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An efficient optimization method for the crystallization of biological macromolecules has been developed and tested. This builds on a successful high-throughput technique for the determination of initial crystallization conditions. The optimization method takes an initial condition identified through screening and then varies the concentration of the macromolecule, precipitant, and the growth temperature in a systematic manner. The amount of sample and number of steps is minimized and no biochemical reformulation is required. In the current application a robotic liquid handling system enables highthroughput use, but the technique can easily be adapted in a nonautomated setting. This method has been applied successfully for the rapid optimization of crystallization conditions in nine representative cases.

Structural crystallography aims to provide a three-dimensional representation of macromolecules. Many parts of the multistep process to produce the three-dimensional structural model have been automated, especially through various structural genomics projects. A key step is the production of crystals for diffraction. The target macromolecule is combined with a large and chemically diverse set of cocktails with some leading ideally, but infrequently, to crystallization. A variety of outcomes will be observed during these screening experiments that typically require human interpretation for classification. Human interpretation is neither scalable nor objective, highlighting the need to develop an automatic computerbased image classification. As a first step towards automated image classification, 147 456 images representing crystallization experiments from 96 different macromolecular samples were manually classified. Each image was classified by three experts into seven predefined categories or their combinations. The resulting data where all three observers are in agreement provides one component of a truth set for the development and rigorous testing of automated imageclassification systems and provides information about the chemical cocktails used for crystallization. In this paper, the details of this study are presented.

The lexical categories noun, verb , and adjective are traditionally distinguished by means of distributional (syntactic) and morphological criteria. But in some languages, lexical categories also have distinct phonological behavior. This point has been discussed by Cohen (1964), Postal (1968), Kenstowicz and Kisseberth (1977), Smith (1997, 2001), Myers (2000), and Bobaljik (2008), among others. However, the cross‐linguistic typology of phonological differences among lexical categories has not received much systematic investigation.

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