dataesr/scientific-paragraphs-categorization huggingface dataset:
These studies put forward the idea of the aphids' symbionts possibly evolving from bacteria originally inhabiting the plant, then transitioning to gut associates, and finally obligate symbionts. In C. cuneomaculata, C. kochiana, and C. curvipes, 'Ca. Erwinia haradaeae' is found inhabiting the bacteriome inside bacteriocytes distinct from those of Buchnera. Previous work referring to these symbionts as 'Erwinia' or 'Erwinia-related' include Jousselin et al. [23]  and Meseguer et al. [14]. Currently available sequences that correspond to 'Ca. Erwinia haradaeae' are deposited under INSDC accession numbers LT670851. 1 and LT670852. 1. All currently known 'Ca. Erwinia haradaeae' species harbour biotin-and thiamin-biosynthetic horizontally transferred genes from a Sodalis or Sodalis-related bacteria both in their chromosome and plasmid.
The transmission from mother to offspring is further supported by our FISH data on three Cinara species. These images show that E. haradaeae strains reside in specialised cells (bacteriocytes), close to Buchnera, typically observed in vertically transmitted symbionts. Phylogenetic studies of the aphid hosts, the Cinara genus, using fossil calibrations, have estimated that the aphid clade hosting E. haradaeae is ~20-30 million years old  [57, 58]. The di-symbiotic system unravelled here has therefore persisted throughout the Pliocene and Miocene and the diversification of the aphids on their hosts-plants (i. e. mainly Pseudotsuga, Larix, Abies, and a few Picea for some species; see see Jousselin et al. [59]  and Meseguer et al. [57]  for phylogenetic reconstructions of aphid host plant associations in this clade) and across two ecozones (the Palearctic and Nearctic)  [57].
Regarding the evolutionary origin of E. haradaeae, phylogenetic analyses of Erwinia genomes available in GenBank suggest that E. haradaeae is most closely related to Erwinia species that have been isolated as both pathogenic and non-pathogenic plant-associated bacteria  [19]. Other Erwinia species have also been found in insects, namely the olive fruit fly (Bactrocera oleae)  [60] , the western flower thrips (Frankliniella occidentalis)  [61]  and aphids  [20, 21, 56, 62]. Concerning the latter, Erwinia spp. were isolated both from the gut of a laboratory strain of the pea aphid A. pisum (i. e. E. aphidicola)  [20, 21]  and from artificial diets exposed to probing by Diuraphis noxia (i. e. Erwinia iniecta)  [62]. The uptake and persistence of Erwinia bacteria within the aphid digestive tract is supported by an experiment on Aphis pomi.
Acknowledgements We would like to acknowledge the talented artist/ scientist  Jorge Mariano Collantes Alegre  for the aphid cartoons in Fig. 6 and  Prof. Colin Favret  for his critical reading of the paper and his feedback. This work was supported by the  Marie-Curie AgreenSkills  + fellowship programme co-funded by the  EU's Seventh Framework Programme  ( FP7-609398 ) to A. M. M. The  Marie-Skłodowska-Curie H2020 Programme  ( H2020-MSCA-IF-2016 ) to E. J. , the  Agriopolis foundation/Labex Agro ("Cinara's microbiome")  to E. J, the  France Génomique National Infrastructure , funded as part of the  Investissement d'Avenir program  managed by the  Agence Nationale pour la Recherche  ( ANR-10-INBS-09 ) to E. J. , C. O. , C. C. and V. B. This publication has been written with the support of the  AgreenSkills + fellowship  programme which has received funding from the  EU's Seventh Framework Programme  under grant agreement No.
FP7-609398  (AgreenSkills + contract) and the  Horizon 2020 Programme  under grant agreement  746189  ( H2020-MSCA-IF-2016-MicroPhan ). We are grateful to the genotoul bioinformatics platform  Toulouse Midi-Pyrenees  ( Bioinfo Genotoul ) for providing help and/or computing and/or storage resources. The authors are grateful to the  CBGP-HPC computational platform. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
The management of crops through the ventilation or the shadowing of tunnels offers some potential to regulate the production schedule and therefore to enhance economic performance. An accurate knowledge of climatological and real-time PAR is valuable here. The relative scarcity of PAR measurements performed at terrestrial stations motivated researchers to seek alternatives by estimating PAR from satellite imagery (PAR-satellite methods). The purpose of this study is to compare five methods to estimate PAR against the measurements performed at three stations located in the area of soft fruit cultivation. Results will be discussed taking into account the constraints and challenges in accessing reliable long-term and real-time PAR datasets that are needed for this type of application. This study has four main advantages: first, the PARsatellite methods anterior to 2010 have never been confronted to each other in UK.
