dataesr/scientific-paragraphs-categorization huggingface dataset:
Rb1 Alterations, Whole-Exome Sequencing and Oncoscan Copy Number Variation Analysis RB1 mutation analysis was performed, as previously reported  [66]. For Patient 2, we also analyzed RB1 promoter methylation status using SALSA MLPA probemix ME002-C1 Tumour suppressor mix 2 (MRC-Holland). For FFPE samples of the primary tumor of patients, genome-wide DNA copy number alterations and allelic imbalances were analyzed using the Affymetrix OncoScan platform after DNA extraction using PureLink Genomic DNA mini kit (Invitrogen, Carlsbad, CA, USA). Array CEL files were processed using the OncoScan Console Software (Thermo Fisher Scientific, Waltham, MA, USA) and they were visualized with Nexus Expression OncoScan software (BioDiscovery, El Secondo, CA, USA), Chromosome Analysis Suite (Thermo Fisher Scientific, Waltham, MA, USA), and a custom script.
Based on the comparison with calls from a normal cohort and the database of benign copy number variants, copy number aberrations greater than 500 kb in size, and copy-neutral LOH greater than 10 Mb are considered to be abnormal based on the established performance characteristics of the assay. For the orbital and lymph node metastasis biopsies of Patient 2 fresh tumors were available for whole exome sequencing (WES). Exome capture was performed by SureSelect Clinical Research Exome v2 (Agilent Technologies, Santa Clara, CA, USA) followed by Next Generation Sequencing on an Illumina HiSeq 4000 instrument (Illumina, San Diesgo, CA, USA). The bioinformatic analysis was performed, as described in Supplementary Materials. Somatic CNA analysis from aligned BAM files was performed using Control-FREEC  [67]  for WES with default parameters, except for ploidy which was set to 3.
Acknowledgments: The authors would like to acknowledge  Lateef Zameer  and  Paromita Roy  for providing the histopathological images from Patient 1 and  Mayur Parihar  for the FISH images of Patient 1. Roxana Marino  is acknowledged for her technical support with Sanger for assessing TP53 mutation status in Patient 2 samples. We also acknowledge that Biorender (https://www. biorender. com) was used to create the graphical abstract.
Domain Definition: 2D Image Segmentation. In order to create a realistic geometry for numerical simulation, we started from a 2D patient-specific angiography. An enhancement technique was done before this image could be segmented. In this phase, different filters were used to improve the contrast of the original image (see  [11] ). en, opening/closing Matlab functions were used to extract a black and white image that contains only the coronary tree in which we are interested. It should be noticed that despite the fact that the original angiography corresponds to a stenotic coronary tree, due to the lower quality of the image and to the small degree of stenosis, the lesion could not appear in the black and white image. Since our aim in this paper is to investigate the impact of the flow model and the boundary conditions on the FFR, we introduced two different artificial lesions in the coronary tree.
e first lesion corresponds to 68% stenosis and was drawn in the same location of the real patient's lesion. e second lesion corresponds to 56% stenosis and was drawn at the entrance of the longest branch in the coronary tree. is choice is justified by the purpose of calculating the fractional flow reserve in the case of traction free outlet boundary conditions. e resulting 2D multistenotic domain, the original extracted tree, and the original angiography are given in Figure  1. Starting from the new multistenotic coronary tree, the segmentation and the meshing were performed later using a homemade FreeFem++ code (see  [12] ).
Laboratory calibrations of turbidity sensors for SSC (Suspend Sediment Concentration) are performed in a 40L black bucket using wet sediments collected under the instruments. SSC are validated by filtration, using GF/C filters on field samples. Times-series data are processed using Matlab routines. Inter-calibration of probes are checked. No significant differences are noticed (paired T-student test with simultaneous field-measurements, p<0. 01). The ROX Optical Dissolved Oxygen sensors (6150 ROX probe) are calibrated using the one point « saturated water » method, mentioned in the « Calibration, Maintenance and Troubleshooting Tips » manual. We carry out a post-deployment calibration on chlorophyll measurement using field samplings. Filters are analysed using a fluorometric method with acetone 90% (Fluorometer Turner TD 700), as proposed by  Lorenz (1967).
