dataesr/scientific-paragraphs-categorization huggingface dataset:
59+24. 18. The robustness of the analysis of both the Planck and AMI data is demonstrated through the use of detailed simulations, which also discount confusion from residual point (radio) sources and from diffuse astrophysical foregrounds as possible explanations for the discrepancies found. For a subset of our cluster sample, we have investigated the dependence of our results on the assumed pressure profile by repeating the analysis adopting the best-fitting GNFW profile shape which best matches X-ray observations. Adopting the best-fitting profile shape from the X-ray data does not, in general, resolve the discrepancies found in this subset of five clusters. Though based on a small sample, our results suggest that the adopted GNFW model may not be sufficiently flexible to describe clusters universally.
In Sections 3 and 4, we describe the Planck and AMI observations of our cluster sample, respectively. In Section 5 we present the pressure profile that we have used to model the clusters and constrain parameters. The analysis of the real data from both experiments is also described in Section 5. We follow this by validating our analysis methodology and investigating the effects of diffuse foreground emission on the Planck constraints with simulations in Section 6. 1. Section 6. 2 presents a similar simulations-based investigation of the effects of residual point sources and analysis methodology on the con-straints derived from the AMI interferometric data. With a view to explaining some of the discrepancies we observe, in Section 7, we investigate the possibility of relaxing the assumptions regarding the universal pressure profile adopted for our cluster sample.
A later version of the data has been used for the analysis here and we use different analysis techniques for reasons which will be explained in Section 5. 1. However, as part of our suite of internal tests, we have repeated our analysis on the older version of the Planck data which was used to derive the ESZ catalogue and we find excellent agreement. In Fig. 1  we present maps of the dimensionless Compton-y parameter for each of the clusters as estimated from the Planck data. The y-parameter is related to the observed brightness as a function of frequency (ν) by ∆T tSZ T CMB (ν) = y • g(ν), (1) where ∆T tSZ is the brightness fluctuation due to the thermal SZ (tSZ) effect and T CMB is the temperature of the CMB, which we take to be 2. 7255 ± 0. 0006  (Fixsen 2009). The function, g(ν) is the frequency dependence of the SZ effect  (Sunyaev & Zeldovich 1972).
Description of AMI data The reduction of the AMI data was performed using a dedicated software tool reduce. This is used to apply pathcompensator and path-delay corrections, to flag interference, shadowing and hardware errors, to apply phase and amplitude calibrations and to Fourier transform the correlator data readout to synthesise the frequency channels, before outputting to disk. Flux calibration was performed using short observations of 3C48 and 3C286 near the beginning and end of each run. The assumed I + Q flux densities for these sources in the AMI channels are listed in Table  3  and are consistent with  Baars et al. (1977). As Baars et al. measure I and AMI measures I + Q, these flux densities include corrections for the polarisation of the sources. An amplitude correction is also made for the intervening air mass during the observation.
As contamination from radio sources at 16 GHz tends to be significant, removing or modelling this emission accurately can often be essential to recover SZ decrements from AMI maps. To address this issue, we use the AMI-developed source extraction software, sourcefind  (Waldram et al. 2003; AMI Consortium: Franzen et al. 2011 ) to determine the position, flux density and spectral index of the radio sources with flux density ≥ 3. 5σ LA on the cleaned LA continuum maps, where σ LA is the LA thermal noise. Spectral indices were fit with a Markov Chain Monte Carlo (MCMC) method using LA maps for all six channels and assuming that source flux densities follow a power-law relation of S ∝ ν -α for the AMI frequencies. These source parameter estimates are subsequently used as priors in our Bayesian analysis of the cluster SZ signals (Section 5.
6. These priors have already been used in Planck detection and extraction algorithms to identify and characterise compact objects buried in a diffuse background  (Planck Collaboration VIII 2011). For the cluster position, however, in order to ensure that we are comparing integrated SZ fluxes centred on identical positions on the sky, we performed an initial analysis of the AMI data using a Gaussian prior centred on the cluster phase centre and with a standard deviation of 1 ′ in order to find the best-fitting cluster coordinates. We then fixed the cluster position to these best-fitting coordinates in the subsequent analysis of the Planck data and also in a subsequent re-analysis of the AMI data.
