dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2016). Thus, when the entire global dataset of PDCs is used, the runout of the more mobile SHV PDCs is underestimated. This reliance on local data poses challenges for forward modeling using ∆H/L cones or empirically-derived basal friction angles for TITAN2D at volcanoes with sparse data. However,  Ogburn et al. (2016)  investigated the use of hierarchical Bayesian models for ∆H/L estimation from PDC volume that allow one to "borrow strength" from the global record of PDCs while retaining characteristics of local data (namely the intercepts), and greatly reduce the uncertainty inherent in traditional linear regression models of sparse data. Other workers have investigated the sensitivity of these models (included in Table  5 ) in detail  (Sheridan et al. , 2005; Kelfoun et al. , 2009; Charbonnier and Gertisser, 2012).
However, because LAHARZ is started within valleys, it is easier to avoid issues with DEM errors at the summit. ∆H/L cones are perhaps the least sensitive to DEM issues, as the start height of the cone does not need to be located on the DEM surface; and, thus, the cone height can be easily adjusted to reflect the most recent measurement of dome height, for example. Another concern for using these models for hazard mapping is the ease of use. The operational aspects of these models are summarized in Table  5. For example, TITAN2D is available as a Linux program, or through a web interface, and uses open-source software, while VolcFlow uses proprietary software. LAHARZ requires the proprietary ArcGIS software, but has a (soon to be public) online interface. ∆H/L cones can be produced using free standalone tools such as the Vhub Energy Cone tool  (Palma, 2013)  or within the LAHARZ toolbox.
∆H/L cones take seconds to produce, LAHARZ takes 5-20 min, while VolcFlow averages around 5 h, and TITAN2D pile sources take around 8-10 h using a single processor on a personal computer. Unfortunately, some TITAN2D flux sources can take a long time (24 h+) to complete, unless super-computing resources are available. The computing time per model run and the output file sizes are important considerations when using modeling for both scenario-based hazard assessment or implementing a probabilistic modeling scheme that samples input parameter space for forecasting purposes. In light of these results, it is clear that LAHARZ should be used cautiously for PDC hazard mapping, perhaps as a very quick initial investigation, and with a range of input coefficients. TITAN2D could be considered as a maximum estimate of lateral spreading of the dense-basal avalanche and a good estimate of runout, especially if there are abundant local data on which to base the regression between basal friction (∆H/L) and volume (see Figures  2, 8A, B ), or if a hierarchical Bayesian approach is used to improve upon global regressions  (Ogburn et al.
Iverson and George (2012)  propose that Coulomb friction forces may vary with the pore-fluid pressure and granular temperature (a measure of granular vibration), which would better capture the complex rheologies of these flows. The resultant numerical model has shown success in replicating both experimental and observed debris flows without calibration  (George and Iverson, 2011; Iverson and George, 2012)  but has not yet been tested for PDCs. While the models compared herein have been tested against natural examples from SHV, Montserrat, and previously against small bench-top experiments  (Kelfoun and Druitt, 2005; Patra et al. , 2005) , it may also be pertinent to test them against other comprehensive field-based datasets at other volcanoes. Ogburn et al. (2016)  showed that differences in mobility of PDCs at different volcanoes may be due to differences in channelization of flows, which could, in turn, affect model selection.
CONCLUSIONS The geophysical and empirical models investigated here are able to reproduce the inundated area and runout of smaller-and larger-volume, confined and unconfined dense-basal avalanche components of PDCs with varying degrees of accuracy. • TITAN2D and VolcFlow are both able to recreate a variety of PDCs, although they perform better when simulating discrete, single pulse, smaller-volume flows. • Larger-volume flows are often sustained, pulsatory and thus highly unsteady, and density stratified, and the simple rheological models of TITAN2D and VolcFlow are not able to recreate all features of these flows. • Empirically-based LAHARZ and ∆H/L cones can be quick, first-approximations of flow runout, provided a database of similar flows, e. g. , FlowDat, is available to properly calculate ∆H/L or the coefficients for planimetric and cross-sectional area.
