dataesr/scientific-paragraphs-categorization huggingface dataset:
8 ). However, based on the crystal structure of the first crystallised clade 7 member, the magnesium chelatase BchI monomer (PDB ID: 1G8P)  38  , aligned onto active hexamers from other clades, AAA+ ATPases of clade 7 were proposed to rely on a trans-acting Sensor 2 contributed by the neighbouring monomer in the hexamer  25. The first pseudo-atomic model of the RavA hexamer, which was based on a fit of the monomer structure into a negative stain C6-symmetric EM map  2, 8  and guided notably by relative positioning of Sensor 2, agreed with this hypothesis and suggested that oligomerisation of MoxR ATPases is required for completion of their ATP binding sites. Specifically, in MoxR-type AAA+ ATPases, the ATP binding site was proposed to be located not between the large (αβα) and small (all-α) AAA+ domains of the same monomer, but between the large domain of one monomer in a hexamer and the small domain of its neighbour  2, 8.
Data collection was performed on an FEI Polara microscope operated at 300 kV. Movies of 40 frames were collected with a total exposure time of 8 s and a total dose of 40 e -Å -2 on a K2 summit direct electron detector (Gatan) at a magnification of 41,270x, corresponding to 1. 21 Å pixel -1 at the specimen level. Specimen motion during data collection was evaluated and corrected with MotionCor2  57, 58. Frames 3-40 of each movie were dose-weighted, summed and kept for further processing. The contrast transfer function (CTF) of each micrograph was determined with GCTF  59. 1819 best micrographs were selected based on visual quality control and CTF inspection. As previously noticed  13  , and despite the high affinity of RavA for the LdcI  12  , the LdcI-RavA cage is extremely sensitive to the cryo-EM grid preparation process, which results in a very low amount of intact particles per image.
This difficulty in sample preparation limits the number of particles available for further analysis, complicates the particle selection process and hinders obtainment of a high-resolution structure. Eventually, 18,902 particles were manually picked using EMAN2 e2boxer  60  and subjected to two rounds of 2D classification with RELION-2. 0 61 to yield a cleaned dataset containing 15,771 particles. For 2D classification and all further steps, CTF-amplitude correction was performed starting from the first peak of the CTF. Visual analysis of 2D class averages immediately revealed considerable heterogeneity in RavA conformations/positions while LdcI appeared more rigid. The initial 3D model based on 2D class averages was calculated with sxviper (SPARX)  62  imposing D5 symmetry and appeared similar to our previously published map  13.
Subsequent 3D classification in RELION-2. 0 was performed with C1 symmetry in order to remove the remaining incomplete cages containing 3 or 4 RavA hexamers. This led to a clean dataset containing 11,866 particles. 3D refinement with Relion auto-refine, using the initial sxviper 3D model low-pass filtered to 40 Å as a reference and imposing C5 symmetry, led to a 7. 3 Å resolution map. Albeit already much better than our previous map  13  , the resulting map showed a lower resolution of RavA in comparison to LdcI, which again pointed to structural heterogeneity of RavA particles inside the LdcI-RavA complex. Thus, a soft mask was created from a fit of one hexameric RavA 8 and two LdcI decamers  12  into the map, and the dataset was expanded by replicating each particle from the C5 consensus refinement and adding n*72°with n = 1,…,5 to its first Euler angle.
A masked 3D classification was then conducted with RELION-2. 0 without angular search. This procedure enabled a reliable separation of the dataset into two classes, containing 47 and 53% of the data, respectively. Unexpectedly, both classes showed left-handed RavA spirals, with a clearly defined seam facing either the upper (orientation A) or the lower (orientation B) LdcI decamer and related exactly by a 180°rotation (Supplementary Fig. 1 ). In order to be able to combine images of both orientations, we applied a -phi, 180°-theta, 180°+psi transformation to the Euler angles of orientation B to bring it into orientation A. Images corresponding to orientation A and rotated orientation B were then subjected to a masked local 3D auto-refinement (RELION-2. 0), with the mask that again included two LdcI decamers and one RavA hexamer.
