DomNet: protein domain boundary prediction using enhanced general regression network and new profiles.

The accurate and stable prediction of protein domain boundaries is an important avenue for the prediction of protein structure, function, evolution, and design. Recent research on protein domain boundary prediction has been mainly based on widely known machine learning techniques. In this paper, we propose a new machine learning based domain predictor namely, DomNet that can show a more accurate and stable predictive performance than the existing state-of-the-art models. The DomNet is trained using a novel compact domain profile, secondary structure, solvent accessibility information, and interdomain linker index to detect possible domain boundaries for a target sequence. The performance of the proposed model was compared to nine different machine learning models on the Benchmark_2 dataset in terms of accuracy, sensitivity, specificity, and correlation coefficient. The DomNet achieved the best performance with 71% accuracy for domain boundary identification in multidomains proteins. With the CASP7 benchmark dataset, it again demonstrated superior performance to contemporary domain boundary predictors such as DOMpro, DomPred, DomSSEA, DomCut, and DomainDiscovery.

CDP840. A prototype of a novel class of orally active anti-inflammatory phosphodiesterase 4 inhibitors.

The discovery, synthesis and biological activity of a series of triarylethane phosphodiesterase 4 inhibitors is described. Structure-activity relationship studies are presented for CDP840 (29), a potent, chiral, selective inhibitor of PDE 4 (IC(50) 4nM). CDP840 is non-emetic in the ferret at 30mgkg(-1) (po), active in models of inflammation and reverses ozone-induced bronchial hyperreactivity in the guinea pig.

Numerical computation of differential-algebraic equations for nonlinear dynamics of multibody android systems in automobile crash simulation.

The principle of virtual work is used to derive the Euler-Lagrange equations of motion in order to describe the dynamics of multibody android systems. The constrained variational equations are in fact differential-algebraic equations of high index and are cast as ordinary differential equations through differentiation of the constraint equations. The integration routine LSODAR and the fourth-order Runge-Kutta method are used to compute the generalized coordinates, their time derivatives and the body forces of two android models. The graphs of the constraint forces reveal the whiplash effect on the neck and that the stiffness of both multibody systems is due to large magnitude impulsive forces experienced by many bodies simultaneously.

A parallel computing engine for a class of time critical processes.

This paper focuses on the efficient parallel implementation of systems of numerically intensive nature over loosely coupled multiprocessor architectures. These analytical models are of significant importance to many real-time systems that have to meet severe time constants. A parallel computing engine (PCE) has been developed in this work for the efficient simplification and the near optimal scheduling of numerical models over the different cooperating processors of the parallel computer. First, the analytical system is efficiently coded in its general form. The model is then simplified by using any available information (e.g., constant parameters). A task graph representing the interconnections among the different components (or equations) is generated. The graph can then be compressed to control the computation/communication requirements. The task scheduler employs a graph-based iterative scheme, based on the simulated annealing algorithm, to map the vertices of the task graph onto a Multiple-Instruction-stream Multiple-Data-stream (MIMD) type of architecture. The algorithm uses a nonanalytical cost function that properly considers the computation capability of the processors, the network topology, the communication time, and congestion possibilities. Moreover, the proposed technique is simple, flexible, and computationally viable. The efficiency of the algorithm is demonstrated by two case studies with good results.