dataesr/scientific-paragraphs-categorization huggingface dataset:
(2011)  and  Lock et al. (2000)  are also used. The parameterization of convection processes is implemented in the CTMs by using different adaptations of the  Tiedtke (1989) ,  Zhang and McFarlane (1995) ,  Bechtold et al. (2008) ,  Rasch and Kristjánsson (1998)  and  Arakawa and Schubert (1974)  schemes. The CTMs used in this experiment are distinguished both by the reanalysis/analysis fields used to drive the CTMs and by their own characteristics (for example, resolution and parameterization schemes). As a result, this experiment studies the impact on methane fluxes of these two contributions together (errors in meteorological drivers and errors in the models themselves) with no real possibility to separate them. Five additional model output datasets (AC-CESS/Australian Community Climate and Earth System Simulator,  Corbin and Law, 2011 ; CAM/Community Atmosphere Model,  Gent et al.
, 2010; CCAM/CSIRO Conformal-Cubic Atmospheric Model, Law et al. , 2006; GEOS-Chem 3-D chemistry transport model, Fraser et al. , 2011; Pickett-Heaps et al. , 2011  and NIES/National Institute for Environmental Studies 3-D chemical transport model,  Belikov et al. , 2011 Belikov et al. , , 2013) )  are available in the TransCom database. Unfortunately, some specific characteristics of these simulations make them unsuitable for our study. Indeed, we have chosen only to focus on the model and forcing errors and neglect all other sources of error on the estimated fluxes. For example, the different OH distribution used in GEOS-Chem simulation influences the chemical sink and may lead to misinterpretation of the impact of transport model errors on the estimated fluxes. One can expect the same issue with CAM, CCAM and NIES since the total atmospheric burden of methane in these three models differs significantly from the atmospheric burden of LMDZ-SACS.
Errors in Globalview-CH 4 are computed at each site as the residual standard deviation (RSD) of the measurements on a smooth curve fitting them. We use the RSD at each site as a proxy of the transport model errors, assuming that the measurement sites with a lot of variability around the mean (e. g. continental sites) are more difficult to model, especially for coarse global models  (Geels et al. , 2007). This simple approach has been used previously in atmospheric inversions  (Bousquet et al. , 2006; Yver et al. , 2011; Rödenbeck et al. , 2003). Errors at stations where Globalview-CH 4 data were not available have been interpolated from stations presenting the same characteristics (background/polluted, Northern/Southern Hemisphere, coastal/continental). A detailed discussion on the specification of the R matrix takes place in Sect.
The synthetic observation datasets The model outputs from the TransCom-CH 4 database are available at selected sites from the most widespread surface networks: Advanced Global Atmospheric Gases Experiment (AGAGE; http://agage. eas. gatech. edu; Prinn et al. , 2000) , the NOAA Earth Research Laboratory, Global Monitoring Division (ttp://www. esrl. noaa. gov/gmd) and the Japan Meteorological Agency (http://www. jma. go. jp/jma/indexe. html). The synthetic observations are output at the locations shown in Fig. 2 , comprising 166 surface stations (including 29 mobile measurements on ships), 6 vertical profiles (aircraft) and 12 tall towers. Some of these sites are continuous in situ stations (red-filled circles). In some other locations, flask   samples are collected on a weekly basis (empty red circles).
Sensitivity to the measurement network The fluxes inferred by atmospheric inversions are sensitive to the location and density of observations used to constrain them. We have used two other networks (NET2 and NET3) to assess the sensitivity of the modelling and transport errors on estimated fluxes to the network. NET2 assumes a mix of flasks and continuous data similar to the current situation. NET3 adds tower and aircraft measurements to NET2 (Sect. 2. 4). Using NET2, the inverse system derives lower global estimates compared to emissions derived using the NET1 configuration for all synthetic datasets, except for IFS (Fig. 14 ). For instance, fluxes derived using ACTM, IMPACT and TM5 drop to 523, 525 and 538 Tg yr -1 in 2005, compared to 533, 534 and 545 Tg yr -1 respectively in the case of NET1.