In Class 1, the densities corresponding to all RavA domains contacting LdcI (i. e. LARA domains) are well resolved. In contrast, in Class 2 as well as in the preceding 7. 3 Å map from the masked refinement containing all particles, the density of one LARA domain is missing. Post-processing of LdcI-RavA Cryo-EM maps and fitting of structures. Local resolution estimation and subsequent filtering of maps were performed in RELION-3. 0, using B-factors of -200, -250 and -300 Å 2 for the masked 3D refinement containing all particles, or particles from Class 1 and 2, respectively. For fitting of atomic models in the resulting filtered maps, we used the previouslydetermined crystal structures of LdcI (PDB ID: 3N75)  12  and RavA (PDB ID: 3NBX)  8. Careful analysis of the RavA crystal packing revealed that RavA was crystallised as a continuous helix.
In each map, two decameric LdcI molecules extracted from PDB 3N75 and one spiral RavA hexamer extracted from a continuous RavA helix generated from PDB 3NBX were first manually placed using Chimera  64  , and then fitted separately using iMODFIT  27  , followed by a single round of B-factor (ADP) refinement in Phenix  65, 66. Cryo-electron microscopy on free RavA. Purified RavA (see above) was diluted to a final concentration of 0. 1 mg ml -1 in the presence of 1 mM ADP and incubated at room temperature for 10 min. 3 μL of RavA:ADP was applied to glowdischarged (20 mA, 45 s) R2/1 400 mesh holey carbon copper grids (Quantifoil Micro Tools GmbH). Grids were plunge-frozen in liquid ethane with a Vitrobot Mark IV (FEI) operated at 100% humidity using blot force 1 and a blot time of 2 s. Data collection was performed on an FEI Polara microscope operated at 300 kV.
A total of 2944 movies comprising 40 frames were recorded at a tilt angle of 0°on a K2 summit direct electron detector (Gatan Inc) operated in counting mode. Movies were collected with a total exposure time of 6 s and a total dose of 40 e -Å -2. Preliminary processing suggested that RavA adopted a strongly preferred orientation on the grid. To overcome preferred orientations of RavA, a further 1083 micrographs were recorded with a 30°tilt, with a total exposure time of 6 s and a total dose of 44 e -Å -2. All movies were recorded at a magnification of 41,270x, corresponding to a pixel size of 1. 21 Å pixel -1 at the specimen level, with a target defocus range of 1. 8-3. 8 µm. RavA:ATPγS grids were prepared as for RavA:ADP, except with a 10 min incubation with ATPγS instead of ADP. Data collection was performed on a Glacios microscope (Thermo Scientific) operated at 200 kV.
A total of 2809 movies (1224 of which were tilted to 30°) comprising 29 frames were recorded on a Falcon II direct electron detector (Thermo Scientific) at a magnification of 116,086×, corresponding to a pixel size of 1. 206 Å pixel -1 at the specimen level. Movies were collected with a total exposure time of 6 s and a total dose of 41 e -Å -2 ,with a target defocus range of 1. 5-3. 5 µm. Image processing and 3D reconstruction of free RavA. Motion correction on both RavA:ADP datasets was carried out using MotionCor2 57. After discarding the first two frames, the remaining frames were aligned, dose-weighted and summed. CTF parameters were determined on aligned dose-weighted sums using CTFFIND4  67  , and micrographs with an estimated resolution by CTFFIND4 of better than 8 Å were kept for further processing.
Because of the observation that RavA was present as a spiral in the LdcI-RavA cage, particles were picked from all micrographs using the particle-picking software FPM 68 using a spiral hexamer extracted from the RavA crystal structure (PDB ID: 3NBX)  8  filtered to a resolution of 20 Å as a reference. Per-particle CTF estimation was then carried out on selected particles using GCTF  59  to account for variations in defocus across the tilted micrographs. A total of 924,000 particles were picked, and particles were extracted with a box size of 256 × 256 pixels. Particles from untitled and tilted micrographs were separately subjected to several rounds of 2D classification in RELION-2. 0, then combined prior to 3D classification resulting in a cleaned dataset of 562,000 particles. 3D classification without imposed symmetry was subsequently carried out with four classes, using an asymmetric initial model generated in RELION-2.
Particles from this class underwent a homogeneous refinement against the resulting volume, resulting in a map with a resolution of 5. 96 Å after post-processing in RELION-2. 1, which was sharpened with a B-factor of -350 Å 2. For the RavA:ATPγS dataset, micrographs were motion corrected using MotionCor2, after discarding the first two frames. CTF estimation was carried out using GCTF. Particles from the best 1044 micrographs after manual screening were picked using Gautomatch (http://www. mrc-lmb. cam. ac. uk/kzhang/), using a. mrcs stack containing projections of both the C2-symmetric and spiral RavA hexamers as a reference. The resulting ~477,000 particles were subject to per-particle CTF estimation using GCTF. Particles were imported into CryoSPARC and 2D classification was then carried out.
