dataesr/scientific-paragraphs-categorization huggingface dataset:
This was tested using Neyman's method of smooth contrasts with a quadratic contrast  [21]  The method was first applied to the ranks of rates of change as estimated, and to the ranks of rates of change after normalizing by the standard errors of these estimates. The normalization step was performed because extreme values in a distribution are expected be more influenced by random noise, and the normalization reduces this effect. The Neyman contrast tests were performed on the complete set of samples, then restricted to the HCC samples and the LC samples. Finally, in order to examine whether some reported biologically significant HCC genes could also be found deregulated in a context of warm ischemia associated with tumor freezing delay, we restricted our analysis of the rates of expression changes in 34 HCC genes reported to be deregulated in more than 4 studies in the public Liverome database  [22].
, 2006; Brunner et al. , 2006; Wohltmann et al. , 2007; Kiesewetter et al. , 2010; Salby et al. , 2011). This study analyses the trends and variability in total column ozone and stratospheric ozone profiles at Haute-Provence Observatory (OHP: 43. 93 • N, 5. 71 • E). A regression model with various explanatory parameters such as QBO, solar flux, aerosol optical thickness, heat flux, NAO and EESC or PWLT functions is applied to the data. Total column ozone observations from the Dobson and Système d'Analyse par Observation Zénithale (SAOZ) spectrometers and ozone profile measurements from the light detection and ranging (lidar), ozonesondes, Stratospheric Aerosol and Gas Experiment (SAGE) II, Halogen Occultation Experiment (HALOE) and Aura Microwave Limb Sounder (MLS) are used for the study. We have tested the regression method and trend estimates with another long-term merged ozone data set, GOZCARDS (Global OZone Chemistry And Related trace gas Data records for the Stratosphere).
Total column ozone observations The measurement principle of Dobson spectrometer is based on the differential absorption of solar light by ozone  (Dob-son, 1957 (Dob-son, , 1968)). It performs ozone observations by measuring the relative intensities of ultraviolet (UV) wavelengths emanating from the Sun, Moon or zenith sky. At OHP, Dobson column data are derived from the direct sun and zenith sky observations, in which 83 % are from the direct sun observations. The precision and accuracy of direct sun observations are ∼ 1 % and ∼ 3 %, respectively, while the zenith observations are less precise  (Basher et al. (1982)  and http://www. esrl. noaa. gov/gmd/ ozwv/dobson/papers/report13/report13. html). We use Dobson total column ozone measurements for the 1984-2010 period (http://woudc. org/data/citation_e.
html). SAOZ observes sunlight scattered from the zenith sky in the 300-600 nm spectral range during sunrise and sunset  (Pommereau and Goutail, 1988). Ozone measurements are carried out in the Chappuis band (450-650 nm) and are retrieved using differential optical absorption spectroscopy method. The SAOZ version (v) 2 data for 1992-2010 are used here. The measurement uncertainty is of the order of 3 %  (Hendrick et al. , 2011).
, 1996). The ozone volume mixing ratio (VMR) profiles v19 for 1991-2005 are used for the analysis. Aura MLS measures thermal emissions from the rotational lines of the measured species through the limb of the atmosphere. Ozone measurements have a vertical resolution of 2. 5-3 km in the stratosphere and an uncertainty of 5-10 % between 16 and 60 km  (Froidevaux et al. , 2008). The ozone VMRs from Aura MLS v3. 3 for 2004-2010, screened as suggested in the v3. 3 validation report  (Livesey et al. , 2011) , are used here. Table  1  summarises the important characteristics of different instruments used for the study. These include the periods considered for the analysis and the measurement frequency of various data sets, vertical resolution and vertical range of the profile measurements, and the latitude and longitude bands chosen for the satellite data sets to find closest profiles at the station location.
Regression analysis of column ozone The temporal evolution of the total column ozone measurements from the Dobson and SAOZ spectrometers is displayed in Fig. 1a. Both data sets follow a similar pattern of ozone evolution. Ozone values are relatively lower after the early 1990s and level off after the early 2000s. A maximum ozone value of about 420 DU is observed in 2010 as found by the Dobson spectrometer. The deseasonalised (monthly mean ozone -monthly climatology over the period) total column ozone measurements are used for the regression analysis. There are slight differences between the two data sets in June and July. These seasonal differences are removed by Table  1. The different instruments used for the analysis along with the analysis period, frequency of ozone measurements, vertical resolution (Ver.
