dataesr/scientific-paragraphs-categorization huggingface dataset:
Molecular genetics studies All patients underwent routine mtDNA diagnostic testing that excluded variants in the mitochondrial genome. Next generation sequencing strategies followed by filtering and candidate variant analysis were undertaken to elucidate the molecular bases of studies on mitochondrial disease patients. GnomAD (https:// gnomad. broadinstitute. org/) database was used for minor allele frequency analysis (≤0. 01%). In silico pathogenicity tools were used to assess the pathogenicity of candidate variants and classified as "likely pathogenic" using the Association of Clinical Genomic Science (ACGS) and The American College of Medical Genetics and Genomics (ACMG) guidelines  (Richards et al, 2015)  (https:// www. acgs. uk. com/media/11631/uk-practice-guidelines-for-variantclassification-v4-01-2020.
In silico analysis and structural modelling The structures of DRP1 (PDB: 4BEJ), DRP1 in complex with GDP. AlF 4 (3W6P), DRP1 in complex with GMP-PCP (3W6O), and co-assembled DRP1-MID49 (PDB: 5WP9) were used to assess the structural implications of the patient mutations using PyMOL by Schr ödinger (https://pymol. org/2/). In silico mutagenesis was performed using Modeler software with standard parameters (https://salilab. org/ modeller/).
Analysis of mtDNA nucleoids, mitochondrial network, and peroxisomal morphology using the Columbus system Cells were synchronised overnight by starvation using DMEM medium with 0. 1% FBS  (Mitra et al, 2009). G0-arrested cells were plated out on to 96-well plates and cultured in DMEM containing 10% FBS for 24 h before incubation with 0. 15% PicoGreen (P7581; Invitrogen) for 30 min at 37°C. After three washes in Flurobrite DMEM (A1896701; Thermo Fisher Scientific), Z-stack images were taken on a Zeiss CellDiscoverer7 microscope with 50× water objective (NA 1. 2). Maximum projection images were analysed using the Columbus (PerkinElmer) software system and for each image field, the proportion of enlarged nucleoids classed as over 1. 5 μm 2 were calculated in the total nucleoid pool. Statistically significant differences were calculated via non-parametric one-way ANOVA using GraphPad Prism.
0 cm, 7. 0 μM) in place. The eluate was detected using a DAWN-EOS multiangle laser light scattering instrument and the Optilab refractive index detector (Wyatt Technologies). Data analysis was performed using the ASTRA software package (Wyatt Technologies). Data were imported into RStudio (1. 4. 1106) (RStudio Team, 2021) and tidied as described above. Traces were then normalized and centre-scaled to allow for easier comparison using caret (6. 0-86)  (Kuhn, 2020). Chromatograms were then generated using ggplot2 (3. 3. 3) (Wickham, 2016)  and RColorBrewer (1. 1-2)  (Neuwirth, 2014)  and plotted with molar mass (right axis) and normalized and scaled dRI (left axis) as a function of time (x-axis) (https://github. com/Hill-Lab/DNM1L-Variants-Scripts). Chromatograms are representative of two independent protein preparations for wild-type and each variant.
SYPRO orange fluorescence was measured in 1-min intervals with a temperature ramp of 1°C per minute using a Stratagene Thermoler Mx3005P. Data were imported into RStudio (1. 4. 1106) (RStudio Team, 2021) and tidied as described above. Melting temperatures (T m ) were determined by the temperature corresponding to the maximum value of the first derivative of the fluorescence signal. Statistical significance of T m value alterations were determined by a one-way ANOVA followed by Tukey's honest significant difference test. Plots were generated using ggplot2 (3. 3. 3) (Wickham, 2016)  and RColorBrewer (1. 1-2)  (Neuwirth, 2014)  with dFluorescence/dTime (y-axis) as a function of time (x-axis). Two biological replicates, each with three technical replicates, were used for analysis and plot generation (https://github.
