dataesr/scientific-paragraphs-categorization huggingface dataset:
6); diluted 1:1,000) or anti-TAF2 þ anti-TAF10 (mAb 6TA-2B11 (ref. 6); diluted 1:1,000) antibody mix followed by incubation (RT, 1 h) with secondary antibody mix including Alexa488labelled goat anti-rabbit mAb (Life Technologies, catalogue number A-11034, diluted 1:3,000; detects anti-TAF2) and Alexa568-labelled goat anti-mouse mAb (Life Technologies, catalogue number A-11004, diluted 1:3,000; detects either anti-TAF8 or anti-TAF10). As negative control, cells were incubated with secondary antibodies only to quantify background signal. Cells were mounted using Vectashield mounting medium with DAPI (Vector laboratories Inc. ). Images were analysed on a Leica widefield fluorescence microscope (DMRXA2) equipped with a CoolSnap HQ camera ( Â 63 or Â 100 magnification). Fluorescence intensity measurements in the cell cytoplasm were performed using Fiji software; intensity values were normalized to background signals.
Specimen was adsorbed onto a thin layer of carbon deposited on an electron microscopy grid and negatively stained for 45 s with 2% of uranyl acetate. Particles were imaged using a transmission electron microscope (Tecnai F20 G2, FEI) equipped with a field emission gun operating at 200 kV. Images were recorded under low-dose condition (total dose of 40-50 e Å À 2 ) on a 2,048 Â 2,048 CCD camera (Ultrascan 1000, Gatan Inc. , Pleasanton) at a magnification of 50,000 resulting in a pixel spacing on the specimen of 0. 21 nm. Random conical tilt reconstructions. The initial reference volumes were obtained by random conical tilt using XMIPP  74  and IMAGIC  75  software packages. Two consecutive images of the same area were taken at 45°and 0°tilt angles under lowdose conditions. A total of 1,546 tilt pairs were selected manually using XMIPP.
Untilted images were aligned using iteratively refined two-dimensional class averages as references and multivariance statistical analysis and Hierarchical Ascendant Classification for clustering into 50 class averages with IMAGIC. Fifty volumes calculated from two-dimensional classes were aligned, clustered and averaged using XMIPP MLtomo to compensate for the missing cone, resulting in five random conical tilt (RCT) reconstructions. Structure refinement. The best volume was used as reference for refinement cycles using a data set of 35,145 untilted molecular images windowed with the Boxer application of the EMAN2 software package  76  and coarsened by two resulting in a pixel spacing of 4. 2 Å. Image sorting was found necessary to select the most homogeneous particles since part of the structure was flexible and prevented convergence.
Acknowledgements We thank the  ESRF-EMBL Joint Structural Biology Group  for access to ESRF beamlines and the  EMBL-ESRF-ILL-INS Partnership for Structural Biology  for access to structural biology instrumentation and protein analysis and characterization facilities. We are grateful to  Andrew Thompson  ( SOLEIL ) for support during data collection and structure solving. We also thank  Aurelian Deniaud  ( EMBL) Andres Palencia (EMBL)  for technical help with AUC, SPR and ITC experiments. S. T. was a  European Commission (EC)   Framework Program (FP) 7 Marie Curie post-doctoral fellow. I. B. is supported by the  EC FP7   ComplexINC  project (contract number  279039 ). C. S. is supported by a  European Research Council (ERC)   Starting Grant  and LT by an  ERC   Advanced grant  ( BIRTOACTION , grant number  340551 ).
I. B. , L. T. and P. S. acknowledge support from the  Agence National de la Recherche ANR Projet Blanc DiscoverIID  and of the  French Infrastructure for Integrated Structural Biology (FRISBI ;  ANR-10-INSB-05-01 ) and INSTRUCT as part of the  European Strategy Forum on Research Infrastructures (ESFRI). J. R. is a  Wellcome Trust Senior Research Fellow  (grant number  084229 ). The  Wellcome Trust   Centre for Cell Biology  is supported by core grant numbers  077707  and  092076 , and the work was also supported by  Wellcome Trust  instrument grant number  091020.
