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.

An efficient algorithm for the detection of Eden

In this paper, a polynomial time algorithm is presented for solving the Eden problem for graph cellular automata. The algorithm is based on our neighborhood elimination operation which removes local neighborhood configurations which cannot be used in a pre-image of a given configuration. This paper presents a detailed derivation of our algorithm from first principles, and a detailed complexity and accuracy analysis is also given. In the case of time complexity, it is shown that the average case time complexity of the algorithm is \Theta(n^2), and the best and worst cases are \Omega(n) and O(n^3) respectively. This represents a vast improvement in the upper bound over current methods, without compromising average case performance.

A basis for instrusion detection in distributed systems using kernel-level data tainting

This project was a step forward in developing intrusion detection systems in distributed environments such as web services. It investigates a new approach of detection based on so-called "taint-marking" techniques and introduces a theoretical framework along with its implementation in the Linux kernel.

ADMRG @ MediaEval 2013 Social Event Detection

This paper elaborates the approach used by the Applied Data Mining Research Group (ADMRG) for the Social Event Detection (SED) Tasks of the 2013 MediaEval Benchmark. We extended the constrained clustering algorithm to apply to the first semi-supervised clustering task, and we compared several classifiers with Latent Dirichlet Allocation as feature selector in the second event classification task. The proposed approach focuses on scalability and efficient memory allocation when applied to a high dimensional data with large clusters. Results of the first task show the effectiveness of the proposed method. Results from task 2 indicate that attention on the imbalance categories distributions is needed.

Electrochemical detection of dopamine and cytochrome c at a nanostructured gold electrode

A nanostructured gold surface consisting of closely packed outwardly growing spikes is investigated for the electrochemical detection of dopamine and cytochrome c. A significant electrocatalytic effect for the electrooxidation of both dopamine and ascorbic acid at the nanostructured electrode was found due to the presence of surface active sites which allowed the detection of dopamine in the presence of excess ascorbic acid to be achieved by differential pulse voltammetry. By simple modification with a layer of Nafion, the enhanced electrocatalytic properties of the nanostructured surface was maintained while increasing the selectivity of dopamine detection in the presence of interfering species such as excess ascorbic and uric acids. Also, upon modification of the nanostructured surface with a monolayer of cysteine, the electrochemical response of immobilised cytochrome c in two distinct conformations was observed. This opens up the possibility of using such a nanostructured surface for the characterisation of other biomolecules and in bio-electroanalytical applications.

A facile chemical screening method for the detection of stress corrosion cracking in 9 carat gold alloys

Stress corrosion cracking (SCC) is a well known form of environmental attack in low carat gold jewellery. It is desirable to have a quick, easy and cost effective way to detect SCC in alloys and prevent them from being used and later failing in their application. A facile chemical method to investigate SCC of 9 carat gold alloys is demonstrated. It involves a simple application of tensile stress to a wire sample in a corrosive environment such as 1–10 % FeCl3 which induces failure in less than 5 minutes. In this study three quaternary (Au, Ag, Cu and Zn) 9 carat gold alloy compositions were investigated for their resistance to SCC and the relationship between time to failure and processing conditions is studied. It is envisaged that the use of such a rapid and facile screening procedure at the production stage may readily identify alloy treatments that produce jewellery that will be susceptible to SCC in its lifetime.

Intrusion detection in distributed systems, an approach based on taint marking

This paper presents a new framework for distributed intrusion detection based on taint marking. Our system tracks information flows between applications of multiple hosts gathered in groups (i.e., sets of hosts sharing the same distributed information flow policy) by attaching taint labels to system objects such as files, sockets, Inter Process Communication (IPC) abstractions, and memory mappings. Labels are carried over the network by tainting network packets. A distributed information flow policy is defined for each group at the host level by labeling information and defining how users and applications can legally access, alter or transfer information towards other trusted or untrusted hosts. As opposed to existing approaches, where information is most often represented by two security levels (low/high, public/private, etc.), our model identifies each piece of information within a distributed system, and defines their legal interaction in a fine-grained manner. Hosts store and exchange security labels in a peer to peer fashion, and there is no central monitor. Our IDS is implemented in the Linux kernel as a Linux Security Module (LSM) and runs standard software on commodity hardware with no required modification. The only trusted code is our modified operating system kernel. We finally present a scenario of intrusion in a web service running on multiple hosts, and show how our distributed IDS is able to report security violations at each host level.