ACKNOWLEDGMENTS Without the dedication of numerous MVO scientists and staff, who originally created and compiled many of the deposit maps, this work would not have been possible. The authors are very grateful for their work and willingness to share data. This research was completed under a Memorandum of Understanding between the MVO and  SUNY University at Buffalo. Furthermore, the authors would like to thank our three reviewers,  Dr. Domenico Doronzo ,  Dr. Gert Lube , and the  USGS  internal reviewer,  Dr. Mary Benage  for their constructive feedback.
In this analysis, we excluded the winter days (days of the year between 15 November and 1 April) where GPP is zero. The 10 predictors most strongly correlated with GPP (and not highly correlated with each other; pairwise r ∈ [-0. 8, 0. 8]) were retained for the analysis. All linear response functions, resulting from a combination of these 10 predictors, were tested and classed according to their Bayesian information criterion (BIC). We also used linear response functions to analyze the relationships between RWhighF and climate variables (same methodology as for GPP). We tested all monthly temperature and precipitation values of the previous and current years as predictors. Time windows of 31 days were used to obtain the time series of monthly data (over the 1950-2010 period) for each day (central day), averaging the values of each window and each year.
Indeed, almost all available carbon in spring was allocated to the canopy and roots (Fig. S5g ; Table  1 ), whereas C allocation to the stem (Dstem) in summer increased with temperature (Fig. 8 ). Furthermore, the ring width series used were highly correlated with July-August temperature, as expected for wood biomass production and for climate-growth analysis for the studied species. Second, some fixed parameters are present in the MAIDEN code (see Eqs. 4 and 6). These parameters may be potentially modified, but their specification is justified in Sect. 2. 1. 2 and limits additional parameter tuning. Third, we modeled GPP and carbon stem increments of a boreal tree species using mechanistic rules, which increased the capability of MAIDEN to reproduce observed variations. However, our choice of mechanistic rules was subjective to some extent and depended on previous physiological knowledge and on model-data comparisons.
Acknowledgements. This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie  grant agreement no. 656896  and from the ECCOREV research federation. The  CRD NSERC   ARCHIVES  project funded the sampling of the tree-ring data. We acknowledge all institutes and persons providing the other data used:  Natural Resources Canada  for the climate data;  NOAA Earth System Research Laboratory  for the CO 2 data; the  Fluxnet  project and  Hank Margolis  ( Université Laval ) for the eddy covariance data;  Gabriel Rodrigue  for the soil data at our tree-ring sites. Edited by:  Anja Rammig Reviewed  by: two anonymous referees.
Sequana is a Python-based software dedicated to the development of Next Generation Sequencing (NGS) pipelines. We use the Snakemake  (Köster and Rahmann 2012)  framework to design our pipelines, which eases the decomposition of pipelines into modular sub-units. We currently have 7 pipelines covering quality control, variant calling, long-reads quality, de-novo and RNA-seq analysis (see https://sequana. readthedocs. io for details). Our pipelines are associated with HTML reports based on JINJA templating and Javascript. The reports are used to store the results of a pipelines but also materials required to reproduce the results. Sequana is also a Python library that provides tools to perform various analysis tasks (e. g. , variant call filtering). Some of the library components provide original tools that are also available as standalone applications.
For instance a fast taxonomic analysis based on Kraken  (Wood and Salzberg 2014)  as well as a tool to perform exhaustive coverage analysis  (Desvillechabrol et al. 2016 ) (bottom right panel in the image here below). Sequana is an open source project (https://github. com/sequana/sequana). It is developed with the aim of simplifying the development of new tools (for developers) and the deployment of the pipelines (for users). The extended documentation (http://sequana. readthedocs. org) and test suite (on Travis. org) provide a high-quality software that is routinely tested. Sequana is now available on bioconda making the installation easier and faster by taking care of the dependencies (e. g. , samtools, bwa, or Python libraries). Finally, for end-users, we also developed a Graphical interface called Sequanix  (Desvillechabrol et al.
The second step of our method is to operate an automated discourse analysis based on the Reinert lexicometric method. The method proposed by  Reinert (1990)  was first implemented in the ALCESTE software and has been recently integrated in an open-source software called IRaMuTeQ that allows for new developments  (Ratinaud, 2014). IRaMuTeQ breaks down texts into smaller parts called segments based on punctuation and on size criteria 3 and then classifies them into clusters based on lexical similarity. This second phase of the analysis uses the top-down hierarchical classification algorithm described by  Reinert (1983) , operating a series of bi-partitions of the corpus based on a correspondence analysis (CA). The first step of this phase is to lemmatize vocabulary and to build a presence/absence matrix (1/0) that crosses tweets (in rows) and full forms (in columns).
