dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments This work was supported by funding from the  European Union   EURATRANS award  ( HEALTH-F4-2010-241504  to N. H. , M. V. , E. C. , S. C. , R. C. J. , M. W. , M. P. ), the  Helmholtz Alliance ICEMED , and the  Deutsche Forschungsgemeinschaft  ( Forschergruppe 1054 ,  HU 1522/1-1 ) to N. H. C. R. was supported by an  EMBO  long-term fellowship ( ALTF 1156 ( ALTF -2009 )). M. P. was supported by grants  P301/10/ 0290  and  LL1204  from the  Grant Agency of the Czech Republic. C. J. , M. C. -T. , and F. J. were supported by grants from the  Netherlands Organization for Scientific Research (NWO).
Data access Raw sequencing and processed data are available via EBI's ArrayExpress (http://www. ebi. ac. uk/arrayexpress)  (Parkinson et al. 2007 ) under accession number E-MTAB-1102 and via the European Nucleotide Archive (ENA; http://www. ebi. ac. uk/ena)  (Leinonen et al. 2011)  under accession number ERP001430. The processed data set is also available at http://www. molgen. mpg. de/;heinig/histoneQTL/.
Birds landed in an open arena and then had to walk 200-300 m back to their perch. Preliminary experiments indicated that the birds flew similar to wild vultures (C. Tromp, unpublished results), reaching heights. 1500 m above ground within 15 minutes. We collected data for one Himalayan griffon (age 15) and one Eurasian griffon (age 30) that performed 11 and 12 flights respectively over four days (mean flight duration 11. 065. 2 min, range 3-19 min). These 2 individuals were chosen for their tame behaviour, tolerance of handling and tolerance for a harness and HR data logger, a prerequisite to obtain normal soaring flight behaviour to reach high altitudes. In contrast birds that were stressed with the harness were more prone to make short flights in the vicinity of the platform where the trainer waited.
b) Data loggers Vultures were equipped with bio-loggers including a GPS, a 3axial accelerometer, and an electrocardiogram recorder (ECG). The 3D positions of birds were recorded 4 times per second using the high-precision GPS module (Gipsy 1, Technosmart, Italy). We performed immobility tests under open sky to assess the precision of the GPS loggers by calculating standard deviation around mean values of longitude, latitude and altitude (respectively 0. 8, and 1. 7 and 1. 2 m). Three-axial accelerations between 22. 3 and +2. 3 G or between 29. 2 and +9. 2 G were acquired at 100 Hz with a LIS302DL accelerometer (STMicroelectronics) and stored in Neurologger 2  [32]. The 52 g ECG logger (UWE-ECG, Little Leonardo, Japan) recorded the ECG at 1000 Hz through 3 cables that were clipped in the skin of the vultures' back with goldenplated safety pins  [17].
c) Analyses ECG and acceleration data were processed using a program developed on Igor Pro for this purpose (Wavemetrics, USA). The vectorial Overall Dynamic Body Acceleration (generally called ODBA or VeDBA) was calculated from the acceleration values, allowing identification of periods of inactivity (resting or soaringgliding) and activity (walking or flapping)  [35] [36] [37]. At first the dynamic acceleration was calculated for each axis by subtracting the smoothed acceleration from total acceleration. Then the ODBA was calculated as a vectorial sum i. e. the square root of the sum of the squares of the dynamic accelerations. To calibrate ODBA with flight behaviour, vultures were observed and filmed after take-off for as long as possible (birds could not be observed 100% of time during flight since they often went out of sight in the canyon), and again while landing.
Both perched and resting birds were on their ''perching roost'', but tourists made for a noisy atmosphere as the show was going on. In contrast, a resting bird was in a sleeping state during the morning hours when tourists were absent and the atmosphere silent. We defined the ''baseline'' HR as the value when birds were ''resting'' and considered it to be a state near Field Basal Metabolic Rate. We considered HR as a proxy for metabolism since HR measured in a resting Eurasian griffon vulture as well as other birds is correlated with oxygen consumption  [29]   [16]. We collected data for a total of 255 min of flight but variability in HR patterns, inter-and intra-individual variations could not be statistically assessed with only 2 individuals. We thus presented only mean 6 SD values.
Acknowledgments We are grateful to the  Rocher des Aigles , particularly directors  R. Arnaud  and  D. Maylin , for their welcome and financial support, and to bird trainers for their technical help. We thank  M. Scialotti  for GPS preparation, and  T. Boulinier ,  K. McCoy ,  B. Nolet ,  M. Klaassen  and four anonymous reviewers for comments.
