MR-IMPACT II: Magnetic Resonance Imaging for Myocardial Perfusion Assessment in Coronary artery disease Trial: perfusion-cardiac magnetic resonance vs. single-photon emission computed tomography for the detection of coronary artery disease: a comparative multicentre, multivendor trial.

Aims Perfusion-cardiac magnetic resonance (CMR) has emerged as a potential alternative to single-photon emission computed tomography (SPECT) to assess myocardial ischaemia non-invasively. The goal was to compare the diagnostic performance of perfusion-CMR and SPECT for the detection of coronary artery disease (CAD) using conventional X-ray coronary angiography (CXA) as the reference standard. Methods and results In this multivendor trial, 533 patients, eligible for CXA or SPECT, were enrolled in 33 centres (USA and Europe) with 515 patients receiving MR contrast medium. Single-photon emission computed tomography and CXA were performed within 4 weeks before or after CMR in all patients. The prevalence of CAD in the sample was 49%. Drop-out rates for CMR and SPECT were 5.6 and 3.7%, respectively (P = 0.21). The primary endpoint was non-inferiority of CMR vs. SPECT for both sensitivity and specificity for the detection of CAD. Readers were blinded vs. clinical data, CXA, and imaging results. As a secondary endpoint, the safety profile of the CMR examination was evaluated. For CMR and SPECT, the sensitivity scores were 0.67 and 0.59, respectively, with the lower confidence level for the difference of +0.02, indicating superiority of CMR over SPECT. The specificity scores for CMR and SPECT were 0.61 and 0.72, respectively (lower confidence level for the difference: -0.17), indicating inferiority of CMR vs. SPECT. No severe adverse events occurred in the 515 patients. Conclusion In this large multicentre, multivendor study, the sensitivity of perfusion-CMR to detect CAD was superior to SPECT, while its specificity was inferior to SPECT. Cardiac magnetic resonance is a safe alternative to SPECT to detect perfusion deficits in CAD.

A-1 - Monthly Public Assistance Statistical Report Family Investment Program, June 2011

A-1 - Monthly Public Assistance Statistical Report Family Investment Program

What trees tell us: dendrochronological and statistical analysis of the data

Trees are a great bank of data, named sometimes for this reason as the "silentwitnesses" of the past. Due to annual formation of rings, which is normally influenced directly by of climate parameters (generally changes in temperature and moisture or precipitation) and other environmental factors; these changes, occurred in the past, are"written" in the tree "archives" and can be "decoded" in order to interpret what hadhappened before, mainly applied for the past climate reconstruction.Using dendrochronological methods for obtaining samples of Pinus nigra fromthe Catalonian PrePirineous region, the cores of 15 trees with total time spine of about 100 - 250 years were analyzed for the tree ring width (TRW) patterns and had quite high correlation between them (0.71 ¿ 0.84), corresponding to a common behaviour for the environmental changes in their annual growth.After different trials with raw TRW data for standardization in order to take outthe negative exponential growth curve dependency, the best method of doubledetrending (power transformation and smoothing line of 32 years) were selected for obtaining the indexes for further analysis.Analyzing the cross-correlations between obtained tree ring width indexes andclimate data, significant correlations (p<0.05) were observed in some lags, as forexample, annual precipitation in lag -1 (previous year) had negative correlation with TRW growth in the Pallars region. Significant correlation coefficients are between 0.27- 0.51 (with positive or negative signs) for many cases; as for recent (but very short period) climate data of Seu d¿Urgell meteorological station, some significant correlation coefficients were observed, of the order of 0.9.These results confirm the hypothesis of using dendrochronological data as aclimate signal for further analysis, such as reconstruction of climate in the past orprediction in the future for the same locality.

Added prognostic value of myocardial blood flow quantitation in rubidium-82 positron emission tomography imaging.

