dataesr/scientific-paragraphs-categorization huggingface dataset:
1 , Data S1, S2, Fig. S1  and Tables  S1-S4 ). The highest read coverage was consistent with the skipping of exon 7 alone, which contains the stop codon of the protein. However, no major modifications are expected in the encoded protein lacking this exon as a supplementary in-frame stop codon is found at the beginning of exon 8. Most importantly, skipping of exon 7 concomitantly to exon 6, which encodes the transmembrane domain, is highly relevant as their absence may generate isoforms that lack the transmembrane domain and the cytoplasmic tail and therefore would constitute still unreported soluble proteins. When RT-PCR was performed with primer G963R, whose sequence is complementary to a region of exon 6, no amplification products could be obtained in combination with the forward primer G257F (exon 3) or G256F (exon 4).
Alternative spliced HLA-G isoforms lack the alpha-1 domain Further analysis of RNAseq data reveals that some of the reads might be initiated at exon 4. This was determined by quantifying the raw count of reads within 20 bp upstream of the exon acceptor site. The predicted N-terminal-truncated protein would lack the peptide signal and the alpha 1 domain. To assess whether the translation into a protein might start in this region, we have examined the nucleotide sequence of exon 4. This analysis revealed the presence of an inframe ATG that might serve as a translation initiation codon. Our preliminary results (not shown) reveal that transcripts that lack the alpha-1 domain may lack also the alpha-2 domain and therefore encode only the alpha-3 domain. Further work is needed to investigate whether this shortened transcript encodes functional proteins.
Another puzzling result was the staining profile of patient 8's tumor. When using the 4H84 antibody (that recognizes all the reported HLA-G isoforms), no stain was detectable, whereas a strong immunostaining was observed with 5A6G7 or 2A12 antibodies (that labels only HLA-G5 and HLA-G6). Altogether, these unexpected findings suggested the presence of still undescribed isoforms and prompted us to initiate a more extensive analysis at the molecular level. Combining RNAseq and RT-PCR analysis provided a highly improved view on the expression of HLA-G isoforms. We describe, to our knowledge for the first time, HLA-G transcripts that are initiated at exon 1. This exon 1 is not included in the IMGT/HLA-G database. Although this sequence is listed on the Ensembl database (ENST00000376828. 6), to our knowledge no articles have been published.
In addition, another very important consequence of the presence of this 5 0 -extended transcript is the regulation by miRNA. Transcripts initiated at exon 1 have a distinct 5 0 -UTRs region than those of previously reported HLA-G transcripts. This may impact the regulation by miRNA and their cross-talk with other miRNA acting on 3 0 -UTRs. Disrupting this link may have substantial repercussion in the outcome of tumor cells  (Lee et al. , 2009; Manaster et al. , 2012; Xie et al. , 2005). The results of our RNAseq approach revealed unreported HLA-G transcripts that may be generated from the interplay of differential exon skipping events. Notably, the skipping of exons 6 and 7 may generate shorter transcripts that would encode proteins that lack the transmembrane domain and the cytoplasmic tail.
The complexity of HLA-G expression in ccRCC together with the extent of inter-and intratumor heterogeneity raises key questions regarding how tumors can be optimally sampled and what are the best targets to improve the clinical outcome of patients with ccRCC. Ultimately, the data also suggest that extensive efforts must be made in the validation of predictive isoforms and reveal an unexpected need to reevaluate treatment strategies. The RNA sequencing of four patients was performed at the Institut du Cerveau et de la Moelle Epini ere (ICM), Paris. This work was funded by CEA and Universit e Paris Diderot, Sorbonne Paris Cite.
Acknowledgements The authors are grateful to  Dr Philippe Moreau  for very instructive discussions. We kindly appreciate the experimental help of  Dr Xinrong Luo  and  Dr Marcela Garcia  at the first stage of the research project. We thank  Zahia Guirous ,  Malika Djouadou , and  Lionel Mockel  for experimental assistance. We thank  Marina Daouya  and  Isabelle Poras  for their technical advices.
