dataesr/scientific-paragraphs-categorization huggingface dataset:
1 ), there is evidence that fracture zones F6 and/or F7 may have ruptured during the events. Many of the published earthquake models show rupture on multiple NNE-SSW-and WNW-ESE-trending faults (Fig. 1A ). However, there is no evidence for WNW-ESE faults at the seafloor in our data (Fig. 1 ). This is most likely because the earthquake rupture on these WNW-ESE faults did not propagate upwards into the overlying sediments, although we acknowledge that the marine geophysical data were collected before the 2012 events. The main structures described here yield a single coherent model of strain in the Wharton Basin (Fig. 1F ). Relative plate motions generate northwest-southeast-oriented P axes, compatible with the left-lateral reactivation of approximately north-south-trending fracture zones (e.
ACKNOWLEDGMENTS This research was supported by a  Marie Curie Intra European Fellowship  within the 7th European Community Framework Programme and the  Natural Environment Research Council  ( NE/D004381/1 ). R/V Sonne cruise SO186-2 was funded by grant  03G0186A  (SeaCause) of the  Federal Ministry of Education and Research (BMBF) ,  Germany. We thank  Jérôme Dyment  and two anonymous reviewers for their careful reviews that helped us to clarify different aspects of this study.
Computational Details Three different programs were used in the present work; namely the DFTB+ program  [64]  for DFTB calculations, GAUSSIAN09  [65]  for Hartree-Fock (HF), DFT and OVGF calculations and the post-SIESTA  [66]  program MBPT_LCAO  [19, 67]  for GW calculations. The DFTB calculations are entirely internally consistent in the sense that DFTB calculations were carried out at geometries optimized at the same level of the DFTB methodology. Vibrational frequencies were calculated to verify true minima by the absence of imaginary vibrational frequencies. Many of the other calculations are also completely internally consistent in the sense that the same basic method (and basis set) was used for geometry optimizations as for subsequent property calculations. However, some of our calculations are property calculations using one method at a geometry optimized with another basis set.
The 3ob set has been optimized at the DFTB3 level for bio and organic molecules containing carbon, hydrogen, nitrogen and oxygen  [70]. It also contains additional parameters for sulfur and phosphorus  [71] , which, however, were not needed in this study. Clearly, any conclusions of our assessment of DFTB are specific to this parameter set. We noticed that some molecules with degenerate orbitals would not converge without the use of fractional occupation numbers. For these molecules, convergence was obtained by using a Fermi orbital occupancy distribution with temperatures in the range 10-40 K. GAUSSIAN09 Several types of calculations were carried out using the GAUSSIAN09 program  [65] , namely DFT (LDA and B3LYP), HF and OVGF calculations. Two different sets of HF calculations were carried out.
For this reason, our OVGF calculations should be considered as indicative, but certainly not conclusive, as we do not believe they are well converged with respect to the orbital basis set used. MBPT_LCAO Our GW calculations were carried out with the recently-developed post SIESTA  [66]  MBPT_LCAO code  [19, 67]  at the B3LYP/6-31G** optimized geometries previously optimized with GAUSSIAN09. The SIESTA part of the calculation used the norm-conserving Trouillier-Martins-type pseudopotential together with a double-zeta polarized numerical atomic orbital basis set. Computational parameters were chosen in a uniform manner to be the same for all molecules, in such a way that calculations for the most computationally-demanding molecule (PCBM) still fit into the random-access memory (RAM). Even so, our PCBM calculation required nine days and 8 h on an Intel 12 core/190-GB RAM machine (model name: Intel(R) Xeon(R) 2.
