dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. We thank  Dr. Pentau Liu  for kindly providing PiggyBack plasmids and  Dr. Domingos Henrique  for fruitful discussions regarding the involvement of Polycomb activity in Nanog function. This work was supported by recurrent funding from the  Institut Pasteur , the  CNRS, and Revive  ( Investissement d'Avenir ;  ANR-10-LABX-73 ). P. N. acknowledges financial support from the  Fondation Schlumberger  ( FRM FSER 2017 ), the  Fondation ARC pour la recherche sur le cancer  ( ARC NAVARRO CA 14/12/2016 ), the  Agence Nationale de la Recherche  ( ANR 16 CE12 0004 01 MITMAT ), and the  Ligue contre le Cancer  ( LNCC EL2018  NAVARRO). V. H is supported by the  Fondation ARC and Revive. N. O. is supported by  Revive.
V 3 Fig. 3. 33 Fig. 3. Nanog rewires the pluripotency network. (A) Representative enrichment profiles (reads per million) of the indicated TFs (ChIP-seq) and chromatin accessibility (ATAC-seq) across an 8kb-long genomic region (mm9: chr10:33,952,000-33,960,000). In black, average signal from 6 publicly available Nanog datasets. In blue and red, average signal of 44iN cells cultured in the presence or absence of Dox, respectively. The peak highlighted by a green box shows reduced signal in the absence of Dox; note an upstream peak displays increased Sox2 binding. (B) Heat map of average enrichment levels in each indicated condition (+/-Dox) across 0. 5kb centred on the Nanog peak summit at all Nanog sites (27,782), ranked from high to low Nanog. The regions were split in two groups depending on the presence (co-binding) or absence (Solos) of other TFs.
This study investigates the means, variability, and trends in integrated water vapour (IWV) from two modern reanalyses (ERA-Interim and MERRA-2) from 1980 to 2016 and ground-based GPS data from 1995 to 2010. It is found that the mean distributions and inter-annual variability in IWV in the reanalyses and GPS are consistent, even in regions of strong gradients. ERA-Interim is shown to exhibit a slight moist bias in the extra-tropics and a slight dry bias in the tropics (both on the order of 0. 5 to 1 kg m -2 ) compared to GPS. ERA-Interim is also generally drier than MERRA-2 over the ocean and within the tropics. Differences in variability and trends are pointed out at a few GPS sites. These differences can be due to representativeness errors (for sites located in coastal regions and regions of complex topography), gaps and inhomogeneities in the GPS series (due to equipment changes), or potential inhomogeneities in the reanalyses (due to changes in the observing system).
In addition, the short residence time of water vapour in the atmosphere makes its study in terms of variability and trends rather challenging. Sherwood et al. (2010)  compared the long-term IWV trends reported in several studies using different datasets. Although there appears to be a global positive trend in the overall IWV data, which is consistent with a global warming trend, it is difficult to compare results from different studies, as they refer to different data sources, time periods, sites, and spatial coverage. Trenberth et al. (2005)  Published by Copernicus Publications on behalf of the European Geosciences Union. found major problems in the means, variability, and trends from 1988 to 2001 for the National Centers for Environmental Prediction (NCEP) reanalyses 1 and 2, and for the 40-year European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA-40) over the oceans.
The reanalyses showed reasonable results over land, where they are constrained by radiosonde observations. Only the reprocessed IWV data from the special sensor microwave imager (SSM/I) appeared to be realistic in terms of means, variability, and trends over the oceans. Their work points to two important issues. First, the reanalyses generally lack assimilation of water vapour information and suffer from model biases and, in the case of ERA40, problems in bias corrections with new satellites (namely after major volcano eruptions). Second, they highlight the need for the reprocessing of data and point to the shortcomings in reanalyses due to the changing observing system. The bias correction of new satellite radiances in the ECMWF reanalysis system has recently been improved using a variational bias-correction scheme, including the detection of instrument calibration errors and longterm drifts, as well as volcano eruptions  (Dee and Uppala, 2009).