Wells containing either 100 μL of serum-free medium and wells with treatments but no cells were utilized as blanks. Following 24 h of incubation at 37°C, 10 μL of the alamarBlue viability reagent were added to each well. Plates were incubated for 5 h at 37°C, and absorbance was measured at 570 nm and 600 nm using an iMark microplate absorbance reader (Bio-Rad Laboratories Inc. ). Collected data were analyzed with the online ala-marBlue colorimetric calculator provided by the manufacturer, available through the following link: http://www. abdserotec. com/colorimetric-calculator-fluorometric-alamarblue. html. The results were expressed as the mean percentage of alamarBlue reduction of treated cells versus non-treated cells. The assay was performed 3 times.
Statistical analyses The data collected from the inhibition halos, the sonication assays and the cell viability tests were compared between pairs of groups using the Mann-Whitney U test. The data were represented as the mean ± standard error of the mean (SEM), and tables containing the mean data, SEM and percentiles (minimum, 25%, median, 75%, maximum) were reported. All the statistical analyses were performed using the GraphPad Prism 5 computer package (La Jolla, California, USA) for Windows. The significance level was set at p<0. 05.
This test is also topical, as there is a growing sample of dwarf galaxies with detected SMBHs  (Reines et al. 2013; Moran et al. 2014). Realistic numerical studies of BH formation and growth in these small galaxies, focusing on their occupation fraction and how they and their host galaxies evolve towards the correlation with the stellar velocity dispersion, would provide vital constraints on BH seed masses and early growth mechanisms  (Volonteri, Lodato & Natarajan 2008; Volonteri & Gnedin 2009; Volonteri 2010). We use the new N-body + SPH code CHANGA  (Menon et al. 2015) , which includes all of the physics modules previously implemented in GASOLINE  (Wadsley, Stadel & Quinn 2004 ) such as hydrodynamics, gas cooling, a cosmic UV background, star formation and SNe feedback. The 'zoomed-in' approach preserves the large-scale tidal field while allowing us to model a small region at high resolution.
AC K N OW L E D G E M E N T S FG and TQ were funded by  NSF  grant  AST-0908499. FG acknowledges support from  NSF  grant  AST-0607819  and  NASA ATP NNX08AG84G. MV acknowledges support from  SAO  award  TM1-12007X , from  NSF  award  AST 1107675  and from a  Marie Curie FP7-Reintegration-Grant  within the  7th European Community Framework Programme  ( PCIG10-GA-2011-303609 ). Some results were obtained using the analysis software PYNBODY (Pontzen et al. 2013). CHANGA was developed with support from  National Science Foundation   ITR grant   PHY-0205413  to the  University of Washington  and  NSF   ITR grant   NSF-0205611  to the  University of Illinois. Simulations were run using  NCSA Bluewaters and NASA Pleiades computers. Resources supporting this work were provided by  NSF   PRAC Award 1144357  and the  NASA High-End Computing (HEC) Program  through the  NASA Advanced Supercomputing (NAS) Division at Ames Research Center.
NOSVA All patients were continuously monitored with a reconstructed 12-lead EKG (using a continuous 6-lead cardiac monitoring, Dräger Infinity ® Acute Care System, Antony, France). EKG data were automatically recorded, digitally stored and checked daily for occurrence of NOSVA during the entire course of septic shock. SVA episodes lasting more than 30 s were assessed by two cardiologists (any discrepancy being solved by consensus) and classified as atrial fibrillation, atrial flutter or atypical flutter, using standard definitions  [15].
