Detection of taeniasis carriers of Taenia solium is essential for control of cysticercosis in humans and pigs. In the current study, we assessed the positive detection rate of a self-detection tool, stool microscopy with direct smear and coproPCR for taeniasis carriers in endemic Tibetan areas of northwest Sichuan. The self-detection tool through questioning about a history of proglottid expulsion within the previous one year showed an overall positive detection rate of more than 80% for Taenia saginata, T. solium and T. asiatica. The positive detection rate was similar for T. saginata and T. solium. In 132 taeniid tapeworm carriers, 68 (51·5%) were detected by microscopy and 92 (69·7%) were diagnosed by coproPCR. A combination of microscopy and coproPCR increased the positive detection rate to 77·3%. There remained 10 cases (7·6%) coproPCR negative but microscopy positive. Due to the high cost and complicated process, coproPCR is required for the identification of Taenia species only when necessary, though it had a significant higher positive detection rate than microscopy. Combined use of self-detection and stool microscopy are recommended in community-based mass screening for taeniases in this Tibetan area or in other situation-similar endemic regions.

This paper addresses the problem of self-detection by a robot. The paper describes a methodology for autonomous learning of the characteristic delay between motor commands (efferent signals) and observed movements of visual stimuli (afferent signals). The robot estimates its own efferent-afferent delay from self-observation data gathered while performing motor babbling, i.e., random rhythmic movements similar to the primary circular reactions described by Piaget. After the efferent-afferent delay is estimated, the robot imprints on that delay and can later use it to successfully classify visual stimuli as either “self” or “other.” Results from robot experiments performed in environments with increasing degrees of difficulty are reported.