Reduction of model biases and enhanced assimilation capabilities of satellite data (e. g. rain-affected radiances) have generally improved the water cycle in modern reanalyses. Reanalysis data generally agree well at representing the short-term variability (e. g. El Niño-Southern Oscillation, ENSO) but their ability for detecting climate trends is still debated  (Dessler and Davis, 2010; Thorne and Vose, 2010; Trenberth et al. , 2011; Robertson et al. , 2014; Schröder et al. , 2016). Chen and Liu (2016)  compared the global variability and trend in water vapour in ERA-Interim and NCEP with GPS, radiosonde, and microwave satellite data, and found that ERA-Interim had better accuracy than NCEP. In this study we will focus on analysing the mean distributions, inter-annual variability, and decadal trends from two recent reanalyses, ECMWF reanalysis ERA-Interim  (Dee et al.
, 2011) , referred to as ERAI, and the Second Modern-Era Retrospective analysis for Research and Applications, MERRA-2  (Gelaro et al. , 2017). The IWV contents from the two reanalyses are intercompared and compared to a global, homogeneously reprocessed Global Positioning System (GPS) dataset over ocean and land. The ground-based GPS observations are independent from the reanalyses as they are not assimilated and thus constitute a valuable validation dataset  (Bock et al. , 2007; Mears et al. , 2015). A critical assessment of the homogeneity of GPS dataset itself was made during this study as previous work detected small offsets associated to GPS equipment changes  (Vey et al. , 2009; Ning et al. , 2016). Furthermore, to add new insights in both the evaluation of ERA-Interim and MERRA-2 reanalyses and in the understanding of IWV trends and variability, we separate the analysis into seasons and consider trends and inter-annual variability of seasons.
This analysis by seasons is rarely provided in other studies, although it helps to better identify regions with higher uncertainty and to understand the physical processes involved in different seasons (e. g. the dynamical component which transports moisture strongly differs between winter and summer). Trenberth et al. (2011)  separated January and July in their analysis of the representation of water and energy budget in ERA-Interim and MERRA and showed the importance of studying the seasons separately. Compared to this study, we added the analysis of the GPS dataset and use MERRA-2, which benefits from several updates compared to MERRA. This paper is organized as follows. Section 2 details the datasets and methods. Section 3 reports on the means and variability found in the GPS and reanalysis data for the 1995-2010 period.
g. in Antarctica). The data set covers the period from January 1995 to December 2010 for 456 stations. Among these, 120 stations have time series with only small gaps over the 15-year period. However, the geographical distribution is quite unequal between hemispheres and even within a given hemisphere, with a cluster of 20 stations in the western USA with inter-station distance smaller than 0. 75 •. In order to avoid over-representation of this region, 16 out of these 20 stations have been discarded (the selection retained those with the longer time series). The final GPS IWV dataset used in this study is thus limited to the selected 104 stations. The basic GPS tropospheric observables are the zenith tropospheric delay (ZTD) estimates, which are available at a 5 min sampling in the IGS repro1 dataset.
5 ), ERA-Interim trends are positive while MERRA-2 trends are negative over the period from 1995 to 2010. Trenberth et al. (2005)  also reported strong moistening and warming trends over the region for the 1988-2003 period using SSM/I IWV data and National Oceanic and Atmospheric Administration (NOAA) sea surface temperature (SST) data. Wang et al. (2016)  confirmed the moistening but found a near-zeroslightly negative temperature trend for the 1995-2011 period (they used more recent releases of the SSM/I and NOAA data). Schröder et al. (2016)  also found moistening trends in all their datasets over the 1988-2008 period, including SSM/I data and the MERRA reanalysis. Although not all studies concern the same period, they confirm the ERA-Interim results. This conclusion points to an inconsistent drying trend in MERRA-2 over the Maritime Continent.
Previous studies have concluded that during recent decades IWV has increased with time over both land and ocean regardless of the time period and dataset analysed, except for some of the older reanalyses and/or some inhomoge- neous observational datasets  (Trenberth et al. , 2005; Dessler and Davis, 2010; Bock et al. , 2014; Schröder et al. , 2016; Wang et al. , 2016). Nevertheless, most global atmospheric reanalyses still have substantial limitations in representing decadal variability and trends in the water cycle components because of assimilation increments and observing system changes  (Trenberth et al. , 2011). In this study we found that trends in IWV and surface temperature in ERA-Interim and MERRA-2 are fairly consistent, with positive IWV trends generally correlated with surface warming over most of the tropical oceans, as well as the Arctic, part of North America, Europe, and the Amazon.
