Investigation of ultra trace detection in functionalised-surface enhanced Raman spectroscopy (SERS)

We have developed an explanation for ultra trace detection found when using Au/Ag SERS nanoparticles linked to biochemical affinity tags, e.g. antibodies. The nanoparticle structure is not as usually assumed and the aggregated nanoparticles constitute hot spots that are indispensable for these very low levels of analyte detection, even more so when using a direct detection method.

Liveness detection based on 3D face shape analysis

In recent years face recognition systems have been applied in various useful applications, such as surveillance, access control, criminal investigations, law enforcement, and others. However face biometric systems can be highly vulnerable to spoofing attacks where an impostor tries to bypass the face recognition system using a photo or video sequence. In this paper a novel liveness detection method, based on the 3D structure of the face, is proposed. Processing the 3D curvature of the acquired data, the proposed approach allows a biometric system to distinguish a real face from a photo, increasing the overall performance of the system and reducing its vulnerability. In order to test the real capability of the methodology a 3D face database has been collected simulating spoofing attacks, therefore using photographs instead of real faces. The experimental results show the effectiveness of the proposed approach.

Examining young recreational sportswomen's intentions to engage in sun-protective behavior : the role of group and image norms

Researchers examined the sun-protective intentions and behavior of young, Caucasian, Australian sportswomen aged between 17 and 35 years (N = 100). The study adopted a 2 x 2 experimental design, comparing group norms (supportive vs. non-supportive) and image norms (tanned vs. pale) related to sun protection and taking into account group identification with friends and peers in the sport. While no significant findings emerged involving image norms, regression analyses revealed a significant two-way interaction for group norm x identification on recreational sportswomen's intentions to engage in sun protection in the next fortnight. Participants identifying strongly with their group had stronger intentions to engage in sun protection when exposed to a norm reflecting fellow recreational sportswomen engaging in sun-protective actions in comparison to those exposed to a non-supportive group. In addition, while prior intentions to engage in sun protection were not significantly related to sun-protection behavior, post-manipulation intentions after exposure to the sun-protective information that was provided were significantly related to follow-up behavior. Overall, the findings supported the importance of group-based social influences, rather than tanned media images, on sun-protective decisions among young recreational sportswomen and provided a targeted source for intervention strategies encouraging sun safety among this at-risk group for repeated sun exposure.

Sampling stored-product insect pests : a comparison of four statistical sampling models for probability of pest detection

Background: Developing sampling strategies to target biological pests such as insects in stored grain is inherently difficult owing to species biology and behavioural characteristics. The design of robust sampling programmes should be based on an underlying statistical distribution that is sufficiently flexible to capture variations in the spatial distribution of the target species. Results: Comparisons are made of the accuracy of four probability-of-detection sampling models - the negative binomial model,1 the Poisson model,1 the double logarithmic model2 and the compound model3 - for detection of insects over a broad range of insect densities. Although the double log and negative binomial models performed well under specific conditions, it is shown that, of the four models examined, the compound model performed the best over a broad range of insect spatial distributions and densities. In particular, this model predicted well the number of samples required when insect density was high and clumped within experimental storages. Conclusions: This paper reinforces the need for effective sampling programs designed to detect insects over a broad range of spatial distributions. The compound model is robust over a broad range of insect densities and leads to substantial improvement in detection probabilities within highly variable systems such as grain storage.

A novel multiplex PCR-RFLP method for simultaneous detection of the MTHFR 677 C > T, eNOS +894 G > T and - eNOS -786 T > C variants among Malaysian Malays

Background Hyperhomocysteinemia as a consequence of the MTHFR 677 C > T variant is associated with cardiovascular disease and stroke. Another factor that can potentially contribute to these disorders is a depleted nitric oxide level, which can be due to the presence of eNOS +894 G > T and eNOS −786 T > C variants that make an individual more susceptible to endothelial dysfunction. A number of genotyping methods have been developed to investigate these variants. However, simultaneous detection methods using polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) analysis are still lacking. In this study, a novel multiplex PCR-RFLP method for the simultaneous detection of MTHFR 677 C > T and eNOS +894 G > T and eNOS −786 T > C variants was developed. A total of 114 healthy Malay subjects were recruited. The MTHFR 677 C > T and eNOS +894 G > T and eNOS −786 T > C variants were genotyped using the novel multiplex PCR-RFLP and confirmed by DNA sequencing as well as snpBLAST. Allele frequencies of MTHFR 677 C > T and eNOS +894 G > T and eNOS −786 T > C were calculated using the Hardy Weinberg equation. Methods The 114 healthy volunteers were recruited for this study, and their DNA was extracted. Primer pair was designed using Primer 3 Software version 0.4.0 and validated against the BLAST database. The primer specificity, functionality and annealing temperature were tested using uniplex PCR methods that were later combined into a single multiplex PCR. Restriction Fragment Length Polymorphism (RFLP) was performed in three separate tubes followed by agarose gel electrophoresis. PCR product residual was purified and sent for DNA sequencing. Results The allele frequencies for MTHFR 677 C > T were 0.89 (C allele) and 0.11 (T allele); for eNOS +894 G > T, the allele frequencies were 0.58 (G allele) and 0.43 (T allele); and for eNOS −786 T > C, the allele frequencies were 0.87 (T allele) and 0.13 (C allele). Conclusions Our PCR-RFLP method is a simple, cost-effective and time-saving method. It can be used to successfully genotype subjects for the MTHFR 677 C > T and eNOS +894 G > T and eNOS −786 T > C variants simultaneously with 100% concordance from DNA sequencing data. This method can be routinely used for rapid investigation of the MTHFR 677 C > T and eNOS +894 G > T and eNOS −786 T > C variants.

