dataesr/scientific-paragraphs-categorization huggingface dataset:
H. C. is granted by the  TUBITAK  project no  110S518. S. v. S, H. M. , U. S. and I. H. received funding from the medical faculty of  Kiel University, Germany. T. D. is a PhD fellow of the  Institute of Science and Technology (IWT). A. S. was a postdoctoral fellow of the  Fund for Scientific Research Flanders  (FWO). S. W. is supported by  French program  " Investissements d'avenir"  ( ANR-10-IAIHU-06 ).
Funding information This study was funded by the following sources:  Assistance Publique des Hôpitaux de Paris  ( AP-HP ),  Dravet Syndrome UK , the  Katy Baggott Foundation , the  Epilepsy Society , the  International Coordination Action (ICA , grant  G0E8614N ), and  EpiPGX (European Union   7th Framework Programme Grant 279062 ). This work was partly undertaken at  UCLH/ UCL , which received a proportion of funding from the  UK Department of Health's NIHR Biomedical Research Centres funding scheme. This work was supported by  Folkh€ alsan Research Foundation  (A-KA, A-EL). Within the  Eurocores program  of the  European Science Foundation P. D. J. ( G. A. 136. 11. N and  FWO/ESF-ECRP)  and  I. H. ( HE5415/3-1 ) received financial support within the  EuroEPINOMICS-RES network. H. C. is granted by the  TUBITAK  project no  110S518.
S. v. S, H. M. , U. S. and I. H. received funding from the medical faculty of  Kiel University, Germany. T. D. is a PhD fellow of the  Institute of Science and Technology (IWT). A. S. was a postdoctoral fellow of the  Fund for Scientific Research Flanders  (FWO). S. W. is supported by  French program  " Investissements d'avenir"  ( ANR-10-IAIHU-06 ).
Gene Ontology (GO) and pathway analyses were carried out to determine molecular processes and biological pathways associated with differentially expressed genes in all comparisons. We associated differentially expressed mRNAs with two structured networks provided by the Genecodis website: biological process (BP) and molecular function (MF) (Supplementary Tables  6 7 8 9 10 11 12 13 14 15 16 17 ). The GO analysis further validated our experimental set up, by demonstrating that many biological processes and molecular functions, already associated with fibrosis, are also enriched in our datasets, including cell adhesion, inflammatory response, apoptosis, cell proliferation, cell differentiation, proteolysis, etc (Supplementary Tables  6 7 8 9 10 11 12 13 14 15 16 17 ). Interestingly, we were able to identify a large number of individual genes in these categories that have not been previously correlated with renal fibrosis or kidney pathologies, even though the specific GO category/term is strongly associated with this disease.
RNA-seq. Total RNA of all samples were sequenced by EMBL GeneCore (EMBL Genomics Core Facility, Heidelberg, Germany). The synthesis of polyadenylated transcriptome libraries for every sample was accompanied by deep sequencing in 3 lanes each generating >50 million reads pair end. The first two lanes comprised of 6 samples and the third of 4 samples. Real-Time PCR Analysis. Expression levels of protein coding genes known to be involved in renal fibrosis or cell cycle and selected lncRNAs were determined with RT-qPCR using SYBR Green and the Lightcycler 96 software (Roche). The reactions were performed using Platinum TaqDNA polymerase (Invitrogen), three-step standard cycling conditions, and sequence-specific primers designed (Supplemental Table  30 ). Cycle conditions consisted of a pre-incubation step at 95 °C for 10 min, 45 cycles of 95 °C for 10 s, 60 °C for 15 s (plate reading), 72 °C for 15 sec.
Then, sequence metrics were collected for the first 10 million properly mapped paired-end reads using Picard Tools 56. Finally, from the resulting metrics that included average cDNA fragment size and its standard deviation, the aforementioned parameters were estimated by subtracting two times the short read length from the average fragment size, and using the standard deviation as-is respectively. Functional and pathway analysis. Each list of differentially expressed genes derived from the different comparisons was subjected to functional and biochemical pathway analysis using the Gene Ontology 57 and KEGG  58  (Kyoto Encyclopedia of Genes and Genomes) and Panther 59 biochemical pathway databases. The analysis was performed using the GeneCodis software  [60] [61] [62]. Among the different GO structured networks we mainly focused on biological process and molecular function.
