-01 ). A. P. K. and D. B. were supported by grants from the  Alberta Mennega Foundation. N. J. M. was supported by the  National Institute for Mathematical and Biological Synthesis , an Institute sponsored by the  National Science Foundation (NSF)  through  NSF  Award no. EFJ0832858 , with additional support from The  University of Tennessee, Knoxville , and is currently supported by a  Discovery Early Career Researcher Award   DE150101773 , funded by the  Australian Research Council , and by  The Australian National University. D. S. is funded by the  Swedish Research Council  ( 2015-04748 ). A. A. is supported by grants from the  Swedish Research Council , the  European Research Council  under the European Union's  Seventh Framework Program  (FP/2007-2013,  ERC  Grant Agreement no.
The first module of Trinity, Inchworm, will try and assemble the most covered contig which hopefully corresponds to the most abundant alternative transcript. Then alternative exons are glued to this major transcript to form a splicing graph. The last step is to enumerate all alternative transcripts. If repeats are present, their high coverage may be interpreted as a highly expressed link between two unrelated transcripts. Overall, assembled transcripts may be chimeric or spliced into many sub-transcripts. In the method we had previously developed, KisSplice, which is a local transcriptome assembler  [12] , repeats are less problematic since the goal is not to assemble fulllength transcripts. KisSplice instead aims at finding variants in transcriptomes (SNPs, indels and alternative splicings).
However, as we reported in  [12] , KisSplice was not able to deal with large portions of a de Bruijn graph containing subgraphs associated to highly repeated sequences, e. g. transposable elements, the so-called complex Biconnected Components. Here, we try and achieve three goals:  (1)  give a clear formalisation of the notion of repeats with high copynumber in RNA-seq data, (2) apply it on local transcriptome assembly by giving a practical way to enumerate bubbles that are lost because of such repeats, and (3) apply it on global transcriptome assembly by showing that the topology of the subgraph around a transcript can give some hints about its confidence level. Recall that we are in a de novo context, so we assume that neither a reference genome/transcriptome nor a database of known repeats, e.
It is therefore important to be able to identify the transcripts coming from such complex regions in order to know that the solution presented is not the only one, and furthermore may not be the right one. We compared our measure against two state-of-the-art methods for de novo transcriptome evaluation, namely Rsem-Eval  [4]  and TransRate  [5] , for the specific task of identifying chimeric transcripts in both real and simulated datasets. We show that our measure provides good results despite the fact that it uses only the graph topology, and not coverage, nor read information. The results obtained thus suggest that exploring the topology of the subgraph around a transcript, an information that is currently disregarded by transcriptome evaluation methods, can be useful to infer some of the transcript's properties, such as confidence level, quality, assembly hardness, etc.
When building the assembly graph, the two copies of R may collapse into a single region of the graph, and connect the unique regions of both transcripts. The only correct assemblies are ARB and CRD, but the assembly graph also allows for the generation of the chimeric transcripts ARD and CRB, or truncated transcripts, in case the assembler chooses to be conservative Fig. 5  The chimeric transcript c12400_g1_i1 that aligns to the gene MOB1A in chromosome 2 and also to the gene PEBP1 in chromosome 12, in which the fusion of these genes is due to a small identical region shared between two different repeats present in their UTR regions (see "Measuring the confidence of a transcript in full-length transcriptome assemblers" section for details of each panel). a The alignment of the transcript c12400_ g1_i1 to reference hg38, visualised using the UCSC Genome Browser.
We tested two values, namely 5 and 15% for the proportion of reads from pre-mRNAs. Those values were chosen so as to correspond to realistic ones (see "Measuring the confidence of a transcript in full-lengthtranscriptome assemblers" section). On these simulated datasets, we ran KisSplice  [12]  versions 2. 1. 0 (Ks_2. 1. 0) and 2. 2. 0 (Ks_2. 2. 0, with a maximum number of branching vertices set to 5) and obtained lists of detected bubbles that are putative alternative splicing (AS) events. We also ran the full-length transcriptome assemblers Trinity version r2013_08_14 and Oases version 0. 2. 09 on both datasets, obtaining a list of predicted transcripts, from which we then extracted a list of putative AS events. For Oases, there was one locus in each dataset for which we were not able to extract the putative AS events.
A manual inspection revealed that they were mostly composed of subparts of introns or UTRs flanked by repeats, usually copies of ALUs. The presence of such high copy-number repeats in these transcripts induced the clustering of all these unrelated sequences into one complex locus. More precisely, in the dataset containing 5% of the reads from pre-mRNAs, the largest locus that Oases predicted comprised 20,769 transcripts, while the second largest contained only 139 transcripts. In the other simulated dataset, the largest locus comprised 39,389 transcripts, and the second largest contained only 205 transcripts. This indicates that Oases faces similar issues to Ks_2. 1. 0. For fairness of comparison, we did not post-process these complex loci, and we were then unable to retrieve the potential AS events that they could describe.