https://www.selleckchem.com/products/gambogic-acid.html To our knowledge, this is the first large-scale study evaluating the potential of machine learning and especially deep learning directly at the level of industry-scale settings and moreover investigating the transferability of publicly learned target prediction models towards industrial bioactivity prediction pipelines. Novel malaria vector control approaches aim to combine tools for maximum protection. This study aimed to evaluate novel and re-evaluate existing putative repellent 'push' and attractive 'pull' components for manipulating the odour orientation of malaria vectors in the peri-domestic space. Anopheles arabiensis outdoor human landing catches and trap comparisons were implemented in large semi-field systems to(i) test the efficacy of Citriodiol or transfluthrin-treated fabric strips positioned in house eave gaps as push components for preventing bites;(ii) understand the efficacy of MB5-baited Suna-traps in attracting vectors in the presence of a human being;(iii) assess 2-butanone as a CO replacement for trapping;(iv) determine the protection provided by a full push-pull set up. The air concentrations of the chemical constituents of the push-pull set-up were quantified. Microencapsulated Citriodiol eave strips did not provide outdoor protection against host-seeking An. arabiensis. Transfluthrin-tonstituent chemicals were only irregularly detected, potentially suggesting insufficient release and concentration in the air for attraction. This step-by-step evaluation of the selected 'push' and 'pull' components led to a better understanding of their ability to affect host-seeking behaviours of the malaria vector An. arabiensis in the peri-domestic space and helps to gauge the impact such tools would have when used in the field for monitoring or control. This step-by-step evaluation of the selected 'push' and 'pull' components led to a better understanding of their ability to affect host-seeking beh