Conclusions A modelling exercise, based on a variational inversion, is presented to quantify the impact of transport model errors on methane emission estimates derived by inverse modelling. Synthetic observations are created from 10 CTMs that participated in the TransCom-CH 4 experiment and are used to constrain one atmospheric inversion framework with a common set-up. Therefore, inversions are only distinguished by the different simulated atmospheric transport, generating dif-ferent synthetic observational datasets. The spread between the inverted emission estimates shows that transport forcing and model errors have a significant impact on CH 4 flux estimates. The range in global fluxes derived in the inversions is 5 % (27 Tg yr -1 ). Ranges of 23 to 48 % are obtained in the fluxes derived at regional scales, and up to 150 % at the model grid-scale.
Acknowledgements. This work is supported by  DGA  ( Direction Générale de l'Armement ) and by  CEA  ( Centre à l'Energie Atomique et aux Energies Alternatives ). The research leading to the IFS results has received funding from the  European Community 's  Seventh Framework Programme  ( FP7 THEME [SPA. 2011. 1. 5-02 ])under grant agreement n. 283576 in the context of the  MACC-II  project ( Monitoring Atmospheric Composition and Climate -Interim Implementation ). The contribution by the LLNL authors was prepared under Contract  DE-AC52-07NA27344 , with different parts supported by the  IMPACTS  project funded by the  US DOE (BER)  and project ( 07-ERD-064 ) funded by the  LDRD program  at  LLNL. The TRANSCOM community is to be thanked for sustained efforts on transport model studies since 1993.
5% (as observed in a parallel Phase III study involving 200 patients)  72  to 55. 3%. 73 Nowadays, one single Phase II clinical trial is ongoing to test the safety and preliminary efficacy of digoxin, employed as a standalone agent, in subjects affected by recurrent prostate carcinoma (NCT01162135). Two additional Phase I studies on the use of digoxin by cancer patients are registered at www. clinicaltrials. gov (NCT00650910, status: completed; NCT01517399, status: recruiting), yet mainly aim at investigating putative pharmacokinetic interactions between digoxin and antineoplastic agents (i. e. , the dual tyrosine kinase inhibitor lapatinib and c-MET inhibitor tivantinib) sensu stricto. Anvirzel TM is a Nerium oleander extract containing the two CGs oleandrin and oleandrigenin that exerts antineoplastic effects of nascent hormone-sensitive malignancies.
Prospective Studies Preclinical and epidemiological data suggested that CGs might be employed as antineoplastic agents, at least in a subset of cancer patients. 66 In spite of these encouraging premises, compelling evidence from randomized prospective clinical studies that would support this notion is still missing (Table  2 ). In a Phase II clinical trial, digoxin failed to increase the response rate of 24 unresectable Stage III/IV NSCLC patients to the epidermal growth factor receptor (EGFR) inhibitor erlotinib  67, 68  employed as a second-line therapy (NCT00281021). In particular, 1 patient manifested a partial response, 9 exhibited stable disease and 14 progressed in spite of chemotherapy, overall leading to the premature termination of the study. This said, the co-administration of digoxin appeared not to increase the rate (2) render immunogenic the demise of cancer cells as triggered by conventional chemotherapy.
Disclosure of Potential Conflicts of Interest No potential conflicts of interest were disclosed. against human melanoma BRO cells in vitro. 36 Patients affected by advanced solid tumors failed to exhibit objective responses to Anvirzel TM in a Phase I trial, though the preparation was shown to be well tolerated at daily doses < 1. 2 mL/m 2. 74 The safety profile and antineoplastic potential of Anvirzel TM , combined with the DNA-damaging agent carboplatin and the microtubular poison docetaxel, are currently being evaluated in a Phase I trial enrolling advanced NSCLC patients (NCT01562301). Along similar lines, the tolerability of a more concentrated Nerium oleander extract (PBI-05204) is under evaluation in a Phase I study that involves individuals bearing advanced solid malignancies (NCT00554268).
