dataesr/scientific-paragraphs-categorization huggingface dataset:
Model input parameters Model inputs were derived from clinical trials and published literature (Table  1 ). Clinical efficacy data on pirfenidone and BSC were obtained from the randomized clinical trials (RCTs) including: ASCEND, CAPACITY-1, CAPACITY-2, and the extension study RECAP  [8, 10, 25, 26]. As patients from the BSC group received pirfenidone after the end of the CAPACITY study, overall survival (OS) data were retrieved from the American Strand registry, for BSC arm  [5]. Parametric survival analyses were conducted to enable extrapolation of the clinical data to the modelled time horizon for BSC and pirfenidone. The following distributions were tested: exponential, Weibull, lognormal, and log-logistic. The selection of the best fitting distribution was made based on the Akaike information criterion (AIC), Bayesian information criterion (BIC), and visual inspection of the survival curves by clinical experts.
The following cost categories were considered for the economic evaluation: treatment acquisition costs, disease management costs, disease-related event costs, adverse event costs, lung transplant costs, and end of life (EoL) costs. Costs of transportation were included in disease management and LT costs. There was no administration cost applied in the analysis since pirfenidone and nintedanib are delivered orally. For treatment acquisition costs, costs of drugs and packaging were extracted from the drugs and tariffs database (BDMIT) of the National Health Insurance  [35]. Pirfenidone is currently available in France as capsules form (267 mg) and as tablets (267 mg or 801 mg). These formulations have an equal cost per mg. An adjusted daily cost of pirfenidone was computed based on the average daily dose observed in the RCTs.
Unit costs were retrieved from official tariffs published by the National Health Insurance  [36] [37] [38]. Costs per cycle were computed from resource uses and unit costs for progression-free and progressed health states. Costs of exacerbation, lung transplant, and end of life were estimated by Cottin et al  [39]. from a French real-world database of hospitalisation data (Programme de Médicalisation des Systèmes d'information, PMSI). Cost of adverse events considered various types of clinical events. Costs of cardiac events were published by Cottin et al  [39]. in their PMSI analysis. Other adverse event costs were retrieved from the 2018 tariffs by diagnosis-related groups (Groupe Homogène de Malades, GHM) published by the ATIH  [40]. Average costs were computed based on the activities by sector (public or private establishments).
Nevertheless, it is conceivable that when nascent IDs are translated, the ribosome may pause or slow down until the interaction partner would arrive and bind, and thus stabilise the nascent ID. However, further systematic studies need to be carried out in order to study the role of translational pausing in co-translational protein assembly. Co-translational assembly in homomeric proteins can also cause premature assembly of protein complexes, if two interacting nascent chains are in close proximity. It has been suggested that homomeric protein IDs are enriched toward the C termini of polypeptide chains across diverse proteomes  39  and this ID localisation is essential to prevent the assembly of homomeric proteins before proper folding. In contrast, our preliminary bioinformatics analyses using a limited curated interaction database  39  suggest that in heterodimeric proteins the N-terminal interaction regions are enriched, further underlining the idea that co-translational protein assembly in heterodimeric proteins is beneficial for assembling cellular machineries.
7 µm. Raw data (. CEL Intensity files) were extracted from the scanned images using the Affymetrix GeneChip® Command Console (AGCC) version 4. 0. CEL files were further processed with Affymetrix Expression Console software version 1. 3. 1 to calculate probe set signal intensities using Robust Multi-array Average (RMA) algorithms with default settings (Sketch quantile normalization). Statistical analysis was performed using the FCROS package version 1. 5. 4  45. Differences are considered significant for p value below 0. 025. Volcano plots were performed using RStudio software version 3. 3. 2. Ribosomal RNA transcripts were filtered out. The microarray results reported in this paper are available in the Gene Expression Omnibus (GEO) under accession number GSE106299. Cell lines, cell culture and transfections.
