dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. The reported work was funded by  ESA  via the  CCI-ECV   Ozone  project, with support from the  Belgian Federal Science Policy Office (BELSPO)  and  ProDEx  via project  A3C. We made use of the versatile Multi-TASTE validation system which was developed in heritage projects and refined recently within the  FP7   EU Project Quality Assurance for Essential Climate Variables  ( QA4ECV ), grant no. 60740. We warmly thank several members of the NDACC ozonesonde and lidar working groups for fruitful discussions. Lidar operation is funded through national collaborators and we are grateful to the following institutes and their co-workers who contributed to generating these data: CNRS and  CNES (Dumont d'Urville station and Observatoire Haute Provence ; PI is Sophie Godin-Beekmann),  DWD  (Höhenpeißenberg station; PI is Hans Claude),  RIVM  and  NIWA  (Lauder station; PIs are  Daan P.
Data availability. The ESA Ozone_cci datasets are available at http://www. esa-ozone-cci. org/?q=node/160 (last access: 18 June 2018). The ground-based ozonesonde and lidar data used in this publication were obtained as part of WMO's Global Atmospheric Watch (GAW) programme, including the Network for the Detection of Atmospheric Composition Change (NDACC) and NASA's Southern Hemisphere Additional Ozonesonde programme (SHADOZ), and they are publicly available via the NDACC Data Host Facility (http://www. ndsc. ncep. noaa. gov/data/, last access: 18 June 2018; De Mazière et al. , 2018), the SHADOZ archive (https://tropo. gsfc. nasa. gov/shadoz/Archive. html, last access: 18 June 2018; Thompson et al. , 2012), and the World Ozone and Ultraviolet Data Centre (WOUDC) archive (https://woudc. org/ data/explore.
EST sequencing and clustering, sequence annotation and database creation. A normalized cDNA library from A. evenia tissues corresponding to non-inoculated roots, roots inoculated with Bradyrhizobium sp. ORS278 and collected 6 hpi, 2 dpi, 4 dpi, 7 dpi, mature nodules and leaves harvested 21 dpi, was pyrosequenced to obtain a reference set of transcripts. A total of 1,488,532 raw reads with an average length of 297 nucleotides corresponding to a total of 442 Mb were obtained (Table  1 ). The sequences were analyzed and clustered using a modified version of a pipeline called Expressed Sequence Tag treatment and investigation kit  13 (ESTtik). A total of 46,934 A. evenia spp. serrulata EST contigs were assembled, of which 71% were blast annotated. The annotation results revealed that 29,120 contigs (61.
3%) had significant matches in the TrEMBL database, 1,719 (3. 61%) in the Nt database, 1,682 (3. 54%) in the Nr database and 1,205 (2. 53%) in the SWISS-PROT database. These A. evenia EST sequences are accessible on the SESAM web site: https://www. genoscope. cns. fr/ agc/sesam. In parallel, the six different non-normalized cDNA libraries corresponding to the infection kinetic time points (T0, 6 hpi, 1 dpi, 2 dpi, 4 dpi, 6 dpi) were individually sequenced in triplicate using HiSeq2000 technology (Illumina Inc. , USA). A total of 327,152,229 reads with a length of 100 nucleotides corresponding to 32,715 MB were obtained and mapped onto the contig clusters generated from the 454 sequencing data using the ssaha2 package (Table  1 ; Fig. 1K ). The expression level of each assembled transcript sequence in different samples was measured using DESeq.
These included 1 serine/threonine kinase, 1 MYB and 4 other transcription factors, as well as 3 genes encoding two component response regulators. Theses latter have been shown to be associated with cytokinin signaling, the hormone associated with nodule organogenesis in legumes  21. These observations suggest that they all play an important role in the shift from root development to nodule organogenesis. The analysis of the genes specifically involved in the symbiotic process is reported in the "Behavior of symbiotic genes in the A. evenia RNA-seq database" section below.
NPR1 is a transcriptional regulator required for defense gene induction in response to salicylic acid  22. Although we were able to identify 5 NPR1 annotated genes in our database, only the CL1720Contig3 gene appeared specifically induced in nodules (Fig. 3C ). It is therefore tempting to speculate that many of the defense-related genes, and especially the NCR genes, that are highly induced in nodules could be regulated through this regulator. A reverse genetic approach such as the one we already performed for DNF1  12  would help to test this hypothesis. The mature nodules also had the highest number of down-regulated genes. Among the 470 genes, 283 were annotated and almost 50% of them (201) were involved in cell wall dynamics (Supplementary Table  1 ) including structural proteins (hydroxyproline-rich glycoprotein also known as extensin, 82 genes) or modeling enzymes (polygalacturonase, 6 genes; pectate lyase, 6 genes; pectin esterase, 13 genes).
