dataesr/scientific-paragraphs-categorization huggingface dataset:
Uncertainties in reconstructed streamflow Several other sources of uncertainty in streamflow reconstructions are not considered in this work. First, all results presented here are conditional on the large-scale information provided by the 20CR atmospheric reanalysis, and using an alternative extended reanalysis like ERA-20C  (Poli et al. , 2016)  may lead to different outputs. Second, the hydrometeorological modelling chain used here to derive the SCOPE Hydro dataset -SCOPE and GR6J + CemaNeige -is only one of the many choices for reconstructing high-resolution meteorological fields and catchment-scale streamflow. In order to assess the uncertainty related to this choice, a follow-up study will compare the SCOPE-Hydro dataset (1) to 20CRdriven reconstructions made by  Dayon et al. (2015)  with a different downscaling method and the physically based Isba-Modcou hydrological chain, and (2) to reconstructions mixing their downscaling method and the GR6J+CemaNeige hydrological model.
Conclusions This paper describes an ensemble reconstruction of spatiotemporal extreme low-flow events since 1871 over 662 nearnatural catchments in France based on reconstructed climate and streamflow from the SCOPE Climate (Spatially COherent Probabilistic Extended Climate) and SCOPE hydro (Spatially COherent Probabilistic Extended Hydrological) datasets. SCOPE Climate builds on a probabilistic downscaling of the 20CR reanalysis over France as described by  Caillouet et al. (2016). This ensemble high-resolution daily meteorological dataset is used as forcings for the GR6J hydrological model -together with the CemaNeige snow-accounting model -to derive the SCOPE Hydro 25-member ensemble daily reconstructed streamflow dataset over a large set of catchments. These two consistent datasets may thus be used for various hydrometeorological studies, and not only for low flows, by extending the limited historical amount of surface meteorological and streamflow observations currently available in databases (see examples in Figs.
9 and 10 for streamflow and  Caillouet et al. , 2016, for precipitation and temperature). This work provides an innovative analysis of extreme lowflow events from the SCOPE Hydro dataset based on two distinct features. First, extreme low-flow events are defined locally using the sequent peak algorithm with a combination of a constant threshold and a daily variable threshold. This allows one to retain periods with both absolute low-flow values and values significantly lower than the average seasonal cycle, thereby combining the advantages of the two types of thresholds. Second, a spatial matching procedure is developed to identify events both in time and space, thus extending previous approaches that could only be applied to data continuous in space like gridded meteorological drought indicators.
This spatial matching procedure is then adapted to the ensemble case as provided by SCOPE Hydro. The above low-flow analysis features are then applied to the SCOPE Hydro dataset to derive and intercompare characteristics of individual spatio-temporal extreme low-flow events at the country scale. For the first time, these events are qualified, quantified, and compared in a spatially and temporally homogeneous way over 140 years on a large set of catchments. Results bring forward well-known recent events like 1976 or 1989-1990, but also older and relatively forgotten ones like the 1878 and 1893 events. These results contribute to improving our knowledge of historical events by taking advantage of the more abundant historical upperair atmospheric data compared to hydrometric data, and by deriving benchmark events over France.
, 2016). This study moreover allows for further detailed analyses of the effect of climate variability and anthropogenic climate change on low-flow hydrology at the scale of France over the last 140 years, by for example extending recent works on the influence of multidecadal variability on trends  (Hannaford et al. , 2013). Data availability. The SCOPE Climate and SCOPE Hydro datasets are available upon request to J. -P. Vidal (jeanphilippe. vidal@irstea. fr). Data papers are currently under preparation to make their access easier in the future. The interannual regimes of the two catchment case studies are presented in Fig. A1. The Corrèze catchment with an oceanic-dominated regime has a summer low-flow period. The Ubaye catchment with a snowmelt-dominated regime has a main low-flow period in winter and a second one at the end of the summer.
The succession of steps (1) SANDHY  (Radanovics et al. , 2013; Ben Daoud et al. , 2016) , (2) subselection with additional analogue levels  (Caillouet et al. , 2016) , (3) bias correction (see above), and (4) Schaake shuffle (see above) is called SCOPE and is summarized by Fig. B1. The use of 20CR as the large-scale reanalysis input provides the SCOPE Climate dataset, described in Sect. 2. 2. 2. L. Caillouet et al. :  Ensemble reconstruction of spatio-temporal extreme low-flow events Appendix C: Hydro-ecoregions Figure  C1  shows the 22 HERs used for the spatial matching procedure. HER 1 ("Pyrénées") and HER 2 ("Alpes internes") include high mountainous areas, whereas HER 3 to HER 5 ("Massif Central sud", "Vosges", "Jura-Préalpes du Nord") include mountainous areas with lower elevations. HER 6 to HER 8 ("Méditerranéen", "Préalpes du Sud", "Cévennes") include Mediterranean areas where convective precipitation events often occur in autumn.
