Hydrotalcite intercalated siRNA:computational characterization of the interlayer environment

Using molecular dynamics (MD) simulations, we explore the structural and dynamical properties of siRNA within the intercalated environment of a Mg:Al 2:1 Layered Double Hydroxide (LDH) nanoparticle. An ab initio force field (Condensed-phase Optimized Molecular Potentials for Atomistic Simulation Studies: COMPASS) is used for the MD simulations of the hybrid organic-inorganic systems. The structure, arrangement, mobility, close contacts and hydrogen bonds associated with the intercalated RNA are examined and contrasted with those of the isolated RNA. Computed powder X-ray diffraction patterns are also compared with related LDH-DNA experiments. As a method of probing whether the intercalated environment approximates the crystalline or rather the aqueous state, we explore the stability of the principle parameters (e.g., the major groove width) that differentiate both A- and A'- crystalline forms of siRNA and contrast this with recent findings for the same siRNA simulated in water. We find the crystalline forms remain structurally distinct when intercalated, whereas this is not the case in water. Implications for the stability of hybrid LDH-RNA systems are discussed.

Edge coding in human vision: a psychophysical and computational investigation

This thesis presents a study of how edges are detected and encoded by the human visual system. The study begins with theoretical work on the development of a model of edge processing, and includes psychophysical experiments on humans, and computer simulations of these experiments, using the model. The first chapter reviews the literature on edge processing in biological and machine vision, and introduces the mathematical foundations of this area of research. The second chapter gives a formal presentation of a model of edge perception that detects edges and characterizes their blur, contrast and orientation, using Gaussian derivative templates. This model has previously been shown to accurately predict human performance in blur matching tasks with several different types of edge profile. The model provides veridical estimates of the blur and contrast of edges that have a Gaussian integral profile. Since blur and contrast are independent parameters of Gaussian edges, the model predicts that varying one parameter should not affect perception of the other. Psychophysical experiments showed that this prediction is incorrect: reducing the contrast makes an edge look sharper; increasing the blur reduces the perceived contrast. Both of these effects can be explained by introducing a smoothed threshold to one of the processing stages of the model. It is shown that, with this modification,the model can predict the perceived contrast and blur of a number of edge profiles that differ markedly from the ideal Gaussian edge profiles on which the templates are based. With only a few exceptions, the results from all the experiments on blur and contrast perception can be explained reasonably well using one set of parameters for each subject. In the few cases where the model fails, possible extensions to the model are discussed.

Computational capabilities of multilayer committee machines

We obtained an analytical expression for the computational complexity of many layered committee machines with a finite number of hidden layers (L < 8) using the generalization complexity measure introduced by Franco et al (2006) IEEE Trans. Neural Netw. 17 578. Although our result is valid in the large-size limit and for an overlap synaptic matrix that is ultrametric, it provides a useful tool for inferring the appropriate architecture a network must have to reproduce an arbitrary realizable Boolean function.

Synthetic, crystallographic, and computational study of copper(II) complexes of ethylenediaminetetracarboxylate ligands

Copper(II) complexes of hexadentate ethylenediaminetetracarboxylic acid type ligands Heda3p and Heddadp (Heda3p = ethylenediamine-N-acetic-N,N',N'-tri-3-propionic acid; H eddadp = ethylenediamine-N,N'-diacetic-N,N'-di-3- propionic acid) have been prepared. An octahedral trans(O) geometry (two propionate ligands coordinated in axial positions) has been established crystallographically for the Ba[Cu(eda3p)]·8HO compound, while Ba[Cu(eddadp)]·8HO is proposed to adopt a trans(O ) geometry (two axial acetates) on the basis of density functional theory calculations and comparisons of IR and UV-vis spectral data. Experimental and computed structural data correlating similar copper(II) chelate complexes have been used to better understand the isomerism and departure from regular octahedral geometry within the series. The in-plane O-Cu-N chelate angles show the smallest deviation from the ideal octahedral value of 90°, and hence the lowest strain, for the eddadp complex with two equatorial ß-propionate rings. A linear dependence between tetragonality and the number of five-membered rings has been established. A natural bonding orbital analysis of the series of complexes is also presented.

Resource allocation in decentralised computational systems:an evolutionary market-based approach

We present a novel market-based method, inspired by retail markets, for resource allocation in fully decentralised systems where agents are self-interested. Our market mechanism requires no coordinating node or complex negotiation. The stability of outcome allocations, those at equilibrium, is analysed and compared for three buyer behaviour models. In order to capture the interaction between self-interested agents, we propose the use of competitive coevolution. Our approach is both highly scalable and may be tuned to achieve specified outcome resource allocations. We demonstrate the behaviour of our approach in simulation, where evolutionary market agents act on behalf of service providing nodes to adaptively price their resources over time, in response to market conditions. We show that this leads the system to the predicted outcome resource allocation. Furthermore, the system remains stable in the presence of small changes in price, when buyers' decision functions degrade gracefully. © 2009 The Author(s).