N-Myc up-regulates JMJD1A gene expression by directly binding to its gene promoter By screening human histone demethylase gene promoter regions with GenoMatix software, we found one Myc-responsive element E-box -420bp upstream of the JMJD1A gene transcription start site (Fig. 1A ). We then examined a c-Myc chromatin immunoprecipitationsequencing (ChIP-Seq) dataset, which was generated by Dr. Michael Snyder's group at Yale University for the ENCODE/SYDH project. As shown in Fig. 1B , the ChIP-seq data showed that the c-Myc oncoprotein bound to the JMJD1A gene core promoter region encompassing the E-Box in K562 and HeLa cells. Consistently, our own ChIP assays showed that an anti-N-Myc antibody efficiently immunoprecipitated the region of the JMJD1A gene core promoter carrying the E-box in BE(2)-C neuroblastoma cells (Fig.
N-Myc modulates target gene expression partly through up-regulating JMJD1A gene expression JMJD1A exerts biological effects by demethylating mono-methyl and di-methyl histone H3K9 and consequently modulating gene transcription  [7] [8] [9]. We therefore performed differential gene expression studies with Affymetrix microarray in BE(2)-C cells 30 hours after transfection with scrambled control, JMJD1A siRNA-1 or N-Myc siRNA-1. Microarray data were subjected to normalization, summarization, annotation and differential gene expression analysis in R (http://www. rproject. org/) with bioconductor package (http://www. bioconductor. org/). We used 1. 5 fold change as the cut-off for JMJD1A siRNA-1 versus control siRNA experiment and 2. 0 fold change as the cut-off for N-Myc siRNA-1 versus control siRNA experiment, since JMJD1A siRNA-1 caused less changes in gene expression in 30 hours.
JMJD1A up-regulates MALAT1 gene expression by demethylating histone H3K9 at the MALAT1 gene promoter As shown in Supplementary Table  S1 , the gene second most significantly reduced by JMJD1A siRNA-1 was the long noncoding RNA MALAT1. In comparison, MALAT1 expression was not reduced by N-Myc siRNA-1 in the Affymetrix microarray data (Supplementary Table  S2) which were acquired from BE(2)-C neuroblastoma cells 30 hours after siRNA transfections. As N-Myc upregulated JMJD1A expression (Fig. 1 ), we examined whether both JMJD1A siRNAs and N-Myc siRNAs reduced MALAT1 expression at a later time point. Realtime RT-PCR analysis showed that knocking-down JMJD1A or N-Myc expression with JMJD1A siRNA-1, JMJD1A siRNA-2, N-Myc siRNA-1 or N-Myc siRNA-2 for 48 hours all significantly reduced MALAT1 expression in both BE(2)-C and CHP134 cells (Fig.
5C ). For cell invasion assays, BE(2)-C cells were transfected with control siRNA, MALAT1 siRNA-1 or MALAT1 siRNA-2 for 20 hours, and then added onto Matrigel-coated BD Falcon Cell Culture Inserts inside BD BioCoat Matrigel Invasion Chambers. Quantification of cells which had   2 )-C and CHP134 cells were transfected with control siRNA, JMJD1A siRNA-1 or JMJD1A siRNA-2. Seventy-two hours later, the numbers of viable cells were analysed by Alamar blue assays. (B) Ibidi Culture Inserts were placed in the center of 6 well plates. BE(2)-C cells were added into the wells outside the inserts, and transfected with control siRNA, JMJD1A siRNA-1 or JMJD1A siRNA-2. Sixteen hours later, the inserts were removed to create "wounds". Photos of the "wounds" were taken under microscope and the area of the remaining "wounds" were measured and analyzed with Image J software.
DISCUSSION. JMJD1A gene expression is down-regulated by the microRNA mir-155  [15] , and up-regulated by hypoxiainducible factor 1α (HIF1α), hypoxia, starvation and iron scavengers in tumor tissues  [16] [17] [18] [19] [20]. Myc oncoproteins, including N-Myc and c-Myc, are well-known to exert oncogenic effects by binding to Myc-responsive element E-boxes at target gene promoters, leading to transcriptional activation  [3] , and by binding to Sp1-binding sites at target gene promoters, leading to transcriptional repression  [21] [22] [23]. In this study, we have identified a canonical Mycresponsive element E-Box at the JMJD1A gene core promoter, and found that c-Myc oncoprotein binds to JMJD1A gene core promoter encompassing the E-Box in a publicly available ChIP-Seq dataset. Our own experiments have confirmed that N-Myc directly binds to JMJD1A gene core promoter and up-regulates JMJD1A mRNA and protein expression in neuroblastoma cells.