Additionally, in order to define whether the differentially expressed genes were previously associated with renal fibrosis, extensive literature searches were performed using Pubmed and the Kidney and Urinary Pathway Knowledge Base (kupkb database)  12. Differentially expressed genes. Differential gene expression analysis was performed with the Bioconductor package metaseqR  63. Briefly, sequence reads were counted over mm 10 exons and after filtering, the final read counts for each gene were calculated as the sums of their exon reads, creating a gene counts table where each row corresponded to an Ensembl gene model and each column corresponded to an RNA-seq sample. The gene counts table was normalized using DESeq  64  after removing genes that had zero counts over all the RNA-seq samples.
Prior to the statistical testing procedure, the gene read counts were filtered for possible artifacts that could affect the subsequent statistical testing procedures. Genes presenting any of the following were excluded from further analysis: i) genes with length less than 500, ii) genes whose average reads per 100 bp was less than the 25 th quantile of the total normalized distribution of average reads per 100 bp, iii) genes with read counts below the median read counts of the total normalized count distribution. The resulting gene counts table was subjected to differential expression analysis for the contrasts SO versus 2D, SO versus 8D and 2D versus 8D, using DESeq. The data discussed in this manuscript have been deposited in NCBI's Gene Expression Omnibus  10, 11  and are accessible through GEO Series accession number GSE79443.
Acknowledgements The authors wish to thank  Dr. Michalis Katsiboulas  for valuable assistance with animal models,  Mrs Valeria Kaltezioti  and  Mrs. Sonia Keddani  for their valuable technical assistance. This study was supported by funds from the  General Secretariat of Research and Technology of the Greek Ministry of Education  in the context of the ARISTEIA grant no  2681  to AC, funded by ESPA 2007-2015. This work was also supported by  ARISTEIA-II  (No. 4786 ), funded by ESPA 2007-2015, and Fondation Sante grants to P. K. P.
Background: The pediatric antiretroviral (ARV) market is poorly described in the literature, resulting in gaps in understanding treatment access. We analyzed the pediatric ARV market from 2004 to 2012 and assessed pricing trends and associated factors. Methods: Data on donor funded procurements of pediatric ARV formulations reported to the Global Price Reporting Mechanism database from 2004 to 2012 were analyzed. Outcomes of interest were the volume and mean price per patient-year ARV formulation based on WHO ARV dosing recommendations for a 10 kg child. Factors associated with the price of formulations were assessed using linear regression; potential predictors included: country income classification, geographical region, market segment (originator versus generic ARVs), and number of manufacturers per formulation.
The mean generic ARV price did not differ by country income level. Prices of originator ARVs were 48 % (p < 0. 001) and 14 % (p < 0. 001) higher in upper-middle income and lower-middle income countries compared to low income countries respectively, with the exception of South Africa, which had lower prices despite being an upper-middle income country. Conclusions: The donor funded pediatric ARV market as represented by the GPRM database is small, and lacks price competition. It is dominated by generic drugs due to the lower prices offered and the practicality of FDC formulations. This market requires continued donor support and the current initiatives to protect it are important to ensure market viability, especially if new formulations are to be introduced in the future.
Methods The GPRM database contains information about prices and volumes of each individual transaction, dosage form and strength of formulations, manufacturers, procurement agents, destination countries, international commercial terms (INCOTERMS), and procurement dates obtained from 11 procurement organizations on a quarterly basis. The analyses were based on GPRM data collected between 2004 and 2012. Prices are reported in current USD. To remove variability arising from the use of different INCOTERMS and to allow comparability, prices were expressed in Ex Works (price of goods at Seller's premises, the Buyer bearing full costs and risks of moving the goods from there to destination) using a published statistical algorithm  [15]. For each of the 21 ARV single formulations, 7 dual FDCs, and 5 triple FDCs , we calculated the quantity per year (QTY) and price per year (PTY) using WHO ARV dosing recommendations for a 10 kg child  (2004) (2005) QTY ¼ number of units purchased ð Þ = units used in daily treatment ð Þ Â 365 ð Þ ½ and PTY ¼ ðunit price USÞ Â units used in daily treatment ð Þ Â 365 ð Þ: Countries were grouped into 7 geographic areas: East Asia and Pacific, Europe and Central Asia, Latin-America and the Caribbean, Middle East and North Africa, South Asia, sub-Saharan Africa excluding South Africa, and South Africa.
