Rapid detection of TNT in aqueous media by selective label free surface enhanced Raman spectroscopy

We report rapid and ultra-sensitive detection system for 2,4,6-trinitrotoluene (TNT) using unmodified gold nanoparticles and surface-enhanced Raman spectroscopy (SERS). First, Meisenheimer complex has been formed in aqueous solution between TNT and cysteamine in less than 15 min of mixing. The complex formation is confirmed by the development of a pink colour and a new UV–vis absorption band around 520 nm. Second, the developed Meisenheimer complex is spontaneously self-assembled onto unmodified gold nanoparticles through a stable Au–S bond between the cysteamine moiety and the gold surface. The developed mono layer of cysteamine-TNT is then screened by SERS to detect and quantify TNT. Our experimental results demonstrate that the SERS-based assay provide an ultra-sensitive approach for the detection of TNT down to 22.7 ng/L. The unambiguous fingerprint identification of TNT by SERS represents a key advantage for our proposed method. The new method provides high selectivity towards TNT over 2,4 DNT and picric acid. Therefore it satisfies the practical requirements for the rapid screening of TNT in real life samples where the interim 24-h average allowable concentration of TNT in waste water is 0.04 mg/L.

Fault detection of wind turbine drivetrain utilizing power-speed characteristics

Wind energy, being the fastest growing renewable energy source in the present world, requires a large number of wind turbines to transform wind energy into electricity. One factor driving the cost of this energy is the reliable operation of these turbines. Therefore, it is a growing requirement within the wind farm community, to monitor the operation of the wind turbines on a continuous basis so that a possible fault can be detected ahead of time. As the wind turbine operates in an environment of constantly changing wind speed, it is a challenging task to design a fault detection technique which can accommodate the stochastic operational behavior of the turbines. Addressing this issue, this paper proposes a novel fault detection criterion which is robust against operational uncertainty, as well as having the ability to quantify severity level specifically of the drivetrain abnormality within an operating wind turbine. A benchmark model of wind turbine has been utilized to simulate drivetrain fault condition and effectiveness of the proposed technique has been tested accordingly. From the simulation result it can be concluded that the proposed criterion exhibits consistent performance for drivetrain faults for varying wind speed and has linear relationship with the fault severity level.

From ImageNet to mining: Adapting visual object detection with minimal supervision

This paper presents visual detection and classification of light vehicles and personnel on a mine site.We capitalise on the rapid advances of ConvNet based object recognition but highlight that a naive black box approach results in a significant number of false positives. In particular, the lack of domain specific training data and the unique landscape in a mine site causes a high rate of errors. We exploit the abundance of background-only images to train a k-means classifier to complement the ConvNet. Furthermore, localisation of objects of interest and a reduction in computation is enabled through region proposals. Our system is tested on over 10km of real mine site data and we were able to detect both light vehicles and personnel. We show that the introduction of our background model can reduce the false positive rate by an order of magnitude.

Redefining dermatologists’ role in skin cancer early detection and follow-up care

Early diagnosis of melanoma leads to the best prognosis for patients and may be more likely achieved when those who are at high risk for melanoma undergo regular and systematic monitoring. However, many people rarely or never see a dermatologist. Risk prediction models (recently reviewed by Usher-Smith et al ) could assist to triage people into preventive care appropriate for their risk profile. Most risk prediction models contain measures of phenotype including skin, eye and hair colour as well as genetic mutations. Almost all also contain the number and size of naevi, as well as the presence of naevi with atypical features which are independently associated with melanoma risk. In the absence of formal population-based screening programs for melanoma in most countries worldwide, people with high risk phenotypes may need to consider regular monitoring or self-monitoring of their naevi , especially since the vast majority of melanomas are found by people themselves or their friend and relatives. Another group of patients that will require regular monitoring are patients who have been successfully treated for their first melanoma, whose risk to develop a second melanoma is greatly increased . In a US study of 89,515 melanoma survivors those with a previous diagnosis of melanoma had a 9-fold increased risk of developing subsequent melanoma compared with the general population, equating to a rate of 3.76 per 1000 person-years, while in an Australian study, risk of subsequent melanoma was 6 per 1000 person-years. Regular follow-up is therefore essential for melanoma survivors, especially during the first few years after initial melanoma diagnosis.

