dataesr/scientific-paragraphs-categorization huggingface dataset:
All the remaining samples were included in the validation set. Our study was approved by ethics committees of Hopital Bicêtre and Hopital Saint Louis (Paris, France; CPPRB 94-40). Everyone provided a written informed consent for the participation in the study. For statistical analysis with the Rare Variant Association Test (RVAT), we added 98 healthy controls extracted from the 1000 genomes project database. We chose CEU individuals (Utah residents with Northern and Western European ancestry) for which whole exome sequences were generated using an Illumina platform.
Variant annotation and classification We classified the remaining variants into rare variants (MAF < 1%), low frequency variants (1% � MAF < 5%) and common variants (MAF � 5%) according to their frequency in public databases. To evaluate these frequencies, we worked on 4 datasets extracted from: (1) the 1000 Genomes Project (2015 August); (2) the Exome Aggregation Consortium project (ExAC); (3) the Exome Sequencing project (ESP6500-6500 exomes); (4) the Complete Genomics project (CG69-69 individuals). We extracted the allele frequency in population with European ancestry when the information was available. To investigate the predicted effect of the genetic variants on proteins, we annotated them with CADD phred-like score  [42]  using ANNOVAR  [43]  and with variant effect defined in SNPeff  [44].
Association analysis of selected genes with burden test We tested the association of genes carrying at least one rare candidate variant using pVAAST  [12]. This software extends the rare variant association test (RVAT) VAAST  [47]  to offer more power in the context of family-based studies. It provides a linkage-association score and a pvalue for each evaluated gene and, a score for each variant carried by the gene to help the prioritization of these variants. Considering the heterozygous nature of our candidate SNVs, we tested the candidate genes under a dominant model and set the maximum disease prevalence to 0. 01, the world-wide prevalence of RA  [48]. In addition, we provided to pVAAST a file containing for each variant included in the analysis the maximum frequency observed in the four public databases described above.
Validation in extended pedigrees of exome selected variants The top 10 candidate variations, selected with the lowest p-values in the 3 rd run of the pVAAST analysis, were re-sequenced in the validation set by using PGM System (Ion Personal Genome Machine). Primers were designed with the Ion AmpliSeq Designer to target the selected variants. We mapped the reads to the reference genome with BWA  [36]  and recalibrated base score with BQSR tool from GATK suite  [31]. We then called variants in the strict limits of the targeted regions with HC and applied the same quality filters used for WES to the observed variants. For samples in both discovery and validation sets, we checked the concordance with whole exome data using VCFtools program package  [49].
Study design and population We analysed data obtained from six multicentre studies on random samples of the general adult population carried out in Europe between 1991 and 2013, which became part of the Ageing Lungs in European Cohorts (ALEC) consortium (www. alecstudy. org). The Global Allergy and Asthma Network of Excellence (GA 2 LEN, www. ga2len. net) is an international cross-sectional study that was carried out on people aged 15-75 years between 2007 and 2009. [7]  The Italian Study on Asthma in Young Adults (ISAYA) is a postal cohort study on subjects aged 20-44 at enrolment in 1998-2000, who were followed up in 2008-2009. [8]  The Genes Environmental Interaction in Respiratory Diseases (GEIRD, www. geird. org) is a cross-sectional study carried out in Italy between 2005 and 2010. [9]  The European Community Respiratory Health Survey (ECRHS, www.
Data on smoking The smoking history of each subject was reconstructed retrospectively using data from postal questionnaires for all studies, in addition to data from clinical interviews for ECRHS. [Table 1 ] In all studies, with the exception of RHINE (where the subjects qualified themselves as "smokers" or "ex-smokers"), smoking was defined as active smoking for as long as a year (regular smoking). Life-long never smokers were excluded from the analyses. A smoker who reported not to have smoked at all in the last month was considered an ex-smoker. Age at initiation was based on the question "How old were you when you started smoking?" for all the studies. Questions on age at cessation referred to the time of stopping or the last time smoking, with a slightly different wording across studies.
