dataesr/scientific-paragraphs-categorization huggingface dataset:
50 mm. A similar t for a pure HA lm gave E ¼ 108 Pa and L ¼ 0. 57 mm (inset). § At strains above 0. 2, the t underestimated the experimental data, indicating that the elastic response becomes nonlinear. In this regime, stress can be determined from the derivative of the force vs. distance curves through 46 P ¼ dW/dd ¼ (2p) À1 d(F/R)/dd. ( 3 ) The Young's modulus as a function of strain is shown in Fig. 4B. Comparison with pure HA brushes shows that the intercalation of aggrecan makes the HA lms somewhat soer: the Young's modulus decreases by 3 to 4 fold at any given strain. Fig. 4  Elasticity of composite HA-aggrecan films: (A) an extrapolated fit with eqn (2) (red line) to a compression force profile (black line with circles) on an aggrecan-loaded HA film (data from Fig. 3 ). The fit over the range 0 < s < 0.
Spectroscopic ellipsometry (SE) Ellipsometry measures changes in the ellipsometric angles, D and J, of polarized light upon reection from a planar sample surface. We employed ellipsometry in situ at ambient temperature, using a custom-designed open glass cuvette. 68 Data were acquired with a spectroscopic rotating compensator ellipsometer (M2000V; J. A. Woollam, Lincoln, NE, USA) over a wavelength range of l ¼ 380-1000 nm, at 70 angle of incidence and with a time resolution of 5 s.
75 MDa. 44 With De Feijter's equation, we can estimate the refractive index change as Dn aggrecan ¼ [(dn/dc) peptide Â c peptide + (dn/dc) GAG Â c GAG ], where c peptide and c GAG are the peptide and GAG concentrations, respectively, and (dn/dc) peptide ¼ 0. 18 cm 3 g À1 and (dn/dc) GAG ¼ 0. 15 cm 3 g À1. Because the GAG chains dominate, we can simplify to Dn aggrecan z (dn/dc) GAG Â c aggrecan. For c ¼ 500 mg mL À1 , this gives Dn aggrecan ¼ 0. 000075. Whenever aggrecan was present in the ambient solution, A sol A + Dn aggrecan ¼ 1. 325075 was therefore used when tting the data instead of A buffer. Areal aggrecan mass densities were determined through eqn (6B).
Colloidal probe AFM/RICM We used a NanoWizard II with a TAO module (JPK, Berlin, Germany), installed on an inverted optical microscope (Axio Observer D1; Zeiss, Oberkochen, Germany) to combine colloidal probe atomic force microscopy (AFM) with reection interference contrast microscopy (RICM), as described previously. 46 FM force curves were acquired in closed-loop mode at an approach speed of v z ¼ 100 nm s À1 , and with a maximal load on the order of typically 10 nN. RICM interferographs were acquired simultaneously at two different wavelengths, 438 and 543 nm, using exposure times of typically 100 ms. Custom-developed algorithms implemented in Matlab were used to quantify the absolute distance between the colloidal probe and the glass substrate from the RICM images. The method, which is described in detail elsewhere,  46  is based on the analysis of radially averaged intensity proles with a simple optical model, the parallel plate approximation with incident light parallel to the surface normal.
Acknowledgements DAL acknowledges support from the  American Society of Hematology  ( Research Award for Fellows-in-Training ) and the  American Cancer Society. CJW acknowledges support from the  Blavatnik Family Foundation , the  Lymphoma Research Foundation ,  NHLBI  ( 1RO1HL103532-01 ;  1RO1HL116452-01 ) and  NCI  ( 1R01CA155010-01A1 ), and is a recipient of a  Leukemia Lymphoma Translational Research Program Award  and an  AACR SU2C Innovative Research Grant.
Using precise full-sky observations from Planck, and applying several methods of component separation, we identify and characterize the emission from the Galactic "haze" at microwave wavelengths. The haze is a distinct component of diffuse Galactic emission, roughly centered on the Galactic centre, and extends to |b| ∼ 35 • in Galactic latitude and |l| ∼ 15 • in longitude. By combining the Planck data with observations from the Wilkinson Microwave Anisotropy Probe we are able to determine the spectrum of this emission to high accuracy, unhindered by the large systematic biases present in previous analyses. The derived spectrum is consistent with power-law emission with a spectral index of -2. 55 ± 0. 05, thus excluding free-free emission as the source and instead favouring hard-spectrum synchrotron radiation from an electron population with a spectrum (number density per energy) dN/dE ∝ E -2.