Second, this analysis includes the most recent outcomes in terms of models for the delivery of spectral radiation values (2015 onwards). Third, it enables growers to immediately access long-term, regularly distributed and worldwide databases of PAR radiation values, which is essential in particular when the size of their exploitation is too small to support the costs of installation and maintenance of a complete meteorological station. Finally, the fourth strength is that the recent and reliable methods also cover water areas, opening new opportunities for other application domains such as the monitoring of the phytoplankton in the ocean. In-situ measurements and quality checks are presented in Sect. 2. Section 3 describes the five methods explored in this analysis to derive PAR-satellite methods, separated into two groups.
All methods are applied to two satellitederived datasets of the solar broadband irradiance received at the surface (SSI) in all-sky conditions: HelioClim-3 version 5 (HC3v5) and CAMS Radiation Service (Copernicus Atmosphere Monitoring Service, abbreviated CAMS-Rad in the rest of the paper). The two datasets are briefly presented in Sect. 3. 1. Section 3. 2 and 3. 3 respectively introduce group 1 and group 2 PAR-satellite methods. Section 4 is dedicated to the validation protocol. Results are presented in Sect. 5. Section 6 proposes an interpretation of the results and explore the cause of the underestimation observed for all methods at one of the three sites. Section 7 provides a few perspectives on potential improvements of the service and to widen the work carried out in the framework of these activities.
HC3v5 and CAMS-Rad HC3v5 and CAMS-Rad are the two SSI data sets selected for this study. They are both available on the SoDa website (http://www. soda-pro. com/, last access: 4 October 2019)  (Gschwind et al. , 2006). The HC3v5 dataset results from the application of the Heliosat-2 model  (Blanc et al. , 2011; Rigollier et al. , 2004)  to images acquired by the Meteosat Second Generation series of satellites combined with outputs from the CAMS McClear clear-sky model  (Lefèvre et al. , 2013)  as proposed by  Qu et al. (2014). The principle of Heliosat is that the appearance of a cloud over a pixel yields an increase in radiance in the images taken in the visible range by the satellite. A cloud index is computed that quantifies the change between the observed radiance and the radiance that should be observed if the sky were cloud-free.
At the origins of McClear, these high quality atmospheric variables were injected into radiative transfer models (RTM) to reach the best accuracy of the components of the radiation in cloud-free conditions. As RTMs are computationally expensive, McClear could not run in real time. Consequently, the strategy adopted in the development of McClear was to reduce the number of runs by using pre-computed abaci (also known as look-up tables) while conserving a satisfactory level of accuracy. The more recent Heliosat-4 method  (Qu et al. , 2017 ) which generates the CAMS-Rad dataset is entirely based on the approximation of  Oumbe et al. (2014). The cloud-free SSI is given by the McClear model with CAMS atmospheric constituents as inputs  (Lefèvre et al. , 2013; Gschwind et al. , 2019). The German Aerospace Center (DLR) processes the multispectral images of the Meteosat satellite every day using the APOLLO method  (Qu et al.
This factor has been implemented in the Kato version of McClear when requesting PAR datasets. The two methods selected for this work are those described in Wandji  Nyamsi et al. (2015 Nyamsi et al. ( ,2019)). Both methods output PAR in cloud-free conditions: we will name the first one "Weighted-Kato" as it corresponds to a weighting of the spectral bands defined in  Kato et al. (1999)  to fit the PAR [400, 700] nm range. The second method proposes a resampling technique of the Kato bands every 1 nm and is called "Discretized-Kato". We propose an extension of these two methods to assess the PAR in all-sky conditions by multiplying the cloud-free PAR by a factor relating to the cloud extinction. This factor is equal to the clear-sky index K clear , which is the ratio of the SSI in all-sky conditions to the SSI in cloud-free conditions.
