dataesr/scientific-paragraphs-categorization huggingface dataset:
Both databases are available online (http://www. soda-pro. com, last access: 29 June 2020) and are used by several hundred academics and engineers each year  (Thomas et al. , 2016a). Many validations have been performed and are still being performed to supply users with the most complete knowledge of the accuracy of each database. For example, validation is performed every quarter that compares ground-based measurements made at several stations in Europe, Africa and South America to E estimated by the CAMS Radiation Service. A report is published and made available on-line at https://atmosphere. copernicus. eu/supplementary-services (last access: 1 May 2020). Joint validations of the two databases have also been performed for various climates in Europe  (Thomas et al. , 2016b)  and for more specific geographical regions, such as the tropical Atlantic Ocean  (Trolliet et al.
, 2018) , Brazil  (Thomas et al. , 2016a) , Egypt  (Eissa et al. , 2015) , Iraq  (Ameen et al. , 2018) , Morocco  (Marchand et al. , 2018) , Oman  (Marchand et al. , 2017) , and The Netherlands  (Marchand et al. , 2019). The present work adds to the continuous documentation of these two databases and demonstrates that both databases capture the temporal and spatial variability of E and are reliable sources of solar radiation data. This study focuses on the verification of the spatial consistency of both databases using a network of measuring stations in Germany. With an installed capacity of 47. 5 GW, Germany is one of Europe's largest users of solar energy and there is a local need to better know the performances of solar plants according to their location. This article addresses the question of how the accuracy of the satellite-derived estimates varies across Germany.
Only non-suspicious data were retained in this work. In addition, we have put one more constraint on measurements. Since the lowest values can be noise and are therefore insignificant in a validation process, any measurement should be greater than a minimum significant value. If it is not, the measurement is removed from the dataset. The threshold was selected in such a way such that there is a 99. 7 % chance that the actual irradiance E is significantly different from 0 and that it can be used for the comparison. It is set to 1. 5 times the uncertainty of measurements of moderate quality as reported by the WMO (2018), and is equal to 75 W m -2. Figure  2  shows the average of the measurements for the period 2010-2018 at each station. The average of E obtained for the different stations ranges between 330 and 370 W m -2 , with the southern stations exhibiting the greatest averages.
Both the HC3v5 and the CAMS-Rad datasets are constructed by processing images acquired by the MSG satellites since their operational inception in February 2004. For HC3v5 the radiances of the two narrow visible bands of SEVIRI at 600 and 800 nm are combined to produce broadband radiances  (Cros et al. , 2006). Then, the Heliosat-2 method combined with the ESRA clear-sky model  (Rigollier et al. , 2000)  is applied to these broadband radiances to yield estimates the solar irradiance (see details in  Rigollier et al. , 2004 and Lefèvre et al. , 2007). A clear-sky model is a model that estimates the solar irradiance at ground level in cloud-free conditions. The clear-sky irradiance is multiplied by a cloud index derived from the satellite images. The cloud index is computed at each instant by comparing the observed radiance to that which should be observed if the sky were cloud free; it quantifies the extinction due to the possible presence of clouds.
Since its inception in February 2004, several versions of HelioClim-3 have been created to improve its performances. The most recent version is the fifth, shortened to HC3v5. The main innovation in HC3v5 is the introduction of a correction factor  (Qu et al. , 2014)  using the very accurate CAMS McClear clear-sky model  (Gschwind et al. , 2019; Lefèvre et al. , 2013)  with inputs on aerosols, and atmospheric column content in ozone and water vapour from CAMS. HC3v5 time series of data are obtained from the SoDa web site (http://www. soda-pro. com, last access: 20 December 2019). This website is managed by both Transvalor and MINES ParisTech and is dedicated to the provision of data for different solar related applications  (Gschwind et al. , 2006). The CAMS-Rad Service is based on the Heliosat-4 method  (Qu et al.
, 2017) , yielding cloud properties that are input to Heliosat-4 to compute the clear-sky index. Other inputs are the solar zenithal angles computed by the Solar Geometry 2 algorithm  (Blanc and Wald, 2012)  and the dataset of  Blanc et al. (2014)  of the bidirectional reflectance distribution function of the ground derived from MODIS dataset. By construction, the CAMS Radiation Service runs the Heliosat-4 method on-thefly at the request of any user. It processes the necessary information and does not create a proper database of the results. It follows that the CAMS-Rad database is a live collection of data and may be corrected a posteriori as flaws or drawbacks are discovered, yielding several versions that apply from now on back to 1 February 2004. For example, the McClear and further, the CAMS-Rad Service, benefits from successive improvements in the assessment of aerosol optical depth by CAMS  (Gschwind et al.