Nevertheless, the spectrum was found to be both significantly softer than free-free emission, and also significantly harder than the synchrotron emission observed elsewhere in the Galaxy as traced by the low-frequency synchrotron measurements of  Haslam et al. (1982)  (see also  Reich & Reich 1988; Davies et al. 1996; Kogut et al. 2007; Strong et al. 2011; Kogut 2012 ). Finally, it was noted that this systematic uncertainty could be almost completely eliminated with data from the Planck 1  mission, which would produce estimates of the CMB signal that were significantly less contaminated by Galactic foregrounds. The synchrotron nature of the microwave haze was substantially supported by the discovery of a gamma-ray counterpart to this emission by  Dobler et al. (2010)  using data from the Fermi Gamma-Ray Space Telescope.
Two data processing centres (DPCs) check and calibrate the data and make maps of the sky (Planck HFI Core Team 2011b;  Zacchei et al. 2011 ). Planck's sensitivity, angular resolution, and frequency coverage make it a powerful instrument for galactic and extragalactic astrophysics as well as cosmology. Early astrophysics results are given in Planck Collaboration VIII-XXVI 2011, based on data taken between 13 August 2009 and 7 June 2010. Intermediate astrophysics results are now being presented in a series of papers based on data taken between 13 August 2009 and 27 November 2010. We take both the WMAP and Planck bandpasses into account when defining our central frequencies. However, throughout we refer to the bands by the conventional labels of  23, 33, 41, 61, and 94 GHz for WMAP and 30, 44, 70, 100, 143, 217, 353, 545, and 857  GHz for Planck; the central frequencies are  22.
The latter involves the complication that no CMB estimate is completely clean of the foregrounds to be measured, which thereore introduces a bias (with the same spectrum as the CMB) in the inferred foreground spectra. As shown by DF08, this bias becomes increasingly large with frequency and renders an exact measurement of the haze spectrum impossible with WMAP alone. This "CMB bias" is the dominant source of uncertainty in all foreground analyses. However, DF08 also pointed out that, because the haze spectrum falls with frequency, the high-frequency data from Planck can be used to generate a CMB estimate that is nearly completely free from haze emission. Thus, pre-subtraction of this estimate should result in an essentially unbiased estimate of the haze spectrum. The CMB estimate that we use consists of a "Planck HFI internal linear combination" (PILC) map, formed from a minimum-variance linear combination of the Planck HFI 143-545 GHz data after pre-subtraction of the thermal dust model of FDS99 at each frequency.
This approach has been advocated by  Eriksen et al. (2007 Eriksen et al. ( , 2008a) )  and  Dickinson et al. (2009)  and has been applied recently to the WMAP 7-year data by  Pietrobon et al. (2012). A detailed description of the algorithm and its validation on simulated data is provided by  Eriksen et al. (2008b, and references therein). The outputs of the sampling are a map-based CMB estimate and the parameters of a foreground model, which can either be template-based, pixel-based, or a combination of the two. We perform the analysis at HEALPix resolution N side = 128. The choice of the foreground model is limited by the number of frequency channels observed since it sets the number of constraints on the model when fitting spectra for each pixel. We separate our results in the following section into two categories, fits using Planck data only and fits using Planck data plus ancillary data sets.
4. 1). However, it is well established that the low-frequency emission actually consists of several components. Following  Pietrobon et al. (2012) , our procedure for separating these components is to perform a template fit as specified in Eq. 2 on the Commander solution for the low-frequency amplitude (i. e. , replacing d ν with the low-frequency amplitude map). Pietrobon et al. (2012)  showed that applying this "post-processing" template regression procedure is effective in extracting the haze from the Commander solution. The addition of the WMAP channels allows us to refine the foreground model further, separating the multiple contributions in the frequency range 23-70 GHz. Moreover, the inclusion of the 408 MHz data improves the characterisation of the synchrotron component and will allow us to investigate the spatial variations of its spectral index (see Sect.
