dataesr/scientific-paragraphs-categorization huggingface dataset:
These two phosphorylation sites are absent from all sponge species and from Trichoplax, suggesting that this regulation by aPKC is an innovation shared by cnidarians and bilaterians (except for Caenorhabditis elegans) since at least one phosphorylation site is present in a Crumbs isoform of cnidarians (Nematostella) (Fig. 5b ). Thus, even though previous studies identified Crumbs-like proteins in all sponge classes, these studies did not verify the conservation of key functional motifs  [24]  required for their functional interactions. Here, we show that the high divergence of key Crumbs residues in glass sponges is hardly compatible with the formation of a fully conserved complex (Fig. 5b ). Finally, the most unexpected result was the absence of any Crumbs-like gene or transcript in M. leidyi (and in other ctenophore transcriptomes available in databases: Pleurobrachia bachei, Beroe abyssicola and Beroe sp.
Discussion By investigating the presence of genes and proteins involved in epithelial polarity and adherens junctions, we found that Placozoa and Porifera (despite some divergence observed in hexactinellids) possess all polarity (See figure on previous page. ) Fig. 5  a Domain composition of Crumbs proteins of D. melanogaster (Dcrb), M. musculus (CRB1, CRB2), O. lobularis, S. ciliatum, A. queenslandica, O. minuta, N. vectensis and T. adhaerens. Domains are shown as detected by SMART and Pfam and scaled with IBS software. Crumbs transmembrane proteins consist of extracellular epidermal growth factor (EGF), laminin-like (LAM) repeats and a short cytoplasmic domain. No crumbs was detected in Ctenophora. In contrast to other animals, sponges have only one copy of Crumbs. b Alignment of the cytoplasmic domain of Crumbs that binds PALS1 and PAR6.
Genome sequencing and assembly Oscarella lobularis (Schmidt 1862) and Oopsacas minuta Topsent, 1927 were collected by SCUBA diving in the north-western Mediterranean Sea (Marseille Bay). All new sequences are available on NCBI website: accession numbers and links are provided in Additional file 1: Tables  S5  and S6. Oscarella lobularis genome sequencing was performed using Illumina technology with DNA-seq paired-end and Nextera mate pair protocols on a HiSeq2500 sequencer. Adapter sequences were removed and low-quality bases were trimmed using Cutadapt  [65]. Remaining reads were assembled using a pipeline including IDBA-UD (  [66] , Platanus  [67] , GapFiller  [68]  and cap3  [69]. A transcriptomic dataset was mapped with Tophat  [70]  to all contigs longer than 1 kb to identify potential Eukaryotic sequences.
Sequence annotation and structure prediction Epithelial hallmarks were investigated using data from various sources: O. lobularis and O. minuta de novo assembled genomes and transcriptomes (this work), and on available poriferan, placozoan, cnidarian and ctenophoran genomes and/or transcriptomes retrieved from the sources listed in Additional file 1: Table  S4. D. melanogaster, M. musculus and A. queenslandica Epithelial cadherin, Crumbs, PAR and Scribble complexes retrieved from NCBI database were used to perform reciprocal best-hits with BLAST 2. 3. 0 run locally using an E-value cutoff of 10 -5. Ab initio protein-coding gene prediction was performed on the best candidate genomic and/or transcriptomic contigs with GeneMark. hmm eukaryotic web serveur (http://exon. gatech. edu/gmhmme. cgi) [79] , Gen-Scan (http://genes.
mit. edu/GENSCAN. html) [80] , Augustus v3. 0. 3 (http://bioinf. uni-greifswald. de/augustus/) [81]  run locally and FgeneSH web server (http://www. softberry. com/) [82]. Protein domains were predicted and checked with Pfam v28. 0 (http://pfam. xfam. org/) run locally  [83] , InterProScan v52 (http://www. ebi. ac. uk/ interpro/)  [84]  and SMART (http://smart. embl-heidel berg. de/) [85]. The newly identified early branching metazoan proteins were added to the previous database sequences for iterative BLAST searches to identify more potential homologues. Proteins containing repeated domains generate false positive and best-hits characteristic of protein motifs and /or domains were used to enhance the detection of real homologues. These motifs and domains were aligned with MAFFT. 7  [86]  (http://mafft.
