dataesr/scientific-paragraphs-categorization huggingface dataset:
). It has already been shown that SPICE could be extended to the majority of physical disciplines  [8] [9] [10] [11] [12] [13] [14] [15] [16]. An extension of SPICE to biology is the next logical step to go toward this unique environment. The idea behind BB-SPICE (SPICE for Biology and Biochemistry) is to offer a way to describe biological functions in a SPICE-like netlist and simulate it with a SPICE distribution. The other requirements for the targeted tool are: i) to be open-source, ii) to be usable by biologists without any knowledge on electronics, on the SPICE language or on the associated simulator and iii) to keep upstream and downstream compatibility with SBML (i. e. a SBML model must be able serve as simulation input file in replacement of the SPICE-like netlist and SBML model must be generated from a BB-SPICE netlist.
Methods BB-SPICE is composed of five modules, as depicted in Fig 1. • A biological netlist parser which takes as input a text file with the description of the biological system in a formalism described hereafter. The parser generates a Python dictionary which contains the list of species, the list of reactions, the reaction parameters, etc. • A SBML file reader which also generates a Python dictionary for a given biological system. • A SBML file generator, which takes as input the Python dictionary described hereabove and generates an SBML description. • A SPICE file generator, which takes as input the Python dictionary described hereabove and generates a SPICE netlist. • A open-source SPICE circuit simulator, NGSPICE in this particular case  [3]. More details on the building blocks are given in the Translators subsection.
Translators BB-SPICE parser. The BB-SPICE netlist is read by a Python script and converted into a set of dictionaries that contain all the relevant information about the system. There is one dictionary for the parameters, one for the species and one for each type of reaction. The simulation directives are also listed. NGSPICE netlist generator. The NGSPICE netlist is generated from the set of dictionaries. First, model parameters are defined as generic parameters (. param) in the NGSPICE netlist. Parameters may depend on other parameters defined previously. Obviously cyclic parameters definition has to be avoided and returns errors during the compilation of the generated NGSPICE netlist. According to the Figs 2 and 3, the definition of a species in the netlist corresponds to the definition of an electrical node with a capacitor and a resistor in the electronic equivalent circuit.
negative) VCCS is instantiated. The value of the VCCS corresponds to the formula encoded in MathML inside the SBML model. Several specific features of SBML language that are supported by the current version of the translator are the following: • Parameters which scope is limited to a given reaction rate are not implemented but substituted by their actual value directly in reaction rate equations. • Assignment rules are implemented as voltage controlled voltage sources. • Rate rules are implemented as voltage controlled current sources. • Compartments are taken into account: the value of the capacitor associated to each species depends on the size of the compartment. • User-defined functions are also translated. As NGSPICE does not provide any mechanism for function definition, a dedicated libSMBL method is used in order to substitute each invocation of the function found in formulas by a in-line copy of the function's body.
SBML generator. A SBML model can also be generated from the set of dictionaries given by the BB-SPICE parser, via the the Python libSBML  [34]  module. Parameters with explicitly defined value are converted into SBML parameters whereas parameters which values depends on other parameters are converted into assignment. Translation of species definition with associated initial concentration is straightforward. Then, each reaction is converted into a SBML reaction. For each of them, reactant, products and modifiers are deduced from the netlist and the MathML formula of the equation rate is built from the reaction type and its associated parameters. Moreover, as degradation is not natively taken into account in SBML, an extra reactions have to be defined for species with non-zero degradation rate.
Model conversion The same comparison has been made with SBML models picked up in the BioModels Database  [19]  in order to validate the SBML to NGSPICE conversion. A benchmark of 25 models has been considered. It is described in Table  4  All the features described in the Methods section are covered by this benchmark. For all of them, simulation results obtained with SBML model on COPASI and with translated model on NGSPICE are in good accordance.
