Influence of cerebral vasodilation on blood reelin levels in growth restricted fetuses

Fetal growth restriction (FGR) is one of the most important obstetric pathologies. It is frequently caused by placental insufficiency. Previous studies have shown a relationship between FGR and impaired new-born neurodevelopment, although the molecular mechanisms involved in this association have not yet been completely clarified. Reelin is an extracellular matrix glycoprotein involved in development of neocortex, hippocampus, cerebellum and spinal cord. Reelin has been demonstrated to play a key role in regulating perinatal neurodevelopment and to contribute to the emergence and development of various psychiatric pathologies, and its levels are highly influenced by pathological conditions of hypoxia. The purpose of this article is to study whether reelin levels in new-borns vary as a function of severity of fetal growth restriction by gestational age and sex. We sub-grouped fetuses in: normal weight group (Group 1, n = 17), FGR group with normal umbilical artery Doppler and cerebral redistribution at middle cerebral artery Doppler (Group 2, n = 9), and FGR with abnormal umbilical artery Doppler (Group 3, n = 8). Our results show a significant association of elevated Reelin levels in FGR fetuses with cerebral blood redistribution compared to the normal weight group and the FGR with abnormal umbilical artery group. Future research should focus on further expanding the knowledge of the relationship of reelin and its regulated products with neurodevelopment impairment in new-borns with FGR and should include larger and more homogeneous samples and the combined use of different in vivo techniques in neonates with impaired growth during their different adaptive phases. © 2021 by the authors. Licensee MDPI, Basel, Switzerland

An Integrated Agent Model Addressing Situation Awareness and Functional State in Decision Making

In this paper, an integrated agent model is introduced addressing mutually interacting Situation Awareness and Functional State dynamics in decision making. This shows how a human's functional state, more specific a human's exhaustion and power, can influence a human's situation awareness, and in turn the decision making. The model is illustrated by a number of simulation scenarios. © 2011 Springer-Verlag

Driver workload detection in on-road driving environment using machine learning

Drivers' high workload caused by distractions has become one of the major concerns for road safety. This paper presents a datadriven method using machine learning algorithms to detect high workload caused by surrogate in-vehicle (IV) secondary tasks performed in an on-road experiment with real traffic. The data were collected using an instrumented vehicle while drivers performed two types of secondary tasks: visual-manual and auditory-vocal tasks. Two types of machine learning methods, support vector machine (SVM) and extreme learning machine (ELM), were applied to detect drivers' workload via drivers' visual behaviour (i.e. eye movements) data alone, as well as visual plus driving performance data. The results suggested that both methods can detect drivers' workload at high accuracy, with ELM outperformed SVM in most cases. We found that for visual intensive workload, using drivers' visual data alone achieveed an accuracy close to using the combination information from both visual and driving performance data. This study proves that machine learning methods can be used for real driving applications