dataesr/scientific-paragraphs-categorization huggingface dataset:
Data and method We analysed the ionospheric data from 6,870,739 echoes (6850 tracks) from the Mars Advanced Radar for Subsurface and Ionospheric Sounding (MARSIS) radar sounder in subsurface mode. The data set consists of vertical Total Electron Content (vTEC, i. e. , the vertical integral of the electron density from the satellite to the ground, expressed in TEC units with 1 TECu = 10 16 e À m À2 ) for the period 19-06-2005 to 01-02-2014  (Safaeinili et al. , 2007; Mouginot et al. , 2008). Only data with acceptable signal to noise ratio threshold (i. e. , SNR is greater than 15 dB,  Grima & Kofman, 2008)  where used. The data from year 2005 were also excluded after quality visual check (i. e. , too much noisy and unrealistic vTEC values). Finally, the data set used consists of 5,308,594 echoes (more than 75% of the entire data set).
MoMo model validation To validate the model, the output from MoMo is compared with a completely independent data set, consisting of 250 electron density profiles of the dayside ionosphere (SZA [50°-85°]) extracted from Mars Express radio occultation experiments MaRs  (Peter et al. , 2014). The mean differences between MoMo and MaRs data is 0. 01 ± 0. 05 TECu for SZA greater than 50°(Fig. 5 ), which is as the same level as the differences with MARSIS data. The mean of the differences in the North is 0. 00 ± 0. 04 TECu, while the differences in the South are 0. 03 ± 0. 06 TECu. This could be due to more intense ionospheric variability in the South not well represented in MoMo which aims at representing the climatology of the vTEC. Furthermore, these differences are lower than 10% of the vTEC values.
It is advised to apply ionospheric corrections even during moderate solar activity level, above all for long term studies (i. e. , more than one Mars sidereal year), such as those aiming at studying the deep interior of the planet. The model provides key radio-science quantities like phase delay and Doppler shift, which can be used to calibrate radio science data like those acquired by RISE on the ongoing InSight mission, by LaRa on the future mission ExoMars 2020 mission, or by historical lander missions (Viking, Pathfinder, MER). In particular, the Mars ephemeris calculations could certainly benefit from the calibration using MOMO of the S-band Viking landers ranging data.
Acknowledgements. This work has been financially supported by the  Solar and Terrestrial Centre of Excellence  (STCE, http://www. stce. be/)as well as by the  Belgian PRODEX program  managed by the  European Space Agency (ESA)  in collaboration with the  Belgian Federal Science Policy Office (BELSPO). Also, support from the  ESA-ESTEC Faculty  is acknowledged. We are grateful for the providers of the tools and findings from SPICE or NAIF/PDS resources, and of the teams that provided the data used to make SPICE kernels. The ensemble of the pictures is produce using the Generic Mapping Tool (GMT, Wessel et al. , 2013). We also thank  ESA  for providing the TEC data set from MARSIS (ftp:// psa. esac. esa. int/) and the  NGDC/NOAA  for providing the F10. 7 data (ftp://ftp. ngdc. noaa. gov). Finally the authors would like to highlight the  Rob Lillis from UC Berkeley Space Sciences Laboratory  and the anonymous person for their constructive review which clearly contributed to the improvement of the paper.
). Such requirements cannot be met by existing standard model atmospheres such as Kurucz  (Kurucz 1979). MARCS model atmosphere  (Gustafsson et al. 2008 ) is able to address these problems, but we decided to use the PHOENIX model atmosphere code  (Allard & Hauschildt 1995)  to take advantage of an already existing collaboration with France Allard and her team. We want to construct, test, and validate a high-resolution synthetic spectral library using PHOENIX model atmospheres  (Allard et al. 2011)  with an accurate description of both molecular and atomic lines associated with a reliable tool to estimate stellar parameters of M dwarfs. Directly fitting observed high-resolution spectra to synthetic library can yield stellar parameters (T eff , log g, and log [M/H]), while simultaneously enabling us to conduct a systematic error analysis.
