dataesr/scientific-paragraphs-categorization huggingface dataset:
De novo motif discovery using HOMER identified a 12 base pair motif in 63% of peaks in WKY LPS stimulated BMDMs with a strong similarity to the consensus AP-1 motif that has been previously recognised and shown to be functionally active  [24]  (Figure  4C ). Additional motifs identified included CAC binding and REST/NRSF in basal WKY BMDMs (Figure  4D ) and the Ascl2, CACCC binding motif for KLF factors, Eomes and Ets (Figure  4E ) in LPS stimulated WKY BMDMs. Furthermore the JunD binding to the promoters of Il1b and Prkca, two of the most markedly differentially expressed transcripts following Jund siRNA were confirmed by ChIP-qPCR in both the basal and LPS stimulated conditions (Figure  4F , Additional file 1: Figure  S4 ) Gene ontology analysis of the JunD-bound genes in the different strains and states identified marked similarities between the enriched terms in basal WKY BMDMs (Additional file 2: Table  S3 ) and WKY.
Integration of microarray and ChIP-Seq datasets identifies primary JunD targets in macrophages In order to identify the set of primary JunD targets, we integrated all the genomic datasets and identified the genes that correlated closely with the expression pattern of Jund during the LPS timecourse that were also differentially expressed following siRNA knockdown of Jund and showed a ChIP-Seq peak in WKY BMDMs. This identified two major networks (basal and LPS) with 24 genes in the basal state (Figure  5 , upper panel) and 36 genes after LPS stimulation (Figure  5 , lower panel) as primary JunD targets through which JunD mediates its effect on macrophage activation. The primary JunD targets correlate with Jund expression levels in two independent datasets (microarray analysis on WKY and WKY.
RNA extraction and microarray preparation Total RNA was extracted using the TRIzol method and purified using RNeasy Plus spin columns (Qiagen). 100ng of RNA was amplified, labelled and hybridised to Rat Gene 1. 0 ST arrays (Affymetrix, Santa Clara, CA, USA) using the Ambion WT Expression Kit (Life Technologies) as per manufacturer's instructions. For timecourse expression analysis, four BMDM preparations from four biological replicates were used for each timepoint and condition. For siRNA expression analysis, four BMDM preparations from at least two biological replicates were used for each timepoint and condition. The microarray data is available in MIAME-compliant (minimum information about a microarray experiment) format at the Array Express database (http://www. ebi. ac. uk/arrayexpress) under accession code E-MEXP-3469.
Microarray data analysis CEL intensity files were produced using GeneChip Operating Software version 1. 4 (Affymetrix) and quality tested using the Affymetrix Expression Console v1. 1. 2. All 32 files in the timecourse data set and 16 files in the siRNA dataset were suitable for further analysis. Probelevel data was normalised using robust multichip average (RMA)  [48, 49]. A custom definition file was created using up-to-date probe information  [50]  and filtered to exclude probes containing the 2,520,602 single nucleotide polymorphisms present between the WKY and LEW genomes (Santosh Atanur, MRC Clinical sciences centre, personal communication). The moderated T test with 40,000 permutations implemented in Statistical Analysis of Microarrays (SAM) version 3. 0 was used to identify differentially expressed genes at an FDR threshold of 5% and timecourse analysis was performed using EDGE with 40,000 permutations and a 5% FDR threshold  [51].
Quantitative PCR All qPCRs were performed with an ABI 7900 Sequence Detection System (Applied Biosystems, Warrington, UK). A two-step protocol was used as previously described  [56]  beginning with cDNA synthesis with iScript select (Bio-Rad) followed by PCR using SYBR Green Jumpstart Taq Ready Mix (Sigma). A total of 10ng of cDNA per sample was used. All samples were amplified using a set of 4 biological replicates with three technical replicates used per sample in the PCR. Sequence detection software (SDS) version (Applied Biosystems) was used to obtain the Ct values. Results were analysed using the comparative Ct method and each sample was normalised to the reference gene Hprt, to account for any cDNA loading differences. The primer sequences used for the qRT-PCR validation of microarray data are listed in Additional file 2: Table  S8.