Generic pediatric ARV pricing does not appear not to be linked to country income levels or geographical region, suggesting a different pricing strategy to that of the originator companies. To our knowledge, this is the first time that a thorough analysis of pricing trends of pediatric ARVs from 2004 to 2012 has been presented. While this database captures mostly donor related pediatric ARV transactions, it reflects almost 80 % of donor transactions worldwide. It is a good representation of the pediatric ARV market since 90 % of the children living with HIV are from sub-Saharan African countries where provision of ARVs is largely donor-funded. This analysis has several limitations that should be noted. It could not take into account ARVs for older children who can use adult formulations. In addition, it could not separate ARVs used for treatment and those used for prevention of mother-to-child transmission (PMTCT).
However, this is likely to have a negligible effect since the use of paediatric ARVs for PMTCT is limited to two single ARVs, namely AZT or NVP liquid formulations  [25] [26] [27]. It should be noted that this database represents procurement data and not actual consumption data, and that the quantity and prices calculated per formulation do not represent quantity and prices of actual treatment regimens. The use of Ex-Works prices in this analysis does not take into account other costs such as transportation, insurance, import duties and taxes. We have also noted differences in characteristics at a national level, such as domestic manufacturing capacity for some countries, but it would be difficult to incorporate these into a global level analysis as conducted here. Another analysis of the GPRM database by Perriens et al.
Moreover, the currently observed negative trend of 1 K decade -1 in upper stratospheric tem-perature data already leads to representation-dependent differences of up to 1 % decade -1 in the ozone trends  (McLinden and Fioletov, 2011). It is important to realise that a drift in temperature data will, in a similar fashion, introduce extra (altitude-dependent) drift in non-native ozone representations. Here, we consider the auxiliary data provided in the satellite data files, see Table  3. The auxiliary profiles for ground-based data are taken either from actual measurements (ozonesonde: interfaced radiosonde), or from reanalysis fields (lidar: ERA-Interim). At altitudes below about 35 km (∼ 5 hPa) there is generally no clear change in bias or comparison spread (both < 1 %) and drift (< 1 % decade -1 ) after the conversion to another representation.
9  and Figs. S8-S9), and to a lesser extent also of the bias (although the ozonesonde and lidar results are somewhat discrepant, Fig. 8 ). The Aura MLS drift changes by about 3 % decade -1 , similar to earlier reports  (Adams et al. , 2014). The dependence has the opposite sign for UARS MLS and is more pronounced, up to ∼ 10 % decade -1. These observations are consistent with the known drifts in absolute pointing of the MLS records. Whereas UARS MLS geopotential height profiles drift upwards, by ∼ 1000 m decade -1  (Livesey et al. , 2003) , the Aura MLS v3. 3/v2. 2 GPH data drift downwards by ∼ 120 m decade -1 , especially between 2005 and 2009  (Livesey et al. , 2015). Obviously, if a more stable and less biased source of auxiliary data were used for the conversion, the reported issues for MIPAS, SCIAMACHY and the MLS records could be easily avoided.
Consistency between satellite records Until now we discussed each satellite Level-2 data set individually. Here, we take advantage of the specific design of the analysis to compare the satellite records directly. What follows is an evaluation of their mutual consistency in terms of bias (Fig. 10 ), short-term variability (Fig. 11 ), decadal stability (Fig. 12 ) and auxiliary data. In the context of the SI2N initiative an extensive literature review was performed of the ground-based validation and satellite intercomparison studies  (Hubert et al. , 2016). We refer the reader to this work for a more in-depth discussion of the global picture that emerges from the different studies.
Impact of auxiliary data Satellite ozone profile data quality is generally not affected by the conversion to another representation with the help of the accompanying pressure and temperature profiles. Bias, spread or decadal stability typically change, respectively, by less than 1 %, 1 % or 1 % decade -1 in the lower and middle stratosphere, and somewhat more in the upper stratosphere. This demonstrates the good mutual consistency of the meteorological data by ozonesonde, MERRA and ERA-Interim. The exceptions are MIPAS and SCIAMACHY (3-5 % change in bias) and the Aura MLS and UARS MLS records (respectively 3 and 10 % decade -1 change in drift). Obviously, the introduction of these artificial effects can be avoided by using less biased or more stable sources of auxiliary data.