The z-stacks were taken with a z-spacing of 300 nm for a total of 4-6 µm. Image processing was performed using the Fiji/Image J software. All images were processed the same way. In detail, the channels of the different images were split and grey values were adjusted to better visualise the spots in the cytoplasm. The nuclear signal in the green channel (TAF10 or TAF8 IF) was removed by masking the nucleus and using the "clear" option. Finally, the processed channels were merged again. For IF-smiFISH, one cell of an image was cropped and one representing z-slice per cell was chosen. For smiFISH, maximum intensity Z-projections of individual images were made and one cell per resulting image was cropped as the representative image. In addition, one single IF or smiFISH spot from the corresponding cells was cropped as well.
The enrichment ratio of the cell was then calculated as the ratio of the median IF intensity at the RNA positions divided by the mean cytoplasmic intensity. Boxplots of enrichment ratios were generated with the Matlab function notBoxPlot. Each dot corresponds to the estimation of one cell. Horizontal lines are mean values, 95% confidence interval is shown in red, and standard deviation in blue. Statistical comparison between different experimental conditions was performed with two-sample Kolmogorov-Smirnov test (Matlab function kstest2). The Matlab script is available upon request. Image analysis of smiFISH co-localization data. Segmentation of nuclei and cells was performed with the DAPI and smiFISH channel channels, respectively. 2D images were obtained with a previously described projection approach based on local and global focus measurements  23.
Segmentation was implemented with the open-source software CellProfiler 54 using a standard workflow: Otsu and watershed separation for nuclei in the DAPI channel. Each nucleus then serves as a seed for a watershed segmentation to obtain the cells in the smiFISH channel. Individual RNA molecules were localised with FISH-quant in 3D and can be treated as point clouds  55. Co-localization analysis between detected RNAs in two colours was solved as a linear assignment problem (LAP) with the Hungarian algorithm (Matlab function hungarianlinker and munkres from Matlab FileExchange). In short, this algorithm finds the best possible global assignment between these two points-clouds such that for each point in the first colour the closest point in the second channel is found. We implemented a user interface for this analysis tool (FQ_DualColor), which is distributed together with a dedicated user manual with FISH-quant: https://bitbucket.
Acknowledgements We would like to thank all members of the  Tora lab  for thoughtful discussions and suggestions throughout the course of the work,  E. Scheer  for help with F9 cell experiment, and  G. Caliskan  for help with the ATAC experiments. In addition, we are grateful to  E. Bertrand  and his group for smiRNA FISH training,  T. Sexton  for advice and carefully reading the manuscript,  V. Alunni  for microarray sample preparation,  S. Bour  for illustrations,  K. Gupta  and  I. Berger  for suggesting TAF deletions and mutations,  D. Van Essen  for providing constructs,  P. Jane Palli  and  G. Travé  for help with protein domain analyses, the IGBMC cell culture facility for cells and media. PM was supported by a fellowship ( DOC20180507393 ) from  Fondation ARC pour la Recherche sur le Cancer.
This work was supported by funds from  CNRS ,  INSERM , and  Strasbourg University. This study was also supported by the  European Research Council (ERC)   Advanced grant  ( ERC-2013-340551 ,  Birtoaction , to L. T. ) and grant  ANR-10-LABX-0030-INRT  (to L. T. ), a  French State  fund managed by the  Agence Nationale de la Recherche  under the frame programme  Investissements d'Avenir   ANR-10-IDEX-0002-02  (to IGBMC) and a grant from the  Collaborative Center for X-linked Dystonia and Parkinsonism  (to H. T. M. T. ).
Data availability The microarray data corresponding to Figs. 1a and 7a are available in the Gene Expression Omnibus (GEO) under accession number GSE106299. The source data corresponding to Figs. 1d-e, 2a-d, 3a-b, 4a-d, 6a-d, 7b-f, 8a-c and Supplementary Figs. 1, 2a-d , 3a-b, 5a-d, 7a-c are provided as a Source Data file. A reporting summary for this Article is available as a Supplementary Information file. Raw image files (~800), their corresponding analyses, and all other data supporting the findings of the study are available from the corresponding author upon request.