Homogenization of the GPS dataset is currently being undertaken using different processing and modelling strategies as well as statistical homogenization techniques that should help detection and correction of the biases and offsets. An extension of the dataset is also planned as a few more years of observations are now available for reprocessing. The GPS and reanalyses do not agree at certain GPS sites. In this appendix we discuss in more detail the various causes for this, and especially those that originate from problems in the GPS data. In order to minimize the representativeness differences between the gridded reanalysis fields and the GPS point observations, a more elaborate intercomparison methodology is required. We used the ERA-Interim 6hourly pressure level data (37 levels between 1000 and 1 hPa, among which 27 levels lie between 1000 and 100 hPa) from the four grid points surrounding each GPS station.
98 kg m -2 decade -1 ), consistent with the large-scale trend in the reanalysis seen in Fig. 4. At WUHN (Fig. B2f ), the GPS trend estimate is positive (0. 34 kg m -2 decade -1 ) while the ERA-Interim estimate is negative (-1. 45 kg m -2 decade -1 ). The IWV difference time series shows several breaks  (in 1999, 2005, and   GPS equipment changes. In fact, the break at the end of 2006 is associated with the change in radiosonde from the Shang-M to Shang-E, which is assimilated by ERA-Interim  (Wang and Zhang, 2008). Zhao et al. (2012)  found that prior to this change there was a 2 kg m -2 wet bias in the radiosonde data at the Wuhan station, in comparison with GPS. This moist bias is also observed in ERA-Interim prior to the end of 2006. At SHAO and CRO1 and a few other sites (e. g. SYOG, DARW, ANKR), inhomogeneity in the IWV difference series coincides with documented GPS equipment changes (not shown).
Representativeness differences are also suspected at some mountainous and coastal sites (e. g. AREQ, CFAG, KIT3, MAW1, SANT, SYOG, and the other sites discussed in the previous section), while some sites also show more gradual drifts in the times series which do not seem connected with known GPS equipment changes (e. g. MAW1, Antarctica). At such sites, drifts in the reanalysis are plausible. Overall, the seasonal trends estimated from the GPS data confirm the trends found in ERA-Interim. The sites with largest differences in the seasonal estimates are also listed in Table  B1. In addition to the sites where issues were noticed in the monthly trends, the list includes a few more sites which are also visible in Fig. 5a  and b (most notably KIRU in Sweden, COCO in the Indian Ocean, IRKT in Russia, and ANKR in Turkey).
Acknowledgements. This work was developed in the framework of the  VEGA  project and supported by the  CNRS program LEFE/INSU  and  ANR   REMEMBER  project (grant  ANR-12-SENV-001 ). This work is a contribution to the  European COST Action   ES1206 GNSS4SWEC  (GNSS for Severe Weather and Climate monitoring; http://www. cost. eu/COST_Actions/essem/ES1206,last access: October 2018) aiming for the development of the global GPS network for atmospheric research and climate change monitoring. Edited by:  Roeland Van Malderen Reviewed  by: three anonymous referees.
Data availability. GPS IWV data have the following DOI: global GPS IWV data at 120 stations of IGS permanent network, https://doi. org/10. 14768/06337394-73a9-407c-9997-0e380dac5591 (Bock, 2016). ERA-Interim data can be downloaded at https://www. ecmwf. int/en/forecasts/datasets/archive-datasets/ reanalysis-datasets/era-interim (last access: October 2018;  Dee et al. , 2011). MERRA-2 data can be downloaded at https://gmao. gsfc. nasa. gov/reanalysis/MERRA-2/ (last access: October 2018;  Gelaro et al. , 2017). The results presented in the paper are also provided in the Supplement.
Acknowledgements The author thanks the  Fundação para a Ciência e Tecnologia (FCT), Portugal , for the financial support to the  Center for Astrophysics and Gravitation (CENTRA) ,  Instituto Superior Técnico ,  Universidade de Lisboa , through the Grant No. UID/FIS/ 00099/201. Moreover, the author also acknowledges the support given by the  Institut d'Astrophysique de Paris  during his sabbatical visit.