Statistical analysis The data were analyzed using SPSS Base 13. 0 (SPSS Inc, Chicago, IL, USA) and R 2. 15. 2 (The R Foundation for Statistical Computing, Vienna, Austria) statistical software packages. Continuous data were expressed as median [25th-75th percentiles], unless otherwise specified and were compared using the Mann-Whitney test for independent samples and the Friedman test for related samples. Categorical variables, expressed as percentages, were evaluated using the Chi-square test or Fisher exact test. To evaluate independent factors associated with NOSVA (time-dependent variable), significant or marginally significant univariate risk factors (p < 0. 10) recorded prior or at the time of the first episode of NOSVA, were examined using backward stepwise multivariate Cox proportional-hazards regression model; septic myocardial dysfunction and patient severity as assessed by SAPS II score at ICU admission were also included in the model; thus, the five variables included in the model were SAPS II score at ICU admission, age, septic myocardial dysfunction, renal and nonrenal SOFA scores at day 1 of septic shock.
Patients' characteristics and NOSVA Eighty-six patients were admitted in our unit with a diagnosis of septic shock during the study period. Among them, 21 patients were excluded because of cardiac surgery within the preceding month (n = 8), chronic heart failure (n = 6), history of permanent atrial fibrillation (n = 4) or logistic difficulties impeding proper data collection (n = 3). Thus, a total of 65 patients were included in the study, with 71 episodes of septic shock (six patients exhibited two distinct episodes of septic shock during the study period). Baseline characteristics of included patients are displayed in Table  1. SAPS II at ICU admission was 51 [39-68], and SOFA score at onset of septic shock was 10  [7] [8] [9] [10] [11] [12] [13] [14]. NOSVA occurred during 30 septic shock episodes (in 27 patients), defining a prevalence of 42 % (95 % CI 30-54 %).
nidulans. Guided by these results, and those of  Clutterbuck (1993) , pacX1 was mapped 8. 3 and 16 map units respectively between the argC3 translocation breakpoint and hisC38. Allelism with suA1palB7 and suD2palA1, also isolated by Dorn (http:// fgsc. net/Archive/nid. html), was confirmed by further genetic analysis (data not shown) and determination of mutant sequence changes (see below). Characterisation of the pacX mutant phenotype: by-passing pH signal transduction pacX1 was found to suppress loss-of-function mutations in each of the pal pH signal transduction genes, specifically, palA1, palB7, palC4, palF15, palH17 and palI30. Phenotype testing of the pacX1 palA -, -B -, -C -, -F -, -H - and -I -, pacX1 pacC +/-20205 (Fig. 4 ), pacX1 pacC +/-207, pacX1 pacC +/-209 and pacX1 pacC +/-210 (data not shown) double mutants showed that, in addition to partial restoration of growth at pH 8.
PacX sequence The pacX sequence specifies a 661 residue protein. A zinc binuclear cluster DNA binding motif is located towards the carboxy-terminus between residues 445 and 472 and a region predicted to form a coiled-coil structure occurs in the region of residues 178 to 224 (Fig. 6  and Fig. S3 ). In BLAST searches of the databases, the region containing the putative DNA binding domain and the amino-terminus extending to residue 228 are the most highly conserved (see below), indicating their functional importance (Fig. 6 , Figs  S3  and S4 ). Different algorithms all predict a nuclear localisation for PacX; ngLOC (17. 6% nuclear 14. 5% cytoplasmic), PSORT II (73. 9% nuclear) with cNLS mapper predicting highly probable monopartite (TPGKRPRSDSGEF, residues 10 to 22, score 6. 5) or bipartite (ETPGKRPRSDSGEFPPIASKVPKT, 9 to 32, score 9.
