During anaphase identical sister chromatids separate and move towards opposite poles of the mitotic spindle. In the spindle, kinetochore microtubules have their plus ends embedded in the kinetochore and their minus ends at the spindle pole. Two models have been proposed to account for the movement of chromatids during anaphase. In the 'Pac-Man' model, kinetochores induce the depolymerization of kinetochore microtubules at their plus ends, which allows chromatids to move towards the pole by 'chewing up' microtubule tracks. In the 'poleward flux' model, kinetochores anchor kinetochore microtubules and chromatids are pulled towards the poles through the depolymerization of kinetochore microtubules at the minus ends. Here, we show that two functionally distinct microtubule-destabilizing KinI kinesin enzymes (so named because they possess a kinesin-like ATPase domain positioned internally within the polypeptide) are responsible for normal chromatid-to-pole motion in Drosophila. One of them, KLP59C, is required to depolymerize kinetochore microtubules at their kinetochore-associated plus ends, thereby contributing to chromatid motility through a Pac-Man-based mechanism. The other, KLP10A, is required to depolymerize microtubules at their pole-associated minus ends, thereby moving chromatids by means of poleward flux.

Text summarization is a data reduction process. The use of text summarization enables users to reduce the amount of text that must be read while still assimilating the core information. The data reduction offered by text summarization is particularly useful in the biomedical domain, where physicians must continuously find clinical trial study information to incorporate into their patient treatment efforts. Such efforts are often hampered by the highvolume of publications. Our contribution is two-fold: 1) to propose the frequency of domain concepts as a method to identify important sentences within a full-text; and 2) propose a novel frequency distribution model and algorithm for identifying important sentences based on term or concept frequency distribution. An evaluation of several existing summarization systems using biomedical texts is presented in order to determine a performance baseline. For domain concept comparison, a recent high-performing frequency-based algorithm using terms is adapted to use concepts and evaluated using both terms and concepts. It is shown that the use of concepts performs closely with the use of terms for sentence selection. Our proposed frequency distribution model and algorithm outperforms a state-of-the-art approach.