ChIP-Seq data analysis Sequencing of the ChIP-Seq libraries was carried out on the high throughput Illumina Genome Analyzer II. Initial data processing was performed using Illumina Real Time Analysis (RTA) v1. 6. 32 software (equivalent to Illumina Consensus Assessment of Sequence and Variation, CASAVA 1. 6) with default filter and quality settings. Quality filtered reads were then realigned to the reference rat genome (RGSC3. 4) using the Burrows Wheeler Alignment tool v0. 5. 9 (BWA)  [57]. Read ends were trimmed if Phred-scaled base quality scores dropped below 20. Reads that uniquely mapped to the reference genome were used to detect areas of enrichment with BayesPeak v1. 1. 3 [22, 23]  using a posterior probability threshold of 0. 9. A stringent posterior probability threshold of 0. 9 was used to filter all bins passing the threshold to form the final contiguous peak regions to produce a more accurate reflection of true peak calls.
An over fitting diagnostic was performed using λ 1 < 0. 7 and score < -2. 25 to filter out regions which showed no enrichment but had a high enough background for the algorithm to call peaks in. Peak regions were annotated using the gene intervals annotator (GIN) implemented in CARPET using a gene priority approach to give the associated transcript I. D. , the associated gene feature and the distance of the peak to the nearest transcriptional start site  [58]. HOMER was used to predict motif occurrence within peaks  [32, 59]  with default settings for a maximum motif length of 12 base pairs. The outputs of the peak calling algorithms were visualised in the Integrative Genomics Viewer  [60]  using custom WIG files generated from the output data generated by BayesPeak. The latter were generated by extending each mapped read by 200 bp and then by using 10 bp bins the overlapping tag count was generated.
Integrated data analysis and identification of primary JunD targets JunD gene expression patterns in WKY and WKY. LCrgn2 BMDMs over the LPS time course were used for Spearman correlation analysis with the rest of the transcripts on the microarrays (P < 0. 001 cut off ). In order to integrate the three different datasets, the list of significantly correlated set of transcripts was filtered and annotated according to two criteria: significant differential expression following Jund siRNA knockdown (both basal and after LPS stimulation) and the presence of the JunD ChIP-Seq peaks in WKY BMDMs (both basal and after LPS stimulation). Two separate networks (basal and LPS) were built with Cytoscape version 2. 8. 3 showing primary JunD targets. Isolation and culture of rat nephritic glomeruli and detection of IL-1β by Western Blot and ELISA Glomeruli were isolated as previously described  [13].
Transcripts associated with a JunD ChIP-Seq peak (within 20kb of TSS or within the gene body) are shown as black circles (232 transcripts, 12%). The transcripts correlating with Jund expression levels and down-regulated following Jund siRNA (basal, upper panel) are shown in blue (125 transcripts, 6. 5%); and those up-regulated (basal, upper panel) are shown as red circles (116 transcripts, 6%). Primary JunD targets in basal BMDMs are given with the gene names and show a JunD ChIP-Seq peak (24 transcripts, basal state, upper panel). This analysis was repeated for the LPS-stimulated macrophages taking into account JunD siRNA and ChIP-Seq datasets in LPS stimulated macrophages and identified 36 primary Jund targets (lower panel).
Acknowledgements We thank  Ailsa Chiu  and  CSC/IC Genome Core facility  for their excellent technical assistance. This work was primarily supported by a  Wellcome Trust   Clinical PhD Fellowship  ( 087182/Z/08/Z  to RPH), a  Junior Fellowship  from  Imperial College  (to JB), by intramural funding from the  MRC Clinical Sciences Centre  (to TJA) and by the  Wellcome Trust  project grant ( WT092523MA  to JB).
Satellite Data Our analysis utilizes information provided by the Geostationary Operational Environmental Satellites (GOES)  [12]. In particular, we use data from the GOES-12 satellite, which was replaced by GOES-13 in April 2010. The satellite is stationed at 75 degrees West longitude. Its Imager instrument captures images of the South American continent every 30 minutes. Full-hemisphere images are produced in one visible band and four infrared bands, centered at wavelengths 3. 9, 6. 5, 10. 7, and 13. 3 µm. These infrared bands are labeled Band 2, Band 3, Band 4 and Band 6 as shown in Figure  2. The bands were chosen to straddle the black-body peak for a typical range of Earth's surface temperatures. In Figure  2 , we show these bands superimposed on the calculated emission spectrum for a 280 K black-body at the surface of the Earth, as viewed from space.