LFigure 8. 8 Figure 8. Ensemble spatial matching for Station 1 in HER 11 during the 1989-1991 period. Events from Safran Hydro, average event series, and the 25 members of SCOPE Hydro are represented. (a) Each colour defines one spatio-temporal event for Safran Hydro and average series. Events from average series and Safran Hydro are independently spatially matched, leading to different colours (not the same datasets). Black bars are independent local events from the 25 members of SCOPE Hydro. (b) Matching between the 25 event members from SCOPE Hydro and average events. Grey events not overlapping average events are considered probabilistic noise and are removed from the dataset. Dashed lines are set at the start and end dates of each average event.
On 2 August 2015, TANSO-FTS TIR (B4) suspended its data processing due to a sudden shutdown of the cryo-cooler. The temperature of the TIR detector increased, while NIR and SWIR detectors (B1, B2, and B3) were operating normally. This anomaly on the cryo-cooler control electronics induced a shift in the zero path difference or ZPD position of the interferogram. The ZPD shift mechanism is sensitive to temperature and it was difficult to simulate the thermal distortion due to the cryo-cooler system failure. The new LlB V201 was able to correct the laser fringe-biased interferogram properly. On 14 September 2015, the cryo-cooler was restarted successfully. A new and stable ZPD position after the cooler restart was on the same side, but with a slightly larger bias than before 2 August. After 15 September, both SWIR and TIR have been acquired simultaneously with nominal three point, target, and glint modes.
TANSO-FTS L1B Spectra Since the end of 2012, the French AERIS/ESPRI  [20]  infrastructure collects daily GOSAT data sets from the JAXA data center in order to sustain national scientific activity based on this data. Two main datasets are regularly recovered through the Connect/Direct protocol: TANSO-FTS (Fourier Transform Spectrometer) data from level 1B and level 2, and TANSO-CAI (Cloud and Aerosol Image) data from level 1B+ and 2. All this data (currently more than 100 Tbytes) are stored on a dedicated machine and can be accessed on a cluster computer system of the IPSL data center  [21]. For TANSO-FTS spectra (unapodized), a preliminary spectral calibration and resampling on a common sampling grid with a 0. 200 cm -1 spacing was performed. Four different versions of the TANSO-FTS L1B spectra have been used in this study.
Radiative Transfer Model and Retrieval The LATMOS (Laboratoire Atmosphère, Milieux, Observations Spatiales, France) Atmospheric Retrieval Algorithm (LARA), which has been developed over the years, is a homemade radiative transfer model associated with an inversion algorithm (see, e. g. ,  [25] [26] [27] ). The corresponding software has been used to analyze atmospheric spectra recorded using ground-based, balloon-, or satellite-borne experiments, both in absorption or emission mode, and for the limb or nadir geometry. LARA has been used to perform simulations of atmospheric spectra for the preparation of satellite experiments and for assessing the information content expected from instruments with different characteristics. The algorithm LARA allows the simultaneous inversion of spectra in several windows for the joint retrieval of vertical profiles (or slant column densities) of various species  [25] [26] [27].
Surface temperature and emissivity, and if needed instrumental line shape or instrumental spectral shift may be fitted together with the concentrations of species. The LATMOS retrieval algorithm includes an accurate line-by-line radiative transfer model and an efficient minimization algorithm of the Levenberg-Marquardt type  [28]. The full a posteriori error covariance matrix of the retrieved parameters is calculated within the retrieval process. The forward model (i. e. , the radiative transfer model) uses molecular parameters that are extracted from the HITRAN 2012 database  [29]. Individual line shapes are calculated with a Voigt profile which uses Lorentzian half-widths (with their temperature dependence exponent) and line shifts from HITRAN 2012. The calculation takes into account the water vapor continuum  [30] , as well as water vapor self-broadening.