Analysis of Planck data The analysis of the Planck data was performed using PowellSnakes (PwS), which is a Bayesian package for discrete object detection, photometry and astrometry, as described in  Carvalho et al. (2009 Carvalho et al. ( , 2011)). PwS is part of the Planck HFI pipeline and is regularly used to produce catalogues of objects (Planck Collaboration VII 2011) and to measure and characterise the SZ signal  (Planck Collaboration VIII 2011). Note that we have chosen to use PwS as our primary SZ extraction algorithm for the Planck analysis in this study as PwS naturally returns the full posterior distribution in the Y 500θ 500 2D parameter space. It is thus naturally suited for combining with the AMI results to produce joint constraints. We will also present a comparison with results obtained using the Matched Multi-Filter algorithm (MMF3;  Melin et al.
2006 ) which was the reference algorithm adopted for the production of the Planck ESZ catalogue (Planck Collaboration VIII 2011). However, in its current implementation, the MMF3 algorithm does not produce any information on the correlation between the two cluster parameters of interest (Y 500 and θ 500 ) and so producing joint constraints as obtained from AMI and Planck via MMF3 is currently not possible. The analysis of the Planck data using the PwS algorithm proceeded as follows. For each cluster, flat patches (14. 7 • × 14. 7 • ; 512×512 pixels) were created using a gnomonic projection, centred on the targeted cluster, for each of the Planck HFI channels. By operating on such a large patch of sky enough statistics are collected in order to produce a smooth cross-channel covariance matrix.
The position of each cluster was assumed to be known precisely (we adopted delta-function priors at the AMIdetermined position) as described in Section 5. The data model for a single isolated cluster located in the centre of the patch is then described by d(x) = Y f Γ(θ s , x) + n(x), (5) where x is sky position, d(x) is a vector containing the data, n(x) is the background composed of instrumental noise plus all other astronomical components except the SZ signal, f is a vector containing the SZ surface brightness at each frequency, Y is the total integrated Comptonisation parameter, θ s is a parameter controlling the cluster radial scale and Γ(θ s , x) is the convolution of the canonical GNFW model integrated along the line of sight with the Planck beam at that channel. It is assumed that the background is a realisation of a stationary Gaussian random field.
On the other hand there is significant overlap in the posterior distributions for the remaining eight clusters. However, taking our cluster sample as an ensemble, there is some evidence that the cluster parameter estimates derived from the AMI data are systematically lower than those derived from the Planck data (i. e. , AMI is finding the clusters to be fainter and smaller in angular extent compared to what the Planck data indicate). In addition, the Y 500θ 500 degeneracies are significantly different for the Planck and AMI constraints, with the degeneracies of the AMI constraints being generally steeper than the Planck ones. This arises because of the interplay between the angular size-redshift relation and the differing angular scales that AMI and Planck are sensitive to, as well as the very different observational techniques and frequencies used by the two instruments.
In the cases where the Planck and AMI-derived constraints are compatible with one another, we also overplot the joint constraints obtained from multiplying the Planck and AMI posteriors. In many cases, the resulting joint constraints are far tighter than either analysis alone which is a direct result of the differing parameter degeneracies for the two instruments, as described above. The marginalised constraints from this combined analysis are also presented in Table  6. In Fig. 5 , we have also over-plotted the constraints as obtained from the Planck data using the MMF3 algorithm. Comparing these results with the PwS Planck results, we see good agreement in most cases, although there may be a tendency for the MMF3 estimates to be systematically brighter and larger than the PwS results. However, it is clear that our broad conclusions regarding the general levels of agreement between the Planck and AMI results remain unchanged if we consider the MMF3 results in place of the PwS constraints.
It is assumed that the noise is uncorrelated between rings and that in each ring it is drawn from a power spectrum. For these simulations, the noise power spectrum used was the mean of the ring-by-ring spectra. In turn, these were determined by applying the noise estimation pipeline (Planck HFI Core Team 2011b) to the exact same version and time-span of the Planck data that was used for the real SZ cluster analysis of the previous section. The simulations were then analysed using the PwS algorithm in exactly the same manner as was applied during the analysis of the real data. For each of the 11 clusters, ten simulations were run. The dust template based on the Planck 857 GHz map was Planck and AMI Collaborations: Comparison of Planck and AMI Sunyaev-Zeldovich measurements for 11 galaxy clusters the same for each of these ten simulations, but the CMB and noise realisations were different.
