dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGEMENTS This work has benefited from the facilities and expertise of the high throughput sequencing core facility of  I2BC (Centre de Recherche de Gif  -http://www. i2bc-saclay. fr/). We also thank  Noémie Ménétrot  and  Benoit Jego  for technical assistance during the doped SELEX, as well as  Larry Gold  for kind suggestions to improve the manuscript. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. FUNDING ' Agence Nationale pour la Recherche ' under the frame of  EuroNanoMed  (project  META );  Investissement d'Avenir  [ ANR-11-INBS-0011 ]--NeurATRIS: A Translational Research Infrastructure for Biotherapies in Neurosciences. N. N. Q. was supported by a Grant from LFB biotechnologies. Funding for open access charge: Laboratory budget.
(Myhre et al. , 2013b). As all these forcings are fairly small, simulations to isolate their impacts would be extremely computationally expensive and hence were not performed but to first order we expect their exclusion is unlikely to greatly affect our results. Characterization of the influence of these other drivers merits future study, particularly as some operate via different physical processes (e. g. tropospheric 10 ozone is both a greenhouse gas and an absorber of incoming solar radiation). Similar analyses were also performed to obtain scaled SLP change (∆SLPscaled), zonal wind change and energy budget change in the atmospheric column. Several observational and reanalysis datasets were also employed in this part of our study. For precipitation, Global Precipitation Climatology Center (GPCC) monthly precipitation data  (Schneider et al.
, 2011) , provided by 15 NOAA/OAR/ESRL from their website (https://www. esrl. noaa. gov/psd/data/gridded/data. gpcc. html#detail), is employed. It is a high quality gridded dataset that is mainly terrestrial station-derived. For SLP, we use HadSLP2 data  (Allan and Ansell, 2006) , which is created by combining marine observations from ICOADS data  (Worley et al. , 2005)  and land (terrestrial and island) observations (available at https://www. esrl. noaa. gov/psd/data/gridded/data. hadslp2. html). We also use NCEP/NCAR reanalysis data  (Kalnay et 20 al. , 1996) , downloaded from https://www. esrl. noaa. gov/psd/data/gridded/data. ncep. reanalysis. derived. surface. html, for the comparisons of zonal wind. All these datasets have undergone rigorous quality control and have been widely used in the climate community, including the IPCC 2013 assessment report  (Hartmann et al.
5 Fig. 7a  shows the overall response of SLP to these forcings, with strong SLP increases at mid-latitudes and strong decreases at higher latitudes. Such patterns of SLP changes are also found in the observed datasets (Fig. 7b ), albeit with a larger magnitude. The combined pattern of zonal wind responses shows winds intensified at the northern edge of the jet stream and weakened at the southern edge (Fig. 7c ). The NCEP dataset depicts a similar pattern of changes, 10 with winds intensifying at 60°N and weakening at 30°N, but as with SLP, with a stronger magnitude (Fig. 7d ). Some previous studies have pointed out that current GCMs significantly underestimate the tropical expansion and jet stream shift, which could be related to the short observational record, large internal variability or model deficiencies  (Johanson and Fu, 2009; Allen et al.
In addition to the wet season, precipitation during the dry season (Apr-Sep) for the PDRMIP model was also analyzed. 15 The modelled dry season precipitation trends, however, do not match the observations well (not shown). The modelled results also show a statistically strong drying trend while the observations do not show significant changes. Two possible reasons may be responsible for the apparent discrepancies. One is that only 30% of the total precipitation occurs during the dry season (boreal summer months) and it is difficult to simulate the uneven distribution of infrequent rainfall events. The other is that there are large uncertainties in the observational data itself. Unlike the wet 20 season, in which nearly half of the grid boxes show statistically significant trends (Fig. 6b ), almost none of the grid boxes show statistically significant trends in the dry season, undermining the robustness of the observational results.
Program through the NASA Center for Climate Simulation at Goddard Space Flight Center for computational resources to run the GISS-E2R model and support from NASA GISS. PDRMIP is partly funded through the Norwegian Research Council project NAPEX (project number 229778). O. Boucher acknowledges HPC resources from CCRT under the gencmip6 allocation provided by GENCI (Grand Equipement National de Calcul Intensif). P. 35 Forster and T. Richardson were supported by NERC grants NE/K007483/1 and NE/N006038/1. Ø. Hodnebrog was Atmos. Chem. Phys. Discuss. , https://doi. org/10. 5194/acp-2018-56 Manuscript under review for journal Atmos. Chem. Phys. Discussion started: 30 January 2018 c Author(s) 2018. CC BY 4. 0 License.