Configuration of the Inversion We have performed retrievals from each individual spectrum (unapodized) of the different versions with our radiative transfer and retrieval code LARA, using as input the European Centre for Medium range Weather Forecast (ECMWF) ERA-Interim temperature and humidity profiles. As mentioned in Section 2, a preliminary spectral calibration and resampling on a common sampling grid with a 0. 200 cm -1 spacing was performed. We selected for the retrieval the spectral window (940-980 cm -1 ), which contains the so-called CO 2 laser bands. To our knowledge, this is the first attempt to retrieve XCO 2 from this spectral region. The molecular species considered in the radiative transfer model were H 2 O (water vapor), CO 2 (carbon dioxide), O 3 (ozone), CFC12 (dichlorodifluoromethane), and SF 6 (sulfur hexafluoride).
As mentioned in Section 2, a major impact on the L1B TANSO-FTS spectra has been the cooler failure in August 2015. This is why we plotted our retrieved time series, with and without August 2015 data. The number of TIR spectra considered for each version (V16x, V201 and V203) as a function of the year/month and OPD direction (0 = forward, 1 = backward, 2 = both). Vertical dashed lines correspond to the platform or instrument events introduced in Table  1. On the graph in Figures  6  and 7 , plotted error bars are empirical uncertainties (1σ) estimated by the corresponding standard deviation (which are higher than theoretical uncertainties calculated in the retrieval process as described in inverse theory  [36] ). A first observation of the results for the retrieved maximum optical path difference shows that OPDmax is significantly different for the V16x data set, with OPDmax(V16x) > OPDmax(V15x, V201, or V203).
On the graph in Figures  6  and 7 , plotted error bars are empirical uncertainties (1σ) estimated by the corresponding standard deviation (which are higher than theoretical uncertainties calculated in the retrieval process as described in inverse theory  [36] ). A first observation of the results for the retrieved maximum optical path difference shows that OPD max is significantly different for the V16x data set, with OPD max (V16x) > OPD max (V15x, V201, or V203). This is not true for August 2015, for which, even if there are few IFOVs/spectra (index 8/2015 on the three panels in Figure  6 ), the mean values are not unexpected. However, the retrieved values are significantly lower for OPD max (V16x) compared to the three other L1B versions' retrievals. This is why we presented plots (with and without the two last monthly samples) allowing to obtain a better view of the time series.
Acknowledgments: The authors wish to acknowledge the IPSL data center ESPRI facility which is supported by  CNRS ,  UPMC ,  Labex  L-IPSL, CNES, and  Ecole Polytechnique  (https://mesocentre. ipsl. fr/),and the  CNES/CNRS-INSUAERIS  web site http://www. aeris. frthat provided GOSAT and IASI data used in this study. Acknowledgments are also due to the  French space agency, CNES , to the  Japanese space agency, JAXA , and to  NIES , which supported the present work. Special thanks are due to  A. Kuze  and  K. Shiomi  from  JAXA. Author Contributions: S. Payan: lead author and writing; C. Camy-Peyret: scientific expertise and writing;  J. Bureau : computer processing and contribution to the text.
Descriptive data There were a total of 125 subjects in this study. The mean ± standard deviation (SD) age was 70. 9 ± 5. 3 years, and subjects were predominantly male (58. 9%) of Caucasian ethnicity (77. 2%) and had a mean body mass index (BMI) of 33. 0 ± 6. 9 kg/m 2 and moderate sleep apnea with an AHI of 31. 9 ± 27. 9 events/hour. Subjects had average scores of 7. 67 ± 5. 92 on the ODSI, 8. 00 ± 4. 65 on the ESS and 17. 6 ± 2. 2 on the FOSQ.
Correlation of ODSI to AHI As a complementary analysis, we tested the associations of the ODSI, ESS and FOSQ as they relate to continuous AHI. There were no correlations between the ODSI and the AHI (Spearman's ρ: -0004, P = 1. 00). Similarly both the ESS (Spearman's ρ: -0. 04, 0. 70) and the FOSQ (Spearman's ρ: 0. 12, P = 0. 19) did not show any significant correlations with AHI (data not shown).
The act of asking whether patients felt sleepy in active or passive situations eliminated any potential for the issue of most older adults not able to answer all of ESS items,  12  due to the irrelevance of the question (i. e. , feeling sleepy while operating a motor vehicle) or potential response bias for sensitive questions. The ODSI is a useful alternative for capturing sleepiness, particularly among older adults, as the general questions allow for a broader range of applicability and thus may be useful for other subject populations as well. The limitations of our study include a limited amount of data obtained from polysomnography. The structure of the ODSI is subject to interviewer bias, when an interviewer influences responses from participants, subtly communicating expected answers.