Replication-based inference algorithms for hard computational problems

Inference algorithms based on evolving interactions between replicated solutions are introduced and analyzed on a prototypical NP-hard problem: the capacity of the binary Ising perceptron. The efficiency of the algorithm is examined numerically against that of the parallel tempering algorithm, showing improved performance in terms of the results obtained, computing requirements and simplicity of implementation. © 2013 American Physical Society.

Investigation of a binding model for anti-cancer cyclohexadienones by computational chemistry and crystallography

DUE TO COPYRIGHT RESTRICTIONS ONLY AVAILABLE FOR CONSULTATION AT ASTON UNIVERSITY LIBRARY AND INFORMATION SERVICES WITH PRIOR ARRANGEMENT

Computational mechanics analysis of the hidden conformational dynamics within a molecular trajectory

DUE TO COPYRIGHT RESTRICTIONS ONLY AVAILABLE FOR CONSULTATION AT ASTON UNIVERSITY LIBRARY AND INFORMATION SERVICES WITH PRIOR ARRANGEMENT

Modelling a biochemical reaction with computational fluid dynamics

Earlier investigations (Cartland Glover et al., 2004) into the use of computational fluid dynamics (CFD) for the modelling of gas-liquid and gas-liquid-solid flow allowed a simple biochemical reaction model to be implemented. A single plane mesh was used to represent the transport and reaction of molasses, the mould Aspergillus niger and citric acid in a bubble column with a height to diameter aspect ratio of 20:1. Two specific growth rates were used to examine the impact that biomass growth had on the local solids concentration and the effect this had on the local hydrodynamics of the bubble column.

Computational methods for traditional Chinese medicine:a survey

Traditional Chinese Medicine (TCM) has been actively researched through various approaches, including computational techniques. A review on basic elements of TCM is provided to illuminate various challenges and progresses in its study using computational methods. Information on various TCM formulations, in particular resources on databases of TCM formulations and their integration to Western medicine, are analyzed in several facets, such as TCM classifications, types of databases, and mining tools. Aspects of computational TCM diagnosis, namely inspection, auscultation, pulse analysis as well as TCM expert systems are reviewed in term of their benefits and drawbacks. Various approaches on exploring relationships among TCM components and finding genes/proteins relating to TCM symptom complex are also studied. This survey provides a summary on the advance of computational approaches for TCM and will be useful for future knowledge discovery in this area. © 2007 Elsevier Ireland Ltd. All rights reserved.

Alpha helical trans-membrane proteins:enhanced prediction using a Bayesian approach

Membrane proteins, which constitute approximately 20% of most genomes, are poorly tractable targets for experimental structure determination, thus analysis by prediction and modelling makes an important contribution to their on-going study. Membrane proteins form two main classes: alpha helical and beta barrel trans-membrane proteins. By using a method based on Bayesian Networks, which provides a flexible and powerful framework for statistical inference, we addressed alpha-helical topology prediction. This method has accuracies of 77.4% for prokaryotic proteins and 61.4% for eukaryotic proteins. The method described here represents an important advance in the computational determination of membrane protein topology and offers a useful, and complementary, tool for the analysis of membrane proteins for a range of applications.

Beta barrel trans-membrane proteins:enhanced prediction using a Bayesian approach

Membrane proteins, which constitute approximately 20% of most genomes, form two main classes: alpha helical and beta barrel transmembrane proteins. Using methods based on Bayesian Networks, a powerful approach for statistical inference, we have sought to address beta-barrel topology prediction. The beta-barrel topology predictor reports individual strand accuracies of 88.6%. The method outlined here represents a potentially important advance in the computational determination of membrane protein topology.

Intonation contour realisation for Standard Yorùbá text-to-speech synthesis:a fuzzy computational approach

This paper presents a novel intonation modelling approach and demonstrates its applicability using the Standard Yorùbá language. Our approach is motivated by the theory that abstract and realised forms of intonation and other dimensions of prosody should be modelled within a modular and unified framework. In our model, this framework is implemented using the Relational Tree (R-Tree) technique. The R-Tree is a sophisticated data structure for representing a multi-dimensional waveform in the form of a tree. Our R-Tree for an utterance is generated in two steps. First, the abstract structure of the waveform, called the Skeletal Tree (S-Tree), is generated using tone phonological rules for the target language. Second, the numerical values of the perceptually significant peaks and valleys on the S-Tree are computed using a fuzzy logic based model. The resulting points are then joined by applying interpolation techniques. The actual intonation contour is synthesised by Pitch Synchronous Overlap Technique (PSOLA) using the Praat software. We performed both quantitative and qualitative evaluations of our model. The preliminary results suggest that, although the model does not predict the numerical speech data as accurately as contemporary data-driven approaches, it produces synthetic speech with comparable intelligibility and naturalness. Furthermore, our model is easy to implement, interpret and adapt to other tone languages.