Computing Program through the NASA Center for Climate Simulation at Goddard Space Flight Center for computational resources to run the GISS-E2R model and support from NASA GISS. PDRMIP is partly funded through the Norwegian Research Council project NAPEX (project number 229778). O. Boucher acknowledges HPC resources from CCRT under the gencmip6 allocation provided by GENCI (Grand Equipement National de Calcul Intensif). P. 35 Forster and T. Richardson were supported by NERC grants NE/K007483/1 and NE/N006038/1. Ø. Hodnebrog was Atmos. Chem. Phys. Discuss. , https://doi. org/10. 5194/acp-2018-56 Manuscript under review for journal Atmos. Chem. Phys. Discussion started: 30 January 2018 c Author(s) 2018. CC BY 4. 0 License.
ACKNOWLEDGMENT We thank  L. Biadala  and  C. Delerue  for fruitful discussion. Z. H. acknowledges support by the  European Commission  via the  Marie-Sklodowska Curie action Phonsi  ( H2020-MSCA-ITN-642656 ), by the  Research Foundation Flanders  (project  17006602 ) and by  Ghent University  (GOA no. 01G01513 ). Z. H. , M. T. and D. D. acknowledge the  Strategisch Initiatief Materialen in Vlaanderen of Agentschap Innoveren en Ondernemen (SIM VLAIO ). "vzw (SBO-QDOCCO, ICON-QUALIDI). This work was supported by the  Université Paul Sabatier , the  Région Midi-Pyrénées , the  CNRS , the  Institut National des Sciences Appliquées of Toulouse  and the  Agence Nationale pour la ACS Paragon Plus Environment Chemistry of Materials Recherche  (Project  ANR-13-IS10-0004-01 ). E. A. B. is grateful to  Marie Curie Actions and Campus France  for a  PRESTIGE postdoc fellowship  ( FP7/2007-2013)  under  REA  grant agreement no.
Non-parametric reconstruction or marginalization over the history of reionization using cosmic microwave background data necessarily assumes a prior over possible histories. We show that different but reasonable choices of priors can shift current and future constraints on the reionization optical depth, τ, or correlated parameters such as the neutrino mass sum, Σm ν , at the level of 0. 3-0. 4 σ, i. e. , that this analysis is somewhat prior dependent. We point out some prior-related problems with the commonly used principal component reconstruction, concluding that the significance of some recent hints of early reionization in Planck 2015 data has been overestimated. We also present the first non-parametric reconstruction applied to newer Planck intermediate (  2016 ) data and find that the hints of early reionization disappear entirely in this more precise dataset.
Introduction The exact details of the "epoch of reionization", during which the universe transitioned from a mostly neutral hydrogen gas into the highly ionized state we see today, are still of considerable uncertainty. This transition left several imprints on the Cosmic Microwave Background (CMB) which can be used to constrain the physics of reionization, but also serve as a "nuisance" uncertainty which must be marginalized over when considering constraints on other parameters. One of the most widely used methods for reconstructing or marginalizing over the reionization history from CMB data has been based on decomposing the history into eigenmodes  (Hu & Holder 2003, hereafter HH03). This has been applied to a number of datasets, for example the WMAP data  (Mortonson & Hu 2008b, hereafter MH08) , and most recently the Planck 2015 data  (Heinrich et al.
2017; Heinrich & Hu 2018; Obied et al. 2018). The latter works argue that the generic reconstruction reveals a > 2 σ preference for a high-redshift (z 15) contribution to the optical depth, τ. Conversely, other works have used alternate methods and not found such evidence in this same dataset  (Hazra & Smoot 2017; Villanueva-Domingo et al. 2018). In this work, we will point out two problems with PCA analyses that have to-date been largely or completely overlooked. Correcting these problems will lead to finding reduced evidence of early reionization in the Planck 2015 data, more consistent with the latter results. The first problem has been mentioned in  Mortonson & Hu (2008b) ,  Heinrich & Hu (2018) , and described in some detail in  Lewis et al. (2006) , although not explicitly in relation to the PCA model.
e. , remains positive but less that the maximum corresponding to a fully ionized universe). Due to the nature of the PCA decomposition, the physicality priors necessarily allow some unphysical models (this may seem quite counter-intuitive, but we will give a simple geometric picture of why this is indeed the case). However, existing analyses have used a sub-optimal set of physicality priors which allowed more unphysical models than necessary. We will derive the optimal set of priors, and show that switching to these leads to significant changes in constraints. Together with removing the impact of the informative τ prior, we find that evidence for early reionization in the Planck 2015 data is reduced from > 2 σ to 1. 4 σ. Since the PCA results depend significantly on the details of the physicality priors, which will always be necessarily imperfect, we conclude that the PCA procedure is poorly tailored to the problem of generic reionization history reconstruction.
