Scholarly publishing and argument in hyperspace

The World Wide Web is opening up access to documents and data for scholars. However it has not yet impacted on one of the primary activities in research: assessing new findings in the light of current knowledge and debating it with colleagues. The ClaiMaker system uses a directed graph model with similarities to hypertext, in which new ideas are published as nodes, which other contributors can build on or challenge in a variety of ways by linking to them. Nodes and links have semantic structure to facilitate the provision of specialist services for interrogating and visualizing the emerging network. By way of example, this paper is grounded in a ClaiMaker model to illustrate how new claims can be described in this structured way.

A nonantisense sequence-selective effect of a phosphorothioate oligodeoxynucleotide directed against the epidermal growth factor receptor in A431 cells

The overexpression of epidermal growth factor receptor (EGFr) has been implicated as a causative factor and a poor prognostic marker in a number of carcinomas. Therefore, strategies that down-regulate EGFr expression may be therapeutically useful. We designed antisense ODNs complementary to the initiation codon region of the EGFr mRNA and evaluated their efficacy in several tumor-derived cells, including the A431 cell line that express amplified levels of EGFr. A 15-mer phosphorothioate (PS) antisense ODN (erbB1AS15) induced a concentration-dependent reduction in proliferation that was accompanied by a change in the morphology of A431 cells into more tightly clustered and discrete colonies. A 15-mer sense (PS) control oligodeoxynucleotide (ODN) and a phosphodiester (PO) version of erbB1AS15 had little or no effect on cell number of morphology, and erbB1AS15 (PS) did not induce these effects in control cell lines expressing lower levels of EGFr. The effects of erbB1AS15 (PS) on A431 cells were not mediated by a true antisense mechanism in that there was no reduction in the level of EGFr mRNA or protein over a 24-hr period, as determined by Northern and Western blotting, respectively. However, autophosphorylation of the receptor was significantly reduced by erbB1AS15 (PS) and not by control ODNs. The results of further studies suggested that this effect was mediated by a direct, dose-dependent inhibition of the EGFr tyrosine kinase enzyme and was not due to impairment of either ligand-binding or receptor dimerization. These data suggest that erbB1AS15 (PS) can inhibit proliferation and alter the morphology of A431 cells by a sequence-selective, but nonantisense mechanism affecting receptor tyrosine kinase activity.

Modelling conditional probability distributions for periodic variables

Most of the common techniques for estimating conditional probability densities are inappropriate for applications involving periodic variables. In this paper we introduce two novel techniques for tackling such problems, and investigate their performance using synthetic data. We then apply these techniques to the problem of extracting the distribution of wind vector directions from radar scatterometer data gathered by a remote-sensing satellite.

Modelling conditional probability distributions for periodic variables

Most of the common techniques for estimating conditional probability densities are inappropriate for applications involving periodic variables. In this paper we apply two novel techniques to the problem of extracting the distribution of wind vector directions from radar catterometer data gathered by a remote-sensing satellite.

Modelling conditional probability distributions for periodic variables

Most conventional techniques for estimating conditional probability densities are inappropriate for applications involving periodic variables. In this paper we introduce three related techniques for tackling such problems, and investigate their performance using synthetic data. We then apply these techniques to the problem of extracting the distribution of wind vector directions from radar scatterometer data gathered by a remote-sensing satellite.

Estimating conditional probability densities for periodic variables

Most of the common techniques for estimating conditional probability densities are inappropriate for applications involving periodic variables. In this paper we introduce three novel techniques for tackling such problems, and investigate their performance using synthetic data. We then apply these techniques to the problem of extracting the distribution of wind vector directions from radar scatterometer data gathered by a remote-sensing satellite.

Estimating conditional volatility with neural networks

It is well known that one of the obstacles to effective forecasting of exchange rates is heteroscedasticity (non-stationary conditional variance). The autoregressive conditional heteroscedastic (ARCH) model and its variants have been used to estimate a time dependent variance for many financial time series. However, such models are essentially linear in form and we can ask whether a non-linear model for variance can improve results just as non-linear models (such as neural networks) for the mean have done. In this paper we consider two neural network models for variance estimation. Mixture Density Networks (Bishop 1994, Nix and Weigend 1994) combine a Multi-Layer Perceptron (MLP) and a mixture model to estimate the conditional data density. They are trained using a maximum likelihood approach. However, it is known that maximum likelihood estimates are biased and lead to a systematic under-estimate of variance. More recently, a Bayesian approach to parameter estimation has been developed (Bishop and Qazaz 1996) that shows promise in removing the maximum likelihood bias. However, up to now, this model has not been used for time series prediction. Here we compare these algorithms with two other models to provide benchmark results: a linear model (from the ARIMA family), and a conventional neural network trained with a sum-of-squares error function (which estimates the conditional mean of the time series with a constant variance noise model). This comparison is carried out on daily exchange rate data for five currencies.

Random graph coloring:Statistical physics approach

The problem of vertex coloring in random graphs is studied using methods of statistical physics and probability. Our analytical results are compared to those obtained by exact enumeration and Monte Carlo simulations. We critically discuss the merits and shortcomings of the various methods, and interpret the results obtained. We present an exact analytical expression for the two-coloring problem as well as general replica symmetric approximated solutions for the thermodynamics of the graph coloring problem with p colors and K-body edges. ©2002 The American Physical Society.