This is to mitigate the effects of short-term cloud cover changes. The data used in this study were obtained over the period of a year in 2007. As was mentioned in Section 1, the Pierre Auger Observatory employs three cloud identification instruments: the CLF, the cloud cameras, and the LIDAR system. We chose the CLF to do our ground truth study because its observations best match the geometry and time frame of the satellite observations. Results from the cloud cameras and satellite are not unambiguously comparable as the two devices detect clouds from very different geometric perspectives. The LIDAR system and satellite share some geometric perspective but because of the manner in which the LIDAR data is presently obtained and analyzed observations are not easily matched in time and space.
Maps were generated for each satellite image available from all the FD running nights since 2004. In general, one image was available every 30 minutes. In addition, nightly animated maps were constructed. These maps (especially the animated versions) are useful in visualizing the cloud cover during and around the occurrence of interesting cosmic ray events. In particular, this helps in distinguishing between cloud-distorted shower profiles and those corresponding to exotic events. The array of cloud probabilities and timing information additionally are stored in the Auger GOES database for further reconstruction analysis of the cosmic ray data.
T2-T4 [K] We have developed a simple routine to be used within the Auger offline  [15]  analysis framework to extract cloud-cover information from the GOES database. At present the code merely extracts the cloud probability for a pixel given a coordinate and a timestamp. In the future we anticipate the development of a more sophisticated routine that will provide the final user with the cloud state of all the pixels between the FD and the shower path. With this information we hope to recover some fraction of the data conservatively thrown away on marginally cloudy nights. We also hope to use it for a fast veto of false exotic events. The Advanced Data Summary Trees (ADST) files, which are used in high-level Auger data analysis, contain cloud information which comes from the LIDAR systems and recently information from the GOES database and IR cloud cameras has been added.
The overlap region may also result from a spreading of the satellite "cloudy" data into the CLF "clear" distribution. This occurs when the satellite is more cloud-sensitive than the CLF system. This can happen due to the mismatch in geometric perspective between the satellite and CLF system. The CLF laser shots probe the cloudy/clear state of only a small portion of the pixel as seen from the satellite. The laser beam illuminates an area less than 100 m across whereas the pixel itself may measure several kilometers on a side. This effect may be important when the cloud cover is non-uniform but will disappear under overcast conditions. Another contribution may arise from the fact that the field of view of the FD is such that clouds whose bottom surfaces are above 14 km are undetectable by the CLF system.
However, clouds at such heights are rare. We also expect a distribution overlap in cases where the CLF is more cloud-sensitive than the satellite. This is the situation for optically thin clouds. Thin clouds produce a negligible change in the overall infrared flux emitted by a pixel rendering them undetectable by the satellite. Optically thin clouds are not important for distortions due to absorption, but could indeed act as side-scatterers. By averaging the profiles of up to 50 laser shots, the CLF system can readily detect thin-cloud echoes above the night-sky background. The presence of thin clouds have the effect of spreading the satellite "clear" data into the CLF "cloudy" distribution as the CLF detects clouds invisible to the satellite. In principle, by the above means the CLF/satellite system can discriminate between thin and thick clouds.
Acknowledgments The successful installation, commissioning, and operation of the  Pierre Auger Observatory  would not have been possible without the strong commitment and effort from the technical and  National Science Foundation , Grant No. 0450696 , The  Grainger Foundation USA ;  NAFOSTED, Vietnam ;  Marie Curie-IRSES/EPLANET ,  European Particle Physics Latin American Network ,  European Union   7th Framework Program , Grant No. PIRSES-2009-GA-246806 ; and  UNESCO. We would like to thank the former  Michigan Tech  students:  Nathan Kelley-Hoskins ,  Kyle Luck  and  Arin Nelson  for their important contribution to the development of this paper. We would like to thank  NOAA  for the GOES satellite data that we freely downloaded from their website. Also, we would like to mention in these acknowledgments  Dr.
WRINKLED1 (AtWRI1) is a key transcription factor in the regulation of plant oil synthesis in seed and non-seed tissues. The structural features of WRI1 important for its function are not well understood. Comparison of WRI1 orthologs across many diverse plant species revealed a conserved 9 bp exon encoding the amino acids "VYL". Sitedirected mutagenesis of amino acids within the 'VYL' exon of AtWRI1 failed to restore the full oil content of wri1-1 seeds, providing direct evidence for an essential role of this small exon in AtWRI1 function. Arabidopsis WRI1 is predicted to have three alternative splice forms. To understand expression of these splice forms we performed RNASeq of Arabidopsis developing seeds and queried other EST and RNASeq databases from several tissues and plant species. In all cases, only one splice form was detected and VYL was observed in transcripts of all WRI1 orthologs investigated.
