dataesr/scientific-paragraphs-categorization huggingface dataset:
Interpolation of meteorological in-situ observations To obtain continuous and spatialized meteorological series for the study area, precipitation and temperature gauge data were interpolated using the inverse distance weighted method (IDW) on a 5-km grid at the regional scale (for details on the purpose of hydrological modelling, see section 3. 2) and on a 500-m grid at the local scale (for the purpose of snow modelling, see section 3. 3). The choice of the IDW technique as interpolation method was based on the study of  Ruelland et al. (2008) , which showed low sensitivity of hydrological models to rainfall input datasets derived using different interpolation methods (IDW, Thiessen, spline, ordinary kriging) with IDW yielding the highest hydrological efficiency. Temperature values were interpolated by accounting for a constant lapse rate of 6.
Satellite precipitation estimates (SPEs) Twelve SPEs with a spatial resolution below or equal to 0. 25° (~25 km at the Equator) were selected for the 2000-2012 period. SPEs with coarser resolution were not used to ensure accurate representation of spatial precipitation variability. The SPEs include the following datasets: Climate Hazards Group InfraRed Precipitation (CHIRP), Climate Prediction Center MORPHing (CMORPH), Global Satellite Mapping of Precipitation (GSMaP), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network (PERSIANN), Soil Moisture to Rain (SM2Rain) method, Tropical Rainfall Measuring Mission (TRMM), Multisatellite Precipitation Analysis (TMPA) and Multi-Source Weighted-Ensemble Precipitation (MSWEP). All the SPEs used a combination of satellite (S) data gathering information from passive microwave (PMW) radiometers and infra-red (IR) data from Low Earth Orbital (LEO) and geosynchronous satellites, respectively except for the SM2RAIN method, which relies on satellite surface soil moisture derived from passive and active microwave, for precipitation estimates.
Finally, using the nearest neighbour technique, all the SPEs were spatially resampled to 5 km to facilitate their comparison. The resulting database consists of 10,500 daily virtual stations at 5 km spatial resolution over 12 hydrological years  (2000) (2001) (2002) (2003) (2004) (2005) (2006) (2007) (2008) (2009) (2010) (2011) (2012)  for each SPE. Additionally, SPEs were resampled to 500 m resolution over the selected subset region to assess SPE potential for snow modelling (see Fig. 1 ). Hydrol. Earth Syst. Sci. Discuss. , https://doi. org/10. Distribution (SCD) represents the percentage of days with snow and can be retrieved for any pixels and periods.
The third step consisted of analysing the sensitivity of snow modelling to the SPEs (precipitation datasets) over a subset mountainous area in the Andes (see Fig. 1 ) using snow cover duration as observed from MODIS gap-filled snow products as reference. The aim of the proposed protocol was to assess the space-time consistency of the SPEs. The spatial consistency was accounted for by the gauge and basin distribution, as well as by the use of a fully distributed snow model for the first, second and third assessment step, respectively. To evaluate the consistency of the SPEs over time, each assessment step was analysed according to three 4-year time windows. It is worth mentioning that simulated streamflow and snow cover using.
Acceptable parameter bounds were defined according to the recommendation of  Perrin et al. , (2003)  and previous experiments in a similar Andean context  (Hublart et al. , 2016; Ruelland et al. , 2014). The following ranges were used for model calibration with all the precipitation datasets tested (P ref and SPEs): 10 mm < X1 <1,800 mm, -5 mm < X2 < 5 mm, 1 mm < X3 < 500 mm, 0. 5 days < X4 <5 days. These ranges are large enough to compensate for the differences in the different precipitation datasets and basins. Beyond these ranges, we assumed that streamflow simulations could not be realistic. In practice, parameter bounds were rarely reached when calibrating the model with the different datasets tested (data not show here for the sake of brevity). The area catchment P and PE averaged values were computed from the 5-km pixels P (P ref and SPEs) and PE included in each catchment considered.
We used a weighted average based on the 5 km*5 km fraction included in the catchment considered. P ref and SPEs were used sequentially as forcing precipitation datasets for the streamflow simulation. For each run, model parameter calibration was based on the shuffled complex evolution (SCE) algorithm  (Duan et al. , 1992 ) by optimising the Nash-Sutcliffe efficiency criterion (NSE, Eq. 6; Nash and Sutcliffe, 1970) at a 10-day time step. The NSE criterion represents the overall agreement of the shape of the hydrograph, while placing more emphasis on high flows. NSE values vary from -∞ to 1 with a maximum score of 1 meaning a perfect agreement between the observed and simulated values. At the contrary, negative values mean that more realistic estimates are obtained using the observed mean values rather than the simulated ones.
