Vibronic Spectra of Jet-Cooled 2-Aminopurine center dot H2O Clusters Studied by UV Resonant Two-Photon Ionization Spectroscopy and Quantum Chemical Calculations

For understanding the major- and minor-groove hydration patterns of DNAs and RNAs, it is important to understand the local solvation of individual nucleobases at the molecular level. We have investigated the 2-aminopurine center dot H2O. monohydrate by two-color resonant two-photon ionization and UV/UV hole-burning spectroscopies, which reveal two isomers, denoted A and B. The electronic spectral shift delta nu of the S-1 <- S-0 transition relative to bare 9H-2-aminopurine (9H-2AP) is small for isomer A (-70 cm(-1)), while that of isomer B is much larger (delta nu = 889 cm(-1)). B3LYP geometry optimizations with the TZVP basis set predict four cluster isomers, of which three are doubly H-bonded, with H2O acting as an acceptor to a N-H or -NH2 group and as a donor to either of the pyrimidine N sites. The "sugar-edge" isomer A is calculated to be the most stable form with binding energy D-e = 56.4 kJ/mol. Isomers B and C are H-bonded between the -NH2 group and pyrimidine moieties and are 2.5 and 6.9 kJ/mol less stable, respectively. Time-dependent (TD) B3LYP/TZVP calculations predict the adiabatic energies of the lowest (1)pi pi* states of A and B in excellent agreement with the observed 0(0)(0) bands; also, the relative intensities of the A and B origin bands agree well with the calculated S-0 state relative energies. This allows unequivocal identification of the isomers. The R2PI spectra of 9H-2AP and of isomer A exhibit intense low-frequency out-of-plane overtone and combination bands, which is interpreted as a coupling of the optically excited (1)pi pi* state to the lower-lying (1)n pi* dark state. In contrast, these overtone and combination bands are much weaker for isomer B, implying that the (1)pi pi* state of B is planar and decoupled from the (1)n pi* state. These observations agree with the calculations, which predict the (1)n pi* above the (1)pi pi* state for isomer B but below the (1)pi pi* for both 9H-2AP and isomer A.

Seasonal variations of ventricular arrhythmia clusters in defibrillator recipients

BACKGROUND: Clustering ventricular arrhythmias are the consequence of acute ventricular electrical instability and represent a challenge in the management of the growing number of patients with an implantable cardioverter-defibrillator (ICD). Triggering factors can rarely be identified. OBJECTIVES: Several studies have revealed seasonal variations in the frequency of cardiovascular events and life-threatening arrhythmias, and we sought to establish whether seasonal factors may exacerbate ventricular electrical instability leading to arrhythmia clusters and electrical storm. METHODS: Two hundred and fourteen consecutive defibrillator recipients were followed-up during 3.3 +/- 2.2 years. Arrhythmia cluster was defined as the occurrence of three or more arrhythmic events triggering appropriate defibrillator therapies within 2 weeks. Time intervals between two clusters were calculated for each month and each season, and were compared using Kruskal-Wallis test and Wilcoxon-Mann-Whitney test with Bonferroni adjustment. RESULTS: During a follow-up of 698 patient years, 98 arrhythmia clusters were observed in 51 patients; clustering ventricular arrhythmias were associated with temporal variables; they occurred more frequently in the winter and spring months than during the summer and fall. Accordingly, the time intervals between two clusters were significantly shorter during winter and spring (median and 95% CI): winter 16 (5-19), spring 11.5 (7-25), summer 34.5 (15-55), fall 50.5 (19-65), P = 0.0041. CONCLUSION: There are important seasonal variations in the incidence of arrhythmia clusters in ICD recipients. Whether these variations are related to environmental factors, change in physical activity, or psychological factors requires further study.

Data-driven estimates of the number of clusters in multivariate time series

An important problem in unsupervised data clustering is how to determine the number of clusters. Here we investigate how this can be achieved in an automated way by using interrelation matrices of multivariate time series. Two nonparametric and purely data driven algorithms are expounded and compared. The first exploits the eigenvalue spectra of surrogate data, while the second employs the eigenvector components of the interrelation matrix. Compared to the first algorithm, the second approach is computationally faster and not limited to linear interrelation measures.