1998 William J. Stickel Bronze Award. Antifungal activity of Melaleuca alternifolia (tea-tree) oil against various pathogenic organisms

Tea-tree oil (oil of Melaleuca alternifolia) has recently received much attention as a natural remedy for bacterial and fungal infections of the skin and mucosa. As with most naturally occurring agents, claims of effectiveness have been only anecdotal; however, several published studies have recently demonstrated tea-tree oil's antibacterial activity. This study was conducted to determine the activity of tea-tree oil against 58 clinical isolates: Candida albicans (n = 10), Trichophyton rubrum (n = 8), Trichophyton mentagrophytes (n = 9), Trichophyton tonsurans (n = 10), Aspergillus niger (n = 9), Penicillium species (n = 9), Epidermophyton floccosum (n = 2), and Microsporum gypsum (n = 1). Tea-tree oil showed inhibitory activity against all isolates tested except one strain of E floccosum. These in vitro results suggest that tea-tree oil may be useful in the treatment of yeast and fungal mucosal and skin infections.     

o-xylene hydrogenation on supported ruthenium catalysts

The influence of the support on the surface properties and catalytic activity of finely divided ruthenium catalysts is reported. The catalysts were prepared using an organometallic precursor, Ru(acac)₂, and three different supports, Al₂O₃, TiO₂ and SiO₂. In order to study the influence of the particle size on the catalytic performance, the effect of the calcination temperature was also evaluated. XPS suggests that the state of ruthenium is essentially Ru⁰, and chemisorption measurements indicate a decrease in metal dispersion from catalysts supported on Al₂O₃ > TiO₂ > SiO₂. The turnover number in the o-xylene hydrogenation showed significant differences depending on the support and on the particle size. Additionally, an increase in the selectivity to cis-dimethylcyclohexane with particle size was observed. This revised version was published online in July 2006 with corrections to the Cover Date.     

Regularized continuous estimation of distribution algorithms

Regularization is a well-known technique in statistics for model estimation which is used to improve the generalization ability of the estimated model. Some of the regularization methods can also be used for variable selection that is especially useful in high-dimensional problems. This paper studies the use of regularized model learning in estimation of distribution algorithms (EDAs) for continuous optimization based on Gaussian distributions. We introduce two approaches to the regularized model estimation and analyze their effect on the accuracy and computational complexity of model learning in EDAs. We then apply the proposed algorithms to a number of continuous optimization functions and compare their results with other Gaussian distribution-based EDAs. The results show that the optimization performance of the proposed RegEDAs is less affected by the increase in the problem size than other EDAs, and they are able to obtain significantly better optimization values for many of the functions in high-dimensional settings.     

Parameter Control of Genetic Algorithms by Learning and Simulation of Bayesian Networks — A Case Study for the Optimal Ordering of Tables

Parameter setting for evolutionary algorithms is still an important issue in evolutionary computation. There are two main approaches to parameter setting: parameter tuning and parameter control. In this paper, we introduce self-adaptive parameter control of a genetic algorithm based on Bayesian network learning and simulation. The nodes of this Bayesian network are genetic algorithm parameters to be controlled. Its structure captures probabilistic conditional (in)dependence relationships between the parameters. They are learned from the best individuals, i.e., the best configurations of the genetic algorithm. Individuals are evaluated by running the genetic algorithm for the respective parameter configuration. Since all these runs are time-consuming tasks, each genetic algorithm uses a small-sized population and is stopped before convergence. In this way promising individuals should not be lost. Experiments with an optimal search problem for simultaneous row and column orderings yield the same optima as state-of-the-art methods but with a sharp reduction in computational time. Moreover, our approach can cope with as yet unsolved high-dimensional problems.     