Results Figure  3  exhibits the bias and the standard deviation for the CAMS Rad database for all-sky conditions. The bias is coded by the colour in the circle, while the standard deviation is given by the diameter of the circle. Table  2  reports the correlation coefficients. The bias ranges between -41 and 32 W m -2 (-11 % and 10 %) with an average of 2 W m -2 (1 %). The standard deviation ranges between 89 and 129 W m -2 (25 % and 39 %) with an average of 107 W m -2 (31 %). This metric represents the scattering of the differences between ground measurements and estimates around the bias. The correlation coefficients range between 0. 83 and 0. 92. The same results were obtained for two classes of clearness index: K T > 0. 7 (cloud-free and low cloudiness conditions -a high value of K T can correspond to clear skies with possible small broken clouds or thin cirrus clouds), and K T < 0.
2 (overcast conditions). For cloud-free conditions, there is an underestimation at all sites. The bias ranges between -101 W m -2 (-16 % of the mean value of E for these conditions) and -55 W m -2 (-8 %). The standard deviation is much less than for all sky conditions: it ranges from 71 to 101 W m -2 for cloud free conditions, compared to 89 and 129 W m -2 for the whole dataset. The correlation coefficient is between 0. 85 and 0. 94. For overcast conditions, on the contrary, the bias for overcast conditions is always positive (overestimation). It ranges between 56 W m -2 (49 %) and 131 W m -2 (109 %). The standard deviation ranges from 78 to 142 W m -2 , and the correlation coefficient from 0. 43 to 0. 56. Resulting maps and tables are not shown here for the sake of available space. Figure  4  displays the bias and the standard deviation for the HC3v5 database for all-skies.
The bias ranges between -22 and 16 W m -2 (-6 % and 5 %) with an average of -6 W m -2 (-2 %). The standard deviation ranges from 70 to 104 W m -2 (20 % and 39 %). The correlation coefficient is comprised between 0. 90 and 0. 95 (Table  2 ). In cloud-free and low cloudiness conditions, the bias for the HC3v5 database is negative (underestimation) at all sites like for CAMS-Rad. It ranges from -71 W m -2 (-11 %) to -14 W m -2 (-2 %). The standard deviation is between 49 and 114 W m -2 , and the correlation coefficient between 0. 80 and 0. 96. In overcast conditions, the bias is positive (overestimation). It ranges between 14 W m -2 (12 %) and 94 W m -2 (79 %). The standard deviation ranges from 54 to 78 W m -2 , and the correlation coefficient from 0. 34 to 0. 52.
Discussion Figure  2  shows that the 2010-2018 average of the 10 min means of irradiance across Germany has a tendency to in- The main features in the spatial variations may be explained by the well-known dependence of the solar radiation with the latitude and the altitude. The stations along the northern seashore exhibit greater averages than their inland counterparts, a feature also reported for The Netherlands  (Marchand et al. , 2019). These differences are due to differences in cloud properties that can have a meteorological origin or can be caused by the interactions between the atmosphere and the underlying surfaces  (Kostsov et al. , 2018). The bias for the CAMS-Rad database is close to 0 W m -2 on average, but shows large discrepancies across Germany (Fig. 3 ). E is underestimated at seashore stations: Arkona (-41 W m -2 ), Sankt Peter-Ording (-27 W m -2 ), Fehmarn (-20 W m -2 ) and Rostock (-18 W m -2 ).
83), and does not exhibit any spatial trend, though one may note a tendency to increase with increasing averages of E. The bias for the HC3v5 database is close to -6 W m -2 on average, and is fairly homogeneous across Germany, between -22 and 16 W m -2. In Fig. 4 , one may note a slight tendency for the bias to increase from East (underestimation) to West (overestimation). On the contrary, there is no spatial feature visible in Fig. 4  for standard deviation, whose relative values range between 20 % and 30 % approximately. Similarly, the correlation coefficient does not exhibit any spatial trend. For the CAMS-Rad database, the greatest bias (32 W m -2 ), the greatest standard deviation (129 W m -2 ) and the smallest correlation coefficient (0. 83) are found for the same station: Zinnwald-Georgenfeld whose elevation is 877 m.
