dataesr/scientific-paragraphs-categorization huggingface dataset:
observed spatial pattern in TS and identify main drivers of thermal streams moderation. The variable selection was based on the most pertinent variables identified in literature and on the results of a principal component analysis (not presented here) to minimize dependency between variables. The distance from the source (D in km) and the elevation (E in m) are determined at the location of each monitoring station. The slope of the river reach (S in m m -1 ) where the station is located is determined with BD ALTI ® 25-m resolution DTM dataset (IGN Paris, France). A higher S increases the flow velocity, and E influences Tw through the association with the adiabatic lapse rates of Ta and also through snow and glacier meltwater inflow, which should cool Tw at higher elevations. Streams with a high D have more time to equilibrate their Tw with Ta.
The matching is based on two criteria: (a) The gauging stations has to be located in the same or nearby streams and (b) the difference of catchment area between the location where Tw was measured and the location where Q was measured was kept to a maximum of ±20%. The daily discharge was extracted from the French river flow monitoring network (HYDRO database, http://www. hydro. eaufrance. fr/). (b) The average specific discharge in August (Q Aug ) is calculated at each station between 2008 and 2012. The goal is to measure the capacity of the catchment to produce a flow in summer, when precipitation is low. The specific discharge is the ratio between the discharge and the corresponding catchment area (in L s -1 km -2 ) and is used to standardize discharge for basin area. Two climatic variables were determined from the Safran reanalysis data: (a) the mean summer cumulated precipitation (P in mm) and (b) the mean summer potential evapotranspiration (PET in mm) both calculated between June 1 and September 30 of each year between 2008 and 2012 for the entire upstream area of each monitoring station.
DATA AVAILABILITY STATEMENT Temperature used in this study are available from the French Biodiversity Agency (AFB; www. afbiodiversite. fr). Restrictions apply to the availability of these data, which were used under license herein. Data are available at http://www. naiades. eaufrance. fr with the permission of the French Biodiversity Agency (www. naiades. eaufrance. fr/formulaire/contact). Discharge data are available from the French river flow monitoring network (HYDRO database, http://www. hydro. eaufrance. fr/). Restrictions apply to the availability of meteorological data, which were used under license herein with the permission of Météo France (www. meteofrance. fr).
This could prevent the individual from competing/working but is unlikely to represent a life-threatening condition. Conversely, potential impact on the competition logistic is very high. It could also seriously damage electronic devices that are used for timing, communication and broadcasting purposes as well as temporary installation, such as tent, banners, scaffoldings. The Khalifa stadium, which is not a fully covered stadium will be affected by a dust storm. Therefore, competitions held in this arena will have to be postponed for 12-24 h waiting for the end of the episode and the complete cleaning of the field of play. The Khalifa ventilation/air conditioning system is protected from this sand/dust and pathogen contamination by a complex system of progressive air filtering. The likelihood of a high WBGT was calculated from the climatological data provided by the Qatari Civil Aviation Authority.
In order to make the calculation of the cumulative distribution function, we have checked that temperatures recorded and provided by the Qatari Civil Aviation Authority, during 24 h in fall in Doha are normally distributed. Then, using the dataset reported in Table  2 , the probability of a mean (calculated on 1 h) WBGT above 23 • C WBGT is extremely high (>99. 9%) between 7 a. m. and 5 p. m. A probability for a temperature above 28 • C WBGT is high (64. 6%) between 7 a. m. and 5 p. m.
S1-Derived Soil Moisture Maps Sentinel-1 soil moisture maps (S1-SSM) at very high spatial resolution (up to a plot scale with a minimum area of 0. 2 hectares) over our study area were analyzed. These soil moisture maps were operationally produced by coupling SAR data from both the S1A and S1B satellite constellations operating at the C-band (wavelength ~6 cm) and optical data from both the S2A and S2B satellite constellations  [16]. To estimate the soil moisture over agricultural areas, El Hajj et al. [16]  inverted the Water Cloud Model (WCM)  [17]  combined with the Integral Equation Model (IEM)  [18]. In their study, they used the WCM parameterized by Baghdadi et al. [19]  for the C-band. The total backscattering coefficient in the WCM is considered the sum of the direct vegetation contribution and the soil contribution multiplied by the attenuation factor.