Statistical analysis Rates of smoking cessation (per 1,000/year) were calculated as the ratio between the number of incident quitters and the total time at risk, which was defined as years from initiation to cessation for quitters, or from initiation to the last survey available for active smokers. To avoid data sparseness, time at risk was left-censored at age 16 years and at 1980, and right-censored at age 60 years and at 2010. All the analyses were stratified by sex, region, and period  (1980-1989, 1990-1999, 2000-2010). Crude rates were calculated for two age groups: 16-40 and 41-60 years. In the main analyses, age-specific trends of smoking cessation rates (with 95% confidence intervals) were estimated using two-level mixed-effects models (with subjects nested into samples), with a negative binomial outcome distribution, a logarithmic link function and an offset for log(person-years).
Ethics approval and consent to participate Ethical approval was not requested for this secondary analysis of pooled data from previous studies. In each of the original studies, ethical approval was obtained for each centre from the appropriate ethics committee. All procedures have conformed to the principles embodied in the Declaration of Helsinki. Written informed consent was obtained from participants in the clinical examinations (ECRHS clinical). In ECRHS-Italy, RHINE, GA 2 LEN, ISAYA and GEIRD, only data from postal questionnaires were used, which were voluntarily sent back giving consent to use the anonymized data.
org/10. 1371/journal. pone. 0201881. s001 and S3 Table ). The risk of relapse is the main issue when studying smoking cessation. About half of smokers attempts to quit every year, but less than 10% achieves abstinence for a period of at least 6 months. [40]  The first year after a quit attempt is the period at highest risk for relapse. [17]  In our study, age at cessation reported in a sub-sample of ex-smokers with follow-up data (ECRHS clinical) increased by two years on average after 20 years, probably indicating smoking relapses. However, we derived age at cessation from the last time point available, and over 90% of quitters had abstained from smoking for at least two years before the interview, suggesting that our findings mainly reflect smoking cessation over an extended period among subjects at a low risk of relapse.
Acknowledgments The ALEC study leader is  Deborah Jarvis. The manuscript was done under ALEC Workpackage 6 led by  Cosetta Minelli. Other Workpackage  leaders in ALEC are  Cecilie Svanes ,  John Henderson  ( Department of Community Based Medicine, University of Bristol, Bristol, UK ),  Judith Garcia-Aymerich ,  Nicole Probst-Hensch  ( Swiss Tropical and Public Health Institute and University of Basel, Basel, Switzerland ). The principal investigators and team members of the original studies are reported in S1 Appendix and in the study website https://www. alecstudy. org/. The  ALEC International Scientific Advisory Board  is:  Marike Boezen  ( University Medical Center Groningen, University of Groningen, Groningen, The Netherlands );  Bernice Elger  ( Institute for Biomedical Ethics, University of Basel, Basel, Switzerland );  Bo Alexander Gleditsch  ( The Norwegian Asthma and Allergy Association, Norway );  Bas Heijmans  ( Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, Leiden, The Netherlands );  Isabelle Romieu  ( National Institute of Public Health, Cuernavaca, Mexico ; and.
This study was conducted within the Ageing Lungs in European Cohorts study (www. alecstudy. org)and has received funding from the  European Union 's  Horizon 2020 research and innovation programme  (https://ec. europa. eu/programmes/horizon2020/) under grant agreement no  633212  (DJ). National funders who supported data collection in the original studies are listed in S1 Appendix. The funders had no role in study Results.
servations permitted new tests of general relativity  (Abbott et al. 2017b) , measurement of the Hubble constant  (Abbott et al. 2017a (Abbott et al. , 2018c)) , and yielded information on neutron star physics (e. g. ,  Abbott et al. 2017g; Levan et al. 2017; Margalit & Metzger 2017; Bauswein et al. 2017). Optical follow-up observations of stellar-mass compact binary coalescence (CBC) sources of GWs are challenging because of the large sky localization uncertainties inherent in the LIGO-Virgo data analysis. With only two GW detectors, sky localizations can be ∼ 100-1000 deg 2  (Singer et al. 2014; Berry et al. 2015) ; adding additional detectors to the network improves sky localization  (Veitch et al. 2012; Abbott et al. 2016f; Pankow et al. 2018)  and enhances three-dimensional localization  (Singer et al.
