Images of desire : cognitive models of craving

Cognitive modelling of phenomena in clinical practice allows the operationalisation of otherwise diffuse descriptive terms such as craving or flashbacks. This supports the empirical investigation of the clinical phenomena and the development of targeted treatment interventions. This paper focuses on the cognitive processes underpinning craving, which is recognised as a motivating experience in substance dependence. We use a high-level cognitive architecture, Interacting Cognitive Subsystems (ICS), to compare two theories of craving: Tiffany's theory, centred on the control of automated action schemata, and our own Elaborated Intrusion theory of craving. Data from a questionnaire study of the subjective aspects of everyday desires experienced by a large non-clinical population are presented. Both the data and the high-level modelling support the central claim of the Elaborated Intrusion theory that imagery is a key element of craving, providing the subjective experience and mediating much of the associated disruption of concurrent cognition.

Towards an automatic road lane marks extraction based on ISODATA segmentation and shadow detection from large-scale aerial images

The automatic extraction of road features from remote sensed images has been a topic of great interest within the photogrammetric and remote sensing communities for over 3 decades. Although various techniques have been reported in the literature, it is still challenging to efficiently extract the road details with the increasing of image resolution as well as the requirement for accurate and up-to-date road data. In this paper, we will focus on the automatic detection of road lane markings, which are crucial for many applications, including lane level navigation and lane departure warning. The approach consists of four steps: i) data preprocessing, ii) image segmentation and road surface detection, iii) road lane marking extraction based on the generated road surface, and iv) testing and system evaluation. The proposed approach utilized the unsupervised ISODATA image segmentation algorithm, which segments the image into vegetation regions, and road surface based only on the Cb component of YCbCr color space. A shadow detection method based on YCbCr color space is also employed to detect and recover the shadows from the road surface casted by the vehicles and trees. Finally, the lane marking features are detected from the road surface using the histogram clustering. The experiments of applying the proposed method to the aerial imagery dataset of Gympie, Queensland demonstrate the efficiency of the approach.

Recombinant protein production using a Tobacco yellow dwarf virus-based episomal expression vector : control of Rep activity