77 More recently, the safety profile and therapeutic potential of HuaChanSu, combined with the nucleoside analog gemcitabine (which per se does not promote ICD), have been investigated in a Phase II clinical trial enrolling 80 individuals affected by locally advanced or metastatic pancreatic cancer (NCT00837239). In this setting, HuaChanSu was well tolerated but failed to improve objective radiographic response rates, time to progression, quality of life and overall survival. 78.
Acknowledgments Authors are supported by the  European Commission (ArtForce) ;  Agence National de la Recherche (ANR) ;  Ligue contre le Cancer (Equipe labellisée) ;  Fondation pour la Recherche Médicale (FRM) ;  Institut National du Cancer (INCa) ;  LabEx Immuno-Oncologie ;  Fondation de France ;  Fondation Bettencourt-Schueller ; AXA Chair for Longevity Research;  Cancéropôle Ile-de-France and Paris Alliance of Cancer Research Institutes (PACRI).
a. Combined with erlotinib Failure to increase overall response rate NCT00281021 Prostate cancer 16 II Recruiting 0. 125 or 0. 25 mg/day As single agent Primary outcome: rate of positive PSADT outcomes NCT01162135 Combined with Solid tumors 30 I Recruiting 0. 25 mg/day caffeine, midazolam, omeprazole, s-war-farin, vitamin K and Primary outcome: pharmacokinetics NCT01517399 tivantinib HuaChanSu HCC NSCLC Pancreatic cancer 15 I n. a. 10-90 mL/m 2 /day As single agent Absence of DLTs disease stabilization Six patients experienced 77 Pancreatic cancer 80 II Completed 20 mL/m 2 / day Combined with gemcitabine Primary outcome: PFS at 4 mo NCT00837239 DLT, dose-limiting toxicity; HCC, hepatocellular carcinoma; IFN, interferon; IL, interleukin; MTD, maximum tolerated dose; n. a. , not available; PSADT, prostate-specific antigen (PSA) doubling time; NSCLC, non-small cell lung carcinoma; PFS, progression-free survival.
High-throughput sequencing and bioinformatics Input and ChIPed DNAs were sequenced on an Illumina Hi-seq 2000 instrument (Fasteris, Switzerland) using the TruSeq TM SBS v3 kit. The sequences produced from the input chromatin and H4R3me2s ChIPed DNA from ES and MEF cells are detailed in the Supplementary Table  S3. ChIP-seq raw data (fastq) were aligned on the M. musculus reference genome (mm9) using the Bowie 0. 12. 7 software. Smoothed tag density compared to input tag density were computed with the Sequence processing pipeline  (20)  and visualized with the integrative Genomics Viewer  (21). Median tag density around the defined peak positions were calculated using an in-house designed R-script. Briefly, peaks' summits coordinates were grouped according to the level of enrichment for the mark examined and median tag densities were calculated every 100 bp across 5 kb windows around the peaks' summits.
Heatmaps of H4R3me2s were clustered and represented using the seqMINER 1. 3. 3 program  (22). H3K4me3 and H3K4me1 peaks generated by Dr Bing Ren's laboratory were downloaded from the UCSC genome browser (http://genome. ucsc. edu/cgi-bin/hgFileUi?db=mm9&g= wgEncodeLicrHistone). Peak predictions for H4R3me2s and PRMT5 in the two cell types were performed with the Model-based Analysis for ChIP-seq (MACS) 1. 4. 2 algorithm with the default parameters (P < 1e -5 )  (23). 100-bp windows with the same H4R3me2s enrichment in MEF and ES cells were grouped together and represented as a density array using the hexbin package. The genome-wide G+C frequency was calculated using the LetterFrequency InSlidingView function from the Biostrings package (http://www. bioconductor. org/packages/2. 11/bioc/html/ Biostrings.
html) in R 2. 13. 0 (http://www. R-project. org/). The MeDIP and hMeDIP-seq in ES cells were from Ficz et al. (24)  (http://www. ebi. ac. uk/ena/data/view/ PRJEB2462). Published ChIP-seq profiles for H3K4me3, H3K27me3 and RNA-seq from ES cells cultivated in 2i serum-free medium  (25)  were retrieved from the GEO database (GSE23943). Genomic coordinates for repeat elements were retrieved from the RepeatMasker database (http://www. repeatmasker. org/).