List of the 25 biological systems that form the benchmark used to compare SPICE with COPASI on files generated from a SBML file from the Biomodels database. For each of them, the identifier in the Biomodels database, the name of the reaction, the number of SBML structures (compartment, used-defined function, global parameters, species, assignment, rules, reactions and events) implemented in the model as well as the reference of the paper in which the model is published are given. https://doi. org/10. 1371/journal. pone. 0182385. t004transcriptionfactor for each gene + the mRNA and the reporter. Long-time transient simulations are carried out on these models. The simulation result, the compilation time of the model and the simulation time are monitored for three different tools: COPASI, NGSPICE and a commercial SPICE simulator optimized for multi-core processing, SPECTRE1.
Acknowledgments Authors would like to thank some students of  Telecom Physique Strasbourg , an engineering school of the  University of Strasbourg , for their valuable contribution to this work ( Margaux Kremer ,  Antonin Rambault ,  Thomas Bonis ,  Katell Berdellou  and  Lucas Santin ),  Simon Wietrich , also from  Telecom Physique Strasbourg , who was in charge of the development of the online version of the tool and  Olufemi Bolaji , student in the Cell Physics Master II of the  University of Strasbourg  who did his master internship on this topic.
85. Surface water storage presents a strong seasonal signal (~496 km 3 estimated by GIEMS/ASTER and ~378 km 3 by GIEMS/HyMAPs), representing ~51% and ~41% respectively of the total water storage signal and it exhibits a large inter-annual variability with strong negative anomalies during the drought-like conditions of 1994 or strong positive anomalies such as in 1998. This new dataset of SWS is a new, highly valuable source of information for hydrological and climate modeling studies of the Ganges-Brahmaputra river basin.
The GB system drains a large part of the Indian sub-continent and hosts more than 700 million people. It is the third largest freshwater outlet to the world's oceans, being exceeded only by the Amazon and the Congo drainage basins  [15]. The basin is facing strong climate variability with alternate periods of floods and droughts. Due to the population growth, the excessive use of water for industrial and agricultural purposes, many water management challenges are emerging in this region  [16]. The Indian sub-continent is facing acute shortages of drinking and agricultural water supply, aggravated by geogenic arsenic contamination of groundwater reservoirs, especially in Bangladesh  [17]. Sections 2 and 3 present respectively the study domain and the datasets used in this study. In Section 4, we briefly describe the SWS estimation technique  [14].
Because the ERS scatterometer encountered serious technical problems after 2000, the processing scheme had to be adapted to extend the dataset and monthly mean climatology of ERS and NDVI-AVHRR observations are used  [2, 5]. Fifteen years of global monthly water surfaces extent for the period 1993-2007 are available  [5]. This dataset has been extensively evaluated at the global scale  [2, 6, 26]  and for a wide range of environments  [9, 21, 27, 28]. It has also been used for climatic and hydrological analyses, such as the evaluation of methane surface emissions models  [29, 30]  and the validation of the river flooding schemes coupled with land surface models  [31] [32] [33] [34] [35]. The spatial distribution of GIEMS was evaluated against high-resolution (100 m) SAR images in  [6]  and in  [36]  over the Amazon basin leading to an overall GIEMS uncertainty of ~10% for GIEMS.
Over the Indian Sub-Continent (and especially GB), the spatial distribution of GIEMS was evaluated against static surface water dataset (Global Lake and Wetland Dataset, GLWD-3, [37]) and other related hydrological variables (precipitation, altimeter-derived river heights, river discharge) in  [5, 21] , as well as using other regional surveys representing various components of wetland and open-water distributions  [38]. Figure  2  shows GIEMS characteristics over the GB basin. Figure  2a ,c show the annual mean and annual maximum extent of surface water respectively, averaged over 15 years (180 months). They exhibit very realistic distributions of major rivers (Ganges-Brahmaputra-Meghna River systems) and their tributaries and distributaries. Associated inundated areas, wetlands and the region of the Bengal delta are well delineated even in the presence of complex areas characterized by extensive flooding.
