The effects of simple procedural, cognitive procedural and declarative learning on sleep /

Sleep spindles have been found to increase following an intense period of learning on a combination of motor tasks. It is not clear whether these changes are task specific, or a result of learning in general. The current study investigated changes in sleep spindles and spectral power following learning on cognitive procedural (C-PM), simple procedural (S-PM) or declarative (DM) learning tasks. It was hypothesized that S-PM learning would result in increases in Sigma power during Non-REM sleep, whereas C-PM and DM learning would not affect Sigma power. It was also hypothesized that DM learning would increase Theta power during REM sleep, whereas S-PM and C-PM learning would not affect Theta power. Thirty-six participants spent three consecutive nights in the sleep laboratory. Baseline polysomnographic recordings were collected on night 2. Participants were randomly assigned to one of four conditions: C-PM, S-PM, DM or control (C). Memory task training occurred on night 3 followed by polysomnographic recording. Re-testing on respective memory tasks occurred one-week following training. EEG was sampled at 256Hz from 16 sites during sleep. Artifact-free EEG from each sleep stage was submitted to power spectral analysis. The C-PM group made significantly fewer errors, the DM group recalled more, and the S-PM improved on performance from test to re-test. There was a significant night by group interaction for the duration of Stage 2 sleep. Independent t-tests revealed that the S-PM group had significantly more Stage 2 sleep on the test night than the C group. The C-PM and the DM group did not differ from controls in the duration of Stage 2 sleep on test night. There was no significant change in the duration of slow wave sleep (SWS) or REM sleep. Sleep spindle density (spindles/minute) increased significantly from baseline to test night following S-PM learning, but not for C-PM, DM or C groups. This is the first study to have shown that the same pattern of results was found for spindles in SWS. Low Sigma power (12-14Hz) increased significantly during SWS following S-PM learning but not for C-PM, DM or C groups. This effect was maximal at Cz, and the largest increase in Sigma power was at Oz. It was also found that Theta power increased significantly during REM sleep following DM learning, but not for S-PM, C-PM or C groups. This effect was maximal at Cz and the largest change in Theta power was observed at Cz. These findings are consistent with the previous research that simple procedural learning is consolidated during Stage 2 sleep, and provide additional data to suggest that sleep spindles across all non-REM stages and not just Stage 2 sleep may be a mechanism for brain plasticity. This study also provides the first evidence to suggest that Theta activity during REM sleep is involved in memory consolidation.

Modeling Metal Protein Complexes from Experimental Extended X-ray Absorption Fine Structure using Computational Intelligence

Experimental Extended X-ray Absorption Fine Structure (EXAFS) spectra carry information about the chemical structure of metal protein complexes. However, pre- dicting the structure of such complexes from EXAFS spectra is not a simple task. Currently methods such as Monte Carlo optimization or simulated annealing are used in structure refinement of EXAFS. These methods have proven somewhat successful in structure refinement but have not been successful in finding the global minima. Multiple population based algorithms, including a genetic algorithm, a restarting ge- netic algorithm, differential evolution, and particle swarm optimization, are studied for their effectiveness in structure refinement of EXAFS. The oxygen-evolving com- plex in S1 is used as a benchmark for comparing the algorithms. These algorithms were successful in finding new atomic structures that produced improved calculated EXAFS spectra over atomic structures previously found.

Modeling short-range ballistic missile defense and Israel's Iron Dome system

This paper develops a model of short-range ballistic missile defense and uses it to study the performance of Israel’s Iron Dome system. The deterministic base model allows for inaccurate missiles, unsuccessful interceptions, and civil defense. Model enhancements consider the trade-offs in attacking the interception system, the difficulties faced by militants in assembling large salvos, and the effects of imperfect missile classification by the defender. A stochastic model is also developed. Analysis shows that system performance can be highly sensitive to the missile salvo size, and that systems with higher interception rates are more “fragile” when overloaded. The model is calibrated using publically available data about Iron Dome’s use during Operation Pillar of Defense in November 2012. If the systems performed as claimed, they saved Israel an estimated 1778 casualties and $80 million in property damage, and thereby made preemptive strikes on Gaza about 8 times less valuable to Israel. Gaza militants could have inflicted far more damage by grouping their rockets into large salvos, but this may have been difficult given Israel’s suppression efforts. Counter-battery fire by the militants is unlikely to be worthwhile unless they can obtain much more accurate missiles.