Benchmark Datasets The identification and tabulation of reliable comparison data are key parts of the test cycle part of the development and successful implementation of a theoretical model for practical applications. The very important and, indeed essential, work involved in establishing such "benchmark datasets" (BDSs) can be substantial and relatively thankless, as it may be regarded primarily as "literature work", rather than as "new science. " An especially well-known example of such a test set is Charlette E. Moore's tables of atomic spectra, now freely available from the American National Institute of Standards and Technology (NIST, [81]), which has proven essential in testing different approaches to quantum chemistry. For small molecules, experimental and quantum chemistry ionization potentials (IPs) are readily available from the NIST Computational Chemistry Comparison and Benchmark Database (CCCBDB,  [82] ).
Fitting data for first EAs of the medium-sized benchmark dataset (BDS) molecules: y = mx + b, where x is the GW EA and y is another theoretical EA, both in eV. HF, Hartree-Fock; OVGF, outer valence Green's function; LDA, local density approximation; DFTB, density-functional tight binding (without self-consistent charge); SCC-DFTB, self-consistent charge DFTB.
Acknowledgments This work is supported, in part, by the  ORGAVOLT (ORGAnic solar cell VOLTage by numerical computation ) Grant  ANR-12-MONU-0014-02  of the  French Agence Nationale de la Recherche  ( ANR) 2012 Programme Modèles Numériques. Mathias P. Ljungberg ,  Peter Koval  and  Daniel Sánchez-Portal  acknowledge support from the  Deutsche Forschungsgemeinschaft (DFG)  through the  SFB (Sonderforschungsbereich) 1083 project , the  Spanish MINECO (Ministerio de Economía y Competitividad )  MAT2013-46593-C6-2-P project  and the  Euroregion Aquitaine-Euskadi program  and support from the  Basque Departamento de Educación, UPV/EHU (University of the Basque Country)  (Grant No. IT-756-13 ). Peter Koval acknowledges financial support from the  Fellows Gipuzkoa program  of the  Gipuzkoako Foru Aldundia through the FEDER funding scheme of the European Union.
MeDIP-chip We performed MeDIP on 200 ng genomic DNA prepared from WT and Ehmt2 -/-littermate embryos collected at E9. 5. Precipitates from three replicates of MeDIP and input samples were hybridized to Roche Nimblegen mm9 HD2 2. 1M deluxe promoter arrays covering -8200 to +3000 bp from 23,517 TSSs. Sample preparation and bioinformatic treatment of microarray data were performed as previously described  (Borgel et al. 2010). For data representation, we smoothed log 2 ratios over 200-bp windows using the Ringo R package  (Toedling et al. 2007).
RNA-seq We performed RNA-seq on single embryos for two WT and two Ehmt2 -/-littermate embryos collected at E8. 5. Total RNAs were extracted with the RNeasy protect mini kit (Qiagen). RNA-seq libraries were prepared with the Ovation RNA-seq System V2 (NuGEN) and sequenced on a HiSeq 2500 (1 × 50 bp). Raw sequences were aligned to the mouse mm10 genome by TopHat (v2. 0. 13, default parameters plus -no-coverage-search -library-type fr-firststrand)  (Kim et al. 2013 ) using a transcriptome index built from RefSeq mm10. Browser tracks in bigWig format were generated with bam2wig. py from the RSeQC package (v2. 4, parameters -u -t 5000000000)  (Wang et al. 2012 ) and visualized with the IGV browser. Read counts in genes were computed with htseq-count (v0. 6. 0, parameters -t exon -s no)  (Anders et al.
2015 ) using a RefSeq mm10 GTF file. We tested for differential gene expression using DESeq2 (v1. 4. 5)  (Love et al. 2014 ) and defined differentially expressed genes as having a fold change greater than three and an adjusted P-value <0. 01. Genes on the Y Chromosome were excluded. Normalized counts and FPKM scores were calculated with the "counts" and "fpkm" functions of DESeq2. For the comparison with genes up-regulated in Dnmt3b -/-embryos (GEO accession no. GSE60334)  (Auclair et al. 2014) , we selected genes up-regulated at least threefold with an adjusted P-value below 0. 01.
