The history of circadian rhythm research in Austria.

In view of the recent revival of interest in circadian biology and circadian epidemiology at the Medical University of Vienna, it seems appropriate to highlight the rich and pioneering history of circadian research in Austria. Among the forefathers of circadian research in Vienna are Otto Marburg (1874-1948), who discovered important elements of the pineal gland physiology, Robert Hofstätter (1883-1970), who used pineal gland extract in obstetrics/gynecology, and Paul Engel (1907-1997), who discovered that the pineal gland was controlled by light. More recently, Vera Lapin (1920-2007) showed that surgical removal of the pineal gland increased tumor growth, while Franz Waldhauser (*1946) investigated melatonin in conjunction with night work.

A comprehensive suite of earthquake catalogues for the 2016-2017 Central Italy seismic sequence.

The protracted nature of the 2016-2017 central Italy seismic sequence, with multiple damaging earthquakes spaced over months, presented serious challenges for the duty seismologists and emergency managers as they assimilated the growing sequence to advise the local population. Uncertainty concerning where and when it was safe to occupy vulnerable structures highlighted the need for timely delivery of scientifically based understanding of the evolving hazard and risk. Seismic hazard assessment during complex sequences depends critically on up-to-date earthquake catalogues-i.

An assessment of what Hans Asperger knew about child euthanasia in Vienna during the Nazi occupation.

Aim: Hans Asperger is probably best known for Asperger syndrome. However, he has been accused of knowingly and willingly participating in the National Socialist Child Euthanasia programme by referring patients to the Am Spiegelgrund children's home in Vienna. This later became notorious for euthanising disabled children.

Machine learning reveals cyclic changes in seismic source spectra in Geysers geothermal field.

The earthquake rupture process comprises complex interactions of stress, fracture, and frictional properties. New machine learning methods demonstrate great potential to reveal patterns in time-dependent spectral properties of seismic signals and enable identification of changes in faulting processes. Clustering of 46,000 earthquakes of 0.