dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements This work was supported by  Inserm  and  University of Montpellier. We thank the  MRI facility  for their assistance,  T. Sauka-Spengler  ( University of Oxford, United Kingdom ) for shipping the Tg(foxd3-mcherry) ct110 zebrafish line,  M. Bagnat  for shipping Tg(rcn3:gal4/UAS:mCherry) line. A. Kawakami  ( Tokyo Institute of Technology, Japan ) for the junbl plasmid,  K. Poss  ( Duke University Medical Center, USA ) for the nrg1 plasmid,  R. Kelsh  ( University of Bath, United Kingdom ) for the Fkd6in situ hybridization plasmid. MP and DS acknowledge financial support from the  France Génomique National  infrastructure, funded as part of " Investissement d'avenir " programme managed by the  Agence Nationale pour la Recherche  (contract  ANR-10-INBS-09 ). We also thank the  Zebrafish facility  of the  University of Montpellier , the  MRI  facility for their assistance and the CARTIGEN platform.
Societal challenges addressed by ocean observations Understanding the synergies and effects of changes that happen in organisms, from cells to communities, is a daunting task for any path towards sustainable development. In the same way that standardized and openly shared weather observations around the world are used for weather forecasts and monitoring climate change, we need interoperable and timely data on marine biodiversity to better forecast ecosystem functions and services  [22, 23]. The EOVs were designed for this purpose. This directly satisfies several United Nations (UN) Sustainable Development Goals  [9, 10, 12, 23, 24]. Marine Life 2030 seeks to advance the implementation of the GOOS Framework for Ocean Observing, building on a collaboration between programs of the Intergovernmental Oceanographic Commission (IOC; including the GOOS Biology and Ecosystems Panel, the Ocean Biodiversity Information System/OBIS, and the Ocean Best Practices System/ OBPS) and the Marine Biodiversity Observation Network (MBON), GEO Blue Planet, the Deep-Ocean Stewardship Initiative (DOSI), the UN Environment Programme World Conservation and Monitoring Centre (UNEP-WCMC), and the Ocean Knowledge-Action Network (Ocean KAN) of Future Earth.
This helps to increase accountability. Different ecosystem-based fisheries management approaches will require different observing and forecasting strategies, and will vary in the implementation to collect information about specific EOVs and in addressing specific capacity building needs  [25]. There are two main sources of information on fish: fisheries data and scientific monitoring (fisheries-independent) data. Commercial fisheries stock status is monitored with an expanding array of methods ranging from direct capture, mark and recapture and more recently genetic approaches, supported by well-developed assessment models and fisheries statistics. While there are commercially fished stocks that are yet to be monitored adequately, fisheries data can be biased as they highly depend on fishing effort and strategy.
While fisheries independent data are costly to acquire, they are essential to monitor and evaluate the status of fish species and marine ecosystems more accurately than using only fisheries-dependent catch estimates. The GOOS Bio-Eco description of the Fish Abundance and Distribution EOV  [42] , provide additional guidelines on the information that may be collected for this EOV. Schmidt et al. [25]  provide case studies and recommendations for the design and use of ocean and fisheries observations in management and research applications. Monitoring priority #3: Marine Megafauna: Tracking changes in 'marine turtles, birds, and mammal abundance and distribution' EOVs. Both within and between national jurisdictions, marine megafauna are instrumental in shaping the structure and function of marine ecosystems.
Well-coordinated, long-term ocean sound observation systems can produce multi-dimensional time-series datasets with which to characterize soundscapes and the interconnections of overlapping biological, physical, and manmade sound sources  [58]. Economic monitoring of the ocean requires a baseline and subsequent acoustic surveys of water column biomass (fish, plankton), benthic biota and habitats, and human-generated sounds at the regional to global scales that complement the Seabed 2030 project, an international initiative to map the bathymetry of the ocean by 2030  [59] [60] [61].
