dataesr/scientific-paragraphs-categorization huggingface dataset:
Mapping speciation rate in the Neotropics Based on the speciation and extinction rates inferred for orchid lineages, and their geographic occurrence, it is possible to identify important areas of diversification as plotted on a heat map  (Condamine & Kergoat, 2013). For this purpose, we designed a novel method that consists on retrieving speciation (lambda) 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.
Phylogenetics, age and biogeography of Andean orchids Analyses of phylogenetic incongruence detection identified 259 and 125 potential conflicting tips in Cymbidieae and Pleurothallidianae, respectively (Fig. S27 , 28), all of which clustered in weakly to moderately supported clades (<75% LBS), or in clades with extremely long branches. In the absence of supported phylogenetic conflicts, nuclear and chloroplast partitions of Cymbideae 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. S3 ). The Pleurothallidinae dataset was composed of 2. 4 kb DNA (two markers) and 684 terminals, including in total 420 newly generated sequences (Fig.
g. Pleurothallinae, 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 trait-dependent approach (GeoSSE) that estimates region-dependent speciation rates. Here, a model with free rates fitted best our Cymbidieae and Pleurothallidinae dataset (Table  S9 ), with differences in speciation (sA -sB) and diversification (dA -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 (Fig 3C  , 4C ) in both Cymbidieae and Pleurothallidinae datasets. We evaluated and confirmed the robustness of these results through extensive data simulations (Fig.
Acknowledgments 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;  L. Lagomarsino  for discussion  F. Pupulin  and  B. Gravendeel  for their support on the Cymbidieae and Pleurothallidinae research. O. A. P. is supported by  Colombian National Science Foundation (COLCIENCIAS)  scholarship, G. C. is supported by the  German Science Foundation  grant ( RE 603/20 ). F. L. C is supported by a  Marie Curie grant  ( BIOMME  project,  IOF-627684 ). A. P. K and D. B. were supported by grants of 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  through  NSF  Award # DBI-1300426 , with additional support from The  University of Tennessee, Knoxville , and is currently supported by  Discovery Early Career Researcher Award   DE150101773 , funded by the  Australian Research Council , and by  The Australian National University.
Background Tandemly duplicated genes are important contributors to genomic and phenotypic variation both among and within species  [1]. Clusters of tandemly duplicated genes have been associated with disease resistance  [2] , stress response  [3] , and other biological functions  [4, 5]. Confounding the analysis of tandem repeats in most organisms is their underrepresentation in genome assemblies constructed from short-read sequence data, typically Illumina reads, for which the sequence reads are shorter than repeats  [6] [7] [8] [9]. The ALLPATHS-LG software  [10]  overcomes some of the assembly limitations of short-read sequencing by clever combination of Illumina paired end reads from both short-insert and long-insert libraries. Applied to human and mouse genomes, the ALLPATHS assembler produced assemblies with more contiguity, as indicated by contig N50 and scaffold N50, than had been attainable from other short-read sequence assemblers.
Unlike other approaches that use Illumina or PacBio sequencing for only certain limited phases of the assembly, Alpaca uses the full capabilities of the data throughout the entire assembly process: 1) contig structure is primarily formed by long reads that are error corrected by short reads, 2) consensus accuracy is maximized by the highly accurate base calls in Illumina SIPE reads, and 3) scaffold structure is enhanced by Illumina LIPE that can provide high-coverage connectivity at scales similar to the PacBio long reads. We targeted low-coverage, longread data in order to make the pipeline a practical tool for non-model systems and for surveys of intraspecific structural variation. We evaluated the performance of Alpaca using data from Oryza sativa Nipponbare (rice), assembling the genome sequence of the same O.
Rice genome assembly The rice Nipponbare genome, which offers an independent reference, was used to evaluate assembly methods. An ALLPATHS assembly was generated from Illumina short reads and these data were used with 33X PacBio long reads to generate PBJelly and Alpaca assemblies. The Alpaca process included correcting long reads by (1) using Celera Assembler  [16] [17] [18]  to generate unitigs (preliminary contigs) from Illumina short-insert paired ends, (2) mapping unitigs to raw long reads with Nucmer  [32] , and (3) correcting the long read base calls with ECTools  [33]. For separate evaluation of the correction step, the raw and corrected long reads were aligned to the reference. On average, raw reads aligned at 82% identity over 89% of their length, while corrected reads aligned at 98% identity over 95% of their length.
