A computational model for the cooling phase of injection moulding

This paper discusses the approaches and techniques used to build a realistic numerical model to analyse the cooling phase of the injection moulding process. The procedures employed to select an appropriate mesh and the boundary and initial conditions for the problem are discussed and justified. The final model is validated using direct comparisons with experimental results generated in an earlier study. The model is shown to be a useful tool for further studies aimed at optimising the cooling phase of the injection moulding process. Using the numerical model provides additional information relating to changes in conditions throughout the process, which otherwise could not be deduced or assessed experimentally. These results, and other benefits related to the use of the model, are also discussed in the paper. © 2007 Elsevier B.V. All rights reserved.

A computational model of cellular response to modulated radiation fields

Purpose:
To develop a model to describe the response of cell populations to spatially modulated radiation exposures of relevance to advanced radiotherapies.

Materials and Methods:
A Monte Carlo model of cellular radiation response was developed. This model incorporated damage from both direct radiation and intercellular communication including bystander signaling. The predictions of this model were compared to previously measured survival curves for a normal human fibroblast line (AGO1522) and prostate tumor cells (DU145) exposed to spatially modulated fields.

Results:
The model was found to be able to accurately reproduce cell survival both in populations which were directly exposed to radiation and those which were outside the primary treatment field. The model predicts that the bystander effect makes a significant contribution to cell killing even in uniformly irradiated cells. The bystander effect contribution varies strongly with dose, falling from a high of 80% at low doses to 25% and 50% at 4 Gy for AGO1522 and DU145 cells, respectively. This was verified using the inducible nitric oxide synthase inhibitor aminoguanidine to inhibit the bystander effect in cells exposed to different doses, which showed significantly larger reductions in cell killing at lower doses.

Conclusions:
The model presented in this work accurately reproduces cell survival following modulated radiation exposures, both in and out of the primary treatment field, by incorporating a bystander component. In addition, the model suggests that the bystander effect is responsible for a significant portion of cell killing in uniformly irradiated cells, 50% and 70% at doses of 2 Gy in AGO1522 and DU145 cells, respectively. This description is a significant departure from accepted radiobiological models and may have a significant impact on optimization of treatment planning approaches if proven to be applicable in vivo.

An economical route to one-way quantum computation

We assess the effects of a realistic intrinsic model for imperfections in cluster states by introducing noisy cluster states and characterizing their role in the one-way computational model. A suitable strategy to counter-affect these non-idealities is represented by the use of small clusters, stripped of any redundancy, which leads to the search for compact schemes for one-way quantum computation. In light of this, we quantitatively address the behavior of a simple four-qubit cluster which simulates a controlled-NOT under the influences of our model for decoherence. Our scheme can be particularly useful in an all-optical setup and the strategy we address can be directly applied in those, experimental situations where small cluster states can be constucted.

Suggested Binding Mechanism of the HIV-gp120 to its CD4 Receptor

The molecular recognition and attachment of the CD4 molecule and the HIV envelope glycoprotein (gp120) might be described as a consecutive three-step molecular recognition process. 1. (a) Long range interaction: electrostatic pre-orientation, 2. (b) short range interaction: electronic attachment followed by a ‘Locking-in’ (via aromatic ring orientation) and 3. (c) internal interaction (induced fit): conformational readjustment of the protein molecules. On the basis of the preliminary investigations (X-ray structures of CD4 and biological studies of CD4 and gp120 point mutants) we described a computational model. This approach consists of empirical calculations as well as ab initio level of quantum chemistry. The conformational analysis of the wild type and mutant CD4 molecules was supported by molecular mechanics and dynamics (Amber force field). The latter analysis involves the application of a novel method, the Amino Acid Conformation Assignment of Proteins (ACAP) software, developed for the notation of secondary protein structures. According to the cardinal role of the electrostatic factors during this interaction, several ab initio investigations were performed for better understanding of the recognition process on submolecular level. Using the above mentioned computational model, we could interpret the basic behaviours and predict some additional features of CD4-gp120 interaction, in spite of the missing gp120 X-ray structure.

Modeled structure of a G-protein-coupled receptor:The cholecystokinin-1 receptor

The Cholecystokinin-1 receptor (CCK1R) mediates actions of CCK in areas of the central nervous system and of the gut. It is a potential target to treat a number of diseases. As for all G-protein-coupled receptors, docking of ligands into modeled CCK1R binding site should greatly help to understand intrinsic mechanisms of activation. Here, we describe the procedure we used to progressively build a structural model for the CCK1R, to integrated, and on the basis of site-directed mutagenesis data on its binding site. Reliability of the CCK1R model was confirmed by interaction networks that involved conserved and functionally crucial motifs in G-protein-coupled receptors, such as Glu/Asp-Arg-Tyr and Asn-Pro-Xaa-Xaa-Tyr motifs. In addition, the 3-D structure of CCK1R-bound CCK resembled that determined by NMR in a lipid environment. The derived computational model was also used for revealing binding modes of several nonpeptide ligands and for rationalizing ligand structure-activity relationships known from experiments. Our findings indeed support that our "validated CCK1R model" could be used to study the intrinsic mechanism of CCK1R activation and design new ligands.