A Bayesian network model for surface roughness prediction in the machining process

The literature reports many scientific works on the use of artificial intelligence techniques such as neural networks or fuzzy logic to predict surface roughness. This article aims at introducing Bayesian network-based classifiers to predict surface roughness (Ra) in high-speed machining. These models are appropriate as prediction techniques because the non-linearity of the machining process demands robust and reliable algorithms to deal with all the invisible trends present when a work piece is machining. The experimental test obtained from a high-speed milling contouring process analysed the indicator of goodness using the Naïve Bayes and the Tree-Augmented Network algorithms. Up to 81.2% accuracy was achieved in the Ra classification results. Therefore, we envisage that Bayesian network-based classifiers may become a powerful and flexible tool in high-speed machining.     

Impact of Heat Moisture Treatment and Hydration Level of Flours on the Functional and Nutritional Value-Added Wheat-Barley Blended Breads

The impact of heat moisture treatment (HMT) of flours on the techno-functional and nutritional patterns of binary flour bread matrices (wheat/barley, WT/CB, 60:40, w/w) was investigated in untreated (?) and HMT (+) samples made at 160 and 170 dough yield (DY) levels. Assessment was performed by determining viscoelastic (stress relaxation test) and mechanical (double compression test) behaviours, volume (seed displacement), colour (Photoshop system), crumb grain (digital image analysis), starch digestibility (enzyme hydrolysis) and staling kinetics (Avrami equation), bioaccessible polyphenol content (digestive enzymatic mild extraction) and anti-radical activity (DPPH●). A superior functional profile was provided by HMT of CB flour in the blend WT?CB+ when hydrated at DY 170 compared to the untreated control WT?CB?. The sample exhibited a similar specific volume, more cohesive, springier, more resilient crumb, with similar rate and extent of crumb firming on ageing, and similar colour pattern but finer and more uniformly sized cell structure, and deserved similar sensory ratings as the control WT?CB? concerning cell uniformity, smoothness and typical smell and taste. Digestible starch kinetic curves of blended breads pointed out samples WT?CB+ and WT+CB+ as matrices expliciting a lower degree and slower rate of starch hydrolysis when mixed at low and high DY, respectively. A similar anti-radical activity for composite bread matrices was evidenced regardless of either HMT or DY.     

Ability of bovine mammary macrophages to enhance proliferation of autologous blood and mammary secretion lymphocytes

Cells were obtained by centrifuging the mammary secretion of healthy udders of 19 cows during the dry-period and during mid-lactation. The suspended cells were incubated in plastic wells. Those adhered cells classified as mammary macrophages were incubated with pokeweed mitogen (PWM). Autologous peripheral blood lymphocytes were added to wells containing untreated macrophage cultures or cultures pretreated with PWM. In seven cows autologous dry-period mammary lymphocytes were added instead of blood lymphocytes. The macrophages + lymphocyte cultures were subjected to the lymphocyte stimulation test (LST). For comparison, peripheral blood lymphocytes and dry-period secretion lymphocytes were also subjected to the LST in the presence of PWM. In all cases, mitogenic responses were higher in pretreated macrophage cultures than in background control cultures. The stimulation indices (SI) showed that PWM-pretreated dry-period mammary macrophages enhanced the proliferation of autologous peripheral blood lymphocytes to a greater extent than did blood lymphocytes plus PWM (49 +/- 10 v. 30 +/- 6; P less than or equal to 0.05). Mammary macrophages taken from the same cows but during midlactation also clearly induced proliferation of autologous peripheral blood lymphocytes but to a lesser extent than dry-period macrophages (16 +/- 2 v. 49 +/- 10; 16 +/- 2 v. 30 +/- 6; P less than or equal to 0.01 and P less than or equal to 0.05). The PWM pretreatment of mammary macrophages increased the proliferation of autologous dry-period mammary lymphocytes by at least a factor of three (28 +/- 8 v. 8 +/- 2 P less than or equal to 0.05). The present results indicate that bovine mammary macrophages pretreated with PWM enhance proliferation as well as modulation of mammary and peripheral blood lymphocytes. The modulation of lymphocyte stimulation as demonstrated here in vitro, has great significance regarding aspects of local immunostimulation related to modern treatment of mastitis.