Turn-on fluorescent detection of cyanide based on the inner filter effect of silver nanoparticles

A simple, sensitive fluorescent method for detecting cyanide has been developed based on the inner filter effect (IFE) of silver nanoparticles (Ag NPs). With a high extinction coefficient and tunable plasmon absorption feature, Ag NPs are expected to be a powerful absorber to tune the emission of the fluorophore in the IFE-based fluorescent assays. In the present work, we developed a turn-on fluorescent assay for cyanide based on the strong absorption of Ag NPs to both excitation and emission light of an isolated fluorescence indicator. In the presence of cyanide, the absorber Ag NPs will dissolve gradually, which then leads to recovery of the IFE-decreased emission of the fluorophore. The concentration of Ag NPs in the detection system was found to affect the fluorescence response toward cyanide greatly. Under the optimum conditions, the present IFE-based approach can detect cyanide ranging from 5.0 x 10 (7) to 6.0 x 10 (4) M with a detection limit of 2.5 x 10 (7) M, which is much lower than the corresponding absorbance-based approach and compares favorably with other reported fluorescent methods.

Glutathione-capped CdTe quantum dots for the sensitive detection of glucose

A simple and sensitive assay system for glucose based on the glutathione (GSH)-capped CdTe quantum dots (QDs) was developed. GSH-capped CdTe QDs exhibit higher sensitivity to H2O2 produced from the glucose oxidase catalyzed oxidation Of glucose, and are also more biocompatible than other thiols-capped QDs. Based on the quenching of H2O2 on GSH-capped QDs, glucose can be detected. The detection conditions containing reaction time, the concentration of glucose oxidase and the sizes of QDs were optimized and the detection limits for glucose was determined to be 0.1 mu M; two detection ranges of glucose from 1.0 mu M to 0.5 mM and from 1.0 mM to 20 mM, respectively Were obtained. The detection limit was almost a 1000 times lower than other QDs-based optical glucose sensing systems. The developed glucose detection system was simple and facile with no need of complicated enzyme immobilization and modification of QDs.

Progress in amperometric detection for capillary electrophoresis

As a high efficiency separation technique, capillary electrophoresis(CE) has been widely used in various fields of analytical science. Amperometry is one of the most sensitive electrochemical detection methods in CE. The capillary/electrode decoupling mechanism, applications,of new electrode systems in CE, detection cell technique are discussed in detail. Amperometric detection is compatible with microfabricated CE chips and will make the concept of lab-on-a-chip become a reality. Because of these progresses, amperometry is becoming a widely acceptable detection method,for more chemical and biological analytes.

Determination of propranolol by capillary electrophoresis with end-column amperometric detection

A capillary electrophoresis-amperometric detection system was developed for the determination of propranolol (PRO) at a 33 mu m carbon fiber microdisk electrode (CFE). The cyclic voltammogram, the hydrodynamic voltammograms and the effect of pH were studied. Under the optimum conditions: separation Voltage 15 kV; injection 3 s at 15 kV; 10 mM pH 7.5 phosphate buffer, 1.15 V (vs. Ag/AgCl) detection potential, the detection limit (LOD) for PRO was 0.05 mu M (S/N = 3). The response for PRO was linear over two orders of magnitude with a linear correlation coefficient of 0.994. The feasibility of this method was demonstrated by the detection of PRO in urine sample.

Sensitive and Rapid Detection of Genetic Modified Soybean (Roundup Ready) by Loop-Mediated Isothermal Amplification

Using the LAMP method, a highly specific and sensitive detection system for genetically modified soybean (Roundup Ready) was designed. In this detection system, a set of four primers was designed by targeting the exogenous 35S epsps gene. Target DNA was amplified and visualized on agarose gel within 45 min under isothermal conditions at 65 degrees C. Without gel electrophoresis, the LAMP amplicon was visualized directly in the reaction tube by the addition of SYBR Green I for naked-eye inspection. The detection sensitivity of LAMP was 10-fold higher than the nested PCR established in our laboratory. Moreover, the LAMP method was much quicker, taking only 70 min, as compared with 300 min for nested PCR to complete the analysis of the GM soybean. Compared with traditional PCR approaches, the LAMP procedure is faster and more sensitive, and there is no need for a special PCR machine or electrophoresis equipment. Hence, this method can be a very useful tool for GMO detection and is particularly convenient for fast screening.