Discussion Efficient and accurate de novo assemblies of genomes will greatly facilitate investigation of the functional importance and evolution of copy number variation within and among species  [39]. We have presented a new opensource hybrid assembler, Alpaca, that integrates PacBio long-read data with Illumina short-read data to produce high quality contigs and scaffolds. We evaluated Alpaca on one rice genome with 33X long-read coverage, and three Medicago genomes using 20X long-read coverage. Alpaca made use of an ALLPATHS-LG  [10]  assembly and the Illumina short reads required by ALLPATHS. Compared to the short-read assembly that it used as input, Alpaca not only improved the contig contiguity of each genome, it also improved the structural accuracy. This was shown by alignment N50 length and tandem repeat content.
Also, Alpaca was able to recover highcopy number variants that were missing from ALL-PATHS assemblies and even the Medicago reference genome. As its name was chosen to indicate, Alpaca is a hybrid assembler that relies on the ALLPATHS and Celera Assembler (CA)  [16] [17] [18]  genome assembly pipelines. Alpaca first corrects the high base-call error expected in the PacBio reads. This read-correction step alters the sequences of individual PacBio reads using their alignments to the ALLPATHS short read contigs by running the ECTools  [33]  software. Alpaca then assembles the corrected long reads into preliminary contigs using Celera Assembler. In parallel, Alpaca relies on ALLPATHS to build scaffolds from the SIPE and LIPE short reads. Alpaca then samples the scaffold sequences to generate synthetic long range mate-pair sequences, and maps those pairs to the long-read contigs.
The gap-filling approach seems predisposed to perpetuate mis-assemblies, if present, such as the collapse of nearby repeats within a contig. In our comparisons using rice and Medicago, Alpaca captured more tandem repeats of sequences 2Kbp and longer. Thus, Alpaca captured more of the gene-length tandem repeats that are difficult to assemble correctly from short reads. PBJelly was much faster as it did not require Alpaca's CPU-intensive long-read correction step or its Celera Assembler scaffolder step. Alpaca's substantial computational burden must be weighed against its repeat detection capability and low coverage requirement. Alpaca is one of several recently-developed hybrid assemblers that incorporate long reads prior to scaffold formation. The hybridSPAdes software for bacterial genomes maps long reads to an assembly graph prior to contig formation  [45].
The MaSuRCA software aligns super-reads and mega-reads derived from Illumina and PacBio reads in a form of correction prior to assembly. MaSuRCA assembled a 4 Gbp wheat genome, an order of magnitude larger than rice or Medicago, using 38X PacBio and 110,000 CPU hr. [46]. The non-hybprid assemblers Falcon  [25]  and Canu  [19]  use only long reads to generate large, high-quality contigs but their CPU and coverage requirements are high. The DBG2OLC hybrid assembler, which avoids correcting base calls in reads prior to assembly, generated a 2 Mbp contig N50 on Arabidopsis using 20X PacBio, though false joins were a concern  [47]. The approach was refined for Drosophila melanogaster and also merged with other approaches to generate N50 s over 10 Mbp with 50X and above. Future work remains to refine and accelerate Alpaca.
Sequence data The rice (Oryza sativa Nipponbare) reference  [29, 48]  version 4. 0, GCA_000005425. 2_Build_4. 0, was downloaded from NCBI. The rice genome was sequenced by Illumina HiSeq to 50X SIPE with 180 bp inserts and to high coverage of LIPE with each of 2Kbp and 5Kbp inserts as previously described  [13]. The LIPE was sampled to 30X per insert size. The genome was further sequenced by PacBio RS II to 34X. The Medicago truncatula A17 reference  [30, 31]  version Mt4. 0 was downloaded from JCVI (medicago. jcvi. org/medicago/). Three accessions of M. truncatula (HM340, HM056, and HM034) were sequenced by Illumina HiSeq to generated on Illumina HiSeq and PacBio RSII platforms. For rice, Illumina library construction targeted 180 bp SIPE inserts and LIPE inserts at 2 Kbp and 5 Kbp. For Medicago, Illumina library construction targeted 150 bp or 180 bp SIPE and 9Kbp LIPE.
