A comparison of the chemical repair rates of free radical precursors of DNA damage and cell killing in Chinese hamster V79 cells.

One of the important temporal stages of radiation action in cellular systems is the chemical phase, where oxygen fixation reactions compete with chemical repair reactions involving reducing agents such as GSH. Using the gas explosion technique it is possible to follow the kinetics of these fast (> 1 ms) reactions in intact cells. We have compared the chemical repair kinetics of the oxygen-dependent free radical precursors leading to DNA single-strand and double-strand breaks, measured using filter elution techniques, with those leading to cell killing in V79 cells. The chemical repair rates for DNA dsb (670s-1 at pH 7.2 and 380s-1 at pH 9.6) and cell killing (530s-1) were similar. This is in agreement with the important role of DNA dsb in radiation induced cell lethality. The rate for DNA ssb precursors was significantly slower (210s-1). The difference in rate between DNA ssb and dsb precursors may be explained on the basis of a dsb free radical precursor consisting of a paired radical, one radical on each strand. The instantaneous probability of one or other of these radicals being chemically repaired and not proceeding to form a dsb will be twice that of a ssb radical precursor. This agrees well with the concept of locally multiply damaged sites (LMDS) produced from clusters of ionizations in DNA (Ward 1985).

The role of non-protein sulphydryls in determining the chemical repair rates of free radical precursors of DNA damage and cell killing in Chinese hamster V79 cells. I

Chinese hamster V79 fibroblasts were irradiated in the gas explosion apparatus and the chemical repair rates of the oxygen-dependent free radical precursors of DNA double-strand breaks (dsb) and lethal lesions measured using filter elution (pH 9.6) and a clonogenic assay. Depletion of cellular GSH levels, from 4.16 fmol/cell to 0.05 fmol/cell, by treatment with buthionine sulphoximine (50 mumol dm-3; 18 h), led to sensitization as regards DNA dsb induction and cell killing. This was evident at all time settings but was particularly pronounced when the oxygen shot was given 1 ms after the irradiation pulse. A detailed analysis of the chemical repair kinetics showed that depletion of GSH led to a reduction in the first-order rate constant for dsb precursors from 385 s-1 to 144 s-1, and for lethal lesion precursors from 533 s-1 to 165 s-1. This is generally consistent with the role of GSH in the repair-fixation model of radiation damage at the critical DNA lesions. However, the reduction in chemical repair rate was not proportional to the severe thiol depletion (down to almost-equal-to 1% for GSH) and a residual repair capacity remained (almost-equal-to 30%). This was found not to be due to compartmentalization of residual GSH in the nucleus, as the repair rate for dsb precursors in isolated nuclei, washed virtually free of GSH, was identical to that found in GSH-depleted cells (144 s-1), also the OER remained substantially above unity. This suggests that other reducing agents may have a role to play in the chemical repair of oxygen-dependent damage. One possible candidate is the significant level of protein sulphydryls present in isolated nuclei.

ARE ARTIFICIAL NEURAL NETS AS SMART AS A RAT

The origins of artificial neural networks are related to animal conditioning theory: both are forms of connectionist theory, which in turn derives from the empiricist philosophers' principle of association. The parallel between animal learning and neural nets suggests that interaction between them should benefit both sides.

Improved structure optimization for fuzzy-neural networks

Fuzzy-neural-network-based inference systems are well-known universal approximators which can produce linguistically interpretable results. Unfortunately, their dimensionality can be extremely high due to an excessive number of inputs and rules, which raises the need for overall structure optimization. In the literature, various input selection methods are available, but they are applied separately from rule selection, often without considering the fuzzy structure. This paper proposes an integrated framework to optimize the number of inputs and the number of rules simultaneously. First, a method is developed to select the most significant rules, along with a refinement stage to remove unnecessary correlations. An improved information criterion is then proposed to find an appropriate number of inputs and rules to include in the model, leading to a balanced tradeoff between interpretability and accuracy. Simulation results confirm the efficacy of the proposed method.

A New Gradient Descent Approach for Local Learning of Fuzzy Neural Models

The majority of reported learning methods for Takagi-Sugeno-Kang fuzzy neural models to date mainly focus on the improvement of their accuracy. However, one of the key design requirements in building an interpretable fuzzy model is that each obtained rule consequent must match well with the system local behaviour when all the rules are aggregated to produce the overall system output. This is one of the distinctive characteristics from black-box models such as neural networks. Therefore, how to find a desirable set of fuzzy partitions and, hence, to identify the corresponding consequent models which can be directly explained in terms of system behaviour presents a critical step in fuzzy neural modelling. In this paper, a new learning approach considering both nonlinear parameters in the rule premises and linear parameters in the rule consequents is proposed. Unlike the conventional two-stage optimization procedure widely practised in the field where the two sets of parameters are optimized separately, the consequent parameters are transformed into a dependent set on the premise parameters, thereby enabling the introduction of a new integrated gradient descent learning approach. A new Jacobian matrix is thus proposed and efficiently computed to achieve a more accurate approximation of the cost function by using the second-order Levenberg-Marquardt optimization method. Several other interpretability issues about the fuzzy neural model are also discussed and integrated into this new learning approach. Numerical examples are presented to illustrate the resultant structure of the fuzzy neural models and the effectiveness of the proposed new algorithm, and compared with the results from some well-known methods.

Determining propeller induced erosion alongside quay walls in harbours using Artificial Neural Networks

In shallow waters, such as those found close to berth structures, the wash from a manoeuvring ship’s propeller can cause erosion of the seabed. This erosion can be increased if the wash intersects a berth structure. A number of researchers have undertaken model studies and used regression analysis to develop predictive relationships for the scouring action. This paper presents an experimental investigation with Artificial Neural Networks (ANN’s), used to analyse the results. The purpose of using ANN’s was to examine the prediction accuracy of the Networks in comparison with previous regression analysis methods. ANN’s were found to provide a more accurate method of predicting propeller wash scour than the equations presented by previous investigators.