An automated neural network-based stage-specific malaria detection software using dimension reduction: The malaria microscopy classifier.

Due to climate change and the COVID-19 pandemic, the number of malaria cases and deaths, caused by the genus, of which is the most common and lethal to humans, increased between 2019 and 2020. Reversing this trend and eliminating malaria worldwide requires improvements in malaria diagnosis, in which artificial intelligence (AI) has recently been demonstrated to have a great potential. One of the main reasons for the use of neural networks (NNs) is the time saving through automatising the process and the elimination of human error.

Reducing data dimension boosts neural network-based stage-specific malaria detection.

Although malaria has been known for more than 4 thousand years, it still imposes a global burden with approx. 240 million annual cases. Improvement in diagnostic techniques is a prerequisite for its global elimination.

Fission of Lipid-Vesicles by Membrane Phase Transitions in Thermal Convection.

Unilamellar lipid vesicles can serve as model for protocells. We present a vesicle fission mechanism in a thermal gradient under flow in a convection chamber, where vesicles cycle cold and hot regions periodically. Crucial to obtain fission of the vesicles in this scenario is a temperature-induced membrane phase transition that vesicles experience multiple times.