Parallel searching on m rays☆☆This research is supported by the DFG-Project “Diskrete Probleme”, No. Ot 64/8-3.

AbstractWe investigate parallel searching on m concurrent rays. We assume that a target t is located somewhere on one of the rays; we are given a group of m point robots each of which has to reach t. Furthermore, we assume that the robots have no way of communicating over distance. Given a strategy S we are interested in the competitive ratio defined as the ratio of the time needed by the robots to reach t using S and the time needed to reach t if the location of t is known in advance.If a lower bound on the distance to the target is known, then there is a simple strategy which achieves a competitive ratio of 9—independent of m. We show that 9 is a lower bound on the competitive ratio for two large classes of strategies if m⩾2.If the minimum distance to the target is not known in advance, we show a lower bound on the competitive ratio of 1+2(k+1)k+1/kk where k=⌈logm⌉ where log is used to denote the base-2 logarithm. We also give a strategy that obtains this ratio

Large-scale benchmark of Endeavour using MetaCore maps

Summary: Endeavour is a tool that detects the most promising genes within large lists of candidates with respect to a biological process of interest and by combining several genomic data sources. We have benchmarked Endeavour using 450 pathway maps and 826 disease marker sets from MetaCoreTM of GeneGo, Inc. containing a total of 9911 and 12 432 genes, respectively. We obtained an area under the receiver operating characteristic curves of 0.97 for pathway and of 0.91 for disease gene sets. These results indicate that Endeavour can be used to efficiently prioritize candidate genes for pathways and diseases. Availability: Endeavour is available at http://www.esat.kuleuven.be/endeavour Contact: [email protected]; [email protected] Supplementary information: Supplementary data are available at Bioinformatics onlin

Decatransin, a novel natural product inhibiting protein translocation at the Sec61/SecY translocon

A new cyclic decadepsipeptide was isolated from Chaetosphaeria tulasneorum with potent bioactivity on mammalian and yeast cells. Chemogenomic profiling in S. cerevisiae indicated that the Sec61 translocon, the machinery for protein translocation and membrane insertion at the endoplasmic reticulum, is the target. The profiles were similar to those of cyclic heptadepsipeptides of a distinct chemotype (HUN-7293/cotransin) that had previously been shown to inhibit cotranslational translocation at the mammalian Sec61 translocon. Unbiased, genome-wide mutagenesis followed by full-genome sequencing in both fungal and mammalian cells identified dominant mutations in Sec61p/Sec61α1 to confer resistance. Most, but not all, of these mutations affected inhibition by both chemotypes, despite an absence of structural similarity. Biochemical analysis confirmed inhibition of protein translocation into the endoplasmic reticulum of both co- and posttranslationally translocated substrates by both chemotypes, demonstrating a mechanism independent of a translating ribosome. Most interestingly, both chemotypes were found to also inhibit SecYEG, the bacterial Sec61 homolog. We suggest "decatransin" as the name for this novel decadepsipeptide translocation inhibitor

Efficient Robot Self-Localization in Simple Polygons

Introduction One of the basic tasks faced by an autonomous mobile robot is the problem of self-localization, that is determining its position in its environment; this is also sometimes called the "where am I?" problem. There are many variations of this problem depending on the environment and the sensor data and a-priori information available to the robot. Here, we consider a robot that is given a map of its environment but has no knowledge of its location on the map. Often, it is assumed that this problem can be solved by only using sensor data and allowing the robot to make a small local pertubation of its current position. But if there are self-similar parts in the environment, then this approach may not suffice to distinguish between the possible locations of the robot. This issue has been addressed in numerous contexts, ranging from aerial photography to autonomous vehicles for the exploration of landscapes. We consider an idealized version of the proble