Over the past decade, plants have been used as expression hosts for the production of pharmaceutically important and commercially valuable proteins. Plants offer many advantages over other expression systems such as lower production costs, rapid scale up of production, similar post-translational modification as animals and the low likelihood of contamination with animal pathogens, microbial toxins or oncogenic sequences. However, improving recombinant protein yield remains one of the greatest challenges to molecular farming. In-Plant Activation (InPAct) is a newly developed technology that offers activatable and high-level expression of heterologous proteins in plants. InPAct vectors contain the geminivirus cis elements essential for rolling circle replication (RCR) and are arranged such that the gene of interest is only expressed in the presence of the cognate viral replication-associated protein (Rep). The expression of Rep in planta may be controlled by a tissue-specific, developmentally regulated or chemically inducible promoter such that heterologous protein accumulation can be spatially and temporally controlled. One of the challenges for the successful exploitation of InPAct technology is the control of Rep expression as even very low levels of this protein can reduce transformation efficiency, cause abnormal phenotypes and premature activation of the InPAct vector in regenerated plants. Tight regulation over transgene expression is also essential if expressing cytotoxic products. Unfortunately, many tissue-specific and inducible promoters are unsuitable for controlling expression of Rep due to low basal activity in the absence of inducer or in tissues other than the target tissue. This PhD aimed to control Rep activity through the production of single chain variable fragments (scFvs) specific to the motif III of Tobacco yellow dwarf virus (TbYDV) Rep. Due to the important role played by the conserved motif III in the RCR, it was postulated that such scFvs can be used to neutralise the activity of the low amount of Rep expressed from a “leaky” inducible promoter, thus preventing activation of the TbYDV-based InPAct vector until intentional induction. Such scFvs could also offer the potential to confer partial or complete resistance to TbYDV, and possibly heterologous viruses as motif III is conserved between geminiviruses. Studies were first undertaken to determine the levels of TbYDV Rep and TbYDV replication-associated protein A (RepA) required for optimal transgene expression from a TbYDV-based InPAct vector. Transient assays in a non-regenerable Nicotiana tabacum (NT-1) cell line were undertaken using a TbYDV-based InPAct vector containing the uidA reporter gene (encoding GUS) in combination with TbYDV Rep and RepA under the control of promoters with high (CaMV 35S) or low (Banana bunchy top virus DNA-R, BT1) activity. The replication enhancer protein of Tomato leaf curl begomovirus (ToLCV), REn, was also used in some co-bombardment experiments to examine whether RepA could be substituted by a replication enhancer from another geminivirus genus. GUS expression was observed both quantitatively and qualitatively by fluorometric and histochemical assays, respectively. GUS expression from the TbYDV-based InPAct vector was found to be greater when Rep was expected to be expressed at low levels (BT1 promoter) rather than high levels (35S promoter). GUS expression was further enhanced when Rep and RepA were co-bombarded with a low ratio of Rep to RepA. Substituting TbYDV RepA with ToLCV REn also enhanced GUS expression but more importantly highest GUS expression was observed when cells were co-transformed with expression vectors directing low levels of Rep and high levels of RepA irrespective of the level of REn. In this case, GUS expression was approximately 74-fold higher than that from a non-replicating vector. The use of different terminators, namely CaMV 35S and Nos terminators, in InPAct vectors was found to influence GUS expression. In the presence of Rep, GUS expression was greater using pInPActGUS-Nos rather than pInPActGUS-35S. The only instance of GUS expression being greater from vectors containing the 35S terminator was when comparing expression from cells transformed with Rep, RepA and REnexpressing vectors and either non-replicating vectors, p35SGS-Nos or p35SGS-35S. This difference was most likely caused by an interaction of viral replication proteins with each other and the terminators. These results indicated that (i) the level of replication associated proteins is critical to high transgene expression, (ii) the choice of terminator within the InPAct vector may affect expression levels and (iii) very low levels of Rep can activate InPAct vectors hence controlling its activity is critical. Prior to generating recombinant scFvs, a recombinant TbYDV Rep was produced in E. coli to act as a control to enable the screening for Rep-specific antibodies. A bacterial expression vector was constructed to express recombinant TbYDV Rep with an Nterminal His-tag (N-His-Rep). Despite investigating several purification techniques including Ni-NTA, anion exchange, hydrophobic interaction and size exclusion chromatography, N-His-Rep could only be partially purified using a Ni-NTA column under native conditions. Although it was not certain that this recombinant N-His-Rep had the same conformation as the native TbYDV Rep and was functional, results from an electromobility shift assay (EMSA) showed that N-His-Rep was able to interact with the TbYDV LIR and was, therefore, possibly functional. Two hybridoma cell lines from mice, immunised with a synthetic peptide containing the TbYDV Rep motif III amino acid sequence, were generated by GenScript (USA). Monoclonal antibodies secreted by the two hybridoma cell lines were first screened against denatured N-His-Rep in Western analysis. After demonstrating their ability to bind N-His-Rep, two scFvs (scFv1 and scFv2) were generated using a PCR-based approach. Whereas the variable heavy chain (VH) from both cell lines could be amplified, only the variable light chain (VL) from cell line 2 was amplified. As a result, scFv1 contained VH and VL from cell line 1, whereas scFv2 contained VH from cell line 2 and VL from cell line 1. Both scFvs were first expressed in E. coli in order to evaluate their affinity to the recombinant TbYDV N-His-Rep. The preliminary results demonstrated that both scFvs were able to bind to the denatured N-His-Rep. However, EMSAs revealed that only scFv2 was able to bind to native N-His-Rep and prevent it from interacting with the TbYDV LIR. Each scFv was cloned into plant expression vectors and co-bombarded into NT-1 cells with the TbYDV-based InPAct GUS expression vector and pBT1-Rep to examine whether the scFvs could prevent Rep from mediating RCR. Although it was expected that the addition of the scFvs would result in decreased GUS expression, GUS expression was found to slightly increase. This increase was even more pronounced when the scFvs were targeted to the cell nucleus by the inclusion of the Simian virus 40 large T antigen (SV40) nuclear localisation signal (NLS). It was postulated that the scFvs were binding to a proportion of Rep, leaving a small amount available to mediate RCR. The outcomes of this project provide evidence that very high levels of recombinant protein can theoretically be expressed using InPAct vectors with judicious selection and control of viral replication proteins. However, the question of whether the scFvs generated in this project have sufficient affinity for TbYDV Rep to prevent its activity in a stably transformed plant remains unknown. It may be that other scFvs with different combinations of VH and VL may have greater affinity for TbYDV Rep. Such scFvs, when expressed at high levels in planta, might also confer resistance to TbYDV and possibly heterologous geminiviruses.

Automated road pavement marking detection from high resolution aerial images based on Multi-resolution image analysis and anisotropic Gaussian filtering

Road features extraction from remote sensed imagery has been a long-term topic of great interest within the photogrammetry and remote sensing communities for over three decades. The majority of the early work only focused on linear feature detection approaches, with restrictive assumption on image resolution and road appearance. The widely available of high resolution digital aerial images makes it possible to extract sub-road features, e.g. road pavement markings. In this paper, we will focus on the automatic extraction of road lane markings, which are required by various lane-based vehicle applications, such as, autonomous vehicle navigation, and lane departure warning. The proposed approach consists of three phases: i) road centerline extraction from low resolution image, ii) road surface detection in the original image, and iii) pavement marking extraction on the generated road surface. The proposed method was tested on the aerial imagery dataset of the Bruce Highway, Queensland, and the results demonstrate the efficiency of our approach.