Biomarker assessments All the biochemical assays (GST, Laccase, SOD, MDA) are performed using the same instrument: SAFAS Flx-xenius at the specific absorbance for each biomarker kind. The assays were carried out on the digestive glands of M. varia at the reference site (marsh) and the contaminated sites (outlet rainwaters and careening areas) at D0, 7 and 21 (D0, D7, D21, n = 10 per site and per date). Before statistical analysis, row data for GST, SOD and Laccase are expressed in U/mf of proteins. MDA row data concentrations are expressed in µM/mg of protein. Total protein concentrations are determined using the BCA Kit methodology (Bicinchononique Acid Kit, Sigma Aldrich). The BCA kit contains bovine serum albumin (BSA) as a standard and involves the reduction of alkaline Cu 2 + by proteins  (Smith et al.
received a doctoral grant (Communauté d'Agglomération de La Rochelle) and financial support from the marina of La Rochelle while working with the La Rochelle University. This study is a contribution to the project ECONAT funded by the Contrat de Plan Etat-Région and the CNRS and the European Regional Development Fund. The LIENSs laboratory would like to thank the general management of the marina of La Rochelle for giving access to the harbour. The authors also thank the people working at the logistic platforms of the LIENSs laboratory (Analyses Élémentaires and Données: Intégration, Structuration et Analyse). The authors would like to thank Oriane Titeca for proofreading this paper. We are deeply indebted to A. Combaud, T. Guyot, V. Becquet, A. Viricel, M.
In the GHz regime, the newly-commissioned MeerKAT telescope in South Africa (Jonas & MeerKAT Team 2016) is proving to be an excellent instrument for GRG studies (e. g. Cotton et al. 2020 ). Although it operates at higher frequencies than LOFAR, MWA and uGMRT (though there is some overlap in observing frequency with the latter), MeerKAT has the excellent sensitivity and uv coverage ideal for such work, including the simultaneous availability of long and short baselines. The MeerKAT International GHz Tiered Extragalactic Exploration survey (MIGHTEE;  Jarvis et al. 2016 ) is a galaxy evolution survey underway with MeerKAT. Among its data products will be high-quality radio continuum data over a relatively wide field (20 deg 2 ). In this paper, we report the discovery of two newly-identified GRGs in 1 deg 2 MIGH-TEE Early Science observations of the COSMOS field.
MIGHTEE L-band data The MIGHTEE survey targets four extragalactic deep fields, namely the European Large Area ISO Survey -South 1 (ELAIS-S1), COSMOS, the XMM-Newton Large Scale Structure (XMM-LSS) field, and the Extended Chandra Deep Field South (E-CDFS), for a total sky area of approximately 20 deg 2. The survey uses total intensity, polarised intensity and spectral line data products to achieve a range of science goals. It has components using MeerKAT's L-band (900 -1670 MHz) and S-band (1. 75 -3. 5 GHz) receivers, and a primary design requirement is to reach the L-band classical confusion limit in total intensity at about 2 µJy beam -1 (for a resolution of ∼ 8 arcsec). The initial release of total intensity continuum within the MIGHTEE consortium included a single pointing in the COSMOS field (J2000 10h00m28.
6s +02d12m21s; see Table  1 ). The two GRGs we present in this paper were discovered in this pointing. Since one of the GRGs was towards the edge of the field, we have also imaged an extra MIGHTEE pointing (COSMOS_8; J2000 10h00m29. 0s +02d33m33. 79s) for which this object was closer to the centre of the primary beam. Full details of the initial MIGHTEE continuum data release and the data processing method 3  will be presented in  Heywood et al. (in prep. ) , however we provide a brief overview here. • The data were converted from their native format into a MeasurementSet format by the South African Radio Astronomy Observatory (SARAO) archive 4  , and averaged from their original L-band frequency resolution to 1024 channels in the process. Flags generated by the telescope control and monitoring system were applied.
This process compensates for the effects of the large fractional bandwidth of MeerKAT, coupled with the fact that the data arrive in MeasurementSet format that only has a single spectral window. The flux scale may be biased if this effect is not taken into account. • All the gain corrections were applied to the target data, which is then flagged using the tricolour 5  package. Removing the edges of the bandpass where the gain sharply rolls off results in 770 MHz of usable bandwidth. A loss of about 50 per cent of the data in this region is typical following RFI removal, although the RFI occupancy is strongly baseline dependent, and most of this loss occurs on spacings shorter than 1 km. • The target data were imaged using wsclean  (Offringa et al. 2014). The data were imaged blindly with 100,000 iterations, and a clean mask was derived from the resulting image, excluding regions below a local threshold of 6σ where σ is the pixel standard deviation.
The data were re-imaged using this mask. • The multi-frequency clean components from the masked image were used to predict a visibility model for selfcalibration using the casa gaincal task. Phase corrections were derived for every 64 seconds of data, and an amplitude and phase correction was derived for every target scan, with the solutions for the former applied while solving for the latter. The self-calibrated data were re-imaged using wsclean and the mask is refined if necessary. • Direction-dependent corrections were made by imaging the data with ddfacet  (Tasse et al. 2018 ). The resulting model was partitioned into ∼10 directions, constrained by the location of off-axis problem sources, and the need to retain suitable flux in the sky model per direction. The killms package (e. g. Smirnov & Tasse 2015) was then used to solve for a complex gain correction for each direction with a time / frequency interval of 5 minutes / 128 channels.
