Overweight, physical activity, tobacco and alcohol consumption in a cross-sectional random sample of German adults

BACKGROUND: There is a current paucity of data on the health behaviour of non-selected populations in Central Europe. Data on health behaviour were collected as part of the EMIL study which investigated the prevalence of infection with Echinococcus multilocularis and other medical conditions in an urban German population. METHODS: Participating in the present study were 2,187 adults (1,138 females [52.0%]; 1,049 males [48.0%], age: 18–65 years) taken from a sample of 4,000 persons randomly chosen from an urban population. Data on health behaviour like physical activity, tobacco and alcohol consumption were obtained by means of a questionnaire, documentation of anthropometric data, abdominal ultrasound and blood specimens for assessment of chemical parameters. RESULTS: The overall rate of participation was 62.8%. Of these, 50.3% of the adults were overweight or obese. The proportion of active tobacco smokers stood at 30.1%. Of those surveyed 38.9% did not participate in any physical activity. Less than 2 hours of leisure time physical activity per week was associated with female sex, higher BMI (Body Mass Index), smoking and no alcohol consumption. Participants consumed on average 12 grams of alcohol per day. Total cholesterol was in 62.0% (>5.2 mmol/l) and triglycerides were elevated in 20.5% (≥ 2.3 mmol/l) of subjects studied. Hepatic steatosis was identified in 27.4% of subjects and showed an association with male sex, higher BMI, higher age, higher total blood cholesterol, lower HDL, higher triglycerides and higher ALT. CONCLUSION: This random sample of German urban adults was characterised by a high prevalence of overweight and obesity. This and the pattern of alcohol consumption, smoking and physical activity can be considered to put this group at high risk for associated morbidity and underscore the urgent need for preventive measures aimed at reducing the significantly increased health risk

Preneoplastic lesions of the lung

Lung cancer is the leading cause of cancer deaths worldwide. If we can define and detect preneoplastic lesions, we might have a chance of improving survival. The World Health Organization has defined three preneoplastic lesions of the bronchial epithelium: squamous dysplasia/carcinoma in situ; atypical adenomatous hyperplasia; and diffuse idiopathic pulmonary neuroendocrine cell hyperplasia. These lesions are believed to progress to squamous cell carcinoma, adenocarcinoma and carcinoid tumors, respectively. In this review we summarize the data supporting the preneoplastic nature of these lesions, and delve into some of the genetic changes found in atypical adenomatous hyperplasia and squamous dysplasia/carcinoma in situ

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino detector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower- or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino de tector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower-or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches