116,735 research outputs found
Interview with Theodore Y. Wu
Full text linkAn interview in three sessions, February-March 2002, with Theodore Y. Wu, professor of engineering science, emeritus, in the Division of Engineering and Applied Science. Dr. Wu was born in China and received his BSc from Chiao-Tung University (1946), his MS from Iowa State University (1948), and his PhD from Caltech (1952).
In this interview, he recalls his boyhood and tribulations during Japan's invasion of China in World War II, his emigration and matriculation at Iowa State in 1948, and his arrival at Caltech a year later. Recollections of H. S. Tsien, R. A. Millikan, Theodore von Kármán, Julian Cole. Works with Paco Lagerstrom's aeronautics group developing asymptotic perturbation method pioneered by Ludwig Prandtl. Joins faculty as a research fellow in 1952. Interest in hydrodynamics. Origins of the department of engineering science in the mid-1950s by Tsien, Milton Plesset, and Charles De Prima.
Interest in bioengineering, beginning in 1960; studies bird flight and fish locomotion. Discusses influence of G. I. Taylor and James Lighthill, and recalls his own work on flagellar and ciliary motion of microorganisms. Caltech's 1974 pioneering symposium on Swimming and Flying in Nature; new field of biofluiddynamics. Recollections of Y. C. (Burt) Fung.
Recalls his sabbatical, 1964-65, at University of Hamburg with Georg Weinblum. Joins Advisory Committee for Reactor Safeguards. Recollections of Caltech presidents Lee DuBridge and Marvin L. Goldberger. Visit to China in 1979.
Discusses his work, since 1996 retirement, on modeling of water waves; solitons and tsunamis. Concludes with comments on good relations between Chinese and Chinese American scientists and the flood of Chinese students to US for graduate work in late 1970s, after reestablishment of diplomatic relations
SPECIFIC RECOGNITION OF N-ACETYLNEURAMINIC ACID IN THE G(M2) EPITOPE BY HUMAN G(M2) ACTIVATOR PROTEIN
No full textG(M2) Activator is a low molecular weight protein cofactor that stimulates the enzymatic conversion of G(M2) into G(M3) by human beta-hexosaminidase A and also the conversion of G(M2) into G(A2) by clostridial sialidase (Wu, Y.-Y., Lockyer, J. M., Sugiyama, E., Pavlova, N. V., Li, Y.-T., and Li, S.- C. (1994) J. Biol. Chem. 269, 16276-16283). Among the five known activator proteins for the enzymatic hydrolysis of glycosphingolipids, only G(M2) activator is effective in stimulating the hydrolysis of G(M2). However, the mechanism of action of G(M2) activator is still not well understood, Using a unique disialosylganglioside, GalNAc-G(D1a), as the substrate, we were able to show that in the presence of G(M2) activator, GalNAc-G(D1a) was specifically converted into GalNAc-G(M1a) by clostridial sialidase, while in the presence of saposin B, a nonspecific activator protein, GalNAc-G(D1a) was converted into both GalNAc-G(M1a) and GalNAc-G(M1b). individual products generated from GalNAc-G(D1a) by clostridial sialidase were identified by thin layer chromatography, negative secondary ion mass spectrometry, and immunostaining with a monoclonal IgM that recognizes the G(M2) epitope. Our results clearly show that G(M2) activator recognizes the G(M2) epitope in GalNAc-G(D1a). Thus, G(M2) activator may interact with the trisaccharide structure of the G(M2) epitope and render the GalNAc and NeuAc residues accessible to beta-hexosaminidase A and sialidase, respectively
Characterization of an alternatively spliced G(M2) activator protein, G(M2A) protein - An activator protein which stimulates the enzymatic hydrolysis of N-acetylneuraminic acid, but not N-acetylgalactosamine, from G(M2)
No full textG(M2) activator protein is a protein cofactor which stimulates the enzymatic hydrolysis of both GalNAc and NeuAc from G(M2). We have previously isolated two cDNA clones, G(M2) activator cDNA and G(M2A) cDNA, for human G(M2) activator protein (Nagarajan, S., Chen, H.-C., Li, S.-C., Li, Y.-T., and Lockyer, J. M. (1992) Biochem. J. 282, 807-813). G(M2A) mRNA is an RNA alternative splicing product that contains exons 1, 2, 3, and intron 3 of the genomic DNA sequence of G(M2) activator protein (Klima, H., Tanaka, A., Schnabel, D., Nakano, T., Schroder, M., Suzuki, K., and Sandhoff, K. (1991) FEES Left. 289, 260-264). G(M2A) cDNA encodes a protein (G(M2A) protein) containing 1-109 of the 160 amino acids of human G(M2) activator protein, plus a tripeptide (VST) encoded by intron 3 at the COOH terminus. Thus, G(M2A) protein can be regarded as a form (truncated version) of G(M2) activator protein. We have expressed G(M2A) cDNA in Escherichia coli using pT7-7 as the vector. The recombinant G(M2A) protein was purified to an electrophoretically homogeneous form and was found to stimulate the hydrolysis of NeuAc from G(M2) by clostridial sialidase, but not the hydrolysis of GalNAc from G(M2) by beta-hexosaminidase A. Like G(M2) activator protein, G(M2A) protein also specifically recognized the terminal G(M2) epitope in GalNAc-GD1a and stimulated the hydrolysis of only the external NeuAc from this ganglioside by clostridial sialidase. These results enabled us to discern the enzymatic hydrolyses of GalNAc and NeuAc from the G(M2) epitope and established that the NeuAc recognition domain of G(M2) activator protein is located within amino acids 1-109. The presence of G(M2A) mRNA in human tissues and the selective stimulation of NeuAc hydrolysis by G(M2A) protein indicate that this activator protein may be involved in the catabolism of G(M2) through the asialo-G(M2) pathway
