dataesr/scientific-paragraphs-categorization huggingface dataset:
experiment summaries exploitation 6. 3. 1. Linked Data querying Based on provenance information, our approach automates the annotation of e-Science workflow results with domain-specific concepts, finally assembled, following Link Data  [1]  principles, into semantic experiment summaries. These summaries can be combined with external data sources such as FMA  [14] , the Foundational Model of Anatomy. We exemplify in Listing 5 a SPARQL query leveraging three kinds of interlinked data, VIP simulated data, VIP organ models, and FMA anatomical concepts. This query aims at retrieving simulated data from VIP organ models which contain brain white matter, as defined in the FMA ontology. PREFIX mo: <http://vip. cosinus. anr. fr/vip-model. owl#> PREFIX partic: <http://www. irisa. fr/visages/team/farooq/ontologies/ particular-owl-lite.
In the Indian Ocean, after the interim phase 2005-2012 covered by PTWC and JMA with the same criteria as in the Pacific, three centers, Australia, India, and Indonesia, are providing alert and forecast products. They are also based on seismic and tide-gage networks, and use, in addition, scenario databases resulting from modeling, and techniques enabling estimation of tsunami height on the shoreline. The criteria implemented in the Northeastern Atlantic and Mediterranean region will be described later in that section. With the partnership of the French Hydrographic and Oceanographic Service (SHOM) and the National Institute for Earth Sciences and Astronomy of the National Center of Scientific Research (CNRS-INSU), the Commissariat al'energie atomique et aux energies alternatives (CEA) was charged in 2009 by French authorities with the creation and operation of a tsunami warning system and center by 2012.
The method is based on the fact that the detection delay can be computed theoretically from travel times to all the tide gages in operation. CEA developed a specific tool that monitors the sea level network and its detection capacity. It reports the network performance continuously and whether each tide gage is operating optimally, delayed or with short data gaps, or is out of order. The outputs of each tide gage are the transmission latency and the percentage of data gaps. For each tide gage, the detection delays are assessed by taking into account the reversibility of the tsunami travel time computed from a tide gage to the source and from the source to the tide gage. The detection delays for each specific tide gage are computed considering the tsunami travel times for each grid cell of the sea or ocean basin, and adding the transmission data delay plus a time for tsunami measurement.
Because this context is rather complex along the North African margin, the choice was made to draw a simplified fault system discretized into unit sources corresponding to the major structural trends of the area. The unit source database used a comprehensive bibliography including seismicity catalogs, focal mechanisms, seismotectonic works (e. g. ,  PONDRELLI et al. 2004   LORITO et al. 2008 or SORENSEN et al. 2012). Fault traces shown on Fig. 6  represent the up-dip edge of the unit sources. The length (L) and width (W) of each unit source are set at 25 km and 20 km, respectively. Values of L and W are defined from empirical relationships linking L and W to the magnitude M w  (WELLS and COPPERSMITH 1994). For each unit source, tsunami propagation modeling was performed using the CEA simulation code solving the hydrodynamic equations under the nonlinear shallow water assumption  (Heinrich et al.
In addition, the PDFM2 method identifies ''tsunami earthquakes'' (earthquakes that cause much bigger tsunamis than their magnitude would imply) by using an estimate of the slowness of the seismic event. Results obtained by use of these methods will be compared in the context of the Mediterranean sea, with frequent large magnitudes varying from magnitude 5. 5-7. 0, to determine which is the most accurate, robust, and fastest which will provide a rapid tsunami effect forecast. Earthquake data obtained by use of these two techniques are automatically input to the tsunami modeling code in the CENALT multiprocessor computers. This code estimates the tsunami amplitude offshore, but in the future the tsunami amplitude on the coastline should also be provided for coastal sites by use of either empirical amplification laws or high-performance simulation when high-resolution bathymetric data are available.
Acknowledgments The authors wish to thank two anonymous reviewers for their very useful and constructive reviews. The CENALT was implemented thanks to the support of the  French Ministries of Interior and Ecology , and the work was also recently supported by the  FP7 ASTARTE-Assessment, STrategy And Risk Reduction for Tsunamis in Europe. Grant  603839 , 7th FP ( ENV. 2013. 6. 4-3ENV. 2013. 6. 4-3 ).
