In the construction of an X-ray selected sample of galaxy clusters for cosmological studies, we have assembled a sample of 495 X-ray sources found to show extended X-ray emission in the first processing of the ROSAT All-Sky Survey. The sample covers the celestial region with declination δ ≥ 0 • and galactic latitude |b II | ≥ 20 • and comprises sources with a count rate ≥ 0.06 counts s −1 and a source extent likelihood of 7. In an optical follow-up identification program we find 378 (76%) of these sources to be clusters of galaxies.It was necessary to reanalyse the sources in this sample with a new X-ray source characterization technique to provide more precise values for the X-ray flux and source extent than obtained from the standard processing. This new method, termed growth curve analysis (GCA), has the advantage over previous methods to be robust, easy to model and to integrate into simulations, to provide diagnostic plots for visual inspection, and to make extensive use of the X-ray data. The source parameters obtained assist the source identification and provide more precise X-ray fluxes. This reanalysis is based on data from the more recent second processing of the ROSAT Survey. We present a catalogue of the cluster sources with the X-ray properties obtained as well as a list of the previously flagged extended sources which are found to have a non-cluster counterpart. We discuss the process of source identification from the combination of optical and X-ray data.To investigate the overall completeness of the cluster sample as a function of the X-ray flux limit, we extent the search for X-ray cluster sources to the data of the second processing of the ROSAT Survey for the northern sky region between 9 h and 14 h in right ascension. We include the search for X-ray emission of known clusters as well as a new investigation of extended X-ray sources. In the course of this search we find X-ray emission from additional 85 Abell clusters and 56 very probable cluster candidates among the newly found extended sources. A comparison of the X-ray cluster number counts of the NORAS sample with the REFLEX Cluster Survey results leads to an estimate of the completeness of the NORAS sample of RASS I extended clusters of about 50% at an X-ray flux of F x (0.1 − 2.4keV) = 3 × 10 −12 erg s −1 cm −2 . The estimated completeness achieved by adding the supplementary sample in the study area amounts to about 82% in comparison to REFLEX. The low completeness introduces an uncertainty in the use of the sample for cosmological statistical studies which will be cured with the completion of the continuing Northern ROSAT All-Sky -3 -(NORAS) cluster survey project.

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AbstractPrimary headaches are common disease of the modern society and it has high negative impact on the productivity and the life quality of the affected person. Unfortunately, the precise diagnosis of the headache type is hard and usually imprecise, thus methods of headache diagnosis are still the focus of intense research. The paper introduces the problem of the primary headache diagnosis and presents its current taxonomy. The considered problem is simplified into the three class classification task which is solved using advanced machine learning techniques. Experiments, carried out on the large dataset collected by authors, confirmed that computer decision support systems can achieve high recognition accuracy and therefore be a useful tool in an everyday physician practice. This is the starting point for the future research on automation of the primary headache diagnosis.

Background: Headaches have not only medical but also great socioeconomic significance, therefore, it is necessary to evaluate the overall impact of headaches on a patient’s life, including their work and work efficiency. The aim of this study was to determine the impact of individual headache types on work and work efficiency. Methods: This research was designed as a cross-sectional study performed by administering a questionnaire among employees. The questionnaire consisted of general questions, questions about headache features, and questions about the impact of headaches on work. Results: Monthly absence from work was mostly represented by migraine sufferers (7.1%), significantly more than with sufferers with tension-type headaches (2.23%; p = 0.019) and other headache types (2.15%; p = 0.025). Migraine sufferers (30.2%) worked in spite of a headache for more than 25 h, which was more frequent than with sufferers from tension-type and other-type headaches (13.4%). On average, headache sufferers reported work efficiency ranging from 66% to 90%. With regard to individual headache types, this range was significantly more frequent in subjects with tension-type headaches, whereas 91–100% efficiency was significantly more frequent in subjects with other headache types. Lower efficiency, i.e., 0–40% and 41–65%, was significantly more frequent with migraine sufferers. Conclusions: Headaches, especially migraines, significantly affect the work and work efficiency of headache sufferers by reducing their productivity. Loss is greater due to reduced efficiency than due to absenteeism.