dataesr/scientific-paragraphs-categorization huggingface dataset:
Testing the crude harvest is considered to be the point with the highest chance of detecting adventitious agents. However, this is challenging as the recombinant Adenovirus platform used in Janssen vaccine production is itself a virus and consequently there are very high virus titres of >10 11 virus particles per ml at this point. The crude harvest also contains a high cell density at infection of >10 7 per ml. The crude harvest is a complex and variable matrix with different transgene proteins that may affect the ability to detect adventitious viruses. Janssen has adopted a stepwise NGS workflow involving biological sample preparation with enrichment, nucleic acid amplification and the use of a sequencer library, followed by sequencing, a bioinformatics pipeline that uses a PathoQuest automated workflow matched to a validated database.
There are a number of potential sources of false positive results including physical sources during the NGS workflow, such as kit components, controls and spikes, operator handling and cross-contamination with the environmental and other samples, as well as sources associated with the bioinformatics pipeline, such as the annotation of host-cell derived sequences, the annotation of viral sequences and the reference databases and associated annotation and scoring. False positives can be mitigated and controlled by optimizing and defining the processes, by operator training, and by careful selection of the raw materials, viral reference databases and the bioinformatics pipeline. These are also important considerations for assay validation. Assay controls are also very important to enable distinction between false and true positive results.
This includes the development of reference virus standards for conducting spiking studies, and comprehensive, correctly annotated viral databases for bioinformatics analysis that can facilitate NGS validation to support applications in biologics. It was noted that NGS is evolving quickly, and although a lot of progress has been made since the 2017 NGS meeting  [5] , some challenges remain regarding its implementation for adventitious virus detection. Therefore, early discussions with regulatory authorities are highly recommended and encouraged to discuss the specific use of NGS for adventitious agent detection for a particular product. The Ph. Eur. and WHO currently recommend the use of NGS as an alternative/substitution method, particularly for the in vivo assays, provided the method has been validated  [17, 18, 41].
The use of NGS is not addressed in either the Japanese or Chinese Pharmacopoeia. It was recognized that increased efforts need to be made to include additional regulatory agencies and regions that were under-represented in the meeting in order for NGS to be more generally applied as a general viral adventitious agent detection assay. It was recognized that NGS and PCR can have similar limits of detection. In contrast to PCR, use of NGS will result in sequencing of all nucleic acids present in the test material, resulting in a large data volume. Panellists stressed that the bioinformatics pipeline and database used are critical components of the NGS workflow for obtaining accurate results for virus detection and removing background noise. False positive results as a consequence of using NGS for adventitious agent detection will need to be further investigated and verified using other assays, e.
Considerable efforts are currently being devoted to advance the use of NGS for adventitious virus detection in human biologics; such efforts should continue and be more inclusive of all regions for global acceptance. • Concerted efforts should be made toward development of publicly available reference materials that can be used for the entire workflow or specific steps in NGS standardization and validation. • The lack of appropriate standards and databases, particularly in the veterinary domain, make it premature at the present time for NGS to be part of compulsory regulatory requirements. However, all stakeholders were encouraged to continue the work highlighted at this meeting with the aim of NGS becoming part of the routine methodological tool kit for manufacturers and regulators.
Acknowledgments In addition to the organizing committee members who were session chairs and are coauthors on the paper, the contributions of the following committee members are appreciated:  William Egan  and  Sven Arnouts. We are grateful to the speakers and panellists for their participation and providing valuable discussions in the meeting. All of the speakers and panellists have reviewed their talks and the panel summary for the meeting report. Ms. Abigail Charlet  and  Ms. Debbie Colman  are acknowledged for providing excellent administrative and technical support.
