Stability control for breath analysis using GC-MS

Gas chromatography mass spectrometry (GC-MS) instruments provide researchers and clinicians with a vast amount of information on sample composition, thus these instruments are seen as gold standard in breath analysis research. However, there are many factors that can confound the data measured by GC-MS instruments. These factors will make interpretation of GC-MS data unreliable for breath analysis research. We present in this paper detailed studies of two of these factors: instrument variation over time and chemical degradation of known biomarkers during storage in sorbent tubes. We found that a single quadrupole MS showed larger variability in measurements than a quadrupole time-of-flight MS when the same mixture of chemical standards was analysed for a period of up to 8 weeks. We recommend procedures of normalising the data. Moreover, the stability studies of breath biomarkers like thioethers, previously found indicative of malaria, showed that there is a need to store the samples in sorbent tubes at low temperature, 6 °C, for no more than 20 days to avoid the total decay of the chemicals

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

Peak alignment of gas chromatography-mass spectrometry data with deep learning

We present ChromAlignNet, a deep learning model for alignment of peaks in Gas Chromatography-Mass Spectrometry (GC–MS) data. In GC–MS data, a compound’s retention time (RT) may not stay fixed across multiple chromatograms. To use GC–MS data for biomarker discovery requires alignment of identical an- alyte’s RT from different samples. Current methods of alignment are all based on a set of formal, math- ematical rules. We present a solution to GC–MS alignment using deep learning neural networks, which are more adept at complex, fuzzy data sets. We tested our model on several GC–MS data sets of various complexities and analysed the alignment results quantitatively. We show the model has very good per- formance (AUC ∼1 for simple data sets and AUC ∼0.85 for very complex data sets). Further, our model easily outperforms existing algorithms on complex data sets. Compared with existing methods, ChromA- lignNet is very easy to use as it requires no user input of reference chromatograms and parameters. This method can easily be adapted to other similar data such as those from liquid chromatography. The source code is written in Python and available online

Using echo state networks for anomaly detection in underground coal mines

We investigate the problem of identifying anomalies in monitoring critical gas concentrations using a sensor network in an underground coal mine. In this domain, one of the main problems is a provision of mine specific anomaly detection, with cyclical (moving) instead of flatline (static) alarm threshold levels. An additional practical difficulty in modelling a specific mine is the lack of fully labelled data of normal and abnormal situations. We present an approach addressing these difficulties based on echo state networks learning mine specific anomalies when only normal data is available. Echo state networks utilize incremental updates driven by new sensor readings, thus enabling a detection of anomalies at any time during the sensor network operation. We evaluate this approach against a benchmark - Bayesian network based anomaly detection, and observe that the quality of the overall predictions is comparable to the benchmark. However, the echo state networks maintain the same level of predictive accuracy for data from multiple sources. Therefore, the ability of echo state networks to model dynamical systems make this approach more suitable for anomaly detection and predictions in sensor networks

Optimising sensor layouts for direct measurement of discrete variables

An optimal sensor layout is attained when a limited number of sensors are placed in an area such that the cost of the placement is minimised while the value of the obtained information is maximised. In this paper, we discuss the optimal sensor layout design problem from first principles, show how an existing optimisation criterion (maximum entropy of the measured variables) can be derived, and compare the performance of this criterion with three others that have been reported in the literature for a specific situation for which we have detailed experimental data available. This is achieved by firstly learning a spatial model of the environment using a Bayesian Network, then predicting the expected sensor data in the rest of the space, and finally verifying the predicted results with the experimental measurements. The development of rigorous techniques for optimising sensor layouts is argued to be an essential requirement for reconfigurable and self-adaptive networks

Relating Fisher information to order parameters

We study phase transitions and relevant order parameters via statistical estimation theory using the Fisher information matrix. The assumptions that we make limit our analysis to order parameters representable as a negative derivative of thermodynamic potential over some thermodynamic variable. Nevertheless, the resulting representation is sufficiently general and explicitly relates elements of the Fisher information matrix to the rate of change in the corresponding order parameters. The obtained relationships allow us to identify, in particular, second-order phase transitions via divergences of individual elements of the Fisher information matrix. A computational study of random Boolean networks supports the derived relationships, illustrating that Fisher information of the magnetization bias (that is, activity level) is peaked in finite-size networks at the critical points, and the maxima increase with the network size. The framework presented here reveals the basic thermodynamic reasons behind similar empirical observations reported previously. The study highlights the generality of Fisher information as a measure that can be applied to a broad range of systems, particularly those where the determination of order parameters is cumbersome

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

Design of an efficient electronic nose system for odour analysis and assessment

This paper presents an efficient electronic nose (e-nose) system, named “NOS.E”, for odour analysis and assessment. In addition to the reliable hardware and software designs, an airflow intake system is implemented to ensure the precise odour analysis procedure in the NOS.E system. Additionally, a particular control logic was introduced to improve the test efficiency of the NOS.E by reducing operation time. Furthermore, the fault detection and alarming design can generate a high-reliability performance by constantly monitoring its working status. To evaluate the performance of the NOS.E, three types of alcohols were tested by the NOS.E and compared to data collected by comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GCxGC-TOFMS). The results indicate that the NOS.E can successfully distinguish three different alcohols with high efficiency and low cost and has the potential to be a universal odour analysis platform implemented in various applications

Quantifying long-range interactions and coherent structure in multi-agent dynamics

We develop and apply several novel methods quantifying dynamic multi-agent team interactions. These interactions are detected information-theoretically and captured in two ways: via (i) directed networks (interaction diagrams) representing significant coupled dynamics between pairs of agents, and (ii) state-space plots (coherence diagrams) showing coherent structures in Shannon information dynamics. This model-free analysis relates, on the one hand, the information transfer to responsiveness of the agents and the team, and, on the other hand, the information storage within the team to the team's rigidity and lack of tactical flexibility. The resultant interaction and coherence diagrams reveal implicit interactions, across teams, that may be spatially long-range. The analysis was verified with a statistically significant number of experiments (using simulated football games, produced during RoboCup 2D Simulation League matches), identifying the zones of the most intense competition, the extent and types of interactions, and the correlation between the strength of specific interactions and the results of the matches

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