Detection of a novel mutation in the CACNA1A gene

Familial hemiplegic migraine (FHM) is a rare autosomal dominant subtype of migraine with aura. It is divided into three subtypes FHM1, FHM2 and FHM3, which are caused by mutations in the CACNA1A, ATP1A2 and SCN1A genes respectively. As part of a regular diagnostic service, we investigated 168 patients with FHM symptoms. Samples were tested for mutations contained within the CACNA1A gene. Some tested samples (4.43%) showed an FHM1 mutation, with five of the mutations found in exon 5, one mutation in exon 16 and one in exon 17. Four polymorphisms were also detected, one of which occurred in a large percentage of samples (14.88%). The exon 16 2094G>A polymorphism, however, has been found to occur in healthy Caucasian control populations up to a frequency of 16% and is not considered to be significantly associated with FHM. A finding of significance, found in a single patient, was the detection of a novel mutation in exon 5 that results in a P225H change. The affected individual was an 8-year-old female. The exact phenotypic effect of this mutation is unknown, and further studies are needed to understand the pathophysiology of this mutation in FHM1. New information will allow for diagnostic procedures to be constantly updated, thus improving accuracy of diagnosis. It is possible that new information will also aid the development of new therapeutic agents for the treatment of FHM.

Utilizing least significant bit-planes of RONI pixels for medical image watermarking

We propose a computationally efficient image border pixel based watermark embedding scheme for medical images. We considered the border pixels of a medical image as RONI (region of non-interest), since those pixels have no or little interest to doctors and medical professionals irrespective of the image modalities. Although RONI is used for embedding, our proposed scheme still keeps distortion at a minimum level in the embedding region using the optimum number of least significant bit-planes for the border pixels. All these not only ensure that a watermarked image is safe for diagnosis, but also help minimize the legal and ethical concerns of altering all pixels of medical images in any manner (e.g, reversible or irreversible). The proposed scheme avoids the need for RONI segmentation, which incurs capacity and computational overheads. The performance of the proposed scheme has been compared with a relevant scheme in terms of embedding capacity, image perceptual quality (measured by SSIM and PSNR), and computational efficiency. Our experimental results show that the proposed scheme is computationally efficient, offers an image-content-independent embedding capacity, and maintains a good image quality

SERS based detection of barcoded gold nanoparticle assemblies from within animal tissue

We have explored the potential of deep Raman spectroscopy, specifically surface enhanced spatially offset Raman spectroscopy (SESORS), for non-invasive detection from within animal tissue, by employing SERS-barcoded nanoparticle (NP) assemblies as the diagnostic agent. This concept has been experimentally verified in a clinic-relevant backscattered Raman system with an excitation line of 785 nm under ex vivo conditions. We have shown that our SORS system, with a fixed offset of 2-3 mm, offered sensitive probing of injected QTH-barcoded NP assemblies through animal tissue containing both protein and lipid. In comparison to that of non-aggregated SERS-barcoded gold NPs, we have demonstrated that the tailored SERS-barcoded aggregated NP assemblies have significantly higher detection sensitivity. We report that these NP assemblies can be readily detected at depths of 7-8 mm from within animal proteinaceous tissue with high signal-to-noise (S/N) ratio. In addition they could also be detected from beneath 1-2 mm of animal tissue with high lipid content, which generally poses a challenge due to high absorption of lipids in the near-infrared region. We have also shown that the signal intensity and S/N ratio at a particular depth is a function of the SERS tag concentration used and that our SORS system has a QTH detection limit of 10-6 M. Higher detection depths may possibly be obtained with optimization of the NP assemblies, along with improvements in the instrumentation. Such NP assemblies offer prospects for in vivo, non-invasive detection of tumours along with scope for incorporation of drugs and their targeted and controlled release at tumour sites. These diagnostic agents combined with drug delivery systems could serve as a “theranostic agent”, an integration of diagnostics and therapeutics into a single platform.