, 2015; Marchand et al. , 2017 Marchand et al. , , 2018;; Thomas et al. , 2016a, b). Statistical results were also generated in clearness index K T -PAR , which corresponds to the PAR received at ground level divided by the same quantity but received from the sun at the top of the atmosphere. The advantage of the normalisation is to minimize the dependence of PPFD with sun elevation. K T -PAR is thus a stricter indicator of the ability of a database in assessing the optical state of the atmosphere in different weather conditions. The results for this    indicator do not change the conclusions drawn in this analysis. A final observation is that the best performing method turns out to be Discretized-Kato. As results are very close to those for Weighted-Kato and taking into consideration that the discretization every 1 nm required by the Discretized-Kato method is quite time consuming, one may wonder if such a precision is necessary depending on the application.
This could also be the case if the geographical coordinates of Cartmel Sands station were rounded and would have erroneously been placed offshore instead of inland. We first checked the sea mask used within HC3v5 to label the Meteosat pixel as water or inland area and serve to define the albedo. This first check confirmed that the pixel in which is located the station has always been considered as inland during the period of data acquisition. Then, we ran the PAR-satellite methods at several locations in the neighbourhood of the considered location. Results were worst in all cases, leading to the conclusion that the issue was not due to a problem of albedo or of a wrong location of the instrument. As CAMS McClear is common for both data sets, a remaining option could have been a problem of the estimation of the radiation in cloud-free conditions, showing a stronger opacity of the atmosphere than the one actually observed at site.
, 2012 (Mueller et al. , , 2015)). In the next release of SARAH (version 3) scheduled for 2021, they contemplate to provide PAR datasets with a monthly time step and we offer them the opportunity to join this comparison. The DWD PAR-satellite method is very similar to the Weighted_Kato method as they are running an implementation equivalent to McClear Kato named SPECMAGIC, in which abaci have also been optimized for each Kato band. Results are fully in line with the ones obtained for the ten other PAR-satellite methods, with a relative bias in percent of approx. 4 % for Aberystwyth University or Abbotts Hall and less than -4 % for Cartmel Sands. As a consequence, the remaining option is that the instrument faces a problem of calibration. Figure  6  displays two days of PAR data for the site of Cartmel Sands.
The selected days are 22 December 2013 for the left hand side picture, and on 15 March 2014 for the right hand side one. For these specific days which belong to the winter period, several 30 min PAR datasets are displayed: the red line corresponds to the PAR TOA , blue is PAR in cloud-free conditions, black is ground PAR measurements, and cyan and green lines represents the values for two PAR-satellite methods: Udo et Aro and Weighted_Kato applied on the SSI HC3v5. The type of weather for these days are mostly covered with broken clouds, nevertheless the ground measurements are very close and sometimes strongly exceeds cloud-free PAR model. The two PAR-satellite models are collected the temporal variation correctly, and the discrepancy between satellite and ground PAR data is very strong due to problem of calibration of the instrument.
Perspectives Concerning the Weighted_Kato and the Discretized_Kato method, authors are currently working on a spectral version of the clear-sky index K clear adjusted for the PAR range. Preliminary results demonstrated that the hypothesis that clouds are not spectrally dependant led to an under-estimation of approximately 10 % in the resulting PAR values. This will be the topic of another publication. Another mean of potential improvement is to provide a better model of the albedo in the PAR range by exploring data sets of bidirectional reflectance distribution functions (BRDF) available from the MODIS mission to estimate the albedo in the PAR range. This could be based on the monthly average maps of BRDF parameters built by  Blanc et al. (2014)  available in the broadband range as well as in ten spectral bands.
Collaboration has already been set up to extend this validation activity across Europe, mainly in France, Spain, and Czech Republic with more than 15 sites in total. PAR data have been collected for a site in Kenya. Discussions have also been initiated with users working in different domains, such as the monitoring of algal blooms along sea shores or in lakes, and for the monitoring of the phytoplankton. They have observed that phytoplankton of the northern hemisphere in the Atlantic Ocean is migrating further north due to global warming and climate change. They are investigating the opportunity to exploit PAR-satellite methods in their monitoring tool.
