dataesr/scientific-paragraphs-categorization huggingface dataset:
4. 4 (http://tree. bio. ed. ac. uk/software/figtree/). Nucleotide substitutions from the reference sequence that define internal nodes of the tree were extracted from the final Nextstrain build file and annotated on the tree using a custom R script (https://www. R-project. org/) with packages tidyverse v1. 3. 0  (Wickham, Averick et al. 2019 ) and jsonlite v1. 6. 1  (Ooms 2014). Adobe Illustrator 2020 was used to prepare final tree figures. Sequence metadata (Supplementary material, Table  S2 ), Nextstrain build, and R script is available at https://github. com/Simon-LoriereLab/SARS-CoV-2-France. The phylogeny can be visualized interactively at https://nextstrain. org/community/Simon-LoriereLab/SARS-CoV-2- France. In this study, we used the proposed nomenclature from GISAID to annotate three major clades V, G, and S according to specific single-nucleotide polymorphisms that are shared by all sequences in the clade.
Acknowledgements We would like to thank all of the health care workers, public health employees, and scientists involved in the COVID-19 response. We acknowledge the hospital laboratories from the RENAL network in the north of France (list of names in Supplementary material, Table  S4 ). We acknowledge the authors, originating and submitting laboratories of the sequences from GISAID and GenBank (Supplementary material, Table  S2 ). We avoided any direct analysis of genomic.
Subjects This is a retrospective study conducted between 2015 and 2019 in the Children's Hospital "Béchir Hamza. " Thirteen patients with clinical features suggestive of HGPPS were enrolled for clinical and radiological examination. After informed consent of legal tutors, the referral doctor examined the children. Upon examination, data including age, sex, Cobb angle of the spinal deformity and treatments of the scoliosis were collected. We also used a standardized questionnaire to inquire genetic history to draw familial pedigrees.
Whole Exome Sequencing and Bioinformatics Analysis Three Patients underwent whole exome sequencing (WES). Libraries were pooled together and reads were sequenced on the IlluminaNovoseq 6000 platform (Illumina, San Diego, CA, USA). Sequence quality control was done with FastQC (https://www. bioinformatics. babraham. ac. uk/projects/fastqc/), Read mapping to Genome Reference Consortium Human Build 37 (GRCh37) was performed with Burrows-Wheeler alignment  (12). SNP and INDEL calling, together with advanced variant annotation were done using GATK and annotation was done with ANNOVAR. WES revealed 52599 homozygous variants within the capture regions in average for each patient. Variants were further filtered using VarAFT according to population frequency of variants that was determined using databases such as 1000 Genomes Project and Exome Aggregation Consortium (ExAC) v0.
The resolution is performed, by the finite element method (FEM), with the commercial software COMSOL Multiphysics (Comsol AB, Stockholm, Sweden). The finite element mesh of the study domain is refined in the vicinity of the walls so that several elements, usually three, are placed across the thickness of the viscous boundary layer. The heat source is obtained by the Beer-Lambert exponential attenuation law, assuming a perfectly collimated illumination of the entrance optical window. The cell is filled with air at standard temperature and pressure, whose physical properties are extracted from the material database of the software. Regarding boundary conditions, all walls are considered rigid and isothermal. The gas inlet and outlet are subject to atmospheric pressure and are not the site of any heat exchange.
Thus, in order to limit the use of computational resources by the optimization process, we decided to resort to the use of metamodels  [20]. The process of metamodeling successively implies evaluating the high-fidelity FEM model at points carefully chosen in the input parameter space (design of experiment) and then training the metamodel on the obtained dataset. Once the metamodel is built, the response at a new evaluation point can be predicted instantaneously. The metamodel can thus be used as a surrogate of the expensive simulator in costly procedures, such as inversion or optimization. Kriging metamodels, based on the theory of Gaussian processes, and known for their efficiency and adaptability, have been used in this work. In addition, the efficient global optimization adaptive algorithm  [21] , that improves the accuracy of the metamodel during the optimization by adding new points to the initial design of experiment, has been implemented.