Acknowledgements. A description of the Planck Collaboration and a list of its members, indicating which technical or scientific activities they have been involved in, can be found at http://www. rssd. esa. int/Planck. The Planck Collaboration acknowledges the support of:  ESA ;  CNES  and  CNRS/INSU-IN2P3-INP (France) ;  ASI ,  CNR , and  INAF (Italy) ;  NASA and DoE (USA) ;  STFC  and  UKSA (UK) ;  CSIC ,  MICINN  and  JA (Spain) ;  Tekes, AoF  and  CSC (Finland) ;  DLR  and  MPG (Germany) ;  CSA (Canada) ;  DTU Space (Denmark) ;  SER/SSO (Switzerland) ;  RCN (Norway) ;  SFI (Ireland) ;  FCT/MCTES (Portugal) ; and  DEISA (EU). The AMI telescope is supported by  Cambridge University  and the  STFC. The AMI data analysis was carried out on the  COSMOS UK National Supercomputer at DAMTP, University of Cambridge  and the  AMI Consortium  thanks  Andrey Kaliazin  for computing assistance.
Methods This research was approved by the UCL Division of Psychology and Language Sciences ethics committee. All participants provided written informed consent before taking part. 44 participants were recruited to take part in the study (26 female; mean age: 28, SD: 8). Two participants failed to make any PM responses, making it unclear whether they had understood task instructions, and one participant performed at chance level (48%) on the ongoing task. These three participants were excluded, along with an additional participant whose data were excluded due to technical problems, leaving a final sample of 40 participants. These participants were divided randomly into equally-sized task switching and dual task groups. Participants were tested individually, sitting approximately 50 cm from a laptop computer in a quiet testing room.
LiCl treatment showed a consistent and significant protective effect against exp PABPN1-induced cell death, compared with non-treated counterparts. As shown in Figure  2a , LiCl rescues cells expressing GFPexp PABPN1-13Ala and GFPexp PABPN1-17Ala-associated cell death (*Po0. 001 versus non-treated samples). LiCl treatment did not show a reduction of protein aggregation (data not shown). In order to validate this live imaging cell survival assay, we used a fluorescence-based flow cytometry (FFC) assay to analyze the ability of cells to exclude the red-fluorescent 7-aminoactinomycin D (7AAD) biomarker for dead cells. The presence of the GFP-tag in the expression vectors used enabled us to establish the fraction of C2C12 cells that were actually transfected. The use of GFP has been described as a means to assess gene expression and transfection efficiency.
The number of nuclei within each individual myotube was counted for 100-150 myotubes per well. At least three different fields containing 100 cells were counted. Data are the mean ± S. E. of three independent experiments. Human Lymphoblastoid Cells (LCLs) cultures. LCLs were established from peripheral blood samples. Cells were maintained in RPMI 1640 with 2 mM L-glutamine and 10% FBS in a 37 1C incubator (5% CO2 LiCl treatment. LiCl (Sigma, Oakville, ON, Canada) was dissolved in water. In the initial cell treatment experiments, we performed dose-response experiments on the non-transfected cells, to establish the best concentration for subsequent tests. LiCl (2. 5 mM) was the most appropriate dose, as it did not cause cell toxicity for the period of experiments, and therefore was used throughout the experiments described below.
Parallel samples were probed using an anti-actin antibody (Chemicon, Billerica, MA, USA) to confirm the equal loading of lysates between lanes. Assessment of protein expression and cell survival using the automated microscope. A 12-well plate containing transfected cells (GFPwt PABPN1-10Ala, GFPexp PABPN1-13Ala, GFPexp PABPN1-17Ala, and GFP as well as non-transfected cells) treated or not with LiCl was placed on a Leica automated microscope stage, connected to Volocity software (PerkinElmer Improvision, Montreal, QC, Canada); the microscope is equipped with digital camera, CO 2 incubation chamber, and an electronically controlled motorized xy axis stage. Cells could be monitored over a long period of time for expressing the fluorescent marker. The microscopic robotic stage allows parallel tracking of multiple fields, both within a single well and in multiple wells.