This requires expanding ocean biological-ecological EOVs (plankton abundance; marine megafauna distribution, seagrass, mangrove, macroalgae, coral cover, and soundscapes)  [81]  in large, standardized databases such as the World Ocean Atlas. There are several important areas of investment to improve seascape monitoring for societal benefit. New hyperspectral satellites promise information on phytoplankton functional types, water quality, and shallow benthic habitats. The Landsat 7 and 8, and Sentinel 2 sensors have been used in many coastal studies, complementing the coarser resolution global ocean observing satellites. An opportunity lies in developing consistent global operational products to address coastal and aquatic issues, including wetlands and shallow benthic habitat mapping. The NASA Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) satellite, the Geosynchronous Littoral Imaging and Monitoring Radiometer (GLIMR) instrument, and the NASA Surface Biology and Geology (SBG) mission will advance global and regional biodiversity and ocean health measurements  [82, 83].
Further applications continue to develop. Omics approaches, including eDNA, are important areas of investment, specifically to improve and to standardize sample collection and processing methods, to expand genetic libraries to identify taxonomies, to have ongoing accuracy assessments, to standardize and broadly adopt data and metadata management schema, and to incorporate these observations into the GOOS framework in order to advance ecological forecasting. Priority methods #3: Applied Models. Advancing ecological forecasting. Society benefits from a capacity to forecast variables that affect the economy and its future well-being. Ecological forecasting of biodiversity has significant potential to advance management and identify trade-offs among human activities  [90]. Skillful forecasts of weather and physical ocean conditions today are possible for daily and longer time scales.
Priority methods #4: Data management. Cleaning up and organizing our data inventories. An investment in knowledge management is needed to organize information and data. Over the past decade, more data have been collected each year from autonomous platforms about the physical and chemical state of the ocean than in the entire century before  [90]. Oceanographic data streams are generally not interoperable, are collected by different methods or devices, are stored in different formats, and are often housed in disconnected databases or the personal computers of individual observers. Many data centers restrict access to information, yet at least 24 international treaties and conventions need information on the status of the marine environment, anticipating an expansion of geographic ocean coverage  [13].
The Ocean Decade offers an opportunity to lower costs of access to information, of sensor deployment and recovery, increasing reliability of sensors, observing platforms, and forecasts, and to advance human capacity. Scientific observations need to be collected based on needs identified from socio-economic indicators. The NASA Socioeconomic Data and Applications Center (SEDAC) and the International Renewable Energy Agency (IRENA) procure societal information to help design and evolve biodiversity indicator applications. Some sectoral data are not openly available due to their sensitive commercial nature (e. g. , fisheries, petroleum and gas extraction, etc. ). Collaborations based on respect and trust may help produce useful indicators by aggregating these data and hiding sensitive details.
For example, the Food and Agriculture Organization reports historical catch levels of fish and invertebrates for many areas and can be supplemented with socio-economic data  [6]. Targeting specific applications requires partnerships between ocean observers and local, regional, indigenous, and national governments in order to strengthen inclusion of different knowledge systems, and to improve the alignment of monitoring efforts with societal needs. Information collected over time is essential to measuring and evaluating the outcome of policies and human behavioral changes and changes related to ecological and environmental conditions. Such outcomes and social changes in principle generates feedback that can improve the entire ocean observing value chain (Fig. 1 ). However, most marine biological and ecological observations are collected today through research projects of limited duration.
Capacity development may also be more successful if cultural heritage is integrated into the process  [112]. Finally, a large amount of marine biodiversity data exists solely in publications or in individual databases. Many biological datasets have not been made accessible to the public. Global data rescue and archaeology initiatives, such as conducted by Syd Levitus  [113] , would revolutionize biological knowledge, like it did for physical and chemical data in the 1990's and the first decade of 2000. This is possible only through partnerships, with the support of and coordination with national and multinational organizations, and with funding and incentives for data.
Acknowledgments We are grateful for the support of the  NASA Applied Sciences Office, Biodiversity & Ecological Forecasting Program  to  Maurice G. Estes, Jr , Grant  NNM11AA01A , for funding support for the  Ecosystem Health and Biodiversity session at OceanObs19 , manuscript preparation and publication charges. Frank Muller-Karger  and  Enrique Montes  received partial support from  NOAA  Grant  NA19NOS0120199 ,  NASA  Grants  NNX14AP62A  and  80NSSC20K0017 ,  NSF  Grants  1762493  and  1728913  and the  Gulf of Mexico Coastal Ocean Observing System. Ana M. M. Sequeira  was supported by a  2020 Pew Fellowship in Marine Conservation  and an  ARC   DP210103091. Jacquomo Monk  and  Nina Bednaršek  were supported by the  Australian Government's National Environmental Science Program Marine Biodiversity Hub.