Molar ellipticity was converted to mean residue ellipticity (Equation (  4 )) and data were scaled to a baseline of 0 at 260 nm using Microsoft Excel. Data were imported into RStudio (1. 4. 1106) (RStudio Team, 2021) and tidied as described above. Plots were generated using ggplot2 (3. 3. 3) (Wickham, 2016)  and RColorBrewer (1. 1-2)  (Neuwirth, 2014)  with CD signal in terms of mean residue ellipticity on the vertical axis and wavelength on the horizontal axis (https:// github. com/Hill-Lab/DNM1L-Variants-Scripts). Protein concentrations were determined again after data collection using the absorbance and theoretical extinction coefficients at 205 and 214 nm to ensure that equivalent amounts of protein were used. Ω = θ × MRW 10 × c × d (4) where Ω = mean residue ellipticity, MRW = mean residue weight calculated as the protein molecular weight/(N-1) where N = total number of residues, c = concentration (mg/ml), and d = path length (cm).
The Columbus (PerkinElmer) software was used to quantify the hyperfusion of patient mitochondrial networks relative to controls and a minimum of 5,500 mitochondria were analysed for each case. The immunofluorescence labelling was performed three times. (D) Analysis of DRP1 co-localisation with the outer mitochondrial membrane protein TOM20 by immunofluorescence labelling of age-matched controls (C1: paediatric, C2: adult) and DNM1L patient (P1-P4) fibroblasts with anti-DRP1 (red puncta) and anti-TOM20 (in blue). DRP1 co-localisation with mitochondria was analysed in.
Acknowledgements R McFarland  and RW Taylor are supported by the  Wellcome Centre for Mitochondrial Research  ( 203105/Z/16/Z ),  Mitochondrial Disease Patient Cohort (UK)  ( G0800674 ), the  Medical Research Council (MRC) International Centre for Genomic Medicine in Neuromuscular Disease  ( MR/S005021/1 ), the  UK NIHR Biomedical Research Centre for Ageing and Age-related disease  award to the  Newcastle upon Tyne Foundation Hospitals NHS Trust , and the  UK NHS Highly Specialised Service for Rare Mitochondrial Disorders of Adults  and  M Ol  áhov á and RW Taylor receive funding from the  Pathology Society. R McFarland ,  M Ol  áhov á, and RW Taylor receive funding from the  Lily Foundation. OM Russell is supported by the  Wellcome Centre for Mitochondrial Research  ( 203105/Z/16/Z ). CL Alston is supported by a  National Institute for Health Research (NIHR)   Post-Doctoral Fellowship  ( PDF-2018-11-ST2-021 ).
The different experiments aiming at retrieving ship-borne GNSS IWV have confirmed the quality of the retrieved IWV at 1-3 kg m -2. Ship-borne GNSS IWV are thus used for the documentation of water vapour space and time distribution during dedicated campaigns  [30] [31] [32] [33]  or for the assessment of satellite-borne water vapour radiometer  [34, 35]. The main foreseen advance will be the use of ship-borne GNSS IWV for climatology and NWP. For climatology, the availability of data over long periods is low, and the systematisation of the recording of raw GNSS data acquired by ships still needs to be developed. Concerning NWP, first recent results show the contribution of ship-borne GNSS IWV assimilation for a better modelling of intense rainfall events, with an improvement of the forecasted precipitation amount and location  [36] ; However, real-time assimilation is still limited by two constraints: firstly, the unavailability of accurate products for GNSS satellites orbit and clock corrections; secondly, the lack of a fast and robust transmission method, which considerably delays data availability.
In this context, GNSS raw data are recorded continuously from the R/V Marion Dufresne as the first ship-borne GNSS water vapour observatory. This dataset is intended to be analysed routinely and operationally with low latency (1 to 3 days) for IWV retrieval along the research vessel route. In the long term, these observations will contribute to the documentation and understanding of atmospheric phenomena in the Southern and Indian Oceans and could be integrated to international meteorological databases for assimilation in NWP systems. The objective of this paper is therefore to assess the quality of the IWV retrieved by GNSS on the R/V Marion Dufresne and to validate the routine restitution of the latter. To this end, this paper is organised as follows. In the first part, the GNSS dataset will be introduced with a first assessment of the quality of the raw data and a description of three analysis strategies that have been set up for the retrieval of ship-borne GNSS IWV (two operational analyses and a reprocessing).