Acknowledgments This material is partially based on research funded by grant  NPRP-4-1058-1-168  from the  Qatar National Research Fund  (a member of the  Qatar Foundation ). The statements made herein are solely the responsibility of the authors. Nizar Habash  performed most of his contribution to this paper while he was at the  Center for Computational Learning Systems at Columbia University. Alla Rozovskaya ,  Kai-Wei Chang ,  Mark Sammons , and  Dan Roth. 2013. The university of illinois system in the conll-2013 shared task. In Proceedings of the Seventeenth Conference on Computational Natural Language Learning:  Shared Task , pages  13-19 ,  Sofia, Bulgaria, August. Association for Computational Linguistics. Grigori Sidorov ,  Anubhav Gupta ,  Martin Tozer ,  Dolors Catala ,  Angels Catena , and Sandrine Fuentes.
Third, we demonstrated that the sequencing depth impacts the size bias and proposed a correction allowing the comparison of expression levels among many samples. The efficiency of our approach was then tested by comparing the correlation between normalized RNA-Seq data and qRT-PCR expression measurements. All the steps are automated in a program written in Perl and available on request. Conclusions: The methodology presented in this article identifies and corrects different biases that influence RNA-Seq quantification, and provides more accurate estimations of gene expression levels. This method can be applied to compare expression quantifications from many samples, but preferentially from the same tissue. In order to compare samples from different tissue, a calibration using several reference genes will be required.
RNA-Seq read counting The first step consists in de-multiplexing the reads by recognizing specific adapter sequences to assign each read to the corresponding sample (three samples were pooled per flow cell lane). From 100 to 240 million pairedend reads were obtained per flow cell lane, corresponding to 27 to 91 million reads for each cDNA library. These paired-end reads were then mapped back to the bovine reference transcriptome, using Bos taurus known transcripts recorded in the Ensembl database v. 61 (Website: ftp://ftp. ensembl. org/pub/release-75/fasta/bos_ taurus/cdna/). This set contains 27,663 transcript sequences assigned to 21,734 known genes and pseudogenes. Paired-end reads located exactly on the same transcript were selected and counted. A total of 21,455 transcripts (17,605 genes) were identified, with at least one paired-end read within the 22 analyzed samples.
qRT-PCR quantification Among the 22 cattle samples, five of them were chosen to perform qRT-PCRs on the basis of a large range of total read numbers. These samples showed around 10. 10 6 (1475), 13. 10 6 (1455), 20. 10 6 (1479), 24. 10 6 (1345), and 30. 10 6 reads (1476), respectively. These experiments were conducted using custom-made TLDA (Taq-Man Low Density Array) cards and ABI PRISM 7900HT sequence detector system (Applied Biosystems). The dataset was built with genes involved in glycosylation metabolism, named glyco-genes in the following. They concern glycosyl-transferases, glycosidases, sulfo-transferases, sugar carriers, and lectines. Among the around 800 genes recorded in the bovine genome (unpublished data), 372 were selected according to two criteria: the greatest diversity of the glyco-gene groups and the availability of primers provided by Applied Biosystems (https://bioinfo.
appliedbiosystems. com/genome-database/gene-expression. html). Twelve housekeeping genes (18S RNA, TFIID transcription factor, etc. , see  [17] ) were added as controls to complete the 384-microwells of each microfluidic card. The quantification was done using the SDS 2. 3 software (Applied Biosystems) according to the ΔCt method (see the User Bulletin #2 for ABI PRISM 7700 of October 2001). Briefly, ΔCt corresponds to the threshold cycle (Ct) for each gene minus that of the mean of the twelve endogenous internal controls.
RNA-Seq data from public datasets (drosophila and human) To validate the first steps of our method, it was necessary to consider public data dealing with other organisms than Bos taurus. As for Drosophila, in the public dataset SRA: SRP009459/GEO: GSE33905 deposited by B. R. Graveley and co-workers, we downloaded 16 read sequence sets obtained from head of male and female adults (GSM838758 to GSM838760, GSM838763 to GSM838766 to GSM838780, and GSM838799 to GSM838802). The sequencing depth varied from 2. 7 to 8. 4 million reads, with a mean value close to 5 million reads. As for Human, we considered the dataset SRA: SRP032775/GEO: GSE52166 deposited by R. Sanka and co-workers. In order to have homogeneous data, the read sequence sets came from whole blood of 20 individuals in a pre-infection state relatively to Plasmodium falciparum (GSM1335718, GSM1335720, GSM1335722 to GSM1335756).
