dataesr/scientific-paragraphs-categorization huggingface dataset:
The relative soil moisture is thus calculated as  [8, 43] : θs(t) = σ 0 (40,t)-σ 0 dry(40,t) σ 0 wet(40,t)-σ 0 dry(40,t) (1) where θs(t) s(t) is the relative measure of the water content in the uppermost soil layer, ranging between zero and one (1%-100%). The difference between the wet and dry reference is defined as the sensitivity. MetOp-A ASCAT (launched 2006, C-Band, VV polarization) together with its predecessors onboard ERS1 and ERS2 (European Remote Sensing Satellite) allows to obtain such references over a time period reaching back to 1991  [9]. The ASCAT incidence angle ranges from 25 • to 65 • and the backscatter is normalized to a standard incidence angle of 40 •  [44]. The MetOp ASCAT derived dataset which has been used for this study is available globally from TU Wien  [8, 9, 27, 43].
The grid format is based on the assumption that the Earth can be modeled as a rotated ellipsoid. Each grid cell over land is attributed a timeseries of backscatter values which are spatially resampled with an equal spacing of 12. 5 km in latitude and longitude. The cells are identified by a unique grid cell index (GPI)  [17]. For this study the global original dataset (without temporal aggregation) has been used, except for the model to remotely-sensed data comparison, where a weekly composite of the ASCAT data was used  [45]. This specific dataset was developed as requested by users from the global modeling community within the DUE-permafrost project. The weekly composite consists in daily records where the retrieved SSM values from the preceding week are averaged. The ASAR WS (C-band) dataset has a higher spatial resolution than ASCAT.
ASAR was a ScanSAR instrument on board ENVISAT. Its spatial resolution in wide swath (WS) mode is 120 m for all sub-swaths  [46]. ASAR had a revisit period of 35 days. Data availability is considerably constraint compared to scatterometer data. Suitable time series can be obtained for a range of locations across Siberia for selected years  [47]. For this study, an ASAR WS dataset which was pre-processed over Northern Eurasia for the time period 2007-2008 within the framework of the European Space Agency (ESA) Support To Science Element (STSE) project ALANIS Methane  [47] [48] [49]  was used. Since the backscatter signal decreases with increasing incidence angle of the radar beam relative to the surface, the backscatter measurements are normalized to a common reference angle (30 • ) using the SAR Geophysical Retrieval Toolbox (SGRT), which is a scripting chain developed by the Institute of Photogrammetry and Remote Sensing (IPF).
The scripting chain calls the commercial SARscape or the free NEST software, allowing for the automatization of the entire preprocessing procedure  [47, 50]. Single images are split up into predefined subsets and stored as time series stacks. In addition, the surface status flag developed for ASCAT  [27]  is used for masking the frozen ground condition and melting snow from the ASAR data. Point-station data measurements of maximum-and minimum temperature, precipitation and wind speed was obtained from the harmonized Global Summary of the Day and Month Observations dataset from the Global Precipitation Climatology Center  [51].
The persistence of lake ice on large lakes well into the summer and the associated water body classification issues have already been pointed out by  [47]. To identify any water surface effects, the water areas identified by the classification were replaced by the value -20 dB, which is a typical C-band backscatter value for smooth water surfaces in the used dataset  [47, 54]. This builds the reference dataset. The mean backscatter result of the masking and replacement by a reference value, henceforth referred to as refilling, was calculated for each ROI and for each available acquisition. This average from the reference datasets was plotted against the original dataset. The mask effect, ∆σ 0 , is the difference between the original and the reference ROI average for each available acquisition.
The acquisitions were also visually interpreted together with meteorological data in order to identify any rain, ice and wind conditions. In addition to the above described specific examination of the surface effects (potentially from ice, rain and/or wind) it was investigated whether the level of agreement between the ASCAT and ORCHIDEE SSM results could be related to the abundance of lakes. For this purpose, open water fraction and lake size were calculated for each ROI from the ASAR WS data by converting the previously described water classification to polygons. Criteria related to total water cover and lake size were defined and used to separate ROI into three categories: (A) More than 20% lake area per total area including only lakes larger than 10 km 2 (B) More than 20% lake area per total area including only lakes smaller than 10 km 2 (C) Less than 20% lake area per total area.