Res. ), measurement range and the latitude and longitude bands (Lat. , Long. ) chosen for the satellite instruments to find the profiles near OHP (43. 93  deseasonalising the data. The deseasonalised data from both instruments are averaged during the overlapping period, as listed in Table  1 , to obtain a single data set from 1984 to 2010. The comparison between Dobson and SAOZ column measurements shows a drift of about -0. 12 % yr -1 , while the combined long-term data show much smaller drift of about 0. 05 % yr -1. This analysis suggests that the combined data from Dobson and SAOZ observations provide relatively stable long-term data compared to the individual Dobson or SAOZ measurements, and a stable data set is a prerequisite Table  2. The regression coefficients of QBO 10, QBO 30, solar flux (SFX), NAO, heat flux (HFX) and volcanic aerosols estimated from the total column ozone measurements, shown for April, November and the average of all months.
The resulting EESC-based and PWLT estimates are about -1. 0 and -1. 1 DUyr -1 , respectively, over the period  1984 -1996. For 1997 -2010  they are of the order of 0. 3 and 0. 55 DUyr -1 , respectively. These results are nearly same as those found for the total column ozone observations. We have seen a clear signature of ozone recovery from the analysis of profile measurements. Therefore, in order to check the robustness of the method and estimated vertical trends, we have also used an additional data set, GOZCARDS, constructed from various satellite observations. Further details about this recently released (and publicly available) GOZCARDS data set (for ozone and other species) can be found in  Froidevaux et al. (2013). In brief, these merged monthly mean time series adjust zonal mean ozone data sets to the SAGE II data set (as a reference), using data from HALOE and Aura MLS, as well as from the UARS MLS and the Atmospheric Chemistry Experiment Fourier transform spectrometer (ACE-FTS) in addition to SAGE II data.
The data set is available as VMRs on a pressure grid. For the analysis, this is converted to number density using GOZCARDS temperature data. The GOZ-CARDS temperature data set was constructed from monthly zonal means based on Modern-Era Retrospective analysis for Research and Applications (MERRA). Rienecker et al. (2011)  provide more information about MERRA (see also http://gmao. gsfc. nasa. gov/merra). Pressure-altitude conversion is made using data from HALOE and Aura MLS and a mean altitude is computed for 1991-2010. The regression is done using monthly ozone anomalies on a vertical grid with 1 km spacing. Ozone trends derived from this zonal mean data set averaged over the latitude bands 40-50 • N using both PWLT (black) and EESC (red) regressions over the periods  1984 -1996 and 1997 -2010 are shown in Fig.
8 (b) are shown in Fig. 8 (b). The analysis exhibits similar and significant negative trends by the PWLT and EESC regressions for the 1984-1996 period, and significant positive EESC-based trends at 15-45 km and PWLTs at 34-45 km over the period 1997-2010. A clear structural similarity in the trends from both data sets (the OHP and GOZ-CARDS) is visible for both periods, although some differences in the PWLTs are found in the middle and lower stratosphere after 1997. This could be due to the differences in the two data sets as the GOZCARDS data are zonally and latitudinally (over 10 • , as available) averaged as compared to the OHP data set (average ozone at the OHP latitude) or a result of unknown issues at this time, which are worthy of further investigation in the future. However, the structure of vertical distribution of ozone trends derived from both data sets and the trend values from EESC in the whole stratosphere and that of PWLT in the upper stratosphere corroborate that the ozone recovery signal in the northern mid-latitudes and the regression method are robust.
Acknowledgements. We would like to thank  J. M. Russell III  for the HALOE ozone data. We thank  Cathy Boonne  and the  ETHER team  for maintaining the ETHER data cluster, and the staff at OHP for operating the ozone monitoring instruments. Work at the  Jet Propulsion Laboratory, California Institute of Technology , was done under contract with the  National Aeronautics and Space Administration (NASA). We also thank the  NASA Langley Research Center (NASA-LaRC ) and the  NASA Langley Radiation  and  Aerosols Branch  for providing SAGE II data, and the collaborative institutes of the  NASA Langley Research Center  for maintaining HALOE data. The ground-based ozone measurements were obtained as part of the NDACC and are publicly available (see http://www. ndacc. org). Reanalysis of the OHP lidar data was performed under the EU NORS contract.
Microarray data acquisition and analyses Microarrays were scanned with the Agilent DNA microarray Scanner (Agilent) at a resolution of 5 microns/pixel using the extended dynamic range function. Spot and background intensities were extracted with the Feature Extraction (v9. 5. 3) software (Agilent) using the GE2-v4_95_Feb07 default protocol. Array quality was assessed through Agilent control features as well as spike-in controls (Agilent 2-Color Spike-in Kit for RNA experiment). Subsequent flagging was done according to the GenePix Pro software (Molecular Devices Sunnyvale, CA) nomenclature, which included four flag levels (good [100], bad [2100], not found [250], moderate [0]). Preprocessing and data normalization were performed with Feature Extraction software (Agilent technologies), with the GE2-v4_95_Feb07 default protocol.