Another run of ddfacet reimaged the data, applying the directional corrections. • Finally, the images were primary-beam corrected by dividing them by an azimuthally averaged Stokes I model, evaluated at 1284 MHz using the eidos  (Asad et al. 2019)  package. MIGHTEE continuum data is imaged twice, with a Briggs' robust parameter of 0. 0 and -1. 2. This is to deliver a higher sensitivity image as well as a higher angular resolution image, the trade-off for which is a loss of sensitivity due to the down-weighting of the many short spacings that MeerKAT's dense core provides. The COSMOS pointing for a total on-source time of 17. 45 h reaches a thermal noise (measured away from the main lobe of the primary beam) of 1. 9 µJy beam -1 in the robust 0. 0 image, with an angular resolution of 8. 4 arcsec × 6.
8 arcsec (position angle -11. 2 deg). The robust -1. 2 image reaches 6 µJy beam -1 with an angular resolution of 4. 8 arcsec × 4. 0 arcsec (position angle -12. 63 deg). We imaged the 6. 25 h of COSMOS_8 data with less aggressive weighting (robust = 0. 3) in order to increase the sensitivity to the diffuse emission from the lobes. This image has a thermal noise of 2. 5 µJy beam -1 and an angular resolution of 11. 6 arcsec × 7. 4 arcsec (position angle -12. 6 deg). The higher resolution map made with COSMOS_8 data was imaged using a robust parameter of -1. 2 and has a thermal noise of 8. 5 µJy beam -1 and an angular resolution of 5. 0 arcsec × 4. 0 arcsec (position angle -15. 44 deg). Note that in practice the central region of the lower resolution images (both COSMOS and COSMOS_8) are limited to root mean squared (rms) 'noise' levels of ∼3 µJy beam -1 by a combination of thermal noise and classical confusion.
VLA-COSMOS 3 GHz Large Project In this paper, we also use data from the VLA-COSMOS 3 GHz Large Project (hereafter VLA-3 GHz). This project was presented by  Smolčić et al. (2017a)  and is a continuum survey with the Karl G. Jansky Very Large Array (VLA;  Perley et al. 2011 ) covering 2. 6 deg 2 over the full COSMOS field. This 384 h survey was centred at 3 GHz with a 2 GHz bandwidth using three sets of 64 pointings in A and C array. It reached an rms sensitivity of 2. 3µJy beam -1 with 0. 75 arcsec resolution. This is similar in sensitivity to the Early Science MIGHTEE data, accounting for the different central frequencies and assuming a standard spectral index for radio galaxies (α = -0. 8; where Sν ∝ ν α ). While the angular resolution of the VLA-3 GHz data is superior to that of MIGHTEE, it has poorer sensitivity to large-scale emission.
GRG1 as seen in the VLA-3 GHz data (background map) and in the MIGHTEE data (black contours). Contour levels are as in Figure  2  and the lowest contour level is again shown in magenta. The hot-spot in the northern lobe is seen faintly at 3 GHz. The inset shows an enlargement of the core region, where a double-lobed/jetted structure can be seen in the VLA-3GHz image. star formation rate (SFR) of 1. 97 M /yr. It is therefore only weakly star-forming and lies below the main sequence of star formation  (Schreiber et al. 2015 ) by a factor of ∼2. This is consistent with the fact that it is undetected in the farinfrared. In the X-ray regime, the galaxy has a [2-10] keV luminosity of LX ∼ 10 41. 6 erg s -1 , based on data from the Chandra-COSMOS  (Elvis et al. 2009; Civano et al. 2012 ) and COSMOS-Legacy catalogues  (Civano et al.
Low frequency surveys do benefit from being more sensitive to the steepest spectrum lobe emission of GRGs, however LoTSS would only surpass MIGHTEE in sensitivity where the spectral index is steeper than α ≈ -1. 7. A further explanation for a previously under-estimated GRG sky density is a dearth of highly sensitive, ancillary multi-wavelength observations. This would make it more difficult to determine whether or not lobes and hot-spots are associated with separate near-infrared/optical counterparts, particularly at higher redshifts. This issue is largely avoided by MIGHTEE-COSMOS thanks to the wealth of high-quality multi-wavelength data available in the field. Therefore, GRGs may be yet more numerous than presently known, despite the rapid increase in the number of these giants discovered in recent years (e.