Convergence tests The corrected tilt angle [φ c ] is determined by the acute angle between the x-axis and the line that joints the core centres with the CoFFE method, meaning its tongue effects have been removed, while the apparent tilt φ a is the tilt deduced from the full magnetogram using the barycentres. Figure  4b  shows the computed φ a and φ c using the model with uniform twist A&A proofs: manuscript no. tilt_correct_corr (N t,0 = 0. 5, h = 0, g = 0). The green and red lines show the evolution of φ c obtained by CoFFE with p = 0 and for n it = 0 and C it , respectively. They correspond to the B z distributions shown in Fig. 3a, c. φ c becomes closer to φ i = 0 as the number of iterations increases, which shows that the method converges well. n it = 0 & p = 0 n it = C it & p = 0 Φ a Φ c : p=1 p=-1 Time step Model N t,0 = 0.
Data Used We applied CoFFE to LOS magnetograms of ARs obtained with the Michelson Doppler Imager (MDI:  Scherrer et al. 1995)  on board the Solar and Heliospheric Observatory (SOHO). The full-disc magnetograms with 96-minute cadence are obtained by averaging either one-minute and five-minute magnetograms. The different averages results in a flux density errors of 16 G and 9 G per pixel, respectively. These data provide 15 magnetograms per day with a size of 1024 × 1024 pixels and a spatial resolution of 1. 98. We use the same temporal and morphological criteria to select ARs as described in  Poisson et al. (2015a). The sample consists of ARs with a low background flux, whose emergence phase is observed during their transit across the solar disc. We limit the latitude and the longitude of the emergence to ± 30 • from the solar equator or the central meridian (CM), respectively.
This criterion aims to minimise foreshortening and projection effects when the AR is close to the solar limb  (Green et al. 2003). In order to describe the behaviour of our new method on observa-tions, we select four bipolar ARs; these ARs are representative of regions with strong tongues. This selection is best to test the ability of CoFFE to remove the tilt deviation associated to magnetic tongues. Furthermore, because CoFFE performs better when the tongues are less extended (lower tilt correction), we prefer not to exemplify such cases. To obtain the set of magnetograms to which we applied CoFFE, we processed the full-disc magnetograms using standard Solar-Software tools. We first transformed the magneticflux density measured in the LOS direction to the solar radial direction, neglecting the contribution of the components on the photospheric plane.
Acknowledgements. The authors are grateful for the insightful referee who stimulated us to improve several parts of the manuscript. MP, MLF, and  CHM  acknowledge financial support from  Argentine  grants  PICT 2012-0973  ( ANPCyT ),  UBACyT   20020130100321  and  PIP 2012-01-403  (CONICET). MLF and CHM are members of the  Carrera del Investigador Científico of the Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET). MP is a fellow of  CONICET. This work was supported by the  Programme National PNST of CNRS/INSU  co-funded by  CNES  and  CEA. The authors acknowledge the use of data from the  SOHO (ESA/NASA ) mission. These data are produced by the MDI international consortia.
g. Gomez et al. , 2015 Gomez et al. , , 2019;; Vaudour et al. , 2014 Vaudour et al. , , 2016 Vaudour et al. , , 2019;; Zaouche et al. , 2017). In addition to these regional attempts, national products have been delivered using all the soil information available in the centralised national database of soil profiles 'DoneSol' and in a centralised database of soil tests realised by various laboratories upon farmer requests, referred to as the French National Soil Test Database. These databases and some of their products are detailed in the Materials and methods section of this paper. The objective of this study was to present an overview of the impacts of these products for various end-users, including citizens and the general public; farmers; private companies; stakeholders and national agencies; policymakers; and international bodies, such as the Global Soil Partnership (GSP) of the United Nations Food and Agriculture Organisation (UN-FAO).
French soil tests database The information stored in the French National Soil Test Database is a compilation of requested soil analyses by farmers and landowners helping to improve the management of their crops and non-permanent grasslands. Samples were taken from topsoil horizons of cultivated or grassland fields, but the specific reasons why individual farmers requested soil analysis were not known; therefore, the sampling strategy could not be controlled. In each sampled field, approximately 15 sub-samples of the ploughed layer (or the 0-30-cm topsoil layer in the case of pasture) were collected using a hand auger, and the extracted soil was bulked to provide a composite sample, which was analysed. Results obtained via standardised analytical procedures were sent by laboratories certified by the French Ministry of Agriculture and were stored in the national database.