cbrc. jp/align ment/server/) and/or MUSCLE v3. 8. 31  [87]  (implemented in Seaview 4. 5. 2 [88] ) depending on the level of conservation of the proteins. HMM profiles were built with HMMER 3. 1b1 (http://hmmer. org/) using aligned sequences with an E-value cutoff of 10 -5. Retrieved motifs of potential homologues to the protein queries were used as new baits to recompute HMM profiles and perform further HMM searches. C-terminal characteristic parts were used to build HMM profiles of Crumbs and E-cadherin proteins. Since Lin2/Lin7 (L27) (N)-terminal domain is important in the mediation of MAGUK family protein interactions with other proteins, a particular attention was given to this domain. To identify all potential MAGUK proteins, local BLAST searches (BLASTp and tBLASTn) were performed against complete or on-going genome projects (Additional file 1: Table  S4 ) using L27 domains retrieved from the Pfam database (PF02828).
To improve the detection, L27 domain HMM profile was rebuilt by iteratively adding the best scoring L27 domains identified in sponges, placozoans, cnidarians and ctenophorans. L27 domain HMM searches led also to the identification of PATJ homologues but for some proteins containing multiple PDZ domains (MPDZ) no L27 domain was detected. For MPDZ lacking the L27 domain, a BLASTp was run locally using the PDZ domains retrieved from the Pfam database (PF00595) as reference. This step led to the identification of Petrosia ficiformis MPDZ which has (N)-and (C)-terminal regions predicted on two different contigs, including L27 and PDZ domains on the first contig and the remaining PDZ domains on a second contig merged with Emboss merger webtool (http://www. bioinformatics. nl/cgi-bin/emboss/merger).
Acknowledgments We thank  Chris Toret  for critical reading and English editing of this manuscript, the  Observatoire des Sciences de l'Univers (Pythéas institute ) and  Christian Marshall  ( IMBE ) for collecting O. lobularis and O. minuta samples and the  Service Commun de Biologie Moléculaire (SCBM) of IMBE  for providing facilities needed to prepare samples for sequencing.
Funding This work was supported by a grant from  A*MIDEX  ( n°ANR-11-IDEX-0001-02 ) funded by the  "investissements d'Avenir" French Government program , managed by the  French National Research Agency (ANR). We acknowledge the use of the  PACA-Bioinfo Platform , supported by  IBISA and France-Génomique  ( ANR-10-INBS-0009 ). The  Le Bivic group  is an "Equipe labellisée 2008 de  La Ligue Nationale contre le Cancer " and is supported by the  labex INFORM  (grant  ANR-11-LABX-0054 ),  CNRS  and  Aix-Marseille University. All funding bodies indicated had no role in the design or collection, analysis or interpretation of the data or in the writing of the manuscript.
This paper presents a new global burned area (BA) product, generated from the Moderate Resolution Imaging Spectroradiometer (MODIS) red (R) and near-infrared (NIR) reflectances and thermal anomaly data, thus providing the highest spatial resolution (approx. 250 m) among the existing global BA datasets. The product includes the full times series (2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016) of the Terra-MODIS archive. The BA detection algorithm was based on monthly composites of daily images, using temporal and spatial distance to active fires. The algorithm has two steps, the first one aiming to reduce commission errors by selecting the most clearly burned pixels (seeds), and the second one targeting to reduce omission errors by applying contextual analysis around the seed pixels.
Several space agencies are working on developing systematic assessments of fire occurrence and fire impacts, as part of their efforts to improve the use of satellite data in climate modelling. This is the main purpose of the Fire_cci project (https://www. esa-fire-cci. org/, last access: July 2018), which is part of the European Space Agency's (ESA) Climate Change Initiative (CCI) programme  (Hollmann et al. , 2013; Plummer at al. , 2017). The Fire_cci project aims to develop long-term time series of burned area (BA) products adapted to the needs of climate modellers  (Chuvieco et al. , 2016). In addition to global BA products, the Fire_cci project is generating a small fire database for the African continent based on mediumresolution sensors on board the Sentinel-2 and Sentinel-1 satellites.
Global BA information is required for many different applications  (Mouillot et al. , 2014). First, it helps to assess fire risk assessment, by comparing estimated risk conditions with actual fire occurrence  (Marlier et al. , 2013; Chuvieco et al. , 2014). Burned area information is also a critical input of dynamic global vegetation models (DGVMs), as it provides an estimation of carbon emissions and vegetation succession  (Lasslop et al. , 2014). In conjunction with other human and physical variables, BA datasets are required to understand factors controlling fire activity  (Forkel et al. , 2017)  and particularly those affecting changes in fire regimes  (Hantson et al. , 2015; Andela et al. , 2017) , with an increasing interest in fire patch identification derived from higher resolution pixel-level information  (Laurent et al.