Classes showed significant heterogeneity and a strongly preferred orientation, even more so than for the RavA:ADP dataset (see Supplementary Fig. 6 ). Fitting of structures and refinement. Local resolutions of 3D reconstructions were calculated in RELION-3. 0  70. All resolution estimates are calculated using the 0. 143 gold-standard Fourier shell correlation (FSC) criterion  63. For fitting of atomic models, a RavA hexamer (for the spiral RavA conformation) and two RavA trimers (for the C2-symmetric RavA conformation) were extracted from a continuous RavA helix generated from the crystal structure (PDB ID: 3NBX)  8  , and were fitted into the corresponding maps using iMODFIT  27. The two resulting models were then subjected to a single round of ADP refinement in Phenix, followed by geometry minimisation  65, 66.
5, 31. 25 or 0 nM LdcI. To check for nonspecific binding during the experiments, nonfunctionalised biosensors were used to measure the signal from the highest ligand concentration as well as running buffer. All data were fitted with the FortéBio Data Analysis 9. 0 software using a 1:1 interaction model. Average values and standard deviations for the RavA-ADP:LdcI interaction measured at four different pH values (8, 7, 6. 5, 5) are: K D = 29. 7 ± 10. 0 nM, k on = 2. 68 × 10 4 ± 2. 13 × 10 3 M -1 s -1 , and k dis = 8. 11 × 10 -4 ± 3. 41 × 10 -4 s -1 (Supplementary Table  2 ). RavA ATPase activity measurements. RavA ATPase activity was measured at 25 °C on a Infinite® 200 microplate reader (TECAN). The reaction was performed in 100 µl of 50 mM MES/Tris pH 5-9, 10 mM MgCl 2 , 2 mM ATP, 0. 02% (v/v) Triton X-100, 1 mM DTT, 17 μg ml -1 RavA.
Acknowledgements We thank  Guy Schoehn  for establishing and managing the cryo-electron microscopy platform and for providing training and support. We are grateful to  Aymeric Peuch  for help with the usage of the EM computing cluster and to  Ambroise Desfosses  and  Leandro Estrozi  for discussions. This work was funded by the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  647784  to IG. For electron microscopy, this work used the platforms of the Grenoble Instruct-ERIC Center (ISBG:  UMS 3518 CNRS-CEA-UGA-EMBL ) with support from  FRISBI  ( ANR-10-INSB-05-02 ) and GRAL, a project of the  University Grenoble Alpes graduate school (Ecoles Universitaires de Recherche) CBH-EUR-GS  ( ANR-17-EURE-0003 ). IBS acknowledges integration into the  Interdisciplinary Research Institute of Grenoble (IRIG, CEA ).
The IBS electron microscope facility is supported by the  Rhône-Alpes Region , the  Fondation pour la Recherche Médicale (FRM) , the fonds  FEDER , the  Centre National de la Recherche Scientifique (CNRS) , the  Commissariat à l'Energie Atomique et aux Energies Alternatives (CEA) , the  University of Grenoble Alpes ,  EMBL , and the  GIS-Infrastructures en Biologie Santé et Agronomie (IBISA). MJ was funded by a  CEA  IRTELIS PhD fellowship, JF was supported by a long-term  EMBO  fellowship ( ALTF441-2017 ) and a  Marie Skłodowska-Curie actions Individual Fellowship  ( 789385 , RespViRALI).
Human subjects Subjects described in this study represent combined cohorts recruited under individual IRB-approved research protocols (KFSHRC RAC# 2121053, 2080006 and 2070023). In each of these protocols, we selectively recruited individuals with at least one of the following features: (a) positive family history consistent with a Mendelian inheritance of the disorder and (b) phenotypic presentation consistent with a previously published Mendelian disease. Informed consent was obtained from all subjects prior to their enrollment. Phenotypic data were collected from all subjects. Blood was collected in EDTA tubes for DNA extraction and in sodium heparin tubes for the establishment of lymphoblastoid cell lines (LCL). Occasionally, blood collected in PAXGene tubes was the only source of RNA.