Phylogenetic analysis Searching with blastp using the PacX protein sequence as in silico probe of the 431 fungal genomes available in the Joint Genetics Institute database on the 30th of January 2015 showed PacX homologues to be present in the Pezizomycotina and not in any other fungal taxon, but being absent from the two sequenced genomes of the Orbiliomycetes and the seven sequenced genomes of the Pezizomycetes, basal classes of the Pezizomycotina. Absence from these and other ascomycete taxons (Saccharomycotina and Taphrynnopmycotina) was confirmed with a tblastn search. Searching the NCBI protein database, excluding the Pezizomycotina, failed to reveal any homologues with either blastp or tblastn. Although within the Eurotiomycetes the sequence is very conserved, the phylogenetic tree (Fig.
fumigatus genome database  (Nierman et al. , 2005)  suggested a possible A. fumigatus homologous gene having a C-terminal zinc binuclear cluster. A larger A. nidulans pacX fragment was PCR amplified using the pacX-specific primer XF2 and a degenerate primer ZNF2 based on the sequence of the zinc binuclear cluster of the putative A. fumigatus homologue. In the first three cycles, an annealing temperature of 45°C was employed, followed by 30 cycles at 55°C; 20 pmoles of XF2 and 200 pmoles of ZNF2 were used per reaction. The resulting ∼ 1. 6 kbp fragment was gel purified and sequenced. A DNA fragment made by PCR using primers XF5 and XR5 was used to isolate pacX clones from a λ gt10 cDNA library  (Osmani et al. , 1988). Two cDNA clones contained the entire pacX coding sequence and sequencing confirmed the presence of one intron.
Acknowledgements This work was supported by the  Wellcome Trust  (Grants  067878  and  084660/Z/08/Z  to HNA and JT),  BBSRC  studentships for JR and LR,  Ministerio de Economía y Competitividad (Spain)  Grants  BIO2009-07281  and  BIO2012-30965 , and  Comunidad de Madrid  (grant  S2010/BMD2414 ) to MAP and by grants to EMB from the  Wellcome Trust  ( WT093596MA ), the  Medical Research Council  ( G0501164 ) and the  BBSRC  ( BB/G009619/1 ). Daniel Lucena-Agell  was holder of a  FPI fellowship  and work in EAE's laboratory is supported by grant  BFU2012-33142  from the  Spanish Ministerio de Economia y Competitividad. We thank  Prof Michael Hynes  for nkuAΔ::bar,  Prof Berl Oakley  for HhoA::mCherry,  Prof Steve Osmani  for gene tagging cassettes and the FGSC for suA1palB7 and suD2palA1 containing strains.
Data Sources Through the InterLymph consortium, nine case-control studies conducted in the USA and five European countries between 1988 and 2008 that had individual level data on BMI and cytokine polymorphisms contributed to this pooled analysis. Data were provided via the InterLymph Data Coordinating Center (DCC) at the Mayo Clinic, Rochester, which was established in 2009 to centrally standardise and harmonise study data so that consistent datasets could be produced to expedite pooling projects. Descriptions of the included studies have been published  (12) (13) (14) (15) (16) (17) (18) (19) (20) ; a brief outline is given in Table  1. Cases were ascertained using rapid identification techniques, and controls were randomly selected from population registers (6 studies), outpatient clinics (1 study) or hospital inpatients with non-neoplastic conditions (2 studies).
Statistical Analyses Risk estimates were calculated using unconditional logistic regression adjusted for study, sex and age. Main and joint associations with BMI and each SNP on the risk of lymphoid neoplasms were estimated. Additive interactions between SNP and BMI were estimated by the relative excess risk due to interaction (RERI). When BMI was a categorical variable, the 95% confidence intervals (CI) for RERI were estimated using likelihood-based 95%CI  (23). For BMI as a continuous variable, 10000 bootstrapping samples (without replacement) of the original sample size were taken from the dataset and the 95%CIs were the 2. 5 th and 97. 5 th centile of the bootstrap sampling distribution  (24). Analyses were repeated for DLBCL, FL and CLL/SLL, and all controls were used irrespective of the individual studies' matching techniques.