sh script, to generate alignments by 'nucmer -maxmatch -l 15 -b 1000 -g 500' using nucmer v3. 1 within the MUMmer v3. 23 package  [32]. Other settings used default values. ECTools was run in parallel on partitions of 20 PacBio sequences each and the output was concatenated into a FASTA file of corrected PacBio reads that was saved for the next step. The post-ECTools corrected PacBio reads provided between 7. 2 and 9. 8X genome coverage for each Medicago accession (9. 8X on HM034, 7. 2X on HM056, 7. 5X on HM340); the rice post-ECTools corrected PacBio reads were filtered to about 10X by using reads of length ≥ 4000 bases. After correction, the corrected PacBio reads are assembled to contigs and scaffolds using CA and the run_alpaca. sh script provided with Alpaca. Using the output FASTA from the ECTools, the corrected PacBio reads are assembled to unitigs with CA using parameters merSize = 22, ovlMinLen = 500, unitigger = bogart, utgGraphErrorRate = 0.
Availability of data and materials The sequence reads are available at NCBI SRA (Additional file 18). The four Alpaca assemblies are available at COGE (Additional file 20) and will be available at NCBI GenBank. The pipeline integration scripts of Alpaca ver 1. 0 are available (http://github. com/VicugnaPacos/ALPACA). All the authors have read and approved the manuscript for publication.
New Dissimilarity Measure In order to deal with different type of data, we propose a new dissimilarity measure. Further, the comparison between each two instances (xi; yi) is 1 http://www. tfidf. com/ calculated based on their similarities and the type of their related concepts (Ci (xi); Ci (yi)). We consider for two individuals X and Y, the same reference vector. Therefore, as we have different types of data (numeric, non-numeric (textual and semantic)), We assume that the concept criteria and noncriteria are determined according to their weight in the database as discussed in the next paragraph. The enhanced similarity measure is then: , , ∈ ∶ 1 , ∈ : 2 , ∈ , ∈ , : 3 , ∈ ∶ 4 0 (3) The similarity is calculated between two users' vector. Therefore, for case1 and case 2, we opt for using Jaccard similarity  (Jeff M.
2013)  for computing similarities between two strings or textual attributes, each attributes is associated to a weight. For this, we use the technique of TF-IDF 1. With this method, we can define which concept (class) is a concept criterion. Each instance is associated with a weight w i to evaluate how important this instances in database. For case3, we compute the similarity using the method proposed in  (Bouhana et al. 2013 ) for numerical attributes. Afterwards, OWA aggregator  (Yager 1988 ) is applied to compute the average similarity. The weights are generated automatically according to the orness measure and dispersion measure. The case 4 discusses uncertain criteria. For this propose, a WordNet 2 is applied to search the synonyms, the hypernym, the hyponym and any existing relation between the attributes of each users.
RNA samples were pooled to create four groups: (i) ipsilateral and (ii) contralateral sides of the dorsal horn of the spinal cord of tumor-bearing mice and (iii) ipsilateral and (iv) contralateral sides of the dorsal horn of the spinal cord of control mice. Pooled RNA samples representing each group were analyzed with microarray and RT-qPCR-based methods to quantify mRNA and miRNA expression, respectively (Fig. 2A ). A total of 3909 mRNAs were differentially expressed between the tumor and the control group (data deposited in ArrayExpress, accession number: E-MTAB-1289). Overall, most of the mRNAs were found to be up-regulated in the bone-cancer-pain condition (Fig. 2B ). These differentially expressed mRNAs represent genes with a wide range of molecular functions, biological processes and cellular components, as assessed by Gene Ontology term enrichment analysis (Supp Fig.