The fold changes calculated from qRT-PCR data are expressed in log2 to facilitate comparison with RNA-seq data. For the same reason, expression profiles, visualized as heatmaps, were subjected to hierarchical clustering using MEV software  24  (MeV 4. 8. 0 software). The results showed that the gene expression patterns evaluated by qRT-PCR (Fig. 4B ) were consistent with the RNAseq data (Fig. 4A ), evidence that our RNA-Seq analysis was robust. Behavior of symbiotic genes in the A. evenia RNA-seq database. We searched our database for genes reported to be involved in all the different steps in the symbiotic process in model legumes. Among the 52 symbiotic genes characterized in M. truncatula and L. japonicus we checked (Fig. 5 ; Supplementary Table  2 ), we were able to identify 39 orthologs in A.
truncatula or L. japonicus  [30] [31] [32] [33] [34] [35] [36] [37] [38] [39] [40]. In the corresponding model legumes, five of the genes (MtNSP1/LjNSP1, MtNSP2/LjNSP2, LjSIP1, LjERF1, MtNOOT) display a moderate, and sometimes transitory (LjSIP1, LjERF1), increase in their transcription level under symbiotic conditions, which could explain why we failed to detect the modulation of transcription of these genes in our dataset. There were still four symbiotic genes (LjNENA, MtLIN/LjCERBERUS, LjnsRING and MtDNF1) whose expression significantly increased during nodule development in model legumes but not in A. evenia. DNF1 encodes the SPC22 subunit of a nodule-specific signal peptidase complex required for maturation of NCR peptides. DNF1 is the only one of the subset of four nodule-expressed genes that have undergone RNAi experiments and whose role in nodulation was demonstrated in A.
This pipeline enabled editing and assembling of the input cDNA sequences in a non-redundant data set. Six mRNA libraries corresponding to non-inoculated roots and roots inoculated at 6 hpi, 1, 2, 4 and 6 dpi, were built and sequenced at the MGX platform (Montpellier Genomix, Institut de Genomique Fonctionnelle, Montpellier France). The RNA libraries were constructed using the TruSeq stranded mRNA library construction kit (Illumina Inc. , USA). The quantitative and qualitative analyses of the library were carried on Agilent_DNA 1000 chip and qPCR (Applied Biosystems 7500, SYBR Green). RNA was sequenced using the Illumina SBS (sequence by synthesis) technique on a Hiseq2000 in single read 100 nt mode. Image analysis, base calling and quality filtering were performed using Illumina software. RNAseq: short read counting method.
The complete transcriptomic high-throughput sequencing data were analyzed with the TAMARA bioinformatic pipeline (Transcriptome Analyses based on MAssive sequencing of RnAs; Cruveiller S. , unpublished) currently implemented on the Microscope platform  60. The pipeline is a "Master" shell script that launches the different parts of the analysis and checks that all tasks are completed without error. We first assessed RNA-Seq data quality including options such as read trimming or the use of merging/split paired-end reads. Reads were then mapped onto the EST contigs of A. evenia spp. serrulata with the SSAHA2 package  61. We minimized the false positive discovery rate by selecting only reliable alignments from SAM-formatted files using SAMtools 62 (v. 0. 1. 8). The number of reads matching each EST contig was then calculated with an enhanced version of the coverageBed software from the BEDtools suite  63.
Reads matching several genomic objects were weighted by the number of objects matched so as to keep the total number of reads in the library constant. Finally, the Bioconductor-DESeq package 64 (v1. 4. 1) was used with default parameters for the analysis of raw count data and to determine whether expression levels differed significantly between conditions. The R script described in Supplementary Method 1 was used to select the up-or down-regulated gene sets specific for each time lapse point. qPCR analysis. One hundred nanograms of total RNA per sample were reverse transcripted using SuperScriptII reverse transcriptase (Invitrogen) and oligo(dT)12-18. Real time qPCR was performed using the Brilliant II SYBR ® Green QPCR Master Mix (Agilent Technologies). Primer sequences are listed in Supplementary Table  3.
Data Availability. The reference transcriptome and the raw FASTQfiles for the 18 libraries were respectively deposited on NCBI under the bioprojects PRJNA396532 and PRJNA399681 with the accession numbers: SRR5983062, SRR5983061, SRR5983060, SRR5983059, SRR5983066, SRR5983065, SRR5983064, SRR5983063, SRR5983069, SRR5983067, SRR5983070, SRR5983068, SRR5983072, SRR5983071, SRR5983073, SRR5983074, SRR5983076, SRR5983075.