We programmed Volocity software to keep track of three different fields/well containing cells, and programmed the stage to return to the same position at pre-fixed time intervals over the 6 days post transfection and post-treatment with LiCl. We monitored cell survival by counting the cells every 24 h for 6 days continuously. Day 1 indicates 24 h post transfection. The percentage of living transfected cells represents the variation of the amount of living transfected cells at different time points compared with the number of transfected cells obtained on day 1. On average, around 150 cells from each field for each construct were counted. Cell viability was assessed by analysis of GFP-positive cells. GFP-expressing cells with fragmented or condensed nuclei were counted as dead. In addition to green images, we also recorded the phase images to get a clear idea about the status of the cells healthy, dead) and to confirm the healthy GFP cells counting.
1 sodium azide) containing 50 mM with 7-aminoactinomycin D (7AAD). Labeled cells were sorted by fluorescence using a BD Flow Cytometer (BD Biosciences) and a minimum of 10 000 events were recorded per sample using BD FACSDiva Software (BD Biosciences). Before each experiment, the flow cytometer was calibrated using three control groups: unstained cells to gate autofluorescence, cells expressing GFP without 7AAD, and cells treated with DMSO to induce cell death having been stained with 7AAD. Using FACSDiva Software, data were displayed in a four quadrant dotplot format on a log-scale. The cell death was expressed as a percentage by dividing the number of transfected cells that stained with 7AAD (green and red) by the total number of transfected cells. Cell death percentages were normalized to that of non-transfected cells.
Acknowledgments The authors thank  Mr. J. Almajid  from  Dubai ,  Prrofessor H. Sadeghi  from  Cardiovascular Surgery, University of Lausanne , and  Dr. K. Farid  from  Hôtel Dieu Hospital, Paris , for their precious help. This study was funded by the  Tajikistan Stem Cells International 9 Ministry of Health , Clinical Trials, Government number,  01011TD053.
The analysis utilizes a data sample corresponding to an integrated luminosity of 12. 1 ± 0. 5 fb -1 , collected by the CMS detector in pp collisions at a centre-of-mass energy of 8 TeV. These data were recorded in the 2012 running period of the LHC. The 14% increase in the energy of the machine relative to the 2011 dataset would result in a larger production cross section for black holes and other new physics with energetic multiparticle final states. This allows the current search to penetrate a previously unexplored regime. Searches for black holes have also been performed by the CMS collaboration in the dijet channel  [14]  and by the ATLAS Collaboration  [15] [16] [17]. A characteristic signature of evaporating semiclassical black holes or string balls is a large number of energetic final-state particles of various types, while quantum black holes typically decay into a few energetic partons.
The HCAL consists of interleaved brass plates and scintillator sheets that extend to |η| = 3. 0. The granularity of the HCAL towers is ∆η × ∆φ = 0. 087 × 0. 087. Extensive forward calorimetry complements the coverage provided by the barrel and endcap detectors. The CMS trigger system is composed of two levels that are used to select potentially interesting events. The first level (L1) trigger ensures negligible dead time and is responsible for reducing the event rate to 100 kHz using the information from calorimeters and muon detectors. After L1 triggering, the data are passed to the software-based high-level trigger, which decreases the event rate to several hundred Hz for further storage. The data used for this analysis are collected with a set of triggers based on the scalar sum of the transverse energies (H T ) of the calorimeter jets found by the trigger.
The thresholds for the H T triggers increased from 200 to 750 GeV, depending on the data-taking period, to cope with the increasing instantaneous luminosity of the LHC. For the earlier part of the data taking, we additionally utilized H T triggers that use jets reconstructed using the particle-flow (PF) technique  [19] , which are corrected for the calorimeter response to calculate the H T variable. The trigger is measured to be fully efficient for jet-enriched collision events with H T above 1 TeV.
Analysis of B. subtilis tRNA by polyacrylamide gel electrophoresis and northern blotting RNAs were analyzed by acid-urea polyacrylamide gel electrophoresis (PAGE)  (19)  and native PAGE [10-15% polyacrylamide in Tris/Borate/EDTA (TBE) buffer] followed by staining with ethidium bromide or northern blotting. The transfer of tRNA onto Hybond-XL (GE Healthcare) or Nytran SPC (Whatman) has been described  (19). Northern blots were analyzed by autoradiography and phosphorimaging using Imagequant software.
