dataesr/scientific-paragraphs-categorization huggingface dataset:
Their output is either an injection height or a full smoke detrainment profile. The Global Fire Assimilation System (GFAS) of the EUfunded operational Copernicus Atmosphere Monitoring Service (CAMS;  Hollingsworth et al. , 2008; Flemming et al. , 2015)  has recently been updated to include injection height information based on the IS4FIRES parameterisation  (Sofiev et al. , 2012)  and a PRM  (Freitas et al. , 2007). These new products have been calculated for the time period since 2003 and are being continued as an operational near-real-time service on a daily basis. They are freely available online at http://apps. ecmwf. int/datasets/data/cams-gfas. In this study, we describe the GFAS update in detail, validate the new injection height information against MISR observations and demonstrate its positive impact on atmospheric aerosol simulations in two case studies.
Validation against MISR observations of plume top heights In this section, the newly computed injection heights are compared with the MPHP2 data set of injection heights derived from MISR observations for each GFAS grid cell in which both MPHP2 observations and GFAS injection heights are available. By taking into account only the plumes with good and fair retrieval quality, the verification data set is composed of 13 454 injection heights. When collocating these observations with the non-null injection heights computed from Terra-based fire observations in GFAS, the sample size is reduced to 4182, or 31 % of the initial verification data set.
Scores per biome type The MPHP2 data set also includes the MODIS landcover-type product following the International Geosphere-Biosphere Programme (IGBP) classification  (Channan et al. , 2014). Scores for each IGBP biome type are shown in Table 3. They help understand the strengths and weaknesses of each algorithm. The PRM shows a large bias of 664 m for evergreen broad-leaf forests, and a bias of more than 350 m for savannas and woody savannas. This could come from the fact that the parameters of the PRM were derived by optimisation using a small number of plumes in North America  (Paugam et al. , 2015) , which may not at all include plumes over this kind of land cover. For these biome types, which represent more than half of the MPHP2 data set, the RMSE is also very large for the PRM. The RMSE and bias are both significantly lower for IS4FIRES injection heights, for all biome types.
Discussion There are three sources of errors in the injection height estimates that were assessed in this study (besides possible errors on the verification data set): uncertainties on the MODIS satellite products that are used as input, uncertainties on the ECMWF profiles that are used to provide information on the environment, and finally, simplifications and/or processes not represented in the algorithms. These three sources of errors all play a role in the scores. MODIS FRP is often underestimated  (Kahn et al. , 2008; Schroeder et al. , 2014). This underestimation seems to be more significant for large fires, because of the opacity effect whereby the smoke hinders the remote detection of thermal anomalies at the surface. Veira et al. (2015a)  improved scores of another version of IS4FIRES relative to the MPHP data set by applying an empirical FRP correction for plumes higher than a threshold height.
A criterion on the PBL height diagnostic was added to prevent emitting biomass burning aerosols too high at night: the injection height provided by the PRM is used only when the PBL height is above 500 m. The PRM was chosen for a first implementation because it appears to estimate better high injection heights, associated with large fires, which have a more important impact on atmospheric composition. The IS4FIRES injection heights will be used in an upcoming test, since they show better scores overall in comparison with the MPHP2 data set. When used in C-IFS, the GFAS emissions of biomass burning aerosols are scaled by a factor of 3. 4. This value was estimated by  Kaiser et al. (2012)  by comparing simu- lations with and without data assimilation, both using GFAS for biomass burning sources.
The difference is especially important for altitudes between 2 and 6 km at 16:00 (35 • N), around 2 km between 18:00 and 19:00 (43-45 • N) and around 4 km between 20:00 and 22:00 (35-40 • N). The simulated values from C-IFS with an injection at the surface underestimated aerosol extinction at altitudes higher than 3-4 km and sometimes overestimated extinction close to the surface. When using injection heights from GFAS, the smoke layer at higher altitudes is slightly better represented, but the overestimation close to surface is also larger. The elevated layer of aerosols around 6 km is not captured by either simulation, which can be caused by errors either in the sources or in the transport and evolution of these smoke plumes. The validation of the injection heights from GFAS against the MPHP2 data set of plume heights observed by MISR show that both algorithms are suitable for plume height estimation, albeit with particular strengths in different respects: the IS4FIRES injection heights achieves better bias and RMSE scores globally and locally, while the PRM appears to represent large injection heights more accurately and to better reproduce the variability of injection heights.