In France, approximately 2600000 soil samples were analysed between 1990 and 2014, leading to a total of approximately 329000000 analytical results. Most of the analyses involve classical agronomic parameters, such as the pH, organic C and N, extractable P with various reagents, cation exchange capacity, and exchangeable cations. The uncontrolled sampling strategy led to a heterogeneous distribution of the samples in both space and time (Figure  1 ). For privacy, the location information in this dataset is limited to the administrative district from which the soil sample was collected. Changes in the sampling resolution may arise in space and time because the soil sample locations were selected according to the needs of farmers rather than a statistical design. <Insert Figure  1  about here>.
French soil profiles database 'DoneSol' This database contains all point-based soil information coming from conventional soil mapping and inventory programs at all scales (Inventaire Gestion et Conservation des Sols, IGCS). Consequently, these points (soil profiles and augering descriptions) are irregularly spread over the French mainland territory and are sometimes clustered in areas of specific economic interest, such as vineyards. As a whole, they represent approximately 160000 point data.
SOILHYDRO and GEVARNOVIA databases The SOLHYDRO database contains data on physical, chemical, and hydraulic characteristics of samples collected from 1985  (Bruand et al. , 2004). The soil types are mainly Cambisols,  Luvisols, Planosols, Albeluvisols, Podzols, and Fluvisols (IUSS Working Group WRB, 2015)  sampled over the French metropolitan territory. The geographical distribution of the sampling locations is mostly concentrated in the northern half of France, with little representation of the more mountainous southern and eastern regions, but covers a wide range of parent materials and textures. The land use varies among the sampling locations, including forests, poplar plantations, vineyards, grasslands, fallow, and oleaginous crops, but mostly consists of cereal monocultures. In 2017, SOLHYDRO comprised 689 horizons, of which 157 were topsoil horizons (depth of 0-30 cm) and 532 were subsurface horizons (depth of >30 cm).
The GEVARNOVIA database was used as an independent validation dataset and consisted of 309 observations from different French institutes, including academic institutes such as INRA and agricultural technical institutes dedicated to cereal crops, oleaginous fruits, or seed selection  (Román Dobarco et al. , 2019b). The soil samples were collected mostly in the northern half and southwest of metropolitan France, with few samples collected in the southeast quadrant. The predominant land use was agricultural land or pastures. The database comprises information regarding the particle-size distribution, SOC content, bulk density, and volumetric soil moisture content measured for soil aggregates after equilibrium at -10 and -1580 kPa.
Soil P In the database, a total of 850606 results from soil analyses contained available P content values (in mg P2O5 kg -1 ). These values were determined by one of the three standardised methods  (AFNOR, 1996)  used by French soil analysis laboratories, i. e. the "Dyer" method (NFX 31-10), which involves the measurement of soluble P in a citric acid monohydrate solution; the "Joret-Hébert" method (NFX 31-161: determination of soluble P in an ammonium oxalate solution); and the "Olsen" method (ISO 11263: determination of P in a sodium hydrogen carbonate solution). The originality of the method used lies in quantitative results originating from the French Soil Test Database and their evaluation using the RegiFert software  (Denoroy et al. 2004 ), which incorporates soil characteristics and crop sensitivity to nutrient availability.
In France, an SAR is an agricultural census unit that combines several districts. The average agricultural area of an SAR is 355. 12 km 2. Employing this spatial unit allows the use of sufficient data to run non-parametric statistics. For running these non-parametric statistics,  Saby et al. (2017)  proposed a statistical approach that takes into account the possible bias in the database using resampling techniques. Detailing this method is outside of the scope of this synthesis; however, a key point is that this methodology does not require any assumption regarding the precise pH measurement location, this is important because we often do not know it due to privacy legislation.