Genetic study Whole-exome sequencing (WES) was performed in nine patients with 46,XX (ovo)testicular DSD; in four additional patients, included later, the coding region of NR5A1 was sequenced. Enrichment for WES was performed with the SureSelectXT Human All Exon V5 kit (Agilent), followed by paired-end sequencing on HiSeq 2000 (2 × 100 cycles) (Illumina). Reads were mapped against the human reference genome sequence (NCBI, GRCh37/hg19) with the CLC Genomics Workbench v6. 4 (Qiagen), followed by postmapping duplicate-read removal, coverage analysis, and quality-based variant calling. More thorough variant annotation was executed with Alamut-HT v1. 1. 5 and Alamut Visual v2. 7 software (Interactive Biosoftware). For case 1, because we had access to parental DNA, a trio-sequencing strategy was used.
Variant filtering was performed with the Ingenuity Variant Analysis platform (Qiagen). Sanger sequencing was used to confirm potentially pathogenic variants identified by WES and to perform segregation analysis. Primers were designed with prim-er3plus. Sequencing was performed on the ABI 3730XL DNA Analyzer (Applied Biosystems) using the BigDye Terminator v3. 1 Cycle Sequencing Kit (Applied Biosystems), followed by data analysis with SeqScape v2. 5 (Life Technologies). Microsatellite analysis with 29 markers (deCODE, Généthon, and Marshfield) was performed to assess disease haplotypes in the three mutation-positive cases. Data analysis was performed with the GeneMapper v3. 7 software (Applied Biosystems).
In silico modeling of the p. (Arg92Trp) and p. (Arg92Gln) mutations All modeling and calculations were performed using model 1 of the NMR structure of the NR5A1 DNA binding domain (PDB code: 2ff0). 15 The effect of the mutations on the stability of the protein was calculated using the FoldX RepairPDB and BuildModel commands, with 20 replicate calculations. 16 Models for p. (Arg92Trp) and, for comparison, p. (Arg92Gln) mutants of the NR5A1 DNA-binding domain in complex with DNA were built using the YASARA structure with the swap and optimize commands to replace R92 by a tryptophan or glutamine, followed by energy minimization with the YASARA forcefield in explicit solvent. 17 The in silico binding energy between the NR5A1 LBD and the DNA fragments was calculated using the YASARA BindEnergy command.
Plasmid construction We constructed a GFP-tagged NR5A1 wild-type (WT) construct starting from the Gateway pcDNA-DEST47 vector (Invitrogen, Life Technologies) and a WT NR5A1 open Original research article reading frame (ORF)-containing entry clone using a Gateway LR-recombination reaction (Invitrogen, Life Technologies). The mutations undergoing study were inserted using the Q5 site-directed mutagenesis kit (New England Biolabs), and mutation-specific primers were designed with the NEBaseChanger software (New England Biolabs). Mutated plasmids were transformed in One Shot TOP10 competent cells (Invitrogen, Life Technologies) and subsequently isolated with the NucleoBond Xtra Midi/Maxi kit (Macherey-Nagel). The entire NR5A1 insert and the surrounding backbone were sequenced and selected plasmids were grown to obtain larger quantities.
1. 0),  19 followed by transcriptome assembly with Cufflinks (v. 2. 2. 1). 20 ubsequently, a read count matrix was generated using HTSeqcount (v. 0. 6. 1p1). 21 Trimmed mean of M-values normalization and differential gene expression were assessed with the Limma software package. 22 The statistical significance of each observed change in expression was tested between the patient group (two patient samples, two replicates per patient) and the control group (four control samples) with a false discovery rate threshold of 5%.
Original research article ESP, ExAC, GoNL, and 1000 Genomes and in an in-house exome database. The affected amino acid and surrounding region are highly conserved up to zebrafish and is part of the Ftz-F1 region (Figure  1b ). 23 Segregation analysis of the three families showed incomplete penetrance in four 46,XX family members (Figure  1a ). In case 1, the mutation was paternally inherited (family 1: II:2); in the other two families, the mutation was transmitted by the mother (family 2: I:2; family 3: I:2). The gynecological history for these unaffected women as well as for the older sister of case 1, who also carries the mutation (family 1: III. 1), was unremarkable (Supplementary Data online, Supplementary Table S12 online). Microsatellite analysis showed a haplotype of approximately 1.
