Cosmological parameters from SDSS and WMAP

We measure cosmological parameters using the three-dimensional power spectrum P(k) from over 200,000 galaxies in the Sloan Digital Sky Survey (SDSS) in combination with WMAP and other data. Our results are consistent with a ``vanilla'' flat adiabatic Lambda-CDM model without tilt (n=1), running tilt, tensor modes or massive neutrinos. Adding SDSS information more than halves the WMAP-only error bars on some parameters, tightening 1 sigma constraints on the Hubble parameter from h~0.74+0.18-0.07 to h~0.70+0.04-0.03, on the matter density from Omega_m~0.25+/-0.10 to Omega_m~0.30+/-0.04 (1 sigma) and on neutrino masses from <11 eV to <0.6 eV (95%). SDSS helps even more when dropping prior assumptions about curvature, neutrinos, tensor modes and the equation of state. Our results are in substantial agreement with the joint analysis of WMAP and the 2dF Galaxy Redshift Survey, which is an impressive consistency check with independent redshift survey data and analysis techniques. In this paper, we place particular emphasis on clarifying the physical origin of the constraints, i.e., what we do and do not know when using different data sets and prior assumptions. For instance, dropping the assumption that space is perfectly flat, the WMAP-only constraint on the measured age of the Universe tightens from t0~16.3+2.3-1.8 Gyr to t0~14.1+1.0-0.9 Gyr by adding SDSS and SN Ia data. Including tensors, running tilt, neutrino mass and equation of state in the list of free parameters, many constraints are still quite weak, but future cosmological measurements from SDSS and other sources should allow these to be substantially tightened.Comment: Minor revisions to match accepted PRD version. SDSS data and ppt figures available at http://www.hep.upenn.edu/~max/sdsspars.htm

Velocity-space sensitivity of the time-of-flight neutron spectrometer at JET

The velocity-space sensitivities of fast-ion diagnostics are often described by so-called weight functions. Recently, we formulated weight functions showing the velocity-space sensitivity of the often dominant beam-target part of neutron energy spectra. These weight functions for neutron emission spectrometry (NES) are independent of the particular NES diagnostic. Here we apply these NES weight functions to the time-of-flight spectrometer TOFOR at JET. By taking the instrumental response function of TOFOR into account, we calculate time-of-flight NES weight functions that enable us to directly determine the velocity-space sensitivity of a given part of a measured time-of-flight spectrum from TOFOR

Relationship of edge localized mode burst times with divertor flux loop signal phase in JET

A phase relationship is identified between sequential edge localized modes (ELMs) occurrence times in a set of H-mode tokamak plasmas to the voltage measured in full flux azimuthal loops in the divertor region. We focus on plasmas in the Joint European Torus where a steady H-mode is sustained over several seconds, during which ELMs are observed in the Be II emission at the divertor. The ELMs analysed arise from intrinsic ELMing, in that there is no deliberate intent to control the ELMing process by external means. We use ELM timings derived from the Be II signal to perform direct time domain analysis of the full flux loop VLD2 and VLD3 signals, which provide a high cadence global measurement proportional to the voltage induced by changes in poloidal magnetic flux. Specifically, we examine how the time interval between pairs of successive ELMs is linked to the time-evolving phase of the full flux loop signals. Each ELM produces a clear early pulse in the full flux loop signals, whose peak time is used to condition our analysis. The arrival time of the following ELM, relative to this pulse, is found to fall into one of two categories: (i) prompt ELMs, which are directly paced by the initial response seen in the flux loop signals; and (ii) all other ELMs, which occur after the initial response of the full flux loop signals has decayed in amplitude. The times at which ELMs in category (ii) occur, relative to the first ELM of the pair, are clustered at times when the instantaneous phase of the full flux loop signal is close to its value at the time of the first ELM