Phoenix model atmosphere PHOENIX is a state-of-the-art stellar and planetary atmosphere code that can compute atmospheres and spectra of stars across the HR-diagram, ranging from T Tauri and main sequence stars to evolved giants and white dwarfs, as well as for novae, supernovae, brown dwarfs, and extra-solar giant planets. This paper concentrates on low-mass stars (0. 8 M ≤ M ≤ 1. 5 M ) on the main sequence. The modelling of a stellar atmosphere is determined by three main parameters: the effective temperature (T eff ), the surface gravity (log g), and the global chemical composition relative to the Sun, i. e. the so-called metallicity (log [M/H]). Depending on these prior parameters, a simultaneous solution of the chemical equilibrium, the hydrostatic equilibrium, and radiative transfer equations is calculated iteratively until a prescribed criterion is reached.
Stellar sample We use observed spectra from the spectropolarimetric observations from PolarBase  (Petit et al. 2014). PolarBase is a database that contains all public stellar data collected with ESPaDOnS at the Canada-France-Hawaii-Telescope (CFHT) and its twin, NARVAL, at Télescope Bernard Lyot (TBL), both of which are high-resolution spectropolarimeters  (Donati 2003). We used intensity spectra normalized to the apparent continuum processed with Libre-ESpRIT data reduction software  (Donati et al. 1997). During the fitting procedure, we correct for any discrepancies due to normalization between observed and synthetic spectra. Instrumental broadening plays a major role when estimating the stellar broadening, it is then essential to characterize the instrumental profile broadening. The spectral resolution of ESPaDOnS and NARVAL is 68000, the instrumental broadening correspond to a Gaussian profile with a full width at half-maximum of 4.
4 km. s -1. Thus, the instrumental profile is simulated with a Gaussian function with the associated broadening. In the present paper, we analyse high S/N (>100) and highresolution ESPaDOnS/NARVAL spectra of 17 main sequence dwarfs with spectral types G and K, including the Sun, 18 Sco, HD 4915, and ξ Boo A (see Table  2 ). The characteristics of the stars in our sample, range from 5000 K to 6000 K in T eff , 4. 0-5. 0 in log g and -0. 5 to 0. 5 in log [M/H]. They are well-characterized stars in common with  Brewer et al. (2016)  and ESPaDOnS/NARVAL database.
Spectral line-list calibration on standard stars We updated the old PHOENIX atomic line data with the latest line data from the Vienna Atomic Line Database, VALD3  (Piskunov et al. 1995; Kupka et al. 1999; Kupka, Dubernet & VAMDC Collaboration 2011)  in early 2017. The database predominantly consists of line data from  Kurucz & Bell (1995) , but VALD is regularly updated with critically evaluated data sets. We extracted all lines between 5 000 and 8 000 Å to be absolutely sure that we did not miss any weak lines. For every line, we used the PHOENIX internal approximation  (Schweitzer, Hauschildt & Allard 1996)  for Stark, radiative, and van der Waals damping parameters, unless any values were manually changed during the line calibration procedure. The molecular line-list contains lines complied from various sources described in  Allard, Homeier & Freytag (2012).
Material and Methods We provide the theoretical background with analytical expressions and a detailed methodology to determine the optimal shape of the TG width from measurements of the aSG (asynchronous sweeping gap) tests with a Farmer ion chamber  [1]. For a difference between adjacent leaves equal to s, the total area of the TG profile to be subtracted from the fluence map, A(s), can be obtained from measurements. The TG width w(s) can then be determined as the first derivative of A(s) and fitted to a function with two parameters a1 and a2. Parameter a1 represents the TG width for large s values and a2 introduces a reduction in the TG width near the leaf tip end to account for the increased transmission through the tongue due to the rounded leaf design. An MLC model similar to the one used by the Eclipse TPS was implemented in MATLAB.
Material and Methods In this pilot study, a clinical 6 MV beam model for a Varian Clinac 2100iX was adapted in Eclipse (AAA 13. 5. 35) to match reference data collected from the IROC Houston site visit program, thus modeling an "average" performance linear accelerator. 23 output measurements, including PDD values, off-axis factors, and 12 square test fields ranging from 2x2 to 30x30 using both primary collimator-and multileaf collimator (MLC)-defined fields, were assessed in order to characterize the goodness of fit with respect to the reference output data. Once the model was verified, ten clinically acceptable head and neck phantom IMRT plans utilizing both dynamic MLC and VMAT techniques were recalculated following modification of the dosimetric leaf gap (DLG), MLC transmission factor, and effective target spot size parameters.