1 VoC 18 (Fig. 4h , supplementary dataset 1). In addition, we noted an L260F substitution in nsp6 associated with SARS-CoV-2 adaptation to weasels 10 that had a 60%-allele frequency in the BZ/2021 inoculum. Four days after infection of ferrets, this mutation had become fully dominant and a second characteristic weasel mutation, Y453F in the spike protein that was first noted in several clusters of SARS-CoV-2 outbreaks in mink farms  10  , had emerged in addition (Fig. 4h , supplementary dataset 1). We furthermore noted the presence of an F184V exchange in nsp6 of the BZ/2021 inoculum, which arose during amplification in VeroE6 cells 18 and was rapidly counterselected against in the ferret host. In contrast, the WA1/2020 inoculum used for our ferret studies did not contain any unreported additional changes (Fig.
4h , supplementary dataset 2). WA1/2020 also acquired a weasel-characteristic mutation when passaged through ferrets, N501T in the receptor binding domain of the spike protein 10 , but no changes in nsp6 were detected and only one animal harbored a virus population with Y453F substitution in low (8%) allele frequency. Neither the GS-621763-experienced BZ/20201 nor WA1/2020 populations contained remdesivir resistance mutations previously selected in SARS-CoV-2 (i. e. E802D in nsp12  19  ) or the related mouse hepatitis virus  20  (i. e. F476 L and V553L in nsp12) nor did any new variants arise in nsp12 at >5% allel frequency, when viruses were extracted from treated animals at the time of termination (Fig. 4h , supplementary datasets 1,2). All vehicle-treated animals efficiently transmitted VoC BZ/2021 to untreated direct contact ferrets (Fig.
4b-e ). Co-housing was started 54 hours after infection and continued until termination of the source animals. Shed BZ/2021 replicated in the contacts without delay, becoming first detectable in nasal lavages within 12 hours after initiation of co-housing. This altered replication profile corroborated BZ/2021 adaptation to the ferret host in the source animals, and virus populations recovered from contacts of vehicle-treated source animals indeed contained both the L260F exchange in nsp6 and the Y453F mutation in spike (Fig. 4h , supplementary dataset 1). Consistent with efficient inhibition of BZ/2021 replication in the treated source animals by oral GS-621763, treatment completely blocked virus transmission to untreated direct-contact animals. None of the contacts of treated source ferrets shed infectious particles or viral RNA at any time (Fig.
Next generation sequencing To authenticate virus stocks, metagenomic sequencing was performed as described  34, 35  , while to sequence lower viral load in vivo samples the COVID-Seq (Illumina) amplicon tiling protocol was used. For metagenomic sequencing, viral RNA was treated with Turbo DNase I (Thermo Fisher), converted to cDNA using random hexamers and SuperScript IV reverse transcriptase, and double stranded cDNA created using Sequenase v2. 0. Sequencing libraries for both sets of libraries were generated using Nextera Flex (illumina) and cleaned using 0. 8´Ampure XP beads and pooled equimolarly before sequencing on an Illumina 1´100 bp NextSeq2000 run. Raw fastq reads were adapter-and qualitytrimmed with Trimmomatic v0. 39  36. To interrogate potential resistance alleles, reference-based mapping to NC_045512.
2 was carried out using our modified Longitudinal Analysis of Viral Alleles (LAVA -https://github. com/michellejlin/lava) 37 pipeline. LAVA constructs a candidate reference genome from early passage virus using bwa  38  , removes PCR duplicates with Picard, calls variants with VarScan 39,40 , and converts these changes into amino acid changes with Annovar 41. Consensus sequences were called with TAYLOR 34 and deposited in NCBI GenBank. Accession numbers (supplementary Table  4 ) are as follows: input strain WA1/2020, MZ433205; WA1/2020 recovered from ferrets, MZ433206 -MZ433213; input strain BZ/2021, MZ433225; BZ/2021 recovered from source ferrets, MZ433214 -MZ433221; BZ/2021 recovered from contacts of vehicle-treated source ferrets, MZ433222 -MZ433224. Raw reads for these sequences are publicly available on SRA (BioProject PRJNA740065).