Inaccuracies in the input data obviously have an impact on the scores. This is thought to be particular relevant in the cases of the planetary boundary layer height diagnostic from ECMWF and a FRP underestimation for particularly large fires, where the MODIS Collection 5 products are known to have larger omission errors due to thick smoke being erroneously classified as cloud. To mitigate these errors, two data sets can also be used together in an optimal combination, with a larger variability than the IS4FIRES data set and a smaller mean error and bias than the PRM data set. The use of injection heights from GFAS for biomass burning emissions has been implemented in the global atmospheric composition model C-IFS. The scaling factors that are used in C-IFS for biomass burning emissions of black carbon and organic matter were recomputed, with and without using injection heights; they showed little variation when the injection heights from GFAS are used.
Data availability The two injection heights data sets are publicly available the CAMS GFAS web page: http://apps. ecmwf. int/datasets/data/ cams-gfas. -The mismatch between ECMWF winds and winds retrieved in MPHP is small. -There is a limited number of fire clusters near the plume's origin. -A plume is clearly and visibly observed. -The plume is in a steady state, i. e. not in a development or dying out stage. The parameter-fitting procedure uses a simulating annealing algorithm and a Markov chain Monte Carlo uncertainty test. Table  A1  summarises the results of the optimisation step for the six parameters. A value of 1 is used for the β parameter for simplicity's sake and because the confidence interval provided by the Monte Carlo uncertainty test was quite large for this particular parameter.
Acknowledgements. The authors wish to thank  NASA  for providing the MODIS data, and  Charles Ichoku  for providing Fig. 2 reproduced from Ichoku and Ellison (2014). The IS4FIRES algorithm was developed within the  IS4FIRES  project of the  Academy of Finland. Airborne data from the SAMBBA field campaign were obtained using the  BAe-146-301 Atmospheric Research Aircraft (ARA)  flown by  Directflight Ltd  and managed by the  Facility for Airborne Atmospheric Measurements (FAAM) , which is a joint entity of the  Natural Environment Research Council (NERC)  and the  Met Office. Support for the DIAL/HSRL operations and measurements during the SEAC4RS field campaign was provided by  NASA Headquarter's Tropospheric Chemistry ,  Radiation Sciences , and  Upper Atmospheric Research Programs. Support for the development and addition of the HSRL capability was provided by the  NASA   Airborne Instrument Technology Transition Program.
In addition, 3 BGCs directing the biosynthesis of putative bacteriocins and lantipeptide, and 2 BGCs containing genes encoding NRPS-independent siderophore (NIS) enzymes are also conserved in the three species. Accession number(s). This whole-genome shotgun project has been deposited in DDBJ/ENA/GenBank under the accession no. MQUH00000000. The version described in this paper is the first version, MQUH00000000. 1.
9A, B ), indicating that the presence of functional key factors of the DDR is necessary but not sufficient to trigger a proper response to DNA damage. In conclusion, we propose that CTCF, by preserving the 3D organization of the chromatin, is critical for the activation of an efficient DDR and, in such context, it functions as a regulator of the structural component of DDR. Coloured arrowheads: orientation of CTCF-binding sites (red: forward; green: reverse). Details about gH2AX ChIP-Seq domains are in Supplementary Methods and Supplementary Fig. 4. ChIP-Seq CTCF profiles were retrieved from publicly available databases (UCSC Accession: Encode wgEH000080, wgEH000543, wgEH000401 and wgEH000470). (b) CTCF occupancy outside or inside gH2AX ChIP-Seq domains. The intensity of each CTCF peak in 100 kb bins upstream and downstream of the border of gH2AX ChIP-Seq domains (grey box) is summed and then presented as one-sided distribution.