Cells were crosslinked with formaldehyde, ChIP was carried out and the recovered DNA fragments were deep sequenced. Specific Pol II bound sequence-reads were mapped to the human genome, and unique reads were considered for further analyses. For the comparative ChIP-seq analyses, all the seven datasets were normalized based on background tag densities calculated on intergenic regions (see Materials and Methods). Note that our control non-irradiated data set was very comparable to that obtained in MCF7 cells by  [13]. Next, Pol II density profiles on the coding regions of all refseq genes were calculated for all datasets by using seqMINER tool  [37]. To this end, average Pol II tag density values on each ORF, starting 21 kb upstream and ending +4 kb downstream from every refseq gene were calculated and compared.
Interestingly, in the UVB-treated samples that were left for 5 and 6 hours to recover before ChIP, Pol II occupancy at the TSSs of all refseq genes increased to the initial levels, suggesting that Pol II transcription has been restarted after TCR has been completed (Figure  2A  and B ). As in the above Pol II binding analyses, when analyzing all refseq genes at gene bodies, we did not observe an obvious GW increase of Pol II occupancy, we next re-analyzed global Pol II tag density changes in the GB regions of all transcribed genes. For this first we selected 4500 expressed genes, from a recently published RNA-seq dataset for MCF-7 cell line  [38]  (for complete gene list see Table  S2 ). These analyses clearly indicated i) a significant quick increase of Pol II tag density at all transcribed genes 1 hour following UVB irradiation; ii) followed by a gradual decrease of Pol II binding in GBs that sink under control levels at 3-4 hours following UVB, and iii) a novel global increase of Pol II signal that rises again above control levels at 5-6 hours following IVB irradiation (Figure  3 panels A-F ).
Moreover, to analyze and find potential differential gene function categories between the detected distinct Pol II behavior patterns on genes belonging to the different groups and subgroups, we carried out Gene Ontology (GO) analyses on the identified categories of genes (Table  1 , carried out with D. A. V. I. D. ; and Table  S1 , carried out with MANTEIA). Interestingly, in many sub-clusters Pol II occupancy increased at 1 hour and then again at 5 and 6 hours following UVB treatment at distinct regions of the transcription units (Figure  4  and Figure  S3 ). Compared to the other patterns, genes in subgroup Aa have a strong increase of Pol II signal at 6 h on TSS, GB and EAG+4000 regions, while they have a somewhat weaker decrease of Pol II signal at their TSS regions between 2 and 4 hours than subgroups From the ChIP-seq datasets heat maps were generated to follow Pol II behavior in time on the 4500 expressed genes at different genomic regions after UVB treatment in MCF7 cells.
Many annotated repair genes show Pol II increase in response to UVB treatment In the above detailed global analysis of Pol II behavior on expressed genes upon UVB treatment we have observed that many genes in group B, belonging to the GO categories 'response to UV' and 'DNA damage response', have increased Pol II occupancy throughout their whole transcription unit. Thus, we analyzed Pol II distribution at annotated repair and UVresponsive genes existing in the KEGG database  [42] , which may have been missed in the above analyses because they are not expressed under 'normal' conditions. We clustered the 164 characterized and annotated repair genes according to their Pol II occupancy and created heat maps as above (Figure  5 ) (for gene lists see Table  S2 ). These analyses indicate that in about half of the annotated repair genes Pol II signals increase in all of the three regions or in only part of them.
A separate search in repair-associated genes in the KEGG database also showed that a portion of genes in this category behave as genes in our group B. Many of these genes seem to be key genes regulating repair, cell cycle, apoptosis and stress responses. Thus, these genes with increase Pol II signals at their TSSs and other regions should have a particularity that differentiates them from the majority of the expressed genes in group A, and it is tempting to speculate that group B are somehow protected from the above described transcription inhibition mechanism. To overcome the negative regulation at the promoters of group B genes, several scenarios are possible: i) these genes may get repaired much faster than the genes in group A and at the 1 hour time point they are transcribing again; or ii) TFIIH is present, but indispensable for transcription initiation on these genes, and thus, the TFIIH sequestration mechanism does not influence the transcription of these genes; or iii) these promoters would have specific requirements for transcription initiation, involving slightly different, noncanonical initiation machinery that could for instance involve specific stress-related transcription factors and a special form of TFIIH.
