Robust and accurate quantification of biomarkers of immune cells in lung cancer micro-environment using deep convolutional neural networks.

Recent years have seen a growing awareness of the role the immune system plays in successful cancer treatment, especially in novel therapies like immunotherapy. The characterization of the immunological composition of tumors and their micro-environment is thus becoming a necessity. In this paper we introduce a deep learning-based immune cell detection and quantification method, which is based on supervised learning, i.

Structures of plasmepsin II from Plasmodium falciparum in complex with two hydroxyethylamine-based inhibitors.

Plasmepsin II (PMII) is one of the ten plasmepsins (PMs) identified in the genome of Plasmodium falciparum, the causative agent of the most severe and deadliest form of malaria. Owing to the emergence of P. falciparum strains that are resistant to current antimalarial agents such as chloroquine and sulfadoxine/pyrimethamine, there is a constant pressure to find new and lasting chemotherapeutic drug therapies.