Statistics and reproducibility The Microsoft Excel (version 16. 48), GraphPad Prism (version 9. 1. 0), and Numbers (version 10. 1) software packages were used for data collection and analysis. One-way or two-way ANOVA with Dunnett's or Tukey's multiple comparisons post-hoc test were used to evaluate statistical significance when comparing more than two groups or two independent variables. When comparing two variables, a two-tailed unpaired t-test was performed to determine statistical significance.
The specific statistical test used to individual studies is specified in the figure legends. RT-qPCR data were collected and analyzed using the StepOnePlus (version 2. 1; Applied Biosystems) software package. Final figures were assembled in Adobe Illustrator (version CS6). All numerical raw data and summaries of individual statistical analyses are provided in supplementary datasets 3 and 4. Effect sizes between groups in the ANOVAs were calculated as η 2 = (SSeffect) / (SStotal) for one-way ANOVA and ω 2 = (SSeffect -(dfeffect)(MSerror)) / MSerror + SStotal for two-way ANOVA (SSeffect, sum of squares for the effect; SStotal, sum of squares for total; dfeffect, degrees of freedom for the effect; MSerror, mean squared error). The statistical significance level α was set to <0. 05 for all experiments.
Acknowledgements We thank  BEI Resources  for SARS-CoV-2 isolates,  X. Xie  and  P. -Y. Shi  for providing SARS-CoV-2-Nluc,  A. Shornikov  of the  Gilead Sample Bank group  for compound distribution, the  High Containment Core  and the  Department for Animal Research of Georgia State University  for support, and  A. L. Hammond  and  D. Porter  for critical reading of the manuscript.
Extrusion was ensured using a pneumatic system, Equalizer and Ultimus V (Nordson EFD, USA). Dispensing pressure was adapted for each suspension. 3D printing was controlled via the Repetier Host software (Repetier, V2. 0. 1, Germany) at a printing speed of 10 mm s -1. The STL file was a homemade hexagonal model of medium complexity,  [6]  which can be printed without any nonprint travel and has a small overhang of 5° (0 being vertical). The file was sliced with the Slic3r software (Slic3r, V3, Italia) using adapted printing parameters. The control sample was printed with Verowhite resin using the Object30 Pro 3D printer (Stratasys, USA). This rigid sample was used as a reference to evaluate the dimensional fidelity of the PDMS samples. Solvent Extraction: The cross-linked silicone containing the hydrophilic gel was immersed in boiling solvent for 12 h, then dried at 80 °C for 12 h and then at 100 °C for a further 12 h.
Acknowledgements This work was supported by the  French government  through  FASSIL  project, selected by  FUI  ( Fonds Unique Interministériel ) and by the  region Auvergne-Rhône-Alpes. The authors especially thank  Jean-Marc Francès  and  Damien Djian  from  Elkem Silicones  for materials supply and very helpful discussions. The authors also acknowledge the  "Centre Technologique des Microstructures (CTμ)" of University Lyon  1 for the SEM experiments and especially  Pierre Alcouffe of IMP, UMR5223 laboratory.
However, monitoring of disease progression includes multiple components, such as symptoms, serial lung function measurements, fibrosis measured by highresolution computed tomography (HRCT) of the chest, exercise capacity assessments, need for supportive care and, potentially, serum biomarkers  [2, 17, 18]. It is expected that healthcare resource consumption will be relatively high in PF-ILD. Healthcare resource consumption may be used as an indirect tool for identifying patients, as there is currently no diagnosis code to identify patients with PF-ILD from medico-administrative databases. Based on insurance claims and survey data in the USA, progressive fibrosis may develop in 18-32% of patients with ILDs other than IPF  [19]. In patients with ILDs other than IPF in the USA over a 3-year time frame, ILD-related mean annual medical costs and hospital claims were approximately 75% and 123% higher in patients with a progressive fibrosing phenotype than in patients with non-progressive disease  [20].
Data source and ethics We conducted a non-interventional, longitudinal, retrospective cohort study using administrative claims data from the French national administrative healthcare database (Système National des Données de Santé [SNDS]), which is managed by the National Health Insurance Fund (Caisse Nationale d' Assurance Maladie [CNAM]). The SNDS includes, in particular, country-wide health insurance data related to ambulatory care (Système National d'Information Inter-Régimes de l' Assurance Maladie [SNIIRAM]), hospitalisations (Programme de Médicalisation des Systèmes d'Information  [PMSI] ) and deaths (Centre d'Epidémiologie des Causes de Décès [CépiDc]), and covers 98. 8% of the French population of over 66 million people  [24]. These merged databases provide comprehensive information on individual activity of healthcare providers, care pathways, healthcare consumption, patient characteristics, hospitalisations and clinical diagnoses in the form of International Classification of Diseases 10th revision (ICD-10) codes.
