dataesr/scientific-paragraphs-categorization huggingface dataset:
Swarm's data are freely accessible at ftp:// swarm -diss. eo. esa. int. Detailed information on Swarm's Langmuir Probes data are provided in Knudsen et al. 40  and Lomidze et al. 41. Right panels of Fig. 6  show median Swarm B T e values binned as a function of Quasi-Dipole 42 magnetic latitude on y-axis (QD), and of Local Time, day of the year, and 81-days running mean of the solar index F10. 7  43  (F10. 7 81 ). Left panels of Fig. 6  show instead median values of the calculated topside effective scale height gradient ∂H Linear ∂z derived from COSMIC dataset, as described in the section "Deriving the effective vertical scale height from Radio Occultation data", and binned as Swarm data. The COSMIC dataset provides a good and quite uniform coverage of different diurnal and seasonal conditions for the QD latitude range ± 70°.
In terms of solar activity the dataset is instead slightly biased toward low solar activity values because most of RO profiles were recorded at the beginning of the mission, i. e. , for years of low solar activity (2006-2010). As demonstrated in the sections "Linking the theory to retrieved effective parameters" and "Deriving the effective vertical scale height from Radio Occultation data", the equalities ∂H Epstein ∂z≡ ∂H Linear ∂z ≡ ∂VSH ∂z can be assumed valid already after a few scale heights above the F2-layer peak. Then, for the sake of brevity, in the following we will refer to ∂H ∂z as the topside vertical scale height gradient. At the altitudes of Swarm B satellite (initial altitude of 520 km) we are usually in a region where a perfect match between effective and theoretical parameters has not yet been achieved (for example, look at lower panels of Figs.
Such differences might be attributed to the fact that Swarm B is a near-polar orbit quasi-solar synchronous satellite, which means that the sampling is not evenly distributed. The solar activity plots are the ones showing the worst similarity. This is partly due to the fact that COSMIC and Swarm B datasets cover different years; then, different solar activity levels are unevenly sampled. Both COSMIC and Swarm B datasets are biased toward low solar activity levels, but COSMIC dataset allows for a better coverage of medium and high solar activity levels than Swarm B. Anyway, similar features are shown also in this case with lower values at low latitudes and higher approaching high latitudes, with a tendency to enlarge the latitudes range of low values for high solar activity. Results of Fig.
This means that from the study of H Epstein (z), information can be deduced for VSH and then for the physical quantities involved in its variability. Following the approach proposed by Pignalberi et al. 13  , effective scale height values and gradients have been calculated for the entire COSMIC RO dataset. Finally, topside scale height gradient values from the COSMIC dataset have been compared with electron temperature values measured by ESA's Swarm B satellite. It was found that both exhibit very similar diurnal, seasonal, and solar activity trends as a function of QD magnetic latitude. This study represents only a first attempt to unveil the physical properties of the topside ionosphere starting from calculated effective values. Conversely, the exploitation of different measurement techniques and satellites missions capable of retrieving physical parameters in the topside ionosphere, can help in improving the topside description made by ionospheric models like NeQuick and IRI through the proposed methodology.
Methods 1. h COSMIC < hmF2 + 150 km; i. e. , we require a topside profile at least 150 km wide to make a reliable fit of the topside scale height; 2. N e < 0 for at least one point at h > hmF2; 3. It was not possible to apply the linear fitting procedure of the topside scale height (see Eq. (  18 )) due to missing or corrupted data in the topside profile; 4. The ionPrf file is corrupted. After this initial filtering procedure, the original COSMIC dataset reduced to 3,069,418 electron density profiles. Then, 557,311 profiles (about 15. 4% of the analyzed dataset) were discarded. Afterwards, the dataset of 3,069,418 profiles underwent a second filtering procedure where COSMIC profiles were discarded if at least one of the following conditions is met: (a) foF2 < 0. 1 MHz or foF2 > 22 MHz; (b) hmF2 < 150 km or hmF2 > 650 km; (c) ∂H Linear ∂z < 0.
The relative differences (relative residuals) between measured and smoothed electron density values are calculated: where N e,residuals_small is the mean value of N e,residuals_small calculated over the whole topside profile (the same holds for the medium and large windows); 5. A profile is considered too noisy, and then discarded, when at least one of the three parameters calculated through Eqs. (23a-c) exceeds definite noise threshold values T window. After a preliminary testing phase on the Pignalberi et al. 13  dataset, in which every topside profile was visually checked, we found that a good compromise is to choose the following noise threshold values: If at least one of these three thresholds is exceeded, the corresponding COSMIC topside profile is discarded. After applying the above described filtering technique, 1,251,124 (40.