ACKNOWLEDGMENTS This work was supported by grants from the  Juvenile Diabetes Research Foundation  ( 1-2005-1056 ), the  Agence Nationale de la Recherche  ( ANR-09-GENO-006-01 ), and the  region Ile-de-France. G. F. was supported by the  DIM STEM-Pôle Ile-de-France. No potential conflicts of interest relevant to this article were reported. G. F. performed research, analyzed data, contributed to discussion, and wrote the manuscript. B. M. C. , V. G. , and R. S. designed research, contributed to discussion, and edited the manuscript. S. G. , A. B. , L. R. , and F. G. performed research. B. L. S. designed research, analyzed data, contributed to discussion, and wrote the manuscript. B. L. S. is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.
1A ). Since both gene cassettes are delivered to the same cells and GFP-HDEL does not move between cells  [28] , primary infected cells should display concomitant green and red fluorescence while secondary infected cells should display red-only fluorescence. This system also allows differentiation between virus RNA replication and virus intercellular movement. A single infiltration with an A. tumefaciens suspension containing the above plasmid was performed on leaves of three-week old N. benthamiana plants, resulting in an agroinfiltrated area of 5-10 mm in diameter (Fig. 1B ). Fluorescence emitted by GFP-HDEL was generally observed at approximately 36 hrs post infiltration (hpinf) and mCherry fluorescence resulting from TuMV replication was detected at approximately 60 hpinf. Systemic TuMV infection was observed at 4-5 days post infiltration (dpinf) in leaves above the infiltrated one by Western blot analysis using a rabbit serum against the CP of TuMV (data not shown).
2D ). This experiment was repeated two more times and the statistical data are presented in Fig. S2A. To assess whether or not BFA or CMA inhibited TuMV replication, we quantified mCherry fluorescence intensity over GFP fluorescence intensity in primary infection foci for all treatments. Fig. 2E  shows that that there was no significant difference in the ratio of red over green fluorescence during BFA and CMA treatments compared with the no inhibitor treatment (TuMV alone) at 4 dpinf, indicating that viral protein production in the primary infected cells was not affected by the drug treatments. Green fluorescence levels were also similar between DMSO-and BFA-or CMA-treated primary cells indicating that steady state level of GFP, which was not associated with virus replication, was not affected by the treatments (data not shown).
thaliana fimbrin fused to GFP (GFP-ABD2-GFP)  [63]  (Fig. S1D ). TuMV intercellular movement was assessed by imaging N. benthamiana leaves at 4 dpinf. Inhibition of TuMV intercellular movement was observed after LatB or CytD treatment (Fig. 6B-D ). This experiment was repeated two more times and the statistical data are presented in Fig. S3A. The ratio of red to green fluorescence in dual expressing cells was unchanged between treatments indicating that virus replication was unaffected by these microfilament antagonists (Fig. 6E ). These results indicate that an intact microfilament network was important for TuMV intercellular movement, but not for replication. The last assertion is in line with the prior observation that LatB treatment did not affect the production of TuMVinduced 6K 2 -tagged perinuclear structures and peripheral vesicles  [40].
Confocal microscopy Agroinfiltrated leaf sections were mounted on a depression microscope slide, aligning the leaf tissue in the well. Cells were observed using a 106 objective, 206, 406 and 636 oil immersion objective on a LSM 510 Metaconfocal microscope (Zeiss) or on a LSM 780 Metaconfocal microscope (Zeiss). For LSM 510 Metaconfocal microscope experiments, argon and HeNe lasers were used to excite fluorescent proteins and for a LSM 780 Metaconfocal multiline argon and DPSS 561 were used. Data from both green and red channels were collected at the same time. After acquisition, images were processed using Metamorph and/or ImageJ to quantify the average intensity of fluorescence, Carl Zeiss LSM Image Browser, and/or Adobe Photoshop software for post-capture imaging processes.
The reason for smaller uncertainties in ET than in precipitation could 300 250 250 200 800 1000 300 400 500 600 700 800 200 300 400 500 600 700 800 900 age 400mm/yr) Precipitation (average 487mm/yr) Precipitation (average 455mm/yr) Global land mean 750 12 700 10 Legend 650 8 GSWP Datasets ET schemes 600 WaterMIP GHM ET 6 WaterMIP LSM 550 Diagnostic 4 Reanalyses 500 All others 450 2 400 0 600 700 800 700 750 800 850 900 950 1000 0 2 4 6 8 10 age 432mm/yr) Precipitation (average 815mm/yr).