Implications While our analysis has considered only enormous (> 2 Mpc) objects, if radio galaxies must grow to reach this size, then we may expect to similarly uncover in our data previously undetected GRGs with smaller sizes. The full 20 deg 2 MIGH-TEE survey will provide an excellent resource for such studies when it is completed in the near future. Based on the GRG sky density we observe in COSMOS (2 per deg 2 ), we could uncover ∼ 40 GRGs with the full survey. With MeerKAT simultaneously facilitating excellent sensitivity, sky coverage and uv coverage with relatively high resolution, and the upcoming Square Kilometre Array (SKA) providing even better angular resolution, we may expect to discover many more diffuse, extended GRGs in the near-to mid-future. The existence of a larger population of GRGs may have implications for our understanding of the AGN duty cycle.
ACKNOWLEDGEMENTS We thank the anonymous referee for providing useful comments which have strengthened the paper. We thank  Pratik Dabhade  for helpful discussions and for generously providing their GRG catalogue. The MeerKAT telescope is operated by the  South African Radio Astronomy Observatory  (SARAO; www. ska. ac. za), which is a facility of the  National Research Foundation (NRF) , an agency of the  Department of Science and Innovation. JD, SVW, IH  and MP acknowledge the financial assistance of SARAO. MP, SMR and LL acknowledge the financial assistance of the NRF. Opinions expressed and conclusions arrived at, are those of the authors and are not necessarily to be attributed to the NRF. MJJ and IH acknowledge support from the  UK Science and Technology Facilities Council  [ ST/N000919/1 ].
MJJ, IH and IHW acknowledge support from the  Oxford Hintze Centre for Astrophysical Surveys  which is funded through generous support from the  Hintze Family Charitable Foundation. IH thanks the  Rhodes University Centre for Radio Astronomy Techniques and Technologies (RATT)  for the provision of computing facilities. ID is supported by the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Skłodowska-Curie  grant agreement No  788679. MP, JC, FXA, MV, KT and MG acknowledge financial support from the  Inter-University Institute  for Data Intensive Astronomy (IDIA). The research of OS is supported by the  South African Research Chairs Initiative  of the NRF. YA acknowledges support by  NSFC  grant  11933011. RB acknowledges support from the  Glasstone Foundation.
LM, MV and IP acknowledge financial support from the  Italian Ministry of Foreign Affairs and International Cooperation  ( MAECI Grant Number   ZA18GR02 ) and the  South African NRF  (Grant Number  113121 ) as part of the  ISARP RADIOSKY2020 Joint Research Scheme. JA acknowledges financial support from the  Science and Technology Foundation (FCT, Portugal)  through research grants  PTDC/FIS-AST/29245/2017 ,  UID/FIS/04434/2019 ,  UIDB/04434/2020  and  UIDP/04434/2020. NA acknowl-.
Methods We have established a network of 46 Italian centers active in the diagnosis, care, and research of JS and connected to the Italian Association for Joubert Syndrome and Congenital Ataxias and have created a comprehensive clinical-genetic database. Patients had a neuroradiologically confirmed diagnosis of JS and variable features ranging from pure neurologic phenotype (developmental delay, hypotonia, and ataxia) to more severe cases with multiorgan involvement (including ocular, renal, hepatic, skeletal, and orofacial defects). 5 Age at diagnosis among probands, inferred from both medical records and brain MRIs, was defined as age at first detection of the MTS. Only patients with JS resident in Italy (60,483,973 inhabitants as of 2018) on October 8, 2018, entered a point-prevalence rate epidemiologic study.
The crude age-and sex-specific prevalence was calculated, assuming a Poisson distribution. The 95% confidence intervals (CIs) were computed with the formula π ± 1:96 ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi πð1 -πÞ=n p , where π means the prevalence and n the absolute number of inhabitants. 9 The database of the Italian National Institute of Statistics provided population data for 2018 (demo. istat. it/pop2018/index. html). Statistical analyses were performed with the t test for age comparison. Linear regression analysis was used to test the relationship between age at point prevalence and age at diagnosis. Data were analyzed with SPSS (version 25. 0, SPSS Inc, Chicago, IL). A value of p ≤ 0. 05 was considered statistically significant, with Bonferroni correction for multiple comparisons.
Next-generation sequencing (NGS) panel analysis of 34 JS genes was conducted on 219 probands (data available from Dryad, table e-1, doi. org/10. 5061/dryad. 2220d7m). Identified variants were classified as pathogenic, likely pathogenic, of uncertain significance, likely benign, or benign, according to current guidelines. 10 Validation and segregation of pathogenic and likely pathogenic variants were performed by Sanger.