S1-SSM maps are produced for agricultural areas masking forest, urban, and high-slope areas (slope >20% calculated based on elevation data provided by the Shuttle Radar Topography Mission "SRTM" at 30 m × 30 m spatial resolution). S1 soil moisture maps for the Occitanie region are available as open access data via the Theia French Land Data Center (http://www. theia-land. fr/en/thematic-products). The SSM map at each date was generated.
S1-Derived Soil Moisture Maps Sentinel-1 soil moisture maps (S1-SSM) at very high spatial resolution (up to a plot scale with a minimum area of 0. 2 hectares) over our study area were analyzed. These soil moisture maps were operationally produced by coupling SAR data from both the S1A and S1B satellite constellations operating at the C-band (wavelength ∼ 6 cm) and optical data from both the S2A and S2B satellite constellations  [16]. To estimate the soil moisture over agricultural areas, El Hajj et al. [16]  inverted the Water Cloud Model (WCM)  [17]  combined with the Integral Equation Model (IEM)  [18]. In their study, they used the WCM parameterized by Baghdadi et al. [19]  for the C-band. The total backscattering coefficient in the WCM is considered the sum of the direct vegetation contribution and the soil contribution multiplied by the attenuation factor.
S1-SSM maps are produced for agricultural areas masking forest, urban, and high-slope areas (slope >20% calculated based on elevation data provided by the Shuttle Radar Topography Mission "SRTM" at 30 m × 30 m spatial resolution). S1 soil moisture maps for the Occitanie region are available as open access data via the Theia French Land Data Center (http://www. theia-land. fr/en/thematic-products). The SSM map at each date was generated using the ascending acquisition mode of the Sentinel-1 images (acquisition time ~17:40 TU). Four maps are required to cover the region on a single date. Thus, a total of 250 soil moisture maps were downloaded to cover the whole region on the studied dates.
IMERG GPM Products Rainfall measurements with 30-minute revisit time that were obtained from the GPM (Global Precipitation Measurements) sensor were considered in this study. The GPM mission is an international satellite mission initiated by the National Aeronautics and Space Administration (NASA) and the Japan Aerospace and Exploration Agency (JAXA) to unify and advance global precipitation measurements from space  [6]. GPM provides several precipitation records at different scales by using different sensor combinations. From the GPM mission, the IMERG (Integrated Multi-satellite Retrievals for GPM) data product was selected to perform our analysis. IMERG data offer global precipitation estimations in both real-time and late mode. While real-time IMERG data are proposed for disaster monitoring and flood risk assessment, the higher quality late IMERG data are intended for meteorological and climatological applications.
The temporal analysis of the S1-SSM values along with GPM data was then studied during the period between 21 December 2016 and 1 February 2017. Different climatological events were observed during this period: the absence of precipitation for 24 consecutive days in some parts of the region between 27 December 2016 and 25 January 2017 and rain episodes at several dates (Figure  2 ). The objective here is to analyze the temporal evolution of S1-derived SSM as a function of cumulative rainfall events (existence or absence of precipitation). As mentioned in Section 2. 2. 1, an S1 soil moisture map can be obtained every six days (during the revisit of the S1 satellite). Thus, a total of eight soil moisture maps acquired between 21 December 2016 and 1 February 2017 were used. For these maps, soil moisture values were classified into four different intervals that were found to best describe the spatiotemporal variation in soil moisture values.
% at most) corresponding to the absence of precipitation in the area for the same prior period. A map derived six days later on 27 December 2016 (Figure  3b   The temporal analysis of the S1-SSM values along with GPM data was then studied during the period between 21 December 2016 and 1 February 2017. Different climatological events were observed during this period: the absence of precipitation for 24 consecutive days in some parts of the region between 27 December 2016 and 25 January 2017 and rain episodes at several dates (Figure  2 ). The objective here is to analyze the temporal evolution of S1-derived SSM as a function of cumulative rainfall events (existence or absence of precipitation). As mentioned in Section 2. 2. 1, an S1 soil moisture map can be obtained every six days (during the revisit of the S1 satellite).