Dataset Several difficulties arise in sampling Twitter on a subject such as the Charlie Hebdo attacks: first, the extraordinary volume of messages produced on the subject; second, the linguistic diversity of messages due to the global impact of the event; and finally, the very high density of interactions between users, especially around the worldwide popular hashtag #JesuisCharlie. Therefore, rather than working on #Jesuis-Charlie, characterized by an emotionally charged and fairly homogeneous discourse centered on condolences and expressions of solidarity or polemical hashtags such as #JesuisAhmed and #JenesuispasCharlie, we decided to analyze a more diverse sample of tweets to shed light on the plurality of debates and interactions that took place. Thus, we collected all messages containing the string charliehebdo, which was the first and most commonly used on the subject, between 7 January 1.
The Discourses Around Charlie Hebdo After having identified the main communities of users around Charlie Hebdo, we wanted to link them to the different discourses that were produced on Twitter. To do that, we therefore submitted the corpus of original tweets containing the string charliehebdo to a downward hierarchical classification with IRaMuTeQ. In other words, we used the software to group in separate categories (lexical clusters) the tweets that tend to contain the same words. Dendrogram 1 and 2 show the classification trees, the size of each cluster, and the overrepresented lexicon in each cluster, respectively, in the French and the English corpus. Through the classification of the French and the English corpus of tweets, we obtained, respectively, 12 and 9 lexical clusters. Some of them refer to common themes and frames, while others are different.
2. To address this problem, we choose to use only open software, the code of which is available and transparent. 3. In our case, we consider tweets as segments. 4. The Digital Methods Initiative Twitter Capture and Analysis Toolset (DMI-TCAT) is an open-source software developed by Erik  Borra and Bernhard Rieder (2014) , https://github. com/ digitalmethodsinitiative/dmi-tcat 5. An important epistemological caveat when working on samples that are extracted from platforms such as Twitter comes from the fact that whatever the method used one can never be absolutely sure that a corpus is completely comprehensive. 6. Fox (2013). 7. Gephi is an open-source software for graph visualization and network analysis: http://gephi. org.
f u n c t i o n m y f i l t e r ( vc ) { var countDP = 0 ; f o r ( var i = 0 ; i < vc. getNSamples ( ) ;++ i ) { var genotype = vc. getGenotype ( i ) ; i f ( ! genotype. hasDP ( ) ) c o n t i n u e ; var dp = genotype. getDP ( ) ; i f ( dp >= 200 ) c o n t i n u e ; i f ( ++countDP >= 2 ) r e t u r n t r u e ; } r e t u r n f a l s e ; } m y f i l t e r ( v a r i a n t ) ; Figure  1 : A simple javascript-based filter retaining the variants having at least two genotypes with a depth lower than 200 reads: an instance of the htsjdk java class htsjdk. variant. variantcontext. VariantContext named 'variant' is injected in the javascript context, the function 'myfilter' is invoked. It runs a loop over all the samples and count the number of genotypes having a depth ('DP') lower than 200. w h i l e ( i t e r.
hasNext ( ) ) { var samread = i t e r. next ( ) ; i f ( samread. getReadUnmappedFlag ( ) ) c o n t i n u e ; out. p r i n t l n ( samread. g e t C o n t i g ( ) + "\ t " + ( samread. g e t S t a r t ( ) -1) + "\ t " + samread. getEnd ( ) ) ; } Figure 2: Reformatting a BAM file to a BED file. An iterator named 'iter ', and scanning some instances of the htsjdk java class htsjdk. samtools. SAMRecord, is injected in the javascript context. As long as we can read a SAM record, mapped on the reference, its' genomic location is printed to 'out', the current output stream. JfxNgs can read local files as long as they've been indexed with tabix or tribble. It can also access the remote files if the hosting server supports 'Byte-Range' requests. The main window provides tools for indexing BAM and VCF files.