The sequencing depth varied from 21. 2 to 72. 9 million reads, with a mean value around 40 million reads. Using STAR aligner software v. 2. 3. 1f [18] , the read sequences were splice-aligned onto the Drosophila v. BDGP5. 75 or the Human genome v. GRCh37. 75, respectively. Transcripts were quantified with sigcufflinks (available upon request at www. sigenae. org), a modified version of the cufflinks code  [19]  providing raw read counts per transcript, by using the GTF reference files provided by Ensembl (version 75).
Simulation of RNA-Seq data As we suspected that the RNA fragment sizes have an impact on the behavior of read counts as a function of transcript sizes, it was useful to conduct simulation using a specific program. We first downloaded the transcript genes of Bos taurus chromosome 20 from Ensembl (version 75genome assembly UMD3. 1). All the sequences were concatenated to obtain a single sequence of 255,601 bp. This sequence was then split into 231 genes in the FASTA format, with increasing sizes from 50 bp to 1,200 bp according to an arithmetic progression with common difference of 5. This file was submitted to rlsim  [20]. Default parameters were chosen, except for sequenced fragment range, and total read number (1 million). Three runs were launched, the first with 250-450 (mean 350) bp, the second with 450-650 (mean 550) bp, and the last 650-850 (mean 750) bp.
Results As qRT-PCR quantification were used to validate our RNA-Seq normalization method, it was necessary to verify that qRT-PCR data were not subject to transcript size and GC content biases. As for transcript size, we tested a relationship with the ΔCt obtained by qRT-PCR for cattle sample 1479 (n = 233). Through polynomial equations of first and third orders, the p-values were 0. 84 and 0. 87, respectively. As for GC content in the same sample, the corresponding polynomial equations gave p-values of 0. 57 and 0. 96, respectively. We verified that for the four other cattle samples, no significant relationships were observed neither for transcript size nor for GC content (Additional file 1). To compare qRT-PCR results with the RNA-Seq approach, several steps of correction are needed. The calculations concerned 14,676 genes for which only one transcript were detected in Cattle.
This idea was tested using the simulation procedure implemented in rlsim software. Three different fragment lengths were considered: 250-450 (mean 350) bp, 450-650 (mean 550) bp, and 650-850 (mean 750) bp, with a fixed total read number of 1 million. The results are summarized in Figure  1E , with LOESS smoothing. It is difficult to give a precise position of the break point between the two regression lines, but it is clear that the greater the sequenced fragments, the more the break point is shifted toward the right. Moreover, the slopes for the regression lines situated before the break points seem to be similar. To assess the efficiency of our method, we calculated the Pearson correlations between qRT-PCR and RNA-Seq counts corrected by FPKM (Fragments Per Kilobase per Million mapped reads)  [23]  or SGTR for the five bovine samples.
Consequently, further comparisons will only be made on the log 2 (FPKM) values. When we compare SGTR correction according to size only with the previous normalization, the p-values were generally of the same order of magnitude. Nevertheless, we observed slightly better results with our method for transcripts < 1,000 bp but faintly worse results for transcripts between 1,000 and 2,000 bp, whatever the sample.
Removing of the GC-content effect For the gene dataset of each cattle sample, we first calculated the trend curve for read numbers according to GC content. Polynomial equations of different order were tested and revealed dissymmetric dome shaped curves: the left increasing part (GC from 35 to 40%) was hardly visible by comparison to the right decreasing one (GC from 45 to 80%), where decreasing trend was getting more and more pronounced for GC > 50% (data not shown). We retained a third order polynomial function that clearly showed this last trend in all the samples, giving the Equation 3 (Figure  3A ). Below the 50% threshold, the mean read numbers remained fairly constant. y ¼ c:x 3 d:x 2 þ e:x þ f ; ð3Þ where y represents the size-corrected mean read number and x the GC content. Second, for each GC percentage, we calculated the difference between the GC content and 50% that we applied to the previous polynomial equation leading to the Figure  3B.