𝑁𝑆𝐸 = 1 -{ ∑ (𝑄 𝑜𝑏𝑠 𝑡 -𝑄 𝑠𝑖𝑚 𝑡 ) 2 𝑁 𝑡=1 ∑ (𝑄 𝑜𝑏𝑠 𝑡 - 𝑁 𝑡=1 𝑄 𝑠𝑖𝑚 ̅̅̅̅̅̅̅ ) 2 } ( 6 ) where NSE Nash-Sutcliffe efficiency,    and    are, respectively, the observed and simulated streamflow for time step t; and N is the number of time steps for which observations are available. The distribution of the basin in the study region (Fig. 1 ) provided the opportunity to assess the hydrological consistency of the SPEs in space by running GR4j in the four basins over the common 2008-2012 period of observed discharge   2012)  and over three 4-year sub-periods  (2000-2004, 2004-2008, and 2008-2012). No validation step was used as the objective was to assess hydrological modelling sensitivity to various precipitation datasets (P ref and SPEs) and not to assess the hydrological model robustness under climate variability.
The following ranges were used for model calibration with all the tested precipitation datasets (P ref and SPEs): 0. 5 mm. °C -1 d -1 < Kf < 20 mm. °C -1 d -1 , 1 mm < SWE_th < 80 mm. Kf ranges were based on ranges adapted from the values reviewed in  Hock (2003). Regarding the SWE_th value, the assumption is that, using remote sensing, snow cover cannot be detected below a certain threshold  (Bergeron et al. , 2014). For instance, based on in-situ measurements to detect snow cover from MODIS in the Pyrenees,  Gascoin et al. , (2015)  found a mean threshold of 40 mm. Since this value may be influenced by the local context (vegetation, topography, and climate) and spatial difference between point (in-situ) and areal satellite (MODIS) observations, SWE_th was tested according to large ranges.
It is worth mentioning that the tested bounds were reached during calibration for all simulations (i. e. Kf=20 mm°C -1 d -1 and SWE_th=1 mm). However we did not consider larger parameter ranges to ensure "realistic" simulations. In association with T ref for temperature forcing data (see section 2. 2. ), P ref and SPEs were sequentially used as forcing precipitation datasets to simulate snow cover with the model. For each run, model parameters were calibrated based on the shuffled complex evolution (SCE) algorithm by optimising the pixel-to-pixel correlation between the snow cover duration (SCD) simulated by the model and that observed by the gap-filled MODIS snow products (see section 2. 3. 2. ), according to the following Eq. (  10 ): 𝑅 2 = 1 - ∑ (𝑆𝐶𝐷 𝑀𝑂𝐷𝐼𝑆(𝑝) -𝑆𝐶𝐷 𝑀𝑂𝐷𝐸𝐿(𝑝) ) 2 𝑛 𝑝=1 ∑ (𝑆𝐶𝐷 𝑀𝑂𝐷𝐼𝑆(𝑝) ) 2 𝑛 𝑝=1 ; ( 10 ) where R² is a determination coefficient, n is the total number of pixels in the study area (see Fig.
In the Keka catchment, for each time window, the best streamflow simulation was obtained using different SPEs. CHIRP, PERSIANN-CDR and CMORPH-BLD resulted in the highest NSE scores over the various sub-periods analysed, with respectively 0. 73 for the period  2000-2004, 0. 83 for 2004-2008 and 0. 77 for 2008-2012. To conclude, the analysis of streamflow model sensitivity to the different precipitation datasets depended to a great extent on the time window and catchment considered. However, MSWEP appeared to be the most 'stable' SPE. It provided the most realistic streamflow simulations with higher NSE scores than P ref for seven out of ten simulations and also outperformed other SPEs in almost all streamflow simulations tested. The results obtained for the 2000-2012 period faithfully reflected global SPE performance for all the time windows with MSWEP as the most and CMORPH-BLD as the least suitable SPE for hydrological modelling over the Katari and Keka catchments.
Space-time consistency of SPEs for snow cover duration Figure  8  compares the snow cover duration (SCD) observed by MODIS and the durations simulated by the snow model using P ref or all the SPEs as input data over the different periods. Whatever the period, the best SCD simulations were obtained using P ref as input data with R 2 values of 0. 83, 0. 80, 0. 79 and 0. 82 for the  2000-2004, 2004-2008, 2008-2012 and 2000-2012 periods , respectively. Simulated SCD using SPEs as input data systematically overestimated SCD compared to the SCD computed from the gap-filled MODIS snow products. The MSWEP (CHIRPS) datasets provided the most realistic (unrealistic) SCD simulations over 2000-2012 with R² of 0. 80 and 0. 67 respectively (see Fig. 8a ). Interestingly, mean annual precipitation over 2000-2012 (see Fig.