H3K9me2 ChIP-seq ChIP was performed as described above on three pools of eight embryos collected at E8. 5. ChIP-seq libraries were prepared with the NEXTflex ChIP-seq Kit (Bioo Scientific) and sequenced on a HiSeq 2500 (1 × 50 bp). Because of the broad distribution of H3K9me2, we merged the FASTQ files from the three experiments. Raw reads were aligned to the mm10 genome with Bowtie 2 (v2. 2. 4, default parameters)  (Langmead and Salzberg 2012). We flagged PCR duplicates with Picard MarkDuplicates (v1. 136) (http://broadinstitute. github. io/picard) and generated density tracks in WIG format with igvtools count (v2. 3. 3. 2, parameters -w 25 -e 300 -minMapQuality 30)  (Thorvaldsdottir et al. 2013).
Data analysis and characterization of HMRs Data processing and representation were performed with the R software (R Core Team 2015) using custom developed scripts. To represent the distribution of methylation over genes (Fig. 1F ), we calculated the average methylation in 20 equal-sized windows of each protein-coding RefSeq gene and in 10 1-kb windows of flanking sequences. To calculate methylation of TEs, we averaged the methylation scores of all the CpGs contained in annotated repeats with a size >400 bp. We used the eDMR algorithm from the methylKit R package  (Li et al. 2013)  to identify RRBS DMRs between WT and Ehmt2 -/-embryos with at least three CpGs, a difference in methylation >20%, and an adjusted P-value <0. 01. We annotated DMRs relative to exons, introns, and repeats using the UCSC RefSeq and Repeatmasker tracks (repeats with a size >400 bp).
DMRs were considered promoter-proximal when the center of the DMR is distant <1000 bp from a RefSeq TSS. We performed gene ontology and tissue expression analyses with DAVID  (Huang da et al. 2009). HMRs were compared with ChIP-seq data for EHMT2 in ESCs (GEO accession no. GSE46545)  (Mozzetta et al. 2014) , H3K9me1/2 in ESCs (GEO accession no. GSE54412)  (Liu et al. 2015) , H3K4me2 in ESCs (GEO accession no. GSE30203)  (Stadler et al. 2011), and H3K9me2 in E8. 5 embryos (this study). To generate the heatmaps, we counted the density of reads from the ChIP-seq WIG files in 200-bp windows and divided by the density of reads in the same windows in the corresponding input sample.
Acknowledgments We thank  Dr. Yoichi Shinkai  for the authorization to use Ehmt2 KO cell lines. We thank  Lauriane Fritsch ,  Julien Pontis , and  Slimane Ait-Si-Ali  for providing cell lines and sharing EHMT2 ChIP-seq data and for scientific discussions. We also thank the staff of the IGBMC high-throughput sequencing facility. This work was supported by the  Agence National de Recherche (ANR Blanc "EMPREINTE") , the  Institut National du Cancer  ( INCa_5960 ,  INCa_7889 ), the  Association pour la Recherche contre le Cancer  ( ARC SFI20101201555 ,  SFI20121205729 ), the  Fondation pour la Recherche Médicale  ( FRM DPM20121125544  and  DEQ20150331703 ), the  ITMO Cancer  ( EPIG201416 ), the  Agency for International Cancer Research (UK) , the  MEDDTL  ( 11-MRES-PNRPE-9-CVS-072 ), the  LABEX EpiGenMEd , the  ATIP-AVENIR program , the  EpiGeneSys Network of Excellence , and the  European Research Council  ( ERC Consolidator  grant no.