Results In what follows, we perform four different types of haze extraction: 1. A masked full-sky (FS) template fit for each input frequency band. 2. Template fits over subsections of the sky (RG) that are combined to give a full-sky haze map for each input frequency band. 3. A Commander fit (CMD1) with a simple two-component foreground model, using Planck 30-353 GHz data. 4. A comprehensive Commander fit (CMD2) including thermal and spinning dust models, a soft power-law component, and a hard power-law component, using Planck 30-545 GHz, WMAP 23-94 GHz, and Haslam 408 MHz data sets. We first discuss our results from the template fitting and Gibbs sampling analyses derived from the Planck data alone, then proceed to include external data sets in the analysis. A direct comparison of the results between the template fits and Commander haze extraction methods boosts confidence that, not only are components being appropriately separated, but the spectrum is relatively free from bias.
Template fitting In order to further our understanding of the spectrum and morphology of the microwave haze component, we augment the Planck data with the WMAP 7-year data set (covering the frequency range 23-94 GHz) and the 408 MHz data. For the template-fitting method, the inclusion of the new data is trivial since Eq. 2 does not assume anything about the frequency dependence of the spectrum and each map is fit independently. The results for the full sky and for smaller regional fits are shown in Figs. 4  and 5. The haze residual is present in both the WMAP and Planck data, and the morphology and spectrum appear consistent between data sets. As before, scaling each residual by ν 2. 5 yields roughly equal brightness per band from 23 GHz to 61 GHz. Including the WMAP data also confirms that the morphology does not change significantly with frequency, thus implying a roughly constant haze spectrum with position.
Our template fit approach can be written as a H h + a F f = b H h ′ + b F f ′ , (6) where we are solving for a H and a F while b H and b F are the true amplitudes. The a H solution to this equation is a H = b H × Γ hh ′ -Γ f h ′ Γ h f 1 -Γ f h Γ h f + b F × Γ h f ′ -Γ f f ′ Γ h f 1 -Γ f h Γ h f , (7) where, for example, Γ h f ′ ≡ h f ′ / h 2 , and the mean is over unmasked pixels. Thus, if h = h ′ and f = f ′ then a H = b H and we recover the correct spectrum. However, if h h ′ then the 61 GHz WMAP haze (FS) Fig. 4. The microwave haze at both WMAP and Planck wavelengths using a full-sky template fit to the data. The morphology of the haze is remarkably consistent from band to band and between data sets implying that the spectrum of the haze does not vary significantly with position. Furthermore, the ν 2.
We measure the spectrum in the GC south region |l| < 35 • and -35 • < b < 0 • by performing a linear fit (slope and offset) over unmasked pixels and convert the slope measurement to a power law given the central frequencies of the Planck and WMAP data (see Fig. 7 ). Specifically, we fit R 23 H = A ν × R ν H + B ν (8) over unmasked pixels in this region for A ν and B ν , and calculate the haze spectral index, β H = log(A ν )/ log(ν/23 GHz), for each ν. This spectrum should now be very clean and -given our use of the PILC -reasonably unbiased. A measurement of the spectrum of the haze emission is shown in Fig. 7. It is evident that the WMAP and Planck bands are complementarily located in log-frequency space and the two experiments together provide significantly more information than either one alone.
Summary We have identified the presence of a microwave haze in the Planck LFI data and performed a joint analysis with 7-year WMAP data. Our findings verify not only that the haze is real, but also that it is consistent in amplitude and spectrum in these two different experiments. Furthermore, we have used Planck HFI maps to generate a CMB estimate that is nearly completely clean of haze emission, implying that we have reduced systematic biases in the inferred spectrum to a negligible level. We find that the unbiased haze spectrum is consistent with a power law of spectral index β H = -2. 55 ± 0. 05, ruling out free-free emission as a possible explanation, and strengthening the possibility of a hard synchrotron component origin. The spectrum of softer synchrotron emission found elsewhere in the Galaxy is β S = -3.
Acknowledgements. The development of Planck has been supported by:  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). A description of the Planck Collaboration and a list of its members, including the technical or scientific activities in which they have been involved, can be found at http://www. rssd. esa. int/Planck. G. Dobler  has been supported by the  Harvey L. Karp Discovery Award. Some of the results in this paper have been derived using the HEALPix (Górski et al. 2005) package.