Growth curve and cell cycle distribution. Cells were seeded 24 h before exposure to IR. After IR, cells were incubated for indicated times, before trypsinization and count with a coulter counter, in triplicates. The remaining cells were then fixed in 2% formaldehyde, permeabilized for 8 min with 0. 5% Triton X-100 in PBS, stained with DAPI (1 mg ml À 1 ) and analysed at the flow cytometer Partec PAS III system (Partec) for cell cycle distribution. Data were analysed with FlowJo software (Tree Star, Inc. ). Apoptosis assay. To detect apoptosis, TUNEL assay was performed according to the manufacturer's instructions (Roche, #11684795910) and a minimum of 1,000 cells was scored by microscopy in two independent experiments. Spectral karyotyping. Cells were treated with colcemid (0. 1 mg ml À 1 ; Invitrogen, Darmstadt, Germany) 2 h before collecting to accumulate metaphase cells.
Three rounds of binary erosion with a 3 Â 3 px-box followed by three rounds of binary dilation were applied to the segmentation results to smooth the borders of segmented objects. 3D-SIM images were exported from the DeltaVision software (softWoRx 6. 0 Beta 19, Applied Precision) and converted to 16-bit images per channel. Foci counting was done using Volocity 6. 3 (Perkin Elmer) or with the 3D foci picker plugin in ImageJ (imagej. nih. gov/ij/). Nearly identical results were obtained and the numbers from Volocity were used. In detail, the individual z-sections were imported and merged to a volume with the above-mentioned pixel sizes and z-spacing. First, the nucleus was identified by setting a manual threshold and a lower volume limit of 200 mm 3 followed by a 'Fill in Holes' step and two iterations of 'Dilate' and 'Close' to fill in all the DAPI weak volumes.
The intensities and voxel coordinates of the whole nucleus were registered. Next, the gH2AX and H3K36me3 or H3K9me3 foci were identified with a lower threshold of 1,000 and a minimum object size of 0. 001 mm 3. To separate close spaced objects, a final 'Separate Touching Objects' step with a nominal volume of 0. 05 mm 3 was used. The foci identified were restricted to the previously defined nuclear volume to remove possible unspecific signals from outside of the nucleus. 3D-SIM pseudo-wide-field imaging: after sample acquisition, the pseudo-widefield images were calculated using softWoRx 6. 0 Beta 19 according to the following workflow: the raw data from each 3D-SIM image z-stack was subdivided to isolate the first angle of acquisition. To this purpose, the maximum number of z-sections in each individual stack is divided by three.
Then the projected five grid shifted section is averaged per z-position and colour channel. After that, the voxel dimensions are adjusted from 0. 625 to 0. 125 mm in the z-dimension by adjusting the file headers. The alignment of the new stack was done with the parameters used for 3D-SIM reconstruction. The following parameters of the softWoRx software were used: normalize intensity, use photosensor, correct bleaching, replace z-lines and smooth z-lines. To reverse the optical distortion in the images, the aligned 3D stack was deconvolved with the instrument-specific optical transfer function (OTF) with the following settings: 'enhanced ratio (aggressive)' and 'noise filtering medium'. For CTCF distance analysis, the previously described protocol was extended as follows: CTCF domains detection was restricted to the nuclear volume, with an automated threshold and a minimum size of 0.
For STED images, object dimensions (for example, diameters) were measured by manual object segmentation of randomly selected foci in ImageJ, using the analyse particle tool. For high-content images, analysis was performed using Harmony software (Perkin Elmer) with the following workflow: maximum projection of the planes, flatfield correction, find nuclei in DAPI channel, method M, splitting coefficient 0. 1, general threshold 0. 4 and guide size of 15 mm in diameter. Calculate intensity and morphology parameters for the nuclei. Discard nuclei touching the border, smaller than 100 mm 2 and larger than 350 mm 2. Filter nuclei for roundness 40. 83 and with a 4 px Haralick contrast 40. 8 and a DAPI signal CV of less than 30%. Measure the mean and integrated intensity for DAPI, gH2AX and CTCF in the selected nuclei areas.
DNA elution was carried out in 1% SDS, 100 mM NaHCO 3 , in a rotary shaker at RT for 15 min. Pure DNA was isolated using the Qiagen PCR purification kit and 15-30 ng of size selected DNA fragments (Qubit fluorometric quantification) were used to produce ChIP-seq libraries (Illumina ChIP-Seq DNA sample Prep Kit). Input sample was essentially prepared following the same protocol, but the immunoprecipitation step was skipped. Next-generation sequencing and data analyses. ChIP-Seq libraries were processed through a high-throughput sequencing pipeline (Illumina Genome Analyzer II). Reads were mapped to the human genome (University of California, Santa Cruz (UCSC) hg19 assembly, based on the National Center for Biotechnology Information (NCBI) build 37. 1) by means of SOAP2 software  51  , allowing up to two mismatches for each 36 bp read.