2 Based on observations made with ESO Telescopes at the La Silla Paranal Observatory under programme ID 299. A-5028. class telescopes, and to search in earnest for candidate EM counterparts to putative strongly-lensed GW sources. The large aperture of Gemini-South and VLT, and the absence of any requirement for us to explore the wider sky localization, enabled us to conduct deep observations that are sensitive to strongly-lensed EM counterparts down to i = 25 independent of the actual source redshift. Our strategy therefore benefits from greater sensitivity than conventional searches, at the expense of a much smaller survey volume. We describe the details of our observing strategy, observations, and data reduction in Section 2, explain how we generate difference images, and search for candidate optical counterparts, and state our results in Section 3, discuss our results in Section 4, and summarize in Section 5.
Gemini and VLT observations We observed Abell 3084 with GMOS on Gemini-S on five occasions, commencing 20 hours after the first LVC alert pertaining to GW170814, on August 15, 2017 UTC. This was the first Chilean night following the detection of GW170814. The GMOS observations of Abell 3084 continued after the revised sky map became available, in order to obtain a comprehensive dataset on one cluster. We also observed SMACS J0304. 3-4401 with GMOS on Gemini-S on two oc- casions -August 18 and 21. The overall aim was to observe for ∼ 45 minutes on each night, with the exposure times and number of exposures adjusted to suit the Moon phase and overhead conditions. The individual exposures were offset from each other randomly within a square region of full width 30 arcsec. Observations were performed in the i-band in order to minimize the impact of the Moon on the sensitivity of the observations, and to probe rest-frame V -band emission from putative lensed GWs at z ∼ > 0.
5. We triggered a rapid ToO on VLT with MUSE within 3 hours after LVC announced the detection of GW170814. This observation was executed at the telescope on August 17, 2017 UTC, and repeated on August 20, 2017 UTC. The delay between our trigger and the first MUSE observation was due to a combination of visitor mode time and engineering time on the intervening nights. Each observation comprised three exposures of duration 980 s and spanned the wavelength range 475 nm < λ < 930 nm. All of the GMOS and MUSE data were obtained at high elevation and with excellent seeing of 0. 5 arcsec FWHM 1 arcsec. Details of the GMOS and MUSE observations are listed in Table  2 , and the reduction of GMOS and MUSE data are described in Sections 2. 5 & 2. 6, respectively.
GMOS data reduction Individual GMOS exposures were de-biased, darksubtracted, flat-fielded, and sky-subtracted in the standard manner using the gemini package in iraf, to produce both a single science frame comprising the mosaiced individual chips, and a bad-pixel map, for each exposure. The bad-pixel maps were applied to the science frames and the individual masked science frames were then combined in to a single stacked frame per visit using the imcombine task in iraf. The full width at half maximum of point sources in the reduced frames is consistently sub-arcsecond (Table  2 ). The reduced and stacked frames were aligned with the first visit for that target to a typical root-mean-square residual accuracy of 0. 03 pixels using the iraf tasks geomap and geotran. We searched the available USNO and GSC catalogues for sources of well calibrated i-band magnitude within the field of view of our GMOS data.
The size and depth of the GMOS imaging even in short exposures meant that there was no overlap between unsaturated bright stars as seen by Gemini and faint stars measured in all-sky surveys. We therefore calibrated the GMOS frames by measuring the (V606 -i) colours of sources detected in both the archival HST /ACS data and our GMOS data, and selecting the photometric zero point that yields the correct colours for massive early-type galaxies in Abell 3084 and SMACS J0304. 3-4401 respectively. These colours were computed using the EzGal code, 3  using a single stellar population that formed at high redshift and evolved passively to the relevant cluster redshifts based on the  Bruzual & Charlot (2003)  population-synthesis code. The predicted colours are insensitive to the fine details of how we choose the formation redshift and the metallicity.