Space-time SPE consistency For each assessment indicator considered (i. e. gauges, streamflow and snow cover duration), the performances of the SPEs varied depending on the time window considered. As shown in the analysis, a given SPEs can appear to be the best option to represent precipitation for one particular period but not for another. Additionally, for the same period, the SPE may be suitable for a specific regional subset but not for another. This space-time inconsistency is of major concern in the current context of climate variability. Indeed, over remote regions, the scarcity of meteorological stations encourages scientists to use remote sensing data to understand precipitation variability and its contribution to meteorological, agricultural and hydrological droughts (see e. g  Agutu et al.
However, the distribution of SCD (Fig. 8a ) indicates permanent snow-covered areas over which seasonal dynamics from optical imagery (MODIS) are difficult to capture as there is more variation in depth than in spatial extent. Consequently, the regional seasonal snow cycle captured from MODIS is weak and erratic (see Fig. 11 ). Preliminary modelling tests showed that the dynamics of these snow covered areas were poorly simulated by the snow model when the P ref and SPE precipitation datasets were used. In addition, the SPEs tested in this study were not originally designed to distinguish between liquid and solid precipitation. The newly released SPEs Integrated Multi-satellite Retrievals for the Global Precipitation Mission (IMERG-v3 v4 v5) are the first SPEs which make it possible to discretize liquid to solid precipitation at 10 km spatial resolution and almost global scale.
-SPEs present space-time errors that cannot be assessed when the time windows are too short and/or the gauge networks are too scarce. In this context, the space-time consistency of SPEs should be carefully considered before using them for hydro-climatic studies. -Ranked SPE potential varied depending on the assessment indicators used. It is therefore recommended to assess SPE potential according to indicators which are related to their final use. -The proposed sensitivity analysis of snow modelling to the SPEs by using MODIS snow-products as control data has great promise for the assessment of SPE potential over completely unmonitored snow-covered regions. -For the three assessment indicators (gauges, streamflow and snow cover duration) considered here, all SPE versions including gauge data for precipitation estimates (TMPA-Adj, CMORPH-BLD and CHIRPS) outperformed their satellite only based version (TMPA-RT, CMORPH-RAW and CHIRP).
The other pixels were not used as P ref is influenced by the interpolation process. For each of the 69 0. 05° pixels, only the daily values available from all precipitation datasets (P ref and SPEs) were used and the 10-day records were only computed when more than 80% of daily values records were available. Next, mean regional 10-day precipitation series were computed from P ref and all SPEs by aggregating the values from all 69 pixels. For each pixel, only 10-day values available for all SPEs and P.
The other pixels were not used as P ref is influenced by the interpolation process. For each of the 69 0. 05° pixels, only the daily values available from all precipitation datasets (P ref and SPEs) were used and the 10-day records were only computed when more than 80% of daily values records were available. Next, mean regional 10-day precipitation series were computed from P ref and all SPEs by aggregating the values from all 69 pixels. For each pixel, only 10-day values available for all SPEs and P 𝐶𝐶 = 𝐶𝑜𝑣(𝑆𝑃𝐸,𝑃 𝑟𝑒𝑓 ) 𝑆𝑇𝐷 𝑆𝑃𝐸 ×𝑆𝑇𝐷 𝑟𝑒𝑓 (1) ref were used. It should be noted that SM2Rain estimates rely on soil moisture observations with many missing data over water bodies and mountainous regions(Dorigo et al. , 2015), leading to significant spatial gaps over Lake Titicaca and the Cordillera region (Fig.
Expansion of long terminal repeat retroelements Given the similar number of genes in the A. mexicanum genome in comparison to other smaller vertebrate genomes, we analysed repetitive sequences (Supplementary Information sections 4. 2. 2, 4. 2. 3). Repetitive sequences made up 65. 6% of the contig assembly, representing a total of 18. 6 Gb. Distinct long terminal repeat (LTR) retroelement classes and endogenous retroviruses made up the largest portion of the repetitive sequences (Fig. 2a , b, Supplementary Table  13 ) and included elements of more than 10 kb in length (Fig. 2c , Extended Data Fig. 1 ). Such long elements pose challenges for assembly, and indeed 97% of contigs ended in LTR elements. The number of substitutions to the repeat consensus, which is an estimate of the relative age of the LTR retroelement, indicates that the axolotl genome has undergone a long period of transposon activity followed by a recent and apparently continuing burst of expansion (Fig.
