106 resultados para variable interest entity

em Cambridge University Engineering Department Publications Database


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This study characterizes the interaction between Campylobacter jejuni and the 16 phages used in the United Kingdom typing scheme by screening spontaneous mutants of the phage-type strains and transposon mutants of the sequenced strain NCTC 11168. We show that the 16 typing phages fall into four groups based on their patterns of activity against spontaneous mutants. Screens of transposon and defined mutants indicate that the phage-bacterium interaction for one of these groups appears to involve the capsular polysaccharide (CPS), while two of the other three groups consist of flagellatropic phages. The expression of CPS and flagella is potentially phase variable in C. jejuni, and the implications of these findings for typing and intervention strategies are discussed.

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Variable selection for regression is a classical statistical problem, motivated by concerns that too large a number of covariates may bring about overfitting and unnecessarily high measurement costs. Novel difficulties arise in streaming contexts, where the correlation structure of the process may be drifting, in which case it must be constantly tracked so that selections may be revised accordingly. A particularly interesting phenomenon is that non-selected covariates become missing variables, inducing bias on subsequent decisions. This raises an intricate exploration-exploitation tradeoff, whose dependence on the covariance tracking algorithm and the choice of variable selection scheme is too complex to be dealt with analytically. We hence capitalise on the strength of simulations to explore this problem, taking the opportunity to tackle the difficult task of simulating dynamic correlation structures. © 2008 IEEE.

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Standard algorithms in tracking and other state-space models assume identical and synchronous sampling rates for the state and measurement processes. However, real trajectories of objects are typically characterized by prolonged smooth sections, with sharp, but infrequent, changes. Thus, a more parsimonious representation of a target trajectory may be obtained by direct modeling of maneuver times in the state process, independently from the observation times. This is achieved by assuming the state arrival times to follow a random process, typically specified as Markovian, so that state points may be allocated along the trajectory according to the degree of variation observed. The resulting variable dimension state inference problem is solved by developing an efficient variable rate particle filtering algorithm to recursively update the posterior distribution of the state sequence as new data becomes available. The methodology is quite general and can be applied across many models where dynamic model uncertainty occurs on-line. Specific models are proposed for the dynamics of a moving object under internal forcing, expressed in terms of the intrinsic dynamics of the object. The performance of the algorithms with these dynamical models is demonstrated on several challenging maneuvering target tracking problems in clutter. © 2006 IEEE.

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A power LDMOS on partial silicon on insulator (PSOI) with a variable low-κ dielectric (VLKD) buried layer and a buried p (BP) layer is proposed (VLKD BPSOI). At a low κ value, the electric field strength in the buried dielectric (EI) is enhanced, and a Si window makes the substrate share the vertical voltage drop, leading to a high vertical breakdown voltage (BV). Moreover, three interface field peaks are introduced by the BP, the Si window, and the VLKD, which modulate the fields in the SOI layer, the VLKD layer, and the substrate; consequently, a high BV is obtained. Furthermore, the BP reduces the specific on-resistance (Ron), and the Si window alleviates the self-heating effect (SHE). The BV for VLKD BPSOI is enhanced by 34.5%, and Ron is decreased by 26.6%, compared with those for the conventional PSOI, and VLKD BPSOI also maintains a low SHE. © 2006 IEEE.

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This paper presents a novel approach using combined features to retrieve images containing specific objects, scenes or buildings. The content of an image is characterized by two kinds of features: Harris-Laplace interest points described by the SIFT descriptor and edges described by the edge color histogram. Edges and corners contain the maximal amount of information necessary for image retrieval. The feature detection in this work is an integrated process: edges are detected directly based on the Harris function; Harris interest points are detected at several scales and Harris-Laplace interest points are found using the Laplace function. The combination of edges and interest points brings efficient feature detection and high recognition ratio to the image retrieval system. Experimental results show this system has good performance. © 2005 IEEE.

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Placing a gene of interest under the control of an inducible promoter greatly aids the purification, localization and functional analysis of proteins but usually requires the sub-cloning of the gene of interest into an appropriate expression vector. Here, we describe an alternative approach employing in vitro transposition of Tn Omega P(BAD) to place the highly regulable, arabinose inducible P(BAD) promoter upstream of the gene to be expressed. The method is rapid, simple and facilitates the optimization of expression by producing constructs with variable distances between the P(BAD) promoter and the gene. To illustrate the use of this approach, we describe the construction of a strain of Escherichia coli in which growth at low temperatures on solid media is dependent on threshold levels of arabinose. Other uses of the transposable promoter are also discussed.

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