We used the SNDS database to identify patients with fibrosing ILDs other than IPF, as defined by ICD-10 diagnostic codes appearing on medical claims, and with a progressive phenotype as defined by proxy criteria (see "Case definitions" section). We then developed an algorithm to select patients with PF-ILD other than IPF within the SNDS using ICD-10 diagnostic codes and healthcare consumption data appearing on medical claims between 1 January 2010 and 31 December 2017. This study was conducted as part of the wider PRO-GRESS portfolio under Reference Methodology (MR) 004  [25]  following approval by the Comité d'Expertise pour les Recherches, les Etudes et les Evaluations dans le domaine de la Santé (CEREES) on 18 August 2018 (TPS 72584) and by the Commission Nationale de l'Informatique et des Libertés (CNIL) on 9 November 2018 (918305).
1 For full list of ICD-10 codes and definitions see Additional file 1: Table  S1. 2 For full list of ICD-10 and ATC codes and definitions see Additional file 1: Table  S2. 3 For full list of CCAM, ATC, GHM, LLP and ICD-10 codes and definitions see Additional file 1: Table  S3   Costs were estimated in euros from the national health insurance perspective. For outpatient healthcare resources (general practitioner visits, pulmonary specialist visits, nursing and physiotherapy appointments, laboratory tests, treatments and medical procedures), ambulance use and sick leave, the amount reimbursed by the public healthcare system was directly extracted from the SNDS database. For hospitalisations, the cost of each stay was valued by the diagnosis-related group (Groupe Homogène de Malades [GHM]) using the official tariffs from the French Diagnosis Related Group prospective payment system (source: Agence technique de l'information sur l'hospitalisation, Médecine chirurgie obstétrique et odontologie 2010-2017 tariffs for private and public institutions).
Discussion This study investigated the epidemiology, clinical characteristics, mortality and healthcare resource use and associated costs of patients meeting criteria for PF-ILD in France. The data presented are estimates based on an algorithm to identify patients with a progressive phenotype among a fibrotic ILD population. In addition, this is one of the first studies presenting information on PF-ILDs as a whole based on such a comprehensive nationallevel database, with an estimated 14,413 patients. A previous US claims-based study was based on 373 patients with PF-ILD  [20]. The percentage of patients with fibrosing ILDs other than IPF that developed progression was estimated at 18-32% in a survey of pulmonary, rheumatology and internal medicine physicians from Japan, the US and four European countries  [19] , and at 15% in a US claims database study  [20].
Immunosuppressive drugs are the main therapy for CTD-ILD  [29] , as seen with mixed CTD-ILD in the current study. Regardless of diagnosis, patients received glucocorticoids as their primary therapy: more than 60% in each underlying disease. Similarly, a US claims database study found that 49-69% of patients with PF-ILDs received glucocorticoids  [19]. For patients with IPF, after adjusting for cost years and currency, the estimated mean total annual cost per capita in North America is $20,000, approximately three times higher than per capita healthcare expenditure for the general population  [30]. In a US study of PF-ILD between 2014 and 2016, mean annual medical costs per patient were $77,666, and were $35,364 for ILD-specific claims  [20] , with 83. 6% of the medical costs associated with hospital claims  [20].
Similarly, in our study, the total mean annual cost per patient with PF-ILD was €81,286, and for all hospitalisations was €54,679 per patient. Most patients were hospitalised at least once during the study period, with 34. 3% having at least one admission to intensive care, demonstrating the high burden of disease on patients. We estimate our data to represent the cost to the French National Health Insurance Fund, and that there would be negligible co-payments by patients, with an estimated 79. 2% of patients in the study having full health coverage. Overall, healthcare resource data for investigations and treatments suggest that patients with PF-ILD may not receive sufficient care, and may benefit from being treated in specialised centres. Limitations of this study include the absence of PFT and imaging data, which precludes individual assessment of disease progression; however, this study provides a macroscopic view of PF-ILD and is complementary to our clinical cohort study, which did include such data  [22].