4 ). Interestingly, miRNA screening revealed that 525 out of the 742 miRNAs investigated were dysregulated in the spinal cord, with 175 up-regulated (fold change > 2) and 350 down-regulated (fold change < 0. 5) in the cancer-pain condition (Fig. 2C ). Since miRNAs exert an inhibitory regulation on mRNA expression, these results suggest that the changes in gene expression observed in the spinal cord in cancer-pain conditions involve a regulation by the miRNA system. To identify which miRNAs regulate which mRNAs in our large dataset, we developed a software to correlate mRNA and miRNA data in the light of the miRNA-target prediction databases. However, since a single miRNA can target dozens of mRNAs, the result of this anti-correlation analysis is a list of 8670 miRNA-mRNA pairs (Supp Table  1 ).
In addition, DWB and the Hargreaves test revealed painful behavior such as mechanical allodynia and thermal hyperalgesia. Then, we screened mRNA and miRNA expression in the dorsal horn of the spinal cord to identify miRNAs and their mRNA targets playing a role in cancer-pain mechanisms. To manage these massive amounts of data, we designed a software, GEASE, which anti-correlates miRNAs and mRNAs regarding their predictive interactions. For example, if a miRNA is up-regulated, the software analyses the expression of all its putative targets and highlights the mRNAs that are down-regulated. Owing to the multi-targeting feature of miRNAs, this analysis revealed 8670 miRNA-mRNA pairs. Targeting of multiple mRNAs by a single miRNA is a fundamental characteristic of miRNA regulation that has been conserved through evolution.
Affymetrix Mouse Gene 1. 0ST was used for mRNA profiling and RMAExpress was used to normalize data and determine the expression value for each gene  64  (http://rmaexpress. bmbolstad. com/). For each gene, the Mann-Whitney test was run to assess whether the differential expression observed between the control and the tumor group was statistically significant. The accession number for data deposited in the ArrayExpress database is E-MTAB-1289. MiRNA profiling was performed with a miRNA qPCR panel, where each miRNA was quantified with a specific forward primer and an universal reverse primer. The miRNA qPCR panel consists in two plates and covers 742 miRNA species known to be expressed in mouse. Each miRNA was quantified in each RNA sample in triplicate (6 control and 5 tumor samples) and the mean was used to determine the miRNA expression level.
Briefly, results were normalized using the ΔΔCp method with multiple reference genes (U6, RNU1A1, miR-191-5p and miR-423-5p)  65. For each miRNA, the Mann-Whitney test was run to assess whether the differential expression observed between the control and the tumor group was statistically significant. Finally, a correlation between mRNA and miRNA expression results was sought according to the miRNA targeting predictions. To increase the relevance of this correlation, Targetscan (http://www. targetscan. org/) and miRanda (http://www. microrna. org/microrna/home. do), two databases for miRNA target predictions, were used as a matrix for miRNA-mRNA interactions. To manage the massive amount of data generated by microarray analysis, our team has set up a comprehensive database server named GEASE (Gene Expression Analysis Software Environment), which is an extension of the standard BASE software package (version 1.
Two micrograms of these miRNA mimics were solubilized in 10 µl of i-Fect reagent and injected intrathecally between the L5 and L6 lumbar vertebrae every 2 days for 14 days. Immunohistochemistry. Immunodetection of synaptopodin was performed on spinal cord cryostat sections (30 µm thickness). Sections were incubated for 72 hours at 4 °C with anti-synaptopodin (1/250; Synaptic Systems) and anti-GFP (1/500; AveLabs). After rising, sections were incubated with Alexa 568-conjugated goat anti-rabbit (1/500; Molecular probes) and Alexa 488-conjugated goat anti-chicken (1/200; ThermoFisher) for 2 hours at room temperature. Image acquisition was performed with an epifluorescent microscope AxioPlan 2 (Zeiss) and a DsRi1 camera (Nikon) using the 10x objective, and the rhodamine and FITC filters. To quantify the percentage of the area labeled for synaptopodin, we developed a macro on image j software.
Acknowledgements This project was supported by  Ligue Nationale contre le Cancer, Association pour la Recherche sur le Cancer  ( SFI20111203977 ),  Institut National du Cancer  ( PLBIO15-300 ) and  European Commission   FP7/2013-2017  under agreement 602133. We are grateful to  Paul Robillard  and  Sébastien Benquet  for their technical help and to  Yves Le Feuvre  and  Pascal Fossat  for their precious comments.