The best fitting polynomial function was then deduced: y ¼ g:x 3 þ h:x 2 þ i:x þ j; ð4Þ where y corresponds to the predicted read number and x represents the difference between a GC content and 50%. Third, we adjusted the size-corrected values by removing "g. x 3 + h. x 2 + i. x " of this last function to all the transcripts. The Figure  3C  illustrates the efficiency of GC bias correction. For the 20 human samples, we obtained the same profiles of size-corrected read number according to GC content as in Cattle (data not shown). In contrast, for the 16 drosophila samples, the polynomial curves were different (Figure  3D  and 3E ). Nevertheless, the correction of the GC content bias using the previous procedure yielded a smoothing curve absolutely flat (Figure  3F ), attesting the efficiency of our method.
We choose the second strategy because of the excellent regression quality of mean read numbers by size classes. We interpret the border 600 bp observed whatever the species dataset (Figure  1A -1C) as a result of sonication and selection of cDNA fragments between 250 and 450 bp. Indeed, fragments > 600 bp are all the more so represented that they are long  [1, 3, 4, 27]. Conversely, the fragments < 600 bp are under-represented as many small segments were not sequenced. Moreover, the simulation conducted with rlsim confirmed our view, and showed that the border increases with the size of the sequenced fragments (Figure  1E ). Hence, this proves the effect of the cDNA fragments size selection on the break point between the two regression lines. As a result, independent corrections are needed for both transcript sizes.
This last point provided slightly better correction than the log 2 (FPKM) for transcripts < 1,000 bp (see Table  1 ). According to  [14, 28]  and  [29] , RNA-Seq protocol including PCR in the first steps introduced biases linked to GC content, as cDNA fragments with high GC and AT content are under-sequenced. To correct this bias,  [10, 14]  and  [30]  proposed to build GC-classes. In our method, we took into account the general trend by calculating a three order polynomial equation, which was used to correct the decrease over 50% GC content. The efficiency of our correction was sample-dependent and more precisely linked to sequencing depth. Indeed, for a low number of reads, the GC bias correction did not improve the normalization, in contrast to samples with higher sequencing depth. SGTR including Size and GC content corrections provide thus globally better results than log 2 (FPKM) (Table  2 ), which is in agreement with the conclusions of  [8]  and  [10].
In contrast, for read counts over 20 million, our method significantly improves the read counting, for the whole dataset and for most gene size classes. The question is to interpret this observation and several considerations have to be taken into account. First, in our samples, when the total number of reads is low, it is particularly true for transcript with sizes shorter than 600 pb, the regression equation between transcript size and read counts is less accurate than the one for transcript sizes longer than 600 pb. Second, the more expressed the transcripts (total read numbers over 20 million), the higher is the probability to generate duplicates and other biases induced by RNAseq. Our method can be compared to GAM (Generalized Additive Model) of  [11] , where the data are corrected for length, GC content, and dinucleotide frequency biases.
Acknowledgements We are grateful to  Diane Esquerré  and  Olivier Bouchez  ( INRA, Toulouse ) for their help in RNA library preparation and sequencing, respectively. We also thank  Dr Mekki Boussaha  ( INRA, Jouy-en Josas ) for helpful discussion. The RNA-Seq work was funded by the  INRA Animal Genetics Department  ( BovRNA-Seq  project). The sampling of the Limousin Longissimus thoracis biopsies was part of the  Qualvigène  project, funded by the  French National Research Agency  (contracts  ANR-05-GANI-005  and  ANR-05-GANI-017-01 ) and  APIS GENE  (contract  01-2005-QualviGenA-02 ). The qRT-PCR work was funded by the  Limousin Regional Council.
, 1996a (Clark et al. , , b, 2004;; Coen, 2005) , which consists of the Clark-Hall atmospheric model, coupled with fire spread implemented by tracers. The SFIRE code currently supports the semiempirical fire-spread model of  Rothermel (1972) , inherited from the CAWFE code. Implementation of alternative firespread models -e. g. ,  Balbi et al. (2009) ;  Fendell and Wolff (2001)  -is in progress. The current code and documentation are available from OpenWFM. org. A version from 2010 is distributed with the WRF release as WRF-Fire  (Coen et al. , 2013; OpenWFM, 2012). Validation studies of WRF-SFIRE are now available for a large-scale wildfire  (Kochanski et al. , 2013b) , as well as for a microscale simulation of a grass-burn experiment  (Kochanski et al. , 2013c) , fuel-moisture data assimilation  (Vejmelka et al.