High throughput sequencing with Hi-seq 2000 The sample preparation for ChIP-seq was the same as described  [11]. To have enough material for sequencing, 5 ChIP samples were pooled together for each time point. Library preparation for sequencing was performed as described by the manufacturer. The generated sequencing data was deposited in the Gene Expression Omnibus (GEO, http://www. ncbi. nlm. nih. gov/gds) database. The 32 base pair tags generated from Hi-seq 2000 were mapped to the human genome (UCSC hg19) using the eland program allowing two mismatches. Only sequences that mapped uniquely to the genome with maximum of two mismatches were used for further analysis. To eliminate non-specific binding sites, we used our control ChIP-seq dataset, which was generated using an antibody that does not recognize any human proteins (GSE34001).
Bioinformatics tools and data-analysis methods Intergenic regions (around 8000 regions) were selected that are far away from genes about 20 kb and tag numbers were counted on 2 kb interval in the middle of them for all the 7 samples. These values were used as an input for DESeq Bioconductor package, which normalizes the samples based on their median values. (http://www. bioconductor. org/packages/2. 9/bioc/vignettes/DESeq/inst/doc/ DESeq. pdf). We carried out analyses on all genes found in the refseq database and on 4500 expressed genes, based on the published RNA-seq dataset for MCF-7 cell line  [38]. The sequenced ChIP-seq reads represented 36 base pair fragments. To illustrate the entire DNA fragments bound by Pol II, basically before analysis, 39 end of each ChIP-seq read was extended to 200 bp in the direction of the reads.
To generate an average gene profile from ChIP-seq results, Pol II tags were counted on the selected genes with seqMINER software  [37]  from 21000 bp relative to the TSS of a given gene until the end of EAG +4000 bp regions by dividing these regions into 120 bins (the same number for long as well as for short genes). The ChIP-seq reads were counted in each bin and used to generate the profile. The promoter regions of all refseq genes were divided into 5 bins, while the GB+EAG+4000 regions together were covered by 115 bins. While doing this analysis, the strand orientation is taken in account in order to orientate all analyzed features in the same direction. For heat map generation, Pol II read numbers for the 4500 expressed genes were counted from the seven datasets around the promoter (+/2300 bp around TSS), on the annotated gene body (TSS+100 until the EAG) and downstream of EAG (EAG+4000 bp).
During heat map generation, Pol II tag densities were subjected to kmeans clustering in order to organize or cluster genes in a same group based upon similar tag enrichment within a defined region. In k-means clustering, number of clusters is fixed and hence the samples are sorted in the clusters based upon the tag enrichment and patterns of Pol II. Cluster and heat map generation was carried out with Cluster 3 and TREEview software. Cluster:http://bonsai. hgc. jp/,mdehoon/software/cluster/manual/ index. html#Top Treeview: http://sourceforge. net/projects/jtreeview/.
Gene ontology (GO) analyses and gene name conversion The Database for Annotation, Visualization and Integrated Discovery (D. A. V. I. D. ) (http://david. abcc. ncifcrf. gov/home. jsp) was used for GO analyses and gene ID conversion. Further GO enrichment analyses were also performed in Manteia using its statistical module (http://manteia. igbmc. fr/). During the analyses only GO categories with lower than 0. 01 p-values (p-value,0. 01) were considered as positive results. Build 37 (hg19 assembly). Gene definitions were given by the refseq genes track  [69].
Acknowledgments We are grateful to  W. Vermeulen ,  M. Fousteri ,  F. Coin ,  J. Pothof ,  E. Balint ,  O. Bensude ,  D. Eick  and  E. Soutoglou  for helpful discussions, advice and materials. We thank  D. Devys ,  T. Pankotai  and  J. H. J. Hoeijmakers  for critically reading the manuscript and for helpful suggestions.
A ´G was supported through a  French-Hungarian Cotutelle program , and by fellowships from  BAYGEN institute Szeged (Hungary) , and from  Fondation pour la Recherche Medicale en France (FRM). The work was supported by funds from  CNRS LEA SKINCHROMA ;  Agence Nationale de la Recherche (ANR)  (grant numbers:  ANR-09-BLAN-0266 ;  ANR-09-BLAN-0052 ) and  European Community (EUTRRAC)  grants to LT and  New Hungary Development Plan  Project  TA ´MOP-4. 2. 2. A-11/1/KONV-2012-0035  to IB and ZU ´. This study was also supported by the grant  ANR-10-LABX-0030-INRT , under the frame programme Investissements  ANR-10-IDEX-0002-02. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
, 2005; Aoki et al. , 2007). A small group of sugar starving responsive genes (SSR) were selected, to perform a co-expression network analysis. This gene subset was loaded into the CressExpress pipeline, a co-expression analysis tool for A. thaliana microarray expression data  (Srinivasasainagendra et al. , 2008) , and Cytoscape software package  (Cline et al. , 2007)  was posteriorly used to build a graphical representation of the network. The SSR network was validated with transcriptional data from starch-impaired mutants. In order to mimic sugar starvation in leaves, microarray analysis was performed on three A. thaliana starch-deficient mutants obtained in our lab  (Dumez et al. , 2006). The starchless mutant be2 be3, knock-out of two genetically independent isoforms of starch branching enzyme  (Dumez et al.