Control and Filtering for Discrete Linear Repetitive Processes with H infty and ell 2--ell infty Performance
No full textRepetitive processes are characterized by a series of sweeps, termed passes, through a set of dynamics defined over a finite duration known as the pass length. On each pass an output, termed the pass profile, is produced which acts as a forcing function on, and hence contributes to, the dynamics of the next pass profile. This can lead to oscillations which increase in amplitude in the pass to pass direction and cannot be controlled by standard control laws. Here we give new results on the design of physically based control laws for the sub-class of so-called discrete linear repetitive processes which arise in applications areas such as iterative learning control. The main contribution is to show how control law design can be undertaken within the framework of a general robust filtering problem with guaranteed levels of performance. In particular, we develop algorithms for the design of an H? and dynamic output feedback controller and filter which guarantees that the resulting controlled (filtering error) process, respectively, is stable along the pass and has prescribed disturbance attenuation performance as measured by and – norms
Assessment of south Atlantic albacore resource by using surplus models on Taiwanese 1968-1993 Longline Data
No full textGenetic algorithm optimization for maximum likelihood joint channel and data estimation
No full textA novel blind equalisation scheme is developed based on maximum likelihood (ML) joint channel and data estimation. In this scheme, the joint ML optimisation is decomposed into a two-level optimisation loop. An efficient version of genetic algorithms (GAS), known as a micro GA, is employed at the upper level to identify the unknown channel model and the Viterbi algorithm (VA) is used at the lower level to provide the maximum likelihood sequence estimation of the transmitted data sequence. The proposed GA based scheme is accurate and robust, and has a fast convergence rate, as is demonstrated in simulation
Genetic algorithm optimization for blind channel identification with higher order cumulant fitting
No full textAbstract—An important family of blind equalization algorithms identify a communication channel model based on fitting higher order cumulants, which poses a nonlinear optimization prob-lem. Since higher order cumulant-based criteria are multimodal, conventional gradient search techniques require a good initial estimate to avoid converging to local minima. We present a novel scheme which uses genetic algorithms to optimize the cumulant fitting cost function. A microgenetic algorithm implementation is adopted to further enhance computational efficiency. As is demonstrated in computer simulation, this scheme is robust and accurate and has a fast convergence performance. Index Terms—Blind channel identification, genetic algorithm, higher order cumulant fitting. I
Reticularisus Wu, Wu & Han, a new subgenus of the genus Rhamnosa Fixsen, 1887 from China, with description of a new species (Lepidoptera: Limacodidae)
No full textA new subgenus, ReticularisusWu, Wu & Han, subgen. n., with type species Rhamnosa henanensis Wu, 2008,of the genus Rhamnosa Fixsen, 1887 is described and illustrated. For the sake of contrast, the type species of theother two subgenera in this genus have been given, including adults and male genitalia. Rhamnosa (Reticularisus) shierbeihoua Wu, Wu & Han, sp. n., a Limacodidae collected from the southwest of China is described as new to science. Also, the new species is illustrated with images of the adult and male genitalia and compared with the similar species Rh. henanensis.Se describe e ilustra un nuevo subgénero, Reticularisus Wu, Wu & Han, subgen. n., con la especie tipo Rhamnosa henanensis Wu, 2008, del género Rhamnosa Fixsen, 1887. Para contrastar, se da la especie tipo de los otros dos subgéneros en este género, incluyendo los adultos y la genitalia del macho. Se describe como nueva para la ciencia a Rhamnosa (Reticularisus) shierbeihoua Wu, Wu & Han, sp. n., un Limacodidae capturado del suroeste de China. También la nueva especie es ilustrada con imágenes del adulto y genitalia del macho y comparada con la especie similar Rh. henanensis
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