S-adenosylmethionine metabolism and DNA methylation in hydrazine-treated rats

The treatment of rats with hepatotoxic doses of hydrazine (NH2-NH2) induces the rapid formation of 7-methylguanine and O6-methylguanine in liver DNA. The methyl moiety in these reactions might be derived from the cellular S-adenosylmethionine pool because radioactivity administered to these rats as methionine rapidly appears in the DNA as methylated guanine. An increased incorporation of radioactivity into 5-methylcytosine was previously reported followed by subsequent suppression. This increased radiolabeling of 5-methylcytosine coincided with time of maximal DNA guanine methylation. To determine the nature of S-adenosylmethionine metabolism during the period of DNA methylation induced by hydrazine treatment, and to determine if the increased radiolabeling of 5-methylcytosine at this time reflected an actual increase in 5-methylcytosine synthesis, liver DNA synthesis and S-adenosylmethionine levels and turnover were assayed. Liver S-adenosylmethionine concentrations varied slightly between control rats and hydrazinetreated rats during the first five hours after hydrazine administration, and no difference was detectable in the incorporation of administered [3H]methionine into S-adenosylmethionine. Because S-adenosylmethionine specific radioactivity in hydrazine-treated rats was not different from control rats, the previously observed increased radiolabeling of 5-methylcytosine appeared to represent an actual increase in synthesis. This conclusion was supported by finding that incorporation of radioactive thymidine into DNA was also accelerated immediately following hydrazine administration, again followed by a decrease. 5-Methylcytosine sythesis, therefore, appears to follow DNA synthesis during hydrazine toxicity, and formation of 7-methylguanine and O6-methylguanine in liver DNA of hydrazine-treated rats occurs during a short period of increased DNA sythesis and 5-methylcytosine formation very early in hydrazine toxicity.     

An algorithm for engineering design optimization

In this paper a new concept for development of algorithms for optimal design of engineering systems is presented. The basic idea is to use upper and lower bounds on optimum cost to develop iterative search strategies. The main feature of the concept is that it does not rely on one-dimensional search to compute a step size at any design iteration. Implication of the feature is that the algorithms based on this concept require evaluation of constraint functions only once at any design iteration. This is highly desirable for optimal design of engineering systems because evaluation of functions for such systems is very expensive due to their implicit dependence on design variables. An algorithm based on the new concept is derived in the paper. Several new step sizes are introduced and their relation to proper reduced optimal design problems are presented. A new step size based on the constant cost requirement at some design iterations is introduced. Numerical aspects for the algorithm are also presented. Based on the new algorithm, a general-purpose computer code GRP2 is developed. The code is used to solve several problems to gain experience and insight for the algorithm. Numerical experience with examples is discussed. It is concluded that algorithms based on bounding optimum cost have substantial potential for applications in optimal design of engineering systems.     

Barrier coverage with wireless sensors

When a sensor network is deployed to detect objects penetrating a protected region, it is not necessary to have every point in the deployment region covered by a sensor. It is enough if the penetrating objects are detected at some point in their trajectory. If a sensor network guarantees that every penetrating object will be detected by at least k distinct sensors before it crosses the barrier of wireless sensors, we say the network provides k-barrier coverage. In this paper, we develop theoretical foundations for k-barrier coverage. We propose efficient algorithms using which one can quickly determine, after deploying the sensors, whether the deployment region is k-barrier covered. Next, we establish the optimal deployment pattern to achieve k-barrier coverage when deploying sensors deterministically. Finally, we consider barrier coverage with high probability when sensors are deployed randomly. The major challenge, when dealing with probabilistic barrier coverage, is to derive critical conditions using which one can compute the minimum number of sensors needed to ensure barrier coverage with high probability. Deriving critical conditions for k-barrier coverage is, however, still an open problem. We derive critical conditions for a weaker notion of barrier coverage, called weak k-barrier coverage.     

Optimization of large‐scale truss structures using sparse SAND formulations

Sparsity features of simultaneous analysis and design (SAND) formulations are studied and exploited for optimization of large‐scale truss structures. Three formulations are described and implemented with an existing analysis code. SAND formulations have large number of variables; however, gradients of the functions and Hessian of the Lagrangian are quite sparsely populated. Therefore, this structure of the problem is exploited and an optimization algorithm with sparse matrix capability is used to solve large‐scale problems. An existing analysis software is integrated with an optimizer based on a sparse sequential quadratic programming (SQP) algorithm to solve sample problems. The formulations and algorithms work quite well for the sample problems, and their performances are compared and discussed. For all the cases considered, the SAND formulations out perform the conventional formulation except one case. Further research is suggested to fully study and utilize sparse features of the alternative SAND formulations and to develop more efficient sparse solvers for large‐scale and more complex applications. Copyright © 2006 John Wiley & Sons, Ltd.     

Ascorbic acid-mediated synthesis and characterisation of iron oxide/gold core–shell nanoparticles.

The current article reports on providing surface modification of magnetic nanoparticles with gold to provide stability against aggregation. Gold-coated magnetite nanoparticles were synthesised to combine both magnetic as well as surface plasma resonance (SPR) properties in a single moiety. The nanocomposites were produced by reduction (using ascorbic acid) of gold chloride on to the surface of iron oxide nanoparticles. Ascorbic acid not only acts as a reducing agent, but also the oxidised form of ascorbic acid i.e. Dehydro-ascorbic acid acts as a capping agent to impart stability to as synthesised gold-coated iron oxide nanocomposites. The synthesised nanocomposite was monodispersed with a mean particle size of around 16 nm and polydispersity index of 0.190. X-ray diffraction analysis confirms presence of gold on the surface of magnetite nanoparticles. The synthesised nanocomposites had a total organic content of around 3.2% w/w and also showed a shifted SPR peak at 546 nm as compared to gold nanoparticles (528 nm). Both uncoated and gold-coated magnetite exhibited superparamagnetic behaviour at room temperature. Upon coating with gold shell, saturation magnetisation of iron oxide nanoparticles decreases from 42.806 to 3.54 emu/gram.