, 2014a) , and coupling with WRF-Chem  (Kochanski et al. , 2014b). The coupling of the fire-heat release with the atmosphere enables a detailed study of the effect of wind shear on fire propagation  (Kochanski et al. , 2013a). Examples of work from other groups using WRF-SFIRE include  Simpson et al. (2013)  and  Peace et al. (2011). For the first time, this paper describes new developments in the SFIRE software system during the 2 years since the last reference paper by  Mandel et al. (2011) , and new results demonstrate the relevance of each. Potential fire-severityassessment tools are described in Sect. 2, ignition in the coupled atmosphere-fire model from a developed fire perimeter in Sect. 3, fuel-moisture model in Sect. 4, and assimilation of RAWS fuel-moisture data in Sect. 5. New software developments include direct input of data in GeoTIFF format (Sect.
33 km, and 444 m, with time steps 162, 54, 18, 6, and 2 s, respectively (Fig. 8a ). The Mellor-Yamada-Janjic PBL scheme  (Janjić, 2001)  and the Kain-Fritsch cumulus scheme  (Kain and Fritsch, 1990)  were used on the three coarsest domains. In order to fully utilize LANDFIRE fuel data and elevation data provided at 30 m resolution, the innermost fire domain had a further-refined fire mesh of 22. 2 m (1 : 20 refinement ratio). The atmospheric component of the model was initialized and forced at the boundaries by the North American Regional Reanalysis (NARR), providing meteorological data at 3 h intervals. There were no ground-fuel-moisture observations available within the fire domain, so the 1 h fuel moisture was initialized with its equilibrium value, while the initial 10, 100, and 1000 h fuel moistures were approximated using data from the National Fuel-Moisture Database (4.
, 2011). First, GeoTIFF can be converted into Geogrid files, which can be read by any standard installation of WPS by a separate utility. This utility, which has a command line, flags to control various Geogrid attributes, such as the size of the tiles. The utility creates a header that contains both a description of the tiles and the geocoding (projection and reference points). TopoGrabber (http: //laps. noaa. gov/topograbber) is a Python application based on this conversion utility, which is capable of downloading and converting topographical data automatically. Conversion of GeoTIFF files, however, creates large Geogrid files, which can present difficulties. For this reason, the WPS has been modified to read GeoTIFF files directly. Here, the GeoTIFF library is wrapped around an abstraction layer that reads the data in tiles.
The main advantage is that it can read floating point data directly, rather than needing to convert to and from fixed point, as required by the Geogrid file format, and thus it can handle large meshes more easily. Such meshes occur naturally as a consequence of high-resolution fire modeling. NetCDF is the standard file format for WRF output files. Visualization pathways that utilize WRF-SFIRE output files in the NetCDF format include VAPOR and KML format for Google Maps and Google Earth  (Beezley et al. , 2012). What has caught the most attention is the utility posted at https://github. com/jbeezley/wrf2kmz. This utility processes the NetCDF files from WRF-SFIRE into KML and the compressed variant KMZ, using several Python libraries. This is the software used to generate the Google Maps and Google Earth images in this paper and in our previous work, referenced here.
Operational use in Israel The Israeli national fire forecasting system 1 is built on top of WRF-SFIRE. From the advances described in this paper, the system uses the potential fire behavior (Sect. 2), the moisture model (Sect. 4), and the geographic information system (GIS) data sources and conversions (Sect. 6). For operational reasons, this deployment uses coarser meshes on a smaller number of processors. The effect of the feedback from the fire to the atmosphere is reduced, but the benefits of the tight (coupled) integration of the fire simulation with a fine-scale weather forecast remain. The system is based on a complete WRF mesoscale weather forecast for Israel (Fig. 12 ). In order to be able to produce a fire forecast on demand, the weather forecasting system uses National Weather Service (NWS) data four times daily, refined into hourly WRF forecasts, from which the fire forecast is made.