COSMIC dh, tTEC modeled = h COSMIC � hmF2 N e,Epstein Linear dh, where N e,COSMIC are electron density values measured by COSMIC, while N e,Epstein Linear are those modeled by Eq. (8) with topside effective scale height values from Eq. (18). To perform a statistical validation, tTEC Root Mean Square Error (RMSE) and Normalized Root Mean Square Error (NRMSE) values are calculated, expressed in TECU (1 TECU = 10 16 el/m 2 ) and in percentage, for the entire COSMIC dataset.
Acknowledgements This research is partially supported by the  Italian MIUR-PRIN  grant  2017APKP7T   on Circumterrestrial Environment: Impact of Sun-Earth Interaction. The authors thank the COSMIC/FORMOSAT-3 team for making freely available Radio Occultation data by means of COSMIC Data Analysis and Archive Center (CDAAC, https :// cdaac -www. cosmic. ucar. edu/cdaac /produ cts. html). Thanks to the  European Space Agency  for making Swarm data publicly available via ftp://swarm diss. eo. esa. int, and for the considerable efforts made for the Langmuir probes data calibration. F 10. 7 values used in this study were downloaded from OMNIWeb Data Explorer website (https ://omniw eb. gsfc. nasa. gov/form/dx1. html)maintained by the  Space Physics Data Facility of the Goddard Space Flight Center (NASA).
DES, VHS and WISE data DES is a (∼5,000deg 2  ) optical survey in the grizY bands used the Dark Energy Camera (DECam;  Flaugher et al. 2015). DECam is a wide-field (3 deg 2 ) imager at the prime focus of the Blanco 4m telescope in Cerro Tololo Inter-American Observatory (CTIO). The DES footprint was selected to obtain an overlap with the South Pole Telescope survey  (Carlstrom et al. 2011)  and Stripe 82 from SDSS  (Abazajian et al. 2009 ). The Galactic plane was avoided to minimize stellar foregrounds and extinction from interstellar dust in order to maintain the DES cosmological goals. Even though the main driver for DES is cosmological, the stellar data have been extensively used by the collaboration to identify new star clusters, streams and satellite galaxies in the MW Halo and beyond  (Bechtol et al.
2015; Drlica-Wagner et al. 2015; Luque et al. 2017). The first public data release of the Dark Energy Survey, DES DR1 (DR1;  Abbott et al. 2018 ) is composed of 345 distinct nights spread over the first 3 years of DES operations, from 2013 August 15 to 2016 February 12. The DES DR1 catalogue contains object flags including several that indicate corrupted values due to image artifacts or reduction problems. For the searches of UCDs, as discussed and presented in Carnero  Rosell et al. (2019) , we demanded that FLAGS_z,Y = 0 (ensures no reduction problems in the z and Y bands) and ISO_MAGFLAGS_i,z,Y = 0 (ensures the object has not been affected by spurious events in the images in i,z,Y bands). We also imposed a magnitude limit cut of z < 22 with a detection of 5σ at least in the z and Y to ensure a high completeness in the i band, and therefore allow construction of colour-colour diagrams useful for the selection of UCDs.
Gaia DR2 The Gaia astrometric mission was launched in December 2013. It is measuring positions, parallaxes, proper motions and photometry for over one billion sources to G 20. 7. Its Data  Release 2 (Gaia DR2; Gaia Collaboration et al. 2018) , has covered the initial 22 months of data taking (from a predicted total of 5 years), with positions and photometry for 1. 7 × 10 9 sources and full astrometric solution for 1. 3 × 10 9. For our purpose, we used Gaia DR2 data to select primary star candidates. Particularly important for this work are the parallaxes, whose precision varies from < 0. 1 mas for G ≤ 17 to 0. 7 mas for G = 20. They allow us to better discern dwarf stars (whose distances will overlap those of the UCDs from DES, VHS and AllWISE) from much more distant giants of similar colours, T eff and chemistry.