For example, the human genome reference sequence is still incomplete, which is demonstrated by the work that the Genome Reference Consortium (GRC) is undertaking to fill remaining sequence gaps, as well as representing different population haplotypes. 8 However, the incompleteness of the human transcriptome should also be a consideration for genome interpretation, since a complete set of transcripts from all the different tissue types and developmental stages that are naturally present is not yet available. Until it is possible to confidently generate a de novo genome assembly for a patient, in conjunction with a complete transcriptome (and for example, proteome), researchers and clinicians must rely upon data that is available in public databases. Therefore, improvements in diagnostics can, in part, be achieved through improvements in gene annotation.
At the current time, most GENCODE (i. e. Ensembl)  9  and RefSeq 10 gene annotations are based on cDNA and EST libraries produced alongside the initial experimental phase of the Human Genome Project, while the large datasets produced by more recent RNA-Seq and long-read sequencing-based projects remain largely unincorporated. Such datasets have the potential to add exons via transcript models to existing gene annotations and these features can provide new insights into genetic disease. For example, 'expanded' exomes offer the potential to capture additional disease-linked variants beyond the reach of previous studies. Additional sequences can be added to existing exome 'panels' used for diagnostics in the clinic and used to select regions for resequencing in people without a molecular genetic diagnosis.
Indeed, this has already been demonstrated for DLG2, where newly identified exons were observed to be deleted in people with neurodevelopmental disorders. 11 Furthermore, the resequencing of annotated regions identified previously missed pathogenic variants linked to epilepsy in SCN8A. 12 Meanwhile, additional transcript features can also be used to reappraise existing variation datasets; providing, for example, new mechanistic explanations for known disease-associated variants and also allowing for the reconsideration of variants from whole-genome studies that had previously been de-prioritised due to an apparent lack of transcription. In fact, modern transcriptomics datasets have the potential to add an enormous number of transcribed features to gene annotation catalogues and the exact proportion of this 'transcriptional complexity' that is linked to gene function and therefore to phenotype, remains hard to fathom.
Overall, this work has added 3550 GENCODE transcript models to 191 genes associated with DEE via the utilisation of publicly available short-and longread transcriptomics datasets. Subsequently, we create an expanded exome panel for eight genes associated with DS, incorporating 125 transcript regions and after resequencing 122 people with a clinical diagnosis of DS or a similar phenotype, discover two de novo variants within SCN1A exonic sequences. Both variants are found within presumptive poison exons that exhibit poor evolutionary conservation. In contrast, we also demonstrate that a third DS-associated variant, previously considered to be intronic and of unknown significance, is present in an alternative SCN1A poison exon that has deep conservation. Although further work is required to understand the biological implications of the transcriptional complexity associated within SCN1A and the larger set of 191 genes, our findings relating to DS show that from the current bioinformatics perspective, uncertainties regarding transcript functionality are not necessarily a barrier to the utility of these transcripts in disease genetics.
Manual re-annotation substantially increases the number of transcripts We manually re-annotated 191 genes associated with epilepsy as part of the GENCODE project, primarily using publicly available long-read transcriptional datasets including brain-derived SLR-Seq and PacBio Iso-Seq reads (see Methods). In total, 3550 alternatively spliced transcripts were annotated across the 191 genes, increasing the number of transcripts for these genes from 1807 to 5357. All transcripts are included in GENCODE v28; contemporary RefSeq annotation contains 1397 NM and XM transcripts. The majority of added transcripts contain either complete exons (37%) or alternative splice sites within existing exons (21%). In total, more than 674 kilobases (kb) of additional exonic sequence was added across the 191 genes.
Updated annotation identifies putative clinically relevant variants Given these considerations, we decided to investigate the clinical impact of the annotations without initial regard to their expression or conservation metrics. First, we compared the overlap of preexisting and updated annotation with the public variation dataset in ClinVar <https://www. ncbi. nlm. nih. gov/clinvar/>. We note that ClinVar is currently heavily biased towards 'known' gene sequences in terms of content, i. e. disease-associated variants are less likely to be found in regions that are less well studied (or have been resequenced less often). Nonetheless, we found that 23 existing ClinVar variants could be made 'exonic' by our annotations (Supplementary Data 1, sheet 1). This set does not include a further 36 ClinVar variants that fall within 8 bp of an existing splice site, due to the possibility that any pathogenicity associated with these variants is due to the disruption of splicing and not directly associated with the annotation (Supplementary Data 1, sheet 2).
