Improving the scientific reliability of biological detection of explosives by Canis familiaris: Active odour signatures and their implications

The use of canines as a method of detection of explosives is well established worldwide and those applying this technology range from police forces and law enforcement to humanitarian agencies in the developing world. Despite the recent surge in publication of novel instrumental sensors for explosives detection, canines are still regarded by many to be the most effective real-time field method of explosives detection. However, unlike instrumental methods, currently it is difficult to determine detection levels, perform calibration of the canines' ability or produce scientifically valid quality control checks. Accordingly, amongst increasingly strict requirements regarding forensic evidence admission such as Frye and Daubert, there is a need for better scientific understanding of the process of canine detection. ^ When translated to the field of canine detection, just like any instrumental technique, peer reviewed publication of the reliability, success and error rates, is required for admissibility. Commonly training is focussed towards high explosives such as TNT and Composition 4, and the low explosives such as Black and Smokeless Powders are added often only for completeness. ^ Headspace analyses of explosive samples, performed by Solid Phase Microextraction (SPME) paired with Gas Chromatography - Mass Spectrometry (GC-MS), and Gas Chromatography - Electron Capture Detection (GC-ECD) was conducted, highlighting common odour chemicals. The odour chemicals detected were then presented to previously trained and certified explosives detection canines, and the activity/inactivity of the odour determined through field trials and experiments. ^ It was demonstrated that TNT and cast explosives share a common odour signature, and the same may be said for plasticized explosives such as Composition C-4 and Deta Sheet. Conversely, smokeless powders were demonstrated not to share common odours. An evaluation of the effectiveness of commercially available pseudo aids reported limited success. The implications of the explosive odour studies upon canine training then led to the development of novel inert training aids based upon the active odours determined. ^

Analysis of organomercurials in environmental and biological samples by capillary column gas chromatography with atomic fluorescence detection

The general method for determining organomercurials in environmental and biological samples is gas chromatography with electron capture detection (GC-ECD). However, tedious sample work up protocols and poor chromatographic response show the need for the development of new methods. Here, Atomic Fluorescence-based methods are described, free from these deficiencies. The organomercurials in soil, sediment and tissue samples are first released from the matrices with acidic KBr and cupric ions and extracted into dichloromethane. The initial extracts are subjected to thiosulfate clean up and the organomercury species are isolated as their chloride derivatives by cupric chloride and subsequent extraction into a small volume of dichloromethane. In water samples the organomercurials are pre-concentrated using a sulfhydryl cotton fiber adsorbent, followed by elution with acidic KBr and CuSO 4 and extraction into dichloromethane. Analysis of the organomercurials is accomplished by capillary column chromatography with atomic fluorescence detection.

Evaluation of the Scent Collection System for Its Effectiveness in Volatile Organic Compound Collection and Use in Canine Training

As a result of increased terrorist activity around the world, the development of a canine training aid suitable for daily military operations is necessary to provide effective canine explosive detection. Since the use of sniffer dogs has proven to be a reliable resource for the rapid detection of explosive volatiles organic compounds, the present study evaluated the ability of the Human Scent Collection System (HSCS) device for the creation of training aids for plasticized / tagged explosives, nitroglycerin and TNT containing explosives, and smokeless powders for canine training purposes. Through canine field testing, it was demonstrated that volatiles dynamically collected from real explosive material provided a positive canine response showing the effectiveness of the HSCS in creating canine training aids that can be used immediately or up to several weeks (3) after collection under proper storage conditions. These reliable non-hazardous training aids allow its use in areas where real explosive material aids are not practical and/or available.

Design strategies for an artificial neural network based algorithm for automatic incident detection on major arterial streets

Traffic incidents are non-recurring events that can cause a temporary reduction in roadway capacity. They have been recognized as a major contributor to traffic congestion on our nation’s highway systems. To alleviate their impacts on capacity, automatic incident detection (AID) has been applied as an incident management strategy to reduce the total incident duration. AID relies on an algorithm to identify the occurrence of incidents by analyzing real-time traffic data collected from surveillance detectors. Significant research has been performed to develop AID algorithms for incident detection on freeways; however, similar research on major arterial streets remains largely at the initial stage of development and testing. This dissertation research aims to identify design strategies for the deployment of an Artificial Neural Network (ANN) based AID algorithm for major arterial streets. A section of the US-1 corridor in Miami-Dade County, Florida was coded in the CORSIM microscopic simulation model to generate data for both model calibration and validation. To better capture the relationship between the traffic data and the corresponding incident status, Discrete Wavelet Transform (DWT) and data normalization were applied to the simulated data. Multiple ANN models were then developed for different detector configurations, historical data usage, and the selection of traffic flow parameters. To assess the performance of different design alternatives, the model outputs were compared based on both detection rate (DR) and false alarm rate (FAR). The results show that the best models were able to achieve a high DR of between 90% and 95%, a mean time to detect (MTTD) of 55-85 seconds, and a FAR below 4%. The results also show that a detector configuration including only the mid-block and upstream detectors performs almost as well as one that also includes a downstream detector. In addition, DWT was found to be able to improve model performance, and the use of historical data from previous time cycles improved the detection rate. Speed was found to have the most significant impact on the detection rate, while volume was found to contribute the least. The results from this research provide useful insights on the design of AID for arterial street applications.