Evaluation of the alkaline comet assay and urinary 3-methyladenine excretion for monitoring DNA damage in melanoma patients treated with dacarbazine and tamoxifen

Purpose: To develop, using dacarbazine as a model, reliable techniques for measuring DNA damage and repair as pharmacodynamic endpoints for patients receiving chemotherapy. Methods: A group of 39 patients with malignant melanoma were treated with dacarbazine 1 g/m2 i.v. every 21 days. Tamoxifen 20 mg daily was commenced 24 h after the first infusion and continued until 3 weeks after the last cycle of chemotherapy. DNA strand breaks formed during dacarbazine-induced DNA damage and repair were measured in individual cells by the alkaline comet assay. DNA methyl adducts were quantified by measuring urinary 3-methyladenine (3-MeA) excretion using immunoaffinity ELISA. Venous blood was taken on cycles 1 and 2 for separation of peripheral blood lymphocytes (PBLs) for measurement of DNA strand breaks. Results: Wide interpatient variation in PBL DNA strand breaks occurred following chemotherapy, with a peak at 4 h (median 26.6 h, interquartile range 14.75- 40.5 h) and incomplete repair by 24 h. Similarly, there was a range of 3-MeA excretion with peak levels 4-10 h after chemotherapy (median 33 nmol/h, interquartile range 20.448.65 nmol/h). Peak 3-MeA excretion was positively correlated with DNA strand breaks at 4 h (Spearman's correlation coefficient, r = 0.39, P = 0.036) and 24 h (r = 0.46, P = 0.01). Drug-induced emesis correlated with PBL DNA strand breaks (Mann Whitney U-test, P = 0.03) but not with peak 3-MeA excretion. Conclusions: DNA damage and repair following cytotoxic chemotherapy can be measured in vivo by the alkaline comet assay and by urinary 3-MeA excretion in patients receiving chemotherapy.

Real-time video event detection in crowded scenes using MPEG derived features : a multiple instance learning approach

This paper presents an investigation into event detection in crowded scenes, where the event of interest co-occurs with other activities and only binary labels at the clip level are available. The proposed approach incorporates a fast feature descriptor from the MPEG domain, and a novel multiple instance learning (MIL) algorithm using sparse approximation and random sensing. MPEG motion vectors are used to build particle trajectories that represent the motion of objects in uniform video clips, and the MPEG DCT coefficients are used to compute a foreground map to remove background particles. Trajectories are transformed into the Fourier domain, and the Fourier representations are quantized into visual words using the K-Means algorithm. The proposed MIL algorithm models the scene as a linear combination of independent events, where each event is a distribution of visual words. Experimental results show that the proposed approaches achieve promising results for event detection compared to the state-of-the-art.

Advances in skin cancer early detection and diagnosis

OBJECTIVES: To provide an overview of 1) traditional methods of skin cancer early detection, 2) current technologies for skin cancer detection, and 3) evolving practice models of early detection. DATA SOURCES: Peer-reviewed databased articles and reviews, scholarly texts, and Web-based resources. CONCLUSION: Early detection of skin cancer through established methods or newer technologies is critical for reducing both skin cancer mortality and the overall skin cancer burden. IMPLICATIONS FOR NURSING PRACTICE: A basic knowledge of recommended skin examination guidelines and risk factors for skin cancer, traditional methods to further examine lesions that are suspicious for skin cancer and evolving detection technologies can guide patient education and skin inspection decisions.

Pulse-coupled neural network performance for real-time identification of vegetation during forced landing

Safety concerns in the operation of autonomous aerial systems require safe-landing protocols be followed during situations where the a mission should be aborted due to mechanical or other failure. On-board cameras provide information that can be used in the determination of potential landing sites, which are continually updated and ranked to prevent injury and minimize damage. Pulse Coupled Neural Networks have been used for the detection of features in images that assist in the classification of vegetation and can be used to minimize damage to the aerial vehicle. However, a significant drawback in the use of PCNNs is that they are computationally expensive and have been more suited to off-line applications on conventional computing architectures. As heterogeneous computing architectures are becoming more common, an OpenCL implementation of a PCNN feature generator is presented and its performance is compared across OpenCL kernels designed for CPU, GPU and FPGA platforms. This comparison examines the compute times required for network convergence under a variety of images obtained during unmanned aerial vehicle trials to determine the plausibility for real-time feature detection.