Measuring energy spectra of TeV gamma-ray emission from the Cygnus region of our galaxy with Milagro

High energy gamma rays can provide fundamental clues to the origins of cosmic rays. In this thesis, TeV gamma-ray emission from the Cygnus region is studied. Previously the Milagro experiment detected five TeV gamma-ray sources in this region and a significant excess of TeV gamma rays whose origin is still unclear. To better understand the diffuse excess the separation of sources and diffuse emission is studied using the latest and most sensitive data set of the Milagro experiment. In addition, a newly developed technique is applied that allows the energy spectrum of the TeV gamma rays to be reconstructed using Milagro data. No conclusive statement can be made about the spectrum of the diffuse emission from the Cygnus region because of its low significance of 2.2 σ above the background in the studied data sample. The entire Cygnus region emission is best fit with a power law with a spectral index of α=2.40 (68% confidence interval: 1.35-2.92) and a exponential cutoff energy of 31.6 TeV (10.0-251.2 TeV). In the case of a simple power law assumption without a cutoff energy the best fit yields a spectral index of α=2.97 (68% confidence interval: 2.83-3.10). Neither of these best fits are in good agreement with the data. The best spectral fit to the TeV emission from MGRO J2019+37, the brightest source in the Cygnus region, yields a spectral index of α=2.30 (68% confidence interval: 1.40-2.70) with a cutoff energy of 50.1 TeV (68% confidence interval: 17.8-251.2 TeV) and a spectral index of α=2.75 (68% confidence interval: 2.65-2.85) when no exponential cutoff energy is assumed. According to the present analysis, MGRO J2019+37 contributes 25% to the differential flux from the entire Cygnus at 15 TeV.

Identification of Francisella tularensis cluster in central and western Europe

We conducted a molecular analysis of Francisella tularensis strains isolated in Switzerland and identified a specific subpopulation belonging to a cluster of F. tularensis subsp. holarctica that is widely dispersed in central and western continental Europe. This subpopulation was present before the tularemia epidemics on the Iberian Peninsula.

Long-term patterns of subjective wellbeing in schizophrenia: cluster, predictors of cluster affiliation, and their relation to recovery criteria in 2842 patients followed over 3 years

To study the longitudinal patterns of subjective wellbeing in schizophrenia using cluster analysis and their relation to recovery criteria, further to examine predictors for cluster affiliation, and to evaluate the sensitivity and specificity of baseline subjective wellbeing cut-offs for cluster affiliation.

Cluster randomized clinical trials in orthodontics: design, analysis and reporting issues

Cluster randomized trials (CRTs) use as the unit of randomization clusters, which are usually defined as a collection of individuals sharing some common characteristics. Common examples of clusters include entire dental practices, hospitals, schools, school classes, villages, and towns. Additionally, several measurements (repeated measurements) taken on the same individual at different time points are also considered to be clusters. In dentistry, CRTs are applicable as patients may be treated as clusters containing several individual teeth. CRTs require certain methodological procedures during sample calculation, randomization, data analysis, and reporting, which are often ignored in dental research publications. In general, due to similarity of the observations within clusters, each individual within a cluster provides less information compared with an individual in a non-clustered trial. Therefore, clustered designs require larger sample sizes compared with non-clustered randomized designs, and special statistical analyses that account for the fact that observations within clusters are correlated. It is the purpose of this article to highlight with relevant examples the important methodological characteristics of cluster randomized designs as they may be applied in orthodontics and to explain the problems that may arise if clustered observations are erroneously treated and analysed as independent (non-clustered).