RNA-Seq and bioinformatics Stranded paired-end sequencing of total poly-A transcripts was performed on an Illumina HiSeq 2500 platform (Aros Applied Biotechnology, Aarhus, Denmark). Reads were mapped and analysed as detailed in Supplementary Materials and Methods. The differential gene analysis was carried out with DESeq2 package  (46). Parameters for differential expression: adjusted P-value < 0. 05 and [log 2 (FC)] ≥ 0. 35. All enrichment analyses for biological functions and transcription factor binding sites were carried out using the Enrichr analysis tool  (47).
Prediction of RNA secondary structures with Mfold As the Mfold program  (48)  does not allow to predict the folding of long RNA sequences (maximum 800 bases), we used chimeric sequences of variable length that included the two regions regulated by DDX5 and DDX17, i. e. the exon 6 and the pri-mir-26a2. The default Mfold parameters were used. The picture presented in Figure  2  shows a part of a typical structure predicted from a 500 nt-long sequence composed of: (i) a 200-nt fragment that includes full exon 6 and the beginning of intron 6; (ii) a 300-nt fragment centered on the miRNA hairpin. Note that a competing base-pairing between the proximal intronic sequence and the lower part of the pri-mir-26a2 hairpin was predicted whatever the length of the tested sequence (up to 800 nt).
Luciferase activity from the DDX17 reporter was significantly reduced upon expression of miR-26a or miR-26b, and fully restored when the predicted miR-26 binding site was mutated, demonstrating that DDX17 is a direct target for these two miRNAs (Figure  3D ). We also carried out similar experiments using a reporter that contained a 1 kb-fragment of the REST 3 UTR, but this gave more contrasted results. Both miR-26a and miR-26b reduced luciferase activity from the REST reporter to a similar extent (although statistical significance was reached only with miR-26b) (Supplementary Figure  S2E ). However, luciferase activity was not restored when we used a reporter carrying a mutation within the most conserved miR-26 binding site (data not shown), suggesting that the REST 3 UTR contains several miR-26 binding sites.
Similar results were obtained when looking at proximity foci between DDX5 and REST (Supplementary Figure  S4B ). The interactions between endogenous REST and both DDX5 and DDX17 were confirmed by coimmunoprecipitation from SH-SY5Y cell extracts (Figure  5C ), which further argued that at least a fraction of these factors are part of the same protein complex. We next used public chromatin immunoprecipitation (ChIP)-seq datasets from ENCODE and ChIP enrichment analysis (ChEA) to look for enriched transcription factor binding sites at the promoter of genes that are specifically activated in the double REST+DDX5/DDX17 knockdown   S5. (B) Scatter plot representation of gene expression in absence of DDX5/DDX17 and/or REST. The 2507 genes of which steady-state expression level is significantly altered only in the siDDX5/DDX17+siREST condition were ranked and plotted according to their expression level (blue dots).
FUNDING Agence Nationale pour la Recherche  [ ANR-16-CE12-0009-01 ]; Fondation ARC [PGA120140200853];  Institut National du Cancer  [ 2014-156 ];  Ligue contre le Cancer  ( Comités du Rh ône et de la Loire );  AFM-Téléthon  and  Association Hubert Gouin 'Enfance et Cancer '. M. P. L. , S. T. and C. B. -P. received fellowships respectively from the  Fondation   ARC (post-doctoral fellowship), AFM-Téléthon (doctoral fellowship ) and the  Région Rh ône-Alpes  (doctoral fellowship). G. G. was supported by the  Agence Nationale pour la Recherche. Funding for open access charge: CNRS.
A comprehensive comparison of more than 70000 synchronous 1-minute aerosol optical depth (AOD) data from three Global Atmosphere Watch-Precision Filter Radiometer (GAW-PFR) and 15 Aerosol Robotic Network-Cimel (AERONET-Cimel) radiometers was performed for the four nearby wavelengths (380, 440, 500 and 870 nm) in the period 2005-2015. The goal of this study is to assess whether, despite the marked differences between both networks and the number of instruments used, their long term AOD data are comparable and consistent. AOD traceability established by the World Meteorological Organization (WMO) consists in determining the percentage of synchronous data within specific limits. If, at least, 95 % of the AOD differences of an instrument compared to the WMO standards lie within these limits, both data populations are considered equivalent.
, 2015; Schmid et al. , 1999; Toledano et al. , 2012). However, these comparisons have been performed during field intercomparison campaigns or during relatively short periods of time, so they are not representative of a large variety of atmospheric conditions. In addition, the type of instrument maintenance and the number and qualifications of staff serving them during campaigns is generally of a higher quality compared to that of the instrument daily operation in unattended mode. This might cause the instrument performance during intensive campaigns to be significantly better than that during operational mode. The growing interest in the analysis of long-term AOD and AE data series for climatological purposes requires an assessment of their quality assurance and long-term intercomparability.
GAW-PFR has a comprehensive calibration system  (Kazadzis et al. , 2018a; Schmid and Wehrli, 1995)  that is transferred by a worldwide suite of reference instruments. However, AERONET-Cimel does not have a CIMO-WMO linked reference and is based on: -Maintaining Master AERONET radiometers based on the Langley calibration technique at Izaña, Spain and Mauna Loa, USA. -Calibration of all other instruments based on raw voltage ratios comparisons with Master instruments at dedicated sites (Carpentras-France, Washington DC-USA, Valladolid-Spain). This is the first study to analyze the long-term traceability of AERONET-Cimel with respect to GAW-PFR, and therefore to assess the validity of the long AOD and AE AERONET-Cimel data series for climatological and climate change studies under specific quality control requirements.
This issue is specifically addressed in Section 5. 4. In general, the agreement obtained with the 1-minute AOD data is slightly lower than that obtained during short campaigns, such as those reported by  (Kazadzis et al. , 2014)  at Athens observatory (465 data-pairs), and  Barreto et al. (2016)  at Izaña Observatory (5566 data-pairs), with agreements > 99 % for AOD 870nm and AOD 500nm in case of  Barreto et al. (2016). However, our results for AOD 500nm (> 95 % of 70833 data-pairs) is significantly better that that observed by Kazadzis et al. (2014) (∼ 48 % of 465 data-pairs) covering a relatively short range of AOD. The probable cause for the poor agreement found by  (Kazadzis et al. , 2014)  was a poor calibration in the 500 nm channel in at least one of the instruments operating at Athens.
Once the most important causes of non-traceability in the first period were ruled out, which were associated with a poor pointing of GAW-PFR due to problems in the sun-tracker, we can conclude that there are no significant differences in the percentages of traceable data of both periods. This means that the continuous change of Master Cimel instruments used in the 2005-2010 period did not have a significant impact on AOD data comparison differences. This proves the consistency and homogeneity of the long AERONET-Cimel AOD data series, and their comparability with the GAW-PFR AOD data series, regardless of the number of instruments used to generate these data series. In our study, with a number of comparison data-pairs one or two orders of magnitude higher than those used in short campaigns, the results shown in Table  4  can be considered excellent.
(2004). However, in this work we have used data from Master instruments (in-situ absolute calibration) in the case of AERONET-Cimel, and field instruments with in-situ Langley calibrations and direct traceability to the reference triad, in the case of GAW-PFR. The typical calibration uncertainty for a single Langley plot is 0. 7-0. 9 % (at the 95 % confidence level), and it is reduced to 0. 4 % in the case of Izaña Observatory when averaging at least 10 Langley-derived extraterrestrial constants which is the normal procedure  (Toledano et al. , 2018). Moreover, in our case, we rule out additional calibration uncertainties introduced by calibration constants transfer at least for the Cimel instruments. Considering the PFRs operated at IZO, a direct yearly comparison of the Langley based V o 's with the reference triad at PMOD/WRC showed differences lower than 1 % for all channels for the 2005-2015 period.
The percentage of AOD traceable data is almost the same regardless of the optical air mass interval for 440, 500 and 870 nm. So, ∼1. 5 % of the 1-minute AOD data-pairs do not meet the WMO criteria for pristine conditions regardless of the  airmass. Taking account of all of the above we can conclude that non-traceability caused by calibration uncertainty might be considered as negligible (or less than 1 %) for these channels. Most of the differences outside the U95 limits for very low AOD values, could be due to instrumental noise. It should be noted that although the few outliers are evenly distributed throughout the airmass range, they are not equally distributed with respect to the zero of the AOD difference, but there is a bias with positive large outliers (higher Cimel AOD), already reported by  Nyeki et al.
