Leibniz Research Centre for Working Environment and Human Factors

About: Leibniz Research Centre for Working Environment and Human Factors is a facility organization based out in . It is known for research contribution in the topics: Computer science & Medicine. The organization has 16 authors who have published 30 publications receiving 94 citations.

Nutrient intake, dietary patterns, and anthropometric variables of children with ADHD in comparison to healthy controls: a case-control study

Abstract: Poor health behaviors and variables are recently more documented in attention-deficit hyperactivity disorder (ADHD) lifestyle which might be relevant to the pathophysiology of this disorder. The objective of this case-control study was to assess the nutrient intake, dietary patterns, and anthropometric variables in children with ADHD compared to normal peers.One hundred children diagnosed with ADHD were included and compared to 100 healthy, sex-matched normal children as the control group. Anthropometric indices, macronutrients, and micronutrients were measured and compared in both groups.ADHD children were significantly consuming more simple sugars, tea, ready-made meals but less protein, vitamin B1, vitamin B2, vitamin C, zinc and calcium compared to the control group. The body mass index (BMI) and waist circumference of children with ADHD were significantly higher and were related to the severity and type of the disease.Unhealthy eating behavior is more frequent in children with ADHD, compared to normal children which might warrant lifestyle intervention in this disorder.

Case‐Control Study on a Decade of Ground‐Based Magnetometers in California Reveals Modest Signal 24–72 hr Prior to Earthquakes

Abstract: Magnetic field changes as earthquake precursors have been the subject of numerous studies and some controversy. Infrequent large earthquakes and sparse magnetometer coverage along fault zones complicate statistical analysis. We present an analysis of ground-based magnetic time-series measurements before 19 earthquakes ≥M4.5 in California drawing from over 330,000 site-days of measurement spanning a decade. To perform a fair existential test for electromagnetic antecedents we applied a pre-specified statistical analysis with two key ideas. First, we combine signals from nearby (≤40 km) sites via spectral cross-power, and then look for large spikes in frequency domain (0.016–25 Hz). The former is only possible with a dense set of sites running over a long period of time. In this statistical case-control study we used the machine learning concept of rigorously separated train and test sets of earthquakes which were generated via a rule-based query of the USGS earthquake catalog. Before each declustered earthquake, we constructed one period 24–72 hr before (the “precursor” or “p-period”) and a series of seven equally-sized preceding periods (“quiescent” or “q-periods”). We distilled the data in each period to a frequency-dependent feature—the 98th percentile of spectral cross power. We trained a model based on Linear Discriminant Analysis and applied the discriminator to the test set revealing a modest effect in the days leading up to an earthquake. While the observed effect size is not directly useful for earthquake prediction (long a scientific goal), it suggests a relationship which should be further investigated for a physical link.