A new methodology using a combination of debiased non-Bayesian retrieval, DINEOF (Data Interpolating Empirical Orthogonal Functions) and multifractal fusion has been used to obtain Soil Moisture and Ocean Salinity (SMOS) Sea Surface Salinity (SSS) fields over the North Atlantic Ocean and the Mediterranean Sea. The debiased non-Bayesian retrieval mitigates the systematic errors produced by the contamination of the land over the sea. In addition, this retrieval improves the coverage by means of multiyear statistical filtering criteria. This methodology allows obtaining SMOS SSS fields in the Mediterranean Sea. However, the resulting SSS suffers from a seasonal (and other time-dependent) bias. This time-dependent bias has been characterized by means of specific Empirical Orthogonal Functions (EOFs).
Mitigation of Time-Dependent Biases: Removal of EOFs from the DINEOF Basis DINEOF (  [36, 37] ) has been used to analyze the L3 SSS binned fields obtained using the debiased non-Bayesian approach detailed in Section 3. 1. DINEOF is a technique to reconstruct missing data and reduce noise in geophysical data sets using an EOF basis. The most dominant EOFs, calculated iteratively to overcome the presence of missing data, are used to infer the missing information. As the amount of missing data in the 9-day L3 binned SSS data set used in this work is very low (5. 3%), the main aim of the use of DINEOF with these data is to reduce noise and remove non-physical patterns. A filter has been applied to the covariance matrix, following  [38]. This filter allows for ensuring a smooth transition between subsequent images, by providing information on the time step between them, which is otherwise not considered in the EOF decomposition.
Spatial Structures: Qualitative Comparison with AVHRR Images In the previous sections (Sections 4. 1-4. 3), we have shown that the corrected SMOS products have lower error than the non-corrected SMOS products (lower bias and std with respect to in situ measurements). However, the performances provided by the corrected L3 and L4 products in the comparison with in situ data are very similar. The main conclusions are that L3 typically provides a slightly lower bias, but L4 provides a slightly lower standard deviation. Therefore, the quality of both products as described by these metrics is very similar. In this section, we want to compare both SMOS SSS corrected products with high resolution AVHRR images in order to assess the kind of spatial structures that each one describes (i. e. , which is the added value of the L4 SMOS corrected product with respect to the L3 SMOS corrected one).
Thus, lower salinity values would have been expected in the Southern part of the SMOS SSS images, which is not the case. Until further investigations are carried out, it must be noted that gyres in the Alboran Sea are extremely variable in both space and time, and that comparison of SMOS data with instantaneous images has its limits. In Figure  13 , the Eastern gyre collapses. In this case, SMOS presents coherent values with what the AVHRR image is showing. In Figure  14 , the main feature of both the SMOS SSS L3 and L4 corrected maps is the frontal zone between the Atlantic and the Mediterranean waters north of the Baleares. Such a zonal gradient does not appear in the AVHRR image. The area of the Rhone river influence (A) is well marked on the three products, with colder SST and lower SSS west of ∼ 5 • E in the gulf, and a tongue extending southward (the latter hardly visible though on the SMOS SSS L3 corrected map).
Conclusions A new methodology using a combination of the new retrieval debiased non-Bayesian algorithm, DINEOF and multifractal fusion has been used to obtain Sea Surface Salinity (SSS) fields over the North Atlantic Ocean and the Mediterranean Sea. The data used for the analysis is the 9-day 0. 25 SMOS SSS L3 binned maps produced by the Barcelona Expert Centre (BEC), and it has been shown that the proposed methodology improves the accuracy with respect to the Objectively Analyzed maps and the L4 products that are currently being produced at the BEC. In particular, a reduction of the seasonal bias has been observed in the final data set as well as a reduction in the standard deviation of the error. The overall comparison of the new products with Argo floats provides an RMSE of 0. 26 PSU in the full domain of study (North Atlantic and Mediterranean Sea) in comparison with an RSME of 0.