Statistical differential analysis were done using the MAnGO software  (Marisa et al. 2007 ), a moderate t-test with adjustment of P-values  (Benjamini 1991)  was computed to measure the significance of each expression difference. Differential analyses have been performed between all the time points at 26°and time T0 for each strain. Fold change values (FC) were calculated from the ratio (R) between normalized intensities at each time in the comparison. FC = R when R. 1; FC = 21/R when R , 1. Genes were considered as differentially expressed during incompatibility if their absolute fold change of up or down regulation in the SI strain was greater than 2 with an adjusted P-value ,0. 001 and if no differential expression was detected in wild-type control.
Data clustering A total of 2285 coding sequences (CDS) that were differentially expressed in the SI strain between 32°and 26°(-fold change. 4 with P , 0. 001) were grouped. A first hierarchical clustering (unweighted pair group method with arithmetic mean, i. e. , UPGMA, method) of this set of genes defined two classes: genes up-regulated (1036 CDS) and genes down-regulated (1249 CDS). A hierarchical clustering (UPGMA method) and a K-mean clustering to 12 classes were computed independently using Spotfire Decision Site software package (Spotfire, Somerville, MA) with correlation coefficient as similarity measure on the 1036 up-regulated genes. Intersection of the clusters obtained by the two clustering methods resulted in eight robust clusters. A total of 999 CDS have been classified and 37 CDS were not robustly classified.
The GO term annotation evokes a general shut down of the basic cell processes such as energy production, ribosome function, and DNA replication and an activation of proteolysis, protein modification, and signal transduction processes. The up-and down-regulated gene sets were then analyzed for enrichment for specific protein domains as annotated in the pfam database (Table  2 ). The up-regulated gene set was found to be enriched in the presence of three domains previously implicated in fungal incompatibility, the HET, the NACHT, and the HeLo domains. More than half of the 130 genes encoding HET domain proteins were found to be up-regulated. The HeLo domain is a toxicity domain found in the HET-S heterokaryon incompatibility protein  (Greenwald et al. 2010)  and the lopB pathogenicity protein of Leptospheria maculans  (Idnurm and Howlett 2003).
The list of genes encoding carbohydrate active enzymes was recovered from the CAZy database and analyzed for gene expression changes (Table  S5 ). Globally, in the different categories more down-regulated than up-regulated enzymes were found. Exceptions to that trend were subcategories CE3, GH16, GH18, GH76, GT1, GT2, GT31, CBM18, and CBM50 (lysM) for which up-regulated genes were more frequent than down regulated genes. Globally, the functional annotation of the up-regulated gene set, indicates an up-regulation of hydrolytic activities in particular lipases and proteases, an up-regulation of a set of STAND signal transduction proteins and numerous HET domain proteins as well as an induction of several secondary metabolism clusters. Of note is also the enrichment for lysM domain proteins. The down-regulated gene set indicates a down regulation of mitochondrial function, translation and ribosome synthesis.
ACKNOWLEDGMENTS This study and the salary of F. B. were funded by the  French National Research Agency  (http://www. agence-nationale-recherche. fr/)grant number  ANR-05-BLAN-0385 , project  SexDevMycol , coordinator  R. Debuchy. The microarray scanner was provided by the  Program Pluri-Formation (PPF) of Universite Paris-Sud 11 "Plate-forme Puces à ADN Gif-Orsay   2005-2008. "The http://podospora. igmors. upsud. fr/methods. phpaddress was hosted on a server funded by IFR115.
n Table 2 List of Pfam-A domains enriched in the up-regulated gene set Pfam Identification Pfam Name Functional Category Number of Up-Regulated Genes With pfam-A Total Number of Genes With pfam-A Fraction of Up-Regulated Genes, % P-Value PF06985 HET Incompatibility 68 130 52 7. 95E-15 PF05729 NACHT Incompatibility. Signal transduction 19 34 56 1. 20E-05 PF12796 Ank_2 Protein2protein interaction 32 77 42 6. 70E-05 PF04145 Ctr Transport 6 6 100 9. 75E-05 PF14479 HeLo Pore-forming toxin 7 9 78 2. 60E-04 PF01734 Patatin Degradation 6 9 67 0. 0045 PF00909 Ammonium_transp Transport 5 7 71 0. 0065 PF00550 PP-binding Secondary metabolism 13 30 43 0. 0066 PF00023 Ank Protein2protein interaction 9 18 50 0. 0069 PF09362 DUF1996 Unknown 12 27 44 0. 0077 PF05199 GMC_oxred_C Oxidoreduction 14 33 42 0. 0088 PF00689 Cation_ATPase_C Transport 6 10 60 0.