Predictors of diet-induced weight loss in overweight adults with type 2 diabetes

textabstractAims A very low calorie diet improves the metabolic regulation of obesity related type 2 diabetes, but not for all patients, which leads to frustration in patients and professionals alike. The aim of this study was to develop a prediction model of diet-induced weight loss in type 2 diabetes. Methods 192 patients with type 2 diabetes and BMI>27 kg/m2 from the outpatient diabetes clinic of the Erasmus Medical Center underwent an 8-week very low calorie diet. Baseline demographic, psychological and physiological parameters were measured and the C-index was calculated of the model with the largest explained variance of relative weight loss using backward linear regression analysis. The model was internally validated using bootstrapping techniques. Results Weight loss after the diet was 7.8±4.6 kg (95%CI 7.2-8.5;p<0.001) and was independently associated with the baseline variables fasting glucose (B = -0.33 (95%CI -0.49, -0.18), p = 0.001), anxiety (HADS; B = -0.22 (95%CI -0.34, -0.11), p = 0.001), numb feeling in extremities (B = 1.86 (95%CI 0.85, 2.87), p = 0.002), insulin dose (B = 0.01 (95%CI 0.00, 0.02), p = 0.014) and waist-to-hip ratio (B = 6.79 (95%CI 2.10, 11.78), p = 0.003). This model explained 25% of the variance in weight loss. The C-index of this model to predict successful (≥5%) weight loss was 0.74 (95%CI 0.67-0.82), with a sensitivity of 0.93 (95% CI 0.89-0.97) and specificity of 0.29 (95% CI 0.16-0.42). When only the obese T2D patients (BMI≥30 kg/m2 ; n = 181) were considered, age also contributed to the model (B = 0.06 (95%CI 0.02, 0.11), p = 0.008), whereas waist-to-hip ratio did not. Conclusions Diet-induced weight loss in overweight adults with T2D was predicted by five baseline parameters, which were predominantly diabetes related. However, failure seems difficult to predict. We propose to test this prediction model in future prospective diet intervention studies in patients with type 2 diabetes

Predictors of diet-induced weight loss in overweight adults with type 2 diabetes

Aims A very low calorie diet improves the metabolic regulation of obesity related type 2 diabetes, but not for all patients, which leads to frustration in patients and professionals alike. The aim of this study was to develop a prediction model of diet-induced weight loss in type 2 diabetes. Methods 192 patients with type 2 diabetes and BMI>27 kg/m2 from the outpatient diabetes clinic of the Erasmus Medical Center underwent an 8-week very low calorie diet. Baseline demographic, psychological and physiological parameters were measured and the C-index was calculated of the model with the largest explained variance of relative weight loss using backward linear regression analysis. The model was internally validated using bootstrapping techniques. Results Weight loss after the diet was 7.8±4.6 kg (95%CI 7.2-8.5;p<0.001) and was independently associated with the baseline variables fasting glucose (B = -0.33 (95%CI -0.49, -0.18), p = 0.001), anxiety (HADS; B = -0.22 (95%CI -0.34, -0.11), p = 0.001), numb feeling in extremities (B = 1.86 (95%CI 0.85, 2.87), p = 0.002), insulin dose (B = 0.01 (95%CI 0.00, 0.02), p = 0.014) and waist-to-hip ratio (B = 6.79 (95%CI 2.10, 11.78), p = 0.003). This model explained 25% of the variance in weight loss. The C-index of this model to predict successful (≥5%) weight loss was 0.74 (95%CI 0.67-0.82), with a sensitivity of 0.93 (95% CI 0.89-0.97) and specificity of 0.29 (95% CI 0.16-0.42). When only the obese T2D patients (BMI≥30 kg/m2 ; n = 181) were considered, age also contributed to the model (B = 0.06 (95%CI 0.02, 0.11), p = 0.008), whereas waist-to-hip ratio did not. Conclusions Diet-induced weight loss in overweight adults with T2D was predicted by five baseline parameters, which were predominantly diabetes related. However, failure seems difficult to predict. We propose to test this prediction model in future prospective diet intervention studies in patients with type 2 diabetes