SOC stocks The most recent product predicting SOC stocks at the national scale  (Mulder et al. , 2016)  used all available point data for France, from both the French Soil Mapping and Inventory Program and RMQS, i. e. the systematic monitoring grid. For estimating SOC stocks, only the soils that these profiles precisely georeferenced and that had measurements of the SOC content were retained, leading to a total of approximately 36000 profiles. Details regarding these two datasets were presented by  Mulder et al. (2016). Briefly,  Mulder et al. (2016)  used these point data and 23 available covariates related to climate, relief, parent material and soils, biomass production, and land use to predict the SOC over a 90 × 90 m grid according to GlobalSoilMap specifications  (Arrouays et al. , 2014).
Trace elements TE data from the RMQS systematic grid were used to deliver various products. A first product was delivered by  Villanneau et al. (2008)  and is available online at https://doi. org/10. 15454/UEZXBY. These authors took into account both the natural pedogeochemical background and the diffuse contamination to quantify background levels of TE by using robust statistics (Tukey's whisker). Whiskers were calculated for each RMQS site using at least 10 neighbour sites within a 50-km circle. High background contents for four elements (Pb, Zn, Cu, and total and extractable Cd) indicated either high natural contents or anthropogenic diffuse contamination. Most importantly, their method allowed the calculation of an indicator for detecting a suspicious TE amount while taking into account the local context.
Soil thickness The first product predicting the ST at a national scale  (Lacoste et al. , 2016)  was produced using 2116 sites from the RMQS database and 14113 sites from the IGCS database. The RMQS dataset had a better spatial coverage and was collected via a standardised procedure. The RMQS dataset was used for calibration; the IGCS dataset was used for validation. The ST (or depth) was defined according to the USDA Soil Survey Manual as the depth (in cm) to a lithic or paralithic contact  (Soil Survey Division Staff, 1993). Chen et al. (2019b)  further demonstrated how right-censored data can be accounted for in the ST modelling of mainland France. They used the random survival forest (RSF) method for ST probability mapping within a DSM framework. Among 2109 sites of the French Soil quality monitoring network (RMQS), 1089 observed STs were defined as being right-censored.
Available water capacity The prediction of the AWC was performed in two steps. First, soil properties used as input for pedotransfer functions and coarse elements were predicted over mainland France using the GlobalSoilMap depth interval. Data of the particle-size distribution and coarse elements were extracted from the IGCS dataset, resulting in a total of 81671 soil profiles and soil cores. Particle-size fractions were predicted with regression-cokriging models [Cubist models  (Kuhn et al. , 2014; Quinlan, 1992)  + linear model of coregionalisation  (Goulard and Voltz, 1992) ], and coarse elements were predicted with a regression model (quantile regression model,  Meinshausen, 2006). All the regression models were fitted using 44 environmental covariates describing scorpan factors. For calibration, data of the particle-size distribution and coarse elements were extracted from the IGCS dataset, resulting in a total of 81671 soil profiles and soil cores.
The RMQS dataset was used as an independent evaluation sample for particle-size distribution and coarse element predictions. Then, the volumetric soil moisture content (cm 3 cm -3 ) at field capacity (-10kPa = θFC) and at a permanent wilting point (-1580 kPa = θPWP) for the fine fraction were estimated with PTFs developed by Román Dobarco et al. (2019a) using the SOLHYDRO database. These PTFs used the content of clay (%) and sand (%) as predictor variables. The predictions of the volumetric soil moisture contents of the fine fraction were evaluated with the GEVARNOVIA georeferenced observations. Finally, the AWC (mm) was calculated as - * 1 - * , where represents the volume of coarse elements (%), and represents the thickness of the GlobalSoilMap depth interval (mm). The uncertainty of the AWC was quantified via first-order Taylor analysis.