A reduced Pax-family complement Next, we interrogated the genome for families of canonical developmental signalling molecules (Supplementary Information section 5). All three hedgehog paralogues as well as a full set of vertebrate Wnt genes were present (Extended Data Fig. 2a, b ). However, we noted that certain members of the paired box family of transcription factors, which have diverse roles in tissue formation, were not found in the assembly. Consistent with the absence of Pax4 in amphibians and other vertebrate lineages  17  , the axolotl genome does not contain Pax4 but does contain Pax10. Notably, despite the presence of the Pax3 and Pax7 paralogues in all other known vertebrate lineages, we were able to identify Pax7 but not Pax3 in the axolotl genome assembly (Extended Data Fig. 2c ).
In the F2 generation, the developmental phenotype described below was observed in 83 out of 345 (24%) progeny from the Pax7 Δ20nt/+ intercrossing and 57 of 232 (24. 6%) progeny from the Pax7 Δ7nt/+ intercrossing (Fig. 4b , Extended Data Fig. 3 ). The phenotype was evident in homozygous mutants, as analysed by PCR and loss of protein (Supplementary Information sections 6. 1, 6. 3). This information, combined with the CRISPRmediated gene mutation results (Supplementary Information sections 6. 2), shows that the homozygous Pax7 Δ20nt/Δ20nt and Pax7 Δ7nt/Δ7nt mutants represent recessive, complete or partial loss of Pax7 function. The Pax7 Δ20nt/Δ20nt and strong F0 Pax7-CRISPR mutants exhibited a curved body, were unable to maintain an erect posture and exhibited a delay in growth compared to controls.
Article reSeArcH In mice, Pax7 deletion affects craniofacial neural crest derivatives, including the facial bones  20  , whereas in zebrafish, pax7 mutants show loss of xanthophores and reduction of melanophores, but no loss of iridophores  24. The AmPax7 mutants lacked a prefrontal bone, had a reduced number of melanophores and were deficient in xanthophores and iridophores except in the eyes (Fig. 4e-g , Extended Data Fig. 8 ). Pax3 deletion in mice is associated with neural tube closure defects  22, 23  (Supplementary Table  18 ). Similarly, Pax7 Δ20nt/Δ20nt -TALEN and Pax7-CRISPR axolotls displayed failed closure of the neural tube in the midbrain (Fig. 4h , Extended Data Fig. 9 ). In summary, mutation of AmPax7 yields a combination of the Pax3-and Pax7-mutant phenotypes that are observed in other vertebrates (Supplementary Information section 6).
Species-restricted genes in regeneration Previous searches for mRNA and microRNA (miRNA) transcripts associated with limb regeneration relied on mapping to de novo transcriptome assemblies. We sought to re-examine these datasets using our newly acquired genomic data. Recent functional work has highlighted the role of diverged gene or protein function during regeneration  [25] [26] [27]. Analysis of published tissue-enriched datasets  28  , combined with regeneration time courses  29, 30  and our own transcriptional profiling of 22 tissues, identified five transcripts that are upregulated in the limb blastema (the mass of proliferating cells involved in regenerating the limb) with orthology limited to non-amniote vertebrates (Supplementary Information section 7). One of these protein sequences shows a weak similarity to tectorin, a basement membrane component normally found in the inner ear, consistent with studies that implicate extracellular matrix components with having an important role in limb regeneration  31, 32.
Notably, another of these transcripts encodes a Ly6 family member in the urokinase type plasminogen activator surface receptor (uPAR) class. Previous studies had identified the salamander-specific Ly6 family member Prod1 as a key factor involved in salamander limb development and regeneration  25, 33. Our results suggest that Ly6 family members have a broader role in limb regeneration. Finally, we also investigated the role of non-coding RNAs by mapping a dataset of small RNA sequences expressed in the limb and limb blastema  34  to our genome assembly. This analysis classified 93 small RNAs as pre-miRNA sequences, of which 42 appear to be novel miRNAs (Supplementary Information section 7. 2). Taken together, these data point to a potential role in limb regeneration for several coding and non-coding sequences that have been lost or diverged rapidly  Article reSeArcH in amniotes.