Searching for transients in imaging data We searched the difference images for transient sources both manually and automatically. The manual search was performed by three authors (GPS, MB, AR), and identified no sources consistent with being a point source in the difference images. The only sources found were either residuals from the PSF matching close to the centres of bright galaxies, artefacts related to saturated stars, or a small number of residual bad pixels and cosmic rays. The automated search was performed with SExtractor  (Bertin & Arnouts 1996) , and found no sources other than PSF matching residuals and artefacts relating to saturated stars. To calibrate the sensitivity of our GMOS search for candidate transients, we injected fake transients in to the data in the following manner, and attempted to recover them both manually and automatically.
(2010). To test the automated search, we generated 10 4 synthetic datasets comprising sources injected at each integer magnitude in the range 21 i 26. SExtractor was then run on these synthetic data sets, with the source being detected if SExtractor finds a source within 0. 5 arcsec of the injected source location. At small angular separations from the BCG (θBCG) the probability of sources being detected is low due to the residuals in the difference images (Figure  6 ). The source recovery rate at the very centre (within < 0. 5 arcsec of the BCG) is artificially boosted because SExtractor detects the residuals at the centre of the BCG (in the absence of an injected source) as a source. However, these scales are generally smaller than the high magnification region close to the radial critical curve, and therefore the residuals at the centre of the BCG are not a major limiting factor in our analysis.
Our search is therefore sensitive at > 80 per cent completeness to point sources at i ∼ < 24 that are adjacent to either critical curve and to point sources at i ∼ < 25 that are adjacent to the tangential critical curve. We performed similar manual and automated tests with MUSE images, injecting sources into the inner 15 arcsec of the Abell 3084 MUSE/HST difference image, obtaining similar results to the GMOS tests described above. As a consequence of the smaller field of view of MUSE compared to GMOS, we did not have a large number of bright stars to use as templates for point sources, but instead used the best-fit Moffat profile discussed in Sec. 2. 6. We interpret the slight shortfall in the fraction of fake sources that are recovered from the MUSE/HST difference images relative to the GMOS images as being caused by the flux sensitivity of the short HST observation being inferior to that of the MUSE observations, after smoothing the HST data to match the ground-based seeing.
Searching for transients in MUSE data cubes In addition to the search for continuum sources based on pseudo-images created from the MUSE datacubes, we have performed an automatic search for emission lines across all wavelengths. This is done using the muselet detection software, which has been used in the past to automatically search for line emitters in MUSE blank fields  (Drake et al. 2017b,a)  as well as lensing cluster fields  (Mahler et al. 2018; Lagattuta et al. 2017). muselet is publicly available as part of the MUSE Python Data Analysis Framework (MPDAF,  Conseil et al. 2016)   based detection tool based on a continuum-subtracted datacube where each wavelength plane is replaced by its corresponding narrow-band image, optimised for the detection of typical FWHM = 150 km s -1 line width line sources.
SUMMARY In the nights immediately following the announcement of the detection of GW170814, we observed two strong-lensing cluster cores -Abell 3084 and SMACS J0304. 3-4401 -identified using the sky localization available from the LVC. Our observations were conducted with the GMOS and MUSE instruments on the Gemini-South and Very Large Telescope, respectively. The data reach a sensitivity to point sources of m(5σ) 25-26, and our search for transient sources is sensitive down to m = 25 in the continuum and line fluxes of 5 × 10 -17 erg s -1 cm -2. We detect no credible candidate transient sources in the data down to these limits. This is the most sensitive search to date for EM counterparts to GW sources, independent of considerations of possible lens amplification. The lens magnification suffered by a lensed GW source cancels out the inverse square law, and therefore the apparent magnitude of a point source EM counterpart of given luminosity is set by the luminosity distance inferred from the GW data assuming no lens magnification.
