California Institute of Technology

About: California Institute of Technology is a education organization based out in Pasadena, California, United States. It is known for research contribution in the topics: Galaxy & Redshift. The organization has 57649 authors who have published 146691 publications receiving 8620287 citations. The organization is also known as: Caltech & Cal Tech.

In vivo footprinting of a muscle specific enhancer by ligation mediated PCR

Abstract: In vivo protein-DNA interactions at the developmentally regulated enhancer of the mouse muscle creatine kinase (MCK) gene were examined by a newly developed polymerase chain reaction (PCR) footprinting procedure. This ligation mediated, single-sided PCR technique permits the exponential amplification of an entire sequence ladder. Several footprints were detected in terminally differentiated muscle cells where the MCK gene is actively transcribed. None were observed in myogenic cells prior to differentiation or in nonmuscle cells. Two footprints appear to correspond to sites that can bind the myogenic regulator MyoD1 in vitro, whereas two others represent muscle specific use of apparently general factors. Because MyoD1 is synthesized by undifferentiated myoblasts, these data imply that additional regulatory mechanisms must restrict the interaction between this protein and its target site prior to differentiation.

Similarity and Dimensional Methods in Mechanics

Abstract: Similiarity and Dimensional Methods in Mechanics, 10th Edition is an English language translation of this classic volume examining the general theory of dimensions of physical quantities, the theory of mechanical and physical similarity, and the theory of modeling. Several examples illustrate the use of the theories of similarity and dimensions for establishing fundamental mechanical regularities in aviation, explosions, and astrophysics, as well as in the hydrodynamics of ships. Other interesting areas covered include the general theory of automodel motions of continuum media, the theory of propagation of explosion waves in gases, the theory of one-dimensional nonestablished motion in gases, the fundamentals of the gas-dynamics theory of atom-bomb explosion in the atmosphere and the theory of averaging of gaseous flows in channels. Aspects of modeling include the dimensionless characteristics of compressor operation, the theories of engine thrust, and efficiency of an ideal propeller for subsonic and supersonic speeds. Similiarity and Dimensional Methods in Mechanics, 10th Edition is an ideal volume for researchers and students involved in physics and mechanics.

Molecular evolution by staggered extension process (StEP) in vitro recombination

Abstract: We have developed a simple and efficient method for in vitro mutagenesis and recombination of polynu-cleotide sequences. The staggered extension process (StEP) consists of priming the template sequence(s) followed by repeated cycles of denaturation and extremely abbreviated annealing/polymerase-catalyzed extension. In each cycle the growing fragments anneal to different templates based on sequence complementarity and extend further. This is repeated until full-length sequences form. Due to template switching, most of the polynucleotides contain sequence information from different parental sequences. The method is demonstrated by the recombination of two genes encoding thermostable subtilisins carrying two phenotypic markers separated by 113 base pairs and eight other point mutation markers. To demonstrate its utility for directed evolution, we have used StEP to recombine a set of five thermostabilized subtilisin E variants identified during a single round of error-prone PCR mutagenesis and screening. Screening the StEP-recombined library yielded an enzyme whose half-life at 65°C is 50 times that of wild-type subtilisin E.

EM Algorithms for PCA and SPCA

Abstract: I present an expectation-maximization (EM) algorithm for principal component analysis (PCA). The algorithm allows a few eigenvectors and eigenvalues to be extracted from large collections of high dimensional data. It is computationally very efficient in space and time. It also naturally accommodates missing information. I also introduce a new variant of PCA called sensible principal component analysis (SPCA) which defines a proper density model in the data space. Learning for SPCA is also done with an EM algorithm. I report results on synthetic and real data showing that these EM algorithms correctly and efficiently find the leading eigenvectors of the covariance of datasets in a few iterations using up to hundreds of thousands of datapoints in thousands of dimensions.