Robust control of nonlinear stochastic systems by modelling conditional distributions of control signals

We introduce a novel inversion-based neuro-controller for solving control problems involving uncertain nonlinear systems that could also compensate for multi-valued systems. The approach uses recent developments in neural networks, especially in the context of modelling statistical distributions, which are applied to forward and inverse plant models. Provided that certain conditions are met, an estimate of the intrinsic uncertainty for the outputs of neural networks can be obtained using the statistical properties of networks. More generally, multicomponent distributions can be modelled by the mixture density network. In this work a novel robust inverse control approach is obtained based on importance sampling from these distributions. This importance sampling provides a structured and principled approach to constrain the complexity of the search space for the ideal control law. The performance of the new algorithm is illustrated through simulations with example systems.

Conditional distribution modeling for estimating and exploiting uncertainty in control systems

This paper presents a general methodology for estimating and incorporating uncertainty in the controller and forward models for noisy nonlinear control problems. Conditional distribution modeling in a neural network context is used to estimate uncertainty around the prediction of neural network outputs. The developed methodology circumvents the dynamic programming problem by using the predicted neural network uncertainty to localize the possible control solutions to consider. A nonlinear multivariable system with different delays between the input-output pairs is used to demonstrate the successful application of the developed control algorithm. The proposed method is suitable for redundant control systems and allows us to model strongly non Gaussian distributions of control signal as well as processes with hysteresis.

Targeted delivery of a novel group of site-directed transglutaminase inhibitors to the liver using liposomes:a new approach for the potential treatment of liver fibrosis

Liver fibrosis and its end-stage disease cirrhosis are a main cause of mortality and morbidity worldwide. Thus far, there is no efficient pharmaceutical intervention for the treatment of liver fibrosis. Liver fibrosis is characterized by excessive accumulation of the extracellular matrix (ECM) proteins. Transglutaminase (TG)-mediated covalent cross-linking has been implicated in the stabilization and accumulation of ECM in a number of fibrotic diseases. Thus, the use of tissue TG2 inhibitors has potential in the treatment of liver fibrosis. Recently, we introduced a novel group of site-directed irreversible specific inhibitors of TGs. Here, we describe the development of a liposome-based drug-delivery system for the site-specific delivery of these TG inhibitors into the liver. By using anionic or neutral-based DSPC liposomes, the TG inhibitor can be successfully incorporated into these liposomes and delivered specifically to the liver. Liposomes can therefore be used as a potential carrier system for site-specific delivery of the TG2 inhibitors into the liver, opening up a potential new avenue for the treatment of liver fibrosis and its end-stage disease cirrhosis.

Using monthly returns to model conditional heteroscedasticity

This empirical study examines the extent of non-linearity in a multivariate model of monthly financial series. To capture the conditional heteroscedasticity in the series, both the GARCH(1,1) and GARCH(1,1)-in-mean models are employed. The conditional errors are assumed to follow the normal and Student-t distributions. The non-linearity in the residuals of a standard OLS regression are also assessed. It is found that the OLS residuals as well as conditional errors of the GARCH models exhibit strong non-linearity. Under the Student density, the extent of non-linearity in the GARCH conditional errors was generally similar to those of the standard OLS. The GARCH-in-mean regression generated the worse out-of-sample forecasts.

Conditional volatility and firm size:an empirical analysis of UK equity portfolios

The techniques and insights from two distinct areas of financial economic modelling are combined to provide evidence of the influence of firm size on the volatility of stock portfolio returns. Portfolio returns are characterized by positive serial correlation induced by the varying levels of non-synchronous trading among the component stocks. This serial correlation is greatest for portfolios of small firms. The conditional volatility of stock returns has been shown to be well represented by the GARCH family of statistical processes. Using a GARCH model of the variance of capitalization-based portfolio returns, conditioned on the autocorrelation structure in the conditional mean, striking differences related to firm size are uncovered.

Incorporation of N-heterocyclic cations into proteins with a highly directed chemical modification

N-Heterocyclic cations are incorporated into proteins using 5-(2-bromoethyl)phenanthridinium bromide, which selectively reacts with either cysteine or lysine residues, resulting in ethylphenanthridinium (Phen) or highly stable cyclised dihydro-imidazo-phenanthridinium (DIP) adducts respectively; these modifications have been found to manipulate the observed structure of lysozyme and bovine serum albumin by AFM.

Typical case behaviour of spin systems in random graph and composite ensembles

This thesis includes analysis of disordered spin ensembles corresponding to Exact Cover, a multi-access channel problem, and composite models combining sparse and dense interactions. The satisfiability problem in Exact Cover is addressed using a statistical analysis of a simple branch and bound algorithm. The algorithm can be formulated in the large system limit as a branching process, for which critical properties can be analysed. Far from the critical point a set of differential equations may be used to model the process, and these are solved by numerical integration and exact bounding methods. The multi-access channel problem is formulated as an equilibrium statistical physics problem for the case of bit transmission on a channel with power control and synchronisation. A sparse code division multiple access method is considered and the optimal detection properties are examined in typical case by use of the replica method, and compared to detection performance achieved by interactive decoding methods. These codes are found to have phenomena closely resembling the well-understood dense codes. The composite model is introduced as an abstraction of canonical sparse and dense disordered spin models. The model includes couplings due to both dense and sparse topologies simultaneously. The new type of codes are shown to outperform sparse and dense codes in some regimes both in optimal performance, and in performance achieved by iterative detection methods in finite systems.