AIMS: We studied the respective added value of the quantitative myocardial blood flow (MBF) and the myocardial flow reserve (MFR) as assessed with (82)Rb positron emission tomography (PET)/CT in predicting major adverse cardiovascular events (MACEs) in patients with suspected myocardial ischaemia. METHODS AND RESULTS: Myocardial perfusion images were analysed semi-quantitatively (SDS, summed difference score) and quantitatively (MBF, MFR) in 351 patients. Follow-up was completed in 335 patients and annualized MACE (cardiac death, myocardial infarction, revascularization, or hospitalization for congestive heart failure or de novo stable angor) rates were analysed with the Kaplan-Meier method in 318 patients after excluding 17 patients with early revascularizations (<60 days). Independent predictors of MACEs were identified by multivariate analysis. During a median follow-up of 624 days (inter-quartile range 540-697), 35 MACEs occurred. An annualized MACE rate was higher in patients with ischaemia (SDS >2) (n = 105) than those without [14% (95% CI = 9.1-22%) vs. 4.5% (2.7-7.4%), P < 0.0001]. The lowest MFR tertile group (MFR <1.8) had the highest MACE rate [16% (11-25%) vs. 2.9% (1.2-7.0%) and 4.3% (2.1-9.0%), P < 0.0001]. Similarly, the lowest stress MBF tertile group (MBF <1.8 mL/min/g) had the highest MACE rate [14% (9.2-22%) vs. 7.3% (4.2-13%) and 1.8% (0.6-5.5%), P = 0.0005]. Quantitation with stress MBF or MFR had a significant independent prognostic power in addition to semi-quantitative findings. The largest added value was conferred by combining stress MBF to SDS. This holds true even for patients without ischaemia. CONCLUSION: Perfusion findings in (82)Rb PET/CT are strong MACE outcome predictors. MBF quantification has an added value allowing further risk stratification in patients with normal and abnormal perfusion images.

Emission of nitrous oxide and carbon dioxide from semi-arid tropical soils in Chiapas México

The semi-arid region of Chiapas is dominated by N2 -fixing shrubs, e.g., Acacia angustissima. Urea-fertilized soil samples under maize were collected from areas covered and uncovered by A. angustissima in different seasons and N2O and CO2 emissions were monitored. The objective of this study was to determine the effects of urea and of the rainy and dry season on gas emissions from semi-arid soil under laboratory conditions. Urea and soil use had no effect on CO2 production. Nitrons oxide emission from soil was three times higher in the dry than in the rainy season, while urea fertilization doubled emissions. Emissions were twice as high from soil sampled under A. angustissima canopy than from arable land, but 1.2 lower than from soil sampled outside the canopy, and five times higher from soil incubated at 40 % of the water-holding capacity (WHC) than at soil moisture content, but 15 times lower than from soil incubated at 100 WHC. It was found that the soil sampling time and water content had a significant effect on N2O emissions, while N fertilizer and sampling location were less influent.

A-1 - Monthly Public Assistance Statistical Report Family Investment Program, July 2011

A-1 - Monthly Public Assistance Statistical Report Family Investment Program

Selecting statistical models to study the relationship between soybean yield and soil physical properties

Statistical models allow the representation of data sets and the estimation and/or prediction of the behavior of a given variable through its interaction with the other variables involved in a phenomenon. Among other different statistical models, are the autoregressive state-space models (ARSS) and the linear regression models (LR), which allow the quantification of the relationships among soil-plant-atmosphere system variables. To compare the quality of the ARSS and LR models for the modeling of the relationships between soybean yield and soil physical properties, Akaike's Information Criterion, which provides a coefficient for the selection of the best model, was used in this study. The data sets were sampled in a Rhodic Acrudox soil, along a spatial transect with 84 points spaced 3 m apart. At each sampling point, soybean samples were collected for yield quantification. At the same site, soil penetration resistance was also measured and soil samples were collected to measure soil bulk density in the 0-0.10 m and 0.10-0.20 m layers. Results showed autocorrelation and a cross correlation structure of soybean yield and soil penetration resistance data. Soil bulk density data, however, were only autocorrelated in the 0-0.10 m layer and not cross correlated with soybean yield. The results showed the higher efficiency of the autoregressive space-state models in relation to the equivalent simple and multiple linear regression models using Akaike's Information Criterion. The resulting values were comparatively lower than the values obtained by the regression models, for all combinations of explanatory variables.