Proper motion The proper motion measurements for the primaries are mostly from Gaia DR2 catalog. However, for the UCDs, the proper motion measurements are from CatWISE Catalog  (Eisenhardt et al. 2019). CatWISE is a catalog of selected sources from WISE and NEOWISE data collected from 2010 to 2016 in the W1 and W2 bands. However, the majority of the UCDs distances are large and the motions are thus small compared to other samples. Also, the objects are faint and the time baselines relatively short, and so most of the proper motion uncertainties are comparable to the measurements themselves, making them consistent with zero. In this situation, proper motions may turn out to be an inefficient diagnostic of association. Nonetheless, we take into consideration these measurements in our binary and multiple systems search to assess their impact.
ACKNOWLEDGMENTS Funding for the DES Projects has been provided by the  U. S. Department of Energy , the  U. S. National Science Foundation , the  Ministry of Science and Education of Spain , the  Science and Technology Facilities Council of the United Kingdom , the  Higher Education Funding Council for England , the  National Center for Supercomputing Applications  at the  University of Illinois at Urbana-Champaign , the  Kavli Institute of Cosmological Physics at the University of Chicago , the  Center for Cosmology and Astro-Particle Physics at the Ohio State University , the  Mitchell Institute for Fundamental Physics and Astronomy at Texas A&M University ,  Financiadora de Estudos e Projetos, Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro ,  Conselho Nacional de Desenvolvimento Científico e Tecnológico  and the  Ministério da Ciência, Tecnologia e Inovação , the  Deutsche Forschungsgemeinschaft  and the  Collaborating Institutions in the Dark Energy Survey.
Based in part on observations at  Cerro Tololo Inter-American Observatory, NSF's National Optical-Infrared Astronomy Research Laboratory , which is operated by the  Association of Universities for Research in Astronomy (AURA)  under a cooperative agreement with the  National Science Foundation. The DES data management system is supported by the  National Science Foundation  under Grant Numbers  AST-1138766  and  AST-1536171. The DES participants from Spanish institutions are partially supported by  MINECO  under grants  AYA2015-71825 ,  ESP2015-66861 ,  FPA2015-68048 ,  SEV-2016-0588 ,  SEV-2016-0597 , and  MDM-2015-0509 , some of which include ERDF funds from the  European Union. IFAE is partially funded by the  CERCA program  of the  Generalitat de Catalunya. Research leading to these results has received funding from the  European Research Council  under the European Union's  Seventh Framework Program  (FP7/2007-2013) including  ERC  grant agreements  240672 ,  291329 , and  306478.
0 mm/Hg ± 5. 0% immediately before the beginning of the tests, in order to achieve a given saturation degree of the air void content of each specimen. After this pre-conditioning stage, the specimens are immersed into water during the tests, in an apparatus speci ically designed for the placement of the water reservoirs (Figure  3 ). A data acquisition system captures the electrical pulses sent by the loading cells and by the Hall effect sensors with a sample frequency of 640 Hz. A computerized terminal programmed in a platform language C ++ converts these pulses into values of force and displacement to a graphical interface in Excel sheet in real time, as for complex modulus (Figure  4 ) as for fatigue tests (Figure  5 ). It is important to emphasize the sharpness of the signals captured by the loading cells and Hall effect sensors (Figures  4  and 5 ), demonstrated through good de inition of the curves, with low noise level, guaranteeing suitable accuracy of the measurements carried out.
0cm of subgrade reinforcement layer in soil treated with lime, while sub-base and base layers with high modulus asphalt mix or also so-called Enrobé Bitumineux à Module ET levé class 2 (EME2), taking into account the formulation tested in the experimental campaign carried out and described in earlier sections. For EME2, thickness layers from 9. 0cm to 13. 0cm were simulated following standardized recommendations (Catalogue LCPC/SETRA, 1998). Rheological parameters of the asphalt mix EME2 were determined with use of Huet-Sayegh viscoelastic linear model (Eq. 3) simulated in the computerized tool so-called Viscoanalyse Ver. Beta (Table  7 ), considering results from complex modulus tests presented in Figure  4  and Table  2. Complex modulus and fatigue test results used in the numerical simulations are compiled in Table  8.
ACKNOWLEDGEMENTS We would like to thank to the  Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR)  from  Nantes, France , by the continuous scienti ic cooperation agreement with our  Group of Research and Development in Pavement (GDPPav) , to Petrobrás for inancing the project concerning the development of FADECOM apparatus (Cooperation Agreement  0050. 0075479. 12. 9  and SAP nº4600373381), as well as to  Gustavo Momm  by the decisive collaboration along the mechanical and logical conception of the FADECOM apparatus.