All data sets were deposited in the Gene Expression Omnibus database (accession number: GSE60526). All gH2AX ChIP-Seq tracks were smoothed with a moving average of five intervals before further analysis. Genomic features and correlation analysis: all genomic features data were retrieved from publicly available databases (UCSC) (Supplementary Table  3 ). Most of the data were generated in HepG2 cells, but not all. Data that were originally generated in the hg18 assembly were transposed to hg19 using LiftOver (http://genome. ucsc. edu/cgi-bin/hgLiftOver). Reads per kilobase per million reads (RPKM)  52  were calculated for non-overlapping 10 kb genomic intervals for all sequence tracks. The features were further normalized to the corresponding genome-wide average and correlation with gH2AX tracks was performed (Spearman's r correlation coefficient with Po2.
Acknowledgements We are indebted to  Dr Diana Pignalosa  and  Dr Sylvia Ritter  for SKY analysis. This work was supported by grants of the  Deutsche Forschungsgemeinschaft  ( GRK1657/TP1B  to M. C. C. ;  GRK1657/TP1C  to A. R. ;  DFG CA198/8-1  and 2 to M. C. C. ), the  Bundesministerium fu ¨r Bildung und Forschung  Grants  02NUK017D  and  02S8355  to M. C. C. and  02NUK036D  to A. R. and the  Beilsetein-Institute, NanoBiC collaboration.
Cystic Fibrosis is the most common lethal autosomal recessive disorder in the white population, affecting among other organs, the lung, the pancreas and the liver. Whereas Cystic Fibrosis is a monogenic disease, many studies reveal a very complex relationship between genotype and clinical phenotype. Indeed, the broad phenotypic spectrum observed in Cystic Fibrosis is far from being explained by obvious genotype-phenotype correlations and it is admitted that Cystic Fibrosis disease is the result of multiple factors, including effects of the environment as well as modifier genes. Our objective was to highlight new modifier genes with potential implications in the lung, pancreatic and liver outcomes of the disease. For this purpose we performed a system biology approach which combined, database mining, literature mining, gene expression study and network analysis as well as pathway enrichment analysis and protein-protein interactions.
There are currently 2,009 mutations listed in the CFTR mutation database (http://www. genet. sickkids. on. ca/cftr/app). The most common one, F508del-CFTR (a 3 bp deletion in exon 10 causing loss of the amino acid phenylalanine at position 508), encodes a cAMP-regulated Cl -channel that is retained in the endoplasmic reticulum (ER) during translation and folding and is targeted to the early proteasomal degradation  [11]. Besides an altered folding, the F508del-CFTR protein exhibits an altered function with an open time period of approximately 0. 1 to 0. 3 seconds  [12, 13]. The phenotype due to a given mutation depends on its interaction with the second mutated CFTR allele and largely on disease modifiers. Whereas CF is a monogenic disease, there is a very complex relationship between genotype and phenotype  [14].
In the present work, a different methodology was used. To search for genetic factors that may play a role in CF, we used a system biology approach. This approach combined database and literature mining, gene expression, and network analysis. Pathway enrichment analysis and protein-protein interactions (PPI) were also searched, to give our results a physiological relevance. This approach allowed us to examine functional relationships between reported genes, and led us to identify novel genes and enriched pathways that may play a role in CF, regarding its lung, pancreatic and liver affections. We found that genes involved in immunity including Ubiquitin C are likely modifiers.
Data sources and gene selection from databases The gene selection was performed using a previously used methodology  [24]. Genes were first identified utilizing the Online Mendelian Inheritance in Man (OMIM) database (http://www. omim. org/) because it is considered to be the best curated resource for genotype-phenotype relationships  [25, 26]. In OMIM, we used the following key words: CF, CF associated genes, PI and Liver Disease. CF and CF associated genes were used to retrieve genes directly involved in CF as well as genes potentially implicated in the disease. The Comparative Toxicogenomics Database (CTD, http://ctdbase. org/, [27] ) was also used. It curates relationships between genes and human diseases in a unique fashion which integrates gene/protein-disease relationships. In CTD, CF, Pancreatic diseases and Liver Disease key words were used.