Sequences were assembled using the MARVEL assembler. Optical mapping was performed using the Saphyr System (Bionano) based on NanoChannel array Technology. DNA was labelled with Nt. BspQI and Nb. BssSI enzymes in separate labelling reactions. Each enzyme reaction was run on the Saphyr System. 2. 813 Tb of data were collected on three Saphyr Chips for Nt. BspQI and 2. 0 Tb of data were collected on two Saphyr Chips for Nb. BssSI samples; single molecule N50 lengths were 240 kb and 184 kb, respectively. Each dataset was de novo assembled using Bionano Solve 2. 1 software. RNA was isolated from 22 tissue types using TRIzol or RNeasy reagents and sequenced using Illumina technology. The Trinity software package was used for transcriptome assembly. In the originally published version of this Article, the sequenced axolotl strain (the homozygous white mutant) was denoted as 'D/D' rather than 'd/d' in Fig.
Acknowledgements E. W. M. was supported by the  Max Planck Society President's fund and BMBF  grants  01IS14014C  and  031L0102. E. M. T. , S. N. and J. -F. F. were supported by  MPI-CBG ,  DFG FZ111  and  IMP. E. M. T. and J. -F. F. were supported by  DFG   TA 274/3-3. J. -F. F. is supported by  NSFC  grant  31771611  and a  Research Starting Grant   S82111-E2  from  SCNU. F. F. was supported by a  CONACYT Masters Fellowship. A. C. was supported by  The Swedish IRLG GRANT   2014-9040-114152-32 ,  CONACYT FOINS-301  and  Ciencia Básica   I0017-CB-2015-01-000000000252126. G. Y. was supported by the  Crick Institute  award  FC001162  (to J. P. Stoye). The Crick receives its core funding from  CRUK , the  MRC  and the  Wellcome Trust. B. H. was supported by  BMBF  grant  01IH11003C. We thank  W.
Code availability. The MARVEL assembler with documentation is available at https://github. com/schloi/MARVEL. Data availability. A browser of the axolotl genome is available at https://genome. axolotl-omics. org. The transcriptome assembly and the genome and transcriptome BLAST database can be accessed at https://www. axolotl-omics. org with no restrictions. The sequence data and both assemblies have been deposited in the NCBI BioProject database with accession numbers PRJNA378970 (genome data) and PRJNA378982 (transcriptome data). Both genome data and transcriptome data were deposited to the NCBI Nucleotide Database (nuccore) with accession numbers PGSH00000000 and GFZP00000000, respectively.
Aims. The Radial Velocity Spectrometer (RVS) on board the ESA satellite mission Gaia has no calibration device. Therefore, the radial velocity zero point needs to be calibrated with stars that are proved to be stable at a level of 300 m s -1 during the Gaia observations. Methods. We compiled a dataset of ∼71 000 radial velocity measurements from five high-resolution spectrographs. A catalogue of 4 813 stars was built by combining these individual measurements. The zero point was established using asteroids. Results. The resulting catalogue has seven observations per star on average on a typical time baseline of six years, with a median standard deviation of 15 m s -1. A subset of the most stable stars fulfilling the RVS requirements was used to establish the radial velocity zero point provided in Gaia Data Release 2.
The RVS will record 40 epochs on average per source during the five years of the nominal mission. The RV precision is expected to be 1 km s -1 for GK type stars down to G = 12-13. The summary of the contents and survey properties of Gaia DR2 are presented in Gaia  Collaboration et al. (2018a). The latest news about Gaia can be found on the Based on observations made at Observatoire de Haute Provence (CNRS), France, at the Telescope Bernard Lyot (USR5026) operated by the Observatoire Midi-Pyrénées, Université de Toulouse (Paul Sabatier) and CNRS, France, at the Euler telescope operated by Observatoire de Genève at La Silla, Chile, and on public data obtained from the ESO Science Archive Facility. Tables are only available in electronic form at the CDS via anonymous ftp to cdsarc. u-strasbg.
2002) , "Radial velocities for 6691 K and M giants"  (Famaey et al. 2005) , and "The Geneva-Copenhagen Survey of the Solar neighbourhood"  (Nordström et al. 2004) , complemented with IAU standards  (Udry et al. 1999). We observed the selected stars between 2006 and 2012 with high-resolution spectrometers. This observing campaign was part of a more general task of the Gaia Data Processing and Analysis Consortium (DPAC), which was to acquire all necessary ground-based observations for Gaia (GBOG). Public archives were queried to complement our observations. The resulting pre-launch version of the catalogue of RV-STDs for Gaia, including 10 214 RV measurements for the 1 420 stars presented in  Soubiran et al. (2013) , is now superseded by the version presented here. The number of RV-STDs needed for the ZP calibration has been re-evaluated in December 2013.