4 vol. % for ∆ 0 to 20. 8 vol. % for ∆ 5 days (S1 image acquired six days after the heavy rainfall event). In the case where the cumulative rainfall is greater than 80 mm in 48 hours (Figure  7b ), we find that SSM average values are similar (23 vol. %) after a heavy rainfall that took place during the past 48 hours of the S1 acquisition or after a heavy rainfall during the 48 hours that took place six days before the S1 acquisition. The effect of the temporal resolution of satellite images on detecting heavy rainfall events was further studied with soil moisture data from SMAP at higher temporal resolution  [7]. For this reason, we have re-established Figure  5  with SMAP data at 9 km × 9 km spatial resolution (Figure  8 ). SMAP data were processed for the period between September 2016 and August 2017, over the Occitanie region, with one day of temporal resolution.
%. The effect of the temporal resolution of satellite images on detecting heavy rainfall events was further studied with soil moisture data from SMAP at higher temporal resolution  [7]. For this reason, we have re-established Figure  5  with SMAP data at 9 km × 9 km spatial resolution (Figure  8 ). SMAP data were processed for the period between September 2016 and August 2017, over the Occitanie region, with one day of temporal resolution. Figure  8  shows the distribution of SMAP soil moisture values as a function of one day of cumulative precipitation obtained from the IMERG-GPM data. In average, SMAP shows an estimation of 15 vol. % following light rain one day before the SMAP acquisition, and about 30 vol. % following 100 mm of cumulative rainfall during one day before the SMAP acquisition.
Conclusions This letter presents an analysis of Sentinel-1 derived surface soil moisture maps (S1-SSM) produced with high spatial resolution (at plot scale) and a revisit time of six days for the Occitanie region located in the south of France as a function of precipitation data derived from the GPM mission, in order to investigate the potential of S1-SSM maps in detecting heavy rainfalls. First, we investigated the temporal evolution of the soil moisture according to precipitation records derived from the GPM data. A general coherence was observed between the soil moisture estimations and precipitation records over the study site. The S1-SSM values increase following rainfall events and decrease after a period without rainfall due to evaporation. Then, we studied the behavior of soil moisture values following rainfall events in order to explore the possibility of detecting heavy rainfall using the revisit time of six days of the S1-SSM product.
1), similar to that used in a previous rMATS analysis of SF3B1mutated samples  [32] , reduced the list of significant AS events to 78, with skipped exon (SE) being the most frequent event (Fig 2A  and 2B ). Then, we investigated whether the two sftb-1 alleles associated with a visible phenotype (the triple mutation allele cer39, and the deletion allele cer6) primarily induced cryptic 3' splice site selection. It is worth noting that, in this experiment, larvae were harvested and processed for RNA isolation at the L2 stage to avoid possible indirect effects due to developmental defects observed after this stage. Both alleles were associated with a high number of deregulated AS events as determined by rMATS, and the predominant alteration was also exon skipping (Fig 2B ). We then explored if there were differentially expressed protein-coding (Fig  2C ) and noncoding (Fig  2D ) transcripts in the three RNA-seq datasets (S2 Table ).
Significant transcripts (padj<0. 05) are colored in blue. (D) MA-plots of expression changes in non-coding transcripts, represented as in panel C. Transcripts that are significantly deregulated are colored by transcript type. (E) Venn diagrams showing the overlap between transcripts that are upregulated (left) or downregulated (right) in the three datasets. The number of transcripts at the intersections is shown, as well as the total number of significantly up-or downregulated transcripts in each mutant strain (padj<0. 05). https://doi. org/10. 1371/journal. pgen. 1008464. g002.
A cross symbol (X) below the conservation score indicates positions where missense mutations have been reported in human cancers (COSMIC database) at different frequencies. Residues that are relevant for this study are highlighted in green. (TIF) S2 Fig. cer6 homozygotes inherit SFTB-1 maternal product. (A) Schematic representation of the sftb-1 locus. Gray boxes indicate exons, connecting lines indicate introns. Left and right orange boxes represent predicted 5' and 3' untranslated regions, respectively. In pink, 29 nucleotides that are deleted in the cer6 allele. Predicted amino acids resulting from translating the WT or cer6 alleles are shown below the DNA sequence. The frameshift caused by the deletion introduces a premature stop codon in the cer6 transcript (colored in red). Scale bar, 100 base pairs (bp).