'Biotype' details the functional effect of the feature as inferred by manual annotation. The alternative final exon within model ENST00000642141 was annotated as 'non-coding' due to the absence of polyadenylation data as per GENCODE guidelines; the functional status of this model is in reality unknown. Feature conservation describes the annotation and structurally identical feature in the mouse ortholog Scn1a showed bifrontal slow waves and rare temporal spikes. MRI was normal apart from an asymptomatic pituitary cyst. She is currently being treated with a combination of valproate, clobazam and topiramate, which has reduced seizure frequency. To quantify the significance of de novo variant enrichment in the 450 bp of CDS sequences (sum of features 7, 9, 12 and 14 from Table  1 ) and to estimate the probability of identifying two or more de novo variants in a cohort of 122 sequenced probands, we performed a de novo variant enrichment statistical test using the fitDNM package.
DISCUSSION We have used human brain-transcriptomic data to rigorously interrogate the human reference transcriptome for missing transcriptional features, focusing on 191 genes associated with DEE. We investigated a cohort of people with DS, which has a robust phenotype/genotype correlation with SCN1A, with the presumption being that any such exon features that capture a variant in SCN1A could support a diagnosis. On investigation, we identified three de novo variants within three distinct poison exons of the SCN1A gene that are associated with DS, each of ENST00000635893. 1 patient 2 c. 79 G>A ENST00000636759. 1 patient 1 c. 301 G>A Family 1 G G A G T G C A G/A T G G C G C G A G G A G T G C G T G G C G C G A G G A G T G C G T G G C G C G A Family 2 which is absent from current diagnostic tests.
Gene annotation Manual re-annotation of the 191 genes was performed on GRCh38 (https://www. ncbi. nlm. nih. gov/grc/human) according to the guidelines of the HAVANA (Human And Vertebrate Analysis and Annotation) group  48  (and ftp://ftp. sanger. ac. uk/pub/project/havana/Guidelines/ Guidelines_March_2016. pdf). In summary, the HAVANA group produces annotation, largely based on the alignment of transcriptomic (ESTs and mRNAs) and protein sequence data from GenBank  49  and Uniprot. 50 hese data were aligned to the individual bacterial artificial chromosome clones that make up the reference genome sequence using BLAST  51  with a subsequent realignment of transcript data by Est2Genome. 52 In addition, SLR-RNA-Seq data  53  mapped using Gmap,  54  PACBIO 55 reads mapped using STAR  56  and foetal and infant RNA-Seq data  19  mapped using cufflinks,  57  were also used to identify transcripts and splice junctions.
Data are available at www. gencodegenes. org/releases/. Updated annotation of the 191 genes described in this study on GRCh38 is represented in GENCODE releases from v27 (August 2017) onwards. In addition, the updated annotation is available remapped to GRCh37 [https://github. com/diekhans/gencode-backmap] here: http:// www. gencodegenes. org/releases/grch37_mapped_releases. html.
RefSeq annotation for transcript counting was extracted from the Ensembl release 84 (March 2016) "RefSeq GFF3 annotation import". Identification of transcriptional features on GRCh38 using foetal and infant RNA-Seq data Illumina data from Jaffe et al. 19  was re-mapped for foetal and infant transcriptome to GRCh38 to identify transcriptional features. FASTQ files from the following datasets were downloaded from ENA: SRR1554537, SRR1554538, SRR1554541, SRR1554544, SRR1554546, SRR1554549, SRR1554551, SRR1554553, SRR1554554, SRR1554566, SRR1554567 and SRR1554568. Data were mapped to GRCh38 with TopHat (tophat-2. 0. 13). 58 eads mapping to the gene regions that were studied, were merged into two files containing foetal and infant alignments using SAMtools. 59 ranscript models were generated from the foetal and infant BAM files using Cufflinks (cufflinks-2.
Quantification of gene expression at exon level Raw reads from Jaffe et al. ,  19  were available via study accession SRP045638. The 36 paired-end libraries were analysed using the iRAP pipeline (https:// github. com/nunofonseca/irap). First, raw reads in the original FASTQ files underwent quality assessment and filtering. 60 They were then aligned against the GRCh38 genome reference using Tophat2,  61  with the option: '-min-intron-length 6'.