The safety of river-crossing tunnels is an important aspect of urban road traffic systems. Previous studies concerning traffic safety mainly focused on urban roads. Given the absence of a number of variables, such as speed and real-time traffic volume, in traffic accident databases, the traffic safety of urban river-crossing tunnels has rarely been studied. This study used the microscopic traffic flow simulation software, VISSIM, to analyse the factors influencing the traffic safety of urban river-crossing tunnels. 12 simulation models based on 12 river-crossing tunnels in Shanghai were established. An exponential model for the speed standard deviation and accident rate was then constructed by calculating the speed standard deviation of the 12 tunnel models. Varied parameters of gratitude, speed limit, heavy vehicle ratio, and traffic flow characteristics were used to set different simulation scenarios in VISSIM.
Introduction Traffic safety is a major concern worldwide. Almost 1. 25 million people died from road accidents in 2013  (WHO, 2015). Therefore, identifying the factors that affect traffic safety is a meaningful research topic. Previous studies showed that analyzing traffic accident databases is simple and useful in evaluating the factors that affect traffic safety. However, traffic accident data is sometimes insufficient or incomplete. Simulation is widely adapted in traffic network analysis, evaluation, and optimization, and the comparison of designs，which is likewise an effective tool to analyze traffic problems  [1]. In existing literature, the analysis of traffic safety has focused on urban roads, especially intersections  [2]. With the development of economy and society, numerous river-crossing tunnels and bridges have been built along river cities, such as London, Paris, and Seoul, to meet the increasing traffic needs of residents.
However, compared with open roads and bridges, river-crossing tunnels impose a unique form of driving behavior. Drivers must adapt to the change of light when passing in and out of the river-crossing tunnels, which results to the "black hole" and "white hole" effect. The severity of an accident happening in a tunnel is usually greater than occur along open stretches of freeways  [3]. In Shanghai China, the percentage of serious injuries and fatal crashes in river-crossing tunnels is twice that of open roadways. The focus of this study is to determine the influence of various factors, such as the gradient, speed limit, and heavy vehicle ratio, on the traffic safety of urban river-crossing tunnels. This study utilizes different simulation scenarios in VISSIM software. Speed standard deviation is selected as an index to measure traffic safety based on the correlation between the speed standard deviation and accident rate of the 12 river-crossing tunnels in Shanghai.
A few studies have focused on the field of urban rivercrossing tunnels. Lu et al. analyzed the temporal and spatial distribution characteristics of traffic accidents of urban rivercrossing tunnels  [13]. Jiang et al. analyzed single-vehicle crash injury severities and hit-and-run crashes in urban rivercrossing tunnels  [14, 15]. These studies focused on analyzing accidents in urban river-crossing tunnels. However, considering the absence of a number of variables, such as speed and real-time traffic volume, in the traffic accident database, the influence of different designs and operational situations on the occurrence of traffic accidents requires further study. The present study uses VISSIM software to simulate urban river-crossing tunnel models and subsequently identify several traffic variables as a reference.
Basic data collection 3. 1. 1 Accident data collection Accident data of 12 crossing-river tunnels derived from the Official Platform of '110' Alarming Receiving Centering (OP110ARC) of the Shanghai Public Security Bureau in 2011 were used to calculate the accident rate of each tunnel. Table  1  shows the calculated annual average daily traffic (AADT), which is the total traffic volume of the tunnel in a year divided by 365 days. The traffic volume was measured in terms of passenger car unit (pcu). The accident rate is the number of accidents occurring in or around the tunnel per million vehicle-kilometers in a year.
Simulation data collection VISSIM was developed by PTV and is a common microscopic traffic flow simulation software. The Wiedemann 74 car following model was used to build the models of urban river-crossing tunnels. Considering the "black hole" effect, the driver needs to adapt to the drastic changes of brightness upon entering and exiting a tunnel (Figure  1 ). The entrance of an urban river-crossing tunnel is downhill; hence, the deceleration starts when the driver approaches the entrance of the tunnel. According to Chen's analysis of the entrance section of mountain expressway tunnels, Point A is the adapting point where the driver begins to decelerate  [16].