Acknowledgements The authors are indebted to all the members of the teams running the CEA-LETI 200 mm cleanrooms facilities where all silicon cells were fabricated and to  Nadine David , in charge of packaging. They also acknowledge the valuable contributions by colleagues from  GSMA ,  Univ. of  Reims Champagne Ardenne, France (B. Parvitte, V. Zéninari) ,  Fraunhofer IPM Freiburg (T. Strahl) , and at mirSense ( G. Aoust ,  F. Boulila ,  M. Brun , and  M. Carras ). The funding of this work has been in part provided by national grants ( MIRIADE ,  ANR-11-ECOT-04 ) or European grants (MIRIFISENS,  FP7 317884 ,  REDFINCH ,  H2020 780240 ) and the  MIRPHAB consortium , as well as by several industrial partners (currently mirSense, in the framework of our joint laboratory). All Allan deviation plots presented in this paper were calculated with the ALAVAR software: thanks to the author,  Alaa Makdissi , for making it freely available at http://www.
Three regions of homozygosity were detected on chr10:37,414,883-43,132,376, chr13:88,327,643-93,518, 692, and chr21:22,370,881-28,338,710. Next, we performed exome sequencing on probands of families 1 and 2 to identify the causative variant(s). On the assumption that the disease follows an autosomal-recessive pattern of inheritance in the families as well as presence of consanguinity in two families, we prioritized the bi-allelic potentially functional variants residing within the runs of homozygosity. These variants were screened through all publicly available population databases and our inhouse database. We excluded synonymous variants, intronic variants (>7 bp from exon boundaries) and common variants (minor allele frequency > 0. 001%). The selected variants were validated, and segregation analysis was performed using Sanger sequencing.
In families 1 and 2, filtered exome-sequencing data narrowed down the variants to the same homozygous nonsense variant (GenBank: NM_021219; c. 685C>T [p. Arg229Ter]) in JAM2 residing within a 5 Mb region of homozygosity on chr21q21. 3 (Figure  S1 ). Sanger sequencing verified the correct segregation of the variant on available samples of both families (Figures  1A, 2A,  and 2B ). This variant was absent in our in-house exome database of more than 10,000 exomes, absent in homozygous state in all publicly available databases, and present in heterozygous state with a minor allele frequency (MAF) of 0. 00002 in gnomAD (6/280662). The early stop codon introduced by c. 685C>T is predicted to result in production of a truncated protein lacking the transmembrane and cytoplasmic regions of JAM2 and cause reduction in total JAM2 as a consequence of nonsense-mediated decay (NMD) of the mutant transcript.
In family 3, Trio-ES revealed compound heterozygous variants in JAM2 (GenBank: NM_021219; c. 395À1dupG [IVS4-1dupG] and c. 323G>A [p. Arg108His]). The c. 395À1dupG was inherited from the father and c. 323G>A was inherited from the mother. The c. 323G>A was reported once in gnomAD in heterozygous state (MAF 0. 00003, 1/31408), three times in GeneDx in-house database (MAF 0. 00002, 3/167854), and was absent in all databases as homozygous. It disrupts a highly conserved residue (CADD:34) and is predicted to be damaging to the protein function by in silico prediction tools (SIFT, PROVEAN, Mutation Taster, Mutation Assessor, and Poly-Phen). The c. 395À1dupG is predicted to cause the retention of the canonical splice acceptor site of intron 4 with insertion of the G nucleotide as the first base of exon 5 causing a frameshift and a truncated protein (p.
Val132-GlyfsX9). This variant was absent from all public databases and present five times in heterozygous state in GeneDx inhouse database (MAF 0. 00003, 5/171284). The variants segregated fully within the family (Figures  1A, 2A,  and 2B ). In family 4, clinical ES uncovered a homozygous 4 bp deletion in JAM2 (GenBank: NM_021219; c. 177_180del-CAGA [p. Arg60Ter]) (Figures  1A, 2A,  and 2B ) that causes a frameshift and an early termination of the protein affecting the extracellular, transmembrane, and cytoplasmic domains of JAM2. The variant was not reported in any public databases and was predicted pathogenic by MutationTaster causing loss of function by NMD. In order to characterize the link between JAM2 variants and the human neurological phenotype, we developed jam2 knockout (jam2 KO) mice.
In addition, we performed automated quantification of the neuropil vacuolation on H&E-stained sections, GFAP immunoreactive gliosis and Iba1-positive microglial activation in young and aged wild-type and jam2 KO mice (Figure  S3 ). Automated quantification of the percentage of vacuolation, gliosis, and microglial activation in the cortex, midbrain, and cerebellum was performed on digitalized slides, using open source software QuPath. There was a significant increase in the degree of neuropil vacuolation (p < 0. 00007) and astrogliosis (p < 0. 0138) in the midbrain of old jam2 KO mice when compared with age-matched wild-type mice, whereas Iba1-positive microglial activation in old jam2 KO mice was less pronounced than in age-matched wild-type mice (p < 0. 035). No mineralization or calcification was observed in the brains of young or old jam2 KO mice or controls at the time of examination.