Population for early follicular phase serum study Patients referred to our department at an academic hospital for hyperandrogenemia, oligo-anovulation, infertility, or all three, were identified from a database that included clinical, hormonal and ultrasound features, which were consecutively recorded between January 2014 and December 2018. A total of 70 women with PCOS (42 obese; BMI ≥30 kg/m² and 28 normal weight; BMI ≤25kg/m²) and 37 controls (16 obese and 21 normal weight) were selected retrospectively, as described below. Patients were included in the PCOS group according to a modified Rotterdam classification (Rotterdam ESHRE/ASRM-Sponsored PCOS consensus workshop group, 2004), i. e. presence of at least two out of three of the following items: hyperandrogenism (clinical or biological), as previously described  (Peigné et al.
Statistical analysis SPSS software 22. 0 (SPSS Inc. ) (Chicago, IL, USA) was used for all statistical analyses. Results were expressed as medians, with fifth to 95th or 10th to 90th percentiles for continuous variables and by the frequencies and percentages for categorical variables. Comparisons between two independent groups were conducted using the Mann-Whitney U test and between dependent groups (for paired comparison between follicular fluid and serum results) using the Wilcoxon signed rank test. Comparisons between four groups were conducted using the analysis of variance test with post-hoc pairwise comparisons between obese and normal weight controls, and obese and normal weight PCOS after applying Bonferroni correction. The correlations between the various parameters were expressed as Spearman's correlation coefficient (r).
Our data confirm and expand upon a recent study showing that the ratio of AMH isoforms do not differ between patients with PCOS and controls  (Wissing et al. , 2019) , thus implying a similar distribution of uncleaved (inactive) and cleaved AMH in the two study groups. Wissing et al. (2019)  analysed a study group composed of women who were either normal weight, slightly overweight,  had PCOS and were taking metformin, or all three. They reported a negative correlation between metabolic parameters and the proportion of inactive AMH form in the serum. Here, we confirmed the correlation results found in  Wissing et al. (2019) , with an even greater coefficient of correlation between the API and BMI (r = -0. 557) and waist circumference (r = -0. 530) and demonstrated that a greater processing of AMH in the circulation does not associate with PCOS but rather with a severe metabolic disturbance, such as obesity.
| Genetic tests Karyotyping was performed for all patients. Genetic testing consisted of targeted Sanger sequencing of four | 3 of 10 FAN et Al. DSD-related genes (SRD5A2, AR, NR5A1, and SRY) and next-generation sequencing (NGS) of a panel of 127 DSDrelated genes or whole-exome sequencing (WES), depending on the clinical features and financial resources of the patients. For Sanger sequencing, SRD5A2 exons 1-5 and exon-intron boundaries were amplified by PCR using previously described primers  (Baldinotti et al. , 2008). NGS was performed by MyGenostics (Beijing, China) or the Beijing Kangso Medical Laboratory. When screening and analysis identified candidate variants, Sanger sequencing and parental validation were performed. All data interpretation was based on the GRCh37/hg19 human genome assembly, and the reference sequence for SRD5A2 was NM_000348.
| Statistical analysis Data are presented as the mean ± SD or median [interquartile range, IQR] as appropriate. Statistical analyses were performed using SPSS version 22. 0 (IBM, Armonk, NY). Graphs were plotted using GraphPad Prism version 7. 0 (San Diego, CA) and Adobe Illustrator CS6 version 16. 0 (San Jose, CA). Normally distributed data were compared between groups using the t-test or one-way ANOVA followed by Bonferroni's correction, and nonnormally distributed data were compared using the Mann-Whitney U test or Kruskal-Wallis test followed by the Dunn-Bonferroni post hoc test. Correlations between variables were evaluated by computing Spearman's correlation coefficients. The chi-square test was chosen to assess differences in proportions. Two-tailed p values <0. 05 were considered to indicate statistical significance.
Q6* (n = 44 alleles, 16. 92%), p. R246Q (n = 35 alleles, 13. 46%), and p. G203S (n = 27 alleles, 10. 38%) (Figure  3 , Table  S2 ). According to ACMG/AMP guidelines, the novel missense variants and nonsense variant were classified as likely pathogenic and pathogenic, respectively. The novel missense variants were predicted to be probably damaging by Polyphen-2 software (Table  S3 ).