ACKNOWLEDGMENTS GPS dedicates this article to the memory of  Robert Smith , and thanks  Olivia Mueller  for her support during August 2017. GPS also thanks  Gary Mamon  and colleagues at the  Institut d'Astrophysique de Paris  for their warm welcome during the latter stages of writing this article, and  Alberto Vecchio  for his advice and support. We thank  Iair Arcavi  and  Leo Singer  for assistance with Figure 1,  Alyssa Drake  for providing us with the results of completeness tests to blindly detect line emitters within MUSE datacubes using muselet, and  Chris Done  and  Sylvain Chaty  for helpful discussions about low mass X-ray binaries. We thank the staff and Directors of the  Gemini and La Silla Paranal Observatories  for their superb support of our observing programmes and awards of Director's Discretionary time respectively.
We used Microsoft Excel 2010 to keep a digital record of all the plant material in this project. A digital database is highly suitable and desirable, however a paper-based system could also be used. The second part was a labelling system that ensured all plant material, plants, and plantlets in the glasshouses and tissue-culture facilities carried a unique identifying code (UIC) (Fig. 6 ) that linked to the database. Each stem cutting, plant, and plantlet that was propagated had its own entry in the spreadsheet. We used a simple sequential UIC, wherein each generation of propagated or micro-propagated plants gained an additional digit. For example, for a plant showing the label "Mal 5. 12. 2. 4" (see Fig. 6 ), "Mal" indicates a variety from Malawi, "5" indicates the code name given for the variety, "12" indicates a plant replicate of that variety, "2" indicates the node number on the plant (e.
g. "2" would be the second node from the top of the plant, a location that is more likely to be virus-free), and "4" indicates the node number  after the first sub-culturing from the previous plant. The spreadsheet was setup such that each row (entry) represented a single bud node from a plant that had been propagated, while the columns contained data on various aspects of that bud node and its history including the UIC, country of origin, cutting number, varietal common name, virus assessments (visual and PCR based), infection status, location (within glasshouse or incubator), date of tissue culture, number of nodes micropropagated, shipping date, and destination. For each entry, additional columns were added to record details of thermo-and chemotherapies and their outcomes, number of nodes that were tissue-cultured, results of repeated PCR tests (used to confirm previous results prior to shipping), dates plantlets were hardened, dates plants were transferred to the glasshouses, or anything else pertinent to the work.
The "Pivot Table " function of Excel was used to quickly generate summaries of the data and track overall progress. We used 'mail merge' function to link between an Excel spreadsheet and a Word document. A template for labels was set up in a Word document, wherein each label included all the relevant fields (e. g. UIC, date, location). The 'mail merge' allows a linked spreadsheet to be filtered by specific fields, and then automatically generates the data into the relevant fields for each label within the Word document. We used 'mail merge' to automatically generate sticky labels for the micro-propagation tubes, storage and shipping containers, thus minimising the potential for human error in labelling. A label printer (LabelStation Pro 200, Advanced Labelling Ltd. , Dorset, UK) and the LabelStation software were used to generate the tags and labels for plants in the glasshouse.
As each plant is propagated, PCR tested, cleaned of viruses, and multiplied to hundreds of plantlets, it is essential to check for viruses, if any, manifested in subsequent steps. If an infection is discovered, any plants linked to the infected plant could then easily be re-tested, cleaned, or removed without having to discard a larger batch of plants. During this project, we employed a relatively simple but encompassing traceability system using commonly available software such as the Microsoft's Excel workbooks. This system allowed us to manage more than 5000 plants through phases I to IV and ensured that we generated and shipped only certified clean material of known origins to our project partners in eastern and southern Africa. It is important to remember that a traceability system is only as good as the data that is put into it, so it is vital that all data input into the system are accurate.
Correct labelling and tagging of all material can minimise mistakes. We used the automated labelling systems to reduce human error that can occur when writing hundreds of labels by hand. Finally, we suggest that if the data in a traceability database is discovered to be incorrectly entered, all the material in that run should be destroyed, and the data should be separated from the main documents to avoid the risk of miscategorising material. In summary, we have demonstrated that it is possible to clean the vegetatively-propagated crops such as cassava from two major virus infections for germplasm exchange at the regional level. This can serve as an example for cleaning and exchange of germplasm of other vegetatively-propagated crops such as bananas, potatoes, sweetpotatoes, sugarcane and others.