In Kharampur, Muzi, Nadym, Taimyr and Vorkuta, ∆σ 0 is generally low and no significant backscatter change that could be related to weather conditions was observed in the ASAR data. Scatterplots of the reference values as a function of the original data for one of the ROI in Cherski (subject to high ∆σ 0 ) and one of the ROI in Kharampur (not subject to a high ∆σ 0 ) are shown in the left and right panel of Figure  4 , respectively. To isolate the potential effect from prevailing ice cover the early summer months are separated from the late summer months: the acquisitions from June are plotted as black diamonds whereas those from July and August as red crosses. Figure  4B  shows that the mean from the unmasked backscatter in the case of Kharampur is similar to that of the masked (reference dataset).
In the cases when the high backscatter events coincide with precipitation events, the impact of dielectric properties changes would need to be taken into account: it is difficult to separate the backscatter variations due to the actual increase in soil moisture from the action that the rain causes on the surface. Furthermore, it should be considered that the ASCAT dataset which has been used for comparison with ORCHIDEE has been aggregated to weekly time steps. One can expect a smoothing of the backscatter variations in this case, so the actual disagreement with the model may be higher when compared on a daily basis. For soil moisture retrieval the signal is impacted if the area covered by open water surface within the footprint is large  [38]. A surface water fraction flag, derived from the Global Lakes and Wetlands Database  [55] , exists but it does not take wetland dynamics, temporary inundation and small lakes into account  [54].
Conclusions This study examines backscatter variabilities other than related to soil moisture in order to address challenges in radar soil moisture retrieval that are specific for in the Arctic. The impact on the soil moisture retrieval is investigated for sites across Siberia, including permafrost longterm monitoring sites. Satellite derived surface soil moisture datasets would provide valuable additional information to point measurements in these regions. Envisat ASAR WS imaging radar data are investigated in order to identify seasonal variations in backscatter that are not related to SSM but to lake cover surface status. This gives an indication for the magnitude of the impact of such features on the coarser spatial resolution ASCAT SSM retrieval, and cast some light on the model-to-ASCAT low agreement in SSM, highlighted by a previously published study  [36].
Regions with high backscatter deviations (averaged over ASCAT grid cell) from the reference dataset representing smooth water surfaces (∆σ 0 ) are however evidently characterized by a high percentage of total lake area, i. e. , many of the ROI in eastern Siberia: Cherski, Andryuskio, Druzhina and Kytalyk. The sites Kharampur, Taimyr, Nadym, Vorkuta and Muzi (western and central Siberia), show low ∆σ 0 and are areas with few lakes. In the case of Markovo (far eastern Siberia) the low agreement between the model output and the SSM product is more likely explained by the exceptionally scarce weather data from this area used to force the model  [36]. Although events of wind and precipitation identified by the meteorological data only occurred infrequently for each site, the correlation between the backscatter deviation and the ASCAT-ORCHIDEE agreement together with the lake size distribution lead to the conclusion that water body fraction and water body size is one explanation for the disagreement between the ASCAT and ORCHIDEE SSM.
Acknowledgments The authors acknowledge financial support by the  European Union   FP7-ENV  project  PAGE21  ( Changing Permafrost  in the Arctic and its Global effects in the 21st century) under contract No. GA282700. The  ALANIS  project was funded by the  European Space Agency (ESA)   Support to Science Element (STSE) program  ( ESRIN Contr. No. 4000100647/10/I-LG ).
The family was referred to the French NDM study group. Informed consent for genetic analysis was obtained from the parents. Genome-wide SNP typing using Illumina Infinium660K-SNP microarrays (according to manufacturer's instructions; Illumina) was carried out in the proband and both parents in order to detect large homozygous regions (runs of homozygosity [ROHs] R2. 5 Mb in size) that are only present in the child, as previously described for homozygosity mapping  (Bonnefond et al. , 2013). Bidirectional Sanger sequencing of all exons 1-19 of the RFX6 gene, flanking intron-exon boundaries, and the proximal promoter were performed from a PCR-amplified genomic DNA sample in the proband using the automated Applied Biosystems 3730xl DNA Analyzer (Life Technologies). Electrophoregram readouts were assembled and analyzedusing the Applied Biosystems Variant Reporter software (Life Technologies).