Blending along the Cut This is the last step of an iteration. Its goal is to construct the new patch P new by blending P old and P in using the previously computed boundary cut. The boundary cut defines a binary mask M that equals one on the left and/or top of the cut and zero otherwise. The patch P new can be defined as P new = M P old + (1 -M ) P in. (4) We noticed that the Matlab implementation of the authors proposes an optional smoothing of the binary mask presumably to avoid noticeable transitions along the cut. We implemented this option but we did not observe noticeable improvements. Since it is not discussed in the original paper we do not use it for the experimental results of this paper.
Background Intracranial electrical recordings (iEEG) and brain stimulation (iEBS) are invaluable human neuroscience methodologies. However, the value of such data is often unrealized as many laboratories lack tools for localizing electrodes relative to anatomy. To remedy this, we have developed a MATLAB toolbox for intracranial electrode localization and visualization, iELVis. New Method iELVis uses existing tools (BioImage Suite, FSL, and FreeSurfer) for preimplant magnetic resonance imaging (MRI) segmentation, neuroimaging coregistration, and manual identification of electrodes in postimplant neuroimaging. Subsequently, iELVis implements methods for correcting electrode locations for postimplant brain shift with millimeter-scale accuracy and provides interactive visualization on 3D surfaces or in 2D slices with optional functional neuroimaging overlays.
, 2013). Secondly, there is mounting evidence that iEBS can improve  (Suthana et al. , 2012)  or manipulate  (Parvizi et al. , 2012; Mégevand et al. , 2014)  brain function with great precision. Finally, a growing number of public databases of iEEG data (e. g. , www. ieeg. org, www. epilepsiae. eu) are increasing access to these rare data. Despite the growing popularity iEEG and iEBS research, many researchers encounter technical challenges when attempting to analyze these data. These include:  [1]  precisely identifying the anatomical location of electrodes,  [2]  correcting for postimplant brain deformities (i. e. , "brain shift") so that postimplant data can be coregistered to preimplant neuroimaging,  [3]  effectively visualizing electrode data in a way that communicates their locations relative to other neural measures (e.
g. , magnetic resonance imaging, MRI, and functional magnetic resonance imaging, fMRI), and  [4]  mapping idiosyncratic electrode montages into a common space for multi-patient analyses. Although some excellent public software has been produced that solves some of these issues (Table  1 ), there is not yet a well-developed, scriptable, public software package for solving all of them. Consequently, iEEG and iEBS research groups currently develop labspecific solutions from existing packages and their own custom code. This inefficient and error-prone solution to localization and visualization makes it difficult to compare findings across research groups. To remedy this problem, we have pooled the resources of five different iEEG/iEBS research groups to develop a freely-available, open source software toolbox and processing pipeline that can assist researchers in localizing electrodes, correcting for brain shift, overlaying electrode locations with neuroimaging data, and visualizing their locations relative to individual anatomy and group-level templates.
The toolbox, called iELVis (Intracranial ELectrode VISualization), consists of MATLAB functions with a handful of Bash scripts for neuroimaging coregistration and overlay creation. iELVis relies on FreeSurfer (www. freesurfer. net) for MRI segmentation and for mapping to the FreeSurfer average brain for group analyses. In addition, iELVis relies on BioImage Suite (www. bioimagesuite. org; Papademetris, Jackowski, & Rajeevan, 2011)  for manually tagging electrode locations in postimplant CT or MRI scans. and FSL or FreeSurfer for coregistering pre-and post-implant neuroimaging. The key contributions of iELVis are that it: 1. implements a set of standard tools for electrode localization/visualization, using MATLAB code supported by thorough tutorials and documentation 2. establishes a pipeline and toolset to standardize localization across multiple laboratories 3.
implements new quality control metrics to help identify erroneous localizations 4. produces interactive, multi-view representations of the brain that can simultaneously represent multiple data modalities (e. g. , iEEG, iEBS, & fMRI) This paper describes the main features and workflow of iELVis. More extensive documentation and tutorial materials are available on the toolbox wiki, http://episurg. pbworks. com. This includes two example datasets and example code that illustrate how to use all the functionalities described here. The toolbox can be downloaded from https://github. com/episurg/EpiSurg.