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Increased amounts of an enzyme called matrix metalloproteinase-8 in the amniotic fluid of pregnant women is a strong risk factor for premature birth. This measurement could be done whenever a pregnant woman has an amniocentesis for genetic reasons.

Increased amounts of an enzyme called matrix metalloproteinase-8 in the amniotic fluid of pregnant women may be a marker for funisitis. Funisitis is an inflammation of the umbilical cord, which is a sign of inflammation in the fetus.

Pregnant women who have fetal fibronectin in cervical fluid and evidence of uterine inflammation have a very high risk of delivering prematurely. In a study of 58 pregnant women with a positive test for fetal fibronectin, uterine inflammation was diagnosed based on increased amounts of interleukin-6 in amniotic fluid.

An automation system for detecting the pilot's diversified mental states is an extremely important and essential technology, as it could prevent catastrophic accidents caused by the deteriorated cognitive state of pilots. Various types of biosignals have been employed to develop the system, since they accompany neurophysiological changes corresponding to the mental state transitions. In this study, we aimed to investigate the feasibility of a robust detection system of the pilot's mental states (i.e., distraction, workload, fatigue, and normal) based on multimodal biosignals (i.e., electroencephalogram, electrocardiogram, respiration, and electrodermal activity) and a multimodal deep learning (MDL) network. To do this, first, we constructed an experimental environment using a flight simulator in order to induce the different mental states and to collect the biosignals. Second, we designed the MDL architecture – which consists of a convolutional neural network and long short-term memory models – to efficiently combine the information of the different biosignals. Our experimental results successfully show that utilizing multimodal biosignals with the proposed MDL could significantly enhance the detection accuracy of the pilot's mental states.