Dynamic, invivo, real-time detection of retinal oxidative status in a model of elevated intraocular pressure using a novel, reversibly responsive, profluorescent nitroxide probe

Changes to the redox status of biological systems have been implicated in the pathogenesis of a wide variety of disorders including cancer, Ischemia-reperfusion (I/R) injury and neurodegeneration. In times of metabolic stress e.g. ischaemia/reperfusion, reactive oxygen species (ROS) production overwhelms the intrinsic antioxidant capacity of the cell, damaging vital cellular components. The ability to quantify ROS changes in vivo, is therefore essential to understanding their biological role. Here we evaluate the suitability of a novel reversible profluorescent probe containing a redox-sensitive nitroxide moiety (methyl ester tetraethylrhodamine nitroxide, ME-TRN), as an in vivo, real-time reporter of retinal oxidative status. The reversible nature of the probe's response offers the unique advantage of being able to monitor redox changes in both oxidizing and reducing directions in real time. After intravitreal administration of the ME-TRN probe, we induced ROS production in rat retina using an established model of complete, acute retinal ischaemia followed by reperfusion. After restoration of blood flow, retinas were imaged using a Micron III rodent fundus fluorescence imaging system, to quantify the redox-response of the probe. Fluorescent intensity declined during the first 60 min of reperfusion. The ROS-induced change in probe fluorescence was ameliorated with the retinal antioxidant, lutein. Fluorescence intensity in non-Ischemia eyes did not change significantly. This new probe and imaging technology provide a reversible and real-time response to oxidative changes and may allow the in vivo testing of antioxidant therapies of potential benefit to a range of diseases linked to oxidative stress

Electrochemical detection of benzo(a)pyrene and related DNA damage using DNA/hemin/nafion-graphene biosensor

A novel electrochemical biosensor, DNA/hemin/nafion–graphene/GCE, was constructed for the analysis of the benzo(a)pyrene PAH, which can produce DNA damage induced by a benzo(a)pyrene (BaP) enzyme-catalytic product. This biosensor was assembled layer-by-layer, and was characterized with the use of cyclic voltammetry, electrochemical impedance spectroscopy (EIS) and atomic force microscopy. Ultimately, it was demonstrated that the hemin/nafion–graphene/GCE was a viable platform for the immobilization of DNA. This DNA biosensor was treated separately in benzo(a)pyrene, hydrogen peroxide (H2O2) and in their mixture, respectively, and differential pulse voltammetry (DPV) analysis showed that an oxidation peak was apparent after the electrode was immersed in H2O2. Such experiments indicated that in the presence of H2O2, hemin could mimic cytochrome P450 to metabolize benzo(a)pyrene, and a voltammogram of its metabolite was recorded. The DNA damage induced by this metabolite was also detected by electrochemical impedance and ultraviolet spectroscopy. Finally, a novel, indirect DPV analytical method for BaP in aqueous solution was developed based on the linear metabolite versus BaP concentration plot; this method provided a new, indirect, quantitative estimate of DNA damage.

Hot spot detection and spatio-temporal dynamics of dengue in Queensland, Australia

Dengue has been a major public health concern in Australia since it re-emerged in Queensland in 1992-1993. This study explored spatio-temporal distribution and clustering of locally-acquired dengue cases in Queensland State, Australia and identified target areas for effective interventions. A computerised locally-acquired dengue case dataset was collected from Queensland Health for Queensland from 1993 to 2012. Descriptive spatial and temporal analyses were conducted using geographic information system tools and geostatistical techniques. Dengue hot spots were detected using SatScan method. Descriptive spatial analysis showed that a total of 2,398 locally-acquired dengue cases were recorded in central and northern regions of tropical Queensland. A seasonal pattern was observed with most of the cases occurring in autumn. Spatial and temporal variation of dengue cases was observed in the geographic areas affected by dengue over time. Tropical areas are potential high-risk areas for mosquito-borne diseases such as dengue. This study demonstrated that the locally-acquired dengue cases have exhibited a spatial and temporal variation over the past twenty years in tropical Queensland, Australia. There is a clear evidence for the existence of statistically significant clusters of dengue and these clusters varied over time. These findings enabled us to detect and target dengue clusters suggesting that the use of geospatial information can assist the health authority in planning dengue control activities and it would allow for better design and implementation of dengue management programs.