Using these reionization models, we calculate new constraints on the history of reionization coming from Planck intermediate data, in particular using the simlow likelihood (Planck Collaboration Int. XLVI 2016). We will refer to the combination of Planck 2015 data and the simlow likelihood as the Planck 2016 data. This likelihood has already been used to explore various parametric reionization models (Planck Collaboration Int. XLVII 2016); however, a generic reconstruction fully capable of detecting an early component to reionization (should it be there) has not yet been performed. We report results from this particular dataset here. The Planck 2016 data represents a significant increase in constraining power on reionization as compared to the 2015 data, and we find that the remaining 1. 4 σ hints of an early reionization component are completely erased.
PCA, flat mode, optimal phys. PCA, flat τ(15, 30), optimal phys. Fig. 4. Constraints on the total optical depth (top panel), and on the optical depth between z = 15 and z = 30 (bottom panel), when using 2015 PlanckTT+lowTEB data. Various reionization models and priors are used (labeled in each plot), in addition to marginalization over other ΛCDM parameters. The top panel shows that the apparent preference for higher τ when using the PCA model as reported in  Heinrich et al. (2017)  was significantly impacted by the choice of prior. The bottom panel shows that evidence for early reionization was also partly impacted, although some preference remains. We consider the shaded green contour, which corresponds to a flat τ prior and optimal physicality region, as the most well-motivated constraint from this dataset.
In our discussion, we focus on the sum of neutrino masses, Σm ν , as it is the cosmological parameter expected to be most impacted by the details of reionization  (Allison et al. 2015). The first dataset we consider is a combination of CMB-S4 with existing Planck data (including the intermediate simlow likelihood). We perform a Fisher forecast using the standard procedure  (Dodelson 2003) , using a fiducial Planck 2015-like cosmology, with τ = 0. 055 and Σm ν at the minimum value of 60 meV. For CMB-S4, we assume a temperature noise level ∆ T = ∆ P / √ 2 = 1 µK-arcmin, a fraction of covered f sky = 0. 5, and a beam size of θ FWHM = 3 arcmin. For the noise levels of the reconstructed lensing deflection power spectrum, we use the noise power spectrum calculated by  Pan & Knox (2015)  7 based on an iterative quadratic EB estimator  (Okamoto & Hu 2003; Smith et al.
2012). We assume the largest scales measurable by CMB-S4 are min = 50. This is combined with the constraints on τ from simlow discussed in the previous section, which are treated as independent. In combining with the remaining Planck 2015 data, to take into account correlations between Planck and CMB-S4 due to sky coverage and multipole overlap, we compute a Planck-like Fisher forecast rather than use the real Planck constraints. The temperature and polarization noise levels assumed for Planck are those given by Planck Collaboration (2005), taking an optimal noise weighting of the 100, 143, and 217 GHz, channels, and a sky coverage of f sky = 0. 75. These reproduce the uncertainties on parameters from the real Planck data to within around 15%, which should be good enough for our purposes. For this first dataset, we forecast a 1 σ error bar on Σm ν of 59.
To the extent that both flat knot and flat τ priors are reasonable, this can be considered as an extra source of "model uncertainty" in the future CMB determination of neutrino mass. The magnitude of the effect is not disastrous, but not negligible either. We comment now on a few avenues to reduce its impact. The first comes from adding in other expected future constrains from measurements of the baryon acoustic oscillations (BAO) feature in the galaxy correlation function by DESI  (Levi et al. 2013). We use the Fisher matrix for DESI calculated by  Pan & Knox (2015) , based on the sensitivities presented in  Font-Ribera et al. (2014). The addition of this dataset brings us significantly closer to a guaranteed detection of the neutrino mass. Here we find a 1 σ error bar on Σm ν of 26. 3 meV or 28.
