The state of the art in integrating machine learning into visual analytics

Visual analytics systems combine machine learning or other analytic techniques with interactive data visualization to promote sensemaking and analytical reasoning. It is through such techniques that people can make sense of large, complex data. While progress has been made, the tactful combination of machine learning and data visualization is still under-explored. This state-of-the-art report presents a summary of the progress that has been made by highlighting and synthesizing select research advances. Further, it presents opportunities and challenges to enhance the synergy between machine learning and visual analytics for impactful future research directions

An investigation into the role of the skin odorants and microbiota in the attraction of malaria mosquitoes to human beings.

Skin microbiota plays an important role in human body odour production, and mosquitoes primarily use olfaction to locate their hosts, therefore, an understanding of the mosquito important compounds released by bacteria could be exploited for a novel intervention. This project investigated the role of skin bacteria on mosquito attractiveness to human beings. In order to investigate this, 30 volunteers were recruited and asked to comply with a washing regime. Bacteria and odour samples were taken from the feet, back, forearm and axilla of 30 volunteers. Odour was collected using nylon stocking that were worn overnight and by carrying out headspace entrainment for each body site. The nylon stockings were tested behaviourally to An. stephensi . Volunteers’ feet increased in attractiveness to An. stephensi between day 1 and day 4, however the other sites (axilla, forearm and upper back) did not. The heaspace entrainment samples were pooled together according to the body site and visit and tested with coupled Gas chromatography - electroantennography (GC - EAG) to test which compounds were detected by mosquitoes. 52 compounds were found to be EAG active across all sites. The samples were individually analysed with GC, and bacteria samples were sequenced with 16S rRna, and a correlation was done for each body site. Over 60 bacteria significantly changed between day 1 and day 5 for feet, however fewer bacteria significantly changed for the other sites (1 bacteria for axilla, 3 for forear m and 10 for the upper back). Furthermore, the correlations for feet revealed that the following compounds: Ethyl - cyclohexane (RI 841), 2 - nonanal (RI 1130 ), menthol (RI 1172) and RI 1232, RI 1711 and RI 1817 ( unidentified ) were associated with Phascolarcto bacterium, Tyzzerella, Sutterella, Turicella, Schlegelella, Oryzihumus, Parabacteroides, Megasphaera, Shingopxis, Paludibacter, Ralstonia, Tuberibacillus and Peptococcus. This study demonstrated that the interactions between bacteria and compounds are highly complex and further research is needed to explore a causal relationship between the two