A data mining approach for fault diagnosis: An application of anomaly detection algorithm

Rolling-element bearing failures are the most frequent problems in rotating machinery, which can be catastrophic and cause major downtime. Hence, providing advance failure warning and precise fault detection in such components are pivotal and cost-effective. The vast majority of past research has focused on signal processing and spectral analysis for fault diagnostics in rotating components. In this study, a data mining approach using a machine learning technique called anomaly detection (AD) is presented. This method employs classification techniques to discriminate between defect examples. Two features, kurtosis and Non-Gaussianity Score (NGS), are extracted to develop anomaly detection algorithms. The performance of the developed algorithms was examined through real data from a test to failure bearing. Finally, the application of anomaly detection is compared with one of the popular methods called Support Vector Machine (SVM) to investigate the sensitivity and accuracy of this approach and its ability to detect the anomalies in early stages.

Multimodal polymer nanoparticles with combined 19F magnetic resonance and optical detection for tunable, targeted, multimodal imagingin vivo

Understanding the complex nature of diseased tissue in vivo requires development of more advanced nanomedicines, where synthesis of multifunctional polymers combines imaging multimodality with a biocompatible, tunable, and functional nanomaterial carrier. Here we describe the development of polymeric nanoparticles for multimodal imaging of disease states in vivo. The nanoparticle design utilizes the abundant functionality and tunable physicochemical properties of synthetically robust polymeric systems to facilitate targeted imaging of tumors in mice. For the first time, high-resolution 19F/1H magnetic resonance imaging is combined with sensitive and versatile fluorescence imaging in a polymeric material for in vivo detection of tumors. We highlight how control over the chemistry during synthesis allows manipulation of nanoparticle size and function and can lead to very high targeting efficiency to B16 melanoma cells, both in vitro and in vivo. Importantly, the combination of imaging modalities within a polymeric nanoparticle provides information on the tumor mass across various size scales in vivo, from millimeters down to tens of micrometers.

Raman spectroscopy techniques for detection of energetic materials at high and low concentration

This thesis presents the development of a rapid, sensitive and reproducible spectroscopic method for the detection of TNT in forensic and environmental applications. Simple nano sensors prepared by cost effective methods were utilized as sensitive platforms for the detection of TNT by surface enhanced Raman spectroscopy. The optimization of the substrate and the careful selection of a suitable recognition molecule contributed to the significant improvements of sensitive and selective targeting over current detection methods. The work presented in this thesis paves the way for effective detection and monitoring of explosives residues in law enforcement and environmental health applications.

Automating marine mammal detection in aerial images captured during wildlife surveys: A deep learning approach

Aerial surveys conducted using manned or unmanned aircraft with customized camera payloads can generate a large number of images. Manual review of these images to extract data is prohibitive in terms of time and financial resources, thus providing strong incentive to automate this process using computer vision systems. There are potential applications for these automated systems in areas such as surveillance and monitoring, precision agriculture, law enforcement, asset inspection, and wildlife assessment. In this paper, we present an efficient machine learning system for automating the detection of marine species in aerial imagery. The effectiveness of our approach can be credited to the combination of a well-suited region proposal method and the use of Deep Convolutional Neural Networks (DCNNs). In comparison to previous algorithms designed for the same purpose, we have been able to dramatically improve recall to more than 80% and improve precision to 27% by using DCNNs as the core approach.

Circulating tumor cell detection in high-risk non-metastatic prostate cancer

Purpose The detection of circulating tumor cells (CTCs) provides important prognostic information in men with metastatic prostate cancer. We aim to determine the rate of detection of CTCs in patients with high-risk non-metastatic prostate cancer using the CellSearch® method. Method Samples of peripheral blood (7.5 mL) were drawn from 36 men with newly diagnosed high-risk non-metastatic prostate cancer, prior to any initiation of therapy and analyzed for CTCs using the CellSearch® method. Results The median age was 70 years, median PSA was 14.1, and the median Gleason score was 9. The median 5-year risk of progression of disease using a validated nomogram was 39 %. Five out of 36 patients (14 %, 95 % CI 5–30 %) had CTCs detected in their circulation. Four patients had only 1 CTC per 7.5 mL of blood detected. One patient had 3 CTCs per 7.5 mL of blood detected, which included a circulating tumor microemboli. Both on univariate analysis and multivariate analysis, there were no correlations found between CTC positivity and the classic prognostic factors including PSA, Gleason score, T-stage and age. Conclusion This study demonstrates that patients with high-risk, non-metastatic prostate cancer present infrequently with small number of CTCs in peripheral blood. This finding is consistent with the limited literature available in this setting. Other CTC isolation and detection technologies with improved sensitivity and specificity may enable detection of CTCs with mesenchymal phenotypes, although none as yet have been validated for clinical use. Newer assays are emerging for detection of new putative biomarkers for prostate cancer. Correlation of disease control outcomes with CTC detection will be important.