Fluorescence detection of plant extracts that affect neuronal voltage-gated Ca2+ channels

Structurally novel compounds able to block voltage-gated Ca2+ channels (VGCCs) are currently being sought for the development of new drugs directed at neurological disorders. Fluorescence techniques have recently been developed to facilitate the analysis of VGCC blockers in a multi-well format. By utilising the small cell lung carcinoma cell line, NCI-H146, we were able to detect changes in intracellular Ca2+ concentration ([Ca2+]i) using a fluorescence microplate reader. NCI-H146 cells have characteristics resembling those of neuronal cells and express multiple VGCC subtypes, including those of the L-, N- and P-type. We found that K+-depolarisation of fluo-3 loaded NCI-H146 cells causes a rapid and transient increase in fluorescence, which was readily detected in a 96-well plate. Extracts of Australian plants, including those used traditionally as headache or pain treatments, were tested in this study to identify those affecting Ca2+ influx following membrane depolarisation of NCI-H146 cells. We found that E. bignoniiflora, A. symphyocarpa and E. vespertilio caused dose-dependent inhibition of K+-depolarised Ca2+ influx, with IC50 values calculated to be 234, 548 and 209 μg/ml, respectively. This data suggests an effect of these extracts on the function of VGCCs in these cells. Furthermore, we found similar effects using a fluorescence laser imaging plate reader (FLIPR) that allows simultaneous measurement of real-time fluorescence in a multi-well plate. Our results indicate that the dichloromethane extract of E. bignoniiflora and the methanolic extract of E. vespertilio show considerable promise as antagonists of neuronal VGCCs. Further analysis is required to characterise the function of the bioactive constituents in these extracts and determine their selectivity on VGCC subtypes.

Nonlinear actuator fault detection for small-scale UAS

This paper presents a recursive strategy for online detection of actuator faults on a unmanned aerial system (UAS) subjected to accidental actuator faults. The proposed detection algorithm aims to provide a UAS with the capability of identifying and determining characteristics of actuator faults, offering necessary flight information for the design of fault-tolerant mechanism to compensate for the resultant side-effect when faults occur. The proposed fault detection strategy consists of a bank of unscented Kalman filters (UKFs) with each one detecting a specific type of actuator faults and estimating corresponding velocity and attitude information. Performance of the proposed method is evaluated using a typical nonlinear UAS model and it is demonstrated in simulations that our method is able to detect representative faults with a sufficient accuracy and acceptable time delay, and can be applied to the design of fault-tolerant flight control systems of UASs.

IS-RT-PCR assay detection of MT-MMP in a human breast cancer cell line

The in situ-reverse transcription-polymerase chain reaction (IS-RT-PCR) is a method that allows the direct localisation of gene expression. The method utilises the dual buffer mediated activity of the enzyme rTth DNA polymerase enabling both reverse transcription and DNA amplification. Labelled nucleoside triphosphates allow the site of expression to be labelled, rather than the PCR primers themselves, giving a more accurate localisation of transcript expression and decreased background than standard in situ hybridisation (ISH) assays. The MDA-MB-231 human breast carcinoma (HBC) cell line was assayed via the IS-RT-PCR technique, using primers encoding MT-MMP (membrane-type matrix metalloproteinase) and human β-actin. Our results clearly indicate baseline expression of MT-MMP in the relatively invasive MDA-MB-231 cell line at a signal intensity similar to the housekeeping gene β-actin, and results following induction with Concanavalin A (Con A) are consistent with our previous results obtained via Northern blotting.

Detection of mRNA for nuclear receptor co-activators 1 and 3 in archival breast cancer tissue and surrounding stroma : a tissue expression study

Before the age of 75 years, approximately 10% of women will be diagnosed with breast cancer, one of the most common malignancies and a leading cause of death among women. The objective of this study was to determine if expression of the nuclear receptor coactivators 1 and 3 (NCoA1 and NCoA3) varied in breast cancer grades. RNA was extracted from 25 breast tumours and transcribed into cDNA which underwent semi-quantitative polymerase chain reaction, normalised using 18S. Analysis indicated that an expression change for NCoA1 in cancer grades and estrogen receptor alpha negative tissue (P= 0.028 and 0.001 respectively). NCoA1 expression increased in grade 3 and estrogen receptor alpha negative tumours, compared to controls. NCoA3 showed a similar, but not significant, trend in grade and a non-significant decrease in estrogen receptor alpha negative tissues. Expression of NCoA1 in late stage and estrogen receptor alpha negative breast tumours may have implications to breast cancer treatment, particularly in the area of manipulation of hormone signalling systems in advanced tumours.