Communicating open systems

Just as conventional institutions are organisational structures for coordinating the activities of multiple interacting individuals, electronic institutions provide a computational analogue for coordinating the activities of multiple interacting software agents. In this paper, we argue that open multi-agent systems can be effectively designed and implemented as electronic institutions, for which we provide a comprehensive computational model. More specifically, the paper provides an operational semantics for electronic institutions, specifying the essential data structures, the state representation and the key operations necessary to implement them. We specify the agent workflow structure that is the core component of such electronic institutions and particular instantiations of knowledge representation languages that support the institutional model. In so doing, we provide the first formal account of the electronic institution concept in a rigorous and unambiguous way.

Application of a mechanobiological simulation technique to stents used clinically

Many cardiovascular diseases are characterised by the restriction of blood flow through arteries. Stents can be expanded within arteries to remove such restrictions; however, tissue in-growth into the stent can lead to restenosis. In order to predict the long-term efficacy of stenting, a mechanobiological model of the arterial tissue reaction to stress is required. In this study, a computational model of arterial tissue response to stenting is applied to three clinically relevant stent designs. We ask the question whether such a mechanobiological model can differentiate between stents used clinically, and we compare these predictions to a purely mechanical analysis. In doing so, we are testing the hypothesis that a mechanobiological model of arterial tissue response to injury could predict the long-term outcomes of stent design. Finite element analysis of the expansion of three different stent types was performed in an idealised, 3D artery. Injury was calculated in the arterial tissue using a remaining-life damage mechanics approach. The inflammatory response to this initial injury was modelled using equations governing variables which represented tissue-degrading species and growth factors. Three levels of inflammation response were modelled to account for inter-patient variability. A lattice-based model of smooth muscle cell behaviour was implemented, treating cells as discrete agents governed by local rules. The simulations predicted differences between stent designs similar to those found in vivo. It showed that the volume of neointima produced could be quantified, providing a quantitative comparison of stents. In contrast, the differences between stents based on stress alone were highly dependent on the choice of comparison criteria. These results show that the choice of stress criteria for stent comparisons is critical. This study shows that mechanobiological modelling may provide a valuable tool in stent design, allowing predictions of their long-term efficacy. The level of inflammation was shown to affect the sensitivity of the model to stent design. If this finding was verified in patients, this could suggest that high-inflammation patients may require alternative treatments to stenting.

Simultaneously uncovering the patterns of brain regions involved in different story reading subprocesses

Story understanding involves many perceptual and cognitive subprocesses, from perceiving individual words, to parsing sentences, to understanding the relationships among the story characters. We present an integrated computational model of reading that incorporates these and additional subprocesses, simultaneously discovering their fMRI signatures. Our model predicts the fMRI activity associated with reading arbitrary text passages, well enough to distinguish which of two story segments is being read with 74% accuracy. This approach is the first to simultaneously track diverse reading subprocesses during complex story processing and predict the detailed neural representation of diverse story features, ranging from visual word properties to the mention of different story characters and different actions they perform. We construct brain representation maps that replicate many results from a wide range of classical studies that focus each on one aspect of language processing and offer new insights on which type of information is processed by different areas involved in language processing. Additionally, this approach is promising for studying individual differences: it can be used to create single subject maps that may potentially be used to measure reading comprehension and diagnose reading disorders.

Computational Verification of Two Universal Relations for Simple Ionic Liquids. Kinetic Properties of a Model 2:1 Molten Salt

Two semianalytical relations [Nature, 1996, 381, 137 and Phys. Rev. Lett. 2001, 87, 245901] predicting dynamical coefficients of simple liquids on the basis of structural properties have been tested by extensive molecular dynamics simulations for an idealized 2:1 model molten salt. In agreement with previous simulation studies, our results support the validity of the relation expressing the self-diffusion coefficient as a Function of the radial distribution functions for all thermodynamic conditions such that the system is in the ionic (ie., fully dissociated) liquid state. Deviations are apparent for high-density samples in the amorphous state and in the low-density, low-temperature range, when ions condense into AB(2) molecules. A similar relation predicting the ionic conductivity is only partially validated by our data. The simulation results, covering 210 distinct thermodynamic states, represent an extended database to tune and validate semianalytical theories of dynamical properties and provide a baseline for the interpretation of properties of more complex systems such as the room-temperature ionic liquids.

A Fast Nonlinear Model Identification Method

The identification of nonlinear dynamic systems using linear-in-the-parameters models is studied. A fast recursive algorithm (FRA) is proposed to select both the model structure and to estimate the model parameters. Unlike orthogonal least squares (OLS) method, FRA solves the least-squares problem recursively over the model order without requiring matrix decomposition. The computational complexity of both algorithms is analyzed, along with their numerical stability. The new method is shown to require much less computational effort and is also numerically more stable than OLS.

Stochastic protein folding simulation in the three-dimensional HP-model

We present results from three-dimensional protein folding simulations in the HP-model on ten benchmark problems. The simulations are executed by a simulated annealing-based algorithm with a time-dependent cooling schedule. The neighbourhood relation is determined by the pull-move set. The results provide experimental evidence that the maximum depth D of local minima of the underlying energy landscape can be upper bounded by D < n(2/3). The local search procedure employs the stopping criterion (In/delta)(D/gamma) where m is an estimation of the average number of neighbouring conformations, gamma relates to the mean of non-zero differences of the objective function for neighbouring conformations, and 1-delta is the confidence that a minimum conformation has been found. The bound complies with the results obtained for the ten benchmark problems. (c) 2008 Elsevier Ltd. All rights reserved.