Simultaneous light emitting diode-induced fluorescence and contactless conductivity detection for capillary electrophoresis

A combined detection system of simultaneous contactless conductometric and fluorescent detection for capillary electrophoresis (CE) has been designed and evaluated. The two processes share a common detection cell. A blue light-emitting diode (LED) was used as the excitation source and an optical fiber was used to collect the emitting fluorescence for fluorescent detection (FD). Inorganic ions, fluorescein isothiocyanate (FITC)-labeled amino acids and small molecule peptides were separated and detected by the combined detector, and the detection limits (LODs) of sub-μ M level were achieved.

Computer technology in detection and staging of prostate carcinoma: a review

Y. Zhu, S. Williams and R. Zwiggelaar, 'Computer technology in detection and staging of prostate carcinoma: a review', Medical Image Analysis 10 (2), 178-199 (2006)

Artefact detection and removal algorithms for EEG diagnostic systems

The electroencephalogram (EEG) is a medical technology that is used in the monitoring of the brain and in the diagnosis of many neurological illnesses. Although coarse in its precision, the EEG is a non-invasive tool that requires minimal set-up times, and is suitably unobtrusive and mobile to allow continuous monitoring of the patient, either in clinical or domestic environments. Consequently, the EEG is the current tool-of-choice with which to continuously monitor the brain where temporal resolution, ease-of- use and mobility are important. Traditionally, EEG data are examined by a trained clinician who identifies neurological events of interest. However, recent advances in signal processing and machine learning techniques have allowed the automated detection of neurological events for many medical applications. In doing so, the burden of work on the clinician has been significantly reduced, improving the response time to illness, and allowing the relevant medical treatment to be administered within minutes rather than hours. However, as typical EEG signals are of the order of microvolts (μV ), contamination by signals arising from sources other than the brain is frequent. These extra-cerebral sources, known as artefacts, can significantly distort the EEG signal, making its interpretation difficult, and can dramatically disimprove automatic neurological event detection classification performance. This thesis therefore, contributes to the further improvement of auto- mated neurological event detection systems, by identifying some of the major obstacles in deploying these EEG systems in ambulatory and clinical environments so that the EEG technologies can emerge from the laboratory towards real-world settings, where they can have a real-impact on the lives of patients. In this context, the thesis tackles three major problems in EEG monitoring, namely: (i) the problem of head-movement artefacts in ambulatory EEG, (ii) the high numbers of false detections in state-of-the-art, automated, epileptiform activity detection systems and (iii) false detections in state-of-the-art, automated neonatal seizure detection systems. To accomplish this, the thesis employs a wide range of statistical, signal processing and machine learning techniques drawn from mathematics, engineering and computer science. The first body of work outlined in this thesis proposes a system to automatically detect head-movement artefacts in ambulatory EEG and utilises supervised machine learning classifiers to do so. The resulting head-movement artefact detection system is the first of its kind and offers accurate detection of head-movement artefacts in ambulatory EEG. Subsequently, addtional physiological signals, in the form of gyroscopes, are used to detect head-movements and in doing so, bring additional information to the head- movement artefact detection task. A framework for combining EEG and gyroscope signals is then developed, offering improved head-movement arte- fact detection. The artefact detection methods developed for ambulatory EEG are subsequently adapted for use in an automated epileptiform activity detection system. Information from support vector machines classifiers used to detect epileptiform activity is fused with information from artefact-specific detection classifiers in order to significantly reduce the number of false detections in the epileptiform activity detection system. By this means, epileptiform activity detection which compares favourably with other state-of-the-art systems is achieved. Finally, the problem of false detections in automated neonatal seizure detection is approached in an alternative manner; blind source separation techniques, complimented with information from additional physiological signals are used to remove respiration artefact from the EEG. In utilising these methods, some encouraging advances have been made in detecting and removing respiration artefacts from the neonatal EEG, and in doing so, the performance of the underlying diagnostic technology is improved, bringing its deployment in the real-world, clinical domain one step closer.