e. , retained Dyrk1a (A) Exons 5 and 6 were floxed in the targeted allele and excised in the conditional knockout (CKO) allele. (B) PCR from thymocyte genomic DNA was performed 2 wk after pI:pC treatment using the indicated primers in A (i and ii) and assessing the presence or loss of the targeted allele in Dyrk1a f/f Mx1-Cre  , Dyrk1a f/w Mx1-Cre + , and Dyrk1a f/f Mx1-Cre1 + ,mice. (C) Dyrk1a mRNA expression measured by qRT-PCR using primers within the excised gene segment in bone marrow and thymus for the indicated mice. (D) Western blot shows DYRK1A protein expression in bone marrow and thymus after loss of 0, 1, or 2 Dyrk1a alleles. Densitometry values were normalized to HSC 70. Loss of DYRK1A expression was verified for all subsequent experiments by qPCR, Western blot, or both. Data are derived from 1 litter and are representative of over 20 cohorts that were analyzed by RT-PCR and/or Western blot.
DYRK1A regulates expression of E2F target genes To gain insights into the mechanism by which Dyrk1a loss affects lymphopoiesis, we performed RNA-sequencing (RNA-seq) of multiple hematopoietic cell populations, including B cells, thymocytes, and mature granulocytes. We used fluorescenceactivated cell sorting (FACS) to isolate five highly purified populations for RNA-seq analysis: quiescent DP thymocytes, cycling DP thymocytes, quiescent small pre-B cells, cycling large pre-B cells, and mature granulocytes. We generated lists of transcripts that were up-or down-regulated in CKO versus control cells, and then performed gene functional enrichment analysis using DAVID bioinformatics resources  (Huang et al. , 2007). Among the lists of up-and down-regulated transcripts in each of the five cell populations, only two populations showed enrichment of cell cycle, proliferative, and mitotic related terms: up-regulated transcripts in small pre-B cells, and up-regulated transcripts in quiescent DP thymocytes.
In contrast, no significant enrichment of proliferative, mitotic, or cell cycle terms was observed in either the cycling cell populations or the granulocytes. Among the up-regulated transcripts specific to small pre-B cells and quiescent DP thymocytes, 177 were shared (Fig. 6 A ). In contrast, there were only 15 commonly down-regulated transcripts (Fig. 6 B ). DAVID analysis of the 177 shared upregulated transcripts revealed highly significant enrichments for terms associated with cell cycle, DNA replication, and mitosis. Furthermore, the top hit for known transcription  by staining for surface pre-BCR (Fig. 7 D ) or intracellular  heavy chain (Fig. 7 E ). Furthermore, we did not detect any changes in the activation of downstream Src family kinases or PDK-1 (activated via PI3K; Fig. 7 F ).
, 2014). Therefore, it remained possible that impaired differentiation and cell cycle exit in CKO cells might result from altered pre-BCR signaling. We did not detect any differences in pre-BCR expression at the large pre-B stage in CKO mice, either dilution in cultured FACS-purified Control and CKO large pre-B cells. Data are representative of two independent experiments. (F) Thymocytes from Control and CKO mice (Lck-Cre) were stained for DNA and RNA content to assess cell cycle status as in A. Representative flow cytometry plots for DP thymocytes from Control and CKO mice are shown; n = 4 mice per genotype, pooled from 2 independent cohorts of 2 mice per genotype. (G) Mean percentages of DP thymocytes in G0 are depicted; n = 4 mice per genotype, pooled from 2 independent cohorts of 2 mice per genotype.
Second, our preliminary work has shown that ALL cells undergo growth inhibition and apoptosis when treated with DYRK1 inhibitors (unpublished data). Third, loss-of-function mutations in DYRK1A have not been reported in ALL; we have directly sequenced the gene in 12 ALL cell lines and did not detect any mutations, and our searches of public databases failed to turn up any DYRK1A mutations or silencing in ALL. In fact, among 3,505 sequenced primary hematopoietic tumor samples in the COSMIC database, only two DYRK1A mutations were found; both were in chronic lymphocytic leukemia (CLL), a mature B cell neoplasm. SIFT and PolyPhen software predict that both mutations (L261R and R300P) could be deleterious to protein function, but we are unable to say definitively how they might affect DYRK1A-mediated Cyclin D3 turnover without functional studies.