ACKNOWLEDGMENTS This work was supported by grants from  ANR Blanc RFX-PancInt  (to R. S. and M. V. ), the  Seventh Framework Programme of the European Union  (no. 241883  to R. S. ), and the  Foundation Bettencourt Schueller  (to R. S. ). R. S. 's laboratory belongs to the  Laboratoire d'Excellence consortium Revive  and to the  Departement Hospitalo-Universitaire (DHU) Autoimmune and Hormonal diseases. We wish to thank  Dr. J. J. Robert  for his help in taking care of the patient. Stephane Lobbens and  Boris Skrobek  (both at the  CNRS UMR 8199, Lille, France ) have contributed to the ROH genetic analysis. Transcriptomic studies were performed at the Microarrays and Deep sequencing platform of the IGBMC. Electrophysiological studies in Oxford were supported by a  Wellcome Trust   Senior Investigator Award  (to P.
The results from genetic analyses have generally supported the accepted geographical subspecies designations  [6, 30] , though confusion has occasionally arisen when using voucher specimens from zoological collections to represent wild populations  [31]. The Moroccan Royal Lion collection, for example, has been used as a proxy for extinct North African Barbary lions [e. g. 28], despite the fact that mitochondrial studies have demonstrated that these samples cluster with lions from Central Africa, rather than North Africa  [19, 20]. These studies have provided crucial information for understanding the phylogeographical relationships among modern lions. However, they were based on very short fragments of mitochondrial DNA, or samples drawn only from the extant populations, or data sets that included individuals with questionable geographical origins.
To estimate the relationships among cytb haplotypes, we generated a median network  [39]  using the medianjoining algorithm in the program Network v4. 610 (www. fluxus-engineering. com). More published sequences were available on GenBank that consisted only of cytb than of cytb + control region. Network analysis allowed us to confirm that congruent trees were inferred using both a shorter DNA dataset containing more sequences and our longer DNA dataset containing fewer sequences. Bayesian phylogenetic analyses were performed using BEAST v1. 7  [40]. The cytb and control region data were concatenated (1186 bp) and the HKY substitution model was selected for each region using the Bayesian information criterion. This criterion has been shown to perform well under a variety of simulation scenarios  [41].
The mutation rate of cytb, relative to the control region, was estimated in the analysis. Second, we added a sequence from a cave lion, P. leo spelaea, which allowed us to specify a calibration for the divergence between P. leo leo and P. leo spelaea. We used a normal prior with a mean of 550,000 years and standard deviation of 25,000 years for the timing of this split, based on the appearance of the ancestral cave lion P. leo fossilis in the European fossil record  [35, 46]. An analysis using Bayes factors  [47]  supported a model with constant population size as the best-fitting coalescent model for the data set. We obtained posterior estimates of parameters via Markov chain Monte Carlo (MCMC) sampling. Samples were drawn every 1,000 steps over 10,000,000 MCMC steps, with the first 10% discarded as burn-in.
Squares correspond to sequences available on Genbank and identified in Additional file 3: Table  S1. Colours of squares and circles correspond to those used in Figure  2. *PL11 and PL12 are medieval English lions that have been identified as North African lions (P. leo leo) based on mitochondrial HVR sequences and morphological data. southern origin has been independently supported by Antunes et al. [28] , who based their findings on mtDNA and lion feline immunodeficiency virus (FIV) sequences amplified from 357 modern East African, South African and Indian lions. Our results, showing longer branches amongst eastern-southern African lions (Figure  2 ), also support the idea that the evolutionary cradle of the modern lion was in eastern-southern Africa. Our results suggest a very late date for the most recent common ancestor of all five phylogeographical groups.
Acknowledgements We thank  F. Renoud  and  D. Robineau  ( Muséum national d'Histoire naturelle, Paris ),  B. Fernholm  and  O. Grönwall , ( Swedish Museum of Natural History, Stockholm ), and  C. Smeenk  ( Natural History Museum, Leiden ) for access to sample material. We also thank  J. Burger  ( Johannes Gutenburg University, Mainz ) for helpful comments and discussion. This project has received funding from the  European Union 's  Seventh Framework Programme  for research, technological development and demonstration under grant agreement no. FP7-PEOPLE-2011-IEF-298820.