In the second part, comparison datasets from more conventional IWV estimates are described; these are GNSS Continuously Operating Reference Stations (CORS), European Centre for Medium-range Weather Forecasts (ECMWF) fifth reanalysis (ERA5) and ground-launched radiosonde data. In the third part, the shipborne GNSS IWV will be assessed by comparisons with these conventional datasets. In the fourth part, the two daily operational data analyses will be assessed with respect to the reprocessing analysis. Finally, the results will be summarised and the perspectives on the continuous monitoring of water vapour over the Indian Ocean by ship-borne GNSS will be drawn.
GNSS Measurements In October 2020, a GNSS acquisition system was installed on the R/V Marion Dufresne. This system is made up of a Trimble Alloy GNSS receiver and Trimble Zephyr 2 antenna. This system is suitable for providing high-quality carrier-phase data that is required to retrieve accurate positions and tropospheric parameters. GNSS raw data is recorded continuously with a time resolution of 15 s. Data is acquired with an elevation cut-off angle of 3°. Such a low elevation cut-off angle may be useful for a better separation of height and troposphere parameter estimates during the GNSS analysis. In addition to GPS measurements, GLONASS (Globalnaïa Navigatsionnaïa Spoutnikovaïa Sistéma) and Galileo measurements were also recorded; however, only GPS measurements were considered in the following as the software used in the GNSS analysis, GIPSY-OASIS v6.
4 (hereafter GIPSY)  [37] , does not process GLONASS and Galileo measurements. GNSS raw data is made available at the end of each day on a FTP server. The R/V Marion Dufresne is based in Reunion Island. Its missions consist of two main activities: first, the delivery of supplies for the benefits of the TAAF (Terres Australes et Antarctiques Françaises, French Austral and Antarctic Lands) administration; secondly, the participation in scientific campaigns at sea. For these activities, the route followed by the vessel presents typical patterns as shown on Figure  1. During the period of interest investigated in this study (1 October 2020 to 30 June 2021) the R/V made two logistical missions in the Austral Islands (Saint-Paul and Amsterdam, Kerguelen and Crozet) as well as various scientific missions off the tropical islands of Mayotte, Mauritius, Tromelin and extra-tropical islands of Marion and Prince Edwards.
In order to assess GNSS raw data quality, a quality diagnostic was performed using the translation, editing and quality check (TEQC) software  [38]. The results of this diagnostic are presented on Figure  2. In the following, we first investigate the multipath indicator (interference in the code and phase measurements induced by reflections or scattering by surfaces in the vicinity of the GNSS antenna) for frequency carriers L1 and L2 of GPS signals, and on the ratio of the number of observations per cycle slip (which happens when a carrier phase is lost; the smaller this ratio is, the worse the quality of the observations is). Two specific periods are identified. First, from 1 October to 10 March, the antenna was installed on an intermediate deck of the vessel (the so-called "Poignée", see Figure  3 , red circle); such a location was shown to be prone to multiple interferences, as can be seen in the time series of the ratio of the number of observations per cycle slip with values around 100.
• One re-analysis of the raw data over the whole period (repro). Note that GIPSY only allows post-processing of the acquired data with a minimum latency of about 1 h, which may limit its use for NWP issues. For real-time analysis of raw GNSS data, another processing tool should be preferred (e. g. , RTKlib  [39] ). The main characteristics of the three strategies are presented in Table  1. We used Jet Propulsion Laboratory (JPL) products for satellite orbits and clocks: ultra-rapid products, with a 300 s sampling, for the ultra analysis; rapid products, with a 30 s sampling, for the rapid analysis; final products, with a 30 s sampling, for the repro analysis. The phase and code raw data were analysed in a 30 h window centred on noon (GPS-Time) of each day from which the 00:00-24:00 parameters were extracted to avoid edge effects.
9% to 0. 4%, which corroborate the gain in quality due to this change. We also observe a larger number of rejected values for the repro solution than for the rapid solution: this difference is mainly due to the periods of interruption of the routine solutions around days 105, 109, 173 and 180 of 2021 (surface measurements data for IWV conversion were unavailable then) when a large number of outliers were rejected for the screening of the repro solution. Figure  5  gives an overview of the final availability of GNSS IWV from the three analyses. Breaks include system failure of GNSS receiver, surface meteorological sensor interruption (affecting ultra and rapid retrieval), and outliers rejected by screening. For the ultra processing, 12. 3% of the data is missing, with 14 interruptions longer than 30 min.
