Attractive silences in cricket courtship song : mate choice using a short-range signal /

Matings systems using signals for sexual communication have been studied extensively and results commonly suggest that females use these signals for locating males, species-identification, and mate choice. Although numerous mating systems employ multiple signals, research has generally focused on long-range signals perhaps due to their prominence and ease of study. This study focused on the short-range acoustic courtship song of crickets. The results presented here suggest this signal is under selection by female choice. Females mated preferentially with males having shorter silences between the two types of ticks within the song. The length of these silences (Gap 1) was correlated with male condition such that males having long silences were significantly lower in mass with respect to body size when compared to males having short silences. Both Gap 1 length and male condition were significantly repeatable within males over time suggesting the possibility these traits have a genetic basis. This study is the first empirical study to test female preferences within the natural variation of the courtship song. It now appears, at least in crickets, that both the longand short-range signals of a multi-signal mating system may contribute to male mating success.

Variational Monte Carlo estimation of the dissociation energy of CuH using correlated sampling

A new approach to treating large Z systems by quantum Monte Carlo has been developed. It naturally leads to notion of the 'valence energy'. Possibilities of the new approach has been explored by optimizing the wave function for CuH and Cu and computing dissociation energy and dipole moment of CuH using variational Monte Carlo. The dissociation energy obtained is about 40% smaller than the experimental value; the method is comparable with SCF and simple pseudopotential calculations. The dipole moment differs from the best theoretical estimate by about 50% what is again comparable with other methods (Complete Active Space SCF and pseudopotential methods).

Bio-inspired optimization & sampling technique for side-chain packing in MCCE

The prediction of proteins' conformation helps to understand their exhibited functions, allows for modeling and allows for the possible synthesis of the studied protein. Our research is focused on a sub-problem of protein folding known as side-chain packing. Its computational complexity has been proven to be NP-Hard. The motivation behind our study is to offer the scientific community a means to obtain faster conformation approximations for small to large proteins over currently available methods. As the size of proteins increases, current techniques become unusable due to the exponential nature of the problem. We investigated the capabilities of a hybrid genetic algorithm / simulated annealing technique to predict the low-energy conformational states of various sized proteins and to generate statistical distributions of the studied proteins' molecular ensemble for pKa predictions. Our algorithm produced errors to experimental results within .acceptable margins and offered considerable speed up depending on the protein and on the rotameric states' resolution used.