5 Mb shared among cases 1-3 (Figure  1a ), pointing to a founder effect. Given the incomplete penetrance, we assessed the WES data for other variants that might contribute to the phenotype. Variant lists with de novo variants for case 1 and rare variants (minor allele frequency < 0. 01) for cases 2 and 3 were added to the Supplementary Data online and Supplementary Tables S13-S16 online. Protein structure modeling of NR5A1 p. (Arg92Trp) and p. (Arg92Gln) We performed protein structural modeling, starting from a model of NR5A1 bound to the α-inhibin promoter, for the following mutations: p. (Arg92Trp) found here in the three cases with 46,XX DSD and p. (Arg92Gln) previously found in 46,XY DSD. 24 FoldX calculations predict that the mutations do not affect the stability of the isolated NR5A1 DNA-binding domain.
Original research article MAMLD1, a direct target gene of NR5A1 (ref. 26) that was previously reported to be involved in 46,XY DSD (ref. 27 ), was upregulated (log 2-fold change = 1. 637, P = 0. 03) in patients compared with controls. BioGPS, an online available expression database, shows expression in both testis and ovaries in mice. LEPR and IRF2BPL (EAP1) are genes involved in hypogonadotropic hypogonadism, which we considered not directly relevant in the context of the phenotypes observed here.
ACKNOWLEDGMENTS We are very grateful to the families who participated in this study. This work was supported by a grant from the  Ghent University Special Research Fund  ( BOF15/GOA/011 ) to E. D. B. , by  Belspo IAP  project  P7/43  ( Belgian Medical Genomics Initiative: BeMGI)  to E. D. B. , by the  Hercules Foundation  ( AUGE/13/023  to E. D. B. ), by a grant from the  Ghent University Special Research Fund  ( BOF Starting Grant ) to M. C. , and by grant  G0D6713N  from the  Research Foundation Flanders (FWO)  to E. D. B. and M. C. E. D. B. and M. C. are Senior Clinical Investigators of the FWO. F. C. is senior postdoctoral fellow of the FWO. We thank  Francis Poulat  for providing the TESCO-luc construct and  Jan Tavernier  for providing the NR5A1 ORF.
The MAT reconstructions are based on a database with > 3500 modern pollen spectra from Europe  (Bordon et al. , 2009)  and the Mediterranean area  (Dormoy et al. , 2009). Here we used the 10 modern assemblages with the smallest chord distances for the reconstructions of the climate parameters. Bisaccate pollen was removed from the evaluated samples and the database due to its over-representation in marine pollen records (e. g. Kotthoff et al. , 2008).
Stable isotopes in foraminifera Stable oxygen and carbon isotope (δ 18 O, δ 13 C) measurements for 36 samples were performed on 10-20 specimens (100-1000 µm fraction) of Elphidium selseyense and Elphidium incertum. The analyses were performed on a Finnigan MAT 251 gas isotope ratio mass spectrometer equipped with a Kiel I automated carbonate preparation device at MARUM, University of Bremen, Germany. The stable isotopic data were calibrated relative to the Vienna Pee Dee Belemnite (VPDB) using the NBS19 standard. The standard deviation of the house standard (Solnhofen limestone) over the measurement period was 0. 03 ‰ for δ 13 C and 0. 06 ‰ for δ 18 O.
Temperature reconstructions Inorganic-(e. g. Mg / Ca and δ 18 O of benthic foraminifera, clumped isotopes) and organic-based temperature proxies (e. g. TEX 86 , LDI) have become indispensable tools in paleoenvironmental research as they provide quantitative information of past climate variations. However, proxy-based temperature estimates may vary significantly and/or show different trends even within the same sample set. This apparent mismatch between proxies severely complicates their application in paleoenvironmental and paleoclimate research. Ideally, multiple proxies should therefore be used simultaneously to provide independent paleotemperature records (e. g. Butruille et al. , 2017; Jonas et al. , 2017). Pollen-based reconstruction of seasonal and mean annual air temperatures as well as estimates of surface water temperature variations using lipid paleothermometers were obtained continuously over the investigated sequence of Site M0059, but clumped isotope and Mg / Ca data are missing, in particular from the freshwater interval reflected in EZ1 and in the lowermost section of EZ2 due to the lack of sufficient carbonaceous micro-and macrofossils.
(e) Ostracod shallow water and deeper-living marine taxa plotted from right to left. (f) Total abundance of benthic foraminifera per cubic centimetre of dried sediment with the relative abundance of the most common species; benthic foraminiferal taxa (Elphidium incertum, E. selseyense, E. magellanicum, and Ammonia beccarii) shown as a percentage of total benthic foraminiferal fauna; total abundance of benthic foraminifera shown as specimens per cubic centimetre of dried sediment; stable carbon and oxygen isotope data analysed on E. selseyense. (g) Diol index indicating relative changes in surface water salinity.