Minding impacting events in a model of stochastic variance

We introduce a generalisation of the well-known ARCH process, widely used for generating uncorrelated stochastic time series with long-term non-Gaussian distributions and long-lasting correlations in the (instantaneous) standard deviation exhibiting a clustering profile. Specifically, inspired by the fact that in a variety of systems impacting events are hardly forgot, we split the process into two different regimes: a first one for regular periods where the average volatility of the fluctuations within a certain period of time is below a certain threshold and another one when the local standard deviation outnumbers it. In the former situation we use standard rules for heteroscedastic processes whereas in the latter case the system starts recalling past values that surpassed the threshold. Our results show that for appropriate parameter values the model is able to provide fat tailed probability density functions and strong persistence of the instantaneous variance characterised by large values of the Hurst exponent is greater than 0.8, which are ubiquitous features in complex systems.Comment: 18 pages, 5 figures, 1 table. To published in PLoS on

Prognostic value of nuclear morphometry in patients with TNM stage T1 ovarian clear cell adenocarcinoma

In 40 patients with TNM stage T1 ovarian clear cell adenocarcinoma, we used nuclear morphometry to study the relations among morphometric variables, clinical prognostic factors and outcome. The presence of one or more giant nuclear cells was positively associated with death (OR = 10.6, P = 0.02) and tended to be associated with disease recurrence (OR = 5.1, P = 0.07). Nuclear irregularity (expressed in terms of the nuclear roundness factor) was positively associated with both death (OR = 8.6, P = 0.02) and disease recurrence (OR = 8.2, P = 0.02). A combination of giant nuclear cell presence or nuclear irregularity proved to be a useful prognostic indicator, with a sensitivity and specificity of 83% and 71% in the prediction of death, and 75% and 71% in the prediction of disease recurrence. Patients' age and substage were of no prognostic value. We conclude that the nuclear morphometric characteristics, especially the presence of giant nuclear cells and nuclear irregularity, may be useful in predicting outcome in patients with early stage ovarian clear cell adenocarcinoma. © 1999 Cancer Research Campaig