Thus, gene promoters were generally marked independently of their levels of expression. Concordantly, H4R3me2s co-localized with H3K4me3-enriched regions in the genome, which are mostly confined to active promoters (Encode/LICR dataset with the UCSC reference number wgEncodeEM001455) (Figure  3D ). In contrast, no colocalization was observed with H3K4me1-enriched regions (Figure  3D ), which include enhancers, nor with H3K9me3-marked regions (data not shown), which include heterochromatin and stably repressed genes. Examples of the lack of correlation between transcriptional activity and H4R3me2s enrichment are provided by the Oct4 (Pou5f1) and Nanog genes, which are expressed in undifferentiated ES cells but not in MEFs (Supplementary Figure  S2G-H ). In both the cell types, however, the promoters of these pluripotency genes were strongly marked by H4R3me2s.
m. domesticus strain C57BL6 and M. m. molussinus strain JF1. For this allele-specific ChIP assay, we generated larger native chromatin fragments of 2-7 nucleosomes in length. Single nucleotide polymorphisms (SNPs) allowed us to directly distinguish the parental chromosomes in the precipitated chromatin fractions (see Supplementary Table  S2 ). At four different ICRs analysed, PCR amplification of ChIPed DNA followed by DNA sequencing clearly showed that H4R3me2s was precipitated on the DNAmethylated allele predominantly. The opposite, unmethylated allele was largely devoid of H4R3me2s (Figure  4G ). These results were confirmed by PCR amplification followed by electrophoretic detection of singlestrand conformation polymorphisms (SSCPs) (data not shown). We conclude from these data that H4R3me2s marks the DNA-methylated alleles of ICRs in MEFs.
Darker shades of grey indicate higher densities of bins as reported on the count scale. MEFs, and a marked reduction was observed at IAP elements as well (Figure  5D ). However, imprinted gene expression was retained in the Prmt5 knock-down MEFs (Supplementary Figure  S3A ), arguing against a role of H4R3me2s in the allelic repression of imprinted genes. We conclude from these data that PRMT5 is recruited to the DNA methylated allele of ICRs on which it methylates H4R3me2s. Next, we asked whether a PRMT5 partner protein, called COPR5, previously reported to recruit PRMT5 to the Cyclin E1 promoter  (34) , could recruit PRMT5 onto the chromatin of ICRs as well. We found that in newly derived Copr5 knock-out MEF cells (E. Fabbrizio, unpublished results), the overall PRMT5 levels were decreased in the nucleus.
ACKNOWLEDGEMENTS We thank members of our teams for discussion and helpful comments, and  Thomas Jenuwein  and  Gunnar Schotta  for kindly providing the Suv420h1/h2 doubleknockout cells. FUNDING  Agence Nationale de la Recherche (ANR) , the  Institut National Contre le Cancer (INCa) ,  La Ligue Contre le Cancer and the Agency for International Cancer Research (AICR, UK) ; We acknowledge post-doctoral fellowships from the  Japanese Society for the Promotion of Science (JSPS)  (to R. H. ), the  'Ligue Contre le Cancer' (France)  (to M. G. ) and the  University of Montpellier-I  (to M. G. ); affiliated to the  European network EpiGeneSys  and the  LABEX EpiGenMed  (to R. F. ). Funding for open access charge:  INCA (French National Cancer Institute ).