Mapping annotation from reference human genome to mouse genome The TransMap cross-species alignment algorithm was used to map all annotated transcripts from the reference human genome (GRCh38) to the reference mouse genome (GRCm38). The alignments are created using synteny-filtered pairwise genome alignments (chains and nets) produced using BLASTZ. 23, 63, 64 All transcript models mapped to mouse were manuallyassessed to identify failures to align correctly at the base, exon and intron level.
Analysis was performed in-house with a standardised pipeline integrated in genomecomb. 66 The pipeline used fastq-mcf (https:// expressionanalysis. github. io/ea-utils/) for adapter clipping. Reads were then aligned using BWA-MEM  67  and the resulting SAM file converted to BAM using Samtools. 59 BAM files were sorted using biobambam. 68 ealignment in the neighbourhood of indels was performed with GATK. 69 mplicon primers were clipped using genomecomb. 66 Variants were called at all positions with a total coverage ≥5 using both GATK  69  and Samtools. 59 t this initial stage, positions with a coverage <5 or a score <30 were considered unsequenced. The resulting variant sets of different individuals were combined and annotated and filtered using genomecomb. 66 ariants with a coverage above seven and a GATK quality score above 50 that were identified by both variant callers (GATK and Samtools) and were absent in publicly available databases (gnomAD,  70  1000 Genomes,  71  Exome Variant Server (http://evs.
gs. washington. edu/EVS/)) and our in house database, were extracted. Furthermore, variants present in homopolymer regions (>8 homopolymers) or simple repeat regions were excluded. The effect of the variant was predicted with the Ensembl Variant Effect Predictor (VEP),  72  using the manually annotated GENCODE dataset as custom gene annotation. Variants were validated and the segregation was checked using bi-directional Sanger sequencing. For de novo variants, maternity and paternity was confirmed using an in-house developed multiplex PCR panel consisting of 16 STR-markers scattered over the genome, including the X and Y chromosome. To predict the effect of the de novo variants on splicing efficiency, we used the 'quick mutant' analysis from Human Splicing Finder,  73  using the CDS from the NMD transcripts as custom sequence input.
Testing for significant excess of de novo variation among SCN1A CDS protein-coding sequence The genomic sequences corresponding to protein-coding features of SCN1A (Features 7, 9, 12 and 14; Table  1 ) were concatenated to reflect a consecutive test sequence of length 450 bp on GRCh38. To test whether the observed number of two de novo variants among this stretch of 450 bp was significant, based on a sampled cohort of 122 individuals, we adopted a modified version of R package fitDNM. 36 The published fitDNM provides both a PolyPhen-2 weighted and Poisson unweighted p-value. Here, since PolyPhen-2 scores do not exist for the entirety of the CDS sequence we focus on the Poisson unweighted P-value. The modification to the original fitDNM package was to correct a type conversion error, which possibly occurred due to different versions of R used for our analysis (v3.
5. 0) compared to the original package version. Specifically, we fixed a clash where a 'T'-valued variable (intended for Thymine) was handled as "TRUE". The adapted fitDNM package accompanied by input and output files from this analysis are available upon request. This R package then takes as input the underlying mutability of the 450 bp (Supplementary Data 3), the total number of observed de novo variants among that 450 bases of sequence and the total number of probands tested for a de novo variant in that 450 bases of sequence. Subsequently, we conservatively corrected the resulting Poisson unweighted P-value by 18,000 to reflect approximately the total number of WES studied protein-coding genes in the human exome.
ACKNOWLEDGEMENTS We thank the following: all patients, their families and carers who participated in this study, as well as the teams who were involved in recruiting patients and gathering samples and data at the respective study sites;  Dr. Gautam Ambegaonkar ,  Dr. Rajiv Chaudhary ,  Ms Helen Dolling ,  Ms Margo Elsworth ,  Dr. Jill Gordon ,  Dr. Alasdair Parker ,  Dr. Elizabeth Radford ,  Ms Kuldeep Stohr  ( NHS England ) for clinical support and advice;  Dr. Andrew Jaffe  ( Lieber Institute for Brain Development, USA ) for advice with utilising the 6 different life-stage transcriptomic datasets used in this study;  Dr. Matthew Hurles  ( Wellcome Sanger Institute, UK ) for initial discussions regarding this study. We also thank  Imogen  and  Jasper Steward , both of whom have rare neurological disorders and have been instrumental in driving this study.