A framework for habitat monitoring and climate change modelling: construction and validation of the Environmental Stratification of Estonia

Environmental stratifications provide the framework for efficient surveillance and monitoring of biodiversity and ecological resources, as well as modelling exercises. An obstacle for agricultural landscape monitoring in Estonia has been the lack of a framework for the objective selection of monitoring sites. This paper describes the construction and testing of the Environmental Stratification of Estonia (ESE). Principal components analysis was used to select the variables that capture the most amount of variation. Seven climate variables and topography were selected and subsequently subjected to the ISODATA clustering routine in order to produce relatively homogeneous environmental strata. The ESE contains eight strata, which have been described in terms of soil, land cover and climatic parameters. In order to assess the reliability of the stratification procedure for the selection of monitoring sites, the ESE was compared with the previous map of Landscape Regions of Estonia and correlated with five environmental data sets. All correlations were significant. The stratification has therefore already been used to extend the current series of samples in agricultural landscapes into a more statistically robust series of monitoring sites. The potential for applying climate change scenarios to assess the shifts in the strata and associated ecological impacts is also examined.</p

The Control of Population Tumor Cells via Compensatory Effect

Mathematical models and simulation studies are powerful tools to investigate dynamic properties of complex systems. Complex models with constant parameters often approach a steady state. In this research, we present an overview of mathematical approaches applied to the description of control population tumor cells. We are researching the influence of D-factor on tumor cells population. We called it compensatory effect. There is Vaccination as biological analogue of the compensator effect. D-factor is presented as catastrophe theory’s fold form. Further, numeric results of model experiments are showing that this approach is positive efficient

The Control of Population Tumor Cells via Compensatory Effect

Abstract: Mathematical models and simulation studies are powerful tools to investigate dynamic properties of complex systems. Complex models with constant parameters often approach a steady state. In this research, we present an overview of mathematical approaches applied to the description of control population tumor cells. We are researching the influence of D-factor on tumor cells population. We called it compensatory effect. There is Vaccination as biological analogue of the compensator effect. D-factor is presented as catastrophe theory’s fold form. Further, numeric results of model experiments are showing that this approach is positive efficient

Objective score from initial interview identifies patients with probable dissociative seizures.

ObjectiveTo develop a Dissociative Seizures Likelihood Score (DSLS), which is a comprehensive, evidence-based tool using information available during the first outpatient visit to identify patients with "probable" dissociative seizures (DS) to allow early triage to more extensive diagnostic assessment.MethodsBased on data from 1616 patients with video-electroencephalography (vEEG) confirmed diagnoses, we compared the clinical history from a single neurology interview of patients in five mutually exclusive groups: epileptic seizures (ES), DS, physiologic nonepileptic seizure-like events (PSLE), mixed DS plus ES, and inconclusive monitoring. We used data-driven methods to determine the diagnostic utility of 76 features from retrospective chart review and applied this model to prospective interviews.ResultsThe DSLS using recursive feature elimination (RFE) correctly identified 77% (95% confidence interval (CI), 74-80%) of prospective patients with either ES or DS, with a sensitivity of 74% and specificity of 84%. This accuracy was not significantly inferior than neurologists' impression (84%, 95% CI: 80-88%) and the kappa between neurologists' and the DSLS was 21% (95% CI: 1-41%). Only 3% of patients with DS were missed by both the fellows and our score (95% CI 0-11%).SignificanceThe evidence-based DSLS establishes one method to reliably identify some patients with probable DS using clinical history. The DSLS supports and does not replace clinical decision making. While not all patients with DS can be identified by clinical history alone, these methods combined with clinical judgement could be used to identify patients who warrant further diagnostic assessment at a comprehensive epilepsy center