Cases and deaths Cholera-associated morbidity and mortality data were prospectively and anonymously collected by the Departmental Health Directorates at the commune level. Departmental databases were sent to the Haitian Directorate of Health (Laboratoire National de SantePublique, LNSP), where data were gathered and analyzed after quality control. According to the WHO standard definition  [17] , a probable cholera case was defined as profuse acute watery diarrhea with severe dehydration. Bacteriological confirmation of cases was recurrently performed at the LNSP for samples collected throughout the entire country using standard methods  [18]. The in-hospital case fatality rate (ICFR) was defined as the ratio of cumulative number of deaths reported at Cholera Treatment Centers (CTCs) to cumulative number of hospitalized cases (severe cases).
The case fatality rate (CFR) was defined as the ratio of cumulative number of in-hospital deaths to cumulative number of cases (reported at any health structure). As some communes lacked proper health facilities, some cholera patients had to travel to health structures of the nearest commune. To avoid overestimating case numbers in such locations and underestimating case numbers in surrounding areas, the data derived from these neighboring communes were aggregated after interviewing local health actors and analyzing local reports. In this study, we did not include personal medical data but included the number of incident cases anonymously reported at each health facility. This study was approved by the Haitian Ministry of Public Health and Population (Ministere de la Sante Ṕublique et de la Population).
Methods First, the mapping of global attack rates, mortality rates, ICFRs and CFRs observed between October 16, 2010 and October 15, 2011 was performed to assess the spatial distribution of the epidemic. Spatial autocorrelation was estimated using Moran's I statistic for areal data  [19]. Second, temporal observations for the entire country were assessed to define epidemic phases and trends. Phases were specified using main slope changes in time series after mobile average (MA) smoothing (order two). The accuracy of this phase specification was then assessed by using sensitive analysis of the MA order, concordance with the wavelet analysis (see above), and the field expertise of the Haitian epidemiologists. For each epidemic phase, communal daily incidence rates (DIRs) were mapped, and spatial clustering was assessed using Kulldorff statistic  [20].
This method provides a multiscale analysis extracting the main evolution and trends of time series at different temporal scales and has been previously utilized to study cholera outbreaks  [26] ,  [27]. The relationship between cases and rainfall time series was assessed via cross-spectrum analysis  [28]. Daily accumulated rainfall data were obtained from NASA Goddard Earth Sciences. These observations (TMPA-RT 3B42RT) were derived from the Tropical Rainfall Measuring Mission (see http://disc. sci. gsfc. nasa. gov/giovanni/overview/index. html for details). For each epidemic phase at the commune level, we also examined the relationship between cases and the following land cover surface factors: plains, mountains and hills, urban zones, rice fields, length of perennial rivers (10 km), area (km 2 ), and number of watersheds, which were.
Because of the over-dispersion of cholera incidences, several models of the Negative Binomial and the Poisson families  [32]  were first graphically verified to meet the conditions of use and then compared using the Generalized Cross-Validation (GCV) score and the Un-Biased Risk Estimator (UBRE) score  [30]. The stepwise selection of variables was performed using the GCV and UBRE scores. The explained deviance was also verified for model goodness-of-fit assessment. The SIRs and corresponding 95% confidence intervals (95CI) were estimated using the final selected model and tested. Spatial cluster analyses were performed using SaTScanH v8. 2. 1 (Martin Kulldorff, Harvard Medical School, Boston, MA, USA and Information Management Services Inc, Silver Spring, MD, USA). Wavelet spectrum analyses were performed using MatlabH v7.
1 (The Mathworks Inc. , Natick, MA, USA). The other statistical analyses were performed using RH v2. 13. 0 (The R Foundation for Statistical Computing, Vienna, Austria) with mgcv package (GAM modeling), the DCluster package (spatial analysis), and the cluster package (HCA). The p-values were compared with the probability threshold a = 0. 05. The maps were generated using Quantum-GISH v1. 7. 3 (Open Source Geospatial Foundation Project, Beaverton, OR, USA).
