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Item does not contain fulltext BACKGROUND: Loss of TP53 function through gene mutation is a critical event in the development and progression of many tumour types including colorectal cancer (CRC). In vitro studies have found considerable heterogeneity amongst different TP53 mutants in terms of their transactivating abilities. The aim of this work was to evaluate whether TP53 mutations classified as functionally inactive (< or=20% of wildtype transactivation ability) had different prognostic and predictive values in CRC compared with mutations that retained significant activity. MATERIALS AND METHODS: TP53 mutations within a large, international database of CRC (n = 3583) were classified according to functional status for transactivation. RESULTS: Inactive TP53 mutations were found in 29% of all CRCs and were more frequent in rectal (32%) than proximal colon (22%) tumours (P < 0.001). Higher frequencies of inactive TP53 mutations were also seen in advanced stage tumours (P = 0.0003) and in tumours with the poor prognostic features of vascular (P = 0.006) and lymphatic invasion (P = 0.002). Inactive TP53 mutations were associated with significantly worse outcome only in patients with Dukes' stage D tumours (RR = 1.71, 95%CI 1.25-2.33, P < 0.001). Patients with Dukes' C stage tumours appeared to gain a survival benefit from 5-fluorouracil-based chemotherapy regardless of TP53 functional status for transactivation ability. CONCLUSIONS: Mutations that inactivate the transactivational ability of TP53 are more frequent in advanced CRC and are associated with worse prognosis in this stage of disease.

A real time road sign detection and recognition system can provide an additional level of driver assistance leading to an improved safety to passengers, road users and other vehicles. Such Advanced Driver Assistance Systems (ADAS) can be used to alert a driver about the presence of a road sign by reducing the risky situation during distraction, fatigue and in the presence of poor driving conditions. This paper is divided into two parts: Detection and Recognition. The detection part includes a novel Combined Colour Model (CCM) for the accurate and robust road sign colour segmentation from video stream. It is complemented by a novel approach to road sign recognition which is based on Local Energy based Shape Histogram (LESH). Experimental results and a detailed analysis to prove the effectiveness of the proposed vision system are provided. An accuracy rate of above 97.5% is recorded.

Izhikevich network is a relatively new neuronal network, which consists of cortical spiking model neurons with axonal conduction delays and spike-timing-dependent plasticity (STDP). In this network polychrony is identified which is neither synchrony nor asynchrony, but a phenomenon of occurence and transmission of a sequence of firing patterns with specific inter-firing intervals. In this work we use van Rossum's distance to measure the correlation between spike trains issued by neurons in a testing polychromous group and analyse the characterisation of information flow in the group of the network.

Active Shape Models (ASM) are applied to the attachment hooks of several species of Gyrodactylus, including the notifiable pathogen G. salaris, to classify each species to their true species type. ASM is used as a feature extraction tool to select information from hook images that can be used as input data into trained classifiers. Linear (i.e. LDA and KNN) and non-linear (i.e. MLP and SVM) models are used to classify Gyrodactylus species. Species of Gyrodactylus, ectoparasitic monogenetic flukes of fish, are difficult to discriminate and identify on morphology alone and their speciation currently requires taxonomic expertise. The current exercise sets out to confidently classify species, which in this example includes a species which is notifiable pathogen of Atlantic salmon, to their true class with a high degree of accuracy. The findings from the current exercise demonstrates that data subsequently imported into a K-NN classifier, outperforms several other methods of classification (i.e. LDA, MLP and SVM) that were assessed, with an average classification accuracy of 98.75%.