Development of a high-throughput enzyme-linked immunosorbent assay for the routine detection of the carcinogen acrylamide in food, via rapid derivatisation pre-analysis

The spontaneous formation of the neurotoxic carcinogen acrylamide in a wide range of cooked foods has recently been discovered. These foods include bread and other bakery products, crisps, chips, breakfast cereals, and coffee. To date, the diminutive size of acrylamide (71.08Da) has prevented the development of screening immunoassays for this chemical. In this study, a polyclonal antibody capable of binding the carcinogen was produced by the synthesis of an immunogen comprising acrylamide derivatised with 3-mercaptobenzoic acid (3-MBA), and its conjugation to the carrier protein bovine thyroglobulin. Antiserum from the immunised rabbit was harvested and fully characterised. it displayed no binding affinity for acrylamide or 3-MBA but had a high affinity for 3-MBA-derivitised acrylamide. The antisera produced was utilised in the development of an ELISA based detection system for acrylamide. Spiked water samples were assayed for acrylamide content using a previously published extraction method validated for coffee, crispbread, potato, milk chocolate and potato crisp matrices. Extracted acrylamide was then subjected to a rapid 1-h derivatisation with 3-MBA, pre-analysis. The ELISA was shown to have a high specificity for acrylamide, with a limit of detection in water samples of 65.7 mu g kg(-1), i.e. potentially suitable for acrylamide detection in a wide range of food commodities. Future development of this assay will increase sensitivity further. This is the first report of an immunoassay capable of detecting the carcinogen, as its small size has necessitated current analytical detection via expensive, slower, physico-chemical techniques such as Gas or Liquid Chromatography coupled to Mass Spectrometry. (c) 2007 Elsevier B.V. All rights reserved.

Unique Challenges in WiFi Intrusion Detection

The Intrusion Detection System (IDS) is a common means of protecting networked systems from attack or malicious misuse. The deployment of an IDS can take many different forms dependent on protocols, usage and cost. This is particularly true of Wireless Intrusion Detection Systems (WIDS) which have many detection challenges associated with data transmission through an open, shared medium, facilitated by fundamental changes at the Physical and MAC layers. WIDS need to be considered in more detail at these lower layers than their wired counterparts as they face unique challenges. The remainder of this chapter will investigate three of these challenges where WiFi deviates significantly from that of wired counterparts:

• Attacks Specific to WiFi Networks: Outlining the additional threats which WIDS must account for: Denial of Service, Encryption Bypass and AP Masquerading attacks.

• The Effect of Deployment Architecture on WIDS Performance: Demonstrating that the deployment environment of a network protected by a WIDS can influence the prioritisation of attacks.

• The Importance of Live Data in WiFi Research: Investigating the different choices for research data sources with an emphasis on encouraging live network data collection for future WiFi research.