Acknowledgments We are grateful to the patients for their essential help. We are grateful for the funding support from  The Medical Research Council (MRC)  ( MR/S01165X/1 ,  MR/S005021/1 ), The  Wellcome Trust  ( Synaptopathies Strategic Award ,  WT093205MA ,  WT104033AIA ),  The Rosetree Trust, Ataxia UK , The  MSA Trust, Brain Research UK ,  Sparks GOSH Charity, Muscular Dystrophy UK (MDUK) , and  Muscular Dystrophy Association (MDA USA ). We also thank  James Polke  and  Conceica ˜o Bettencourt  for assistance with the text and interpretation. S. B. and H. H. are partly funded by the  National Institute of Health Research (NIHR) UCLH/UCL Biomedical Research Centre and Dementia Biomedical Research Unit. J. R. M. deO. receives funding from  CNP ,  CAPES , and  DECIT-MS. We thank  Joanne Lau  for technical support.
The aims of the work presented in this paper are twofold. Our first aim is to investigate the low-frequency radiocontinuum spectral behaviour of NGC 6251 and re-examine the pressure balance in its lobes, taking into account the new LOFAR data. Our second aim is to probe the environment and source structure using the high-resolution Faraday spectra obtained using the LOFAR HBA data. The material in this paper is split between Section 2, in which we describe the observational data and its basic processing, Section 3, in which we derive results regarding spectra and polarisation, Section 4, in which we discuss these results with reference to the above aims, and Section 5, in which we summarise our conclusions. A preliminary report of this work has been previously published  (Cantwell 2018) , and contains additional details of some intermediate results that are omitted here for clarity.
OBSERVATIONS AND IMAGING NGC 6251 was observed with LOFAR HBA on 23 August 2013 during LOFAR's cycle 0. A summary of the observations is provided in Table  1. Data were taken in interleaved mode, with scans alternating between the target and the flux calibrator. This mode was used in early LOFAR cycles to compensate for gain instability, before other approaches were developed. The calibrator 3C295 was observed for 2 minutes per scan and the target scans were 10 minutes long.
Final imaging was carried out using AWImager using Briggs weighting with a robustness parameter of 0. Due to issues with radio-frequency interference (RFI), only 63 MHz of the 80 MHz total bandwidth was used. In order to investigate the diffuse emission, each band was imaged separately, with an outer uv limit of 3kλ in units of wavelength λ, achieving a resolution of 40 arcsec. At this point the flux scale was corrected as described in Section 2. 1. 2. The fluxcorrected images were combined to produce a weighted aver- age X = i σ -2 rms, i X i i σ -2 rms, i where X i is the image in band i and σ rms,i is its root-mean-square (rms) noise. The effective frequency for this weighted average is 140 MHz, though throughout the rest of this document we label these data with the nominal 150 MHz midpoint frequency for this LOFAR band.
Polarisation imaging The commissioning of a pipeline to process LOFAR polarisation data is not yet complete, and so it is not currently possible to calibrate the instrumental polarisation and thus to determine the absolute polarisation angle. However, it is still possible to detect polarised emission with LOFAR  (Mulcahy et al. 2014; O'Sullivan et al. 2019) , and science commissioning has shown that linearly-polarised intensity and Faradaydepth values can be reliably recovered for known polarised sources. The Faraday depth is defined as φ = 0. 81 ∫ n e B • dl rad m -2 , ( 1 ) where n e is the electron density in cm -3 , B the magnetic field in µG, and dl the path length in parsec. To image polarised emission from NGC 6251 we must account for Faraday rotation due to the ionosphere, which results, per equation (1), from ionospheric free electrons and the geomagnetic field.