In CTD, disease-gene associations are reported as curated or inferred. We selected curated associations due to a higher confidence than inferred associations. Solely the genes that may be biomarkers of a disease or play a role in the etiology of a disease were selected. Lastly, the Human Genome Epidemiology encyclopedia (HuGE Navigator, http://www. hugenavigator. net/HuGENavigator/startPagePubLit. do, [28] ) which mines the scientific literature on human gene-disease associations was used. It is a database of population-based epidemiologic studies of human genes  [29]. In HuGE Navigator we used the CF, PI and Liver Disease key words. The genes with a genetic association above 5 were selected (score formula is described in  [30] ). Different key words were used among databases because some of them did not permit to retrieve any result.
Expression analysis Datasets restricted to humans, were collected from the Gene Expression Omnibus (GEO,  [45] ) and GEO2R was used for the analysis  [46]. For this purpose, experimental groups were reassigned. The following datasets were passed through the GEO2R online application: GSE40445 (Gene expression in CF-vs-non CF airway epithelial cells from nasal brushing), GSE48452 (Gene expression in control liver-vs-NASH) and GSE44314 (Gene expression in type 1 diabetes-vs-healthy controls). Type 1 diabetes was used because no PI dataset was found. Samples were assigned to the case or control groups. The GEO2R uses the limma (Linear Models for Microarray Data) package from Bioconductor and was designed to analyze complex experiments involving comparisons between many RNA targets simultaneously, with the idea to fit to a linear model to the expression data for each gene.
Regulated genes shared with [31] [33] UP: ACAA2, CDKN2B  several p-value options, we applied the adjustment to the p-values also called multiple-testing corrections, to correct the occurrence of false positive results. The Benjamini & Hochberg false discovery rate method, commonly used to adjust microarray data, was selected. After GEO2R dataset analysis, genes of interest were selected by using the criteria of a Bonferroni-corrected p-value 0. 05. According to the annotation files, probes that were not corresponding to any genes and genes which were present several times within a single study were eliminated. Differentially expressed transcripts between normal and pathologic were selected and common genes between GSE40445, GSE48452 and GSE44314 were searched by drawing a Venn diagram (not shown).
Analysis of enrichment of KEGG biochemical pathways in CTD Our target genes were mapped to biological process pathways using CTD (http://ctd. mdibl. org). Biocurators at CTD manually curated gene-disease relationships from the literature as well as pathways. The core data are integrated to construct pathway networks. The Venn-Viewer function, which permits to compare associated datasets, was used for the genes that were found to be potential modifiers in CF, Liver Disease and Chronic Pancreatitis. The Pathway associations function was selected to compare annotated enriched pathways, as well as pathways for disease for these genes. Associated data for interacting genes and diseases, inferred KEGG and REACTOME pathways, or enriched GO terms were downloaded. Annotated pathways for genes are associations established by KEGG and REACTOME curation.
PPI networks design PPI networks were obtained by STRING 10. 0 (Search Tool for the Retrieval of Interacting Genes and Proteins, http://string-db. org/). STRING is a global resource for searching connections (edges) between genes or proteins (nodes) currently covering 9,643,763 proteins. Each edge has a confidence value between 0 and 1, lowest and highest confidence, respectively. The gene's names were used as an initial input and experimental data sets were selected to display interactive networks. The parameters were defined as follows: all PPI prediction methods were enabled, except for High-throughput Lab Experiments; maximum of 5 interactions by node; cut-off criterion of combined score ! 0. 7 (interactions at high confidence or better); Homo sapiens. Common genes were searched in CF, pancreatic disease and Liver Disease.
Differentially expressed genes in CF were also retrieved from published results in PubMed. Finally, Datasets were collected from the GEO and re-analyzed. Because the CF, Chronic Pancreatitis and Liver Disease keywords did not give any result, type 1 diabetes-vs-healthy control and control liver-vs-NASH were used in GEO. Candidate genes from these databases were compared and analyzed to search for potential common genes and pathways. PPI were further searched to give our results a physiological relevance.