Evaluation of object detection proposals under condition variations

Object detection is a fundamental task in many computer vision applications, therefore the importance of evaluating the quality of object detection is well acknowledged in this domain. This process gives insight into the capabilities of methods in handling environmental changes. In this paper, a new method for object detection is introduced that combines the Selective Search and EdgeBoxes. We tested these three methods under environmental variations. Our experiments demonstrate the outperformance of the combination method under illumination and view point variations.

Effectiveness of in-office blood pressure measurement by eye care practitioners in early detection and management of hypertension

AIM To investigate the number of hypertensive patients, the optometrist is able to identify by routinely taking blood pressure (BP) measurements for patients in "at -risk" groups, and to sample patients' opinions regarding in -office BP measurement. Many of the optometrists in Saudi Arabia practice in optical stores. These stores are wide spread, easily accessible and seldom need appointments. The expanding role of the optometrist as a primary health care provider (PHCP) and the increasing global prevalence of hypertension, highlight the need for an integrated approach towards detecting and monitoring hypertension. METHODS Automated BP measurements were made twice (during the same session) at five selected optometry practices using a validated BP monitor (Omron M6) to assess the number of patients with high BP (HBP) - in at -risk groups -visiting the eye clinic routinely. Prior to data collection, practitioners underwent a two-day training workshop by a cardiologist on hypertension and how to obtain accurate BP readings. A protocol for BP measurement was distributed and retained in all participating clinics. The general attitude towards cardiovascular health of 480 patients aged 37.2 (依12.4)y and their opinion towards in-office BP measurement was assessed using a self -administered questionnaire. RESULTS A response rate of 83.6% was obtained for the survey. Ninety -three of the 443 patients (21.0% ) tested for BP in this study had HBP. Of these, (62 subjects) 67.7% were unaware of their HBP status. Thirty of the 105 subjects (28.6%) who had previously been diagnosed with HBP, still had HBP at the time of this study, and only 22 (73.3%) of these patients were on medication. Also, only 25% of the diagnosed hypertensive patients owned a BP monitor. CONCLUSION Taking BP measurements in optometry practices, we were able to identify one previously undiagnosed patient with HBP for every 8 adults tested. We also identified 30 of 105 previously diagnosed patients whose BP was poorly controlled, twenty-two of whom were on medication. The patients who participated in this study were positively disposed toward the routine measurement of BP by optometrists.

Microscopy for the detection, identification and quantification of malaria parasites on stained thick and thin blood films in research settings

Summary This manual was developed to guide a move towards common standards for undertaking and reporting research microscopy for malaria parasite detection, identification and quantification. It contains procedures based on agreed quality assurance standards for research malaria microscopy defined at a consultation of: TDR, the Special Programme for Research and Training in Tropical Diseases; the Worldwide Antimalarial Resistance Network (WWARN), United Kingdom; the Foundation for Innovative New Diagnostics (FIND), Switzerland; the Centers for Disease Control and Prevention (CDC), USA; the Kenya Medical Research Institute (KEMRI) and later expanded to include Amref Health Africa (Kenya); the Eijkman-Oxford Clinical Research Unit (EOCRU), Indonesia; Institut Pasteur du Cambodge (IPC); Institut de recherche pour le Développement (IRD), Senegal; the Global Good and Intellectual Ventures Laboratory (GG-IVL), USA; the Mahidol-Oxford Tropical Medicine Research Unit (MORU), Thailand; Queensland University of Technology (QUT), Australia, and the Shoklo Malaria Research Unit (SMRU), Thailand. These collaborating institutions commit to adhering to these standards in published research studies. It is hoped that they will form a solid basis for the wider adoption of standardized reference microscopy protocols for malaria research.