Variations in the ionospheric electron content and the projection angle of the geomagnetic field during the observations will lead to different degrees of Faraday rotation throughout the data, causing a smearing of any signal in Faraday space  (Sotomayor-Beltran et al. 2013). We corrected for ionospheric Faraday rotation with RMExtract 4  (Mevius 2018), which calculates the expected Faraday rotation over the LOFAR stations from a model of the geomagnetic field and maps of the ionospheric total electron content (TEC). The geomagnetic field is taken from the International Geomagnetic Reference Field (IGRF), and the TEC maps may be obtained from either the Centre for Orbital Determination in Europe (CODE) 5  or the Royal Observatory of Belgium (ROB) 6. Tests during commissioning investigating pulsars of known properties suggest that using CODE ionospheric maps recovers more of the true polarised flux, and so we used these as the input for the ionospheric correction (  Van Eck 2017).
CODE calculates the TEC using data from ∼200 GPS and Global Navigation Satellite System (GLONASS) sites of the International GPS Service (IGS) and other institutions, with a time resolution of about an hour, and a spatial resolution of 2. 5 • ×5. 0 •  (Dow et al. 2009). Fig. 3  shows a plot of the ionospheric rotation-measure (RM) correction produced by RMExtract. Once the RM correction had been applied to every subband and time step, individual channels of 48 kHz were split from sub-bands and imaged in Stokes Q and U using AWImager. An inner uv limit of 200λ, corresponding to an angular scale of ∼ 20 arcmin, was used in order to avoid imaging Galactic foreground emission.
Archival data We have used a number of archival datasets in our analysis of NGC 6251. We have used the Westerbork Synthesis Radio Telescope (WSRT) 325 MHz and 610 MHz images as well as the Effelsberg 10 GHz images from  Mack et al. (1997a) , which are discussed in detail in that work and by  Mack et al. (1998). A number of VLA datasets from the archive were also used; the details of these are summarised in Table  2. The VLA datasets used were chosen to best match the resolution of the LOFAR observations. Observations at 8 GHz in D configuration as well as 1. 4 GHz and 325 MHz in B configuration were used to image the core of NGC 6251. Observations at 1. 4 GHz in D configuration were used to analyse the large-scale structure of NGC 6251. The B-configuration 325 MHz data and D-configuration 1.
4 GHz data were imaged and reduced in casa 4. 7  (Mc-Mullin et al. 2007). A simple calibration strategy was adopted for the D-configuration 1. 4 GHz data. The flux scale was that of  Perley & Butler (2013). An initial phase calibration was performed using the flux calibrator followed by the bandpass calibration and a final amplitude and phase calibration. NGC 6251 was observed as two pointings, one centred on the core and the other on the southern lobe. Both pointings were imaged in two steps. In the first round of imaging we applied a mask that excluded large extended regions, and did not carry out multi-scale cleaning. Once all compact emission or narrow emission, such as the jet, was The VLA 325 MHz data were calibrated similarly, with one additional step at the start of the procedure. Data from the Global Positioning System (GPS) were used to generate a map of the ionospheric electron content, and a phase correction based on this map was applied to the data using the casa task gencal.
Spectral index The integrated spectral index was calculated for each component of NGC 6251 using the fluxes shown in Table  4. Fig. 8  shows the best-fitting power law for the spectrum of each component and Table  4  lists the fitted spectral indices. It should be noted that the uv range of the interferometric maps used to measure the flux densities are not matched as we did not have access to the uv data for all the images. This could lead to an artificial steepening of the measured spectral index in regions of diffuse extended emission such as the lobes. The core of NGC 6251 can not be separated from the inner jet region in the LOFAR, WSRT or low-resolution 1. 4 GHz images. The flux from the core contributes to the inner jet region. In order to subtract the core contribution from the inner jet region, archival 325 MHz, 1.
4 GHz and 8 GHz VLA data were used. Table  4  shows the core fluxes measured from each of these datasets. The spectral index of the core as measured from these data is inverted and has a value of α = +0. 3 ± 0. 1. Using this spectral index, the core flux was predicted for each of our datasets and subtracted from the integrated flux of the inner jet region. To investigate the variation of the spectral index across the source and reduce the ambiguity associated with inconsistent uv coverage, the LOFAR data were re-imaged with a uv range of 140-4400 λ, matching that of the VLA. This ensures that both total-intensity maps used to calculate the spectral index include emission from the same spatial scales, although they may still differ in the uv coverage of the specific observations. The resulting LOFAR image has an rms of 1.