Heterogeneity between studyspecific risk estimates was considered present when a test for interaction between the variable of interest and study was statistically significant (p-value<0. 05). Potential sources were investigated using sensitivity analyses by: study design (population-or hospital-based); diagnosis classification; participation rates; continent; proportions of cases and controls with SNP data; whether the controls' SNP data were in Hardy-Weinberg equilibrium; or where there was no relationship between obesity and the SNP among controls. All analyses were conducted using Stata 13. 1.
Among our controls TNF genotype and BMI overall were not correlated in all but two of our studies (NCI-SEER, EpiLymph-Spain); the removal of these did not alter our findings. Strengths of our study include its large sample size, giving the potential to examine interactions and explore differences in interactions among the most common lymphoid neoplasms. Obesity prevalence varies across countries, which could relate differently to subjects' participation and responses in the studies included. Most studies had not published on this topic before, and while data were a subset of studies and subjects included in the main effects analyses, the risk estimates for BMI and SNPs were consistent with those published previously  (1, (6) (7) (8). A reduced risk of CLL/SLL with obesity was found in some subsets of data, which could relate to disease-related weight loss; but in larger InterLymph datasets, no obesity associations for CLL/SLL have been reported  (1).
Acknowledgments Financial Support: Studies that contributed data to this pooled analysis were supported by:  NCI  contracts  N01-PC-65064 ,  N01-PC-67008 ,  N01-PC-67009 ,  N01-PC-67010 ,  N02-PC-71105  and the  Intramural Research Program of the National Cancer Institute, National Institutes of Health , and  Public Health Service  ( P. Hartge ,  NCI-SEER  study); grants  R01 CA92153  and  P50 CA97274 ,  National Institutes of Health  ( J. R. Cerhan ,  Mayo Clinic ); grants  R01 CA45614 ,  R03 CA89745 ,  R01 CA87014 ,  NCI 263-MQ-701711 ,  U01 CA66529 ,  R01 CA1046282  and  CA154643 ,  National Institutes of Health and National Cancer Institute , and collection of cancer incidence data was supported by the  California Department of Public Health  as part of the statewide cancer reporting program; the  NCI 's  SEER Program  under contract  HHSN261201000140C  awarded to CPIC; and the  CDC's National Program of Cancer Registries , under agreement #  U58DP003862-01  awarded to the  California Department of Public Health (E.
The atmosphere-chemistry model EMAC The EMAC model is a global atmospheric chemistry climate model described in  Jöckel et al. (2006 Jöckel et al. ( , 2010)). ECHAM5/MESSy includes submodels describing processes of the troposphere and middle atmosphere as well as interaction with land and human influences. Air-sea gas exchange is calculated in EMAC with the submodule AIRSEA, as described by  Pozzer et al. (2006). The numerical simulations were nudged towards the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim reanalysis  (Dee et al. , 2011)  every 6 h (temperature, divergence, vorticity, surface pressure). The resolution of the EMAC atmosphere was ∼ 2. 8 • × 2. 8 • (T42) and 39 vertical hybrid pressure levels up to 0. 01 hPa (L39). The effect of resolution on the results tested with a finer resolution (T106) was only minor (see Table  S2  in the Supplement).
These two parameterizations have been derived from in situ DMS eddy covariance measurements and deviate from previously published parameterizations. Bell et al. (2003)  observed that the transfer velocity does not increase at wind speeds higher than 11 m s -1. Marandino et al. (2009)  found a linear dependency between wind speed and the transfer velocity k w for DMS. Both simulations cover the period of 2004-2013, since observations from this period were available for comparison. These two parameterizations for k w were added to the submodule code of AIRSEA (for equations see Table  4 ). The modification of the code included a parameterization based on results of the study from  Bell et al. (2013, B13m)  with a conservative approach, in which the N00 parameterization was used at wind speeds below 11 m s -1 and kept constant at higher wind speeds to account for the missing increase of k w with increasing wind speed.
