The drone ambulance [A-UAS]: golden bullet or just a blank?

Defibrillation within the first minutes after sudden cardiac arrest can save many quality-adjusted life years. Yet, despite enormous investments, 'healthcare' is still unable to provide this for the majority of patients. Emergency Medical Services often have a too long mean response time and many issues surround Public Access Defibrillation programs. In this article we argument that AED-equipped drones could be the 'magic bullet'. They are easily deployed and fast, and have a relatively low operational cost. As such they could rapidly bring an AED next to the victim, irrespective of most geographical circumstances, give visual feedback and situational awareness to the EMS dispatcher and thus assist a bystander to provide better CPR. Although there are many real-life barriers to actual deployment, we argument these might all get solved once we have solved the described technological issues.

Automatic identification of Caenorhabditis elegans in population images by shape energy features.

Experiments on model organisms are used to extend the understanding of complex biological processes. In Caenorhabditis elegans studies, populations of specimens are sampled to measure certain morphological properties and a population is characterized based on statistics extracted from such samples. Automatic detection of C. elegans in such culture images is a difficult problem. The images are affected by clutter, overlap and image degradations. In this paper, we exploit shape and appearance differences between C. elegans and non-C. elegans segmentations. Shape information is captured by optimizing a parametric open contour model on training data. Features derived from the contour energies are proposed as shape descriptors and integrated in a probabilistic framework. These descriptors are evaluated for C. elegans detection in culture images. Our experiments show that measurements extracted from these samples correlate well with ground truth data. These positive results indicate that the proposed approach can be used for quantitative analysis of complex nematode images.