Conclusion This paper reports on a comparison of five methods to derive PAR from two SSI datasets derived from satellite imagery against the measurements performed at three UK stations. A great attention has been paid to the quality of the measurements prior to the comparison. An in-depth analysis of the measurements available for the Cartmel Sands station demonstrated that this dataset should not be used. The variability of the measurements in time is well reproduced by all methods for this area. Overall, methods based on HC3v5 exhibit better results. Those using the CAMS-Rad data set globally overestimates irradiances as it misses cloudy situations, which is in agreement with the literature. Group 2 methods, and in particular Discretized-Kato combined with the cloud extinction of the SSI HC3v5, outperform methods of group 1 confirming the importance of successfully modelling the spectral distribution of the solar radiation in cloudfree conditions, with a bias equal to zero or slightly negative, a RMSE of 140 µmol m -2 s -1 (approx.
30 %) relative to the mean of the measurements) and a correlation coefficient of 0. 95. This methods turns to be satisfactory to meet the diverse challenges of the specific application of assessment of the PAR potential and the real-time monitoring at more than sixty soft fruit farms relying upon tunnel production. The validation carried out in this analysis will be extended to other sites in Europe and in Kenya in a forthcoming effort. To help in understanding the merits and drawbacks of the different PAR-satellite methods, they will be confronted to other methods, such as the alternative proposed by EUMET-SAT which plans to enlarge the panel of the CM-SAF products with monthly PAR data sets as part of the next version of SARAH (version 3) developed and maintained by the DWD. This work belongs to a wider project which aims at providing irradiances in any spectral range to meet the numerous requirements from users in various applications, for instance in solar energy, human health, agro-meteorology and e-cosmetics.
S-PLUS DR1 is available at datalab. noao. edu/splus, and it is characterised in Section 4 and in the NOAO data lab site, as well as in  Molino et al. (in prep. ). Section 2 describes the technical aspects of the survey -the telescope, optics, control system, camera, filter system -and survey strategy, including a description of the five sub-surveys of S-PLUS. Section 3 presents the key science areas of each sub-survey. In Section 4, a brief description of the data reduction pipeline is given. In addition, this section specifies the production of catalogues and data calibration strategies, tests of the PSF stability over the images, photometric and photometric redshift depths, and our plans for future data releases. In Section 5, we present a table with the characteristics of S-PLUS DR1 and describe some preliminary results from the analysis of the first S-PLUS dataset.
Our MS observing strategy is a modification of the J-PLUS strategy, and it is expected that the datasets from both S-PLUS and J-PLUS can be combined in the future for scientific projects where a large area (∼ 16000 square degrees) is desirable. The S-PLUS MS strategy is mainly motivated by the requirements set by the extragalactic science. The original goal was to match the photometric depth of SDSS in the broad-band filters; however, S-PLUS images are, on average, shallower than SDSS (see §4. 6 and Table  8 ). The MS has significant overlap with Pan-STARRS  (Schlafly et al. 2012 ), DES (Dark Energy Survey Collaboration et al. 2016 ), KiDS (de Jong et al. 2015) , and ATLAS  (Shanks et al. 2015) , and can thus provide improved photometric redshifts for objects in these fields down to rAB ∼ 20 (see §4.
7). Figure  6. The Javalambre 12-filter system. The y-axis shows the total efficiency of the S-PLUS filters, obtained through the multiplication of the average filter transmission curves, the atmospheric transmission, the CCD efficiency, and the primary mirror reflectivity curves. Different filters are coloured according to the labels shown in the legend at the right. The determinations of photo-z, environment indicators, and star-galaxy separation (described in Section 5) using DR1, will form the basis for a number of important extragalactic studies. For example, we expect to detect several million galaxies in the MS -from these data we plan to build a new multi-wavelength galaxy catalogue, with uniform environment criteria, choosing from isolated galaxies to groups/clusters. This will extend previous Southern Hemisphere catalogues to a complete, volume-limited sample, mitigating projection effects by using the more precise S-PLUS photometric redshift information (δz/(1 + z) = 0.
2011). The VFS data will be inspected for new asteroids and other moving objects. Some SNe may also be identified, although this is not a primary goal of VFS. In addition, the follow-up of Fermi LAT triggers is interesting due to the matching of the typical error box of these triggers (of about 1 degree diameter) to the field of view of the camera. About one third of the sources in the latest Fermi/LAT Source Catalogue (3FGL) are of unknown type  (Acero et al. 2015) , and their identification may result in a large number of new blazars. Finally, identification and follow-up of the electromagnetic counterparts of gravitational wave events  (Abbott et al. 2017)  are areas in which VFS may bring important contributions. At the time of this writing, there is one long-term program that was awarded VFS observing time in 2018B and continuing through 2019, aiming to detect cataclysmic variable stars.
