Malignant bone pain: pathophysiology and treatment

2-acetyl aminofluorene (AAF) reacts in acidic conditions with nitrous fume yielding N-nitroso-AAF (N-NO-AAF), as previously described, that exerts more toxic and mutagenic effects than its parental compound. In this study, the effect of sodium nitrite (NaNO2) on the tumorigenicity of AAF in rats fed with AAF and NaNO2 was observed. Wistar rats were divided into five groups: group I served as control; group II were treated with NaNO2 (0.3%); group III was given 0.02% AAF alone; groups IV and V received both AAF and NaNO2 (0.2 and 0.3% respectively) in their diet for 12 weeks. At the end of the experiment, all rats in groups III, IV and V developed early stage phenomena of hepatocellular carcinoma, including hepatomegaly with variable-sized foci and neoplastic nodules. Severe damage was observed in the rats treated with AAF and NaNO2. Feeding of AAF (0.02%) for 3 months elevated the levels of c-Fos, c-Jun and c-Myc proteins in the rat livers. The AAF-induced c-Jun, c-Fos and c-Myc expressions were significantly magnified (P < 0.001) by NaNO2. These data confirmed that the strengthening of AAF-induced hepatocarcinogenesis by NaNO2 should be associated with its enhancing effect on the AAF-induced increases in the expressions of c-Jun, c-Fos and c-Myc.     

Performance evaluation of rule grouping on a real-time expertsystem architecture

Uses a Markov process to model a real-time expert system architecture characterized by message passing and event-driven scheduling. The model is applied to the performance evaluation of rule grouping for real-time expert systems running on this architecture. An optimizing algorithm based on Kernighan-Lin heuristic graph partitioning for the real-time architecture is developed and a demonstration system based on the model and algorithm has been developed and tested on a portion of the advanced GPS receiver (AGR) and manned manoeuvring unit (MMU) knowledge bases     

Quantization effects in digitally behaving circuit implementationsof Kohonen networks

Implementing a neural network on a digital or mixed analog and digital chip yields the quantization of the synaptic weights dynamics. This paper addresses this topic in the case of Kohonen's self-organizing maps. We first study qualitatively how the quantization affects the convergence and the properties, and deduce from this analysis the way to choose the parameters of the network (adaptation gain and neighborhood). We show that a spatially decreasing neighborhood function is far more preferable than the usually rectangular neighborhood function, because of the weight quantization. Based on these results, an analog nonlinear network, integrated in a standard CMOS technology, and implementing this spatially decreasing neighborhood function is then presented. It can be used in a mixed analog and digital circuit implementation.     

Large-deviation inequalities for parameter estimators in stochastic systems

Translated from Kibernetika i Sistemnyi Analiz, No. 2, pp. 95–112, March–April, 1994.     

Approximation of the optimal-time problem for controlled differential inclusions

Translated from Kibernetika i Sistemnyi Analiz, No. 3, pp. 172–176, May–June, 1994