Bacteria classification using Cyranose 320 electronic nose

Background An electronic nose (e-nose), the Cyrano Sciences' Cyranose 320, comprising an array of thirty-two polymer carbon black composite sensors has been used to identify six species of bacteria responsible for eye infections when present at a range of concentrations in saline solutions. Readings were taken from the headspace of the samples by manually introducing the portable e-nose system into a sterile glass containing a fixed volume of bacteria in suspension. Gathered data were a very complex mixture of different chemical compounds. Method Linear Principal Component Analysis (PCA) method was able to classify four classes of bacteria out of six classes though in reality other two classes were not better evident from PCA analysis and we got 74% classification accuracy from PCA. An innovative data clustering approach was investigated for these bacteria data by combining the 3-dimensional scatter plot, Fuzzy C Means (FCM) and Self Organizing Map (SOM) network. Using these three data clustering algorithms simultaneously better 'classification' of six eye bacteria classes were represented. Then three supervised classifiers, namely Multi Layer Perceptron (MLP), Probabilistic Neural network (PNN) and Radial basis function network (RBF), were used to classify the six bacteria classes. Results A [6 × 1] SOM network gave 96% accuracy for bacteria classification which was best accuracy. A comparative evaluation of the classifiers was conducted for this application. The best results suggest that we are able to predict six classes of bacteria with up to 98% accuracy with the application of the RBF network. Conclusion This type of bacteria data analysis and feature extraction is very difficult. But we can conclude that this combined use of three nonlinear methods can solve the feature extraction problem with very complex data and enhance the performance of Cyranose 320

How do you lose a river?

In this paper I explore the concept of the 'lost river' and the implications this term has for our understanding of the history of changing urban environments. In taking a voyage down one of the London 2012 Olympic Park’s now-filled waterways, the Pudding Mill River, charting it and its surrounding area’s diverse history, I explore how rivers end up becoming ‘losable’. Drawing on diverse methodologies from archaeology and geography and with a particular emphasis on mapping, I argue that a literal and metaphorical exploration of such a rapidly changing environment reveals a multitude of buried narratives and fluid histories. This research suggests that the labeling of a river as ‘lost’ is not a politically neutral act and that, with its romantic connotations, the term may actually serve to legitimize insensitive and contentious changes to our environment

Letter to Andrew Inglis Clark, Tasmania from J.W. Gardner, Victoria, 1 Mar 1875

Letter to Andrew Inglis Clark, Tasmania from J.W. Gardner, Victoria, 1 Mar 1875, regarding his visit to Melbourne, thanks for photos and 'valuable enclosures'. C4/C8

The Mechanical Behavior of a 25Cr Super Duplex Stainless Steel at Elevated Temperature

Peer reviewedPostprin

Flow cytometric analysis of inflammatory and resident myeloid populations in mouse ocular inflammatory models

Myeloid cells make a pivotal contribution to tissue homeostasis during inflammation. Both tissue-specific resident populations and infiltrating myeloid cells can cause tissue injury through aberrant activation and/or dysregulated activity. Reliable identification and quantification of myeloid cells within diseased tissues is important to understand pathological inflammatory processes. Flow cytometry is a valuable technique for leukocyte analysis, but a standardized flow cytometric method for myeloid cell populations in the eye is lacking. Here, we validate a reproducible flow cytometry gating approach to characterize myeloid cells in several commonly used models of ocular inflammation. We profile and quantify myeloid subsets across these models, and highlight the value of this strategy in identifying phenotypic differences using Ccr2-deficient mice. This method will aid standardization in the field and facilitate future investigations into the roles of myeloid cells during ocular inflammation

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

CMOS integration of inkjet-printed graphene for humidity sensing.