Multi-Satellite Surface Water Storage from GIEMS and ENVISAT Radar Altimeter Maps of water levels over the floodplains of the Ganges-Brahmaputra basin were obtained by combining observations from GIEMS and altimetry-based water levels at monthly time-scale over the 2003-2007 period where all the datasets overlap  [13]. Water levels for 58 ENVISAT RA-2 altimetry stations were interpolated with respect to the inverse of the distance from the gridpoint over inundated surfaces from GIEMS  [2, 5]. Each monthly map of surface water levels has a spatial resolution of 0. 25 • and is referenced to the EGM2008 geoid  [49]. The error on these estimates is lower than 10%  [13]. A map of minimum water levels was estimated for the entire observation period using a hypsometric approach to take into account the difference of altitude between the river and the floodplain (see  [12, 13]  for more details).
GPCP Monthly Rainfall Product In order to further evaluate our various estimates of the satellite-derived surface water storage, we will compare them with precipitation over the Ganges and Brahmaputra watersheds estimated by the Global Precipitation Climatology Project (GPCP). GPCP, established in 1986 by the World Climate Research Program, quantifies the distribution of precipitation over the globe  [56]. We use the Satellite-Gauge Combined Precipitation Data product of GPCP Version 2. 1 data (monthly means from 1993 to 2008) with a spatial resolution of 2. 5 • in latitude and longitude. Over land surfaces, the uncertainty in the rate estimates from GPCP is generally lower than over the oceans due to the in situ gauge input (in addition to satellite) from the GPCC (Global Precipitation Climatology Center).
GIEMS Surface Water Extent Thresholding Previous analysis  [2, 21, 38]  suggested that GIEMS overestimates the actual surface water extents in regions of very saturated soils. To overcome this issue, we use external information on flood coverage from the Dartmouth Flood Observatory (DFO) database that provides surface water extent for the period 2002-2015  [59]. It comprises the Surface Water Record (SWR), a comprehensive record of satellite-observed changes in the Earth's inland surface waters, compiled from the flooding history over the period 2002-2015. Extent of surface water is mostly derived from NASA MODerate-resolution Imaging Spectroradiometer (MODIS) Terra and Aqua sensors with, in some cases, additional information from Radarsat, ASTER, or other higher spatial resolution data  [60].
Water areas are accumulated over 10 days to minimize the effect of cloud cover. Inundation maps are made available at a spatial resolution of 250 m on 10 • × 10 • tiles. A color code indicates maximum flood extent each year. This dataset is commonly used for estimating flood extent limits when processing other remotely sensed observations (e. g. ,  [13, 61, 62] ). It was resampled on the GIEMS low resolution grid providing a percentage of inundation for each grid point equal to the inundation extent given by the SWR divided by the area of the GIEMS grid element (773 km 2 ). The resulting MODIS inundation mask over the Ganges-Brahmaputra is presented in Figure  3. One can see that the main river channels, along with the major floodplains and wetlands are well depicted over the basin. However, compared to GIEMS estimates (Figure  2 ), many pixels present lower maximum extents, especially in the upstream regions.
Water areas are accumulated over 10 days to minimize the effect of cloud cover. Inundation maps are made available at a spatial resolution of 250 m on 10° × 10° tiles. A color code indicates maximum flood extent each year. This dataset is commonly used for estimating flood extent limits when processing other remotely sensed observations (e. g. ,  [13, 61, 62] ). It was resampled on the GIEMS low resolution grid providing a percentage of inundation for each grid point equal to the inundation extent given by the SWR divided by the area of the GIEMS grid element (773 km 2 ). The resulting MODIS inundation mask over the Ganges-Brahmaputra is presented in Figure  3. One can see that the main river channels, along with the major floodplains and wetlands are well depicted over the basin. However, compared to GIEMS estimates (Figure  2 ), many pixels present lower maximum extents, especially in the upstream regions.
The Hypsographic Curve Approach The method to estimate surface freshwater storage consists in the combination of the surface water extent from GIEMS product with a global digital elevation model (GDEM), using a hypsographic curve approach that relates the flooded area to the elevation. We will derive here two estimates using two global datasets of hypsographic curves derived from ASTER-GDEM and SRTM30 DEM (simply named as HyMAP in the following) as processed for CaMa-Flood and HyMAP models. The three-step methodology to construct the hypsographic curve and estimate SWS is described in details in  [14] , and is briefly summarized below: 1. The first step is to construct the hypsographic curve for each pixel of the surface water extent dataset (GIEMS). The corresponding GDEM elevation points for each pixel of GIEMS product (equal-grid of 773 km 2 ) are selected.