We focus here on climate applications which rely on stable data sets spanning multiple decades on a global scale. The Global Climate Observing System (GCOS), for instance, requests an accuracy (Joint Committee for Guides in Metrology, 2012) better than 10 % in the UTLS and 5-20 % above, and a stability better than 1 % decade -1 (GCOS, 2011). Within ESA's Climate Change Initiative program (Ozone_cci) similar requirements were set for accuracy (< 8-15 %) and somewhat looser targets for stability (< 1-3 % decade -1 ) (van der A et al. , 2011). In practice, the accuracy and stability of a particular record can of course only be tested to a level determined by the accuracy and stability of the reference data and by constraints from the metrology of the comparison. From Figs. 10 and 11 we conclude that ground-based studies are indeed able to verify an accuracy of 5-10 %, and resolve altitude-latitudeseason patterns, in the middle and upper stratosphere.
g. Arctic SCIAMACHY data. We also reported www. atmos-meas-tech. net/9/2497/2016/ Atmos. Meas. Tech. , 9, 2497-2534, 2016 that the single Level-2 profile noise of SMR and SCIA-MACHY is considerably higher than that of other records. Averaged profiles will be sufficiently precise over large bins (monthly, 5 • latitude) since both instruments are dense samplers, but this may not be the case at finer spatiotemporal resolutions. Our assessment of stability furthermore demonstrates the potential of drift correction schemes, especially when HALOE, OSIRIS or SCIAMACHY data are involved (and likely GOMOS and SMR as well). Eckert et al. (2014)  have recently explored this approach, by correcting MIPAS trends for a drift relative to Aura MLS. In practice, however, the drift estimate between two satellite records is not sufficiently well constrained, especially for a short overlap period, which makes it very challenging to obtain robust corrections.
Finally, the impact of the auxiliary data should not be forgotten, since profile representation conversions are typically required. We observed considerable changes in bias (MIPAS, SCIAMACHY and, to a lesser extent, UARS/Aura MLS) and stability (UARS/Aura MLS) due to the auxiliary data provided along with the ozone data sets. The use of a common source of stable auxiliary profiles eliminates additional discrepancies between the contributing records. Our results suggest that ECMWF (operational and ERA-Interim) and MERRA fields impact ozone trends in a consistent way over the entire stratosphere.
, 2014). A combined SAGE-OSIRIS record, on the other hand, produces more positive post-1998 trends in the uppermost stratosphere, by 3-4 % decade -1 at mid northern latitudes  (Bourassa et al. , 2014; Tummon et al. , 2015; Harris et al. , 2015). Two records that combine SAGE and GOMOS data lead to considerably more negative trends than other data sets in the lower stratosphere  (Tummon et al. , 2015; Harris et al. , 2015). Many of the ozone trend differences cannot be explained by statistical uncertainty. Our ground-based assessment of decadal stability suggests that these may be interpreted, at least for the better part, in terms of instrumental drift. Indeed, we noted a +8 % decade -1 drift above 40 km for OSIRIS and a -9 % decade -1 drift for SCIAMACHY 5  around 35 km. Additionally, we found indications of a -5 % decade -1 drift of GOMOS below 20 km.
5 % decade -1 depending on altitude. Since GOZCARDS and SWOOSH include HALOE data, the drift is possibly somewhat larger in the middle stratosphere. Producing post-1998 estimates is a more intricate problem, due to the increasing number of contributing instruments, and due to the fact that none of these cover the entire period. We are inclined toward a conservative approach, giving figures that should be considered upper limits to the actual drift. The SAGE-GOMOS record will be impacted by negative GOMOS drifts in the lower stratosphere. The SAGE-OSIRIS trends should be considered more uncertain in the upper stratosphere due to drifting OSIRIS data. Records that use Aura MLS as backbone (GOZCARDS, SWOOSH) should not be more unstable than about 2 % decade -1 in the stratosphere. A merged Ozone_cci data set is likely also prone to larger uncertainty in the upper stratosphere (drifting OSIRIS, SCIAMACHY, SMR) and to some extent in the lower stratosphere as well (GOMOS).
Nevertheless, the altitude at which the quality of UTLS observations starts to degrade rapidly is clearly not only determined by the tropopause. It also depends on the measurement technique and instrument (e. g. UTLS observations of UV-visible star occultations being less sensitive than those of infrared emissions at the limb). There were furthermore no evident signs of seasonal patterns, except for the Arctic SCIAMACHY data which exhibit a 10 % increase in bias and spread in boreal winter and a 10 % decrease in bias and spread in boreal summer. We found no significant drifts at decadal time scales, most records are stable within about ±5 % decade -1 in the middle and upper stratosphere and, for some records, even within ±3 % decade -1 (SAGE II, Aura MLS and MIPAS). However, the drift uncertainty should not be neglected, as our analysis is typically not sensitive (at 2 σ ) to drifts smaller than 2-3 % decade -1 in the middle stratosphere and 3-4 % decade -1 at lower and higher altitudes.