The VCF and the BAM windows have common functionalities: filtering the data with javascript, displaying the data in a very simple genome browser, viewing the selected item in a web browser for a common database like Exac  (Song et al. , 2015) , displaying simple statistics (Fig. 3 ), exporting the data with javascript, viewing the current selected genomic interval in IGV, viewing the components of the file header, saving the filtered data in a new file. The javascript areas contain a growing library of code snippets to help the user. The BAM window shows the standard columns of the BAM specification (Read-Name, Sam-Flags, etc. ), it provides some tables to view the details of the cigar string, the sam flags, the supplementary alignments. A pileup-like table displays the bases covering each position.
Availability The software is available in the jvarkit package  (Lindenbaum, 2015). It has been tested under Linux, Windows and MacOS and is freely available at https://github. com/lindenb/jvarkit/blob/ma At the time of writing, our tool is also available, packaged as a java webstart application, at http://redonlab. univ-nantes. fr/public html/jnlp/jfxngs, meaning that it doesn't require any installation but java and an always up-to-date application is downloaded each time the user invokes it.
GBE Genome Biol. Evol. 9(3):593-618. doi:10. 1093/gbe/evx030 Advance Access publication February 15, 2017  (English et al. 2012)  with default settings, except for the assembly stage where "-maxTrim" and "-maxWiggle" were set to 15 kb. A single region on chromosome 9 was not completely assembled de novo using MHAP, and this region was inferred from the previous S. sclerotiorum genome assembly  (Amselem et al. 2011). The final assembly was error corrected after manual gap filling using Quiver  (Chin et al. 2013). Illumina short reads were first trimmed using Trimmomatic version 0. 36  (Bolger et al. 2014 ) then mapped to the de novo assembly using Bowtie version 2. 2. 6  (Langmead et al. 2009). Modules from the Genome Analysis Toolkit version 3. 5-0-g36282e4  (McKenna et al. 2010)  and Picard Tools version 2.
1. 0 (available at http://broadinstitute. github. io/picard/) were then used to call variants between the de novo assembly and the Illumina reads. After mapping to the version two genome sequence, GATK was used to create a consensus sequence from the Illumina reads and PacBio de novo assembly. Substitutions, of which there were very few, were ignored and only insertions and deletions (InDels) were considered. Polymorphisms that were identified based on a depth of less than 30Â or a mapping quality of less than 30 were also excluded.
Alignment of RNA Sequencing Data for Use in Gene Predictions RNASeq data obtained from the experimental conditions enumerated in the previous section were used for gene prediction. Reads were trimmed using Trimmomatic version 0. 36  (Bolger et al. 2014). Reads from in planta samples were first binned based on whether they mapped better to the S. sclerotiorum genome or the host genome using BBSplit version 34. 86 (available at https://sourceforge. net/projects/bbmap/ files/). For this purpose, the B. napus genome was downloaded from http://www. genoscope. cns. fr/brassicanapus/ and the A. thaliana genome was downloaded from https:// www. arabidopsis. org/ (Chalhoub et al. 2014; 2000). Reads were then mapped to the S. sclerotiorum genome using Top Hat version 2. 1. 0 (Trapnell et al. 2012)  with the following nondefault settings: "-min-intron-length 10" and "-max-intron-length 5000.
" Cufflinks version 2. 2. 1  (Trapnell et al. 2012)  was used with default settings to assemble spliced transcripts from read mappings. Transcripts from all experimental conditions were merged for use in gene prediction using the cuffmerge module of Cufflinks. Gene Prediction Using Ab Initio and RNASeq-Guided Softwares, Protein Homology, Transcript, and Expressed Sequence Tag Evidence The following gene prediction software packages were used to generate predictions for the version two S. sclerotiorum genome: Augustus version 2. 5. 5  (Stanke and Morgenstern, 2005) , Coding Quarry version 1. 2  (Testa et al. 2015) , and GeneMark-ES version 3. 1  (Borodovsky and Lomsadze, 2011). These softwares were used with default settings and gene predictions were supplied to Evidence Modeler  (Haas et al.
Comparison of Version One Gene Predictions with Version Two Gene Predictions To determine what genomic loci had changed or remained the same, two approaches were used: 1) Bedtools version 2. 17. 0  (Quinlan and Hall, 2010)  was used to determine which new gene predictions overlapped annotations that had been transferred from the previous assembly to infer fusions or splits and 2) nucleotide sequence predictions from both genome assemblies were subjected to a BLAST analysis in both directions using BLASTn version 2. 2. 31 to determine differences in gene sequence length and content. Approach two was used to generate a table of version two loci and corresponding version one loci. The script used for the reciprocal BLAST analysis is available at: https://github. com/markcharder/GeneralBioinformaticsScripts/ blob/master/reciprocalBestHitsBlast.