Interannuality of the Airborne Annual Fungal Spore Integral at Saclay The interannuality of AFS Integral (AFSIn) was studied in depth. The annual sum of fungal spores ranges from 2. 5 10 6 Nb#/m 3 to 5. 2 10 6 Nb#/m 3. We observed a strong interannuality of the AFSIn and no biannual cycle has been found. Instead, a general decrease of the atmospheric concentration was found, as illustrated in Figure  3. The AFSIn data from January to June 2014 have been estimated by a linear modeling with the most basic model which is the simple linear regression where a variable X is explained and modeled by an affine function of a comparable variable Y. The interpolation of the Saclay data set from January to June 2014 has been done by comparing the daily concentrations of Paris and Saclay data set for year 2014 provided by the RNSA observations in Paris.
Geographical Origins of AFS Impacting the Observation Site The calculations using ZeFir from this 5-year dataset showed an interesting result regarding the general origin of AFS concentrations. The model designates a main origin from the northwest sector, that is, independent from the SW prevailing winds (Figure  2 ), however still in the general "wet" W sector, which mostly carries marine air masses. Two point-sources were identified, the major one is associated with wind speeds ranging from 10 km/h to 13 km/h and a minor one with wind speeds of 17 km/h (Figure  13 ). We also evaluate the geographical origin of total AFS by running the model year to year as illustrated by Figure  A7  to investigate the effect of the meteorology on the Saclay ecosystem. sector, that is, independent from the SW prevailing winds (Figure  2 ), however still in the general "wet" W sector, which mostly carries marine air masses.
The AFSIn data from January to June 2014 have been estimated by a linear modeling with the most basic model which is the simple linear regression where a variable X is explained and modeled by an affine function of a comparable variable Y. The interpolation of the Saclay data set from January to June 2014 has been done by comparing the daily concentrations of Paris and Saclay data set for year 2014 provided by the RNSA observations in Paris. The results obtained without the data set of the January to June 2014 were also showing a decrease of the atmospheric concentration (Figure A1, Appendix B) and pointed out the importance to follow the AFS all over the year as it is done at the Saclay observatory.
We have seen an increasing number of assessments that place observations at the base of a value chain, stretching from ocean observation through to societal applications, for an example see the EU-H2020 Atlantic Observing System Project (AtlantOS) linking sustained ocean observations with societal benefit. 2 Ocean data portals (e. g. Copernicus Marine Services and EMODnet), with streams of high-quality data available for download, have developed to serve scientists, expert users and the developers of downstream services. Most of the European observing infrastructures (e. g. JERICO, EMSO-ERIC, EuroARGO ERIC) are also delivering their data through these portals. They are a valuable source of data for value creation but are less well known by the wide range of potential users that could benefit from the data and forecasts.
SOCIB is a key European example of advanced operational ocean observing systems that are leading a new era of ocean observation, supporting key grand challenges that society is facing, in particular climate change, sustainable ocean resource and blue growth. SOCIB is an integrated facility of facilities that has evolved and continues to evolve, to promote the paradigm shift that has occurred in ocean observation and data availability  (Delaney and Barga 2009; Tintoré et al. 2013) : namely from the historical, single platform ship-based observation with delayed mode data availability to the current multi-platform, integrated and multidisciplinary observing systems (Figure  1 ). SOCIB provides streams of data, added value products and forecasting services from the coast to the open ocean. The objective of this paper is to present the development of a sector-focused Products and Services Strategy at SOCIB, with the aim of increasing the use of data and knowledge developed from its observing and forecasting system.
