Cilia are microtubule-based organelles and perform motile, sensing and signaling functions. The assembly and maintenance of cilia depend on intraflagellar transport (IFT). Besides ciliary localization, most IFT proteins accumulate at basal bodies. However, little is known about the molecular mechanism of basal body targeting of IFT proteins. We first identified the possible basal body-targeting sequence in IFT46 by expressing IFT46 truncation constructs in an ift46-1 mutant. The C-terminal sequence between residues 246-321, termed BBTS3, was sufficient to target YFP to basal bodies in the ift46-1 strain. Interestingly, BBTS3 is also responsible for the ciliary targeting of IFT46. BBTS3::YFP moves bidirectionally in flagella and interacts with other IFT complex B (IFT-B) proteins. Using IFT and motor mutants, we show that the basal body localization of IFT46 depends on IFT52, but not on IFT81, IFT88, IFT122, FLA10 or DHC1b. IFT52 interacts with IFT46 through residues L285 and L286 of IFT46 and recruits it to basal bodies. Ectopic expression of the C-terminal domain of IFT52 in the nucleus resulted in accumulation of IFT46 in nuclei. These data suggest that IFT52 and IFT46 can preassemble as a complex in the cytoplasm, which is then targeted to basal bodies.

Autonomous underwater vehicles (AUVs) rely on a mechanically scanned imaging sonar that is fixedly mounted on AUVs for underwater target barrier-avoiding and tracking. When underwater targets cross or approach each other, AUVs sometimes fail to track, or follow the wrong target because of the incorrect association of the multi-target. Therefore, a tracking method adopting the cloud-like model data association algorithm is presented in order to track underwater multiple targets. The clustering cloud-like model (CCM) not only combines the fuzziness and randomness of the qualitative concept, but also achieves the conversion of the quantitative values. Additionally, the nearest neighbor algorithm is also involved in finding the cluster center paired to each target trajectory, and the hardware architecture of AUVs is proposed. A sea trial adopting a mechanically scanned imaging sonar fixedly mounted on an AUV is carried out in order to verify the effectiveness of the proposed algorithm. Experiment results demonstrate that compared with the joint probabilistic data association (JPDA) and near neighbor data association (NNDA) algorithms, the new algorithm has the characteristic of more accurate clustering.

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