For the ethanol-preserved tissues, the complete mitochondrial cytb gene was amplified as described in  [2]. In instances where amplification failed (8 samples), a shorter fragment was sequenced as described above. The complete cytb sequences of the three wild-caught M. indutus specimens presented positions with double peaks. PCR products were cloned and the sequences (9-14 per specimen) were compared to those of the museum samples. The new cytb sequences are deposited in the EMBL databank under accession numbers HG934890 to HG934979.
Phylogenetic Analysis A phylogenetic analysis was performed using the cytb sequences of the museum, wild-caught specimens as well as available sequences of M. minutoides; we also included sequences from several closely related species (M. musculoides, M. indutus, M. mattheyi and M. haussa; Table  S2 ), the complete data set comprise 139 sequences (File S1). The best-fit model of sequence evolution was selected based on the corrected AIC score implemented in jmodeltest v2. 1  [17]. The molecular phylogeny was reconstructed by Maximum Likelihood (ML) with RAxML v8. 0  [18]  and Bayesian analyses were performed using MrBayes v3. 2. 2  [19]  under the model selected by jmodeltest (GTR + I + G) and three partitions corresponding to each codon position. The robustness of the nodes was estimated by bootstrap (BP) (1000 pseudoreplicates, starting tree: best tree obtained from 200 randomized MP starting tree) with RAxML and Posterior Probability (PP) using MrBayesall parameters except topology are unlinked.
g. [26] ). Two other species of pygmy mice have been described in South Africa: M. orangiae and M. neavei  [7]. Mus orangiae is restricted to the Free State and Lesotho whereas M. neavei has a more extensive distribution from the north of South Africa to south Tanzania  [7]. No molecular data are available for these taxa (a recent study morphologically identified two specimens from uMkhuze, Kwazulu-Natal, South Africa as cf neavei but these were subsequently non-ambiguously attributed to M. minutoides following mitochondrial DNA sequencing  [3] ). Interestingly, M. neavei has, on occasion, been considered a close relative of M. sorella  [4]  for which there is only one sequence available in GenBank. As M. sorella is distantly related to M. minutoides  [8] , our specific primers might not have been able to amplify cytb of this species and consequently we cannot rule out the presence of M.
Statistical analysis The reproductive status of the female worms was analyzed using one qualitative and three quantitative criteria. Qualitative aspects. Following the definition proposed by Kla ¨ger and colleagues  [23] , we considered as ''productive'' any live female (neither dead, nor calcified) presenting with an active embryogram, i. e. all worms whose embryogram contained any viable (not degenerating) embryonic stages (morulae and/or coiled mf and/or stretched mf). Thus, worms containing only degenerating embryos were not taken into account in the analyses of productive worms. The proportions of ''productive'' worms were first compared between the sites using simple chi-squared tests. Then, to account for the hierarchical structure of the data (several worms from a same nodule, several nodules from a same individual) the comparisons were performed using a multilevel (random-effects) logistic regression (see Text S2 in supporting information for explicit specification).
Significance of the random effects was tested using log-likelihood tests. Quantitative aspects. The reproductive status was also analyzed according to three quantitative indicators: the mean number of all embryos (morulae, coiled and stretched mf, either viable or degenerating) per worm, the mean number of viable mf per worm and the mean number of degenerating mf per worm. The distributions of these quantitative indicators among female worms were compared between the sites using the Kolmogorov-Smirnov test. Random-effect Poisson regression models were used to compare the quantitative criteria between the two groups while accounting for the data structure. Adjustment factors. It has been shown that O. volvulus skin microfilarial density, assessed after IVM treatment, is associated with host factors, such as age and the number of palpable nodules  [24].
Acknowledgments We are grateful to the populations from the  Mbam and Nkam  valleys for having kindly agreed to participate to this study. We are also grateful to all those who helped in the execution of this study, and particularly  Mr. U. Olanguena , former Minister of Public Health,  Professor L. Kaptue, Dr. P. Ongolo Zogo , as well as  Drs J. C. Akono Emane ,  R. Atangana ,  X. Crespin ,  S. Efonle ,  Fifen Alassan ,  X. Garde ,  M. Ntep  and  Mrs/Messrs. I. Behalal ,  C. Evini ,  F. Mafo ,  S. Kemleu ,  L. Fossouo ,  L. -P. Nguetsop ,  S. Nnouk ,  G. Kweban ,  I. Jato  and  J. P. Agbor.