Reproducible and label-free biosensor for the selective extraction and rapid detection of proteins in biological fluids

Erythropoietin (EPO), a glycoprotein hormone of ∼34 kDa, is an important hematopoietic growth factor, mainly produced in the kidney and controls the number of red blood cells circulating in the blood stream. Sensitive and rapid recombinant human EPO (rHuEPO) detection tools that improve on the current laborious EPO detection techniques are in high demand for both clinical and sports industry. A sensitive aptamer-functionalized biosensor (aptasensor) has been developed by controlled growth of gold nanostructures (AuNS) over a gold substrate (pAu/AuNS). The aptasensor selectively binds to rHuEPO and, therefore, was used to extract and detect the drug from horse plasma by surface enhanced Raman spectroscopy (SERS). Due to the nanogap separation between the nanostructures, the high population and distribution of hot spots on the pAu/AuNS substrate surface, strong signal enhancement was acquired. By using wide area illumination (WAI) setting for the Raman detection, a low RSD of 4.92% over 150 SERS measurements was achieved. The significant reproducibility of the new biosensor addresses the serious problem of SERS signal inconsistency that hampers the use of the technique in the field. The WAI setting is compatible with handheld Raman devices. Therefore, the new aptasensor can be used for the selective extraction of rHuEPO from biological fluids and subsequently screened with handheld Raman spectrometer for SERS based in-field protein detection.

Complete-linkage clustering for voice activity detection in audio and visual speech

We propose a novel technique for conducting robust voice activity detection (VAD) in high-noise recordings. We use Gaussian mixture modeling (GMM) to train two generic models; speech and non-speech. We then score smaller segments of a given (unseen) recording against each of these GMMs to obtain two respective likelihood scores for each segment. These scores are used to compute a dissimilarity measure between pairs of segments and to carry out complete-linkage clustering of the segments into speech and non-speech clusters. We compare the accuracy of our method against state-of-the-art and standardised VAD techniques to demonstrate an absolute improvement of 15% in half-total error rate (HTER) over the best performing baseline system and across the QUT-NOISE-TIMIT database. We then apply our approach to the Audio-Visual Database of American English (AVDBAE) to demonstrate the performance of our algorithm in using visual, audio-visual or a proposed fusion of these features.

Gamma tocotrienol targets tyrosine phosphatase SHP2 in mammospheres resulting in cell death through RAS/ERK pathway

Background There is increasing evidence supporting the concept of cancer stem cells (CSCs), which are responsible for the initiation, growth and metastasis of tumors. CSCs are thus considered the target for future cancer therapies. To achieve this goal, identifying potential therapeutic targets for CSCs is essential. Methods We used a natural product of vitamin E, gamma tocotrienol (gamma-T3), to treat mammospheres and spheres from colon and cervical cancers. Western blotting and real-time RT-PCR were employed to identify the gene and protein targets of gamma-T3 in mammospheres. Results We found that mammosphere growth was inhibited in a dose dependent manner, with total inhibition at high doses. Gamma-T3 also inhibited sphere growth in two other human epithelial cancers, colon and cervix. Our results suggested that both Src homology 2 domain-containing phosphatase 1 (SHP1) and 2 (SHP2) were affected by gamma-T3 which was accompanied by a decrease in K- and H-Ras gene expression and phosphorylated ERK protein levels in a dose dependent way. In contrast, expression of self-renewal genes TGF-beta and LIF, as well as ESR signal pathways were not affected by the treatment. These results suggest that gamma-T3 specifically targets SHP2 and the RAS/ERK signaling pathway. Conclusions SHP1 and SHP2 are potential therapeutic targets for breast CSCs and gamma-T3 is a promising natural drug for future breast cancer therapy.

Comparison of human and animal Chlamydia pneumoniae responses to interferon gamma and penicillin treatment

This thesis has made a significant contribution to future chlamydial research by uncovering the chlamydial pathogenic mechanisms which will potentially help in the development of targeted vaccine against the pathogen. This thesis has made important new contributions to our understanding of Chlamydia pneumoniae specific adaptations to stress responses and has provided new perspectives on the survival of this successful pathogen. This thesis has used two well established microbial stressors and has identified major differences in stress responses between human and animal Chlamydia pneumoniae isolates.