Some Arabidopsis transcription factors repress their own function through producing alternative splice variants and by forming heterodimers  [18, 22]. Currently, three alternative splice forms are predicted for AtWRI1 and it is unknown whether all three AtWRI1 splice variants can be found in plant cells; or if present, whether these may have different patterns of expression or roles in plant fatty acid biosynthesis. The recent availability of very comprehensive EST and transcriptomic datasets for many Arabidopsis tissues provided the opportunity to evaluate the possible occurrence of the predicted alternative AtWRI1 splice forms. Here we show that expression of EgWRI1 in the Arabidopsis wri1-1 mutant is able to complement reduced seed oil content and other wri1-1 impaired phenotypes. In addition, we provide evidence that WRI1 splice form 3 (At3g54320.
Analysis of WRI1 alternative splice forms In AtWRI1, "VYL" is encoded by a 9 bp exon (Figure  6 ). Micro-exons of 2-25 bp are known to sometimes facilitate alternative splicing events  [25]. AtWRI1 is predicted to have three alternative splice forms (Figure  6 ) of different lengths and different protein sequences. Notably, "VYL" is absent from one of the predicted splice forms (At3g54320. 2) In addition, Multiple Sequence Alignment examination of predicted protein sequences from approximately 34 plant genomes in the Phytozome database 9. 0 (http://www. phytozome. org) indicated that VYL is also missing from the predicted AtWRI1orthologous amino acid sequences of 13 species (Figure  S5B ). These results indicate that the prediction of alternative protein sequences (or splice forms) for WRI1-like proteins occurs throughout the plant kingdom, and is not peculiar to Arabidopsis or its close relatives.
In the case of AtWRI1, the three splice forms are based on predicted gene models, rather than experimental evidence, but each has been previously assigned the same confidence level. These predictions for Arabidopsis and for at least 33 other plant species with sequenced genomes, raised the question whether different WRI1 splice forms are present in vivo and have different functions. We specifically asked: 1) Are all three AtWRI1 splice forms expressed in Arabidopsis? 2) Can we find multiple WRI1 splice forms in other plant species? 3) If we can find multiple WRI1 splice forms, are these expressed differently in different tissues? To evaluate the possible expression of multiple spice forms of WRI1, we tested directly by reverse transcription polymerase chain reaction (RT-PCR), performed RNASeq analysis of developing Arabidopsis seeds, and also searched publicly available large transcript databases.
The primer set (FW2 + RV2) was designed to amplify a PCR product of 269 bp (in form 2 or 3) or 167 bp (in form 1) respectively. As shown in Figure  S6C , all the samples that we tested supported the existence of form 2 or 3 (a PCR product of 269bp). A PCR product of form 1 (167bp) was not detected. Further sequencing of each PCR product in Figure  S6C  also confirmed this conclusion. Combining the results, this evidence indicated that splice form 3 of AtWRI1 is the major form expressed in Arabidopsis seedlings. As a second approach to evaluate expression of alternative splice forms in Arabidopsis, we searched a number of databases of Sanger ESTs, 454 pyrosequencing and Illumina RNASeq. Although large EST and RNASeq databases are available from several Arabidopsis tissues, the datasets do not include large numbers of reads from developing seeds, where WRI1 is most highly expressed.
999) that splice forms 1 or 2 of WRI1 are not expressed in Arabidopsis developing seeds. Alternative splice forms of genes are sometimes expressed only in specific tissues  [20, 21]. To evaluate this possibility, we also examined publicly available Ilumina RNASeq data from Arabidopsis roots and flowers. Although WRI1 is expressed at much lower levels in these tissues (13. 1 FPKM root, 10. 9 FPKM flower), the 9 bp exon 3 was represented by over 80 RNASeq reads from roots and 20 reads from flowers. No RNASeq reads were found that lacked exon 3. Examination of the 3' end (represented by 130 RNASeq reads in roots and 60 reads in flowers) revealed that the predicted 3' intron was not spliced out in any reads. Thus, splice form 1 was also not detected in either root or flower datasets. Taken together, although expression of the alternative splice forms at very low levels cannot be ruled out, these data indicate that splice forms 1 or 2 are unlikely to play a biological role in Arabidopsis flowers and roots grown under standard condition.