Acknowledgments We acknowledge the members of the  Ceron Laboratory  for helpful discussions and comments on the manuscript. We also thank  Alan Zahler  and  Sol Katzman  for their advice in transcriptomic analyses. The teg-4(oz210) strain was kindly provided by  Dave Hansen. Pladienolide B and sudemycin D6 were gifts from Juan Valcarcel. We thank  CERCA Program / Generalitat de Catalunya  for their institutional support.
This work has been supported by a grant from the  Instituto de Salud Carlos III (ISCIII) , http:// www. isciii. es/, to JC ( PI15-00895 ), co-funded by  FEDER funds/European Regional Development Fund (ERDF)  -a way to build Europe, https://ec. europa. eu/regional_policy/en/funding/erdf/. AEC is funded by  ISCIII , http://www. isciii. es/, of the MINECO (reference  PT17/0009/0019 ) and cofinanced by  FEDER , https://ec. europa. eu/regional_policy/en/funding/erdf/. EC is supported by a  Pasteur-Roux postdoctoral fellowship program , https://research. pasteur. fr/en/call/call-forapplication-pasteur-roux-and-pasteurcantarinipostdoctoral-fellowships-2019-spring-session/. XS has an  FPU PhD fellowship  from  MINECO , http:// www. educacionyfp. gob. es/servicios-al-ciudadanomecd/becas-ayudas. html.
U2AF ligand motifs (ULMs), p14-interacting region and HEAT domains are indicated in boxes. A cross symbol (X) below the conservation score indicates positions where missense mutations have been reported in human cancers (COSMIC database) at different frequencies. Residues that are relevant for this study are highlighted in green. (TIF) S2 Fig. cer6 homozygotes inherit SFTB-1 maternal product. (A) Schematic representation of the sftb-1 locus. Gray boxes indicate exons, connecting lines indicate introns. Left and right orange boxes represent predicted 5' and 3' untranslated regions, respectively. In pink, 29 nucleotides that are deleted in the cer6 allele. Predicted amino acids resulting from translating the WT or cer6 alleles are shown below the DNA sequence. The frameshift caused by the deletion introduces a premature stop codon in the cer6 transcript (colored in red).
In risk analyses, two components are taken into account: (1) hazard analysis, including susceptibility and temporal occurrence, and (2) consequence analysis, including characterization of elements at risk (EaRs) and their vulnerability. This study focused on characterization of EaRs, in which items are spatially displayed and impacted by natural events. Several methods can assess these EaRs through expert or engineering approaches. Among the expert approaches, the multicriteria method is more flexible and allows integration of a wide range of information in order to characterize and discretize different EaRs. Traditionally, the mapping and criteria accuracy of EaRs is the same at all spatial analysis scales, while the hazard accuracy changes according to the spatial scales. Therefore, we propose an approach based on the selection of different information/criteria among several private or open access multiple geographical databases to adapt the mapping and criteria accuracy of each EaR according to the hazard analysis spatial scale.
After harmonizing the different databases and merging them under GIS, a single database per work scale is created through a specific procedure with interoperability of results between scales. Thus, the number of criteria used to describe these EaRs will depend on the scale of work and the spatial scale of the analysis. To develop and test the transposability of this method, three experimental coastal study sites subject to several hazards (multirisk) have been selected in Normandy (France) with error estimations ranging between 10% and 20%. Subsequently, these data can be integrated into risk and multirisk analyses.