Validation of Version Two Gene Annotations To determine overall concordance with RNASeq data, CDS sequences from both genome versions' gene predictions were compared with Cufflinks transcripts (derived from the previously mentioned read mapping) using BLASTn. Overall concordance with RNASeq data was inferred from coverage of query sequence with the best BLAST hit from this analysis. Sequencing depth for each site in all exonic regions for both genome assemblies was calculated using Bedtools version 2. 17. 0. Both sets of gene predictions were also tested against the NCBI nonredundant (nr) database using BLASTp, excluding previous S. sclerotiorum gene models. Percent identity and query coverage of subject sequence based on best BLAST hits were used to infer quality of gene models. Finally, a random set of 30 sequences selected from the 300 loci that differed the most between the two genome versions were manually reinspected to check which version's prediction most closely matched supporting evidence and why there might have been a discrepancy.
Analysis of Differential Expression of Putative Effectors A subset of the RNASeq data, data set 1, that were used for gene prediction were aligned to the version two assembly using the previously described methods. This subset included samples taken from the following conditions: In vitro, 1, 3, 6, 12, 24, and 48 hpi of B. napus with S. sclerotiorum strain 1980. Samples were replicated three times as per previously mentioned. Aligned reads were then used in conjunction with the version two gene annotations to determine differential expression using the cuffdiff module of Cufflinks  (Trapnell et al. 2012). All sequences that exhibited homology to ribosome-related sequences were specified to cuffdiff at run-time. Fold-change in expression was considered significantly different if it was at a P-adjusted value of below 0.
To identify paralogous effectors in S. sclerotiorum, OrthoMCL version 2. 0. 9  (Li et al. 2003)  was used with default settings. The Botrytis cinerea reference genome version 3. 0 (van  Kan et al. 2016)  was downloaded from the ENSEMBL Fungi database (http://fungi. ensembl. org/Botrytis_cinerea/ Info/Index) for this purpose  (Pedro et al. 2016). For effector sequences that were determined to be recent paralogs by OrthoMCL, regions of 4-5 kb encompassing the gene models were extracted and aligned using ClustalW version 2. 1  (Thompson et al. 1994). Up to 20-kb regions surrounding paralogous genes were manually inspected for the presence of LTRs using LTRharvest  (Ellinghaus et al. 2008) , TEs predicted by REPET, and overlapping gene models containing typical retro-element Pfam domains (based on the previously mentioned InterProScan analysis).
Consensus TE sequences were used to search the Dfam database version 2. 0  (Wheeler et al. 2013)  to identify homologous TE families. Assessment of RIP in the Most Abundant Repeats and at the Whole-Genome Scale in S. sclerotiorum Before identifying RIP in repeat families, the whole genomes of S. sclerotiorum and the fungal and oomycete species Blumeria graminis f. sp. hordei (herein referred to as B. graminis), Leptosphaeria maculans, and Phytophthora infestans were scanned to determine whether there was a bimodal GC content. This was done using OcculterCut version 1. 1 with default settings. The additional genome sequences were used to provide a context for genome bimodality against which S. sclerotiorum was compared. These additional genomes were downloaded from GenBank. After identifying repeat families de novo using REPET, the five TE sequences with the highest copy number were subjected to analyses to identify RIP.
For each repeat, only the 50 longest sequences were used. First, the most numerous repeat was subjected to analysis using RipCal version 1. 0  (Hane and Oliver, 2008)  through the alignment method to show dominant forms of RIP. Second, the ApT/TpA RIP index was calculated for each repeat family member and a random set of sequences from the S. sclerotiorum genome of the same size and lengths. Student's t-tests were used to determine whether the repeat sequences had a significantly higher ApT/TpA index than an equivalent random set of sequences. Random sequences were produced and RIP indices were calculated using a custom Perl script available at: https://github. com/markcharder/GeneralBioinformaticsScripts/blob/master/ getRandom. pl.