In this case, we find a 0. 35 σ shift in Σm ν depending on the τ prior used. While this is a larger relative shift, on an absolute scale it is about half the shift as compared to without DESI. To reduce ρ and thus the impact of reionization uncertainty, we need data which directly constrains τ and/or A s. This could be provided, for example, by more precise large-scale polarization data. There is room for some improvement as the Planck data is not yet at the cosmic variance limit. We thus consider the limiting case of a full-sky cosmic-variance limited EE measurement across multipoles 2 to 30 (which we will label cvlowEE). This should be the upper bound of what could be achieved with nextgeneration satellite missions, e. g. , LiteBIRD, PIXIE, COrE, or PICO  (Matsumura et al. 2016; Kogut et al.
MariusMillea and François Bouchet: Cosmic Microwave BackgroundConstraints in Light of Priors Over Reionization Histories Fisher forecasted 1 σ error bars on the sum of neutrino masses, Σm ν , assuming a fiducial value at the minimum allowed sum of 60 meV. Different datasets are labeled on the left, with P16 corresponding to Planck 2015 data combined with the intermediate simlow likelihood, S4 corresponding to CMB-S4, BAO to DESI-BAO, and cvlowEE to a cosmic-variance-limited full-sky EE measurement up to = 30. Orange error bars use the TANH reionization model, and blue errors bars are slightly degraded due to having marginalized over all possible reionization histories via the FlexKnot model. The black arrows show the expected shift when considering a flat knot prior vs. a flat τ prior.
Acknowledgements. We thank  Zhen Pan  for his forecasting code which we adapted to use in this work,  Torsten Ensslin ,  Douglas Scott , and  Martin White  for their helpful comments on the draft of this paper, and  Silvia Galli ,  William Handley , and  Wayne Hu  for useful discussions. MM was supported by the  Labex ILP  (reference  ANR-10-LABX-63 ). This work was aided by the use of several software packages, including CAMB (Lewis et al. 2000),  CosmoSlik  (Millea.
MariusMillea and François Bouchet: Cosmic Microwave BackgroundConstraints in Light of Priors Over Reionization Histories Fisher forecasted 1 σ error bars on the sum of neutrino masses, Σm ν , assuming a fiducial value at the minimum allowed sum of 60 meV. Different datasets are labeled on the left, with P16 corresponding to Planck 2015 data combined with the intermediate simlow likelihood, S4 corresponding to CMB-S4, BAO to DESI-BAO, and cvlowEE to a cosmic-variance-limited full-sky EE measurement up to = 30. Orange error bars use the TANH reionization model, and blue errors bars are slightly degraded due to having marginalized over all possible reionization histories via the FlexKnot model. The black arrows show the expected shift when considering a flat knot prior vs. a flat τ prior. P16+S4 66.
Its total area is about 86. 4 ha and the mean slope is 58%. Its bottom elevation is presented in the figure  1  The soil is mostly constituted of black marls and 68% of the surface is a bare soil. Thanks to data of the Draix-Bleone observatory  [10] , it is possible to evaluate the model by comparing the results to measured discharges and sediment fluxes from field campaigns on this catchment. At the outlet, the discharges are available for many rainfall events with a time step of 60 s. The rainfall intensity associated to the discharge is also measured every 60 s. Two different events are selected, one spring rain following three rainy days with a high soil moisture initial condition, and a summer storm with a dry initial soil state.
Watershed hydrology The hydrological model is confronted to field data by comparing the measured and simulated outlet discharges on two events. The events are selected because they are the most erosive of 2012, in terms of sediment volume exported at the outlet. The first event is recorded the 29th of May succeeding six rainy events from 21st to the 27th of May. The maximal value of the rain intensity is 84 mm/h and the peak discharge is 3 m 3 /s. Concerning the other event, it is a summer storm of the 25th August with an initial dry state because the last recorded event is the 25th July. The maximal intensity of the rain is 156 mm/h and the peak discharge is 6. 6 m 3 /s. To simulate these events, we set the properties of the soil constant, modifying only the initial soil moisture (θ i in the infiltration model).
HCV core sequences reported in the present study have been submitted to European Nucleotide Archive (ENA) and have been assigned accession numbers ERZ664228, ERZ655053, ERZ655054 and ERZ672786. reporter pTOP and pFOP plasmids  19  and pA-EUA2 + lacZ plasmid were kindly provided by C. Neuveut (Institut Pasteur, Paris, France) and A. L. Epstein (University Claude Bernard, Lyon, France), respectively. Nucleotide substitutions encoding amino acid mutations P64S (amino acid numbering according to position within core; C530T, nucleotide numbering according to genome-length cDNA position) or S71T (T551A) were introduced into pCI/core-1aH77 and pCI/core-4aR, respectively, using QuickChange Site-Directed Mutagenesis Kit (Agilent Technologies, Santa Clara, USA) and appropriately designed primers. Plasmid pJad  26  was derived from pJFH1 (genome-length HCV cDNA of subtype 2a)  23  and contains cDNA substitutions resulting in three amino acid changes that confer high-titer cell culture adaptation  15.