Introduction to Wireless Intrusion Detection Systems

The IDS (Intrusion Detection System) is a common means of protecting networked systems from attack or malicious misuse. The development and rollout of an IDS can take many different forms in terms of equipment, protocols, connectivity, cost and automation. This is particularly true of WIDS (Wireless Intrusion Detection Systems) which have many more opportunities and challenges associated with data transmission through an open, shared medium.
The operation of a WIDS is a multistep process from origination of an attack through to human readable evaluation. Attention to the performance of each of the processes in the chain from attack detection to evaluation is imperative if an optimum solution is to be sought. At present, research focuses very much on each discrete aspect of a WIDS with little consideration to the operation of the whole system. Taking a holistic view of the technology shows the interconnectivity and inter-dependence between stages, leading to improvements and novel research areas for investigation.
This chapter will outline the general structure of Wireless Intrusion Detection Systems and briefly describe the functions of each development stage, categorised into the following 6 areas:
• Threat Identification,
• Architecture,
• Data Collection,
• Intrusion Detection,
• Alert Correlation,
• Evaluation.
These topics will be considered in broad terms designed for those new to the area. Focus will be placed on ensuring the readers are aware of the impact of choices made at early stages in WIDS development on future stages.

The ultrasensitive detection of prostate cancer biomarker based on resonant Rayleigh light-scattering response of single Au nanoparticle

A proof-of-concept study was reported on analysis of antigen-antibody recognition based on resonant Rayleigh scattering response of single Au nanoparticles on a microimaging chamber. As benefited by a traditional dark-field microscope and a spectrograph, tiny 30 nm Au nanoparticles were effectively used as nanosensors to monitor changes in refractive index induced by every single binding of the adsorbates. The individual Au nanoparticles were observed with very high signal-to-noise ratio, and a LSPR ?max shift of about 2.5 nm accounting for the detection of PSA antigen with concentration as low as 0.1 pg ml-1 was recorded. This resulted in the successful demonstration of a non-labelling detection system for proteins as well as thousands of different chemical or biological species with possibility of miniaturization and multiplexing scheme.

An automatic COLREGs-compliant obstacle avoidance system for an unmanned surface vehicle

Unmanned surface vehicles are becoming increasingly vital tools in a variety of maritime applications. Unfortunately, their usability is severely constrained by the lack of a reliable obstacle detection and avoidance system. In this article, one such experimental platform is proposed, which performs obstacle detection, risk assessment and path planning (avoidance) tasks autonomously in an integrated manner. The detection system is based on a vision-LIDAR (light detection and ranging) system, whereas a heuristic path planner is utilised. A unique property of the path planner is its compliance with the marine collision regulations. It is demonstrated through hardware-in-the-loop simulations that the proposed system can be useful for both uninhabited and manned vessels.

Low-power DWT-based quasi-averaging algorithm and architecture for epileptic seizure detection

In this paper, we have developed a low-complexity algorithm for epileptic seizure detection with a high degree of accuracy. The algorithm has been designed to be feasibly implementable as battery-powered low-power implantable epileptic seizure detection system or epilepsy prosthesis. This is achieved by utilizing design optimization techniques at different levels of abstraction. Particularly, user-specific critical parameters are identified at the algorithmic level and are explicitly used along with multiplier-less implementations at the architecture level. The system has been tested on neural data obtained from in-vivo animal recordings and has been implemented in 90nm bulk-Si technology. The results show up to 90 % savings in power as compared to prevalent wavelet based seizure detection technique while achieving 97% average detection rate. Copyright 2010 ACM.

Stateful Intrusion Detection for IEC 60870-5-104 SCADA Security

Cyber threats in Supervisory Control and Data Acquisition (SCADA) systems have the potential to render physical damage and jeopardize power system operation, safety and stability. SCADA systems were originally designed with little consideration of escalating cyber threats and hence the problem of how to develop robust intrusion detection technologies to tailor the requirements of SCADA is an emerging topic and a big challenge. This paper proposes a stateful Intrusion Detection System (IDS) using a Deep Packet Inspection (DPI) method to improve the cyber-security of SCADA systems using the IEC 60870-5-104 protocol which is tailored for basic telecontrol communications. The proposed stateful protocol analysis approach is presented that is designed specifically for the IEC 60870-5-104 protocol. Finally, the novel intrusion detection approach are implemented and validated.