, 2018). Atmospheric emission models require precise information on spatio-temporal patterns of fire occurrence, as well as combustion characteristics (van der  Werf et al. , 2017; Knorr et al. , 2016). Finally, BA information is needed to model fire impacts on human health  (Reid et al. , 2016)  and the safety of properties  (Moritz et al. , 2014; Sturtevant et al. , 2009). A few global BA products have been developed in the last years. Two sensors have been particularly used to develop these global BA time series datasets: VEGETATION (VGT), on board the SPOT satellites since 1998, and Moderate Resolution Imaging Spectroradiometer (MODIS), on board the Terra and Aqua satellites since 2000 (https://modis. gsfc. nasa. gov/about/, last access: October 2018). The former images have been used to create BA products funded under different European projects: L3JRC  (Tansey et al.
usgs. gov/dataset_discovery/modis/ modis_products_table/mcd64a1_v006, last access: October, 2018). From the BA detections of the MCD64A1 product with additional variables on fuel properties and emission coefficients, the Global Fire Emissions Database (GFED) was first created in 2005. The current version is named GFED4, and includes data from the MCD64A1 collection 5 (c5) product, as well as from the Along Track Scanning Radiometer (ATSR) sensor (on board the European Remote Sensing satellites) for the pre-MODIS era  (1995) (1996) (1997) (1998) (1999) (2000). The GFED4s adds an estimation of the area burned by small fires (not detected by the MCD64A1), which is derived from the relation between the number of hotspots and burned patches  (van der Werf et al. , 2017). Generally, small fires are considered as those below the detection threshold of coarse-resolution sensors (for instance, smaller than 100 ha).
0 of the Fire_cci BA products. The former versions were derived from MERIS images  (Chuvieco et al. , 2016). Therefore, FireCCI50 is the first version of Fire_cci BA products based on the MODIS sensor. The goal of generating this product was to complement existing global BA datasets using higher spatial-resolution bands. The paper describes the BA algorithm used for generating the FireCCI50 product, presents a spatio-temporal validation of the results, compares estimations with existing BA products (FireCCI41, GFED4 and MCD64A1 c6) and describes an example of using our product for fire-related climate models.
Algorithm design The main inputs for developing the FireCCI50 product are the MOD09GQ and the MCD14ML datasets, both from collection 6. The MOD09GQ reflectance product includes the daily red (R) and near-infrared (NIR) corrected reflectances of the Terra-MODIS bands 1 and 2, respectively, both at approximately 250 m resolution in sinusoidal projected tiles of 1200 × 200 km. Pixel quality information was obtained from the MOD09GA product, as recommended by the MODIS Science team (https://lpdaac. usgs. gov/dataset_discovery/ modis/modis_products_table/mod09ga_v006, last access: July 2018). Additionally, our algorithm used the MODIS thermal anomalies product (MCD14ML: http://modis-fire. umd. edu/pages/ActiveFire. php?target=MCD14ML, last access: July 2018), which includes active fires (named here hotspots, HSs), detected from the sensor's middle infrared and thermal channels at 1 km resolution  (Giglio et al.
Product specifications As the result of a user requirement analysis, two BA datasets (pixel and grid) were generated for the FireCCI50 product. Both were provided in geographical coordinates. The pixel products include the date of detection (0-366), the uncertainty of the estimation (0-100) and the land cover affected by the fire (taken from the Land Cover CCI product of a similar time period). Pixel products had the full resolution of the MODIS input images. Monthly files were generated to avoid missing double burns in a single year. The output datasets were created in a GeoTIFF format and comprised quasi-continental tiles (Table  1 ). The grid product was produced for biweekly periods and incorporated 23 layers at 0. 25 • resolution: sum of burned area (m 2 ), standard deviation of the estimation (m 2 ), fraction of burnable area (0-1), fraction of observed area (0-1), number of burned patches (0 -N) and sum of burned area in 18 land cover classes (again taken from the Land Cover CCI product).
The output datasets are NetCDF CF files (http://www. unidata. ucar. edu/software/netcdf/docs, last access: July 2018). The uncertainty layer represents the probability that each pixel was correctly classified as burned. It was derived from four input layers related to the BA detection algorithm: the number of daily images to build the monthly composite, the percentile of the NIR reflectance in the CDF of burned and unburned pixels, the CDF of the GEMI differences and the distance to the closest HS. These variables were normalized and combined to obtain the pixel confidence level, from which the standard error of the grid product was calculated. The fraction of burnable area was computed from the land cover CCI product, while the fraction of observed area from the monthly integrated values of the MODIS QA data.