Non Celiac Gluten sensitivity (NCGS) was originally described in the 1980s and recently a "re-discovered" disorder characterized by intestinal and extra-intestinal symptoms related to the ingestion of gluten-containing food, in subjects that are not affected with either celiac disease (CD) or wheat allergy (WA). Although NCGS frequency is still unclear, epidemiological data have been generated that can help establishing the magnitude of the problem. Clinical studies further defined the identity of NCGS and its implications in human disease. An overlap between the irritable bowel syndrome (IBS) and NCGS has been detected, requiring even more stringent diagnostic criteria. Several studies suggested a relationship between NCGS and neuropsychiatric disorders, particularly autism and schizophrenia.
Epidemiology The overall prevalence of NCGS in the general population is still unknown, mainly because many patients are currently self-diagnosed and start a GFD without medical advice or consultation. However, new data confirm that this is not an uncommon disorder at all. In a region of New Zealand, 5% of children reported non-CD-related avoidance of gluten-containing food  [8]. Gluten avoidance was associated with improvement of nonspecific behavioral and gastrointestinal complaints  [9]. It remains to be elucidated how many children reporting gluten avoidance were indeed affected by NCGS, as the vast majority of the children involved in this study were not tested for CD nor underwent to an intestinal biopsy. In a US study performed on 7762 unselected persons aged six years or older who participated in the National Health and Nutrition Examination Survey (NHANES) 2009-2010, Digiacomo et al.
The above data suggest that removing gluten from the diet may positively affect the clinical outcome in some children diagnosed with ASD, indicating that autism may be part of the spectrum of NCGS, at least in some cases. However, a word of caution is necessary to stress the fact that only a small, selected sub-group of children affected by ASD may benefit from an elimination diet. Additional investigations are required in order to identify phenotypes based on best-and non-response to dietary modifications and assess any biological correlates including anthropometry before considering a dietary intervention.
Is NCGS permanent or transitory? Is the threshold of sensitivity the same for everybody, or change from subject to subject and in the same subject over time? How frequent is NCGS? The range reported in the literature is between 0. 5% and 6%, based on poorly conducted studies and on definitions of the disease that varies widely from one report to another. Only recently, well-conducted studies based on double blind, placebo control design are providing evidence-based data on the prevalence of NCGS in specific clinical conditions, particularly IBS  [13]. There is the strong need for more coordinated efforts to perform large multicenter studies for those conditions, including autism and schizophrenia, in which NCGS has been indicated as a possible cause in a subgroup of these patients. The lack of validated biomarkers for a diagnosis not based on exclusion criteria is judged to be of paramount importance by many experts in the field.
A control simulation representative of the present-day climatic state that uses 8 plant functional types (PFTs), prescribed phenology and generic PFT-specific isoprene emission potentials (fraction of electrons available for isoprene synthesis) reproduces 50 % of the variability across different ecosystems and seasons in a global database of 28 measured campaign-average fluxes. Compared to time-varying isoprene flux measurements at 9 select sites, the model authentically captures the observed variability in the 30 min.
The modeling methodology and isoprene emission algorithm are detailed in Sect. 2. In Sect. 3, we specify the control and sensitivity global simulations that are carried out for this work. Section 4 summarizes the global model results and presents the model evaluation including: summary of simulated global GPP and isoprene emission (Sect. 4. 1), isoprene emission sensitivity to GPP and canopy temperature (Sect. 4. 1. 1), evaluation of global GPP and isoprene emission (Sect. 4. 2), FLUXNET-derived global GPP (Sect. 4. 2. 1), GPP and latent heat seasonal cycle at 6 benchmark sites (Sect. 4. 2. 2), global database of campaign-average isoprene flux measurements (Sect. 4. 2. 3), time-varying isoprene emission through campaign periods (Sect. 4. 2. 4), isoprene and GPP diurnal cycle (Sect. 4. 2. 5).
Global database of campaign-average isoprene flux measurements Our goal is to assess the model's ability to capture the isoprene emission variability across a wide range of ecosystem types and seasons. We have assembled a global database of campaign-average above-canopy isoprene flux measurements obtained between 1995-2010 (Table  4 ). The measurements are sorted by ecosystem type. The dominant vegetation fraction in the model grid cells for both the standard and SiB2 vegetation cover data sets corresponds to the local ecosystem at the measurement towers for all sites in the database except for South Africa and SW China. vary greatly for a single plant species  (Goldstein et al. , 1998)  and across plant species lumped into each PFT class e. g. (Rinne et al. , 2009). For example, the deciduous PFT includes species that are strong isoprene emitters (willows, aspen) and others that do not emit isoprene (birches).