Crystal data D-HÁ Á ÁA D -H HÁ Á ÁA D Á Á ÁA D -HÁ Á ÁA N1-HN1Á Á ÁO1 0. 86 (2) 2. 38 (2) 3. 066 (2) 138 (2) N1-HN1Á Á ÁO1 i 0. 86 (2) 2. 38 (2) 3. 066 (2) 138 (2) N1-HN2Á Á ÁO2 ii 0. 89 (1) 2. 41 (1) 3. 109 (4) 135 (1) N1-HN2Á Á ÁO2 iii 0. 89 (1) 2. 41 (1) 3. 109 (4) 135 (1) N1-HN2Á Á ÁO2 iv 0. 89 (1) 2. 41 (1) 3. 109 (4) 135 (1) N1-HN2Á Á ÁO2 v 0. 89 (1) 2. 41 (1) 3. 109 (4) 135 (1) Symmetry codes: (i) Àx þ y À 1; Àx; z; (ii) x À y; x; z À 1 2 ; (iii) Àx À 1; Ày; z À 1 2 ; (iv) y À 1; Àx þ y À 1; z À 1 2 ; (v) y À 1; x; z À 1 2. Data collection: COLLECT (Nonius, 1998); cell refinement: DIRAX/LSQ  (Duisenberg, 1992) ; data reduction: EVALCCD  (Duisenberg, 1998) ; program(s) used to solve structure: SIR97  (Altomare et al. , 1999) ; program(s) used to refine structure: SHELXL97  (Sheldrick, 2008) ; molecular graphics: DIAMOND  (Brandenburg, 2005) ; software used to prepare material for publication: WinGX  (Farrugia, 2012).
Computing details Data collection: COLLECT (Nonius, 1998); cell refinement: DIRAX/LSQ  (Duisenberg, 1992) ; data reduction: EVALCCD  (Duisenberg, 1998) ; program(s) used to solve structure: SIR97  (Altomare et al. , 1999) ; program(s) used to refine structure: SHELXL97  (Sheldrick, 2008) ; molecular graphics: DIAMOND  (Brandenburg, 2005) ; software used to prepare material for publication: WinGX  (Farrugia, 2012).
25 mL H20 were added to each well. After brief centrifugation, the PCR plate was subjected to thermocycling for 40 cycles with PCR activation at 95uC for 2 min, denaturation at 95uC for 3 seconds, and annealing/ extension at 60uC for 20 s. All samples and controls were run in triplicates. Quantitative RT-PCR data were analyzed by the comparative cycle number threshold method. Results are shown as the ratio of the reference gene to target gene by using the formula: DCt = Ct (target genes) 2 Ct (18S). To determine the relative expression levels, we used: DDCt = DCt (FSHD) -DCt (Control). The n-fold difference was determined by the expression 2 2DDCt.
RNA Isolation and Microarray Total RNA was isolated from myotubes derived from different cell lines for DYSF (n = 4), CAV3 (n = 4), FSHD (n = 4), FHL1 (n = 3), and healthy controls (n = 7) by using the standard TRIzol reagent (Invitrogen, Darmstadt, Germany) according to the manufacturer's instructions. cDNAs were synthesized using the Ambion's WT expression kit (http://www. ambion. com/). Fragmentation and labeling was done by using the standard Affymetrix protocol. Fragmented cDNA was hybridized for 16 h at 45uC (Gene Chip hybridization oven 640) to the human exon array. Arrays were washed and stained with streptavidin-phycoerythrin in the Affymetrix Fluidics Station 450 and further scanned using the AFFYMETRIX GeneChip Scanner 3000 7G (http://www. affymetrix. com). The image data were analyzed with the Gene Chip command console using Affymetrix default analysis settings.
Analysis of Expression Data Arrays were quantile-normalized with respect to the probe GC content using the Robust Multi-array Average (RMA) algorithm (GC content adjustment, RMA background correction and mean probe set summarization). No or low expressed transcripts (max. native signal ,100) were removed. Data filtering led to a set of 25,271 meta-probe sets. Differential expression was tested by using ANOVA followed by controlling the false discovery rate (FDR) according to Benjamini et al. [21]. We found 59 differentially expressed probe sets between the five sample groups (5% FDR). The data discussed in this publication have been deposited in NCBI's Gene Expression Omnibus  [22]  and are accessible through GEO series accession number GSE44874.