DATA FLOW, FROM RAW DATA TO SCHEDULED DATA RELEASES This paper presents the first S-PLUS data release, DR1, on Stripe 82. This section characterises these data. Further characterisation of DR1 is reported in  Molino et al. (in prep. )  and  SamPedro et al. (in prep. ). The raw imaging data of S-PLUS are processed daily and data catalogues are generated at the data centre, located in the T80S technical room on Cerro Tololo. Full backups of the raw data are made with LTO6 tapes, for any eventual re-processing, if needed. The processed data are transferred through fibre connection to IAG/USP, in São Paulo. An overview of the data reduction process is given in §4. 1. Multi-band photometric catalogues are generated by running the SExtractor software  (Bertin & Arnouts 1996; Bertin 2010 ) on a combined reduced image, which is the weighted-sum of the reddest (griz) broad-band images.
Overview of the Data Reduction Process The S-PLUS raw data are reduced using an early version (number 0. 9. 9) of the data processing pipeline jype (developed by CEFCA's Unit for Processing and Data Archiving, UPAD) designed to reduce data for the J-PLUS and the J-PAS surveys  (Cristóbal-Hornillos et al. 2014). This, in turn, is based on the photometric pipeline originally developed for the ALHAMBRA survey (see  Cristobal-Hornillos et al. 2009; Benitez et al. 2014; Molino et al. 2014 ). The basic reduction strategy consists of four steps: i) Generating a master bias; ii) Creating a master flat; iii) Reducing the individual frames; and iv) Combining the individual frames into the final astrometrically-aligned images. Bias frames are obtained every night, and twilight flats are obtained, whenever the sky is clear, at dawn and at dusk.
Data Calibration and Final Catalogues A new photometric calibration technique is employed here, specifically developed for wide-field multi-band photometric surveys such as S-PLUS. A similar version of such technique is planned to be used for calibrating J-PAS  (Gruel et al. 2012). The calibration takes advantage of other surveys such as SDSS  (Ivezić et al. 2007; Padmanabhan et al. 2008 ), Pan-STARRS  (Schlafly et al. 2012) , DES  (Drlica-Wagner et al. 2018; Burke et al. 2018)  or  KiDS (de Jong et al. 2015) , which derived photometric calibrations for millions of stars, typically in 4-5 bands, in areas overlapping with S-PLUS. In addition, instead of using complex (and sometimes inaccurate) transformation equations between filter systems, our calibration strategy relies on libraries of stellar models as if they were spectrophotometric standard stars.
(in prep. ). The rms of the distributions for the five filters, ugriz, are 0. 06, 0. 05, 0. 03, 0. 05 and 0. 03 mag, respectively, proving the good consistency between the two data sets. The mean differences between the SDSS and the S-PLUS filter systems give an offset in the x-axes of 0. 06, -0. 02, -0. 03, -0. 01 and 0. 03 mag for the five bands respectively. This is due to small differences in the filter systems described in Table  A1. Once the zero-points are obtained, the final catalogues with calibrated magnitudes are derived. The final data catalogues include the basic astrometric (coordinates), photometric (e. g. , fluxes and magnitudes), and morphological (e. g. , ellipticity, position angles, major and minor axis ratio, and stellarity) information for all sources detected in the images.
Determination of the Stellar FWHM across the Field The S-PLUS DR1 Stripe 82 data were used for checking the average variation of the FWHM of stellar objects across the field. Detection images (i. e. , a combination of griz bands) were used for this exercise. The differences in the FWHM measurements for a given star, in the four bands, g, r, i, z, was never more than half a pixel, therefore a simple combination of the four images was appropriate (using only the r-band yields very similar results). The FWHM values of typically 500 bright non-saturated stars across each field were measured (using SExtractor) and they were normalised to the average FWHM of the bright, isolated, and non-saturated stars in each image. The result is shown in Fig. 14. Note that the average FWHM corresponds to unity, on the scale shown in the right-hand side of the figure, and the variation from the centre to the border is 10%.