Effective eigenvalue for the intensity correlations of single-mode and two-mode lasers

Exact formulas for the effective eigenvalue characterizing the initial decay of intensity correlation functions are given in terms of stationary moments of the intensity. Spontaneous emission noise and nonwhite pump noise are considered. Our results are discussed in connection with earlier calculations, simulations, and experimental results for single-mode dye lasers, two-mode inhomogeneously broadened lasers, and two-mode dye ring lasers. The effective eigenvalue is seen to depend sensitively on noise characteristics and symmetry properties of the system. In particular, the effective eigenvalue associated with cross correlations of two-mode lasers is seen to vanish in the absence of pump noise as a consequence of detailed balance. In the presence of pump noise, the vanishing of this eigenvalue requires equal pump parameters for the two modes and statistical independence of spontaneous emission noise acting on each mode.

Statistics of avalanches in martensitic transformations, II. Modelling

Using a scaling assumption, we propose a phenomenological model aimed to describe the joint probability distribution of two magnitudes A and T characterizing the spatial and temporal scales of a set of avalanches. The model also describes the correlation function of a sequence of such avalanches. As an example we study the joint distribution of amplitudes and durations of the acoustic emission signals observed in martensitic transformations [Vives et al., preceding paper, Phys. Rev. B 52, 12 644 (1995)].

A-1 - Monthly Public Assistance Statistical Report Family Investment Program, August 2011

A-1 - Monthly Public Assistance Statistical Report Family Investment Program

A-1 - Monthly Public Assistance Statistical Report Family Investment Program, September 2011

A-1 - Monthly Public Assistance Statistical Report Family Investment Program

A-1 - Monthly Public Assistance Statistical Report Family Investment Program, October 2011

A-1 - Monthly Public Assistance Statistical Report Family Investment Program

Implications of the variability in soil penetration resistance for statistical analysis

Soil penetration resistance (PR) is a measure of soil compaction closely related to soil structure and plant growth. However, the variability in PR hampers the statistical analyses. This study aimed to evaluate the variability of soil PR on the efficiency of parametric and nonparametric analyses in indentifying significant effects of soil compaction and to classify the coefficient of variation of PR into low, medium, high and very high. On six dates, the PR of a typical dystrophic Red Ultisol under continuous no-tillage for 16 years was measured. Three tillage and/or traffic conditions were established with the application of: (i) no chiseling or additional traffic, (ii) additional compaction, and (iii) chiseling. On each date, the nineteen PR data (measured at every 1.5 cm to a depth of 28.5 cm) were grouped in layers with different thickness. In each layer, the treatment effects were evaluated by variance (ANOVA) and Kruskal-Wallis analyses in a completely randomized design, and the coefficients of variation of all analyses were classified (low, intermediate, high and very high). The ANOVA performed better in discriminating the compaction effects, but the rejection rate of null hypothesis decreased from 100 to 80 % when the coefficient of variation increased from 15 to 26 %. The values of 15 and 26 % were the thresholds separating the low/intermediate and the high/very high coefficient variation classes of PR in this Ultisol.

Statistical hadronization of supercooled quark-gluon plasma

The fast simultaneous hadronization and chemical freeze-out of supercooled quark-gluon plasma, created in relativistic heavy ion collisions, can lead to the reheating of the expanding matter and to the change in a collective flow profile. We use the assumption of statistical nature of the hadronization process, and study quantitatively the freeze-out in the framework of hydrodynamical Bjorken model with different simple quark-gluon plasma equations of state.