Variable-density flow in the subsurface of oceanic islands : physical experiments and numerical modeling

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Neurons against Noise : Neural adaptations for dim light vision in hawkmoths

All animals perceive the world through their senses, which form the basis for their decisions and motor actions. However, when these all-important senses reach their limit and cease to provide reliable information, the animal’s survival is threatened. Among the senses, vision is brought to its limits on a daily basis, because its signal strength is diminished as night falls, and increases again as the sun rises. In this thesis, I investigated adaptations that enable the visual system of hawkmoths, a group of insects, to cope with the low light intensities they face at night. I have focused on neural adaptations, manifested in the processing of visual neurons, in contrast to anatomical adaptations, such as modifications of the eye. I showed that neural adaptations exist in the motion vision system of hawkmoths, in the form of integration of visual information in space and time. Furthermore, I demonstrated that a combination of such spatial and temporal summation increased sensitivity and information content in dim light (Paper I). The amount of spatial and temporal summation matched the ecological needs of different hawkmoth species, as well as their anatomical adaptations for visual sensitivity: night active species, and species with less sensitive eyes had more extensive spatial and temporal summation than day-active species and species with very sensitive optics (Paper II). Furthermore, I identified and characterised candidate neurons that carry out spatial and temporal summation in the brain of hawkmoths (Paper III). Finally, I quantified the effects of temporal summation on the ability of hawkmoths to track flowers in hovering flight at different light levels, and showed that a subset of the observed behavioural phenomena could be explained by temporal processing in the nervous system (Paper IV). Taken together, this work has provided detailed insight into how neural processing can increase visual reliability in dim light. The results presented are not only relevant to hawkmoths, since neural summation is also expected to increase visual sensitivity in other species of nocturnal insects, and can be compared to similar mechanisms in vertebrates. Furthermore, this work is instructive for the development of artificial visual systems, for which insect brains have proven to be a successful biomimetic model

Uric acid is more strongly associated with impaired glucose regulation in women than in men from the general population: the KORA F4-Study.

High serum uric acid (UA) levels are associated with the metabolic syndrome, type 2 diabetes and cardiovascular disease. It is largely unknown whether there are gender-specific differences regarding the association between UA and prediabetic states. We examined the possible association between UA levels and known as well as newly diagnosed diabetes (NDD), isolated impaired fasting glucose (i-IFG), isolated impaired glucose tolerance (i-IGT), and combined IFG/IGT in a population-based sample of 32-to-81-year-old men and women. An oral glucose tolerance test was carried out in all 2,740 participants without known diabetes of the Cooperative Health Research in the Region of Augsburg (KORA) F4 Study conducted between 2006 and 2008 in Southern Germany. Serum UA was analysed by the uricase method. In women after multivariable adjustment the associations between UA and i-IFG (OR 1.57, 95% CI 1.15-2.14), IFG/IGT (OR 1.52, 1.07-2.16), NDD (OR 1.67, 95% CI 1.28-2.17), and known diabetes (OR 1.47, 95% CI 1.18-1.82) remained significant, but the association with i-IGT (OR 1.14, 95% CI 0.95-1.36) lost significance. In contrast in men, after multivariable adjustment there was only a significant association between UA levels and i-IFG (OR 1.49, 95% CI 1.21-1.84), all other associations were non-significant (i-IGT: OR 1.09, IFG/IGT: OR 1.06, NDD: OR 0.91, known diabetes: OR 1.04; all p-values>0.05). Serum UA concentrations were associated with different categories of impaired glucose regulation in individuals from the general population, particularly in women. Further studies investigating the role of UA in the development of derangements in glucose metabolism are needed

Machine Learning Based Compensation for Inconsistencies in Knitted Force Sensors

Knitted sensors frequently suffer from inconsistencies due to innate effects such as offset, relaxation, and drift. These properties, in combination, make it challenging to reliably map from sensor data to physical actuation. In this paper, we demonstrate a method for counteracting this by applying processing using a minimal artificial neural network (ANN) in combination with straightforward pre-processing. We apply a number of exponential smoothing filters on a re-sampled sensor signal, to produce features that preserve different levels of historical sensor data and, in combination, represent an adequate state of previous sensor actuation. By training a three-layer ANN with a total of 8 neurons, we manage to significantly improve the mapping between sensor reading and actuation force. Our findings also show that our technique translates to sensors of reasonably different composition in terms of material and structure, and it can furthermore be applied to related physical features such as strain