87 rad m -2 and φ max-scale ≈ 0. 46 rad m -2. φ max-depth ≈ √ 3 δλ 2 (3a) δφ ≈ 2 √ 3 ∆λ 2 (3b) φ max-scale ≈ π λ 2 min , (3c) We calculated the Faraday spectrum from the Stokes Q and U images, neglecting spectral dependence of the polarised flux, using the RM synthesis code pyrmsynth 7. In our first iteration, we searched the entire range of Faraday depths to which our observations were sensitive, from -1000 rad m -2 to +1000 rad m -2 , using a coarse Faradaydepth cell size of 2 rad m -2. From this spectrum we excluded 7 https://github. com/mrbell/pyrmsynth any structure at large Faraday depths. In our second iteration, we searched over Faraday depths from -300 rad m -2 to +300 rad m -2 with a cell size of 0. 2 rad m -2 to properly sample the rotation-measure spread function (RMSF). Fig. 11  shows the RMSF of the LOFAR data.
We calculate the age t of both components following  Alexander & Leahy (1987) + B m µG 2 , (8) where B m = 3. 18 (1 + z) 2 is the equivalent field strength of the cosmic microwave background radiation assuming the present day temperature of 2. 726 K. Using the equipartition magnetic fields in Table  5  (see Section 4. 1. 1) this places lower limits of t 250 Myr for the age of the northern extension and t 210 Myr for the southern backflow. The data for both the northern lobe and southern lobe show no sign of a break. Taking 10 GHz as a lower limit for the break frequency we find that the ages of both the northern and southern lobes have an upper limit of t 40 Myr.
(2011)  show that the galaxy overdensity is larger in the direction of the shorter main jet of NGC 6251. There is therefore reason to believe the environment of NGC 6251 could be asymmetric. However, the current available X-ray data is not sufficient to investigate this directly. A separate estimate of the particle energetics comes from observations of inverse-Compton emission. Takeuchi et al. (2012)  fit a model to radio, X-ray and gamma-ray data. They find the combined data are best fitted with a magnetic field in the lobe of B = 0. 37 µG (approximately 3 times smaller than the equipartition magnetic field calculated in Section 4. 1. 1) and an injection spectral index of α = -0. 5, which breaks to α = -0. 75 at E b = 1. 5 × 10 9 eV. This gives an energy ratio of U e /U B = 45 and an internal pressure of 8.
ACKNOWLEDGEMENTS JHC acknowledges support from the  Science and Technology Facilities Council (STFC)  under grants  ST/M001326/1  and  ST/R00109X/1. We would like to thank  Karl-Heinz Mack  for providing fits images for the previously published WSRT and Effelsberg maps. AMMS, JDB & TMC gratefully acknowledge support from the  European Research Council  under grant  ERC-2012-StG-307215  LODESTONE. RM gratefully acknowledges support from the  European Research Council  under the European Union's  Seventh Framework Programme  (FP/2007-2013) / ERC  Advanced Grant  RADIOLIFE-320745. PNB is grateful for support from the  UK STFC  via grant  ST/M001229/1. MJH acknowledges support from the  UK Science and Technology Facilities Council  [ ST/M001008/1 ]. This research made use of Astropy, 8 a community-developed core Python package for Astronomy (Astropy Collaboration et al.
Agnostic bioinformatic analysis The raw data were analyzed as previously described  [12]. Briefly, reads of high-quality aligned with the human genome from A549 cells were discarded. To increase the precision of the taxonomic assignation, the analysis was performed on all bacterial transcripts except ribosomal rRNA, which were discarded. Remaining reads were considered as sequences of interest and assembled into contigs. Resulting contigs and non-assembled reads (singletons) were aligned using BLAST (November 2017 release) alignment  [16]  of the EMBL-EBI nucleotide sequence database (https://www. ebi. ac. uk/).
Estimation of T > C conversion rates and conversion indexes Based on the results of the agnostic analysis (see results), resulting contigs were mapped back with minimap2  [17]  onto Acholeplasama laidlawii strain PG8A genome (RefSeq AccNum. CP000896. 1). Positive hits were then tiled on Acholeplasama laidlawii using Mummer 3  [18]  to evaluate the degree of completeness of the newly built sequence. The set of quality filtered reads for each point was mapped back to the corresponding reference sequence with minimap2 and resulting alignments filtered using the samtools  [19]  view module (0x904 flag and Quality Map ≥ 30). The pileup module of the htsbox software (https:// github. com/lh3/htsbox) was then used to detect all mismatches which had a base quality of 30 or more at every position of alternatively internal and external (LC5_ALAID_CNS built from the LC5 condition data) reference sequence.