Results We ascertained 284 patients with JS (220 white) from 251 families. The male/female ratio was 1. 22 (156/128). The overall mean age at point prevalence was 16. 7 ± 10. 4 years (median 14. 7 years, range 1-60 years, interquartile range [IQR] 8. 3-23. 2 years). The mean male-and female-specific ages at point prevalence were 17. 5 ± 11. 2 years (median 15 years, range 2-60 years, IQR 8. 8-23. 5 years) and 15. 7 ± 9. 2 years (median 14. 1 years; range 1-39 years; IQR 8. 8-23. 5 years), respectively, without significant differences between sexes. Among patients, 124 (44%) presented exclusive neurologic manifestations and 134 (47%) had additional multiorgan involvement, while data were missing in 26 cases (9. 1%). The crude prevalences of JS in Italy on October 8, 2018, for total, females, and males were 0.
47 (95% CI 0. 41-0. 53), 0. 41 (95% CI 0. 32-0. 49), and 0. 53 (95% CI 0. 45-0. 61) per 100,000 population, respectively (table  1 ). When we focused on patients 0 to 19 years of age, the crude prevalences for total, females, and males increased to 1. 7 (95% CI 1. 49-1. 97), 1. 62 (95% CI 1. 31-1. 99), and 1. 80 (95% CI 1. 49-2. 18) per 100,000 population (table  1 ). Two hundred of the 251 (80%) probands have both parents of Italian origin; 44 (17. 5%) have at least 1 non-Italian parent; and 23 (9. 1%) descend from consanguineous unions. Table e-2 available from Dryad (doi. org/10. 5061/dryad. 2220d7m) summarizes the proband genetic status. Among the 219 tested probands, 131 had a confirmed molecular diagnosis, resulting in a mutation rate of 60%, in accordance with the literature. 3 he commonest mutated genes are AHI1 (9.
1%), CPLANE1 (8. 7%), TMEM67 (8. 2%), and CEP290 (6. 9%), similarly distributed between sexes (figure  1 ). When we also considered affected siblings, molecularly confirmed cases increased to 152. In this latter group, the crude prevalences for total, females, and males were 0. 25 (95% CI 0. 21-0. 29), 0. 24 (95% CI 0. 20-0. 31), and 0. 26 (95% CI 0. 21-0. 32) per 100,000 population (data available from Dryad, table e-3, doi. org/ 10. 5061/dryad. 2220d7m). Abbreviations: CI = confidence interval; JS = Joubert syndrome.
For all these reasons, we consider our data minimum prevalence estimates. However, it must be noted that the distribution of our cohort among the 3 macro-areas (North, Center, South Islands) is in accordance with that reported in the general population. This study considerably increases knowledge of JS epidemiology, demonstrating an overall population-based crude prevalence rate of 0. 47 per 100,000 population. This rate is approximately halved for molecularly confirmed cases (ratio 0. 5 = 0. 25/0. 47 per 100,000 population) (data available from Dryad, tables e-1 and e-3, doi. org/10. 5061/dryad. 2220d7m). When we considered pediatric age, JS prevalence was 1. 7 per 100,000 population, 10 times higher than the single report published to date (0. 17 per 100,000 population). 7 However, this comparison is not completely appropriate because the latter study was performed before the MTS identification.
Acknowledgment The authors thank all patients and clinicians for enthusiastically encouraging this study and  R. Cutigni  for her contribution to the data collection. Study funding Supported by the  Ministry of Health  ( Ricerca Finalizzata   NET-2013-02356160 , and  Ricerca Corrente   2019  to IRCCS Eugenio Medea),  European Research Council  ( ERC Starting Grant   260888 ),  Italian Ministry of University and Research (Progetto Dipartimenti di Eccellenza) , and  Pierfranco and Luisa Mariani Foundation  ( PADAPORT  project).
Chemical identification, chemical and radiochemical purities determination and molar activity measurement. Identification, purities and molar activity were assessed according to the "Liquid chromatography" monograph n°01/2019:20229 of the European Pharmacopeia 9. 7 by analytical reverse phase HPLC equipped with a 717plus Autosampler system, a 1525 binary pump, a 2996 photodiode array detector (Waters, USA) and a Flowstar LB 513 (Berthold, France) gamma detector. The system was operated with the Empower 3 (Waters) software. HPLC were realized on a reverse phase analytical Symmetry C18 (150 x 3. 9 mm, 5 μm, Waters) column using a mixture of H2O/CH3CN/PicB7 ® (55/45/0. 1 v/v/v, 2 mL/min) as.