This may question the quality of this site, but one notes that there is no major difference between this site and similar ones for the HC3v5 database. It is possible that the CAMS-Rad does not correctly estimate at this site because of the surrounding environment and the altitude. The standard deviations are greater than those reported in previous studies  (Ameen et al. , 2018; Eissa et al. , 2015; Marchand et al. , 2017 Marchand et al. , , 2018 Marchand et al. , , 2019;; Thomas et al. , 2016a, b; Trolliet et al. , 2018). This is partly explained by the difference in the duration of the measurements: 10 min here, against 1 h in the other studies. In overcast conditions, both databases overestimate irradiance. The bias, standard deviation of error and correlation coefficients do not exhibit any spatial trend according to latitude, longitude or altitude of stations.
For both databases, the results for cases where K T > 0. 7 shows underestimation at all sites. One may question the quality of the McClear-v3 clear-sky model. However, several comparisons between McClear-v3 estimates and 1-min means of irradiance measured at several stations in the world show that this model provides very satisfactory results  (Gschwind et al. , 2019; Sun et al. , 2019). We therefore suspect that the errors of CAMS-Rad and HC3v5 in clear-sky condition originate from errors in classification of clear skies by the Heliosat algorithms. This observation has been reported in previous investigations of the CAMS-Rad database  (Marchand et al. , 2018; Thomas et al. , 2016b; Trolliet et al. , 2018)  and in the quarterly validation reports available on-line at https://atmosphere. copernicus.
However, this is a complex issue that also involves the detection and classification of thin clouds, which is beyond the scope of this work. For the HC3v5 database, the origin of the bias results from errors in the determination of the cloud-index that may be themselves related to errors in estimating the ground reflectance. Several previous investigations on HC3v5 have shown that the bias is small under cloud-free conditions when such conditions are frequent, such as over the equatorial part of the Atlantic Ocean  (Trolliet et al. , 2018 ), Morocco (Marchand et al. , 2018) , or Oman  (Marchand et al. , 2017). In other cases, investigations have reported similar observations to ours, i. e. a noticeable underestimation in the HC3v5 database  (Thomas et al. , 2016b). We then evaluate whether CAMS-Rad and HC3v5 solar radiation estimates fulfil the definition of "moderate quality" for measurements as set up by  WMO (2018).
Under this quality, the relative uncertainty of E should not exceed 20 %, the uncertainty being equal to twice the standard deviation of errors. To perform this evaluation, we assume that the bias for CAMS-Rad or HC3v5 can be removed. We also assume that the ground measurements are themselves of moderate quality. The results of the comparisons include the combination of the uncertainty of measurements and the uncertainty of the estimates. This combination can be expressed in a first approximation as the quadratic sum of both uncertainties (ISO/IEC, 2009). The total uncertainty is equal to twice the standard deviations reported above. Knowing the total uncertainty and the uncertainty of estimates, the uncertainty of estimates can be calculated. It is found that the relative uncertainty of estimates is approximately respectively 60 % for the CAMS-Rad and 50 % for the HC3v5 databases.
These numbers exceed the relative uncertainty set by WMO and it can be concluded that to a first approximation, the quality of CAMS-Rad and HC3v5 estimates is less than "moderate quality". These findings agree with those reported by  Marchand et al. (2019)  for the Netherlands which experiences a similar climate to that of Germany. They disagree with the findings of previous studies which report that both databases meet the moderate quality for the Arabic Peninsula  (Marchand et al. , 2016) , Morocco  (Marchand et al. , 2017)  and tropical Atlantic Ocean  (Trolliet et al. , 2018). This disagreement is likely related to the higher occurrence of cloud-free days in these three regions compared to Germany and The Netherlands. The re-examination of the results of  Ameen et al. (2018) ,  Eissa et al.
Conclusions and perspectives This paper reports on a comparison of two satellite-derived radiation databases against the measurements of 26 stations in Germany. The reference dataset includes 10 min measurements of global radiation that underwent a quality control to use only non-suspicious data. It was found that the two databases reproduce the 10 min changes in irradiance well with correlation coefficients around 0. 83-0. 92 for CAMS-Rad and 0. 90-0. 95 for HC3v5. No systematic errors could be highlighted in all sky conditions with biases ranging from -40 to 32 W m -2 for CAMS-Rad and from -22 to 16 W m -2 for HC3v5. The spatial consistency of the uncertainties of the satellite estimates has been investigated. For the CAMS Rad database, overestimation is observed in the South, and underestimation in the North with a faint tendency of the bias to increase from East to West.
