We describe and test a coarse-grained molecular model for the simulation of the effects of pressure on the folding/unfolding transition of proteins. The model is a structure-based one, which takes into account the desolvation barrier for the formation of the native contacts. The pressure is taken into account in a qualitative, mean field approach, acting on the parameters describing the native stabilizing interactions. The model has been tested by simulating the thermodynamic and structural behavior of protein GB1 with a parallel tempering Monte Carlo algorithm. At low effective pressures, the model reproduces the standard two-state thermal transition between the native and denatured states. However, at large pressures a new state appears. Its structural characteristics have been analyzed, showing that it corresponds to a swollen version of the native structure. This swollen state is at equilibrium with the native state at low temperatures, but gradually transforms into the thermally denatured state as temperature is increased. Therefore, our model predicts a downhill transition between the swollen and the denatured states. The analysis of the model permits us to obtain a phase diagram for the pressure-temperature behavior of the simulated system, which is compatible with the known elliptical shape of this diagram for real proteins.

A novel synthetic strategy based on the combination of the chlorination of an organometallic precursor followed by solvothermal treatment is found to be successful in the synthesis of tetragonal nano-ZrO(2) or nano-ZrO(2), embedded in an amorphous carbon matrix, depending on the solvent employed in the solvothermal step. The chemical and structural features (chemical composition, size and surface defects) of the intermediate and final materials have been determined experimentally mainly by high resolution transmission electron microscopy, electron energy loss spectroscopy, and Z-contrast images. These local techniques reveal that the nanoparticles consist of tetragonal ZrO(2) with an average size of 1.7 ± 0.4 and 6.2 ± 0.9 nm for the embedded in carbon and the free nano-ZrO(2), respectively.

Background: Brain connectivity has shown to be a key characteristic in the study of both Parkinson’s Disease (PD) and the response of the patients to the dopaminergic medication. Time series analysis has been used here for the first time to study brain connectivity changes during motor activation in PD. Methods: A 64-channel EEG signal was registered during unilateral motor activation and resting-state in 6 non-demented PD patients before and after the administration of levodopa and in 6 matched healthy controls. Spectral entropy correlation, coherence, and interhemispheric divergence differences among PD patients and controls were analyzed under the assumption of stationarity of the time series. Results: During the motor activation test, PD patients showed an increased correlation coefficient (both hands p < 0.001) and a remarkable increase in coherence in all frequency range compared to the generalized reduction observed in controls (both hands p < 0.001). The Kullback­–Leibler Divergence (KLD) of the Spectral Entropy between brain hemispheres was observed to increase in controls (right hand p = 0.01; left hand p = 0.015) and to decrease in PD patients (right hand p = 0.02; left hand p = 0.002) with motor activation. Conclusions: Our results suggest that the oscillatory activity of the different cortex areas within healthy brains is relatively independent of the rest. PD brains exhibit a stronger connectivity which grows during motor activation. The levodopa mitigates this anomalous performance.

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Extended abstract of a paper presented at Microscopy and Microanalysis 2012 in Phoenix, Arizona, USA, July 29 – August 2, 2012.