Spatial distribution of indoor radon in Triveneto (Northern Italy): a geostatistical approach.

The study of spatial distribution of the indoor radon has assumed in the last years a lot of interest. The geostatistical techniques turn out to be particularly promising. The present work presents the results of a study where around 4000 indoor radon data from Veneto, Friuli Venezia-Giulia and Alto Adige, collected during the sampling campaigns performed in dwellings and in schools, have been analyzed. After the definition of the common data set, the study of the spatial distribution of the phenomenon has been performed by examining the experimental variograms. Declustering techniques have been applied. Predictive maps were defined by using simulation techniques; they allow to determine the probabilities of exceeding defined concentration levels, the 'radon-prone' areas. Systematic results regarding the validation of these maps are reported. This methodological study indicates how it is possible to understand the geographical variability of the phenomenon, trying to find out correlations among indoor radon, geological characteristics (i.e. lithology, morphology, tectonics, soil gas) and building-specific features, which can significantly influence radon concentrations.

Machine learning for the analysis of indoor radon distribution, compared with ordinary kriging.

Having a reliable forecasting tool is necessary to correctly identify radon prone areas, especially in cases where the variable of interest is the indoor radon concentration. An appropriate characterisation of the features of the buildings becomes fundamental. In this work, the results obtained (in global and local scale) using the following approaches for estimating the concentration of indoor radon at locations that were not sampled were compared: geostatistical model, based on ordinary kriging, and machine learning (ML) technique. In the first case, algorithms designed for the specific and fine treatment (by modelling the variographic structure) of the spatial component of the phenomenon were used, whereas in the second case a model that can also exploit information linked to other variables that characterise each single dwelling in which the measure was conducted was used. For locations having large errors, the ML approach provides better results, due to the information related to 'soil contact' and 'building material'.

[Cat scratch disease. Bartonella henselae antibodies and DNA detection in regional lymphadenopathy]. / Katzenkratzkrankheit. Bartonella-henselae-Antikörper und Nukleinsäurenachweis bei regionaler Lymphadenopathie.

HISTORY AND CLINICAL FINDINGS: Two hard, pressure-sensitive nodules developed in the lower jaw of a 22-year-old woman. After a dental cause had been excluded, she was treated for suspected tonsillitis with Ceftibuten. Erythrocyte sedimentation rate was increased to 18 mm in the first hour. There were no other significant biochemical findings and fine-needle biopsy of one of the nodules showed nonspecific inflammatory reaction. INVESTIGATIONS: Sonography revealed two lymph nodes, 7 and 22 mm in diameter. Suspected cat scratch disease was confirmed by immunofluorescence with Bartonella (Rochalimaea) henselae and quintana antigens. TREATMENT AND COURSE: After a course of Clarithromycin (250 mg twice daily) for 6 weeks the lymph nodes had shrunk and the overlying skin was thin and discoloured brown. One node was incised and drained and the material examined. Microbiology was negative, but DNA sequencing confirmed Bartonella henselae. As a consequence, Rifampicin was given for 2 months (600 mg daily). Wound healing was very slow and the scar had regressed little after 9 months.