Road network extraction with new vectorization and pruning from high-resolution RS images

With the increasing resolution of remote sensing images, road network can be displayed as continuous and homogeneity regions with a certain width rather than traditional thin lines. Therefore, road network extraction from large scale images refers to reliable road surface detection instead of road line extraction. In this paper, a novel automatic road network detection approach based on the combination of homogram segmentation and mathematical morphology is proposed, which includes three main steps: (i) the image is classified based on homogram segmentation to roughly identify the road network regions; (ii) the morphological opening and closing is employed to fill tiny holes and filter out small road branches; and (iii) the extracted road surface is further thinned by a thinning approach, pruned by a proposed method and finally simplified with Douglas-Peucker algorithm. Lastly, the results from some QuickBird images and aerial photos demonstrate the correctness and efficiency of the proposed process.

Improved simultaneous computation of motion detection and optical flow for object tracking

Object tracking systems require accurate segmentation of the objects from the background for effective tracking. Motion segmentation or optical flow can be used to segment incoming images. Whilst optical flow allows multiple moving targets to be separated based on their individual velocities, optical flow techniques are prone to errors caused by changing lighting and occlusions, both common in a surveillance environment. Motion segmentation techniques are more robust to fluctuating lighting and occlusions, but don't provide information on the direction of the motion. In this paper we propose a combined motion segmentation/optical flow algorithm for use in object tracking. The proposed algorithm uses the motion segmentation results to inform the optical flow calculations and ensure that optical flow is only calculated in regions of motion, and improve the performance of the optical flow around the edge of moving objects. Optical flow is calculated at pixel resolution and tracking of flow vectors is employed to improve performance and detect discontinuities, which can indicate the location of overlaps between objects. The algorithm is evaluated by attempting to extract a moving target within the flow images, given expected horizontal and vertical movement (i.e. the algorithms intended use for object tracking). Results show that the proposed algorithm outperforms other widely used optical flow techniques for this surveillance application.

Temporal-based support vector machines for facial expression recognition

When classifying a signal, ideally we want our classifier to trigger a large response when it encounters a positive example and have little to no response for all other examples. Unfortunately in practice this does not occur with responses fluctuating, often causing false alarms. There exists a myriad of reasons why this is the case, most notably not incorporating the dynamics of the signal into the classification. In facial expression recognition, this has been highlighted as one major research question. In this paper we present a novel technique which incorporates the dynamics of the signal which can produce a strong response when the peak expression is found and essentially suppresses all other responses as much as possible. We conducted preliminary experiments on the extended Cohn-Kanade (CK+) database which shows its benefits. The ability to automatically and accurately recognize facial expressions of drivers is highly relevant to the automobile. For example, the early recognition of “surprise” could indicate that an accident is about to occur; and various safeguards could immediately be deployed to avoid or minimize injury and damage. In this paper, we conducted initial experiments on the extended Cohn-Kanade (CK+) database which shows its benefits.

Design of a Web interface for anatomical images

Interactive documents for use with the World Wide Web have been developed for viewing multi-dimensional radiographic and visual images of human anatomy, derived from the Visible Human Project. Emphasis has been placed on user-controlled features and selections. The purpose was to develop an interface which was independent of host operating system and browser software which would allow viewing of information by multiple users. The interfaces were implemented using HyperText Markup Language (HTML) forms, C programming language and Perl scripting language. Images were pre-processed using ANALYZE and stored on a Web server in CompuServe GIF format. Viewing options were included in the document design, such as interactive thresholding and two-dimensional slice direction. The interface is an example of what may be achieved using the World Wide Web. Key applications envisaged for such software include education, research and accessing of information through internal databases and simultaneous sharing of images by remote computers by health personnel for diagnostic purposes.

Large margin vector quantization

In this paper we describe the Large Margin Vector Quantization algorithm (LMVQ), which uses gradient ascent to maximise the margin of a radial basis function classifier. We present a derivation of the algorithm, which proceeds from an estimate of the class-conditional probability densities. We show that the key behaviour of Kohonen's well-known LVQ2 and LVQ3 algorithms emerge as natural consequences of our formulation. We compare the performance of LMVQ with that of Kohonen's LVQ algorithms on an artificial classification problem and several well known benchmark classification tasks. We find that the classifiers produced by LMVQ attain a level of accuracy that compares well with those obtained via LVQ1, LVQ2 and LVQ3, with reduced storage complexity. We indicate future directions of enquiry based on the large margin approach to Learning Vector Quantization.