Detection of anuran calling activity in long field recordings for bio-acoustic monitoring

This paper presents a system to analyze long field recordings with low signal-to-noise ratio (SNR) for bio-acoustic monitoring. A method based on spectral peak track, Shannon entropy, harmonic structure and oscillation structure is proposed to automatically detect anuran (frog) calling activity. Gaussian mixture model (GMM) is introduced for modelling those features. Four anuran species widespread in Queensland, Australia, are selected to evaluate the proposed system. A visualization method based on extracted indices is employed for detection of anuran calling activity which achieves high accuracy.

Early detection of melanoma metastases using microRNAs as novel biomarkers

Early detection of melanoma skin cancer, prior to metastatic spread, is critical to improve survival outcomes in patients. This study identified a melanoma-related panel of blood markers that can detect the presence of melanoma with high sensitivity and accuracy which is superior to currently used markers for melanoma progression, recurrence, and survival. Overall, the findings discussed in this thesis may lead to more precise measurement of disease progression allowing for better treatments and an increase in overall survival.

Improving spoken term detection using complementary information

This research has made contributions to the area of spoken term detection (STD), defined as the process of finding all occurrences of a specified search term in a large collection of speech segments. The use of visual information in the form of lip movements of the speaker in addition to audio and the use of topic of the speech segments, and the expected frequency of words in the target speech domain, are proposed. By using these complementary information, improvement in the performance of STD has been achieved which enables efficient search of key words in large collection of multimedia documents.

In vivo non-invasive high resolution MR-based method for the determination of the elastic modulus of arterial vessels

The mechanical properties of arterial walls have long been recognized to play an essential role in the development and progression of cardiovascular disease (CVD). Early detection of variations in the elastic modulus of arteries would help in monitoring patients at high cardiovascular risk stratifying them according to risk. An in vivo, non-invasive, high resolution MR-phase-contrast based method for the estimation of the time-dependent elastic modulus of healthy arteries was developed, validated in vitro by means of a thin walled silicon rubber tube integrated into an existing MR-compatible flow simulator and used on healthy volunteers. A comparison of the elastic modulus of the silicon tube measured from the MRI-based technique with direct measurements confirmed the method's capability. The repeatability of the method was assessed. Viscoelastic and inertial effects characterizing the dynamic response of arteries in vivo emerged from the comparison of the pressure waveform and the area variation curve over a period. For all the volunteers who took part in the study the elastic modulus was found to be in the range 50-250 kPa, to increase during the rising part of the cycle, and to decrease with decreasing pressure during the downstroke of systole and subsequent diastole.

A Bayesian Framework for the Assessment of Vision-based Weed and Fruit Detection and Classification Algorithms

This paper proposes new metrics and a performance-assessment framework for vision-based weed and fruit detection and classification algorithms. In order to compare algorithms, and make a decision on which one to use fora particular application, it is necessary to take into account that the performance obtained in a series of tests is subject to uncertainty. Such characterisation of uncertainty seems not to be captured by the performance metrics currently reported in the literature. Therefore, we pose the problem as a general problem of scientific inference, which arises out of incomplete information, and propose as a metric of performance the(posterior) predictive probabilities that the algorithms will provide a correct outcome for target and background detection. We detail the framework through which these predicted probabilities can be obtained, which is Bayesian in nature. As an illustration example, we apply the framework to the assessment of performance of four algorithms that could potentially be used in the detection of capsicums (peppers).

Nonparametric rank regression for analyzing water quality concentration data with multiple detection limits

Environmental data usually include measurements, such as water quality data, which fall below detection limits, because of limitations of the instruments or of certain analytical methods used. The fact that some responses are not detected needs to be properly taken into account in statistical analysis of such data. However, it is well-known that it is challenging to analyze a data set with detection limits, and we often have to rely on the traditional parametric methods or simple imputation methods. Distributional assumptions can lead to biased inference and justification of distributions is often not possible when the data are correlated and there is a large proportion of data below detection limits. The extent of bias is usually unknown. To draw valid conclusions and hence provide useful advice for environmental management authorities, it is essential to develop and apply an appropriate statistical methodology. This paper proposes rank-based procedures for analyzing non-normally distributed data collected at different sites over a period of time in the presence of multiple detection limits. To take account of temporal correlations within each site, we propose an optimal linear combination of estimating functions and apply the induced smoothing method to reduce the computational burden. Finally, we apply the proposed method to the water quality data collected at Susquehanna River Basin in United States of America, which dearly demonstrates the advantages of the rank regression models.