Time-varying isoprene emission through campaign periods We examine the time-varying performance at 9 select measurement sites described in Table  6. Some of the sites are part of the global database of campaign-average fluxes (Table  3 ). There are 4 tropical sites on 2 different continents, 4 temperate broadleaf sites on 2 different continents, and 1 temperate mixed site in Europe. At 3 of the temperate sites, measurements are available across multiple years  : Harvard Forest (1995 and 2007) ; UMBS  (2000 , 2001 , 2002 , 2003 , 2005); and Belgium (2009-2011). These three sites provide measurements that span an entire growing season. The remaining 6 sites offer measurements over periods of a few days to several weeks. The sites in southern France (La Verdière and Montmeyan) occur in the same model grid cell.
Acknowledgements. Funding for this research was provided by  Yale University. This project was supported in part by the facilities and staff of the  Yale University Faculty of Arts and Sciences High Performance Computing Center. The authors thank  M. Jung ,  G. Bohrer ,  W. Munger ,  A. Friend ,  E. Blyth , and  M. J. Puma. The authors are grateful to the  NASA   Model-E2  climate model development team who are supported by the  NASA   Modeling, Analysis and Prediction Program. Harvard Forest  flux tower measurements are supported by the  US Department of Energy Office of Science and US NSF   Long-term Ecological Research program. Edited by:  F. McNeill.
6 for Sim-CONT and SimH2O results). The model results are monthly or seasonal averages that correspond to the time period dur- has the largest impact on the magnitude of isoprene emission rate in the North American deciduous sites. Based on linear regression, the control run SimCONT is able to reproduce 50 % of the variability across different ecosystems and sea-sons in the global database. SimH2O and SimSiB2 capture 64 % and 40 ing which the observations were made. The campaign periods are typically shorter than one month and as such the observations tend to refer to shorter averaging periods than in the model output. In some cases the published observational campaign-average is for mid-day average or daytime aver-% of the variability in the global database, respectively. The model/measurement discrepancies in the isoprene emission magnitude are most likely due to the model's assignment of PFT-specific emission potentials (fraction of electrons available for isoprene synthesis, ε) that in reality.
Patients One hundred and seventeen patients have been consecutively referred to our dermatology unit for MM between January 2007 and May 2012. Selection criteria included availability of tumor tissue from primary melanoma and metastasis, and pathologically confirmed stage IIIb, IIIc or IV on American Joint Committee on Cancer  [34]. Clinical and follow-up data were reviewed and 18 patients were excluded for unavailability of paired melanoma samples (n = 13) or inappropriate fixation of material (Bouin's fluid) (n = 5). The cohort finally consisted of 99 patients who contributed a total of 142 metastatic tumors and 88 primary tumor specimens for analysis. Among the 99 patients, 51 (51. 5%) were stage III and 48 (48. 5%) were stage IV. According to the French Public Health and Bioethical Law, our study was considered by our research direction lawyer as a noninterventional study without need for ethics committee approval (Article L1121-1 and Article R1121-3).
The HRM protocol consisted of an initial denaturation of 95uC for 10 minutes, followed by 55 cycles of amplification consisting of denaturation at 95uC for 15 seconds, annealing at 60uC for 25 seconds, and extension 72uC for 15 seconds. Melting was performed with a denaturation step at 95uC for 1 minute, followed by an annealing step at 40uC for 1 minute and a melt from 70uC to 95uC at a ramp rate of 0. 03uC/second with 25 acquisitions per degree Celsius. The Roche analysis software version 1. 5. 0. SP3 (Roche Diagnostics) was used to detect variant sequences and the settings were optimized to achieve maximum sensitivity. These primers amplify a 107 bp long fragment allowing the detection of Sensitivity testing experiments were performed using the Horizon Diagnostics control slides of FFPE cell lines block with defined ratios of mutant alleles (1 wild type sample, 1 at 50%, 1 at 3.