41 PSU corresponding to the initial SMOS products. In the Mediterranean basin, the methodology also allows for reducing the error (0. 39 PSU against 0. 70 PSU in the case of the L4 products). The comparison with the SSS TRANSMED in situ data shows that, in spite of the limitation of the differences in the spatial and temporal resolutions, the corrected SMOS SSS products not only provide a coherent description of the dynamics in the Algerian basin (as pointed out in  [19] ), but also a coherent description of the Balearic frontal zone. The short time and space scales of the freshwater discharges impair the detection of the latter by the SMOS SSS products, as seen in the gulf of Lion. An analysis at the seasonal scale might improve the comparison in this region. On the other hand, the L4 corrected products provide a description of the mesoscale salinity in regions so challenging as the Alboran Sea, where the water is surrounded by land a few kilometers away, and, therefore, the land sea contamination is especially strong.
Mediterranean Sea"  ( [50]), funded by the  European Space Agency "Support to Science (STSE)  PATHFINDERS" call. Part of this work was also supported by the  Ministry of Economy and Competitiveness, Spain , through the  National R+D Plan  under L- Band  Project  ESP2017-89463-C3-1-R ,  PROMISES  Project  ESP2015-67549-C3  and previous grants. Argo data were collected and made freely available by the international  Argo  project and the national programs that contribute to it [51]. SSS time series from the moorings located in the Western Mediterranean were collected and made freely available by the  Copernicus  project and the programs that contribute to it (available online: [25] (accessed on 19 March 2018) and available online: [26] (by the  Commission Internationale pour l'Exploration de  This work is a contribution to the  ANR ASICS- MED  project (grant  ANR-12-BS06-0003 ).
The  MIO laboratory  acknowledges the support received from the  European FEDER Fund  under project  1166-39417. OSTIA SST data were provided by GHRSST, UKMO and CMEMS. ,  Aida Alvera-Azcárate  and  Antonio Turiel  conceived and designed the algorithm for the SMOS SSS products generation. Estrella Olmedo  and  Aida Alvera-Azcárate  implemented the new algorithm. Estrella Olmedo  and  Isabelle Taupier-Letage  performed the validation of the new SMOS SSS product. Isabelle Taupier-Letage  conducted the oceanographic interpretation of the results. The manuscript was prepared by  Estrella Olmedo. All authors revised and approved the final manuscript.
Population data First, we used phantom data that we acquired at our institutional hospital; then we used testretest images from the RIDER public dataset  [7] ; finally, we evaluated whole body CT scan datasets available from our hospital. Our whole body CT scan (WBCT) dataset consisted of imaging examinations of cancer patients acquired from a Positron Emission Tomography (PET)-CT. By Whole Body PET/CT we mean acquisition from top of the skull to bottom of the feet. In our institution, theses examinations are not usually performed, except for suspicion of lower limb metastasis or melanoma. Over a one year period, from December 2014 to December 2015, we were able to retrospectively collect 35 adults WBCT dataset from the picture archiving system. After a quality control review of the images, 26 WBCT patients were included in the study as 8 out of 35 patient's images had truncated field of view (arms and legs) and one patient's image featured motion blur.
This study was approved by the monthly Review Board of the institute. External ethic committee's approval was not asked considering all the aspects of the study. Indeed, patient management information was not part of the study nor modified. Moreover, medical records and imaging data were transmitted and studied retrospectively in a fully anonymized manner. Acquisitions were performed using a PET-CT scanner (Siemens, BioGraph 40™) using 50% reconstruction overlap and a single slice thickness of 3. 0mm with a B30f reconstruction kernel filter. As required for assessing the 18-FDG injection dose, weight and height at the time of the exam were recorded using a scale and a wall mounted stature meter during pre-examination consultation with the physician.
Then segmentation masks were processed with mathematical morphology and mesh  [11]  before summing surface of vertexes. Software outputs were iBSA value in cm 2 and body masks. To ensure the integrity of segmentations, we visually compared them to the original registered CT. The quantitative measurement of an imaging biomarker is expected to be accurate and reproducible  [8]. We validated the software in three different ways; each of these validations relied on a dedicated dataset: Accuracy. We measured the accuracy of surface assessments obtained on CT phantom data of known geometry corresponding to a surface of 0. 23m 2. For this metrological evaluation, we scanned 14 times the same synthetic object acquired at different random positions and locations on the table. Phantom images were processed and the distribution of the percent relative error of the surface area was obtained according to Eq 1.