, 2004). According to  Cachorro et al. (2004 Cachorro et al. ( , 2008) )  fictitious differences of up to 0. 06 between the minimum and the maximum AOD can be recorded in a day with constant AOD as a result of a non-accurate calibration or not cleaned instruments. Certainly, the amplitude of the diurnal differences depends on the calibration factor. Thus, we have calculated for the non-traceable AOD data the diurnal range of AOD variation (maximum value minus minimum value of AOD in one day) for each instrument. This can be considered as a hint pointing to the suggestion that the instrument that shows the highest daytime AOD range is the one that is responsible for the outlier, since a real symmetric AOD variability around noon of one instrument measurements, while the other shows a constant AOD during the day, is very unlikely.
Differences in cloud-screening and sun tracking In this section we examine the effect that the presence of clouds might have on AOD differences and the number of cases outside the U95 limits. AERONET-Cimel Version 2 data uses the so-called "triplet check" cloud-screening algorithm developed by  Smirnov et al. (2000)  and a second-order temporal derivative constraint  (McArthur et al. , 2003)  to rule out AOD measurements potentially contaminated by clouds. GAW-PFR algorithms also use the Smirnov triplet measurement, and the second-order derivative check, but add a test for optically thick clouds with AOD 500nm > 2  (Kazadzis et al. , 2018b). This algorithm, used by both networks with certain variants, assumes a transitory character in the presence of a cloud, which causes a sudden change of AOD.
Acknowledgements. The authors thank  Luc Blarel  and  Philippe Goloub  ( LOA, CNRS-University of Lille, France ) for supervising the periodic calibrations of the Cimel Masters. This study has been performed in the frame of the  WMO CIMO Izaña Testbed for Aerosols and Water Vapour Remote Sensing Instruments. The work was supported by the project " The Global Atmosphere Watch Precision Filter Radiometer (GAW-PFR) Network for Aerosol Optical Depth long term measurements " supported by  Bundesamt für Meteorologie und Klimatologie MeteoSchweiz -GCOS Swiss Office. Part of the AERONET-Cimel radiometers have been calibrated at  Izaña Observatory by AERONET-EUROPE Calibration Service , financed by the  European Community  specific programs for Integrating Activities:  Research Infrastructure Action  under the  Seventh Framework Programme  (FP7/2007-2013),  ACTRIS  grant agreement No.
262254 , and  Horizon 2020 Research and Innovation Program ,  ACTRIS-2  grant agreement No. 654109. This research has received funding from the  European Union 's  Horizon 2020 Research and Innovation Programme  under grant agreement No. 654109  ( ACTRIS-2 ). The funding by  MINECO  ( CTM2015-66742-R ) and  Junta de Castilla y León  ( VA100P17 ) is also gratefully acknowledged. We thank the staff of the  Izaña observatory  for their effort and dedication in maintaining the instruments. Our colleague  Celia Milford  has improved the English language of the paper.
Results Microbiota characterization of biopsies, faeces, and blood samples. We used 16S rRNA V3-V4 targeted sequencing to characterize the microbiota composition of 89 samples from cirrhotic patients (17 caecum, 35 faeces, 30 peripheral blood, 7 portal blood) and 20 samples from controls (6 caecum, 14 faeces). We obtained a total of 9209053 filter-quality reads (84487 per sample on average), which were clustered into a total of 1990 OTUs (see Supplementary Fig. S1 , Supplementary Table  S2 ) and classified with the NCBI database. Estimates of the richness (observed OTUs) and biodiversity (Shannon) evidenced a marked dysbiotic status in cirrhotic faeces, with an α diversity similar to that of blood samples (Fig. 1A, B ). PCoA analysis (for β diversity) computed on the relative abundances of OTUs showed a significant separation among the samples classified by their origin (biopsies, faeces, blood) (AMOVA P < 0.
82% P = 0. 0082; Oscillibacter ruminantium, 2. 95% vs 0. 72% P = 2. 6 * 10 -5 ; Roseburia faecis, 1. 73% vs 0. 81% P = 0. 0081; Faecalibacterium prausnitzii, 5. 47% vs 1. 76% P = 0. 0047, see supplementary file), and Lefse analysis showed their importance in the controls (Fig. 1F ). Due to their involvement in the production of SCFAs (especially butyrate), they could exert a homeostatic role within the intestine of the controls  19, 20  : Figure  1. Microbiota compositional analysis. Average rarefaction curves (with 95% confidence interval) (panel A) and box plots (panel B) of α-diversity richness (observed OTUs) and biodiversity (Shannon index) estimators are reported for each dataset. The PCoA analysis (for β diversity) was based on the Yue & Clayton measure of dissimilarity (panel C), while the within-group Bray-Curtis average distance is reported in (panel D).