A search sequence of 32 nt located just before the last intron (spliced out in AtWRI1 splice form 1; Figure  S9A ), was used to identify B. napus ESTs that encoded the 3' end (see Figure  S9B ). In the B. napus EST database, 80 ESTs were identified that closely matched the 3' sequence of WRI1 and in all cases the stop codon position matched that of AtWRI1 splice forms 2 and 3, but not 1. The same conclusion was reached by analysis of 90 castor ESTs containing the 3' stop codon. Taken together, for B. napus, castor and oil palm, we could not detect evidence for splice forms other than those corresponding to WRI1 splice form 3.
Given the fact that WRI1 splice form 1 has been observed in only this one case and differs by only eight additional amino acids at the WRI1 C-terminal region (Figure  6B ), we consider that a specific function of WRI1 splice form 1 under normal plant growth is very unlikely. Taken together, we conclude that the WRI1 splice form 1 and 2, while predicted by automated annotation at the same confidence level as splice form 3, are unlikely to play a role in gene regulation, in the diverse tissues we examined. The prediction of forms 1 and 2 may be related to the fact that the sequences at the intron/exon junctions are not 'cannonical' which can lead to incorrect exon identification  [27, 28]. This 'error' in gene prediction is not specific to Arabidopsis because in the Phytozome database approximately one third of predicted protein sequences of WRI1 orthologs lack the VYL sequence (Figure  S5B ).
RNASeq analysis of WRI1 transcripts from Arabidopsis Developing seeds were collected from liquid nitrogen frozen siliques at 7-8, 9-10, and 11-12 d after flowering. Siliques were opened over dry ice and frozen seeds were separated from the silique walls by filtering through a liquid nitrogen cooled sieve into a tube on dry ice. Approximately 100 mg of developing seeds was finely ground and RNA extracted as described  [32]  and analyzed for yield and quality by capillary electrophoresis (Agilent 2100). Libraries for sequencing were prepared from 2-4 µg total RNA using Illumina TruSeq RNA kits and sequenced with Illumina HiSeq2000. Reads (50 nt) were trimmed, filtered and aligned to TAIR10 using TopHat v 1. 4. 1 (Parameters: --nonovel-juncs; -G TAIR10. gff) and Bow tie v 0. 12. 7. Cufflinks v 2.
It is therefore critical that the detected changes are rigorously analyzed to determine whether they represent disease-associated changes. Conditions for this could include the following: (i) the demonstration that the novel variant segregates with disease in familial cases. The lack of segregation would normally indicate that the variant is not disease associated; (ii) the demonstration of rarity of the disease-associated variant/ mutation in large population databases in addition to ethnicspecific databases; and (iii) the demonstration that the mutation affects protein function. Although this has been considered the gold standard, functional testing will be impractical for all variants as more and more variants are identified. In addition, functional experiments are often contrived and do not reflect the true biology.
DISCLOSURE GBA is a consultant to Alexion Pharmaceuticals and Genentech and a Principal Investigator for an Alexion-funded research grant. FF has received fees from Alexion Pharmaceuticals for invited lectures and is member of an expert board supported by Alexion Pharmaceuticals. VF-B has received fees from Alexion Pharmaceuticals for invited lectures and is member of an expert board supported by Alexion Pharmaceuticals. VMH is a consultant to Alexion Pharmaceuticals, has received sponsored research from Taligen Therapeutics and Alexion, and has received licensing and royalty fees from Taligen and Alexion. SJ is a member of a scientific advisory board for Alexion Phamaceuticals. BPM is a consultant for Baxter International. CMN is a participant on the C3 Glomerulopathy Advisory Board, sponsored by Alexion Pharmaceuticals.
Supplementary information such as climatic data (monthly rain, evapotranspiration (ETP) and temperature) and detailed land cover are available at the DONESOL database (http://www. gissol. fr/programme/rmqs/ RMQS_manuel_31032006. pdf  [42, 43] ). All parameters characterizing the 100 soils used in this study, including soil texture, soil chemistry, land-use, climatic data and spatial localization are detailed as supporting information (Table  S1 ). Moreover, the textural classification of the 100 soils is represented in supplementary figure (Figure  S1 ).