We report on the integration of inkjet-printed graphene with a CMOS micro-electro-mechanical-system (MEMS) microhotplate for humidity sensing. The graphene ink is produced via ultrasonic assisted liquid phase exfoliation in isopropyl alcohol (IPA) using polyvinyl pyrrolidone (PVP) polymer as the stabilizer. We formulate inks with different graphene concentrations, which are then deposited through inkjet printing over predefined interdigitated gold electrodes on a CMOS microhotplate. The graphene flakes form a percolating network to render the resultant graphene-PVP thin film conductive, which varies in presence of humidity due to swelling of the hygroscopic PVP host. When the sensors are exposed to relative humidity ranging from 10-80%, we observe significant changes in resistance with increasing sensitivity from the amount of graphene in the inks. Our sensors show excellent repeatability and stability, over a period of several weeks. The location specific deposition of functional graphene ink onto a low cost CMOS platform has the potential for high volume, economic manufacturing and application as a new generation of miniature, low power humidity sensors for the internet of things.S.S. acknowledges Department of Science and Technology (DST), India for Ramanujan Fellowship to support the work (project no. SR/S2/RJN-104/2011). This work was (partly) supported through the EU FP7 project MSP (611887). T.H. acknowledges support from the Royal Academy of Engineering through a fellowship (Graphlex).This is the final version of the article. It was first available from NPG via http://dx.doi.org/10.1038/srep1737

Enhanced spectroscopic gas sensors using in-situ grown carbon nanotubes

In this letter, we present a fully complementary-metal-oxide-semiconductor (CMOS) compatible microelectromechanical system thermopile infrared (IR) detector employing vertically aligned multi-walled carbon nanotubes (CNT) as an advanced nano-engineered radiation absorbing material. The detector was fabricated using a commercial silicon-on-insulator (SOI) process with tungsten metallization, comprising a silicon thermopile and a tungsten resistive micro-heater, both embedded within a dielectric membrane formed by a deep-reactive ion etch following CMOS processing. In-situ CNT growth on the device was achieved by direct thermal chemical vapour deposition using the integrated micro-heater as a micro-reactor. The growth of the CNT absorption layer was verified through scanning electron microscopy, transmission electron microscopy, and Raman spectroscopy. The functional effects of the nanostructured ad-layer were assessed by comparing CNT-coated thermopiles to uncoated thermopiles. Fourier transform IR spectroscopy showed that the radiation absorbing properties of the CNT adlayer significantly enhanced the absorptivity, compared with the uncoated thermopile, across the IR spectrum (3 μm–15.5 μm). This led to a four-fold amplification of the detected infrared signal (4.26 μm) in a CO2 non-dispersive-IR gas sensor system. The presence of the CNT layer was shown not to degrade the robustness of the uncoated devices, whilst the 50% modulation depth of the detector was only marginally reduced by 1.5 Hz. Moreover, we find that the 50% normalized absorption angular profile is subsequently more collimated by 8°. Our results demonstrate the viability of a CNT-based SOI CMOS IR sensor for low cost air quality monitoring.This work was partly supported through the EU FP7 project SOI-HITS (No. 288481). MTC thanks the Oppenheimer Trust and the EPSRC IAA for their generous financial support.This is the author accepted manuscript. The final version is available from AIP at http://scitation.aip.org/content/aip/journal/apl/106/19/10.1063/1.4921170

Intelligent Bayes Classifier (IBC) for ENT infection classification in hospital environment

Electronic Nose based ENT bacteria identification in hospital environment is a classical and challenging problem of classification. In this paper an electronic nose (e-nose), comprising a hybrid array of 12 tin oxide sensors (SnO(2)) and 6 conducting polymer sensors has been used to identify three species of bacteria, Escherichia coli (E. coli), Staphylococcus aureus (S. aureus), and Pseudomonas aeruginosa (P. aeruginosa) responsible for ear nose and throat (ENT) infections when collected as swab sample from infected patients and kept in ISO agar solution in the hospital environment. In the next stage a sub-classification technique has been developed for the classification of two different species of S. aureus, namely Methicillin-Resistant S. aureus (MRSA) and Methicillin Susceptible S. aureus (MSSA). An innovative Intelligent Bayes Classifier (IBC) based on "Baye's theorem" and "maximum probability rule" was developed and investigated for these three main groups of ENT bacteria. Along with the IBC three other supervised classifiers (namely, Multilayer Perceptron (MLP), Probabilistic neural network (PNN), and Radial Basis Function Network (RBFN)) were used to classify the three main bacteria classes. A comparative evaluation of the classifiers was conducted for this application. IBC outperformed MLP, PNN and RBFN. The best results suggest that we are able to identify and classify three bacteria main classes with up to 100% accuracy rate using IBC. We have also achieved 100% classification accuracy for the classification of MRSA and MSSA samples with IBC. We can conclude that this study proves that IBC based e-nose can provide very strong and rapid solution for the identification of ENT infections in hospital environment