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     

Development of a recurrent Sigma-Pi neural network rainfall forecasting system in Hong Kong

At the moment, weather forecasting is still an art — the experience and intuition of forecasters play a significant role in determining the quality of forecasting. This paper describes the development of a new approach to rainfall forecasting using neural networks. It deals with the extraction of information from radar images and an evaluation of past rain gauge records to provide shortterm rainfall forecasting. All of the meteorological data were provided by the Royal Observatory of Hong Kong (ROHK). Preprocessing procedures were essential for this neural network rainfall forecasting. The forecast of the rainfall was performed every half an hour so that a storm warning signal can be delivered to the public in advance. The network architecture is based on a recurrent Sigma-Pi network. The results are very promising, and this neural-based rainfall forecasting system is capable of providing a rain storm warning signal to the Hong Kong public one hour ahead.     

CMV colitis masquerading as colon cancer--an unusual presentation of acquired immunodeficiency syndrome

To determine whether there is any correlation between sudden decrease in barometric pressure and onset of labor, a non-experimental, retrospective study at a 948-bed tertiary care hospital was done. Pregnant patients of 36 weeks gestation or more who presented with spontaneous onset of labor during the 48 hours surrounding the 12 occurrences of significant drop in barometric pressure in 1992 were included in the study. Significantly more occurrences of onset of labor were identified in the 24 hours after a drop in barometric pressure than were identified in the 24 hours prior to the drop in barometric pressure (P < 0.05). Therefore, the overall number of labor onsets increased in the 24 hours following a significant drop in barometric pressure.     

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.     

Power Distribution Fault Cause Identification With Imbalanced Data Using the Data Mining-Based Fuzzy Classification E-Algorithm

Power distribution systems have been significantly affected by many outage-causing events. Good fault cause identification can help expedite the restoration procedure and improve the system reliability. However, the data imbalance issue in many real-world data sets often degrades the fault cause identification performance. In this paper, the E-algorithm, which is extended from the fuzzy classification algorithm by Ishibuchi to alleviate the effect of imbalanced data constitution, is applied to Duke Energy outage data for distribution fault cause identification. Three major outage causes (tree, animal, and lightning) are used as prototypes. The performance of E-algorithm on real-world imbalanced data is compared with artificial neural network. The results show that the E-algorithm can greatly improve the performance when the data are imbalanced     

The Transmogrifier-2: a 1 million gate rapid-prototyping system

This paper describes the Transmogrifier-2 (TM-2), a second-generation multifield programmable gate array (FPGA) rapid-prototyping system. The largest version of the system will comprise 16 boards that each contain two Altera 10K50 FPGA's, four I-Cube interconnect chips, and up to 8 Mbytes of memory. The inter-FPGA routing architecture of the TM-2 uses a novel interconnect structure, a nonuniform partial crossbar, that provides a constant delay between any two FPGA's in the system. The TM-2 architecture is modular and scalable, meaning that systems of various sizes can be constructed from copies of the same board, while maintaining routability and the constant delay feature. Other features include a system-level programmable clock that allows single-cycle access to off-chip memory, and programmable clock waveforms with edge resolution of 10 ns. The first Transmogrifier-2 boards have been manufactured and are functional. They have recently been used successfully in some simple graphics acceleration applications