The network of platforms includes surface and profiling drifters, fixed platforms (moorings, coastal stations), a research vessel, high-frequency radar, animal-borne instruments, autonomous underwater gliders and coastal monitoring stations and beach surveying campaigns. Continuously recorded multidisciplinary data are transmitted to the SOCIB Data Centre, and quality-controlled data are made available, freely and openly, for science, education, authorities, industry and the general public. SOCIB focuses on scientific excellence with relevance to and impact on society. Since 2011, the team has contributed to 110 papers in peer-reviewed international journals, participated as a partner in 10 projects, including EU-FP7 (JERICO-FP7), EU-H2020 (PERSEUS, JER-ICO-NEXT), and other international projects such as Copernicus Marine INSTAC, EMODnet MedSea-Checkpoint and ODIP2.
As an extension to its scientific and operational activities, SOCIB also undertakes significant outreach, providing visibility in the international landscape of new ocean observing systems and developing education materials. The SOCIB Outreach Service promotes ocean literacy and raises awareness on the impact of modern ocean observing systems on the advancement of knowledge, science-based management and the subsequent preservation of marine and coastal resources. A summary of SOCIB outputs across the observing, forecasting, data centre facilities and outreach can be seen in Figure  2. The knowledge, data, operational forecasting capabilities, tools and products developed to date are an indication of SOCIB achievements. Since 2015, SOCIB has also developed and implemented a sector-focused Products and Services Strategy, as presented in this article, in order to broaden the use of its data, forecasts and knowledge within societal end-users.
A SOCIB products and services strategy SOCIB's mission, to serve both science and society, initiated the need to develop a more comprehensive strategy for its data products and services. With multiplatform observations, high-resolution modelling and a data centre SOCIB is a 'complete' observing system on a regional scale and therefore an ideal laboratory to understand how to make the last link in the observing data value chain, from data to regional societal endusers. And in doing so create more value from the investment in ocean observations and forecast. There are examples of ocean observatories successfully creating great value for end-users in focused projects, e. g. Irish Marine Institute for regional aquaculture  (Dabrowski et al. , 2016) , SOCIB Bluefin Tuna project for ICCAT  (Alvarez-Berastegui et al.
, 2016 , Reglero et al. , 2018) , IOOS MARACOOS for butterfish population modelling, 3 Prooceano using gliders to monitor deep eddies that impact oil field operations 4 and the observing system response to the Deepwater Horizon oil spill. These are excellent examples, but in a sense are based on existing science norms, research or rapid response projects with key outcomes. How do we now move beyond this and broaden the appeal of data, knowledge and research outcomes? How can greater value be created from the increasing wealth of ocean data and knowledge that is being collected and developed for the benefit of science and society? These questions were raised at SOCIB, where despite developing a broad range of high-quality freely available datasets, and having developed dedicated applications for visualising data, after 2 years of operational capacity  (2013) (2014) (2015)  there was no clear evidence of widespread use of the data beyond the regional scientific community.
Although many of the scientists were using SOCIB data for societally relevant investigations, there was a belief at SOCIB that the data could additionally offer more direct and real-time benefits to end-users. To understand how to deliver more to more societal end-users, SOCIB developed a Products and Services Strategy. To do this we took an innovative approach, combining business best practice, innovation and science. The business best practice follows new market and start-up development concepts (e. g. Crossing the Chasm, Geoffrey  Moore, 1991) , and mainstream business strategy concepts (e. g. What is Strategy? Michael Porter, HBR The Essentials, 2011). The startup concepts are also useful here as although there are some existing ocean data products and great potential, they are not yet in mainstream use.
Review of internal and external factors To inform the strategy development, a review of internal and external factors was undertaken; through web-based research, publication review, discussions with thought leaders, participation in workshops, 6 attendance to trade exhibitions (e. g. Oceanology International held in 2016 (https://www. oceanologyinternational. com/Oi-News/ Press-releases-/OCEANOLOGY-INTERNATIONAL-2016--LOOKING-BACK/)), and meetings with SOCIB Facilities and Projects. The following questions were addressed: External:. What users do other ocean observing systems have, how do they serve them?. What information is there on the value of ocean data (observations and forecast)?. What potential users of our ocean data are there in the Balearic Islands, Western Mediterranean region? Internal:.