DC = 2E 11 E 1+ + E +1 , (3) where E 11 is the area correctly classified according to the reference and E 1+ and E +1 are the BA reported by the product and reference data respectively. Relative bias is computed as the ratio of the difference between omission and commission errors and the true burned pixels. The formulae of accuracy measures can be found in  Padilla et al. (2015). The details of the sampling design and the formulae used to infer accuracy at the global scale, for the whole time period of study and on a yearly basis, can be found in the validation report of the Fire_cci project (see technical documents at https://www. esa-fire-cci. org/documents, last access: October 2018). As explained above, each year is delimited by a group of strata (delimited by biomes); therefore the same formulae used for the whole population can be used to infer accuracy for each year.
Product intercomparison Spatial and temporal trends of the FireCCI50 were compared with existing global BA products. We selected products that are currently operational: the BA of the GFED4, widely used for fire emissions, and the MCD64A1 c6, now the standard NASA BA product. For comparison with outputs from the CCI project, the MERIS Fire_cci v4. 1, with its shorter time series  (2005) (2006) (2007) (2008) (2009) (2010) (2011) , was also assessed. Global temporal trends of these products were generated. Spatial comparison between products was performed at 0. 25 • resolution. In addition, BA detections were compared with existing databases of fire perimeters, given the importance of understanding performance against established approaches by national fire agencies. Three sites representative of different fire regimes were selected: boreal forest, selecting the h12v03 MODIS tile located in Manitoba and Saskatchewan (fire perimeters were downloaded from the Canadian Wildland Fire information System: http://cwfis.
cfs. nrcan. gc. ca/ ha/nfdb/, last access: July 2018); tropical woodlands and savannas, selecting the h30v10 MODIS tile located in northern Australia (fire perimeters were downloaded from the North Australia and Rangelands Fire Information database: http://www. firenorth. org. au/nafi3/, last access: July 2018); and temperate forest, including the h08v05 MODIS tile intersecting the state of California (fire perimeters were obtained from the Fire and Resource Assessment program's web page: http://frap. fire. ca. gov, last access: July 2018). These perimeters were mainly obtained from Landsat images for the Canadian and Californian databases and MODIS 250 m resolution for the Australian one. In all cases, fire perimeters were verified by visual interpretation and field reports. We downloaded data from four different years, covering the full fire season.
Product assessment: fire patch analysis As an example of the potential uses of the FireCCI50 product in fire modelling, a fire patch morphological analysis was carried out. The input data were the pixel-level products, which were converted to fire patches based on a spatiotemporal aggregation process. The same methods used to generate the FRY database  (Laurent et al. , 2018)  were applied. Individual fire patch indices, such as the fire patch area and the so-called shape index (SI), defined by the ratio between the perimeter P and the square root of the of the patch area A (SI = 0. 25 × P /(A) 0. 5 ), were considered. The shape index allows the investigation of (i) the ability of the product to identify individual fire patches and capture their power-law size distribution following the self-criticality hypothesis  (Hantson et al.
Fire size accuracy The assessment of the impact of the higher spatial resolution of the MODIS Fire_cci product with reference to the MCD64A1 product conducted over the sampled African sites confirms the limited performance of both global products in detecting fires smaller than 100 ha. Both FireCCI50 and MCD64A1 products show omission values larger than 85 %, mostly caused by the coarse resolution of the input images. However, for smaller fire patches, the FireCCI50 product shows a better performance than MCD64A1, with 3. 5 % less omission error in both the 5-25 and the 25-100 ha fire size ranges (97. 39 % and 92. 13 % for the MCD64A1 and 93. 91 % Figure  5. Dice coefficient (DC), relative bias (relB) and reference burned area (BAref; m 2 ) for FireCCI50 in TSAs. TSAs with reference data but without accuracy measurements available are represented by empty polygons (white polygons with grey borders).
