Mensing. Community stroke knowledge: a new information strategy using a joint project of the public health service and the hairdressers' guild of the Wesel district. Journal of Public Health, Springer Verlag, 2009, 17 (6) Abstract Objectives The public health programme "Healthy Lower Rhine…against Stroke" is aimed at improving the population's knowledge about stroke and thus at reducing the prehospital phase in patients with suspected stroke. First evaluation results indicate that apart from providing information through the mass media, there is an urgent need to further develop the face-to-face communication approach. This has to be achieved by efficient but also effective means, given that financial and personnel resources are scarce. Study design In cooperation with lögd Bielefeld, the Lower Health Authority of the Wesel District (health department) developed a postcard-sized quiz card containing exclusively correct answers on the issue of stroke, risk factors as well as symptom and action knowledge. For face-to-face communication, the hairdressers could be convinced to be included in the project. The hairdressers posed the corresponding questions and marked those answers of the clients that were identical with the quiz card answers with a cross. Answers not given by the clients were read out loud to them by the hairdressers, who were thus "styling up" the knowledge of their clients. To increase participation in the project, prizes were offered for the hairdressers with the most filled-in quiz cards as well as for three of the participating clients (drawing of prizes 1-3). More than 380 hairdressers in the Wesel district were sent a letter inviting them to participate as facilitators in this project, which is probably the first of its kind worldwide. Methods The machine-readable quiz cards were collected and statistically evaluated including data regarding age and gender of the participants. Results were to be presented in the form of a descriptive statistic. Results Thirty-three hairdressers from 12 cities and municipalities of the Wesel district participated in this joint action of the Wesel district Department of Health and the Wesel hairdressers' guild, dealing with the monitoring and imparting of basic knowledge on the issue of stroke. Almost 2,000 clients were interviewed by the participating hairdressers, and knowledge gaps were closed by information read out to them. Discussion This innovative approach of imparting knowledge can be regarded as the model of an effective and economical way of communicating health information to the broader public.

No abstract

No abstract

No abstract

Motivation: Mathematical models take an important place in science and engineering. A model can help scientists to explain dynamic behavior of a system and to understand the functionality of system components. Since length of a time series and number of replicates is limited by the cost of experiments, Boolean networks as a structurally simple and parameter-free logical model for gene regulatory networks have attracted interests of many scientists. In order to fit into the biological contexts and to lower the data requirements, biological prior knowledge is taken into consideration during the inference procedure. In the literature, the existing identification approaches can only deal with a subset of possible types of prior knowledge.Results: We propose a new approach to identify Boolean networks from time series data incorporating prior knowledge, such as partial network structure, canalizing property, positive and negative unateness. Using vector form of Boolean variables and applying a generalized matrix multiplication called the semi-tensor product (STP), each Boolean function can be equivalently converted into a matrix expression. Based on this, the identification problem is reformulated as an integer linear programming problem to reveal the system matrix of Boolean model in a computationally efficient way, whose dynamics are consistent with the important dynamics captured in the data. By using prior knowledge the number of candidate functions can be reduced during the inference. Hence, identification incorporating prior knowledge is especially suitable for the case of small size time series data and data without sufficient stimuli. The proposed approach is illustrated with the help of a biological model of the network of oxidative stress response.Conclusions: The combination of efficient reformulation of the identification problem with the possibility to incorporate various types of prior knowledge enables the application of computational model inference to systems with limited amount of time series data. The general applicability of this methodological approach makes it suitable for a variety of biological systems and of general interest for biological and medical research.