The pressure and/or temperature data that accompany the satellite ozone data sets are generally well suited for the conversion between ozone quantities or vertical coordinates. Bias, spread and drift in non-native ozone profile representations differ, respectively, not more than about 1 %, 1 % and 1 % decade -1 , and somewhat more in the uppermost stratosphere. There are of course exceptions to these general observations. We noted more pronounced biases (∼ 10 %) over much of the stratosphere for POAM II and SCIAMACHY, the latter also exhibits a clear hemispheric asymmetry. Two records show markedly poorer single-profile precision: SMR (entire atmosphere) and SCIAMACHY (upper stratosphere and Arctic). And three records drift significantly: HALOE in the middle stratosphere (-5 % decade -1 ) and in the upper stratosphere SCIAMACHY (-9 % decade -1 ) and OSIRIS (+8 % decade -1 ).
There are also indications of a -5 % decade -1 or more drift in the lower stratosphere for GOMOS, and in the upper stratosphere for SMR. Further confirmation is needed however for the latter two data sets. In the meantime, we advise caution when using GOMOS and SMR measurements at these altitudes. Finally, we observed for a few records a considerable impact of the accompanying auxiliary data (e. g. GPH retrievals) on ozone quality in non-native profile representations. The ozone bias changes by 3-5 % for MIPAS and SCIAMACHY; both MLS records (UARS and Aura) show a dependence of the drift (by 3 % decade -1 or more) on vertical coordinate and/or ozone quantity, and perhaps of the overall bias as well. We stress that these representation-dependent quality issues are unrelated to the satellite ozone retrievals themselves, and can be avoided by using another, external source of auxiliary information for any necessary conversions.
Overall, the observing system of limb and occultation instruments produces ozone profiles that meet the ∼ 10-15 % accuracy requirements by climate users, most certainly over 20-40 km, and perhaps also in the lower stratosphere. However, it remains unclear whether the current Level-2 records comply with the 1-3 % decade -1 target on decadal stability. The combination of different data sets has received widespread interest in recent years, but also poses several challenges. Our results show that the merging schemes should be sufficiently refined to temper additional artefacts in the Level-3 data sets. Even then, the characteristics of merged records remain mostly defined by those of their contributors  (Tummon et al. , 2015). Multi-instrument comparison studies are therefore crucial to establish observational evidence.
Data availability The satellite ozone profile Level-2 data used in this work were obtained from: -SAGE II v7. 0 (available at: https://eosweb. larc. nasa. gov/project/sage2/sage2_v7_table, see also  Damadeo et al. , 2013) , -SAGE III v4. 0 (available at: https://eosweb. larc. nasa. gov/project/sage3/sage3_table, see also  Thomason et al. , 2010) , -HALOE v19 (available at: http://disc. gsfc. nasa. gov/ uui/datasets/UARHA2FN_V001/summary, see also  Nazaryan et al. , 2005) , -UARS MLS v5 (available at: http://mls. jpl. nasa. gov/ uars/data. php, see also  Livesey et al. , 2003) , -Aura MLS v3. 3 (available at: http://mls. jpl. nasa. gov/ data/overview. php, see also  Livesey et al. , 2013b) , -POAM II v6 (available at: https://eosweb. larc. nasa. gov/project/poam2/poam2_table, see also  Lumpe et al.
0 (available at: http://www. ace. uwaterloo. ca/data. html, see also  Boone et al. , 2013)  and -MAESTRO v1. 2 (available at: http://www. ace. uwaterloo. ca/data. html, see also  McElroy et al. , 2007). The ground-based ozonesonde and stratospheric ozone lidar measurements were downloaded from the NDACC Data Host Facility (http://www. ndsc. ncep. noaa. gov/data), the WOUDC data archive (http://woudc. org/data/explore. php) and the SHADOZ data archive (http://croc. gsfc. nasa. gov/shadoz, see also  Thompson et al. , 2012). ERA-Interim meteorological data were obtained from the MARS catalogue at ECMWF (http://apps. ecmwf. int/mars-catalogue). The Supplement related to this article is available online at doi:10. 5194/amt-9-2497-2016-supplement. contribution via BIRA-IASB. Sweden's Odin satellite carries the atmospheric and astronomical missions OSIRIS and SMR, developed and funded jointly by the space agencies of Sweden, Canada, Finland and France.