In the Amazon Basin, floodplain inundation is a key component of surface water dynamics and plays an important role in water, energy and carbon cycles. The Model for Scale Adaptive River Transport (MOSART) was extended with a macroscale inundation scheme for representing floodplain inundation. The extended model, named MOSART-Inundation, was used to simulate surface hydrology of the entire Amazon Basin. Previous hydrologic modeling studies in the Amazon Basin identified and addressed a few challenges in simulating surface hydrology of this basin, including uncertainties of floodplain topography and channel geometry, and the representation of river flow in reaches with mild slopes. This study further addressed four aspects of these challenges. First, the spatial variability of vegetationcaused biases embedded in the HydroSHEDS digital elevation model (DEM) data was explicitly addressed.
A vegetation height map of about 1 km resolution and a land cover dataset of about 90 m resolution were used in a DEM correction procedure that resulted in an average elevation reduction of 13. 2 m for the entire basin and led to evident changes in the floodplain topography. Second, basin-wide empirical formulae for channel cross-sectional dimensions were refined for various subregions to improve the representation of spatial variability in channel geometry. Third, the channel Manning roughness coefficient was allowed to vary with the chan-nel depth, as the effect of riverbed resistance on river flow generally declines with increasing river size. Lastly, backwater effects were accounted for to better represent river flow in mild-slope reaches. The model was evaluated against in situ streamflow records and remotely sensed Envisat altimetry data and Global Inundation Extent from Multi-Satellites (GIEMS) inundation data.
Topography data are essential inputs in hydrologic modeling. At present, the common practice is to use the DEM to represent topography. Because the coverage of high-accuracy DEM data (e. g. , with elevation errors less than 1 m) is limited, hydrologic modeling at regional or larger scales uses DEM data obtained by spaceborne sensors. The Shuttle Radar Topography Mission (SRTM) DEM data have been widely used for hydrologic modeling, but some factors limit their accuracy. In forested regions such as the Amazon Basin, primary biases in the SRTM DEM data were caused by vegetation cover because the radar signal was not able to penetrate the vegetation canopy  (Sanders, 2007). Previous studies in the Amazon Basin adopted various approaches to alleviate the vegetation-caused biases embedded in the SRTM data.
In some modeling studies, elevation values were lowered by a constant in forested areas of the entire basin, so the spatial variability of vegetation heights was ignored  (Coe et al. , 2008; Paiva et al. , 2013a). In a few hydrodynamic modeling studies for the central Amazon Basin, the vegetation-caused biases in the SRTM elevations were derived from spatially varying vegetation heights. For example,  Wilson et al. (2007)  estimated the vegetation height distribution based on their surveyed heights of various vegetation types, and a map of vegetation types by  Hess et al. (2003)  and  Baugh et al. (2013)  utilized a global dataset of spatially distributed vegetation heights developed by  Simard et al. (2011). These two studies estimated the vegetation-caused biases as products of spatially varying vegetation heights and a fixed percentage.
In this study, we used the HydroSHEDS DEM data which were derived from the SRTM data and inherited the vegetationcaused biases. To alleviate those biases, we used a method similar to that of  Baugh et al. (2013). Besides the vegetation height map by  Simard et al. (2011) , we also used a land cover dataset for wetlands of the lowland Amazon Basin developed by  Hess et al. (2003 Hess et al. ( , 2015a)). A "bare-earth" DEM of the Amazon Basin was created and employed in the hydrologic modeling for the entire basin. To our knowledge, this was the first time that the spatial variability of vegetationcaused biases in the DEM data was explicitly considered in hydrologic modeling for the entire Amazon Basin. Channel cross-sectional geometry affects the channel conveyance capacity in modeling of surface water dynamics.
In this study, river flow was modeled with the diffusion wave method that could represent backwater effects. Moreover, the impacts of backwater effects on surface hydrology of the Amazon Basin were investigated through numerical experiments in a comprehensive manner. The four factors described above could have important impacts on modeling surface hydrology in the Amazon Basin and were accounted for in the simulations conducted with the MOSART-Inundation model. The model performance was evaluated against gauged streamflow data, as well as riverstage and inundation data obtained by satellites. In a sensitivity study, the roles of the following factors in hydrologic modeling for the Amazon Basin were separately examined and demonstrated: (1) representing floodplain inundation; (2) alleviating vegetation-caused biases in the DEM data; (3) refining channel cross-sectional geometry; (4) adjusting Manning roughness coefficients; and (5) representing backwater effects.