Sequential frame averaged scans were set up for each fluorophore to eliminate emission crosstalk. In infected cultures, only effectively infected cells were considered on the basis of HCV core immunolabeling. All Z-stack images were saved in a 16-bit TIFF format with a resolution of 512 × 512 pixels. 3D images were deconvolved with Huygens Essential software (Version X11, Scientific Volume Imaging BV, Hilversum, The Netherlands). Deconvolved images centered on single cells were subjected to object segmentation using Object Analyzer. Object analysis (core, β-catenin and DAPI channels taken individually) and colocalization analyses of intersecting volumes between two channels of interest (DAPI and β-catenin) were performed using Huygens Professional software (Version X11). For each channel, an individual threshold was selected and maintained for all processed samples.
Voxels with intensity above the established threshold were quantified and the number of intersecting voxels between channels of interest was calculated for each individual cell analyzed. Voxels may then be translated in volumes expressed in µm 3. Intersecting volumes of β-catenin and DAPI channels provide a quantification of nuclear β-catenin. Microsoft Excel and GraphPad Prism 8 softwares were used for data analysis, chart generation and statistical analyses.
Statistical analyses. Student's tests were used to compare the effects of various core sequences in core-expressing DNA transfected or HCV infected cells. The normal distribution of data was assessed in GraphPad Prism software. Differences were considered to be significant when P values were <0. 05. The Holm-Sidak method (GraphPad Prism) was used to evaluate the statistical significance of the intersecting volume analyses in immunofluorescence experiments.
Acknowledgements This work was supported in part by an  ACIP grant  (# A23/2013 ) from the  Institut Pasteur, Paris, France  and by the  Hellenic Pasteur Institute, Athens, Greece. SA, a PhD student of the  University of Patras, School of Health Sciences, Department of Pharmacy , Greece was the recipient of doctoral fellowships from the  Calmette and Yersin program  and the  Virology Department of the Institut Pasteur , and of a young researcher prize from the SCIENTIFIC RepoRts | (2018) 8: 11185 | DOI:10. 1038/s41598-018-29078-2 Fondation des Treilles*. The authors gratefully acknowledge:  Christine Neuveut  ( IP Paris ) for helpful discussions and for sharing pFOP and pTOP plasmids;  Alberto L. Epstein  ( Université Claude Bernard, Lyon ) for sharing pA-EUA2 + lacZ plasmid;  Amadou Sall  ( IP Cambodia ) for sharing HCV 1aC isolate;  Takaji Wakita  ( NIID, Tokyo, Japan ),  Ralf Bartenschlager  ( Univ.
After exclusion of common variants and application of technical and biological filters  (Coutton et al, 2018) , three genes were found to be homozygously mutated in at least two subjects. Only one gene carried a homozygous variant scored as "high" and was predicted to induce loss of function by the "Variant Effect Predictor" tool (Ensembl). Interestingly, the same variant, p. Arg160Ter, c. 478C>T in PATL2 transcript ENST00000434130, was detected in five different patients. Since the orthologue of PATL2 in Xenopus is described as an important factor in Xenopus oocyte maturation  (Nakamura et al, 2010) , it was possible that this variant could be the cause of these subjects' infertility. The variant identified is expected to lead to either the production of a truncated protein  (Fig 1A)  or a complete absence of expression due to possible nonsense-mediated mRNA decay.
The truncated protein would contain less than one-third of the complete amino acid sequence, in particular lacking the topoisomerase II-associated protein PAT1 domain. This domain, common to all Pat1 proteins, has been shown to be necessary for its paralogue, PATL1, to function through interaction with its partners  (Braun et al, 2010)   (Fig 1A). Because we did not have access to the relevant biological material (patients' ovaries), it was impossible to assess RNA decay in the presence of this mutation. The presence of the genetic variant was confirmed by Sanger sequencing for the five mutated patients  (Fig 1B). This variant was also identified in a heterozygous state in five out of 148,732 alleles (rs548527219) in the Genome Aggregation  Database (gnomAD). This rate corresponds to a very low frequency of 0.