, 2006; MATE/METL, 2016). However, the subjectivity in the choice of variables induces difficult comparisons of the results  (Kappes et al. , 2012). Consequently, a multicriteria method has been developed to reduce the uncertainties involved in the use of ranking methods  (Malet et al. , 2006; Puissant et al. , 2013). This method consists of assessment using criteria and a weighting system(s) index  (Puissant et al. , 2013; Carlier et al. , 2017). The main advantage of this method is its flexibility, which allows adaptation to the number of criteria and databases used to describe EaRs  (Reghezza-Zitt and Rufat, 2016; Carlier et al. , 2017; Puissant et al. , 2013). However, the number of criteria used and their spatial accuracies must be consistent with the spatial scale analysis. Based on this multicriteria method (expert approach), two main objectives are highlighted to enhance EaR analysis using multiple scale analysis methods and geographical databases.
, 2012; Chen et al. , 2016). In this study, we consider scales ranging from medium to local-scale analysis. Beyond the medium scale, EaRs are not considered accurate. From these different scale analyses, we used multiple geographical databases (GDBs) to characterize criteria and describe EaRs. The second objective concerns the acquisition and harmonization of multiple databases to assess EaRs and their criteria with a coherent degree of detail. The mapping accuracy and criteria integrated to describe these EaRs depend of\n the spatial analysis scale (see above). At the global scale, the use of remote sensing is privileged; however, at the medium to large scale, the use of GDBs must prevail. Three types of GDBs are identified: (1) GDBs provided by institutions under conditions, (2) GDBs originating from volunteered geographical information (VGI), which is an open data format, and (3) archive documents available under conditions that require digitalization  (Foody et al.
, 2015; Bol et al. , 2016; Olteanu-Raimond et al. , 2017). At the local scale, these databases are merged with field data acquisition or photointerpretation  (Papathoma-Köhle et al. , 2007; Lissak, 2012; Puissant et al. , 2013; Hénaff et al. , 2014). Difficulties associated with these different databases are related to harmonization and verification of these data and their integration into data warehouses. Consequently, the main challenge is defining EaRs and their criteria at different analysis scales using multiple GDBs. Multiple EaR scale descriptions and characterization are lacking in risk analyses. These terms have been defined at only one analysis scale and then zoomed in (or vice versa), and thus the accuracy of the EaRs is the same at a local or watershed scale. Furthermore, various EaRs that are incomparable are taken into account in the same analysis, such as biological areas and building functions.
The development of GDBs has allowed identification of different EaRs at different scales, but different datasets must be merged to obtain adequate accuracy after verifying their reliability. The use of multiple GDBs will provide a better assessment of EaRs at the medium and large scales. At the local scale, field data acquisition is leading to identification of the intrinsic characteristics of buildings. Thus, the accuracy (spatial and attribute) varies according to the analysis scale. In the first part of this paper, we perform a bibliographical synthesis to identify EaRs and the criteria used in different risk analysis contexts. In second part, based on this synthesis, we define a set of criteria to characterize EaRs at each spatial analysis scale. In the third part, two data integration models are calibrated for EaR assessment at medium and large scales and from the use of multiple GDBs.
Geographical databases used in integration models The use of multiple GDBs to identify EaRs at different scales is a recent addition to risk analyses due to the development of multiple data platforms and sources  (Jokar Arsanjani et al. , 2015; Calazans Campelo et al. , 2017; Johansson et al. , 2017). These different platforms and sources represent an alternative to traditional data collection from the field or photointerpretation  (Defossez et al. , 2017; Ambrosi et al. , 2018). In this paper, multiple GDBs were integrated for the medium-and large-scale analyses, and local-scale field data were promoted. Three types of GDBs are available for open access or under conditions (Table  2 ). The GDBs produced by national institutes are regularly updated and controlled. Consequently, confidence in these GDBs is particularly high.
The second type of GDB comes from (2) VGI  (Foody et al. , 2015; Bol et al. , 2016; Olteanu-Raimond et al. , 2017) , which is an open data format, such as Open Street Map (OSM). Multiple associations or users supply the GDBs produced by this institution. These GDBs are updated in near real-time but require an audit before integration with other GDB types. The third type of GDB comes from archive documents available in municipalities, such as the Local Urbanism Plan (PLU) or the Intercommunal Local Urbanism Plan (PLUi). New platforms, such as Geoportail Urbanism, are increasingly being developed. This platform provides digitalized urban documents with geo-referencing and harmonized data. The confidence in these data is high, but verification is required when the data update is long-standing.