For the HC3v5 database, the bias is fairly homogeneous, though one may note a slight tendency for underestimation in the East and overestimation in the West. For both databases, there is no noticeable spatial trend in the standard deviation. In contrast, a negative bias is identified for situations with a clearness index greater than 0. 7. This bias most likely stems from issues in the Heliosat algorithms because several studies showed that the performance of the clear-sky model Mc-Clear is unbiased. Generally, this work validates the results obtained by  Marchand et al. (2019)  who show that the analysed satellite databases have a lower quality than a "moderate quality" standard as defined by the WMO in the case of The Netherlands. This result disagrees with previous studies because of the high occurrence of cloudy cases, for which the uncertainty of satellite based radiation estimate is worse  (Marchand et al.
Data availability. Measurements performed at the DWD stations are available online at the German climate data center (CDC) portal (https://opendata. dwd. de/climate_environment/CDC/ observations_germany/climate/10_minutes/solar/historical/, DWD, 2020). CAMS-Rad and HC3v5 are available online at the SoDa website (http://www. soda-pro. com/web-services/radiation/ cams-radiation-service, Atmospheric Monitoring Service, 2020). Time series used for this work are freely available on request (mathilde. marchand@transvalor. com).
Identifying insertion sites for IS elements Locations of CLJ-ISL3 insertions in the gaps between CLJ3 contigs were detected by checking for the inverted repeat sequences at contig ends, while taking the shared gene synteny between CLJ1, PA7 and PAO1 genomes into account. PanISa version 0. 1. 0 (https://github. com/bvalot/ panISa)  [30]  was also used, with default parameters, to search for ISs in CLJ3 reads. Reads were mapped to the PA7 and CLJ1 genomes using the BWA-MEM algorithm from BWA version 0. 7. 15  [31].
MS-based quantitative proteomics analyses Proteomics samples were prepared and analysed by nano-LC coupled to tandem MS (Ultimate 3000 coupled to an LTQ-Orbitrap Velos Pro; Thermo Scientific) as described previously  [33]  with slight modifications (see Supplementary Materials and Methods). Each fraction was verified by western blot for purity using appropriate antibodies. The protein content in the whole-cell, membrane and secretome fractions of CLJ1 and CLJ3 was analysed independently. Statistical analyses were performed using ProStaR  [34]. In total, a list of 2852 quantified proteins was obtained. The MS proteomics data were deposited with the ProteomeXchange Consortium via the PRIDE  [35]  partner repository under dataset identifier PXD011105.
RT-qPCR To quantify selected transcripts, total RNA from 2. 0 ml cultures (OD 600 of 1. 0) was extracted with the TRIzol Plus RNA Purification Kit (Invitrogen) and treated with DNase I (Amplification Grade; Invitrogen). Realtime quantitative PCR (RT-qPCR) was performed as described  [38]  with minor modifications (see Supplementary Materials and Methods). Primer sequences were designed using Primer3Plus (http://www. bioinformatics. nl/ cgi-bin/primer3plus/primer3plus. cgi/) and are listed in Table  S2. Transcript quantifications are shown with SEM.
05). The labels on the outermost ring highlight genes that were differentially expressed in both RNA sequencing (RNA-Seq) and at least one of the proteomics datasets. The protein subcellular localization (outermost ring) was coloured according to the coding used in the Pseudomonas Genome Database  [30]. CLJ-ISL3 insertions are indicated by red, green and blue triangles, depending on whether they were present in CLJ1, CLJ3 or both strains, respectively. Downloaded from www. microbiologyresearch. org by IP: 132. 168. 74. 211  On: Tue, 09 Apr 2019 06:58:07 contained 15 regions that are absent from the PA7 genome, and lacked 26 PA7 regions, including some outside recognizable RGPs (Table  S4 ). The differences between PA7 and CLJ1 are presented in Tables  S3  and S4. All CLJ1 regions absent from PA7 (CLJ-SR), except CLJ-SR14, were detected in other P.
Evidence of mobile genetic elements in key pathogenic regions The CLJ3 isolate was sampled from the same patient after a series of aggressive antibiotic treatments and displayed different cytotoxicity and antibiotic-sensitivity profiles compared to CLJ1  [16]. The genomes of the two strains were almost identical, with a reciprocal best-hit average nucleotide identity  [46]  estimated at 99. 97 %, and >600 SNPs between them (Fig. 1 ). When analysing the genomes of CLJ1 and CLJ3, the most striking feature was the presence of multiple copies of a 2985 bp fragment corresponding to a mobile genetic element, absent from the reference PA7 genome. At the nucleotide level, the whole sequence of this element was 99 % identical to ISPst2 from P. stutzeri, ISpu12 from P. putida and IS1396 from Serratia marcescens, found in the ISfinder database  [47]  (Table  S6 ).