0091 PF14856 Hce2 Pathogen effector 5 8 62 0. 0095 PF13460 NAD_binding_10 Oxidoreduction 7 13 54 0. 0105 PF00732 GMC_oxred_N Oxidoreduction 13 31 42 0. 0134 PF05630 NPP1 Necrosis inducer 4 6 66 0. 0153 PF00690 Cation_ATPase_N Transport 6 11 55 0. 0165 PF13637 Ank_4 Protein2protein interaction 6 11 55 0. 0165 PF01476 LysM Defense 8 17 47 0. 0167 PF08659 KR Secondary metabolism 7 14 50 0. 0171 PF02668 TauD Oxidoreduction 10 23 43 0. 0185 PF01565 FAD_binding_4 Oxidoreduction 15 40 38 0. 0194 PF12697 Abhydrolase_6 Degradation 15 40 38 0. 0194.
Deep-sequencing Total cellular RNA (5 mg), extracted using Isol-RNA Lysis Reagent (5PRIME) was processed into sequencing libraries using adapted Illumina protocols and sequenced at Fasteris (http:// www. fasteris. com, Switzerland) using the HiSeQ 2000 sequencer. All next-generation sequencing data have been deposited to the NCBI Gene Expression Omnibus (GEO) and are accessible with the accession nu GSE43110 (WT and Dcr 2/2 ) and GSE43153 (IP_FHA-hAgo2).
sRNA analysis The sRNA-seq analyses were performed using the ncPRO pipeline  [24]. Briefly, the reads were aligned on the mm9 genome using the Bowtie software and allowing multiple matches. Profiling of repeats was estimated from the intersection of the mapped reads with the RepeatMasker annotation. As annotated L1Md_Tf L1 repeats are often truncated or have different full length, the median size of full length was considered, and all LdMd_Tf L1 repeats were scaled to this median size when computing positional read coverage. The positional read coverage was computed by summing up the normalized counts (RPM) of reads covering each position, which was further normalized to the number of L1Md_Tf L1 repeats in the genome containing the position.
PCR Strand-specific RT-PCR was performed using the Transcriptor Reverse Transcriptase kit (Roche) using 1 mg total RNA and following the manufacturer's instructions. PCR using primer for the specific Tf LINE from chromosome 17 were conducted at 95uC for 10 min, followed by 35 cycles at 95uC for 15 s, 60uC for 30 s, 72uC for 30 s and 10 min at 72uC and revealed on 1% agarose gel. Real-time PCR reagents for miRNAs, 59_UTR sRNAs and control U6 snRNA were from Qiagen. 59_UTR sRNAs sense and antisense discrete sequences have been extracted from deep-sequencing data and chose because their higher level of expression. For RT reactions, 1 mg total RNA was reversetranscribed using the miScript Reverse Transcription Kit (Qiagen) according to the manufacturer's instructions. Following the RT reactions, cDNA products were diluted five times in distilled water, and 2 mL of the diluted cDNAs was used for PCR using QuantiTect SYBR Green PCR Master Mix and miScript Universal Primer (Qiagen).
L1 copy number analysis Using the ePCR package from NCBI (http://www. ncbi. nlm. nih. gov/sutils/e-pcr/) we identified 10 806 hits on the mouse mm9 reference genome using the L1_Tf specific primers as designed on the L1spa Nu. AF016099  [29]. These primers generate 67 bp amplicons, present in the L1_Tf 59_UTR repeated regions. Real-time quantitative PCR analysis of the L1_Tf copy number in Dicer Flx/Flx mESCs provided a figure of 9,478 at P10 and 10,465 at P30 PCR hits, remarkably close to the ePCR estimation  (10, 806). The small difference could be explained by the hybrid background of the Dicer mESC line used compared to the genome reference in mm9. The copy number assay shown in Figure  2A , involved a comparison of Dcr Flx/Flx mESCs sampled at passage 10 (9,478 amplicons detected) and at passage 30 (10,465 amplicons detected) with Dcr 2/2 mESCs sampled at passage 30 (13,821 amplicons detected).