05), expressed as increase (I) or decrease (D) when at least one of the mean values was not significantly different from the others, and intermediate (inter. ) when at least one value was different. In the first column, m/z values in green correspond to m/z peaks observed only in IC analysis (a total of 135m/z). Rows with color in IC, SD and ID columns correspond to specific peaks for the sample preparation. A list of endogenous biomolecules identified by top-down MS is shown in Supplementary Table  2. This table shows the raw file name associated to the NCBInr accession number, the gene name, the protein description, the location, the characterized post-translational modifications, the number of b ions, y ions and total ions, the delta mass (Da and ppm) between the ProSight theoretical mass M and the mass M observed by nano-ESI-FTMS, the mass M (Da) observed by nano-ESI-FTMS, the ProSight theoretical mass M (Da), the ProSight PDE score, the E-value identification probability, the p score, the precursor m/z and mass type with corresponding charge state (z), the fragmentation mode, the database used for identification, the signal/noise, the ProSight search type mode, the precursor mass type, the precursor mass tolerance (Da or ppm), the fragment mass type, the fragment mass tolerance (ppm), the delta M mode and disulfide activation/deactivation, the minimum of matching fragment between observed and theoretical fragmentation mass spectra, the include modified forms activation/ deactivation, the sequence, the calculated theoretical average and monoisotopic mass [M þH] þ (Da) and the mass [MþH] þ (Da) observed previously by MALDI-MS.
MALDI-TOF profiling of whole sperm cells and sub-cellular fractions All samples were analyzed by a MALDI-TOF mass spectrometer (Waters Corporation, Micromass Ltd. , Manchester, UK) operating in positive linear mode as previously described  [1]. Spectral profiles were collected in the 2000-20,000m/z mass range. Data processing was performed using MassLynx ™ 4. 0 software. To increase mass accuracy, internal calibration was performed. Thus, the major unknown 6797m/z constant was used as "lock mass" for all spectra. Intact spermatozoa and corresponding detergent-soluble and -insoluble extracts obtained from 4 epididymal regions (E2, E4, E6 and E9) of both epididymes from 4 animals were analyzed by MALDI-TOF MS, with 8 replicates for each of the three sample preparations. A total of 768 spectra were generated in this study.
The spectra were analyzed by Progenesis MALDI ™ 1. 2 software (NonLinear Dynamics) as previously described  [1]. After alignment of all spectra, a total of 253m/z peaks were detected (Supplementary Table  1 ). A total of 135m/z molecular species were characterized by Intact Cell MALDI-MS and 118m/z were newly observed by MALDI MS from SD and ID fractions.
Top-down mass spectrometry Identification of peptidoforms and proteoforms (endogenous species) was achieved by acquiring nano-ESI tandem high resolution mass spectrometry (MS and MS/MS). Molecular species were previously extracted with 1% and 5% formic acid from IC (region 2 or 9) or from ID fraction (region 9), desalted and concentrated using ZipTip C4 (Millipore Corporation, Billerica, MA). Eluted peptides and proteins were directly analyzed using a LTQ Orbitrap Velos mass spectrometer (Thermo Fisher Scientific, Germany) operating in positive mode, as previously described  (1). Data were acquired using Xcalibur software v2. 1 (Thermo Fisher Scientific, San Jose, CA). All analyses were performed manually using a high-high strategy, meaning that a MS spectrum in the 400-2000m/z mass range was followed by a MS/MS spectrum obtained by High energy Collisional Dissociation (HCD).
Thus 412m/z corresponding to 217 non-redundant molecular ions were selected to induce HCD fragmentation. Identification and structural characterization were performed using ProSight PC software 2. 0 (Thermo Scientific, San Jose). Raw data files were processed by THRASH (signal/noise: 2-3), and data were compared to a simple annotated "Sus scrofa" house database generated from NCBInr using Proteome Discoverer (Thermo Fisher Scientific). Automated searches were performed using the "Absolute Mass and Biomarker" search options. The mass tolerances were set at 5 ppm for the monoisotopic precursors, 5 Da for the average precursor and 15 ppm for fragment ions mass tolerance. Disulfide modifications and N-terminal post-translation modifications (acetylation and initial methionine cleavage) were activated.