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     

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.     

An outbreak of malignant catarrhal fever in young rusa deer (Cervus timorensis)

On the basis of clinical signs and histological findings eight 9-month-old male rusa deer (Cervus timorensis) were diagnosed with sheep associated-malignant catarrhal fever. Following a variable course involving rectal temperatures around 40.5 degrees C, depression, inappetence, diarrhoea, corneal opacity and hypopyon all animals died or were euthanased over a 5-week period. Severe multifocal vasculitis, mainly periglomerular and in the arcuate vessels were consistent histological findings which in the past have been adequate to confirm clinical diagnosis of sheep associated-malignant catarrhal fever. A nested polymerase chain reaction test has been used to detect a sheep associated-malignant catarrhal fever PRC product, 238 base-pairs in size, in DNA extracted from lymphocyte preparations. The result supported the diagnosis of sheep associated-malignant catarrhal fever in these deer.     

Running programs backwards: The logical inversion of imperative computation

Imperative programs can be inverted directly from their forward-directed program code with the use of logical inference. The relational semantics of imperative computations treats programs as logical relations over the observable state of the environment, which is taken to be the state of the variables in memory. Program relations denote both forward and backward computations, and the direction of the computation depends upon the instantiation pattern of arguments in the relation. This view of inversion has practical applications when the relational semantics is treated as a logic program. Depending on the logic programming inference scheme used, execution of this relational program can compute the inverse of the imperative program. A number of nontrivial imperative computations can be inverted with minimal logic programming tools.     

APPLICATION OF THE POLYMERASE CHAIN REACTION TO THE RAPID ANALYSIS OF BREWERY YEAST STRAINS

The rapid discrimination of closely-related Saccharomyces cerevisiae strains can pose a significant problem to breweries, in particular where closely related strains are being used simultaneously to manufacture different products. In this study, two PCR approaches have been examined to assess their usefulness for the discrimination of brewery ale and lager yeast strains. PCR using arbitrary primers (RAPD PCR) was found unsuitable for such an application since the DNA profiles generated from brewery strains were generally found to be identical, due presumably to the close genetic relatedness of these yeasts. In contrast, PCR using δ sequence primers could rapidly differentiate between many ale and lager strains and characteristic profiles for these were generated. This method could also be applied directly to yeasts isolated from brewery worts or from active dried yeast preparations. Results of such analyses were available within the working day.