Changes in soil pH The authors showed that the soil pH increased in 36% of arable soils monitored from 1996 to 2010, indicating that soils became less acidic across the country. Conversely, reductions in the pH were almost never detected. Their statistical framework smoothed the effects of some limitations of the database, i. e. the variable number of samples collected during both periods and sampling location uncertainties. For large parts of France, no trend was confirmed, indicating that the program should either gather more samples or wait for a longer time to detect such evolutions. 2017 ) used geostatistical methods to map the expected concentrations of As and Hg in the topsoil and determine the probabilities that legislative thresholds are exceeded. They found that with the exception of some areas where the geogenic concentrations and soil adsorption capacities are very low, the As concentrations are often higher than the thresholds, indicating that further assessment of some areas is required.
Soil thickness Lacoste et al. (  2016 ) compared three spatially explicit modelling approaches for the ST for mainland France (540 km 2 ): i) regression tree modelling (RTM), ii) gradient boosting modelling (GBM), and iii) multi-resolution kriging (MrK) for large datasets. The RMQS dataset was used for calibration; the IGCS dataset was used for validation. Exhaustive environmental data were used to characterise the climate, the organisms, the topography, and the other known soil forming factors, according to the Soil-Landscape paradigm  (Jenny, 1941; McBratney et al. , 2003). The maps' accuracy and difference were assessed by comparing the ST predictions to observed data (RMQS and IGCS datasets) and to an available soil map bootstrapping approach was used to assess the 90% confidence intervals.
The results indicated that the RSF method was able to correct for right-censored data entries occurring within a given dataset. The RSF was more reliable for thin (0. 3 m) and thick soils (1-2 m), as they performed better (overall accuracy ranging from 0. 793 to 0. 989) than soils with a thickness between 0. 3 and 1 m. This study provided a new approach for modelling rightcensored soil information. They also investigated the bacterial interactions (co-occurrence) network of the bacterial community according to land use. In contrast to the bacterial richness, the network complexity was found to decrease from vineyards to forests, with a loss of >80% of the interactions  (Karimi et al. , 2019). Overall, they concluded that soil perturbation due to intensive cropping significantly reduces the complexity of the bacterial network, although the richness is increased, suggesting a potential negative impact on the soil bacterial functioning of the crop system.
Trace elements The whiskers product  (Villanneau et al. , 2008)  and more elaborated products  (Saby et al. , 2011; Marchant et al. , 2017)  are available online at https://data. inra. fr/dataverse/gissol. They are used by private companies in charge of assessing potentially contaminated sites as references to determine whether a given site is locally contaminated and requires further investigation/remediation. Using these tools, and as a major stakeholder being the organisation in charge of orphan sites (an orphan site is a site for which the person or corporation that created the pollution is unknown or out of business), the French.
Arrouays In France, an important data rescue effort led to a 69% increase of the number of soil profiles data from 2009 to 2015 giving an impressive coverage at adequate density of the French territory. In 2015, the total number of points in mainland France soil database was about 160000 and is now (end of 2019) about 190000. This effort of capturing data benefited from the implementation of the 1:250,000 soil mapping program for which it was mandatory to deliver the soil profiles information to the national database. For this purpose, INRA developed an easy-to-use data entry interface with a smallest error potential as possible. Meanwhile, many other legacy data from maps at other scales and from other programs were rescued by INRA, by entry and collation of legacy soil profiles and data (e.
g. point location and year of recording, soil classification, soil morphologic observations and soil analytical measurements including values, units and methods used) from soil reports into the national soil profile database. The number of measurements, however, was highly variable, depending on the soil properties. In particular, AWC measurements are much less dense and irregularly spread over the territory. A sampling strategy has been designed to complete AWC measurements geographical coverage in the framework of the second campaign of the RMQS. Other hydraulic properties, such as for instance hydraulic conductivity, still remain very sparse in the national database. This type of measurements has mainly been realised in localised areas for drainage of irrigation projects. Rescuing them to build PTFs would require specific efforts and funding that are still under discussion.