Application in the Amazon Basin The MOSART-Inundation model was applied to the entire Amazon Basin. The 3 arcsec HydroSHEDS DEM data developed by the United States Geological Survey (USGS) was used in this study. The hydrologically conditioned Hy-droSHEDS DEM was used to generate the digital river network and subbasins. Relatively coarse-resolution subbasins were adopted, as MOSART-Inundation is intended for global earth system modeling which is constrained by computational cost. The study domain of 5. 89 million km 2 was divided into 5395 subbasins (the average area was 1091. 7 km 2 and the standard deviation was 921. 5 km 2 ), which were used as computation units (Fig. 2a  and b ). Each subbasin had a main channel and the entire river network consisted of 5395 main channels (Fig. 2a ). To ensure stable computation, the time-step size was determined based on the CFL condition and sensitivity tests.
e. , the main stem subregion). The inputs of surface and subsurface runoff, which were of 1 • resolution, were produced by the ISBA land surface model  (Getirana et al. , 2014)  driven by the ORE-HYBAM precipitation dataset  (Guimberteau et al. , 2012). The area-weighted averaging method was used to convert the grid-based runoff data to subbasin-based runoff data for driving the simulations of this study.
The resolution of the vegetation height data was coarser than that of the DEM data. It might not be appropriate to assume a uniform vegetation height for all the DEM pixels within the grid cell of the vegetation height dataset. Hess et al. (2003 Hess et al. ( , 2015a ) developed a high-resolution (3 arcsec) land cover dataset for floodplains (or wetlands) located in the lowland Amazon Basin (i. e. , areas with elevations lower than 500 m). This land cover dataset was used in our DEM correction process. In the floodplains of the lowland Amazon Basin, vegetation height removal was conducted differently for different land cover classes. For DEM pixels with forest or woodland classes, 50 % of the vegetation height was deducted from the original DEM. In the high-resolution land cover dataset, shrubs were defined to be less than 5 m tall  (Junk et al.
Then, the normalized elevation profiles of all subbasins were averaged to give the average elevation profile of the entire basin. Figure  2f  illustrates that the DEM processing evidently lowers the average elevation profile. O'Loughlin et al. (  2016 ) estimated the vegetation-caused biases in the SRTM DEM data based on vegetation height data, canopy density data and the distribution of five climatic zones (i. e. , tropical, arid, temperate, cold and polar). They created the first global bare-earth high-resolution (3 arcsec) DEM from the SRTM DEM data. They compared their method with the static correction method (i. e. , estimating the vegetation-caused bias as the product of vegetation height and a fixed percentage) used by  Baugh et al. (2013)  and this study, and noted that the static correction method was effective but moderately worse than their method.
Channel geometry At regional or larger scales, channel cross-sectional shape is usually simplified to be a rectangle since the channel top width is much larger than the channel depth (or bank height). The channel cross section can be determined by channel width and channel depth. Beighley and Gummadi (2011)  presented a methodology for estimating channel cross-sectional dimensions (i. e. , channel width and channel depth) at stream-gauge locations by using stage-discharge relationship data and Landsat imagery. They implemented the approach to derive channel cross-sectional dimensions of 82 streamflow gauging locations spread over the Amazon Basin, which were further used to develop the general relationships between channel cross-sectional dimensions and upstream drainage area (or channel geometry formulae) for the entire basin.
The channel geometry formulae of Beighley and Gummadi (2011) were obtained through regression analysis of data from 82 locations over the Amazon Basin and reflected the average feature of the basin. Directly applying the same formulae and parameters to the entire basin could cause large biases in the estimated channel cross-sectional dimensions for some subregions. In order to reduce those biases, in this study, the coefficients in the basin-wide channel geometry formulae of  Beighley and Gummadi (2011)  were adjusted for the majority of the 10 subregions (Fig. 2c ) based on channel cross-sectional dimensions of local locations. The 82 streamflow gauging locations scattered over the Amazon Basin and each subregion contained a few streamflow gauging locations. For the streamflow gauging locations of the same subregion, the root mean square error (RMSE) between the channel cross-sectional dimensions estimated with the channel geometry formulae and the corresponding dimensions presented in  Beighley and Gummadi (2011)  could be calculated.
