The design and construction of effective delivery vectors for drugs is very important. We have discovered that octadecyl quaternized carboxymethyl chitosan (OQCMC) in combination with cholesterol (Chol) could form stable vesicles with structure similar to that of conventional liposomes prepared from phosphatidylcholine/cholesterol (PC/Chol). Compared to conventional liposomes, our polymeric liposomes formed by OQCMC/Chol have many excellent features, such as good physical and thermal stability, excellent solubility in water, and high effectiveness in drug encapsulation. Trans-activating transcriptional activator protein (TAT peptide) could be connected on the surface of cationic polymeric liposomes by using cross-linking reagent N-hydroxysuccinimidyl-3-(2-pyridyldithio) propionate (SPDP). Also, oil-soluble magnetic nanoparticles were used to verify the bilayer structure of the polymeric liposomes and their ability to solublize hydrophobic materials. Using different preparation methods, OQCMC/Chol could easily be made into nanoscale particles by encapsulating both hydrophilic and hydrophobic components. We have successfully prepared polymeric liposomes encapsulating quantum dots (QDs), superparamagnetic nanoparticles, or both. Vincristine was also encapsulated in the polymeric liposomes with high drug encapsulation efficiency (90.1%). Vincristine-loaded magnetic polymeric liposomes were stable in aqueous solution and exhibited slow, steady release action over 2 weeks under physiologic pH (7.4). This allows the use of multifunctional cationic polymeric liposomes, such as those developed here from modified chitosan, in various applications such as cancer diagnosis and treatment.

This article presents a novel path planning algorithm for autonomous land vehicles. There are four main contributions: Firstly, an evaluation standard is introduced to measure the performance of different algorithms and to select appropriate parameters for the proposed algorithm. Secondly, a guideline generated by human or global planning is employed to develop the heuristic function to overcome the shortcoming of traditional A-Star algorithms. Thirdly, for improving the obstacle avoidance performance, key points around the obstacle are employed, which would guide the planning path to avoid the obstacle much earlier than the traditional one. Fourth, a novel variable-step based A-Star algorithm is also introduced to reduce the computing time of the proposed algorithm. Compared with the state-of-the-art techniques, experimental results show that the performance of the proposed algorithm is robust and stable.

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