First, we have not detected were filtered. The data were processed largely following the procedure described in  Trapnell et al. (2012). In brief, the reads were aligned to the Mus musculus genome (mm10) using TopHat (v2. 0. 8b). Subsequently, the aligned reads, in conjunction with a gene annotation file for mm10 obtained from the UCSC website, were used to determine the expression of known genes using Cufflinks (v2. 1. 1). The individual transcript files generated by Cufflinks for each sample were merged into a single gene annotation file, which was then used to calculate RPKM values. Transcripts with greater than 5 RPKM in either CKO or control conditions were kept, and the log 2 ratio (CKO:control) was calculated. Finally, transcripts with an absolute value of log 2 ≤ 0. 9 were removed.
Sequencing data are available in the Gene Expression Omnibus under accession no. GSE67052. Functional enrichment analysis. All mRNA transcripts classified as up and down-regulated mRNA from each of the five cell populations were examined independently using DAVID  (Huang et al. , 2007). Additionally, to compare sets, the top 47 up or down-regulated transcripts were submitted to DAVID for each population and enriched terms were compared (47 was used because it was the minimum number of transcripts that were expressed at log 2 > ±0. 9). qRT-PCR. Total RNA was isolated using RNeasy kits (QIAGEN) and reverse transcribed using SuperScript III First Strand Synthesis kits (Life Technologies). Real-time PCR was performed using PerfeCTa SYBR Green (Quanta Biosciences) and gene-specific primers. Data were quantified using the standard curve method.
The reference simulation is compared to data from the AirBase network and two ChArMEx stations, and from the AERONET network and the MODIS satellite instrument, to quantify the ability of the model to reproduce the observations. It is shown that the correlation ranges from 0. 19 to 0. 57 for surface particulate matter and from 0. 35 to 0. 75 for AOD. For the summer of 2012, the model shows that the region is mainly influenced by aerosols due to mineral dust and anthropogenic emissions (62 and 19 %, respectively, of total surface PM 10 and 17 and 52 % of total surface PM 2. 5 ). The western part of the Mediterranean is strongly influenced by mineral dust emissions (86 % for surface PM 10 and 44 % for PM 2. 5 ), while anthropogenic emissions dominate in the northern Mediterranean basin (up to 75 % for PM 2.
The purpose of our analysis is to estimate the relative contributions of this diversity of sources, and thus to draw a regional context of aerosol pollution during the summer of 2012 in the framework of the ChArMEx program. Aerosol optical properties are important to characterize aerosols for air pollution monitoring. They are needed to es-timate the radiative effects, which have themselves an impact on photochemistry by modifying the photolysis rates  (Mailler et al. , 2015). Moreover, optical properties are directly related to aerosol particle concentration, type and size. The observations of aerosol optical properties from remote sensing instruments thus provide an important complement to the surface PM databases. In particular, extensive measurements of the aerosol optical depth (AOD), characterizing the light extinction associated with the aerosols along the full vertical column, are provided by the surface AErosol RObotic NETwork (AERONET; http://aeronet.
Surface PM Surface PM concentrations from the model are evaluated through comparisons to PM 2. 5 and PM 10 observations from the European Air quality database (AirBase) of the European Environment Agency (http://acm. eionet. europa. eu/ databases/airbase). This database provides air quality monitoring data and information from 38 countries around The average concentration from 1 June to 31 August 2012 at the PM 10 stations is shown in Fig. 2 , with the number of days with data shown in Fig. 1. Almost all stations present data every day of the studied time period. Most of the stations are concentrated in the central north of the domain, so that the analysis over the Mediterranean Sea itself and around most of its coasts is not possible. The observations show that some regions are recurrently affected by pollution.