CressExpress computes patterns of correlated expression between query genes and the rest of the genes in the genome  (Srinivasasainagendra et al. , 2008). The co-variation analysis was carried out on the data release 3. 0 with all available samples, and with CressExpress default settings. Finally, Cytoscape software package (http://www. cytoscape. org/) was used to perform a graphical representation of the network  (Cline et al. , 2007). Highly interconnected modules were identified by AllegroMCODE (http://allegroviva. com/allegromcode/), a highperformance MCODE cluster finder for Cytoscape  (Saito et al. , 2012). All genes with at least two neighbors were represented in the final SSR network.
IN SILICO ANALYSIS OF CIS-REGULATORY ELEMENTS ATHENA (http://www. bioinformatics2. wsu. edu/Athena) promoter sequence analysis tool  (O'Connor et al. , 2005)  was used to identify statistically over-represented (p < 10 -3 ) transcription factor binding sites occurring in the selected set of promoters (1000 bp upstream of the transcriptional start site). The PROMOMER tool (http://bar. utoronto. ca/ntools/cgi-bin/ BAR_Promomer. cgi) was also used to identify statistically overrepresented cis-elements in the promoters of the SSR genes  (Toufighi et al. , 2005). PROMOMER is a word-counting program that allows the identification of significantly over-represented motifs in a particular set of promoters compared with randomly generated promoter sets from the genome  (Toufighi et al. , 2005). This tool uses the Boyer-Moore algorithm for matching and counting n-mers in a set of sequences  (Boyer and Moore, 1977).
STATISTICAL ANALYSIS OF MICROARRAY DATA Normalization and statistical analysis was based on three dye swaps (i. e. , six arrays, each containing 24,576 GSTs and 384 controls) as described in  Gagnot et al. (2008). To determine differentially expressed genes, we performed a paired t-test on the log ratios, assuming that the variance of the log ratios was the same for all genes. Spots displaying extreme variance (too small or too large) were excluded. The raw p-values were adjusted by the Bonferroni method  (Ge et al. , 2003). We considered as being differentially expressed the genes with a Bonferroni p ≤ 0. 05, as described in  Gagnot et al. (2008). Microarray data were deposited at Gene Expression Omnibus (http://www. ncbi. nlm. nih. gov/ geo/), accession number GSE20964, and at CATdb (http://urgv.
Interestingly, three of the highly connected genes encode for plant-specific regulatory proteins with unknown metabolic function: a protein kinase (STY46), a transcription factor (UPBEAT1) and a blue light receptor (PLPB). All these regulator genes were overexpressed in the starch-deficient mutant lines (Supplemental Data Sheet 1). The gene encoding for the isovaleryl-CoA-dehydrogenase (IVD, At3g45300) is the only candidate regulatory gene that belongs to the starting SSR gene set. The SSR network was searched for modules, defined as a group of densely connected nodes (genes) that have a sparsely connected periphery. Two highly interconnected modules were clearly identified by AllegroMCODE (Supplemental Image Remarkably, the protein kinase STY46 tightly connects these two modules (Supplemental Data Sheet 1).
Only 23% of genes (  783 ) showed an altered expression pattern at both times of sampling, but the majority, 77% (2549), were modulated either at dawn or at dusk, strongly suggesting a circadian-regulated expression (Figure  2B ). Microarrays data from the three starch-deficient mutant lines were cross-examined with the SSR gene network. Two hundred and twenty genes of the network (27%) (Figure  3A ) were overexpressed at dawn in at least one of the mutants (Supplemental Data Sheet 1), and 74 of these genes were overexpressed in the three mutant lines (Figure  3B ). At this time point, starch-deficient plants suffer from sugar starvation as a consequence of the absence of photosynthesis during the dark phase. Interestingly, the expression of SSR genes were nearly not modified at dusk, when plants are not under starvation.