A novel formulation of an approximate valence bond model (AVB2) and its application to the tautomeric forms of porphyrin and porphycene.

This study presents a novel formulation of the approximate valence bond method, which can be applied as a very fast generator of the molecular potential energy function. The AVB2 model was formulated and parameterized for porphyrin and porphycene using results of quantum mechanical computations at the B3LYP/6-31G (d, p) level. The DFT potential energy, its gradients, and the Hessian-matrix elements, as well as effective atomic charges at local energy minima and transition states, were used for the parameterization of the AVB2 Hamiltonian matrix. The AVB2 method, and in particular its anharmonic version, very well reproduce the potential energy maps for all representative geometries of the studied systems, including harmonic frequencies, and possible proton translocations. For validation of the method, we performed molecular dynamics simulations for isolated molecules accounting for internal double proton transfer processes, which are strongly correlated with changes of the electronic charge density. The simulated power spectra were compared with the experimental infrared spectra. More precise simulations of IR spectra at the classical and quantum dynamics levels, as well as extensions of the AVB2 parameterization to electronic excited states, are the subject of further research.

Estimation of Posturographic Trajectory Using k-Nearest Neighbors Classifier in Patients with Rheumatoid Arthritis and Osteoarthritis.

Rheumatoid arthritis (RA) and osteoarthritis (OA) are common rheumatic diseases and account for a significant percentage of disability. Posturography is a method that assesses postural stability and quantitatively evaluates postural sways. The objective of this study was to estimate posturographic trajectories applying pattern recognition algorithms. To this end, k-nearest neighbors (k-NN) classifier was used to differentiate between healthy subjects and patients with OA and RA. The following parameters of trajectories were computed: radius of sways, developed area, total length, and two directional components of sways: length of left-right and forward-backward motions. Posturographic tests were applied with eyes open and closed, and with biofeedback control. We found that in RA, the radius of sways, the trajectory area, and the biofeedback coordination were related to the patients' condition. The trajectory dynamics in OA patients were smaller compared to those in RA patients. The smallest misclassification errors were observed after feature selection in the biofeedback test compared with the eyes open and closed tests. We conclude that the estimation of posturographic trajectory with k-NN classifier could be helpful in monitoring the condition of RA patients.