The estimated precision of the direct AOD measurements is ∼ 0. 01 (at 440 nm) for a recently calibrated instrument  (Holben et al. , 1998) , and the uncertainties are well documented  (Dubovik et al. , 2000). During the period of the simulation, data from 53 stations of the network are available within the domain, with values each day of the summer of 2012 (Fig. 1 ). Surface site measurements do not allow a full regional evaluation, particularly around the Mediterranean basin, which is poorly covered. Satellite observations therefore offer a good complement. MODIS is carried on board the Terra and Aqua satellites (NASA) and enables a daily nearglobal coverage of the Earth with its large swath of almost 2330 km. The satellites cross the equator at about 10:30 and 13:30 UTC (ascending node), respectively.
The measurements over 36 spectral bands (from 0. 41 to 15 µm) allow the retrieval of many aerosols properties. For this study, the aerosol products MOD04 (for Terra) and MYD04 (for Aqua) from L2 5. 2 collection data are used  (Remer et al. , 2006). It includes daily AOD at 550 nm, as well as the Deep-Blue AOD  (Sayer et al. , 2013 ) that provides AOD over bright land areas (especially useful here for North Africa). They are available at 10 km × 10 km resolution. The expected error for the AOD is ±0. 05 ± 0. 15 AOD over land and ±0. 03 ± 0. 05 AOD over ocean, with a good agreement with ground based measurements  (Remer et al. , 2005). The data coverage over the studied time period is shown in Fig. 1. The Mediterranean basin presents AOD values almost every day. For eastern Europe and the Balkans, fewer AERONET or AirBase stations are available.
The north of Europe is under-represented during the period, due to recurrent clouds in the area. The AOD at 550 nm from the AERONET and MODIS data sets have been compared over the studied region and time period. Both data sets are coherent in variability and levels (not shown): correlation coefficients range from 0. 71 in MED-Ea to 0. 96 in NEU-We and there is a slight overestimation of MODIS compared to AERONET measurements (mean fractional bias of 7 % on average). Spatial patterns of AOD over the region are similar to the ones observed in terms of surface PM concentrations (Fig. 2 , bottom right panel), with high values at the south of the domain (up to 2 for North Africa and all around the Mediterranean Sea on a seasonal average), the Po Valley and northern Europe (up to 1. 2). MODIS allows a better coverage than surface measurements in some regions, like North Africa, the Mediterranean Sea, the Atlantic Ocean, the Balkans and the eastern part of the domain (eastern Europe, Middle East, etc.
Modeling tools The sensitivity of aerosol concentrations in the region to each source is evaluated using the CHIMERE regional CTM, driven by meteorological simulations performed using the mesoscale non-hydrostatic Weather Research and Forecasting (WRF) model. The analysis is focused on the summer of 2012 (1 June to 31 August 2012), with a 10-day spin-up. The simulation domain is chosen to include all contributing sources around the basin (including North Africa until the Sahel, a part of the Atlantic Ocean, and eastern and northern Europe). A Lambert conformal projection is used with a constant horizontal resolution of 50 km × 50 km. The domain is represented in Fig. 5. This section introduces the models used, their configuration for this analysis, the emissions databases as well as the numerical approach used.
The CHIMERE chemistry-transport model CHIMERE is an off-line regional CTM forced by emissions (see Sect. 3. 3), meteorological fields (see Sect. 3. 1) and boundary conditions. The model version chimere2014  (Menut et al. , 2013)  is used here on the 50 km horizontal resolution grid described above. The vertical discretization consists in 18 uneven levels in hybrid sigma-pressure coordinates, from the surface up to 200 hPa and with 8 levels under 800 hPa. The reduced chemical mechanism called MELCHIOR2 is used here and includes 44 species involved in almost 120 reactions. The list of species and reactions is provided in  Menut et al. (2013). The aerosol module developed by  Bessagnet et al. (2004)  allows the simulation of primary particulate matter and secondary species: nitrates, sulfates, ammonium, primary organic matter (POM), secondary organic aerosol (SOA), elemental carbon (EC), marine aerosols and mineral dust.