Ground GNSS Antenna Along its track, the R/V Marion Dufresne passed close to ground continuously operating reference GNSS stations (CORS) as indicated in Figure  1. For each territory highlighted on the map, only one reference station was considered, the selection criteria being based on data availability, proximity of the station to the coastline and to mean sea level. The GNSS raw data was also analysed using GIPSY, following a procedure that has been widely used in previous studies  [22, 23]. Hereafter, we present the main characteristics of the processing. Only GNSS data for the GPS constellation is considered and processed in PPP mode with ambiguity resolution; we used JPL final products for orbits and clocks of GPS satellites. Analysis was performed in static mode with the estimation of one single position per day for each station.
ZWDs and horizontal gradients were estimated after a priori correction for ZHD and ZWD from ECMWF analysis, with a sampling of 300 s. Elevation dependency for troposphere delays was modelled by VMF1 mapping function. As for the ship-borne ZTDs, a screening of the ground estimated ZTDs was also performed in order to reject spurious data or outliers that would degrade comparisons. The screening was reduced to a range check on the formal errors of the estimated ZTDs. The threshold value of 4. 0 mm was chosen on the basis of the distribution of formal errors over the period of interest. This threshold induces a low rejection rate in the range 0. 0-0. 1% except for the station DRBA, where it reaches 17. 8%. This large rate may be linked to a large number of interruptions of acquisition from this antenna over the period.
A very good match is observed with the reprocessing analysis showing low difference rms and high correlation coefficient. These results highlight the performance of a routine observation of water vapour over the oceans with the help of ship-borne GNSS antennas. The deployment of the GNSS antenna on the R/V Marion Dufresne is now sustainable. The IWV measurements retrieved, as presented in this study, will be used for long-term periods, as it is customary for ground-based antennas. Rms difference of routine analysis are consistent with threshold requirement mentioned by  [55]  for global and regional NWP, but the latency should be still reduced. This could be reached thanks to the maritime VSAT carried on the R/V Marion Dufresne. An hourly transmission of GNSS raw data is thus expected in the near future and will enable an hourly analysis with a latency of a few hours (typically below 2 h).
The operational use of ship-borne GNSS IWV as assimilated data in NWP thus becomes reachable. Further assessments of GNSS and IWV data quality collected onboard the RV Marion Dufresne will be conducted in the near future through organising dedicated GNSS verification experiments, based on the launch of collocated radiosondes onboard the vessel and also on the use of satellite-borne water vapour measurements  [49, 56]. Moreover, further intercomparison procedures will also be set up in the the tropical Indian Ocean to benefit from the recent, and relatively dense, ground-based GNSS network deployed in Madagascar and Scattered Island of Grande Glorieuse, Juan de Nova, Europa and Tromelin in the frame of the IOGA4MET research program  [57, 58]. following GNSS data providers: the GAGE facility operated by UNAVCO, Inc.
, with support from the National Science Foundation and the National Aeronautics and Space Administration under NSF cooperative agreement no. EAR-1724794; the SONEL service (https://www. sonel. org/, accessed on 17 February 2022); the RGP service, operated by the IGN (https://rgp. ign. fr, accessed on 17 February 2022). We also acknowledge the ICARE data and services center (https://www. icare. univ-lille. fr, accessed on 17 February 2022) for providing radiosondes data.
Funding: MAP-IO is a scientific programme led by the  LACy/La Réunion University  and was funded by the  European Union  through the  ERDF programme , the  University of Reunion , the  SGAR-Réunion , the région Réunion, the  CNRS , the  TAAF , the  IFREMER  and the  Flotte Océanographique Française. This work was supported by the  CNRS programme LEFE/INSU  through the project  GEMMOC.
STRENGTHS AND LIMITATIONS OF THIS STUDY ⇒ The study was conducted using a well-recognised and robust populational medical administrative database that confers many advantages, such as the completeness of mortality data and comprehensiveness of healthcare reimbursed for all patients. ⇒ This is the first study, to our knowledge, to specifically compare patients with uncontrolled severe asthma with the overall asthmatic population, in the French population. ⇒ Comparability of studied groups was ensured by careful matching process. ⇒ The lack of clinical data was mitigated by the use of a comprehensive algorithm to identify the uncontrolled severe asthmatic population with great care in the definitions of asthma, severity and control to ensure the accuracy of the cohorts. ⇒ The criteria requiring continuous insurance coverage may have resulted in a marginally lower number of younger patients being included due to studentspecific insurance offered during school years.