Positional mapping, WES, and variant identification The method of combining positional mapping and variant identification using WES has been described elsewhere  [1, 22]. Briefly, all samples were genotyped on an Axiom SNP platform, and the regions of homozygosity (ROH) were determined to guide the search for the likely causal variant whenever the phenotype and family history are compatible with autosomal recessive inheritance. WES was performed as described before, and the resulting variants were filtered by the autozygome coordinates  [3, 23]. Variants were filtered using gnomAD and a local population database (2379 exomes) for allele frequency of < 0. 001 and were interpreted by following the ACMG guidelines  [24]  to determine the likely causal variants. Although protein-truncating variants may exert their pathogenic effect at the level of the final transcript via NMD, we have chosen to exclude them because it is very difficult to disentangle their effect on protein from that on RNA.
RNA-Seq and computational analysis RNA samples of the subjects were prepared at KFSHRC and sent to the KAUST core lab for RNA sequencing. The quality of each RNA sample was determined based on its RNA Integrity Number (RIN) using Agilent 2100 BioAnalyzer. Those samples that scored RIN < 6. 0 were not considered further. The sequencing libraries were prepared using Illumina TruSeq Stranded mRNA. Paired-end 150 bp reads were generated on Illumina NovaSeq6000. GTEx RNA-Seq samples  [25]  for blood and skin tissue types were downloaded from the Database of Genotypes and Phenotypes (dbGaP) and transformed into the fastq format using SRA Toolkit (https://www. ncbi. nlm. nih. gov/sra/ docs/toolkitsoft/). Samples with RIN < 8. 0 were not included in our GTEx controls. RNA-Seq reads from both patients and GTEx were also aligned to hg38 (GENCODE 25) using STAR 2.
6  [26]  with the two-pass option. Only reads mapped to chromosomes 1-22 and X were considered. SAMtools  [27]  and BEDTools  [28]  were applied to the BAM files to quantify the occurrence of annotated and unannotated splicing junctions, as well as to count nonsplit reads mapped to intronic regions. Splicing junctions with < 5 read supports were filtered out. To quantify the transcript abundance levels, RNA-Seq reads were also mapped to the reference transcript sequences for hg38 (GENCODE 25) using Kallisto  [29]. Using the generated BAM files and transcript abundance levels, error-free normal transcript abundance levels were estimated with the omega quantification  [30]. Briefly, omega computes an adjusted count per million (CPM) value for each coding gene g, ω g , as follows: ω g ¼ X t∈T g w t x t where T g is the set of mRNA transcripts for gene g, w t is the rate to express annotated, normal transcript t based on the RNA splicing data, and x t is the CPM level of transcript t.
Thus, low values of ω g can indicate low abundance outliers or splicing outliers that escaped NMD. From the GTEx data, RNA-Seq datasets of the "Cells -EBV-transformed lymphocytes" and "Cells -Cultured fibroblasts" tissue types were selected as the control for cases derived from blood and skin tissue types, respectively. To ensure the use of an appropriate set of samples in control for each patient, we measured the median of the ω g values of each coding gene for all the blood and skin tissue types in the GTEx datasets and confirmed that the selected tissue type gave the highest level of correlation with the patient data. Based on the second percentile of the ω g values in the corresponding control, two scores, α g and β g , were measured to analyze the severity of transcriptional aberrations in gene g for each patient.
Similarly, a high beta score means that the fraction of the normal transcripts of a given gene is lower compared with a lower end of the same gene in the control set. With these scores, each coding gene g was selected as a causative candidate for each patient i if all of the following criteria are met: 1. Either α g (i) ≥ 3. 0 or β g (i) ≥ 3. 0. 2. For all the other patients j with RNA samples being the same cell type, α g (i) < α g (j). Note that criterion 2 is based on 11 RNA-Seq datasets from RNA samples with RIN > 8. 5 (4 from fibroblasts and 7 from LCL) and that this criterion was set specifically for comparison based on a small number of patients. To visualize splicing events, BAM files were first converted into the hg19 coordinate using CrossMap  [31]  and Integrative Genomic Viewer  [32]  was used.
The breakdown of the six classes of TDVs is summarized in Fig. 4  and is described below in detail. (1) Class (a) variants: a total of 175 (representing 64. 3% of all TDVs) unique variants involving the canonical intronic splice sites were identified. RTPCR data were available for 93 (4 from literature and 89 from this cohort). Although this class is generally classified as "loss of function," we note that several resulted in in-frame rather than frameshift indel (Additional file 2: Table  S1 ). More concerning was the finding of canonical splicing variants in established disease genes with no resulting phenotype, i. e. , non-penetrance (Additional file 8: Table  S6 ). For example, the variant NM_001172818. 1:c. 300 + 1G > A in PGM1, was identified in homozygosity in individuals with no phenotype despite its deleterious effect on splicing (confirmed by RTPCR), which explains its high population frequency.
