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A lipase gene ( lip3) was cloned from the Pseudomonas aeruginosa strain LST-03 (which tolerates organic solvents) and expressed in Escherichia coli. The cloned sequence includes an ORF consisting of 945 nucleotides, encoding a protein of 315 amino acids (Lip3 lipase, 34.8 kDa). The predicted Lip3 lipase belongs to the class of serine hydrolases; the catalytic triad consists of the residues Ser-137, Asp-258, and His-286. The gene cloned in the present study does not encode the LST-03 lipase, a previously isolated solvent-stable lipase secreted by P. aeruginosa LST-03, because the N-terminal amino acid sequence of the Lip3 lipase differs from that of the LST-03 lipase. Although the effects of pH on the activity and stability of the Lip3 lipase, and the temperature optimum of the enzyme, were similar to those of the LST-03 lipase, the relative activity of the Lip3 lipase at lower temperatures (0?35°C) was higher than that of the LST-03 lipase. In the absence of organic solvents, the half-life of the Lip3 lipase was similar to that of the LST-03 lipase. However, in the presence of most of the organic solvents tested in this study (the exceptions were ethylene glycol and glycerol), the stability of the Lip3 lipase was lower than that of the LST-03 lipase. [ABSTRACT FROM AUTHOR]

We develop exoskeletal robots to assist the motion of physically weak persons such as elderly persons or handicapped persons. In our previous research (2001), a prototype of a two degree of freedom exoskeletal robots for shoulder joint motion assist have been developed. In this paper, we propose an effective fuzzy-neuro controller, a moving mechanism of the center of rotation (CR) of the shoulder joint of the exoskeletal robot, and an intelligent interface in order to realize a practical and effective exoskeletal robot for shoulder joint motion assist. The fuzzy-neuro controller enables the robot to assist a person's shoulder motion. The moving mechanism of the CR of the robot shoulder joint is used to fit the CR of the robot shoulder joint to that of the physiological human shoulder joint during the shoulder motion. The intelligent interface is realized by applying a neural network and used to cancel out the effect the human subject's arm posture change. The effectiveness of the proposed method was evaluated by experiment. [ABSTRACT FROM PUBLISHER]