Bisulfite sequencing-based DNA methylation analysis Genomic DNA was extracted using Isol-RNA Lysis Reagent (5PRIME). Bisulfite treatment was performed using the EpiTect Bisulfite Kit (Qiagen). Bisulfite-treated DNA was then amplified using the DreamTaq DNA Polymerase and primers listed in see accompanying primer list. PCR cycling conditions and primers design were made following the recommendations in  [61]. PCR fragments were purified and cloned into pGEM-T Easy (Promega) and individual colonies were sequenced using M13 primers. Sequences were then analysed using Kismeth and BISMA softwares  [62, 63]  to obtain the percentage of methylated sites for each sequence context. Results shown were obtained in two independent experiments.
Antibodies The following antibodies were used: anti-L1_ORF1 (gift of Dr Alex Bortvin, Carnegie Institution for Science, USA), anti-AGO1 (D84G10, Cell Signaling Technology, Beverly, MA, USA), antimouse AGO2 (clone 6F4, gift of Dr Gunter Meister, University of Regensburg, Germany), anti-XRN2 (A301-101A, Lubio Science, Switzerland), anti-EXOSC10 (Rrp6) (ab50558, Abcam, Cambridge, UK) and anti-OCT4 (ab19857, Abcam, Cambridge, UK). Bisulfite sequencing-based methylation analysis at the L1 59_UTR in Dcr Flx/Flx , Dcr 2/2 , Dgcr8_KO and TKO mESCs. Data were analysed with the Kismeth and BISMA online softwares  [62, 63]. D. Expression of eGFP detected by qRT-PCR in WT and Dcr 2/2 mESCs carrying the human eGFP-tagged L1 transgene after 4 (P4) and 6 (P6) passages post-puromycin treatment for selection of stable transformants.
Acknowledgments We thank members of the  Voinnet laboratory  for critical reading of the manuscript and for stimulating discussions,  Dr. G. Meister  and  Dr. A. Bortvin  for the gift of the mouse  AGO2  ( 6F4 ) antibody, and L1_ORF1 antibodies, respectively,  Dr. X. Wang  for the gift of the inducible Ago1,2,3,4_KO mESC line and  Dr. G. Hannon  for the gift of Dcr Flx/Flx mice. Isabelle Grandjean  and  Patricia Diabangouaya  are deeply acknowledged for their key contribution to mice crosses and genotyping at the  Curie Institute.
Solar energy applications need global aerosol optical depth (AOD) information to derive historic surface solar irradiance databases from geostationary meteorological satellites reaching back to the 1980's. This paper validates the MATCH/DLR model originating in the climate community against AERONET ground measurements. Hourly or daily mean AOD model output is evaluated individually for all stations in Europe, Africa and the Middle East -an area highly interesting for solar energy applications being partly dominated by high aerosol loads. Overall, a bias of 0. 02 and a root-mean-square error (RMSE) of 0. 23 are found for daily mean AOD values, while the RMSE increases to 0. 28 for hourly mean AOD values. Large differences between various regions and stations are found providing a feedback loop for the aerosol modelling community.
They often rely on a cloud index method (e. g. ,  Cano et al. , 1986; Beyer et al. , 1996; Rigollier et al. , 2004; Schillings, 2004 ) making use of external aerosol databases as auxiliary input. Atmospheric aerosol extinction typically reaches 30 % of the incoming direct irradiance in most locations and can reach values up to 100 % in strong pollution or dust events. Global irradiance is also affected by extinction, but with a typical range of only up to 10 %. Additionally, dust soiling on concentrating solar power mirrors or photovoltaic panels significantly decreases the performance of the system. Wittmann et al. (2008)  discuss the interaction of atmospheric extinction with solar electricity generation reporting electricity production losses of 24 % for a typical aerosol loaded day in Southern Spain.
, 2003) , and a significant variability in the range of hours to single days is typically observed in aerosol ground measurements. Therefore, the existing AOD datasets derived in the climate modelling community are no longer sufficient for this purpose. Even if exact modelling of aerosol processes and sources remains an on-going research question, it is the objective of this paper to evaluate a chemistry transport model (CTM) representing the existing state-of-the-art with respect to information needs from the solar electricity generation sector. Such a CTM needs to fulfil the criteria of being applicable with reasonable processing effort on a global scale, including all relevant aerosols and especially a dust aerosol component, covering the time period from 1983 as the first year of Meteosat satellite observations onwards, providing at least an hourly temporal resolution, and dealing not only with particulate matter mass concentrations at ground level, but also with total column AOD suitable for the use in radiative transfer models.