Fire patch analysis Figure  8  displays the global pattern of fire patch density (number of fire patches km -2 ) calculated from the FireCCI50 and MCD64A1 c6 products. A similar global pattern was observed for both datasets, with the savannas and temperate grasslands yielding higher fire patch density. When plotting the difference between the two products, it was observed that MODIS Fire_cci v5. 0 detected more fire patches in woody savannas and open shrubland/grasslands worldwide (+52 %), while it detected fewer fire patches in savannas    (-42 %). In other biomes, no clear spatial pattern was observed, with a mixture of fire patch densities varying ±70 %. The total number of fire patches without and with consideration of a 107 ha fire size threshold estimated globally for FireCCI50 was 1.
Discussion This paper has presented a new global BA product aimed to improve the information available to climate modellers on spatio-temporal patterns of fire occurrence. The FireCCI50 is the first global BA product derived from the two highest spatial-resolution channels (R, NIR) of this sensor, with approximately 250 m pixel size. This product was generated as theoretically it provides an improved representation of small fire patches and is thus more suitable to analyse spatial properties of burned patches than other existing global BA datasets. A first assessment of the product indicates it is more sensitive to small fire patches for the African continent than the MCD64A1 c6 product. However, further assessment is needed to determine to what extent this observation applies to other continents.
Data availability The FireCCI50 includes two types of BA files: monthly fullresolution continental tiles (doi: https://doi. org/cpk7, Chuvieco et al. , 2018a)  and biweekly global grid files at a degraded resolution of 0. 25 degrees (doi: https://doi. org/gcx9gf, Chuvieco et al. , 2018b). Both datasets are freely available through the Fire_cci website (https://www. esa-fire-cci. org, last access: October 2018), or through the CCI Open Data Portal (http://cci. esa. int/data, last access: October 2018). To receive updates on the products and information on new datasets, users are encouraged to register with the Fire_cci project at https://geogra. uah. es/fire_cci/ (last access: October 2018).
Conclusions This paper has presented the FireCCI50 BA product. It is based on Terra-MODIS bands R and NIR, thus providing the highest spatial resolution of existing global BA datasets (approx. 250 m). The BA algorithm followed a two-phase approach, first detecting the core burned pixels and then applying a contextual analysis to discriminate the burned patches. Information from thermal anomalies was used to define probability functions adapted to the spatio-temporal variation of fire activity. The algorithm was used to process the 2001-2016 Terra-MODIS time series. Two set of products were generated: pixel (at full resolution), with date of detection, confidence level and land cover; and grid (at 0. 25 • resolution), with BA, standard error, fraction of burnable area, fraction of observed area, number of patches and BA for 18 different land covers.
Maple  [42]  and Maxima  [43]  softwares were used to perform the manipulations of the algebraic equations. Firstly we define a set of intermediate variables (k 1 to k 4 ) and the coefficients of the polynomial equation: k 1 = 2 * J b -J a k 2 = J b 2 + J a * (J a -2 * J b ) k 3 = J a -J b k 4 = 2 * J a * v f c 4 = -J b * (J b 3 + J a * (J a * (5 * J b -2 * J a ) -4 * J b 2 )) c 3 = 0 c 2 = -6 * (2 * J a * k 2 * v i -J b 2 * a i 2 + J a * (k 1 * a i 2 + 2 * k 2 * v f ) + a f 2 * (J a * k 1 -J b 2 )) c 1 = -8 * (a i * (3 * J a * J b * v i -(3 * J a 2 * v i + J b * a i 2 )) + J a * a i 3 -3 * J a * k 3 * x f ) + a f * (3 * J a * k 3 * v f -a f 2 * k 3 )) c 0 = 3 * (4 * J a * v i * (J a * v i -(a i 2 + k 4 -a f 2 )) + a i 4 + 2 * ((k 4 -a f 2 ) * a i 2 + 2 * J a 2 * v f * v f ) + a f 2 * (a f 2 -4 * J a * v f )) The quartic polynomial equation is then defined by: c 4 * x 4 + c 2 * x 2 + c 1 * x + c 0 = 0.
Net3 The WFEBC analysis was first carried out on Net3, a case study network of 92 nodes, 5 sources or reservoirs and 119 links. The network is included as an example with the distribution of the open-source hydraulic solver software EPANET 2. 0  (Rossman 2000). WFEBC is computed for all network links as described above and the results, ranging from 0 to 1, are represented by the colours of the links on Fig. 2. The WFEBC of a link is the sum of all partial flows going through the link (as computed by the random walk for all source-target pairs of nodes) divided by the total demand of the target nodes partially supplied by the link. The greater the WFEBC of a link, the more important it is compared to alternative supply paths. A WFEBC value of 1 indicates an exclusive supply link (bridge, or bottleneck).