Synthesis of azulenylsilane nitrones as diagnostic tools for superoxide detection

The superoxide radical is considered to play important roles in physiological processes as well as in the genesis of diverse cytotoxic conditions such as cancer, various cardiovascular disorders and neurodegenerative diseases such as amyotrophic lateral sclerosis (ALS), Parkinson’s disease (PD) and Alzheimer’s disease (AD). The detection and quantification of superoxide within cells is of critical importance to understand biological roles of superoxide and to develop preventive strategies against free radical-mediated diseases. Cyclic nitrone spin traps such as DMPO, EMPO, DEPMPO, BMPO and their derivatives have been widely used in conjunction with ESR spectroscopy to detect cellular superoxide with some success. However, the formation of unstable superoxide adducts from the reaction of cyclic nitrones with superoxide is a stumbling block in detecting superoxide by using electron spin resonance (ESR). A chemiluminescent probe, lucigenin, and fluorogenic probes, hydroethidium and MitoSox, are the other frequently used methods in detecting superoxide. However, luceginen undergoes redox-cycling producing superoxide by itself, and hydroethidium and MitoSox react with other oxidants apart from superoxide forming red fluorescent products contributing to artefacts in these assays. Hence, both methods were deemed to be inappropriate for superoxide detection. In this study, an effective approach, a selective mechanism-based colorimetric detection of superoxide anion has been developed by using silylated azulenyl nitrones spin traps. Since a nitrone moiety and an adjacent silyl group react readily with radicals and oxygen anions respectively, such nitrones can trap superoxide efficiently because superoxide is both a radical and an oxygen anion. Moreover, the synthesized nitrone is designed to be triggered solely by superoxide and not by other commonly observed oxygen radicals such as hydroxyl radical, alkoxyl radicals and peroxyl radical. In vitro studies have shown that these synthesized silylated azylenyl nitrones and the mitochondrial-targeted guanylhydrazone analog can trap superoxide efficiently yielding UV-vis identifiable and even potentially fluorescence-detectable orange products. Therefore, the chromotropic detection of superoxide using these nitrones can be a promising method in contrast to other available methods.

Design Strategies for an Artificial Neural Network Based Algorithm for Automatic Incident Detection on Major Arterial Streets

Traffic incidents are non-recurring events that can cause a temporary reduction in roadway capacity. They have been recognized as a major contributor to traffic congestion on our national highway systems. To alleviate their impacts on capacity, automatic incident detection (AID) has been applied as an incident management strategy to reduce the total incident duration. AID relies on an algorithm to identify the occurrence of incidents by analyzing real-time traffic data collected from surveillance detectors. Significant research has been performed to develop AID algorithms for incident detection on freeways; however, similar research on major arterial streets remains largely at the initial stage of development and testing. This dissertation research aims to identify design strategies for the deployment of an Artificial Neural Network (ANN) based AID algorithm for major arterial streets. A section of the US-1 corridor in Miami-Dade County, Florida was coded in the CORSIM microscopic simulation model to generate data for both model calibration and validation. To better capture the relationship between the traffic data and the corresponding incident status, Discrete Wavelet Transform (DWT) and data normalization were applied to the simulated data. Multiple ANN models were then developed for different detector configurations, historical data usage, and the selection of traffic flow parameters. To assess the performance of different design alternatives, the model outputs were compared based on both detection rate (DR) and false alarm rate (FAR). The results show that the best models were able to achieve a high DR of between 90% and 95%, a mean time to detect (MTTD) of 55-85 seconds, and a FAR below 4%. The results also show that a detector configuration including only the mid-block and upstream detectors performs almost as well as one that also includes a downstream detector. In addition, DWT was found to be able to improve model performance, and the use of historical data from previous time cycles improved the detection rate. Speed was found to have the most significant impact on the detection rate, while volume was found to contribute the least. The results from this research provide useful insights on the design of AID for arterial street applications.