Fault detection in wind turbine : a systematic literature review

Wind power has become one of the popular renewable resources all over the world and is anticipated to occupy 12% of the total global electricity generation capacity by 2020. For the harsh environment that the wind turbine operates, fault diagnostic and condition monitoring are important for wind turbine safety and reliability. This paper employs a systematic literature review to report the most recent promotions in the wind turbine fault diagnostic, from 2005 to 2012. The frequent faults and failures in wind turbines are considered and different techniques which have been used by researchers are introduced, classified and discussed.

Distributed low-power image processing in wireless sensor networks for intelligent video surveillance applications

Distributed Wireless Smart Camera (DWSC) network is a special type of Wireless Sensor Network (WSN) that processes captured images in a distributed manner. While image processing on DWSCs sees a great potential for growth, with its applications possessing a vast practical application domain such as security surveillance and health care, it suffers from tremendous constraints. In addition to the limitations of conventional WSNs, image processing on DWSCs requires more computational power, bandwidth and energy that presents significant challenges for large scale deployments. This dissertation has developed a number of algorithms that are highly scalable, portable, energy efficient and performance efficient, with considerations of practical constraints imposed by the hardware and the nature of WSN. More specifically, these algorithms tackle the problems of multi-object tracking and localisation in distributed wireless smart camera net- works and optimal camera configuration determination. Addressing the first problem of multi-object tracking and localisation requires solving a large array of sub-problems. The sub-problems that are discussed in this dissertation are calibration of internal parameters, multi-camera calibration for localisation and object handover for tracking. These topics have been covered extensively in computer vision literatures, however new algorithms must be invented to accommodate the various constraints introduced and required by the DWSC platform. A technique has been developed for the automatic calibration of low-cost cameras which are assumed to be restricted in their freedom of movement to either pan or tilt movements. Camera internal parameters, including focal length, principal point, lens distortion parameter and the angle and axis of rotation, can be recovered from a minimum set of two images of the camera, provided that the axis of rotation between the two images goes through the camera's optical centre and is parallel to either the vertical (panning) or horizontal (tilting) axis of the image. For object localisation, a novel approach has been developed for the calibration of a network of non-overlapping DWSCs in terms of their ground plane homographies, which can then be used for localising objects. In the proposed approach, a robot travels through the camera network while updating its position in a global coordinate frame, which it broadcasts to the cameras. The cameras use this, along with the image plane location of the robot, to compute a mapping from their image planes to the global coordinate frame. This is combined with an occupancy map generated by the robot during the mapping process to localised objects moving within the network. In addition, to deal with the problem of object handover between DWSCs of non-overlapping fields of view, a highly-scalable, distributed protocol has been designed. Cameras that follow the proposed protocol transmit object descriptions to a selected set of neighbours that are determined using a predictive forwarding strategy. The received descriptions are then matched at the subsequent camera on the object's path using a probability maximisation process with locally generated descriptions. The second problem of camera placement emerges naturally when these pervasive devices are put into real use. The locations, orientations, lens types etc. of the cameras must be chosen in a way that the utility of the network is maximised (e.g. maximum coverage) while user requirements are met. To deal with this, a statistical formulation of the problem of determining optimal camera configurations has been introduced and a Trans-Dimensional Simulated Annealing (TDSA) algorithm has been proposed to effectively solve the problem.

Separable and non-separable discrete wavelet transform based texture features and image classification of breast thermograms

Highly sensitive infrared cameras can produce high-resolution diagnostic images of the temperature and vascular changes of breasts. Wavelet transform based features are suitable in extracting the texture difference information of these images due to their scale-space decomposition. The objective of this study is to investigate the potential of extracted features in differentiating between breast lesions by comparing the two corresponding pectoral regions of two breast thermograms. The pectoral regions of breastsare important because near 50% of all breast cancer is located in this region. In this study, the pectoral region of the left breast is selected. Then the corresponding pectoral region of the right breast is identified. Texture features based on the first and the second sets of statistics are extracted from wavelet decomposed images of the pectoral regions of two breast thermograms. Principal component analysis is used to reduce dimension and an Adaboost classifier to evaluate classification performance. A number of different wavelet features are compared and it is shown that complex non-separable 2D discrete wavelet transform features perform better than their real separable counterparts.