] [5] [6] [7]  In the context of the rapidly evolving landscape of expensive GINA step 5 asthma treatments, it is crucial to generate up-to-date data on the real-life population burden of uncontrolled severe asthma in adolescents and adults and to understand the relative impact of the disease in patients with severe asthma compared with the general and non-severe asthmatic populations. The framework of the French Health Data Hub project provided the opportunity to access the public medico-administrative database to address this specific issue. The RESONANCE study was undertaken to: (1) characterise uncontrolled severe asthma in patients aged 12 years and older and (2) compare the disease burden in terms of mortality, healthcare utilisation and associated costs within this population to two series of matched controls from the general population and the asthmatic population (ie, total asthmatic population excluding those with uncontrolled severe asthma).
Study design A retrospective, non-interventional cohort study was carried out using a large populational database from France, the Echantillon Généraliste des Bénéficiaires (EGB). All individuals aged 12 years and over in the EGB were included in the source population. A cohort of adolescent and adult patients who had uncontrolled severe asthma during 2014 were identified. Their index date was the date of the first event identifying non-control during 2014. Two control cohorts were defined, one from the general population and another of patients with asthma. For each selected control, the assigned index date was the calendar index date of respective matched patients from the uncontrolled severe asthma cohort. Historical information on comorbidities, healthcare use and treatments received was collected up to 5 years preceding the index date to assess baseline characteristics.
Data sources The EGB is an anonymised permanent sample of health insurance databases representing 1/97th of the entire French population. 8 9 Given the universal healthcare system in France, the EGB is a sizeable and representative sample of all subjects covered by various social security schemes, and now includes nearly 660 000 beneficiaries. The EGB contains information on demographics (age, sex, area of residence and so on); outpatient care reimbursement (including drug dispensing); medical care and reimbursement received in outpatient and hospital settings; specific information on the right to universal supplementary health coverage (CMU-c); whether beneficiaries are affected by a long-term condition (affection de longue durée; ALD) and the date of death. The database benefits from the interlinkage of several administrative data sources, thereby allowing an assessment of beneficiaries' healthcare utilisation over time.
Collected data and outcomes The following sociodemographic and clinical characteristics were considered: ► Up to 5 years prior to index date: comorbidities, treatments received, healthcare utilisation (medical visits, emergency department (ED) visits, hospitalisations). ► At index date: age, sex, CMU-c status. ► During follow-up: -Date of death, if applicable. -Drugs of interest: asthma treatments and other classes of interest (online supplemental file 1). -Healthcare utilisation: medical and paramedical visits (general medicine, respiratory specialists, otolaryngology, nursing support, physiotherapy), ED visits and hospitalisations (all-cause and asthma-related). -Direct medical costs associated with medications and healthcare use (outpatient and hospital settings). The primary objective of this study was to assess the number and proportion of patients with uncontrolled severe asthma aged 12 and older.
The final model included the following variables: population (uncontrolled severe asthmatic cohort vs general population), age, history of cardiovascular disease, diabetes, psychiatric disease and cancer. Interaction terms between the variable 'population' and other covariates included in the model were tested. Interaction between population and history of psychiatric disease was significant and was thus included in the final model, with other variables listed above. Costs were evaluated from the collective perspective, and all costs collected between 2014 and 2016 were reevaluated to correspond to 2018 € using the 2018 consumer price index (health data index 4011-E, published by national institute of statistics and economic studies, Institut National de la Statistique et des Études Économiques), since 2018 was the date of data access.
The asthmatic control cohort was selected and matched with patients with uncontrolled severe asthma using propensity scores to control potential confounders, using all available and relevant variables. A conservative approach was used, since some potential confounders included in propensity scores may be preponderant in patients with uncontrolled severe asthma, leading to a potential minimisation of the burden of asthma lack of control and severity. More, given that this control population includes severe but controlled or non-controlled but non-severe patients with asthma, it is not a population exclusively comprising low-risk patients. The study was conducted using the EGB, a wellrecognised and robust populational medical administrative database that confers many advantages, such as the completeness of mortality data and comprehensiveness of healthcare reimbursed for all patients.