Drosophila olfactory receptors as classifiers for volatiles from disparate real world applications

Olfactory receptors evolved to provide animals with ecologically and behaviourally relevant information. The resulting extreme sensitivity and discrimination has proven useful to humans, who have therefore co-opted some animals' sense of smell. One aim of machine olfaction research is to replace the use of animal noses and one avenue of such research aims to incorporate olfactory receptors into artificial noses. Here, we investigate how well the olfactory receptors of the fruit fly, Drosophila melanogaster, perform in classifying volatile odourants that they would not normally encounter. We collected a large number of in vivo recordings from individual Drosophila olfactory receptor neurons in response to an ecologically relevant set of 36 chemicals related to wine ('wine set') and an ecologically irrelevant set of 35 chemicals related to chemical hazards ('industrial set'), each chemical at a single concentration. Resampled response sets were used to classify the chemicals against all others within each set, using a standard linear support vector machine classifier and a wrapper approach. Drosophila receptors appear highly capable of distinguishing chemicals that they have not evolved to process. In contrast to previous work with metal oxide sensors, Drosophila receptors achieved the best recognition accuracy if the outputs of all 20 receptor types were used

Diurnal variation in expired breath volatiles in malaria-infected and healthy volunteers

We previously showed that thioether levels in the exhaled breath volatiles of volunteers undergoing controlled human malaria infection (CHMI) with P. falciparum increase as infection progresses. In this study, we show that thioethers have diurnal cyclical increasing patterns and their levels are significantly higher in P. falciparum CHMI volunteers compared to those of healthy volunteers. The synchronized cycle and elevation of thioethers were not present in P. vivax-infection, therefore it is likely that the thioethers are associated with unique factors in the pathology of P. falciparum. Moreover, we found that time-of-day of breath collection is important to accurately predict (98%) P. falciparum-infection. Critically, this was achieved when the disease was asymptomatic and parasitemia was below the level detectable by microscopy. Although these findings are encouraging, they show limitations because of the limited and logistically difficult diagnostic window and its utility to P. falciparum malaria only. We looked for new biomarkers in the breath of P. vivaxCHMI volunteers and found that a set of terpenes increase significantly over the course of the malaria infection. The accuracy of predicting P. vivax using breath terpenes was up to 91%. Moreover, some of the terpenes were also found in the breath of P. falciparum CHMI volunteers (accuracy up to 93.5%). The results suggest that terpenes might represent better biomarkers than thioethers to predict malaria as they were not subject to malaria pathogens diurnal changes

Feature selection for chemical sensor arrays using mutual information

We address the problem of feature selection for classifying a diverse set of chemicals using an array of metal oxide sensors. Our aim is to evaluate a filter approach to feature selection with reference to previous work, which used a wrapper approach on the same data set, and established best features and upper bounds on classification performance. We selected feature sets that exhibit the maximal mutual information with the identity of the chemicals. The selected features closely match those found to perform well in the previous study using a wrapper approach to conduct an exhaustive search of all permitted feature combinations. By comparing the classification performance of support vector machines (using features selected by mutual information) with the performance observed in the previous study, we found that while our approach does not always give the maximum possible classification performance, it always selects features that achieve classification performance approaching the optimum obtained by exhaustive search. We performed further classification using the selected feature set with some common classifiers and found that, for the selected features, Bayesian Networks gave the best performance. Finally, we compared the observed classification performances with the performance of classifiers using randomly selected features. We found that the selected features consistently outperformed randomly selected features for all tested classifiers. The mutual information filter approach is therefore a computationally efficient method for selecting near optimal features for chemical sensor arrays

Bio-Benchmarking of Electronic Nose Sensors

BACKGROUND:Electronic noses, E-Noses, are instruments designed to reproduce the performance of animal noses or antennae but generally they cannot match the discriminating power of the biological original and have, therefore, been of limited utility. The manner in which odorant space is sampled is a critical factor in the performance of all noses but so far it has been described in detail only for the fly antenna. METHODOLOGY:Here we describe how a set of metal oxide (MOx) E-Nose sensors, which is the most commonly used type, samples odorant space and compare it with what is known about fly odorant receptors (ORs). PRINCIPAL FINDINGS:Compared with a fly's odorant receptors, MOx sensors from an electronic nose are on average more narrowly tuned but much more highly correlated with each other. A set of insect ORs can therefore sample broader regions of odorant space independently and redundantly than an equivalent number of MOx sensors. The comparison also highlights some important questions about the molecular nature of fly ORs. CONCLUSIONS:The comparative approach generates practical learnings that may be taken up by solid-state physicists or engineers in designing new solid-state electronic nose sensors. It also potentially deepens our understanding of the performance of the biological system