What data products do we have, how do they function and who uses them?. What data are currently being used and do we have any contact with end-users around any specific data?. What knowledge, intellectual property or product ideas do we have that could be useful for users? From this review, major 'global' societal benefit areas for ocean data were defined. We then looked at these with reference to a series of criteria; the users in the Balearic region, the economic value of ocean data/knowledge that was required, our ability to deliver and the potential for engagement with end-users, in order to assess the following:. the type of data required,. user importance (regionally, but also at national/global level), size,. societal benefit/value,. whether SOCIB had data of relevance and value of this data,.
existence of current users (a clear indicator of value),. whether SOCIB had developed products to address the users, and whether SOCIB had additional data or knowledge to offer,. if we were in contact with or could effectively contact end-users. In practice, this was a working spreadsheet with a series rows for the 'global' user sectors and a series of columns with metrics for the sector analysis, e. g. potential users in the region, sector size/importance to the region, what data do they require, types of use of data/applications, economic/socio-economic benefit, SOCIB data/knowledge available now/future, current SOCIB products for the sector, any future developments underway, do we have contact/can we contact these end-users, do user requirements match SOCIB core competence. The working spreadsheet was revised as information became available, however, it formed the basis for the analysis to define target sectors, priorities and the potential development projects.
We looked at how current SOCIB products were used (science and anecdotal societal use) and were on the lookout for ideas for products, especially those that had developed through contact with user groups. The review identified the following issues with the existing SOCIB product offer, as developed from 2011 to 2015:. development was led by observing facility and generally platform based;. availability not well communicated, products could be difficult to find and not always intuitive to use;. many products were not developed with a specific user group in mind;. important regional user groups were not supported by any products. 3. 3. Identification regional sectors, priorities and product ideas Following the review, SOCIB defined 10 target user sectors, as listed in Table  1. The sectors represent groups of users with common data needs, for which SOCIB could provide data/knowledge/IP, and that are important, economically, societally, in number of users or in terms of regional impact.
From these 10 sectors, 3 were identified as already having a number of useful products, namely Marine and Coastal Research, Coastal Protection, Planning and Governance, and Education and Kids, Maritime Safety was identified as being supported through a third party developed application (the Environmental Data Server (EDS) developed by RPS). The other sectors were prioritised against their ability to fulfil the aims of the strategy, namely:. economic value of data;. uniqueness of SOCIB contribution;. readiness of technology/development capability;. no existing 'commercial' products for this sector. This resulted in the selection of three user group targeted products for development, two of which were initiated in 2017, as described in Section 4.
New product development The development of new products for target user groups is a challenge that addresses both cross-platform integration and the development of an 'end-to-end' observing system. The two product ideas selected as the initial development projects were: 1. A tool for the Sustainable Marine Resources sector; an oceanography exploration tool, using experience, knowledge and contacts developed through the SOCIB-Spanish Institute of Oceanography (IEO) Bluefin Tuna Project focused around sustainable fisheries management 2. An interface for the Beach, Coastal Communities & Tourism sector; focused around beach lifeguards that require specific ocean and meteorological data every day of the beach monitoring season (spring through to autumn). Both product ideas filled gaps in SOCIB delivery to the target sectors had identified 'early adopter' users, 7 with needs that matched high economic value data (forecasts and derived variables).
2018; Berx et al. 2011). These gaps are related to: 1. the design of operational oceanography data products, 2. the data delivery process, 3. the technical background of the fisheries management community and 4. the techniques used in fisheries assessment (population models). SOCIB worked with the IEO, as a key external collaborator, to understand and look at how to bridge these gaps within the SOCIB-IEO Bluefin Tuna Project (Alvarez-  Berastegui et al. , 2016). The tool is designed to facilitate fisheries researchers and managers in accessing time series and realtime data for key oceanographic variables driving species ecology. Thus helping bridge the gap for the exploration of oceanographic physical-biological scenarios, enabling access and analysis of multiple datasets, and easing the integration of environmental variability into the design of field sampling and fisheries assessment processes.