Modeling of the hemodynamic responses in block design fMRI studies

The hemodynamic response function (HRF) describes the local response of brain vasculature to functional activation. Accurate HRF modeling enables the investigation of cerebral blood flow regulation and improves our ability to interpret fMRI results. Block designs have been used extensively as fMRI paradigms because detection power is maximized; however, block designs are not optimal for HRF parameter estimation. Here we assessed the utility of block design fMRI data for HRF modeling. The trueness (relative deviation), precision (relative uncertainty), and identifiability (goodness-of-fit) of different HRF models were examined and test-retest reproducibility of HRF parameter estimates was assessed using computer simulations and fMRI data from 82 healthy young adult twins acquired on two occasions 3 to 4 months apart. The effects of systematically varying attributes of the block design paradigm were also examined. In our comparison of five HRF models, the model comprising the sum of two gamma functions with six free parameters had greatest parameter accuracy and identifiability. Hemodynamic response function height and time to peak were highly reproducible between studies and width was moderately reproducible but the reproducibility of onset time was low. This study established the feasibility and test-retest reliability of estimating HRF parameters using data from block design fMRI studies.

Eye-safe UV stand-off Raman spectroscopy for the ranged detection of explosives in the field

Increasing worldwide terrorist attacks involving explosives presents a growing need for a rapid and ranged explosive detection method that can safely be deployed in the field. Stand-off Raman spectroscopy shows great promise; however, the radiant exposures of lasers required for adequate signal generation are often much greater than what is safe for the eye or the skin, restricting use of the technique to un-populated areas. Here, by determining the safe exposure levels for lasers typically used in Raman spectroscopy, optimal parameter values are identified, which produce the largest possible detection range using power densities that do not exceed the eye-safe limit. It is shown that safe ultraviolet pulse energies can be more than three orders of magnitude greater than equivalent safe visible pulse energies. Coupling this to the 16-fold increase in Raman signal obtained in the ultraviolet at 266 nm over that at 532 nm results in a 131 times larger detection range for the eye-safe 266-nm system over an equivalent eye-safe 532-nm laser system. For the Raman system described here, this translates to a maximum range of 42 m for detecting Teflon with a 266-nm laser emitting a 100-mm diameter beam of 23.5-mJ nanosecond pulses.

Robotic detection and tracking of Crown-Of-Thorns starfish

This paper presents a novel vision-based underwater robotic system for the identification and control of Crown-Of-Thorns starfish (COTS) in coral reef environments. COTS have been identified as one of the most significant threats to Australia's Great Barrier Reef. These starfish literally eat coral, impacting large areas of reef and the marine ecosystem that depends on it. Evidence has suggested that land-based nutrient runoff has accelerated recent outbreaks of COTS requiring extensive use of divers to manually inject biological agents into the starfish in an attempt to control population numbers. Facilitating this control program using robotics is the goal of our research. In this paper we introduce a vision-based COTS detection and tracking system based on a Random Forest Classifier (RFC) trained on images from underwater footage. To track COTS with a moving camera, we embed the RFC in a particle filter detector and tracker where the predicted class probability of the RFC is used as an observation probability to weight the particles, and we use a sparse optical flow estimation for the prediction step of the filter. The system is experimentally evaluated in a realistic laboratory setup using a robotic arm that moves a camera at different speeds and heights over a range of real-size images of COTS in a reef environment.

Acoustic adaptation in cross database audio visual SHMM training for phonetic spoken term detection

Visual information in the form of lip movements of the speaker has been shown to improve the performance of speech recognition and search applications. In our previous work, we proposed cross database training of synchronous hidden Markov models (SHMMs) to make use of external large and publicly available audio databases in addition to the relatively small given audio visual database. In this work, the cross database training approach is improved by performing an additional audio adaptation step, which enables audio visual SHMMs to benefit from audio observations of the external audio models before adding visual modality to them. The proposed approach outperforms the baseline cross database training approach in clean and noisy environments in terms of phone recognition accuracy as well as spoken term detection (STD) accuracy.