Further examination of publicly available genome sequences (NCBI release February 2019) by BLASTN searching revealed the fragment to be 100 % identical to a part of the pU12A_D plasmid present in the Escherichia coli ST131 urinary tract isolate (accession number: PRJNA516746). The complete sequence was also detected in a few other bacterial strains, including a multi-drug-resistant Acinetobacter baumannii isolate recovered from bronchoalveolar lavage fluid  [48]. The element is an isoform of ISPst2 and belongs to the ISL3 family, and we therefore named it CLJ-ISL3. In addition to coding for a transposase, CLJ-ISL3 encodes a putative seven-transmembrane domain inner-membrane protein (344 aa, 36 kDa) from the permease superfamily cl17795 (with the conserved domain COG0701  [49] ) and a putative transcriptional metalloregulator from the ArsR family (Fig.
As mobile genetic elements contribute significantly to phenotypic modifications by altering gene expression or by inactivating genes  [50, 51] , we examined how these insertions affected overall gene expression and specific phenotypes. To do so, we compared CLJ1 and CLJ3 transcriptomes and proteomes using RNA-Seq and MSbased quantitative proteomics, respectively. The proteomes for three different bacterial fractions (whole bacteria, total membranes and secretomes) were analysed. Stringent statistical analyses of extracted data revealed 75 differentially expressed elements between CLJ1 and CLJ3, at both mRNA and protein levels (Fig. 1 , Tables S8, S9 and S10). Among these elements, 27 (35 %) were phage-related, while 32 (42 %) were predicted to be localized in bacterial membranes, the periplasm or secreted.
4b ). This gene encodes a cytoplasmic actinlike protein, and is the first gene in the pilMNOPQ operon, which is reported to be essential for both T4P biogenesis and twitching motility  [66]. Expression of the entire operon appears to be affected, as no proteins were detected by proteomic analysis, unlike several other Pil proteins encoded by other operons (Table  S8 ). This effect is probably due to the polar effect of the IS element on the downstream genes. This finding was intriguing as the action of Exolysin, responsible for CLJ1 cytotoxicity, relies heavily on T4P in another exlA + strain, IHMA87  [67]. Therefore, we examined CLJ1 proteomes for the presence of other putative adhesive molecules that may substitute for the T4P function during host cell invasion. Based on proteomic datasets, the CLJ1 strain synthesizes components of five two-partner secretion (TPS) systems, the predicted secreted components, some of which are annotated as haemolysins/ haemagglutinins, including Exolysin (CLJ1_4479).
Among the other genes in the cluster are found a putative sulfite reductase-encoding gene (CLJ1_0707) and a gene (CLJ1_0708) coding for an oxidoreductase probably involved in sulfite reduction; however, their exact role is unknown. Examining the data for differential expression of putative virulence determinants indicated that CLJ3 overproduces Lipotoxin F (LptF, CLJ1_1186), an outer-membrane protein contributing to adhesion to epithelial A549 cells which is known to activate the host inflammatory response  [79]. The lptF gene is located in a putative operon together with a gene encoding a hypothetical protein predicted to be a lipoprotein located in the periplasm (CLJ1_1187). Proteomics analysis revealed this hypothetical protein to be upregulated in CLJ3 (Table  S8 ). As lptF upregulation was not detected by initial RNA-Seq analysis, we performed RT-qPCR assays.
Nevertheless, a homologue of PrtN (CLJ1_0535), an activator of pyocin biosynthetic genes  [83] , was expressed at higher levels in CLJ3 than in CLJ1. Indeed, the prtN gene is located in a region encoding phagerelated proteins, all of which are overexpressed in CLJ3 (Fig. 1  and Table  S10 ). The genes for AlpR (CLJ1_4295, CLJ3_4269) and AlpA (CLJ1_4296, CLJ3_4268), transcriptional regulators of a programmed cell death pathway in PAO1  [84] , were also identified in both CLJ genomes. Finally, alpA and all the genes in the alpBCDE lysis cassette were highly expressed in CLJ3 (Fig. 1  and Table  S10 ). The alp genes are present in a variable, phage-related region that differs in gene composition from PAO1 and PA7 strains, but is identical to that found in three recently sequenced exlA + isolates: AR441 (GenBank: CP029093.