Observational data Simulated atmospheric mixing ratios of the trace gases from PWC and PE are compared to observations from ship cam- A total of 23 aircraft campaigns providing halocarbon data are considered in order to create annual zonal mean climatologies of these trace gases. The combined data set ranges from 90 • N to 75 • S, transecting from the surface to the upper troposphere/lower stratosphere over land and ocean from 1992 to 2012 (see Table  S1  for details on the aircraft campaigns). Many of the more recent data sets are intercalibrated (see e. g. Brinckmann et al. , 2012; Hall et al. , 2014; Sala et al. , 2014; Wisher et al. , 2014). The latitudinal and longitudinal distributions and names of the aircraft campaigns are illustrated in Fig. 3a. The measurements were averaged in zonal 10 • wide latitude bins with a vertical extent ranging from 10 to 50 hPa (10 hPa in boundary layer and TTL regions).
These data are currently available at the HalOcAt (Halocarbons in the Ocean and Atmosphere) database (https://halocat. geomar. de/). Two time series stations situated distant to the coast (Park Falls, Wisconsin, Niwot Ridge Forest, Colorado, both USA) were chosen to assess to contribution of marine halocarbon emissions to the atmospheric mixing ratio over land. Monthly means of the time series were compared to monthly means of simulations 1 and 2 for the PWC and PE set-up. DMS was directly compared to measurements from ship campaigns in the marine boundary layer, because only few data from ground-based time series stations is available. The campaigns chosen were  PHASE-I (2004 , Marandino et al. , 2007) , two campaigns on RV Knorr  (Marandino et al. , 2007 (Marandino et al. , , 2008)) , and M98 on RV Meteor (2009, A.
Even though it is clear that more data for all compounds are needed globally, the PWC set-up can be used to identify oceanic regions where more measurements will be needed to improve the global emission estimate. For example, there are clear discrepancies in the Northern Hemisphere for CHBr 3 and the tropics for CH 3 I. Global emission estimates display a large sensitivity towards the parameterization of the transfer velocity k w , with relative differences between 15. 6 % (CH 2 Br 2 ) and 35. 9 % (CH 3 I) compared to the mean global emissions of the four tested simulations including k w parameterizations according to  Liss and Merlivat (1986, LM86) ,  Wanninkhof and McGillis (1999, W99) ,  Nightingale et al. (2000, N00)  and  Ho et al. (2006, H06). Sensitivity towards rain or bubble-mediated transfer was generally low (< 10 % change in global emission estimate).
Acknowledgements. This work was supported through the  German Federal Ministry of Education and Research  through the project  ROMIC-THREAT  ( BMBF-FK01LG1217A  and  01LG1217B ). Additional funding for  C. Marandino  and  S. Lennartz  came from the  Helmholtz Young Investigator Group  of  C. Marandino ,  TRASE-EC (VH-NG-819 ), from the  Helmholtz Association through the President's Initiative and Networking Fund  and the  GEOMAR Helmholtz-Zentrum für Ozeanforschung Kiel. We thank  A. Pozzer  for advice on the use of AIRSEA and valuable comments on the manuscript. Thanks to  Donald R. Blake  from the  University of California, Irvine  for advice and the data access. Thanks to  A. Lana  for providing emission and concentration fields of DMS and to  A. C. Zavarsky  for atmospheric DMS measurements on the Meteor 98 cruise.
We thank  Prabir Patra  for his help and for providing the OH field used in the EMAC simulations. NOAA measurements were supported in part by  NOAA's Atmospheric Chemistry ,  Carbon Cycle  and  Climate Program of its Climate program  Office. Data on halocarbon mixing ratios from aircraft campaigns were obtained from the  ESPO NASA  archive and from the  EOL-NCAR database. We acknowledge operational, technical and scientific support provided by  NCAR's Earth Observing Laboratory , sponsored by the  National Science Foundation. The University of Frankfurt would like to thank the  DLR  for organizing and funding the  ESMVal campaign and DFG  (grant no. 367/8  and  EN367/11 ) for funding the TACTS campaign and the measurements. Edited by:  L. M. Russell.