Formulation of sage essential oil (Salvia officinalis,L.) monoterpenes into chitosan hydrogels and permeation study with GC-MS analysis

This study deals with the formulation of natural drugs into hydrogels. For the first time, compounds from the sage essential oil were formulated into chitosan hydrogels. A sample preparation procedure for hydrophobic volatile analytes present in a hydrophilic water matrix along with an analytical method based on the gas chromatography coupled with the mass spectrometry (GC-MS) was developed and applied for the evaluation of the identity and quantity of essential oil components in the hydrogels and saline samples. The experimental results revealed that the chitosan hydrogels are suitable for the formulation of sage essential oil. The monoterpene release can be effectively controlled by both chitosan and caffeine concentration in the hydrogels. Permeation experiment, based on a hydrogel with the optimized composition [3.5% (w/w) sage essential oil, 2.0% (w/w) caffeine, 2.5% (w/w) chitosan and 0.1% (w/w) Tween-80] in donor compartment, saline solution in acceptor compartment, and semi-permeable cellophane membrane, demonstrated the useful permeation selectivity. Here, (according to lipophilicity) an enhanced permeation of the bicyclic monoterpenes with antiflogistic and antiseptic properties (eucalyptol, camphor and borneol) and, at the same time, suppressed permeation of toxic thujone (not exceeding its permitted applicable concentration) was observed. These properties highlight the pharmaceutical importance of the developed chitosan hydrogel formulating sage essential oil in the dermal applications.     

Lignin from Annual Plants as Raw Material Source for Flavors and Basic Chemicals

The enzymatic conversion of lignins, possibly in combination with electrochemical oxidation, makes aromatics such as syringol, guaiacol, vanillin and catechol available in the qualities required by the fragrance industry. The lignins were obtained by soda digestion from wheat straw and Miscanthus, characterized and then converted with laccases. The overall yield amounted up to 9 wt % with a product spectrum confined to four substances. Catechol was the major product, with a fraction of ≈75 %. It can easily be isolated by extraction with acetone.     

Quantitative pharmacophore models with inductive logic programming

Three-dimensional models, or pharmacophores, describing Euclidean constraints on the location on small molecules of functional groups (like hydrophobic groups, hydrogen acceptors and donors, etc.), are often used in drug design to describe the medicinal activity of potential drugs (or ‘ligands’). This medicinal activity is produced by interaction of the functional groups on the ligand with a binding site on a target protein. In identifying structure-activity relations of this kind there are three principal issues: (1) It is often difficult to “align” the ligands in order to identify common structural properties that may be responsible for activity; (2) Ligands in solution can adopt different shapes (or `conformations’) arising from torsional rotations about bonds. The 3-D molecular substructure is typically sought on one or more low-energy conformers; and (3) Pharmacophore models must, ideally, predict medicinal activity on some quantitative scale. It has been shown that the logical representation adopted by Inductive Logic Programming (ILP) naturally resolves many of the difficulties associated with the alignment and multi-conformation issues. However, the predictions of models constructed by ILP have hitherto only been nominal, predicting medicinal activity to be present or absent. In this paper, we investigate the construction of two kinds of quantitative pharmacophoric models with ILP: (a) Models that predict the probability that a ligand is “active”; and (b) Models that predict the actual medicinal activity of a ligand. Quantitative predictions are obtained by the utilising the following statistical procedures as background knowledge: logistic regression and naive Bayes, for probability prediction; linear and kernel regression, for activity prediction. The multi-conformation issue and, more generally, the relational representation used by ILP results in some special difficulties in the use of any statistical procedure. We present the principal issues and some solutions. Specifically, using data on the inhibition of the protease Thermolysin, we demonstrate that it is possible for an ILP program to construct good quantitative structure-activity models. We also comment on the relationship of this work to other recent developments in statistical relational learning. Editors: Tamás Horváth and Akihiro Yamamoto     

MANAGING DISTRIBUTED COMPUTING Five Practices for Success

Management of distributed environments requires corporate IS managers to shift from being operators of a central utility to facilitators who can recognize the synergy among loosely related groups of users. Success depends on the development of new ways of visualizing and measuring the service delivery process.     

Simulation of magnetic component models in electric circuitsincluding dynamic thermal effects

It is essential in the simulation of power electronics applications to model magnetic components accurately. In addition to modeling the nonlinear hysteresis behavior, eddy currents and winding losses must be included to provide a realistic model. In practice the losses in magnetic components give rise to significant temperature increases which can lead to major changes in the component behavior. In this paper a model of magnetic components is presented which integrates a nonlinear model of hysteresis, electro-magnetic windings and thermal behavior in a single model for use in circuit simulation of power electronics systems. Measurements and simulations are presented which demonstrate the accuracy of the approach for the electrical, magnetic and thermal domains across a variety of operating conditions, including static thermal conditions and dynamic self heating     

Ozonation of Two Acidic Azo Dyes with Different Substituents

The ozonation of two differently substituted azo dyes (Schwarz GRS and Orange Acid 8) in water media is studied. The influence of pH on the effectiveness of the ozonation at various initial concentrations of each dye is explored. It was found out that the rates of decolorization for amino-group substituted dyes reflect the considerable influence by the widely varying initial pH from 4.5 to 10. Specifically, the highest effect of decolorization of this dye was obtained at the highest pH studied (pH 10) for all initial concentrations of the solutions. Considering the dye without an amino-group substitute, the rates of color disappearance in ozonation reflected to a lower degree the variations of the initial pH. Pseudo-first-order trends of decolorization were observed in all the experimental runs. Regarding the kinetic results obtained, an attempt to explain the different dyes reactivity was made based on the absolute electronegativity (E_lumo + E_homo) of both dyes. The COD/BOD analysis shows that the ozonation of both azo dyes can reduce the sample COD but it could not improve the biodegradability ratio (BOD₅/COD). BOD decrease with ozonation time indicates that the intermediates of the ozonation are of lower biodegradability. Oxalic acid was found as the final product of ozonation of both dyes.     

What We Mean by Future-Proofing

Perhaps the most powerful claim that advocates of fiber can make,on top of the massive bandwidthfiber makes possible today, is that fiber is future-proof. That is, the fiber that is laid today can be upgraded so that the same strands of glass can carry massively more bandwidth in the future.     

HIPAA's Effect on Web Site Privacy Policies

Healthcare institutions typically post their privacy practices online as privacy policy documents. We conducted a longitudinal study that examines the effects of HIPAA's enactment on a collection of privacy policy documents for a fixed set of organizations over a four-year period. We present our analysis of 24 healthcare privacy policy documents from nine healthcare Web sites, analyzed using goal mining, a content-analysis method that supports extraction of useful information about institutions' privacy practices from documents. We compare our results to our pre-HIPAA study of these same institutions' online privacy practices and evaluate their evolution in the presence of privacy laws