The elevation distribution function is then created and converted (by integration) to a curve of cumulative frequencies. The latter function presents the so-called hypsographic curve that consists of an area-elevation relationship, constructed for each pixel of the GIEMS data set. 2. In the second step, a translation is applied to set to zero the lowest elevation of the hypsographic curve by subtracting the lowest value from all other elevations. The hypsographic curve is then converted into an area-surface water volume relationship by estimating the surface water volume associated with an increase of the pixel fractional open water coverage by filling the hypsographic curve from its base level to an upward level, following  [13] : V(α) = α ∑ i=1 (h(i) -h(i -1)) × S 100 × i (2) where V is the surface water volume (in km 3 ) for a percentage of flood/inundation α (an increment i of 1% in percentage of inundation is chosen), S is the area of a GIEMS pixel (773 km 2 ), and h the elevation (in km) for a percentage of flood/inundation α given by the hypsographic curve.
In order to evaluate the present method, our results were compared to SWS variation estimates from  [13]. The technique used in the latter study is based on the combination of water extents given by GIEMS and altimetry-based water levels. It should be noted that the technique used by  [13]  also applies a MODIS mask to the GIEMS dataset. The SWS time series obtained by the present study (ASTER in blue and HyMAP in red) and by  [13]  (green) are presented in Figure  7  along with GRACE-derived TWS variations (black) over the same period and same geographical locations mentioned above. where ∆ℎ , , is the anomaly of TWS at time t of the pixel of coordinates ( , ) and is the radius of the Earth (6378 km).
In order to evaluate the present method, our results were compared to SWS variation estimates from  [13]. The technique used in the latter study is based on the combination of water extents given by GIEMS and altimetry-based water levels. It should be noted that the technique used by  [13]  also applies a MODIS mask to the GIEMS dataset. The SWS time series obtained by the present study (ASTER in blue and HyMAP in red) and by  [13]  (green) are presented in Figure  7  along with GRACE-derived TWS variations (black) over the same period and same geographical locations mentioned above. The variations of SWS (Figure  6 ) after the use of the MODIS mask maintain a similar variation pattern (correlation coefficients higher than 0. 94 over G, B and GB for ASTER and HyMAP) along with a significant decrease in amplitude.
A strong seasonal cycle is observed with maximum surface water volume in August, one month before the TWS peak and a minimum volume in March-April one month before the TWS minima (Figure  7 ). The maximum lagged correlation coefficient between TWS and the SWS time series (ASTER and HyMAP) is always higher than 0. 9, with a lag time of one month. The delayed seasonal phases of TWS relative to SWS can be explained by the lower flow velocities of water in the soil and in groundwater in comparison to the surface water movement, causing recharge and drainage to continue after the maximum and minimum of SWS. In agreement with the results from  [13] , these new datasets confirm that SWS contributes annually to ~50% of TWS variations for both river basins. The variations of SWS (Figure  6 ) after the use of the MODIS mask maintain a similar variation pattern (correlation coefficients higher than 0.
95 for the different couples of time series (SWS GIEMS/Alt-SWS GIEMS/ASTER and SWS GIEMS/Alt-SWS GIEMS/HyMAP) over G, B, and GB. A strong seasonal cycle is observed with maximum surface water volume in August, one month before the TWS peak and a minimum volume in March-April one month before the TWS minima (Figure  7 ). The maximum lagged correlation coefficient between TWS and the SWS time series (ASTER and HyMAP) is always higher than 0. 9, with a lag time of one month. The delayed seasonal phases of TWS relative to SWS can be explained by the lower flow velocities of water in the soil and in groundwater in comparison to the surface water movement, causing recharge and drainage to continue after the maximum and minimum of SWS. In agreement with the results from  [13] , these new datasets confirm that SWS contributes annually to ~50% of TWS variations for both river basins.
