Meibomian gland dysfunction is the most common cause of dry eye disease, which is a prevalent condition that can damage the ocular surface and cause reduced vision and substantial pain. Meibum secreted from the meibomian glands makes up the majority of the outer, protective lipid layer of the tear film. Changes in the secreted meibum and markers of glandular damage can be detected through tear sampling.
Several studies have investigated the tear film protein expression in meibomian gland dysfunction, but less work apply machine learning to analyze the protein patterns. We use machine learning and methods from explainable artificial intelligence to detect potential clinically relevant proteins in meibomian gland dysfunction. Two different explainable artificial intelligence methods are compared. Several of the proteins found important in the models have been linked to dry eye disease in the past, while some are novel. Consequently, explainable artificial intelligence methods serve as a promising tool for screening for proteins that are relevant for meibomian gland dysfunction. By doing so, one may be able to discover new biomarkers and treatments, and gain a better understanding of how diseases develop.

Optical networks are spanning the world. They are the main medium for carrying all types of data-traffic. From a vulnerability and security perspective, it is therefore the most important part of the network to protect against any physical impacts that may cause disruptions or security incidents. In this paper, we demonstrate and describe a scalable, low-cost monitoring system based on detecting the state of polarisation in the fibre cables. We give examples on how any direct movement of fibre-cables or fibre-cords as well as indirect vibrations on the cable can be detected, and how basic characteristics in the patterns can be used for recognition of these events.