Please note that blood-derived RNA was extracted from PAXGene and/or LCL and these are listed separately in Additional file 2: Table  S1. We found that 84. 1% (195 out of 232) of the tested genes are expressed in the blood-derived RNA, 85. 8% (199 out of 232) in fibroblast-derived RNA and 90% (209 out of 232) in the renal epithelial cells-derived RNA. The majority of genes were expressed in all three sources of RNA (75. 5%), while only 2. 6% (6 out of 232 genes) were not expressed in any of these sources. We were able to detect the aberrant transcript associated with TDVs in controls who lack the respective variant in only 11/169 (6. 5%) of those that were empirically tested. In all these instances, the aberrant transcript was much less abundant in controls, and in none of these cases was the aberrant transcript listed in Ensembl or UCSC Genome Browser.
Second, our finding of aberrant transcripts not described in databases that are detected in controls, despite their very low frequency, recapitulates the challenge described in previous studies in identifying the signal from noise when interpreting RNA-Seq. We suggest that while greater in magnitude, this challenge is no different in principle from the challenge of identifying the candidate causal variant in WES/WGS, and that filters that improve the signal/noise ratio are even more acutely needed in RNA-Seq. For example, we show in this study that the use of autozygome coordinates drastically reduces the search space in RNA-Seq (up to a factor of 300 in one case). While we acknowledge this filter is not always applicable, it should be pursued even in the absence of clear history of consanguinity since its integration into existing WES/WGS is straightforward as has been shown before  [3].
Availability of data and materials The raw RNA-seq data from 11 human samples are deposited in NCBI Sequence Read Archive under bioproject ID PRJNA625628  [50]  (https://www. ncbi. nlm. nih. gov/bioproject/PRJNA625628). Source code is deposited in Zenodo  [51]. Pipe line for Alpha/beta-based scoring and filtering of candidates is deposited in guthub  [52]. Whole exome sequence data sets are deposited in NCBI under dbSNP  [53]. The expression dataset of Genotype-Tissue Expression (GTEx) Common Fund Project is available through Genotypes and Phenotypes (dbGaP) under accession number phs000424. v8. p2 (third party data).
Index case sample and data collection Demographic and clinical data of index cases were collected with standardized case report forms. Blood samples were collected for RT-PCR twice: on the day of enrollment (D1) and on the day fever subsided (Day of defervescence, Ddef). Additional blood samples were collected once a day during the febrile phase between D1 and Ddef and at 2-week and 2-month follow-up time points for a heme-agglutination inhibition (HI) test and other immunological studies. Index cases were classified into DF and DHF according to WHO criteria (World Health Organization, 1997) (Figure  1 ).
Household member samples and data collection Once the index cases were confirmed for DENV infection, their HHM were enrolled within 1-2 days. Their body temperature and clinical symptoms were recorded on standardized case report forms throughout the follow-up period. Blood samples were taken for DENV RT-PCR on the first day of enrollment (D1) and then between 24À72 hours later. The result of DENV RT-PCR became available within 24 h after blood collection and dictated subsequent investigation and follow-up. If DENV was detectable by RT-PCR, two drops of blood were taken onto filter paper  (Aubry et al. , 2012)  every 2-4 days until DENV become undetectable twice in a row (or the subject was lost to follow-up). The clinical followup also ended when DENV was undetectable twice consecutively by RT-PCR (Figure  1 ).
METHODS A document search was performed using PubMed; guidelinespecific databases (Guidelines International Network); and online government portals. Search terms included specific disease name and relevant jurisdiction ('ovarian cancer treatment guidelines OR pathway in Canada'). Inclusion criteria were guidelines widely used in routine ovarian cancer treatment as validated by working group members from each country, and guideline revisions from the past 20 years to highlight the frequency of updates over time. Information was extracted for jurisdiction; organization(s); publication year(s); and treatment modality. A working group of 19 clinicians was formed to validate the guideline comparison and provide additional insight into clinical practice differences. A questionnaire was developed based on a previous survey conducted by Farrell et al investigating changes in surgical practice for ovarian cancer in Australia and New Zealand  15  and extended to include additional questions.