Evaluation of the enthalpy of formation, proton affinity, and gas-phase basicity of gamma-butyrolactone and 2-pyrrolidinone by isodesmic reactions

The knowledge of thermochemical parameters such as the enthalpy of formation, gas-phase basicity, and proton affinity may be the key to understanding molecular reactivity. The obtention of these thermochemical parameters by theoretical chemical models may be advantageous when experimental measurements are difficult to accomplish. The development of ab initio composite models represents a major advance in the obtention of these thermochemical parameters,. but these methods do not always lead to accurate values. Aiming at achieving a comparison between the ab initio models and the hybrid models based on the density functional theory (DFT), we have studied gamma-butyrolactone and 2-pyrrolidinone with a goal of obtaining high-quality thermochemical parameters using the composite chemical models G2, G2MP2, MP2, G3, CBS-Q, CBS-4, and CBS-QB3; the DFT methods B3LYP, B3P86, PW91PW91, mPW1PW, and B98; and the basis sets 6-31G(d), 6-31+G(d), 6-31G(d,p), 6-31+G(d,p), 6-31++G(d,p), 6-311G(d), 6-311+G(d), 6-311G(d,p), 6-311+G(d,p), 6-311++G(d,p), aug-cc-pVDZ, and aug-cc-pVTZ. Values obtained for the enthalpies of formation, proton affinity, and gas-phase basicity of the two target molecules were compared to the experimental data reported in the literature. The best results were achieved with the use of DFT models, and the B3LYP method led to the most accurate data.

From EEG to BOLD: Brain mapping and estimating transfer functions in simultaneous EEG-fMRI acquisitions

Simultaneous acquisition of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) aims to disentangle the description of brain processes by exploiting the advantages of each technique. Most studies in this field focus on exploring the relationships between fMRI signals and the power spectrum at some specific frequency bands (alpha, beta, etc.). On the other hand, brain mapping of EEG signals (e.g., interictal spikes in epileptic patients) usually assumes an haemodynamic response function for a parametric analysis applying the GLM, as a rough approximation. The integration of the information provided by the high spatial resolution of MR images and the high temporal resolution of EEG may be improved by referencing them by transfer functions, which allows the identification of neural driven areas without strong assumptions about haemodynamic response shapes or brain haemodynamic`s homogeneity. The difference on sampling rate is the first obstacle for a full integration of EEG and fMRI information. Moreover, a parametric specification of a function representing the commonalities of both signals is not established. In this study, we introduce a new data-driven method for estimating the transfer function from EEG signal to fMRI signal at EEG sampling rate. This approach avoids EEG subsampling to fMRI time resolution and naturally provides a test for EEG predictive power over BOLD signal fluctuations, in a well-established statistical framework. We illustrate this concept in resting state (eyes closed) and visual simultaneous fMRI-EEG experiments. The results point out that it is possible to predict the BOLD fluctuations in occipital cortex by using EEG measurements. (C) 2010 Elsevier Inc. All rights reserved.

Microscopic origin of non-Gaussian distributions of financial returns

In this paper we study the possible microscopic origin of heavy-tailed probability density distributions for the price variation of financial instruments. We extend the standard log-normal process to include another random component in the so-called stochastic volatility models. We study these models under an assumption, akin to the Born-Oppenheimer approximation, in which the volatility has already relaxed to its equilibrium distribution and acts as a background to the evolution of the price process. In this approximation, we show that all models of stochastic volatility should exhibit a scaling relation in the time lag of zero-drift modified log-returns. We verify that the Dow-Jones Industrial Average index indeed follows this scaling. We then focus on two popular stochastic volatility models, the Heston and Hull-White models. In particular, we show that in the Hull-White model the resulting probability distribution of log-returns in this approximation corresponds to the Tsallis (t-Student) distribution. The Tsallis parameters are given in terms of the microscopic stochastic volatility model. Finally, we show that the log-returns for 30 years Dow Jones index data is well fitted by a Tsallis distribution, obtaining the relevant parameters. (c) 2007 Elsevier B.V. All rights reserved.

Proposal for a Trigonometric Method to Evaluate the Abduction Angle of the Lower Limbs in Neonates

It is difficult to precisely measure articular arc movement in newborns using a goniometer. This article proposes an objective method based on trigonometry for the evaluation of lower limb abduction. With the newborn aligned in the dorsal decubitus position, 2 points are marked at the level of the medial malleolus, one on the sagittal line and the other at the end of the abduction. Using the right-sided line between these 2 points and a line from the medial malleolus to the reference point at the anterior superior iliac spine or umbilical scar, an isosceles triangle is drawn, and half of the inferential abduction angle is obtained by calculating the sine. Twenty healthy full-term newborns comprise the study cohort. Intersubject and intrasubject variability among the abduction angle values (mean [SD], 37 degrees [4]degrees) is low. This method is advantageous because the measurement is precise and because the sine can be used without approximation.