No abstract

A new procedure for molecular replacement is presented in which an ef®cient six-dimensional search is carried out using an evolutionary optimization algorithm. In this procedure, a population of initially random molecular-replacement solutions is iteratively optimized with respect to the correlation coef®cient between observed and calculated structure factors. The sensitivity and reliability of the method is enhanced by uniform sampling of the rotational-search space and the use of continuously variable rotational and translational parameters. The process is several orders of magnitude faster than a systematic six-dimensional search, and comparisons show that it can identify solutions using signi®cantly less accurate or less complete search models than is possible with two existing molecular-replacement methods. A program incorporating the method, EPMR, allows the rapid and highly automated solution of molecular-replacement problems involving single or multiple molecules in the asymmetric unit. EPMR has been used to solve a number of dif®cult molecular-replacement problems.

Clinical trials are time consuming, expensive, and often burdensome on patients. Clinical trials can fail for many reasons. This survey reviews many of these reasons and offers insights on opportunities for improving the likelihood of creating and executing successful clinical trials. Literature from the past 30 years was reviewed for relevant data. Common patterns in reported successful trials are identified, including factors regarding the study site, study coordinator/investigator, and the effects on participating patients. Specific instances where artificial intelligence can help improve clinical trials are identified.

Natural evolution is a population-based optimization process. Simulating this process on a computer results in stochastic optimization techniques that can often outperform classical methods of optimization when applied to difficult real-world problems. There are currently three main avenues of research in simulated evolution: genetic algorithms, evolution strategies, and evolutionary programming. Each method emphasizes a different facet of natural evolution. Genetic algorithms stress chromosomal operators. Evolution strategies emphasize behavioral changes at the level of the individual. Evolutionary programming stresses behavioral change at the level of the species. The development of each of these procedures over the past 35 years is described. Some recent efforts in these areas are reviewed.