These findings may be related to variations in 5α-reductase type 2 activity with age. Over 100 SRD5A2 variants have been recorded to cause 5α-RD (Human Gene Mutation  Database, www. hgmd. cf. ac. uk, 2019. 06. 13). Consistent with previous studies, missense and nonsense mutations predominated in our cohort, and exons 1 and 4 were predominantly affected  (Abaci et al. , 2019; Gui et al. , 2019). Homozygous SRD5A2 mutations are far more common than compound heterozygous mutations in countries other than China  (Cheng et al. , 2015; Gui et al. , 2019; Mendonca et al. , 2016) , which may be ascribable to the vast population, ethnic diversity, and prohibition of consanguineous marriages in China. Reported SRD5A2 variants differ across ethnic groups: p. R227Q, p. P212R, p. Q126R and p. G34R have been detected in Asians, Mexicans, Portuguese, and Egyptians, respectively  (Samtani, Bajpai, Ghosh, & Saraswathy, 2010).
Patients Patients included in this study were recruited within the COMETE (COrtico-et MEdullo-surrénale, les Tumeurs Endocrines) -HEGP (Hôpital Européen Georges Pompidou) protocol (authorization CPP 2012-A00508-35). Among the 67 patients undergoing adrenalectomy for lateralized PA at the Hypertension Unit of the HEGP between 2013 and June 2016, we included 49 patients for whom informed consent was available for research purposes. 4] [25]  Thirty-nine of 40 patients with outcome data at 6 months after surgery had complete biochemical remission according to the Primary Aldosteronism Surgery Outcome criteria. 26 Further details are available in the online-only Data Supplement. All patients gave written informed consent for genetic and clinical investigation. Procedures were in accordance with the institutional guidelines.
Two (1-5) (median [minimum to maximum]) FFPE blocs per patient were investigated for a total of 110 blocs. Two PA cases who underwent adrenalectomy at the University of Michigan were also included for the intratumoral genetic analysis. The use of the specimen for the analysis was approved by the Institutional Review Board at the University of Michigan (HUM00083056). The number of patient samples that underwent each procedure is described in Figure  S1  in the online-only Data Supplement.
Analysis and Quantification of Histological Features Images were acquired using a Vectra automated imaging system (PerkinElmer). Quantifications were done either by blinded observation using Phenochart (CYP11B2, CYP11B1, and GIRK4) or by automated image analysis and feature selection (β-catenin, colocalization) using the InForm image analysis package (both programs from PerkinElmer). Details are available in the onlineonly Data Supplement.
Sanger Sequencing and Targeted NGS Targeted Sanger sequencing of KCNJ5, CACNA1D, ATP1A1, ATP2B3, CTNNB1, and CLCN2 is described in the online-only Data Supplement. The list of primers used for Sanger sequencing is indicated in Table  S1. NGS was performed using a newly developed NGS kit, covering all coding exons and intron-exon junction of the KCNJ5 (NM_000890), ATP1A1 (NM_000701), ATP2B3 (NM_0010001344), CTNNB1 (NM_001904), CACNA1D (NM_001128839. 2 and NM_000720), APC (NM_000038. 5), CACNA1H (NM_021098 and NM_001005407), PRKACA (NM_002730), and ARMC5 (NM_002730) genes (MASTR_PA kit; Multiplicom/Agilent, Santa Clara, CA). The 9 target genes were amplified with a total of 3 April 2020 multiplex polymerase chain reactions generating 593 amplicons. Further details are available in the online-only Data Supplement.
Liquid Chromatography coupled to tandem Mass Spectrometry Steroid Profiling Thirteen steroids were measured simultaneously with steroid profiling: 18-oxocortisol, 18-hydroxycortisol, aldosterone, cortisone, cortisol, 11-deoxycortisol, 21-deoxycortisol, 18-hydroxy-11-deoxycorticosterone, 11-deoxycorticosterone, 18-hydroxycorticosterone, corticosterone, 17-hydroxyprogesterone, delta-4-androstenedione in a 13-minute run. The complete steroid profiling procedure is described in the online-only Data Supplement.
Acknowledgments We wish to thank  A. Venisse ,  C. Travers , and  M. Mandavit  ( HEGP, Paris, France ) for technical support,  C. E. Gomez-Sanchez  ( University of Mississippi Medical Center, Jackson, MS ) for providing antibodies against CYP11B1 and CYP11B2 and 18-oxocortisol, and  M. F. Rossier  ( Hospital of Valais, Sion, Switzerland ) for providing 18-hydroxy-11-deoxycorticosterone. We would also like to thank  Drs Thomas J. Giordano  and  Scott A. Tomlins  at the  University of Michigan  for preparing tissue material and next-generation sequencing analysis, respectively.