Topological and Functional Characterization of an Insect Gustatory Receptor

Insect gustatory receptors are predicted to have a seven-transmembrane structure and are distantly related to insect olfactory receptors, which have an inverted topology compared with G-protein coupled receptors, including mammalian olfactory receptors. In contrast, the topology of insect gustatory receptors remains unknown. Except for a few examples from Drosophila, the specificity of individual insect gustatory receptors is also unknown. In this study, the total number of identified gustatory receptors in Bombyx mori was expanded from 65 to 69. BmGr8, a silkmoth gustatory receptor from the sugar receptor subfamily, was expressed in insect cells. Membrane topology studies on BmGr8 indicate that, like insect olfactory receptors, it has an inverted topology relative to G protein-coupled receptors. An orphan GR from the bitter receptor family, BmGr53, yielded similar results. We infer, from the finding that two distantly related BmGrs have an intracellular N-terminus and an odd number of transmembrane spans, that this is likely to be a general topology for all insect gustatory receptors. We also show that BmGr8 functions independently in Sf9 cells and responds in a concentration-dependent manner to the polyalcohols myo-inositol and epi-inositol but not to a range of mono- and di-saccharides. BmGr8 is the first chemoreceptor shown to respond specifically to inositol, an important or essential nutrient for some Lepidoptera. The selectivity of BmGr8 responses is consistent with the known responses of one of the gustatory receptor neurons in the lateral styloconic sensilla of B. mori, which responds to myo-inositol and epi-inositol but not to allo-inositol

Inositol trisphosphatase and bisphosphatase activities in the retina of crab

AbstractInositol trisphosphate appears to be an excitatory second messenger in the transduction cascade of invertebrate visual photoreceptors. The high time-resolution of visual transduction demands an efficient system for the removal of the second messenger. It is now demonstrated that soluble extracts of crab retina promote rapid magnesium-dependent release of inorganic phosphate from D-myo-1,4,5,-inositol trisphosphate. Experiments in which the inositol trisphosphate had been labelled with 32P in the 4′ and 5′ positions indicated that both inositol trisphosphatase and bisphosphatase activities are present. The breakdown involves loss of at least one of the pair of vicinal phosphates, which is sufficient to inactivate the compound

Comparison of the performance of metal oxide and conducting polymer electronic noses for detection of aflatoxin using artificially contaminated maize

The electronic nose offers potential as a rapid and cost effective field portable diagnostic device that would allow for quick screening of produce for aflatoxin contamination at the market entry level. This study aimed to compare the performance of three electronic nose sensor technologies: metal oxide semiconductor sensors (Fox 3000), conducting polymer sensors (Cyranose 320) and doped metal oxide semiconductor sensors with thermocycling (DiagNose), for the detection of volatiles associated with maize contaminated with aflatoxins. Australian maize (variety DK703w) samples were artificially inoculated with aflatoxigenic and non-aflatoxigenic Aspergillus flavus isolates and 2 % v/v Tween 20 as a control. Mutual information was used to select features from the electronic nose sensor signals for classification of the samples. The effectiveness, of selected features to discriminate between the different classes of samples was evaluated by support vector machines and k-nearest neighbour with leave-one-out cross-validation. Cross-validated classification accuracy for the different sample classes ranged from 81 % to 94 % for DiagNose, 76 to 79 % for Fox 3000 and 68 to 75 % for Cyranose. The results suggest that an electronic nose equipped with doped metal oxide semiconductor sensors and thermocycling is more effective for detection of aflatoxin contamination of maize

Evaluation of performance of metal oxide electronic nose for detection of aflatoxin in artificially and naturally contaminated maize

Aflatoxins are of great concern for food safety and security due to their impact on human health and the agriculture economy in developing countries. This study aimed to evaluate the potential use of a field portable metal oxide sensors based electronic nose to detect aflatoxin contamination in Kenyan maize varieties that were artificially and naturally infected with Aspergillus flavus. Mutual information was used to select features from the electronic nose sensor signals for classification of the samples. The effectiveness of selected features to discriminate between the different classes of samples was evaluated by support vector machines and k-nearest neighbour with leave-one-out cross-validation. External validation was also conducted by analysing samples naturally contaminated with A. flavus using the classification model generated with samples that had been artificially inoculated with the aflatoxigenic A. flavus. Cross-validated classification accuracies ranged from 72% to 88% for maize samples artificially inoculated with A. flavus and 61–86% for samples naturally infected with A. flavus. Classification accuracies achieved with external validation for maize samples naturally contaminated with aflatoxins ranged from 58% to 78% and were relatively consistent with accuracies obtained from internal validation. Results suggest that the electronic nose could be a promising cost-effective screening method to detect aflatoxin contamination in maize