On the other hand, it is interesting to note that SWS estimates in 1994, which is characterized as a drought year, are below the mean seasonal cycle only for the Brahmaputra basin as reported in  [64]. Nevertheless, Figure  10d  shows that major drought patterns are well observed over the Brahmaputra and the main channel of the Ganges. The drought observed over the main Ganges channels is compensated by positive anomalies distributed over the rest of the basin, which diminish the signature of the drought over the entire basin. This case scenario highlights the importance of the new SWS dataset that helps spatialize large-scale drought/flood patterns. Nevertheless, it should be also noted that when estimating SWS during severe droughts (which involved the low end of the hypsographic curves), the proposed method assumes that we cannot have access to water storage below the minimum values that GIEMS/ASTER and GIEMS/HyMAP can provide.
This methodology had already been tested in the Amazon basin. The present study shows its validity in a very different environment. As GIEMS and the DEMs are available globally, this study is also a first step towards the development of such a database at the global scale. There is also ongoing work to extend the GIEMS time series from 2007 to present. A consistent global SWS dataset from 1993 to present will play a key role in the definition and development of the future hydrology-oriented satellite missions such as the NASA-CNES SWOT (Surface Water and Ocean Topography) dedicated to surface hydrology  [68, 69].
Acknowledgments: This work was supported by the  Centre National d'Etudes Spatiales (CNES) TOSCA  ( Terre Solide, Océan, Surfaces Continentales et Atmosphère ) and  OSTST (Ocean Surface Topography Science Team ) grants "Variability of terrestrial freshwater storage in the Tropics from multi-satellite observations" managed by  Selma Cherchali  and by the  Belmont Forum  project  BAND-AID  ( ANR-13-JCLI-0002 , http: //Belmont-BanDAiD. org or http://Belmont-SeaLevel. org) with  PI C. K. Shum from Ohio State University. We thank  Raffael Maurer  for his involvement in processing ASTER-GDEM data during his work at  NASA-GISS.
Data collection Heart rate, mean arterial blood pressure, pressure in the inferior vena cava considered as a surrogate of central venous pressure, left carotid and left renal arteries blood flow, core temperature and urine output were recorded every 10 min. Doppler probes on left renal and left carotid arteries allowed a continuous monitoring of blood flows (displayed as a numeric mean value in milliliter per minute). The quality of signal was assessed by the adequate pulsatility and the absence of short-term lability (the lack of these criteria led to probe adjustments). The absence of contralateral stenosis, dilatation or renal malformation was controlled after animal killing. Renal microcirculation was evaluated by measuring velocity in cortical capillaries with Side Dark Field Camera movies (Micro Vision Medical Inc.
Data analysis Quantitative data are expressed as the mean and standard deviation. A nonparametric Mann-Whitney test was used for intergroup comparisons. For repeated measurements, we used a mixed linear regression model  [19]. All statistics were calculated with R software 3. 2. 2 (free software developed by R Core Team (2008) for the R Foundation for Statistical Computing, Vienna, Austria) and Nlme R package version 3. 1-122 (free software developed by Pinheiro J, Bates D, DebRoy S, Sarkar D and R Core Team (2015) for the R Foundation for Statistical Computing, Vienna, Austria). All tests were bilateral, and a p value of <0. 05 was considered statistically significant.
Sepsis induction After cecal ligation and puncture, hypotension occurred within similar time frames in the two groups (205 ± 40 min in the 0. 9% NaCl group vs 190 ± 32 min in the PL group, p = 0. 85). Hemodynamics at hypotension time were similar in both groups (Table  1 ). Fig. 1  Experimental design. After catheter insertion and surgical CLP, rats were randomized into the 0. 9% NaCl or PlasmaLyte ® groups. After randomization (T0, supervision baseline), rats were hydrated with the corresponding crystalloid and were monitored until arterial pressure dropped below 90 mm Hg (T1, end of supervision, resuscitation baseline). At that time, they received the allocated crystalloid for 200 min for a targeted blood pressure of 90 mmHg. After 200 min, the animals were killed and their urine and plasma were sampled (T2, end of resuscitation) Data are presented as mean and standard deviation.