Streamflow The observed daily streamflow data for model evaluation were from 13 stream gauges operated by the Brazilian Water Agency. A total of 8 of the 13 gauges either control the major area of a tributary subregion or are typical gauges in their tributary subregions. None of the 13 gauges are located in the tributary subregion "upper Solimões tributaries" in the western Amazon Basin. Most of this subregion is controlled by the Santo Antônio do Içá gauge at the upper main stem. The remaining four gauges are located along the middle or lower main stem. The simulated daily streamflow results were compared with the observed data for a 12-year period  (1995) (1996) (1997) (1998) (1999) (2000) (2001) (2002) (2003) (2004) (2005) (2006)  at the 13 stream gauges (Fig. 3 ). The Nash-Sutcliffe efficiency coefficient (NSE) and the relative error of mean annual streamflow (RE) were calculated for each gauge (Fig.
3 ). For the majority of the 13 gauges, daily streamflow values were reproduced fairly well. The NSE value is higher than 0. 62 at seven gauges. The four gauges with NSE values lower than 0. 5 have high absolute values of RE (i. e. , > 0. 20), which suggests that large biases in runoff inputs for the areas upstream of those gauges degrade the streamflow results. Overall, runoff inputs have large negative biases in the western portion of the Amazon Basin and large positive biases in the southern and southeastern portions. The runoff biases could be caused by errors in the precipitation forcing dataset or errors in the land surface water fluxes calculated by the land surface model (e. g. , canopy evaporation, plant transpiration and soil evaporation). In general, the simulated streamflow results are comparable to those of a few previous studies (e.
River stage The observed river stages were based on altimetry data obtained by the Envisat satellite. The altimetry data were stored in the HydroWeb server (http://ctoh. legos. obs-mip. fr/ products/hydroweb). This study utilizes river stages of 11 virtual stations which correspond to 11 of the 13 stream gauges used in Sect. 3. 1. Each of the 11 virtual stations is close to one gauge: the virtual station and the gauge are located in either the same subbasin or two neighboring subbasins. There is no virtual station close to the Altamira or Cachoeira da Porteira gauges. The simulated river stages are relative elevations as they were calculated from the riverbed elevation and the channel water depth. The method for estimating the riverbed elevation is described in Sect. 2. 5. Considerable uncertainties in the riverbed elevation are expected due to the large uncertainties in the riverbed elevation at the mouth and the riverbed slopes.
Flood extent The simulated flood extent results were evaluated using the Global Inundation Extent from Multi-Satellites (GIEMS) data  (Papa et al. , 2010; Prigent et al. , 2007 Prigent et al. , , 2012)). The GIEMS data contained monthly surface water area during a 15-year period  (1993) (1994) (1995) (1996) (1997) (1998) (1999) (2000) (2001) (2002) (2003) (2004) (2005) (2006) (2007)  for each of the land pixels of equal area (i. e. , 773 km 2 ). The area-weighted averaging method was used to convert the grid-based surface water extent data to subbasin-based data for use in this study. Lake area was not deducted from the GIEMS data because in the Amazon Basin the lakes usually were located in the low portion of one subbasin and the simulated inundated area also contained lake areas. The simulated monthly flood extent results (including channel surface area and flooded area over floodplains) were compared to the GIEMS data during a 13-year period  (1995) (1996) (1997) (1998) (1999) (2000) (2001) (2002) (2003) (2004) (2005) (2006) (2007)  for 10 subregions and the entire Amazon Basin (Fig.
; (2) surface water bodies (e. g. , lakes and swamps) not represented by the model that were lumped into the inundated floodplains; (3) subsurface processes and wetlands sustained by groundwater that were not simulated; and (4) inundation that could be underestimated or overestimated in the GIEMS data which were of comparatively low resolution  (Hess et al. , 2015a; Prigent et al. , 2007). The effects of model parameters (including floodplain topography, channel cross-sectional geometry and channel Manning coefficients) on the inundation results were investigated in the sensitivity study. Although the GIEMS data have non-negligible uncertainties, it is useful to check how our results may differ from those of previous studies using the GIEMS data as the common benchmark. Overall, compared to the GIEMS data, the spatial inundation patterns of this study were slightly better than those of  Getirana et al.